diff --git a/.Rhistory b/.Rhistory
index 68a53c1..3e97812 100644
--- a/.Rhistory
+++ b/.Rhistory
@@ -1,4 +1,221 @@
-here()
+agesex <- copdata %>%
+# Filter for cases only:
+filter(case == TRUE) %>%
+# Create age sex pyramid:
+apyramid::age_pyramid(
+# Specify column containing age categories:
+age_group = "age_cat",
+# Specify column containing sex:
+split_by = "sex",
+# Don't show midpoint on the graph:
+show_midpoint = FALSE
+)
+# Print plot:
+agesex
+# Create summary table:
+tabsymptoms <- copdata %>%
+# Select person characteristics to summarise:
+select(case, diarrhoea, bloody, vomiting,
+abdo, nausea, fever,headache, jointpain) %>%
+# transform clinical symptoms to factors, so NA can be accounted properly in the table
+dplyr::mutate(
+across(.cols = c(diarrhoea, bloody, vomiting,
+abdo, nausea, fever,headache, jointpain),
+.fns = ~as.factor(.))) %>%
+# The enxt paragraph does not work with Rversion 4.4.0
+# Make NA a explicit level of factor variables
+# dplyr::mutate(
+#   across(.cols = c(diarrhoea, bloody, vomiting,
+#          abdo, nausea, fever,headache, jointpain),
+#          .fns = ~forcats::fct_na_value_to_level(.))) %>%
+# Create the summary table:
+gtsummary::tbl_summary(
+# Stratify by case:
+by = case,
+# take care of missing values
+missing = "no",
+# Calculate row percentages:
+percent = "column",
+# Create nice labels:
+label  = list(
+diarrhoea   ~ "Diarrhoea",
+bloody      ~ "Dysentary",
+vomiting    ~ "Vomiting",
+abdo        ~ "Abdominal pain",
+nausea      ~ "Nausea",
+fever       ~ "Fever",
+headache    ~ "Headache",
+jointpain   ~ "Joint pain")
+) %>%
+# Add totals:
+add_overall() %>%
+# Make variable names bold and italics:
+bold_labels() %>%
+italicize_labels() %>%
+# Modify header:
+modify_header(
+label = "**Characteristic**",
+stat_0 = "**Overall**\n **N** = {N}",
+stat_1 = "**Non-case**\n **N** = {n}",
+stat_2 = "**Case**\n **N** = {n}",
+)
+# Create summary table:
+tabsymptoms <- copdata %>%
+# Select person characteristics to summarise:
+select(case, diarrhoea, bloody, vomiting,
+abdo, nausea, fever,headache, jointpain) %>%
+# transform clinical symptoms to factors, so NA can be accounted properly in the table
+dplyr::mutate(
+across(.cols = c(diarrhoea, bloody, vomiting,
+abdo, nausea, fever,headache, jointpain),
+.fns = ~as.factor(.))) %>%
+# The enxt paragraph does not work with Rversion 4.4.0
+# Make NA a explicit level of factor variables
+# dplyr::mutate(
+#   across(.cols = c(diarrhoea, bloody, vomiting,
+#          abdo, nausea, fever,headache, jointpain),
+#          .fns = ~forcats::fct_na_value_to_level(.))) %>%
+# Create the summary table:
+gtsummary::tbl_summary(
+# Stratify by case:
+by = case,
+# take care of missing values
+missing = "no",
+# Calculate row percentages:
+percent = "column",
+# Create nice labels:
+label  = list(
+diarrhoea   ~ "Diarrhoea",
+bloody      ~ "Dysentary",
+vomiting    ~ "Vomiting",
+abdo        ~ "Abdominal pain",
+nausea      ~ "Nausea",
+fever       ~ "Fever",
+headache    ~ "Headache",
+jointpain   ~ "Joint pain")
+) %>%
+# Add totals:
+add_overall() %>%
+# Make variable names bold and italics:
+bold_labels() %>%
+italicize_labels() %>%
+# Modify header:
+modify_header(
+label = "**Characteristic**",
+stat_0 = "**Overall**\n **N** = {N}",
+stat_1 = "**Non-case**\n **N** = {n}",
+stat_2 = "**Case**\n **N** = {n}",
+)
+# Print the table:
+tabsymptoms
+# Create list of symptom variables:
+symptoms <- c("diarrhoea",
+"bloody",
+"vomiting",
+"abdo",
+"nausea",
+"fever",
+"headache",
+"jointpain")
+# Create nice labels for case definition:
+caselabs <- ggplot2::as_labeller(c("0" = "Non-case",
+"1" = "Case"))
+# Select variables and cases:
+symptom_bar <- copdata %>%
+# Select symptom columns:
+select(case, c(all_of(symptoms))) %>%
+# Drop NAs:
+drop_na() %>%
+# Reshape (pivot longer):
+pivot_longer(!case,
+names_to = "Symptoms",
+values_drop_na = TRUE) %>%
+# Keep only TRUE values:
+filter(value == "1") %>%
+# Group by symptoms and case:
+group_by(Symptoms, case) %>%
+# Count for each symptom by case:
+dplyr::summarise(count = n()) %>%
+# Create plot:
+ggplot(
+mapping = aes(
+# Order symptom bars so most common ones are ontop:
+x = reorder(Symptoms, desc(count), decreasing = TRUE),
+y = count)) +
+# Display bars as proportions
+geom_bar(stat = "identity") +
+# Update x axis label:
+xlab("Symptoms") +
+# Update y axis label:
+ylab("Proportion of respondents") +
+# Flip plot on its side so symptom labels are clear:
+coord_flip() +
+# Facet the plot by (labelled) case:
+facet_wrap(facets = "case",
+labeller = caselabs,
+ncol = 2)
+# Print plot:
+symptom_bar
+# Create table of case status:
+total_ap <- tabyl(copdata, case) %>%
+# Add row totals:
+adorn_totals(where = "row") %>%
+# Add percentages with 1 digit after the decimal point:
+adorn_pct_formatting(digits = 1) %>%
+# Filter to rows where case is TRUE:
+filter(case == "1") %>%
+# Select the column percent:
+select(percent) %>%
+# Extract (pull) the value from this cell:
+pull()
+# Print result:
+total_ap
+# Table to calculate attack proportions:
+attack_prop <- copdata %>%
+# Select columns:
+select (case, class, group, sex) %>%
+# Create table:
+tbl_summary(
+# Stratified by case
+by = case,
+# with row percentages
+percent = "row") %>%
+# Add totals:
+add_overall() %>%
+# Make variable names bold and italics:
+bold_labels() %>%
+italicize_labels() %>%
+# Modify header:
+modify_header(
+label = "**Characteristic**",
+stat_0 = "**Overall** **N** = {N}",
+stat_1 = "**Non-case** **N** = {n}",
+stat_2 = "**Case** **N** = {n}"
+)
+# Table to calculate attack proportions:
+attack_prop <- copdata %>%
+# Select columns:
+select (case, class, group, sex) %>%
+# Create table:
+tbl_summary(
+# Stratified by case
+by = case,
+# with row percentages
+percent = "row") %>%
+# Add totals:
+add_overall() %>%
+# Make variable names bold and italics:
+bold_labels() %>%
+italicize_labels() %>%
+# Modify header:
+modify_header(
+label = "**Characteristic**",
+stat_0 = "**Overall** **N** = {N}",
+stat_1 = "**Non-case** **N** = {n}",
+stat_2 = "**Case** **N** = {n}"
+)
+# Print table:
+attack_prop
 # Load the required libraries into the current R session:
 pacman::p_load(rio,
 here,
@@ -16,7 +233,7 @@ scales,
 EpiStats,
 broom)
 # Import the raw data set:
-copdata <- rio::import(here::here("data", "Copenhagen_clean2_2024.rds"))
+copdata <- rio::import(here::here("data", "Spetses_clean2_2024.rds"), trust = T)
 # Check if age overall follows a normal distribution:
 shapiro.test(copdata$age)
 # Can simply have a look at
@@ -32,22 +249,122 @@ copdata %>%
 select(sex, case) %>%
 tbl_summary(by = case) %>%
 add_p()
-EpiStats::riskratio(copdata$orzoD,
-copdata$case)
-epitools::riskratio(copdata$orzoD,
-copdata$case)
-epitools::riskratio(copdata$orzoD,
-copdata$case) %>%
+copdata %>%
+select(class, case) %>%
+tbl_summary(by = case) %>%
+add_p()
+copdata %>%
+select(group, case) %>%
+tbl_summary(by = case) %>%
+add_p()
+copdata %>%
+select(group, case) %>%
+tbl_summary(by = case) %>%
+add_p()
+copdata %>%
+select(sex, class, group, case) %>%
+tbl_summary(by = case) %>%
+add_p()
+copdata %>%
+select(sex, class, group, case) %>%
+tbl_summary(by = case) %>%
+add_p()
+# You could use the EpiStats package for each food item
+CS(copdata, "case", "feta")
+CS(copdata, "case", "sardines")
+CS(copdata, "case", "eggplant")
+CS(copdata, "case", "pasta")
+rr_tbl <- CSTable(copdata, "case", food_vars) %>%
+as.data.frame() %>%
+rownames_to_column() %>%
+flextable() %>%
+set_header_labels(
+values = c("Food Item",
+"Total exposed",
+"Cases exposed",
+"AR among exposed",
+"Total unexposed",
+"Cases unexposed",
+"AR among unexposed",
+"RR",
+"95% lower CI",
+"95% upper CI",
+"p-value"))
+# You can save time (and probably typos!) by creating a vector for food variables...
+food_vars <- c("feta", "sardines", "eggplant", "pasta",
+"veal", "tomsal", "dessert", "bread",
+"champagne", "beer", "redwine", "whitewine")
+# ...and using EpiStats::CSTable() to run all variables together!
+CSTable(copdata, "case", food_vars)
+rr_tbl <- CSTable(copdata, "case", food_vars) %>%
+as.data.frame() %>%
+rownames_to_column() %>%
+flextable() %>%
+set_header_labels(
+values = c("Food Item",
+"Total exposed",
+"Cases exposed",
+"AR among exposed",
+"Total unexposed",
+"Cases unexposed",
+"AR among unexposed",
+"RR",
+"95% lower CI",
+"95% upper CI",
+"p-value"))
+rr_tbl
+epitools::riskratio(copdata$pastaD,
+copdata$case,
+conf.level = 0.95)
+# Let's get the results directly exponentiated
+binom_pastaD_exp <- glm(case ~ pastaD, data = copdata,
+family = binomial(link = "log")) %>%
 tidy(exponentiate = TRUE,
 conf.int = TRUE)
-asd<-epitools::riskratio(copdata$orzoD,
+binom_pastaD_exp
+epitools::riskratio(copdata$champagneD,
 copdata$case,
 conf.level = 0.95)
-asd$measure
-asd$measure[2,2]
-asd$measure[1,2]
-asd$measure[2,1]
-exp(asd$measure[2,1])
+# Let's get the results directly exponentiated
+binom_champagneD_exp <- glm(case ~ champagneD, data = copdata,
+family = binomial(link = "log")) %>%
+tidy(exponentiate = TRUE,
+conf.int = TRUE)
+binom_champagneD_exp
+# Check if the 'pacman' package is installed, if not install it:
+if (!requireNamespace("pacman", quietly = TRUE)) install.packages("pacman")
+# Load the required libraries into the current R session:
+pacman::p_load(rio,
+here,
+skimr,
+plyr,
+janitor,
+lubridate,
+gtsummary,
+flextable,
+officer,
+epikit,
+apyramid,
+scales,
+tidyverse
+)
+# Import the raw data set:
+copdata <- import(here::here("data", "spetses_school.csv"))
+copdata <- copdata %>% rename(temp = veal,)
+copdata <- copdata %>% rename(temp = veal, tempD = vealD)
+copdata <- copdata %>% rename(veal = temp, vealD = tempD)
+copdata <- copdata %>%
+dplyr::rename(veal = temp, vealD = tempD)
+copdata <- copdata %>%
+dplyr::rename(temp = veal,
+tempD = vealD)
+copdata <- copdata %>%
+dplyr::rename(veal = pasta,
+vealD = pastaD)
+copdata <- copdata %>%
+dplyr::rename(pasta = temp,
+pastaD = tempD)
+export(copdata, "data/spetses_school.csv")
 # Load the required libraries into the current R session:
 pacman::p_load(rio,
 here,
@@ -61,29 +378,9 @@ flextable,
 officer,
 epikit,
 apyramid,
-scales)
-# Import the clean data set:
-copdata <- rio::import(here::here("data", "Spetses_clean2_2024.rds"))
-lab <- rio::import(here::here("data", "Lab data.xlsx"),
-skip = 1) # skip the first row
-head(lab)
-View(lab)
-copdatalab <- left_join(copdata, lab,
-by = "id")
-# Tabulate:
-janitor::tabyl(dat = copdatalab, RT-PCR_ecoli_etec)
-copdatalab <- left_join(copdata, lab,
-by = "id")
-# Tabulate:
-janitor::tabyl(dat = copdatalab, "RT-PCR_ecoli_etec")
-copdatalab <- left_join(copdata, lab,
-by = "id")
-# Tabulate:
-janitor::tabyl(dat = copdatalab, "RT-PCR_ecoli_etec", "ct value_ecoli_etec")
-copdatalab <- left_join(copdata, lab,
-by = "id")
-# Tabulate:
-janitor::tabyl(dat = copdatalab, "RT-PCR_ecoli_etec")
+scales,
+EpiStats,
+broom)
 # Load the required libraries into the current R session:
 pacman::p_load(rio,
 here,
@@ -101,8 +398,56 @@ scales,
 EpiStats,
 broom)
 # Import the raw data set:
-copdata <- rio::import(here::here("data", "Spetses_clean2_2024.rds"))
-?CS
+copdata <- rio::import(here::here("data", "Spetses_clean2_2024.rds"), trust = T)
+# Check if age overall follows a normal distribution:
+shapiro.test(copdata$age)
+# Can simply have a look at
+hist(copdata$age)
+# Looking only at the students:
+students <- copdata %>%
+filter(group == "student")
+hist(students$age)
+# Perform Wilcoxon rank sum test on age and sex:
+wilcox.test(age ~ case,
+data = copdata)
+copdata %>%
+select(sex, case) %>%
+tbl_summary(by = case) %>%
+add_p()
+# You could use the EpiStats package for each food item
+CS(copdata, "case", "feta")
+CS(copdata, "case", "sardines")
+CS(copdata, "case", "eggplant")
+CS(copdata, "case", "pasta")
+# You can save time (and probably typos!) by creating a vector for food variables...
+food_vars <- c("feta", "sardines", "eggplant", "pasta",
+"veal", "tomsal", "dessert", "bread",
+"champagne", "beer", "redwine", "whitewine")
+# ...and using EpiStats::CSTable() to run all variables together!
+CSTable(copdata, "case", food_vars)
+rr_tbl <- CSTable(copdata, "case", food_vars) %>%
+as.data.frame() %>%
+rownames_to_column() %>%
+flextable() %>%
+set_header_labels(
+values = c("Food Item",
+"Total exposed",
+"Cases exposed",
+"AR among exposed",
+"Total unexposed",
+"Cases unexposed",
+"AR among unexposed",
+"RR",
+"95% lower CI",
+"95% upper CI",
+"p-value"))
+rr_tbl
+# Let's get the results directly exponentiated
+binom_pastaD_exp <- glm(case ~ pastaD, data = copdata,
+family = binomial(link = "log")) %>%
+tidy(exponentiate = TRUE,
+conf.int = TRUE)
+binom_pastaD_exp
 # Load the required libraries into the current R session:
 pacman::p_load(rio,
 here,
@@ -120,14 +465,48 @@ scales,
 EpiStats)
 # Import the raw data set:
 copdata <- rio::import(here::here("data", "Spetses_clean2_2024.rds"))
+# Import the raw data set:
+copdata <- rio::import(here::here("data", "Spetses_clean2_2024.rds"), trust = T)
 stratall <- copdata %>%
 # Mutate across to convert cases to numeric:
 mutate(across(.cols = case,
 .fns = ~ as.numeric(.)))
 # Pass data to the csinter function:
-orzostrata <- csinter(x = stratall,
+pastastrata <- csinter(x = stratall,
+cases = "case",
+exposure = "veal",
+by = "pasta")
+pastastrata
+# Perform Wilcoxon rank sum test on veal and pasta:
+wilcox.test(veal ~ pasta,
+data = copdata)
+# Pass data to the csinter function:
+champstrata <- csinter(x = stratall,
 cases = "case",
-exposure = "moussaka",
-by = "orzo")
-orzostrata
-orzostrata$df1
+exposure = "champagne",
+by = "pasta")
+champstrata
+pastastrata
+csinter(x = stratall,
++                        cases = "case",
+csinter(x = stratall, cases = "case",exposure = "pasta",by = "veal")
+# Load the required libraries into the current R session:
+pacman::p_load(rio,
+here,
+tidyverse,
+skimr,
+plyr,
+janitor,
+lubridate,
+gtsummary,
+flextable,
+officer,
+epikit,
+apyramid,
+scales,
+EpiStats,
+broom)
+# Import the raw data set:
+copdata <- import(here::here("data", "spetses_school.csv"))
+tabyl(copdata$ill)
+View(copdata)
diff --git a/Case_definition.qmd b/Case_definition.qmd
index 88ba379..c168504 100644
--- a/Case_definition.qmd
+++ b/Case_definition.qmd
@@ -1,6 +1,8 @@
 ---
 title: "Case Definition"
 editor: visual
+editor_options: 
+  chunk_output_type: console
 ---
 
 ## 1. Learning outcomes
@@ -11,7 +13,7 @@ At the end of the session, participants will be able to:
 
 ## 2. Story/plot description
 
-You will now create a new column in the data set to hold the case definition you decided in a previous step during your investigation. You can call this column `case` and set it to `TRUE` if the individual meets the case definition criteria and `FALSE` if not. You will use this column later on for any calculations needed (descriptive statistics, two-by-two tables to compute measures of association, etc.) to figure out the culprit of this outbreak.
+You will now create a new column in the data set to hold the case definition you decided in a previous step during your investigation. You can call this column `case` and set it to `1` if the individual meets the case definition criteria and `0` if not. You will use this column later on for any calculations needed (descriptive statistics, two-by-two tables to compute measures of association, etc.) to figure out the culprit of this outbreak.
 
 ## 3. Questions/Assignments
 
@@ -56,13 +58,13 @@ pacman::p_load(rio,
 
 ```{r, Import_data}
 # Import the clean data set:
-copdata <- rio::import(here::here("data", "Spetses_clean1_2024.rds"))
+copdata <- rio::import(here::here("data", "Spetses_clean1_2024.rds"), trust = TRUE)
 ```
 
 ## 3.3 Identify the variables you need to apply the case definition criteria.
 
 ::: {.callout-tip title="Need a little bit of help?" collapse="true"}
-The variables we need from the dataset to apply the above case definition are: `meal`, `onset_datetime`, `diarrhoea`, `bloody` and `vomiting`.
+The variables we need from the dataset to apply the above case definition are: `meal`, `ill`, `onset_datetime`, `diarrhoea`, `bloody` and `vomiting`.
 :::
 
 ## 3.4 Create a new `case` column to hold the binary case definition variable. Let's think about how to do this little by little:
@@ -72,7 +74,7 @@ The variables we need from the dataset to apply the above case definition are: `
 You decide to exclude any people from the cohort who didn't eat at the dinner, because we specifically hypothesised a food item to be the vehicle of infection in this outbreak. Thus, filter your dataset to those who ate a meal: Keep in your dataset only those who ate a meal.
 
 ::: {.callout-tip title="Need a little bit of help?" collapse="true"}
-`filter` by those with `meal == TRUE`.
+`filter` by those with `meal == 1`.
 :::
 
 ::: {.callout-warning title="Let's stop and... think!" collapse="true"}
@@ -81,7 +83,7 @@ What are some of the implications this decision may lead to? (excluding any peop
 
 ```{r}
 copdata <- copdata %>% 
-  filter(meal == TRUE)
+  filter(meal == 1)
 ```
 
 ::: {.callout-note title="Once you've thought about the above, have a look here" collapse="true"}
@@ -89,14 +91,14 @@ Seven of the respondents actually said they did not eat the meal, but when it ca
 
 \- At the survey state, one could adjust the design of an electronic questionnaire to prevent key questions from being skipped. This can come with both pros and cons. Allow fellows to discuss if time allows.
 
-\- Explore your data further, realise this is the case, and recode the `meal` variable for these individuals as `TRUE.` =\> This would be the way to go, but is not what we did in our example because we tried to keep it simple, and also because it is good to show that you may not always clean the data perfectly, and that has consequences: You can highlight the importance or really explore your data in depth.
+\- Explore your data further, realise this is the case, and recode the `meal` variable for these individuals as `1.` =\> This would be the way to go, but is not what we did in our example because we tried to keep it simple, and also because it is good to show that you may not always clean the data perfectly, and that has consequences: You can highlight the importance or really explore your data in depth.
 
 By making the above decision, we may be missing cases and non-cases people, and thus, modifying the final estimate of our measure of association. =\> It is very important to know your data, explore it deeply and try to clean it as well as possible. Every step one makes when cleaning the data may have a consequence, and we should be aware of it when making the data cleaning decisions and when interpreting the results.
 :::
 
 ### b) Fell ill after the start of the meal
 
-We define "fell ill" as any person having had diarrhoea with OR without blood, OR vomiting. To capture this information easily, you will create a new `gastrosymptoms` variable. This variable will indicate that the person had one OR ("or" is R is achieved by using `|`) more of the clinical symptoms in your definition.
+The questionnaire included a question on whether the person fell ill after the school dinner party (column `ill`). However, the person may have experience symptoms that are very unspecific to this outbreak (e.g., headache, joint pain...). We may want to only include those who develop gastrointestinal symptoms. That is any person having had diarrhoea with OR without blood, OR vomiting. To capture this information easily, you will create a new `gastrosymptoms` variable. This variable will indicate that the person had one OR ("or" is R is achieved by using `|`) more of the clinical symptoms in your definition.
 
 Note that we the concept of having eaten a meal is already included as per one of the steps above.
 
@@ -109,7 +111,7 @@ What are some of the implications this decision may lead to? (defining "fell ill
 :::
 
 ::: {.callout-note title="If you've thought about the above, have a look here" collapse="true"}
-Having one clinical symptom enough to be considered a potential case at this point may be considered too unspecific (low specificity). For example, a person who ate at the dinner party and developed diarrhoea for other reasons other than food poisoning (say, they recently started on antibiotics known for unbalancing the intestinal flora and causing diarrhoea) could be misclassified as a potential case. (Note we talk about **potential case**, and not **case**; that is because here we are not talking about cases per-se yet, but this decision has implications for when applying the case definition below).
+Having one gastrointestinal symptom is enough to be considered a potential case at this point, which may still be considered too unspecific (low specificity). For example, a person who ate at the dinner party and developed diarrhoea for other reasons other than food poisoning (say, they recently started on antibiotics known for unbalancing the intestinal flora and causing diarrhoea) could be misclassified as a potential case. (Note we talk about **potential case**, and not **case**; that is because here we are not talking about cases per-se yet, but this decision has implications for when applying the case definition below).
 
 Moreover, those who did not report clinical symptoms will be defined as non-cases. Thus, we are assuming that these individuals did not develop symptoms because they didn't report them. The missing values could be due to, for example, them skipping the questions in the questionnaire. Some individuals may be reluctant to report symptoms, due to shame, fear of repercussion, or others. It is important to think ahead, before the interview, about ways to minimise these situations. For example, through questionnaire design, you may impede skipping questions; you could promote trust by using the right interviewers (in some cases this will be someone from the community, in others someone form specific NGOs, someone of a specific race or gender, etc); choose to carry out online questionnaires vs in person (or vice versa, depending on the situation), etc.
 :::
@@ -118,13 +120,13 @@ Moreover, those who did not report clinical symptoms will be defined as non-case
 copdata <- copdata %>% 
   mutate(gastrosymptoms = case_when(
     # Those had diarrhoea...
-    diarrhoea == TRUE |
+    diarrhoea == "1" |
       #or bloody diarrhoea...
-    bloody == TRUE |
-      # or vomiting, are marked as TRUE (fell ill after the meal)
-    vomiting == TRUE ~ TRUE,
-    # The rest are FALSE. This includes those who ate a meal but had no symptoms (did not fell ill after the meal)
-    .default = FALSE)
+    bloody == "1" |
+      # or vomiting, are marked as 1 (fell ill after the meal)
+    vomiting == "1" ~ 1,
+    # The rest are 0 This includes those who ate a meal but had no symptoms (did not fell ill after the meal)
+    .default = 0)
     )
 ```
 
@@ -178,21 +180,21 @@ What are some of the implications this decision may lead to? (implications of th
 All those not developing at least one symptom (diarrhoea with OR without blood, OR vomiting) 48h after the dinner are considered non-cases. This could (depending on how you decide to analyse your data) include those who had no symptoms at all, those who have missing data on the `onset_datetime` variable, and/or those who had symptoms before eating the meal. *This is a reminder that you need to be both careful and aware of the implications of your data analysis decisions.* If a person had clinical symptoms before eating the meal, they are considered as not-cases. However, it could be that a person had symptoms before the meal, and yet, still got infected by the pathogen when eating their meal (bad luck, we know...). According to this definition, we would be missing that case.
 :::
 
-### d) Finally, with this information you can create a new `case` column to hold the binary (`TRUE`/`FALSE`) case definition variable.
+### d) Finally, with this information you can create a new `case` column to hold the binary (`1`/`0`) case definition variable.
 
 ```{r}
 
 copdata <- copdata %>% 
   mutate(case = case_when(
-    # Those who had symptoms <48h from the meal are cases (TRUE)
-    gastrosymptoms == TRUE & 
+    # Those who had symptoms <48h from the meal are cases (1)
+    gastrosymptoms == 1 & 
       onset_datetime >= meal_datetime &
-      onset_datetime <= (meal_datetime + days(2)) ~ TRUE,
+      onset_datetime <= (meal_datetime + days(2)) ~ 1,
     # Those who had symptoms >48h from the meal are non-cases (FALSE)
-    gastrosymptoms == TRUE & 
-      onset_datetime > (meal_datetime + days(2)) ~ FALSE,
+    gastrosymptoms == 1 & 
+      onset_datetime > (meal_datetime + days(2)) ~ 0,
     # The rest are considered non-cases. Including, those who had no symptoms at all, who have missing data on the onset_datetime variable, or who had symptoms before eating the meal 
-    .default = FALSE)
+    .default = 0)
   )
 ```
 
@@ -219,9 +221,9 @@ Let's have a look at how many people ate a meal, had symptoms, and were consider
 
 ```{r overview}
 copdata %>% 
-  summarise(atemeal = sum(meal == TRUE),
-            hadsympt = sum(gastrosymptoms == TRUE),
-            nb_cases = sum(case == TRUE)
+  summarise(atemeal = sum(meal == 1),
+            hadsympt = sum(gastrosymptoms == 1),
+            nb_cases = sum(case == 1)
             )
 ```
 
diff --git a/Data_import_cleaning.qmd b/Data_import_cleaning.qmd
index 56365fe..d595ce7 100644
--- a/Data_import_cleaning.qmd
+++ b/Data_import_cleaning.qmd
@@ -1,6 +1,8 @@
 ---
 title: "Data import and cleaning"
 editor: visual
+editor_options: 
+  chunk_output_type: console
 ---
 
 # **1. Learning outcomes**
@@ -36,7 +38,6 @@ if (!requireNamespace("pacman", quietly = TRUE)) install.packages("pacman")
 # Load the required libraries into the current R session:
 pacman::p_load(rio, 
                here, 
-               tidyverse, 
                skimr,
                plyr,
                janitor,
@@ -46,7 +47,9 @@ pacman::p_load(rio,
                officer,
                epikit, 
                apyramid, 
-               scales)
+               scales,
+               tidyverse 
+)
 
 ```
 
@@ -56,7 +59,7 @@ pacman::p_load(rio,
 
 ```{r, Import_data}
 # Import the raw data set:
-copdata <- rio::import(here::here("data", "spetses_school.csv"))
+copdata <- import(here::here("data", "spetses_school.csv"))
 ```
 
 ## 3.3. Explore and clean your data
@@ -79,7 +82,7 @@ You could use `head()`, `dim()`, `str()`, or `skim()` to have a quick look at th
 head(copdata)
 dim(copdata)
 str(copdata)
-skimr::skim(copdata)
+skim(copdata)
 names(copdata)
 
 ```
@@ -94,7 +97,7 @@ Through visual exploration of the `age` histogram we see that there is at least
 # Have a look at the histogram  
 hist(copdata$age)   
 # Create cross-tab with the group variable:  
-janitor::tabyl(dat = copdata, 
+tabyl(dat = copdata, 
                var1 = age, 
                var2 = group)
 ```
@@ -113,7 +116,7 @@ Now, have a look at your cross-tab again:
 
 ```{r cross-tab_age_group}
 
-janitor::tabyl(dat = copdata, 
+tabyl(dat = copdata, 
                var1 = age, 
                var2 = group) 
 ```
@@ -165,7 +168,7 @@ drtable <- copdata %>%
   # Select all the columns with column names that end in upper case 'D':   
   select(ends_with("D", ignore.case = FALSE)) %>%       
   # Create the summary table, excluding missing values:   
-  gtsummary::tbl_summary(missing = "no") 
+  tbl_summary(missing = "no") 
 
   # Print the summary table: 
 drtable
@@ -186,7 +189,9 @@ You can use `mutate` to modify the type of the following variables:
 +-------------------------------------------------------------------------------------------------------------+---------------------+-----------------+--------------------------------------------------------------------------------------+
 | class                                                                                                       | integer             | factor          | mutate(), as.factor()                                                                |
 +-------------------------------------------------------------------------------------------------------------+---------------------+-----------------+--------------------------------------------------------------------------------------+
-| All the clinical symptom variables                                                                          | integer             | logical         | mutate(across()), as.logical()                                                       |
+| ill                                                                                                         | integer             | factor          | mutate(), as.factor()                                                                |
++-------------------------------------------------------------------------------------------------------------+---------------------+-----------------+--------------------------------------------------------------------------------------+
+| All the clinical symptom variables                                                                          | integer             | factor          | mutate(across()), as.factor()                                                        |
 +-------------------------------------------------------------------------------------------------------------+---------------------+-----------------+--------------------------------------------------------------------------------------+
 | All the food variables representing the amount of specific foods eaten (those finishing with a capital "D") | integer             | factor          | mutate(across()), as.factor()                                                        |
 +-------------------------------------------------------------------------------------------------------------+---------------------+-----------------+--------------------------------------------------------------------------------------+
@@ -199,15 +204,16 @@ You can use `mutate` to modify the type of the following variables:
 
 : Table 1: Variable types to modify
 
-#### Sex, group and class
+#### Sex, class and ill
 
-Let's start transforming one-by-one the first two variables in the table: `sex`, and `class`.
+Let's start transforming one-by-one the first three variables in the table: `sex`, `class` and `ill`.
 
 ```{r, mutate_simple}
 copdata <- copdata %>% 
-  dplyr::mutate(
+  mutate(
     sex = as.factor(sex),
-    class = as.factor(class))
+    class = as.factor(class),
+    ill = as.factor(ill))
  
 ```
 
@@ -219,11 +225,11 @@ For these variables, we are going to show you a couple of different ways to carr
 
 ```{r, mutate_cs}
 copdata <- copdata %>% 
-  dplyr::mutate(
+  mutate(
     # clinical symptoms
     across(.cols = c(diarrhoea, bloody, vomiting,
              abdo, nausea, fever,headache, jointpain), 
-           .fns = ~ as.logical(.)
+           .fns = ~ as.factor(.)
            )
     )
 ```
@@ -234,18 +240,18 @@ copdata <- copdata %>%
 # Create a vector with all the food variables representing the amount of specific foods items eaten (those finishing with a capital "D")
 # One way of doing it:
 food_dose <- copdata %>% 
-    dplyr::select(
+    select(
       ends_with("D", ignore.case = FALSE)) %>% 
     names()
 
 # Another way of doing it:
-# food_dose <- c("fetaD", "sardinesD", "eggplantD", "moussakaD", 
-#                 "orzoD", "greeksalD", "dessertD", "breadD", 
+# food_dose <- c("fetaD", "sardinesD", "eggplantD", "pastaD", 
+#                 "vealD", "greeksalD", "dessertD", "breadD", 
 #                 "champagneD", "beerD", "redwineD", "whitewineD")
 
 
 copdata <- copdata %>% 
-  dplyr::mutate(
+  mutate(
     # food dose variables
     across(.cols = all_of(food_dose), 
            .fns = ~as.factor(.))) 
@@ -253,7 +259,7 @@ copdata <- copdata %>%
 ```
 
 ::: {.callout-note title="Note" collapse="true"}
-The tilde (`~`) above is used to apply the transformation `as.logical(.)` to each selected column, which in our case is either all columns included in `food_items` and `food_dose.`
+The tilde (`~`) above is used to apply the transformation `as.factor(.)` to each selected column, which in our case is either all columns included in `food_items` and `food_dose.`
 :::
 
 #### Date and time variables
@@ -274,7 +280,7 @@ class(copdata$dayonset)
 # Update copdata:
 copdata <- copdata %>% 
   # Change column to date class:
-  dplyr::mutate(
+  mutate(
     dayonset = lubridate::dmy(dayonset))
 
 # Check class of updated column:
@@ -291,7 +297,7 @@ We can check if we have any missing values by cross-tabulating `starthour` with
 
 ```{r crosstab_dayonset_starthour}
 # Cross-tabulate dayonset with starthour:
-janitor::tabyl(dat = copdata, 
+tabyl(dat = copdata, 
                var1 = starthour, 
                var2 = dayonset)
 ```
@@ -302,7 +308,7 @@ This shows us that there are two respondents who had an onset date, but are miss
 copdata <- copdata %>% 
   # Combine dayonset and starthour in a new date time variable:
   mutate(onset_datetime = 
-           lubridate::ymd_h(
+           ymd_h(
              str_glue("{dayonset}, {starthour}"), 
                                            # Deal with missing starthour:
                                            truncated = 2))
@@ -333,7 +339,7 @@ Use `rio::export()`.
 
 ```{r export_clean_data}
 
-rio::export(x = copdata, 
+export(x = copdata, 
             file = here::here("data", "Spetses_clean1_2024.rds"))
 
 ```
diff --git a/IntroCourse_inCop.Rproj b/IntroCourse_inCop.Rproj
index 8e3c2eb..7b2a3ff 100644
--- a/IntroCourse_inCop.Rproj
+++ b/IntroCourse_inCop.Rproj
@@ -1,4 +1,5 @@
 Version: 1.0
+ProjectId: 7ab6702e-aa74-4dd3-8f10-1a4b9b06add9
 
 RestoreWorkspace: Default
 SaveWorkspace: Default
diff --git a/Lab_and_Descriptive.qmd b/Lab_and_Descriptive.qmd
index 6f22880..c292559 100644
--- a/Lab_and_Descriptive.qmd
+++ b/Lab_and_Descriptive.qmd
@@ -46,7 +46,7 @@ pacman::p_load(rio,
 
 ```{r, Import_data}
 # Import the clean data set:
-copdata <- rio::import(here::here("data", "Spetses_clean2_2024.rds")) 
+copdata <- rio::import(here::here("data", "Spetses_clean2_2024.rds"), trust = T) 
 lab <- rio::import(here::here("data", "Lab data.xlsx"),
                    skip = 1) # skip the first row.
 ```
@@ -76,7 +76,7 @@ What can you infer from the incubation periods in the histogram?
 
 # Create a dataset with only cases
 cases <- copdata %>% 
-  filter(case == TRUE)
+  filter(case == 1)
 
 incplot <- cases %>% 
   # Create an empty ggplot frame:
@@ -359,8 +359,8 @@ symptoms <- c("diarrhoea",
               "jointpain")
 
 # Create nice labels for case definition:
-caselabs <- ggplot2::as_labeller(c(`FALSE` = "Non-case", 
-                                   `TRUE` = "Case"))
+caselabs <- ggplot2::as_labeller(c("0" = "Non-case", 
+                                   "1" = "Case"))
 # Select variables and cases:
 symptom_bar <- copdata %>% 
   # Select symptom columns:
@@ -372,7 +372,7 @@ symptom_bar <- copdata %>%
                names_to = "Symptoms", 
                values_drop_na = TRUE) %>% 
   # Keep only TRUE values:
-  filter(value == TRUE) %>% 
+  filter(value == "1") %>% 
  
    # Group by symptoms and case:
   group_by(Symptoms, case) %>% 
@@ -414,7 +414,7 @@ total_ap <- tabyl(copdata, case) %>%
   # Add percentages with 1 digit after the decimal point:
   adorn_pct_formatting(digits = 1) %>% 
   # Filter to rows where case is TRUE:
-  filter(case == TRUE) %>% 
+  filter(case == "1") %>% 
   # Select the column percent:
   select(percent) %>% 
   # Extract (pull) the value from this cell:
diff --git a/README.md b/README.md
index ff5b0d3..2b9e7a3 100644
--- a/README.md
+++ b/README.md
@@ -8,13 +8,11 @@ This repository contains the R teaching material that can be used (it is not com
 
 The SimOb is an 4-day exercise where fellows are provided a series of injects through which they will follow a 10-steps framework to investigate a specific outbreak. Some of the injects are dedicated to R-exercises that can be proved quite handy during an outbreak investigation. Those injects are maintained in this repository and are stored as different .qmd files, under the root folder:
 
-- Data_import_cleaning.qmd
-- Case_definition.qmd
-- Lab_and_Descriptive.qmd
-- Univariate.qmd
-- Stratified.qmd
-
-
+-   Data_import_cleaning.qmd
+-   Case_definition.qmd
+-   Lab_and_Descriptive.qmd
+-   Univariate.qmd
+-   Stratified.qmd
 
 # REST (FROM TO HEREFORWARDS) NEEDS UPDATING
 
diff --git a/Stratified.qmd b/Stratified.qmd
index 247548d..bc50d6f 100644
--- a/Stratified.qmd
+++ b/Stratified.qmd
@@ -15,7 +15,7 @@ At the end of the session, participants will be able to:
 
 ## 2. Story/plot description
 
-From the univariable analysis, it seems that eating orzpo and eating moussaka as well as drinking champagne are associated with the highest risk of becoming ill. There are, however, many other food items that are associated with an increased risk (even if not statistically significant).
+From the univariable analysis, it seems that eating pasta and eating veal as well as drinking champagne are associated with the highest risk of becoming ill. There are, however, many other food items that are associated with an increased risk (even if not statistically significant).
 
 You should next think about potential confounders and about effect modification. Think about which variables you might want to check for effect modification or confounding. One common strategy is to base this decision on the results obtained in the univariable analysis and a p-value threshold of 0.20-0.25. Also, food items that are known risk factors for gastroenteritis could also be included regardless of their univariable p-value.
 
@@ -49,24 +49,24 @@ pacman::p_load(rio,
 
 ```{r, Import_data}
 # Import the raw data set: 
-copdata <- rio::import(here::here("data", "Spetses_clean2_2024.rds"))
+copdata <- rio::import(here::here("data", "Spetses_clean2_2024.rds"), trust = T)
 ```
 
 ## 3.4. Consider and assess for confounding and/or effect modification
 
-Have a look at the relative risk for being a case having eaten a specific food item (for example, moussake), when stratified by another variable (for example, orzo). You may consider stratifying by orzo, as it has the highest RR in the univariable analysis.
+Have a look at the relative risk for being a case having eaten a specific food item (for example, veal), when stratified by another variable (for example, pasta). You may consider stratifying by pasta, as it has the highest RR in the univariable analysis.
 
 There are many variables in this dataset and it might not make sense to stratify each variable by each other variable on our search for effect modifiers and confounders.
 
-However, we also don't want to be too restrictive as a variable which actually (i.e. causally) is associated with the outcome might not show a significant association at the significant level we decided (say 5%) in the univariable analysis due to confounding. Therefore, we could test all variables statistically significant at the 15%, 20%, or 25% level (specific percentage to be decided by your group). In our solutions here, we are looking at moussaka and champagne, stratified by orzo, but you may decide to look at other food items as well.
+However, we also don't want to be too restrictive as a variable which actually (i.e. causally) is associated with the outcome might not show a significant association at the significant level we decided (say 5%) in the univariable analysis due to confounding. Therefore, we could test all variables statistically significant at the 15%, 20%, or 25% level (specific percentage to be decided by your group). In our solutions here, we are looking at veal and champagne, stratified by pasta, but you may decide to look at other food items as well.
 
 ::: {.callout-tip title="Need a little bit of help?" collapse="true"}
 Use the function `csinter()` of the EpiStats package.
 :::
 
-### a) Moussaka as exposure of interest, stratified by having eaten orzo
+### a) Veal as exposure of interest, stratified by having eaten pasta
 
-If we stratify the **effect of moussaka by orzo** we ask the question: does eating orzo modify or confound the association between eating moussaka and being a case?
+If we stratify the **effect of veal by pasta** we ask the question: does eating pasta modify or confound the association between eating veal and being a case?
 
 ```{r}
 stratall <- copdata %>% 
@@ -75,42 +75,42 @@ stratall <- copdata %>%
                 .fns = ~ as.numeric(.)))
 
 # Pass data to the csinter function:
-orzostrata <- csinter(x = stratall, 
+pastastrata <- csinter(x = stratall, 
                        cases = "case", 
-                       exposure = "moussaka", 
-                       by = "orzo")
+                       exposure = "veal", 
+                       by = "pasta")
 
-orzostrata
+pastastrata
 ```
 
-Let's check if orzo is associated with moussaka (if we are thinking moussaka may be a confounder, we need to see if there is an association between the potential confounder (moussaka) and the exposure (orzo)):
+Let's check if pasta is associated with veal (if we are thinking pasta may be a confounder, we need to see if there is an association between the potential confounder (pasta) and the exposure (veal)):
 
 ```{r}
-# Perform Wilcoxon rank sum test on orzo and moussaka:
-wilcox.test(orzo ~ moussaka, 
+# Perform Wilcoxon rank sum test on veal and pasta:
+wilcox.test(veal ~ pasta, 
             data = copdata)
 ```
 
-### b) Champagne as exposure of interest, stratified by having eaten orzo
+### b) Champagne as exposure of interest, stratified by having eaten pasta
 
 ```{r}
 # Pass data to the csinter function:
 champstrata <- csinter(x = stratall, 
                        cases = "case", 
                        exposure = "champagne", 
-                       by = "orzo")
+                       by = "pasta")
 ```
 
 ::: {.callout-warning title="Let's stop and... think!" collapse="true"}
 Are there any indications to make you think there may be effect modification and/or confounding?
 :::
 
-We could stratify by orzo (the strongest risk factor in the univariable analysis), to examine if orzo confounds the association between eating moussaka and being a case. Before stratification, we will need to check if orzo meets the conditions of being a confounder. For a variable to be a confounder it needs to be associated both with the outcome (being a case) and with the exposure (and not be in the causal pathway between exposure and outcome). We know from univariable analysis that orzo is associated with being a case. If we run a Wilcoxon rank sum test, we will see that orzo is also associated with moussaka (indeed, you can see that most people either had both moussaka and orzo or neither of these food items, so they are associated with each other).
+We could stratify by pasta (the strongest risk factor in the univariable analysis), to examine if pasta confounds the association between eating veal and being a case. Before stratification, we will need to check if pasta meets the conditions of being a confounder. For a variable to be a confounder it needs to be associated both with the outcome (being a case) and with the exposure (and not be in the causal pathway between exposure and outcome). We know from univariable analysis that pasta is associated with being a case. If we run a Wilcoxon rank sum test, we will see that pasta is also associated with veal (indeed, you can see that most people either had both pasta and veal or neither of these food items, so they are associated with each other).
 
-Above, we uses `ccinter` to stratify and save the object as "orzostrata".
+Above, we uses `ccinter` to stratify and save the object as "pastastrata".
 
-orzostrata: Within the stratum of the people who ate orzo, moussaka has no significant effect (RR = 1.20, CI: 0.60 - 2.41). The same holds within the stratum of people who didn't eat orzo (RR = 1.05, CI = 0.38, 2.92). The adjusted MH-RR also suggests that moussaka has no effect (RRadj = 1.15, CI: 0.80 - 2.85).  To identify confounding, we want to look at the % change between the crude and the adjusted RR. This is given by the csinter output "Adjusted/crude relative change".  The difference between the crude and the MH-RR in this case is \>20% suggesting that orzo confounds the association between moussaka and the disease.
+pastastrata: Within the stratum of the people who ate pasta, veal has no significant effect (RR = 1.20, CI: 0.60 - 2.41). The same holds within the stratum of people who didn't eat pasta (RR = 1.05, CI = 0.38, 2.92). The adjusted MH-RR also suggests that veal has no effect (RRadj = 1.15, CI: 0.80 - 2.85).  To identify confounding, we want to look at the % change between the crude and the adjusted RR. This is given by the csinter output "Adjusted/crude relative change".  The difference between the crude and the MH-RR in this case is \>20% suggesting that pasta confounds the association between veal and the disease.
 
-This result suggest that moussaka is not a risk factor of the disease and that the crude observed effect was due to the confounding effect of orzo.
+This result suggest that veal is not a risk factor of the disease and that the crude observed effect was due to the confounding effect of pasta.
 
-If you stratify by moussaka, you see that moussaka does not confound the association between orzo and the disease. The same applies if you stratify the exposure to orzo by other variables. The above, the higher RR for orzo and the dose response relationship we found earlier for orzo (remember this was optional) provide additional evidence that there was something going on with the orzo with pesto dish!
+If you stratify by veal, you see that veal does not confound the association between pasta and the disease. The same applies if you stratify the exposure to pasta by other variables. The above, the higher RR for pasta and the dose response relationship we found earlier for pasta (remember this was optional) provide additional evidence that there was something going on with the pasta with pesto dish!
diff --git a/Univariate.qmd b/Univariate.qmd
index 33fcf33..84dffc8 100644
--- a/Univariate.qmd
+++ b/Univariate.qmd
@@ -65,7 +65,7 @@ pacman::p_load(rio,
 
 ```{r, Import_data}
 # Import the raw data set:
-copdata <- rio::import(here::here("data", "Spetses_clean2_2024.rds"))
+copdata <- rio::import(here::here("data", "Spetses_clean2_2024.rds"), trust = T)
 ```
 
 ## 3.3. Hypothesis tests for other variables
@@ -169,13 +169,13 @@ Create a `food_vars` vector containing food variables of interest and use the fu
 CS(copdata, "case", "feta")
 CS(copdata, "case", "sardines")
 CS(copdata, "case", "eggplant")
-CS(copdata, "case", "moussaka")
+CS(copdata, "case", "pasta")
 ```
 
 ```{r}
 # You can save time (and probably typos!) by creating a vector for food variables...
-food_vars <- c("feta", "sardines", "eggplant", "moussaka", 
-               "orzo", "greeksal", "dessert", "bread",  
+food_vars <- c("feta", "sardines", "eggplant", "pasta", 
+               "veal", "tomsal", "dessert", "bread",  
                "champagne", "beer", "redwine", "whitewine")
 
 # ...and using EpiStats::CSTable() to run all variables together!
@@ -215,68 +215,68 @@ rr_tbl <- CSTable(copdata, "case", food_vars) %>%
 · Do you think there are any confounders or effect modifiers? If so, how would you investigate these further?
 :::
 
-The interesting results here are that the food items that are most suspicious are orzo, moussaka and champagne. Orzo as such is unlikely to be contaminated, but as you can see in the picture (and from the dinner night), it was served with pesto! Maybe it was the pesto? Who-ho-ho!
+The interesting results here are that the food items that are most suspicious are pasta,veal and champagne. Pasta as such is unlikely to be contaminated, but as you can see in the picture (and from the dinner night), it was served with pesto! Maybe it was the pesto? Who-ho-ho!
 
-Before one jumps into conclusions, consider that this result could be due to confounding! Maybe orzo was "clean" but eaten by all the people who ate the food item that actually was contaminated!(Optional)
+Before one jumps into conclusions, consider that this result could be due to confounding! Maybe pasta was "clean" but eaten by all the people who ate the food item that actually was contaminated!(Optional)
 
 ## 3.5. (Optional) Dose Response
 
 Check for a dose-response relationship between the food items with the highest RR values (the top 3) and being a case.
 
-#### a) Orzo
+#### a) Pasta
 
 Using `epitools::riskratio` function:
 
 ```{r}
-epitools::riskratio(copdata$orzoD,
-                    copdata$case,
+epitools::riskratio(copdata$pastaD,                     
+                    copdata$case,                     
                     conf.level = 0.95)
 ```
 
 Using a binomial regression:
 
 ```{r}
-# Binomial regression for RRs. 
-# The outcome needs to be exponentiated so we can interpret it properly!
-binom_orzoD <- glm(case ~ orzoD, data = copdata, 
-             family = binomial(link = "log"))
 
-# To get exponentiated:
-binom_orzoD_exp <- glm(case ~ orzoD, data = copdata, 
+# Let's get the results directly exponentiated
+binom_pastaD_exp <- glm(case ~ pastaD, data = copdata, 
                        family = binomial(link = "log")) %>% 
   tidy(exponentiate = TRUE, 
        conf.int = TRUE)
 
-binom_orzoD_exp
+binom_pastaD_exp
 ```
 
 ::: {.callout-warning title="Let's stop and... think!" collapse="true"}
 What do these results tell you?
 :::
 
-Results suggest a dose response relationship of having eaten orzo, pointing towards orzoas the potential vehicle. The higher the amount of orzothey ate, the stronger is the association (RR) with getting ill/being a case.
+Results suggest a dose response relationship of having eaten pasta, pointing towards pasta as the potential vehicle. The higher the amount of pasta they ate, the stronger is the association (RR) with getting ill/being a case.
 
-#### b) Moussaka
+#### b) Veal
 
 Using `epitools::riskratio` function:
 
 ```{r}
-epitools::riskratio(copdata$moussakaD,                     
-                    copdata$case,                     
+epitools::riskratio(copdata$vealD,
+                    copdata$case,
                     conf.level = 0.95)
 ```
 
 Using a binomial regression:
 
 ```{r}
+# Binomial regression for RRs. 
+# The outcome needs to be exponentiated so we can interpret it properly!
+binom_vealD <- glm(case ~ vealD, data = copdata, 
+             family = binomial(link = "log"))
 
-# Let's get the results directly exponentiated
-binom_moussakaD_exp <- glm(case ~ moussakaD, data = copdata, 
+# To get exponentiated:
+binom_vealD_exp <- glm(case ~ vealD, data = copdata, 
                        family = binomial(link = "log")) %>% 
   tidy(exponentiate = TRUE, 
        conf.int = TRUE)
 
-binom_moussakaD_exp
+binom_vealD_exp
 ```
 
 #### c) Champagne
diff --git a/data/Spetses_clean1_2024.rds b/data/Spetses_clean1_2024.rds
index 28ce645..bec68e0 100644
Binary files a/data/Spetses_clean1_2024.rds and b/data/Spetses_clean1_2024.rds differ
diff --git a/data/Spetses_clean2_2024.rds b/data/Spetses_clean2_2024.rds
index 2bb10ad..88652f7 100644
Binary files a/data/Spetses_clean2_2024.rds and b/data/Spetses_clean2_2024.rds differ
diff --git a/data/spetses_school.csv b/data/spetses_school.csv
index e4c7916..f347db1 100644
--- a/data/spetses_school.csv
+++ b/data/spetses_school.csv
@@ -1,398 +1,398 @@
-id,sex,age,group,class,diarrhoea,bloody,vomiting,abdo,nausea,fever,headache,jointpain,starthour,meal,feta,fetaD,sardines,sardinesD,eggplant,eggplantD,moussaka,moussakaD,orzo,orzoD,greeksal,greeksalD,bread,breadD,dessert,dessertD,champagne,champagneD,beer,beerD,redwine,redwineD,whitewine,whitewineD,dayonset
-sp-2-001,male,18,1,2,1,0,0,1,0,,0,0,9,1,1,2,1,2,0,0,1,2,1,3,1,1,1,2,1,2,1,1,1,3,0,0,0,0,6oct2024
-sp-3-003,female,18,1,3,,,,,,,,,,1,0,0,0,0,0,0,1,1,1,3,1,3,1,3,1,3,1,1,0,0,1,3,0,0,
-sp-1-005,female,17,1,1,,,,1,1,,1,,,1,,,,,,,1,0,1,1,,,1,1,,,0,0,0,0,0,0,0,0,
-sp-2-006,male,17,1,2,,,,,,,,,,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,3,1,3,1,3,1,3,
-sp-3-007,female,18,1,3,1,0,0,1,1,0,1,0,15,1,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,1,1,2,0,0,1,3,6oct2024
-sp-2-008,male,180,1,2,1,0,0,1,0,0,0,0,15,1,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,1,1,3,0,0,1,2,7oct2024
-sp-0-009,male,16,0,,,,,,,,,,,1,1,2,1,2,1,2,1,1,1,2,1,2,1,2,0,0,1,2,,,1,2,1,1,
-sp-1-010,female,15,1,1,0,0,0,0,1,0,1,0,,1,1,1,0,0,0,0,1,3,1,3,1,2,1,2,1,1,1,1,1,1,0,0,1,3,
-sp-0-011,female,43,0,,1,,,,1,,1,1,3,1,1,2,1,2,,,1,2,1,2,1,2,1,2,,,1,1,,,1,2,,,7oct2024
-sp-1-012,male,16,1,1,,,,,,,,,,1,1,2,1,2,1,2,1,2,1,3,1,2,1,2,,,1,1,1,2,0,0,0,0,
-sp-2-013,male,18,1,2,,,,,,,,,,1,0,0,0,0,0,0,1,1,1,1,0,0,0,0,0,0,0,0,1,3,0,0,1,3,
-sp-3-015,male,18,1,3,1,0,0,1,1,0,1,1,21,1,0,0,0,0,0,0,1,3,1,2,0,0,1,2,0,0,1,3,1,3,0,0,0,0,6oct2024
-sp-3-016,male,20,1,3,1,0,0,1,0,0,0,,9,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,3,1,3,0,0,0,0,6oct2024
-sp-0-017,male,58,0,,1,,,,1,,,,21,1,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,,,,,1,3,,,6oct2024
-sp-3-018,male,19,1,3,1,0,0,1,1,0,0,0,9,1,0,0,0,0,0,0,1,2,1,3,1,1,1,2,1,2,1,3,1,3,0,0,0,0,7oct2024
-sp-0-019,male,17,1,,,,,,,,,,,1,0,0,0,0,0,0,1,2,1,2,0,0,1,2,0,0,1,1,1,3,1,1,1,1,
-sp-0-020,female,39,0,,0,0,0,1,1,0,1,,,1,1,2,1,2,1,2,1,3,1,3,1,2,1,2,1,1,1,1,,,1,2,1,1,
-sp-2-021,male,18,1,2,1,1,0,1,1,,1,1,21,1,0,0,1,1,1,2,1,3,1,3,1,2,1,2,1,3,1,3,1,3,1,3,1,1,6oct2024
-sp-2-022,male,17,1,2,,,,,,,,,,1,1,2,0,0,1,2,1,2,1,2,1,2,1,2,1,2,1,3,1,3,1,1,1,3,
-sp-2-023,female,17,1,2,1,0,1,1,1,0,1,0,9,1,0,0,0,0,0,0,1,2,1,2,0,0,1,2,0,0,1,1,1,1,0,0,1,2,6oct2024
-sp-1-024,female,16,1,1,,,,,,,,,,1,1,2,1,2,1,2,1,2,1,2,1,2,1,2,0,0,1,1,1,1,1,1,1,3,
-sp-1-025,female,17,1,1,,,,,,,,,,1,1,2,1,2,1,2,1,3,1,3,1,2,1,1,0,0,1,1,1,1,1,1,1,3,
-sp-3-026,female,19,1,3,0,0,0,1,1,0,1,0,,1,1,3,1,3,1,3,1,2,1,2,1,2,1,2,1,2,0,0,0,0,1,1,1,1,
-sp-2-027,male,17,1,2,1,,,,,,,,15,1,0,0,0,0,,,1,2,1,2,,,1,2,0,0,,,1,3,1,1,1,1,6oct2024
-sp-3-028,female,18,1,3,0,0,0,1,1,1,1,,,1,0,0,1,2,0,0,1,2,1,2,0,0,1,1,0,0,1,2,0,0,0,0,0,0,
-sp-1-029,male,16,1,1,1,0,0,1,1,1,1,0,15,1,1,2,1,2,1,2,1,2,1,3,1,3,1,2,1,2,0,0,1,3,0,0,1,1,6oct2024
-sp-1-030,male,16,1,1,1,,,,1,,1,,9,1,1,1,0,0,1,2,1,3,1,2,0,0,1,2,0,0,0,0,1,2,0,0,1,1,6oct2024
-sp-2-031,male,18,1,2,1,0,0,1,1,0,1,0,15,1,1,2,0,0,1,2,1,1,1,2,0,0,,,1,1,1,1,1,3,0,0,0,0,6oct2024
-sp-2-032,female,18,1,2,1,0,0,1,1,,0,0,9,1,1,2,0,0,0,0,1,3,1,3,0,0,1,2,1,3,1,1,1,3,,,,,6oct2024
-sp-3-033,female,19,1,3,1,,,1,1,0,0,0,21,1,0,0,1,1,0,0,1,1,1,1,,,0,0,,,1,2,1,3,0,0,0,0,6oct2024
-sp-2-034,female,17,1,2,1,,0,1,1,1,1,1,9,1,1,1,1,1,0,0,1,1,1,2,1,2,1,2,1,2,1,2,0,0,0,0,1,3,6oct2024
-sp-1-036,male,16,1,1,,,,,,,,,,1,1,3,0,0,0,0,1,0,1,2,1,2,1,2,0,0,1,1,1,3,0,0,0,0,
-sp-2-037,female,17,1,2,0,0,0,1,0,0,0,0,,1,0,0,0,0,0,0,1,2,1,2,0,0,0,0,1,2,1,1,1,3,0,0,1,3,
-sp-1-038,female,17,1,1,1,0,0,1,1,0,1,0,15,1,0,0,0,0,0,0,1,2,1,1,0,0,1,2,0,0,1,1,1,3,0,0,0,0,6oct2024
-sp-1-039,female,17,1,1,1,,,1,1,,,,15,1,1,1,1,2,,,1,0,1,2,0,0,1,1,0,0,1,1,1,3,0,0,1,3,6oct2024
-sp-3-040,male,19,1,3,1,0,1,1,1,,0,0,9,1,1,2,1,2,1,2,1,3,1,2,1,2,1,2,1,2,1,3,1,3,0,0,0,0,6oct2024
-sp-2-041,female,16,1,2,0,0,0,1,0,0,0,0,,1,1,1,1,2,0,0,1,2,1,2,1,2,1,2,0,0,1,1,1,1,0,0,1,1,
-sp-2-042,male,17,1,2,1,0,0,0,1,1,0,0,9,1,1,1,0,0,0,0,1,3,1,2,1,2,1,2,0,0,1,1,1,3,0,0,0,0,6oct2024
-sp-0-045,female,18,1,,1,0,0,1,1,0,0,0,3,1,0,0,0,0,0,0,1,1,0,0,0,0,1,2,0,0,1,1,1,3,0,0,0,0,6oct2024
-sp-2-046,male,17,1,2,1,0,0,1,0,0,0,1,9,1,1,2,1,2,1,2,,2,,,,,1,2,1,2,1,2,1,2,1,2,1,2,6oct2024
-sp-2-047,male,18,1,2,,,,,,,,,,1,0,0,0,0,0,0,1,3,1,3,0,0,1,2,0,0,0,0,1,2,0,0,0,0,
-sp-1-048,female,17,1,1,,,,1,,,,,,1,1,2,1,2,1,2,1,2,1,2,1,2,1,1,0,0,1,1,0,0,0,0,0,0,
-sp-1-049,female,16,1,1,1,0,1,1,1,0,1,1,9,1,0,0,0,0,0,0,1,2,1,1,0,0,1,2,1,2,1,1,1,2,0,0,1,3,6oct2024
-sp-2-050,female,19,1,2,,1,,1,1,,,,9,1,1,2,1,2,0,0,1,2,1,2,1,3,1,2,,,1,1,1,3,1,1,1,3,6oct2024
-sp-3-051,female,18,1,3,0,0,0,1,1,0,0,0,,1,1,3,1,2,1,2,1,2,1,3,1,3,1,2,1,3,1,1,1,1,0,0,1,2,
-sp-0-052,male,56,0,,,,,,,,,,,1,1,3,1,3,1,3,1,3,1,3,1,3,1,3,1,3,1,2,,,1,2,,,
-sp-2-053,female,17,1,2,,,,,,,,,,1,1,1,0,0,0,0,1,0,1,2,0,0,1,2,0,0,1,1,1,3,0,0,1,2,
-sp-1-054,female,16,1,1,1,0,0,1,1,0,1,0,21,1,0,0,0,0,0,0,1,0,1,1,1,1,1,2,0,0,1,1,0,0,0,0,0,0,6oct2024
-sp-0-055,male,18,1,,1,,,1,,,1,,15,1,1,2,1,2,1,2,1,2,1,2,0,0,1,2,1,1,1,3,1,3,0,0,0,0,6oct2024
-sp-2-056,female,18,1,2,,,,,,,,,,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,2,1,3,0,0,1,1,
-sp-1-057,male,16,1,1,1,0,1,1,1,0,0,0,15,1,0,0,0,0,1,1,1,2,1,2,1,1,1,2,,,1,1,1,3,0,0,1,2,6oct2024
-sp-3-058,male,20,1,3,,,,1,,,,,21,1,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,3,1,3,1,3,,,6oct2024
-sp-3-059,female,18,1,3,1,0,1,1,1,,1,,9,1,1,2,1,2,1,2,1,1,1,2,1,2,1,2,0,0,1,1,1,2,0,0,1,1,6oct2024
-sp-2-060,female,18,1,2,1,0,0,0,1,0,1,0,15,1,1,1,0,0,1,1,1,1,1,1,1,2,1,2,1,1,1,1,1,3,0,0,1,1,7oct2024
-sp-1-062,male,16,1,1,1,0,0,1,1,0,1,0,15,1,1,2,1,2,1,2,1,2,1,2,,,1,2,,,0,0,1,3,0,0,1,3,6oct2024
-sp-3-063,female,19,1,3,1,,0,1,1,,1,0,9,1,1,1,0,0,0,0,1,2,1,2,1,2,1,2,0,0,1,3,1,1,0,0,1,2,6oct2024
-sp-1-064,male,15,1,1,,,,,,,,,,1,0,0,1,1,0,0,1,3,0,0,0,0,1,2,0,0,1,1,,,,,1,3,
-sp-1-066,male,16,1,1,,,,,,,,,,1,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,1,1,3,0,0,1,3,
-sp-1-067,female,17,1,1,0,0,0,0,1,1,1,0,,1,1,2,1,2,1,2,1,1,1,2,1,1,1,2,0,0,1,1,1,1,,,1,1,
-sp-1-068,female,17,1,1,,,,,,,,,,1,1,2,1,2,1,2,1,2,0,0,1,2,0,0,1,2,1,1,1,2,0,0,1,1,
-sp-2-069,female,16,1,2,,,,,,,,,,1,0,0,0,0,0,0,1,1,1,2,0,0,1,1,0,0,1,1,1,3,0,0,1,1,
-sp-1-070,male,16,1,1,,,,,,,,,,1,1,2,1,2,1,3,1,3,0,0,0,0,1,2,0,0,0,0,1,3,0,0,1,3,
-sp-1-072,female,17,1,1,1,1,0,1,1,1,1,1,9,1,1,2,1,2,1,2,1,2,1,2,1,2,1,2,,,1,1,,,,,1,3,6oct2024
-sp-2-073,male,18,1,2,0,0,0,1,1,0,1,0,,1,0,0,0,0,0,0,1,2,0,0,0,0,1,3,0,0,1,1,1,3,0,0,0,0,
-sp-1-075,male,15,1,1,,,,,,,,,,1,1,2,1,2,1,2,1,0,1,2,1,2,1,2,1,2,0,0,0,0,0,0,0,0,
-sp-1-076,female,17,1,1,1,,,,,,1,,9,1,0,0,0,0,0,0,1,3,1,1,0,0,1,1,0,0,1,2,1,1,,,1,3,6oct2024
-sp-1-077,female,17,1,1,,,,1,1,,1,,,1,0,0,0,0,0,0,1,1,1,1,0,0,0,0,0,0,1,1,0,0,0,0,1,3,
-sp-3-078,female,18,1,3,,,,,,,,,,1,1,1,1,1,0,0,1,2,1,2,1,2,1,2,1,2,1,1,1,3,0,0,1,2,
-sp-2-079,female,18,1,2,1,0,1,1,1,,,0,9,1,1,1,0,0,0,0,1,2,1,2,1,2,1,2,0,0,1,2,1,2,0,0,1,2,6oct2024
-sp-1-080,female,16,1,1,1,0,0,1,1,0,1,0,9,1,0,0,1,1,1,2,1,1,1,1,0,0,1,2,0,0,1,2,1,3,0,0,1,3,6oct2024
-sp-3-081,male,18,1,3,1,0,0,0,1,0,0,0,9,1,1,2,1,2,1,2,1,2,1,3,1,2,1,2,1,2,1,3,1,3,0,0,1,2,6oct2024
-sp-2-082,female,16,1,2,,,,,,,,,,1,0,0,0,0,0,0,1,2,1,3,1,1,0,0,0,0,1,1,1,3,0,0,1,1,
-sp-1-083,male,16,1,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-sp-2-084,male,17,1,2,1,0,1,1,1,0,1,0,15,1,1,1,0,0,1,2,1,2,1,2,1,2,1,2,1,2,1,1,1,3,0,0,0,0,6oct2024
-sp-1-085,female,16,1,1,,,,,,,,,,1,1,2,1,2,1,2,1,1,1,1,0,0,1,1,1,1,1,1,1,2,0,0,1,3,
-sp-2-086,female,17,1,2,1,0,1,1,1,0,0,0,9,1,1,1,1,2,1,2,1,2,1,2,0,0,1,2,0,0,1,1,1,1,0,0,1,2,6oct2024
-sp-1-087,female,16,1,1,,,,,,,,,,1,1,2,1,2,0,0,1,2,1,2,1,2,1,2,0,0,1,1,1,3,0,0,0,0,
-sp-1-088,female,17,1,1,,,,,,,,,,1,1,1,1,2,1,1,1,2,1,1,1,1,0,0,0,0,1,1,0,0,0,0,1,3,
-sp-2-089,female,18,1,2,,,,1,,,,,,1,1,1,1,1,0,0,1,2,0,0,0,0,1,2,0,0,1,1,1,3,0,0,0,0,
-sp-0-090,male,16,1,,,,,,,,,,,1,1,2,1,2,1,2,1,3,1,3,0,0,1,2,1,3,1,1,0,0,0,0,0,0,
-sp-3-091,female,19,1,3,0,0,0,1,0,0,0,0,,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,3,0,0,0,0,0,0,
-sp-2-092,female,16,1,2,1,,,1,,,,,15,1,0,0,0,0,0,0,1,1,1,1,1,1,1,1,0,0,1,1,0,0,0,0,1,3,6oct2024
-sp-3-093,female,19,1,3,0,0,1,1,1,,1,0,21,1,1,1,1,1,1,1,1,1,0,0,1,1,1,2,1,1,1,3,0,0,0,0,0,0,5oct2024
-sp-2-094,female,16,1,2,,,,1,,,1,,,1,1,2,,,,,1,2,1,2,,,1,2,1,2,,,,,,,1,3,
-sp-1-095,female,16,1,1,1,0,0,1,0,0,1,0,15,1,1,2,1,2,1,2,1,2,1,2,1,2,1,2,,,1,2,1,3,1,3,0,0,6oct2024
-sp-1-096,male,16,1,1,,,,,,,,,,1,1,3,1,3,1,3,1,3,1,3,1,3,,,1,3,,,1,2,,,,,
-sp-3-097,female,17,1,3,1,0,0,1,1,0,1,0,9,1,1,3,0,0,1,2,1,2,1,3,1,2,1,2,1,3,1,3,1,3,0,0,1,1,6oct2024
-sp-1-099,female,17,1,1,,,,,,,,,,1,1,2,1,2,1,1,1,2,1,2,1,2,1,2,1,2,1,1,1,1,1,1,1,3,
-sp-2-101,male,17,1,2,1,0,0,1,0,0,0,0,15,1,1,2,1,3,0,0,1,3,1,3,0,0,1,2,1,2,1,1,1,3,0,0,0,0,6oct2024
-sp-2-102,female,18,1,2,1,0,0,0,1,,1,,15,1,0,0,1,2,0,0,1,2,1,2,1,2,1,2,0,0,1,3,1,3,0,0,1,1,6oct2024
-sp-0-103,male,16,1,,,,,,,,,,,1,1,1,1,1,1,1,1,1,1,1,,,1,1,1,1,0,0,1,1,0,0,1,2,
-sp-3-104,female,18,1,3,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-sp-0-105,male,18,1,,1,,,,1,,1,,15,1,1,2,1,2,1,2,1,3,1,3,1,2,,,1,2,1,1,1,3,0,0,0,0,6oct2024
-sp-1-106,female,16,1,1,1,0,0,1,1,,1,,9,1,1,2,1,2,0,0,1,2,1,3,1,3,1,1,1,3,0,0,1,1,0,0,1,3,6oct2024
-sp-1-107,female,17,1,1,,,,,,,,,,0,,,,,,,,,,,,,,,,,,,,,,,,,
-sp-1-108,male,16,1,1,1,0,0,0,0,0,0,0,21,1,0,0,0,0,1,2,1,2,1,2,0,0,1,2,0,0,1,1,1,3,0,0,1,1,6oct2024
-sp-0-109,female,17,1,,1,,,1,,,,1,9,1,1,2,1,2,1,2,1,2,1,2,1,2,1,2,,,1,1,1,1,0,0,1,3,6oct2024
-sp-2-110,female,17,1,2,1,,,1,,,1,,9,1,1,1,1,1,0,0,1,0,1,1,1,1,1,1,0,0,1,1,1,1,,,1,2,6oct2024
-sp-1-111,male,17,1,1,1,0,1,1,1,0,1,1,15,1,1,1,1,3,0,0,1,3,1,2,0,0,1,2,1,2,1,3,1,3,1,1,1,3,6oct2024
-sp-2-112,male,17,1,2,0,0,0,1,0,0,0,0,,1,1,2,1,2,1,2,1,2,1,2,1,2,1,2,,,1,1,1,3,0,0,1,2,
-sp-0-113,female,17,1,,,,,,,,,,,1,1,2,1,2,0,0,1,2,1,2,1,2,1,2,1,2,1,2,1,3,0,0,1,2,
-sp-1-114,male,16,1,1,1,0,0,1,0,0,0,0,9,1,1,2,1,2,1,2,1,2,1,2,0,0,1,2,1,2,1,3,1,3,1,3,1,1,7oct2024
-sp-3-115,male,18,1,3,0,0,0,1,1,0,1,0,,1,1,3,1,3,1,3,1,3,1,3,1,3,1,3,0,0,1,3,1,3,0,0,0,0,
-sp-1-116,male,17,1,1,1,,1,,1,,,,9,1,1,2,0,0,1,2,1,3,1,3,0,0,1,3,0,0,1,3,1,3,0,0,1,2,6oct2024
-sp-3-117,male,18,1,3,0,0,0,0,0,0,0,0,,1,0,0,1,2,1,1,1,3,1,2,1,1,1,2,0,0,1,3,1,3,0,0,1,1,
-sp-1-118,male,16,1,1,0,0,0,1,,0,0,0,,1,1,2,1,2,1,2,1,3,1,3,1,1,1,2,1,3,1,1,1,3,1,1,1,1,
-sp-1-119,male,17,1,1,1,0,0,1,0,0,0,1,21,1,1,1,1,2,,,1,3,1,3,0,0,1,2,0,0,1,3,1,2,0,0,1,3,6oct2024
-sp-0-120,male,17,1,,1,0,1,1,1,,1,,9,1,1,2,1,2,1,2,1,3,1,1,0,0,1,1,1,3,1,1,1,3,0,0,1,1,6oct2024
-sp-1-121,male,16,1,1,0,0,0,1,0,,1,0,,1,0,0,0,0,0,0,1,3,1,1,0,0,1,3,0,0,1,1,1,3,0,0,0,0,
-sp-3-122,female,18,1,3,0,0,0,1,1,1,1,0,,1,1,1,0,0,1,2,1,2,1,3,0,0,1,2,1,3,1,3,1,3,0,0,0,0,
-sp-1-123,male,16,1,1,,,,,,,,,,1,0,0,0,0,1,2,1,2,1,2,1,2,1,2,0,0,0,0,1,3,0,0,1,3,
-sp-1-124,female,16,1,1,1,,1,1,,,1,,9,1,0,0,0,0,0,0,1,1,0,0,0,0,1,2,0,0,1,1,0,0,1,1,1,1,7oct2024
-sp-2-125,female,18,1,2,1,0,0,1,1,0,0,0,9,1,1,2,1,2,,,1,0,1,1,1,1,1,2,0,0,1,2,1,3,0,0,1,3,6oct2024
-sp-1-126,male,17,1,1,0,0,0,0,0,0,1,0,,1,1,2,1,2,1,2,1,2,1,2,0,0,1,2,0,0,1,1,1,3,0,0,0,0,
-sp-3-127,female,18,1,3,1,0,0,1,1,,,,15,1,1,2,1,2,,,1,2,1,2,1,2,1,2,1,2,1,2,1,2,0,0,0,0,6oct2024
-sp-2-128,female,18,1,2,0,0,0,1,1,0,0,0,,1,1,1,0,0,1,1,1,1,1,2,1,2,1,2,0,0,1,1,1,2,0,0,1,2,
-sp-1-129,female,17,1,1,1,0,0,1,1,0,1,0,9,1,1,3,1,3,1,3,1,3,1,3,1,2,1,2,0,0,1,1,1,1,0,0,1,1,6oct2024
-sp-1-131,female,16,1,1,1,0,0,1,1,0,0,0,9,1,0,0,0,0,1,1,1,0,1,3,1,2,0,0,1,1,1,1,0,0,0,0,1,3,6oct2024
-sp-3-132,male,19,1,3,,,,,,,,,,1,0,0,0,0,1,2,1,2,1,2,1,2,1,2,1,2,1,3,1,3,0,0,0,0,
-sp-1-134,female,16,1,1,,,,,,,,,,1,1,2,1,2,1,2,1,2,1,2,1,2,1,2,0,0,1,1,1,3,0,0,1,1,
-sp-3-135,male,20,1,3,1,,0,1,0,,0,0,15,1,1,3,1,2,0,0,1,2,1,1,1,1,1,2,1,2,1,3,1,3,0,0,1,3,6oct2024
-sp-2-136,female,17,1,2,0,0,1,1,0,0,0,0,9,1,0,0,0,0,0,0,1,2,1,2,0,0,1,2,0,0,1,2,1,3,0,0,1,3,6oct2024
-sp-1-137,female,16,1,1,1,,,1,1,,,,21,1,1,1,0,0,0,0,1,1,1,3,0,0,1,2,0,0,1,1,1,3,0,0,1,1,6oct2024
-sp-3-138,female,19,1,3,0,0,0,0,1,1,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-sp-1-139,male,16,1,1,,,,,,,,,,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,,,1,3,5oct2024
-sp-1-140,male,16,1,1,,,,,,,,,,1,1,2,1,2,0,0,1,3,1,3,1,2,1,2,1,2,1,1,1,2,,,1,3,
-sp-1-141,female,16,1,1,,,,,,,,,,1,1,1,1,2,0,0,0,1,0,0,0,0,1,1,0,0,1,1,1,3,0,0,0,0,
-sp-3-142,male,18,1,3,1,0,0,0,1,0,1,0,15,1,1,2,1,2,0,0,1,2,1,2,1,2,1,2,0,0,1,1,1,3,0,0,1,2,6oct2024
-sp-3-143,male,18,1,3,1,0,0,1,1,0,0,0,9,1,1,3,1,3,1,3,1,2,1,2,1,2,1,3,1,2,1,1,1,3,0,0,0,0,6oct2024
-sp-3-144,male,18,1,3,1,1,0,1,0,1,0,0,15,1,0,0,1,2,0,0,1,3,1,3,0,0,0,0,0,0,1,1,1,3,0,0,1,2,6oct2024
-sp-1-145,female,15,1,1,,,,,,,,,,1,1,3,1,2,1,3,1,2,1,2,0,0,1,2,0,0,0,0,0,0,0,0,1,1,
-sp-2-146,female,18,1,2,1,0,0,1,,0,0,0,15,1,1,2,1,2,0,0,1,2,1,2,1,2,1,2,0,0,1,1,1,1,0,0,1,2,6oct2024
-sp-1-147,male,16,1,1,1,0,1,1,1,0,0,0,21,1,1,2,1,2,0,0,1,3,1,3,1,2,0,0,0,0,1,1,1,3,0,0,1,3,6oct2024
-sp-2-148,female,17,1,2,,,,,,,,,,1,0,0,0,0,0,0,1,2,1,2,1,2,1,2,0,0,1,1,1,3,1,1,1,3,
-sp-3-149,female,18,1,3,1,0,1,1,1,0,1,0,15,1,1,3,1,3,1,3,1,2,1,2,1,1,1,3,0,0,1,2,1,1,0,0,1,2,6oct2024
-sp-1-150,male,16,1,1,,,,,,,,,,1,1,3,1,3,1,3,1,3,1,2,1,2,1,2,1,3,1,1,0,0,0,0,0,0,
-sp-3-151,female,18,1,3,1,0,0,1,1,,1,1,9,1,1,2,1,2,1,2,1,,1,1,0,0,1,2,0,0,1,1,1,3,0,0,1,2,6oct2024
-sp-3-152,female,19,1,3,1,0,0,1,0,0,0,0,9,1,1,2,1,2,1,2,1,1,1,3,1,3,1,2,1,3,1,2,0,0,0,0,0,0,6oct2024
-sp-3-154,male,18,1,3,1,0,0,1,0,1,1,0,15,1,1,2,1,2,0,0,1,3,1,1,0,0,1,2,0,0,1,1,1,3,0,0,0,0,6oct2024
-sp-3-155,male,19,1,3,1,,1,1,1,,1,0,3,1,1,2,1,2,1,2,1,3,1,3,1,3,1,3,1,3,1,1,1,2,1,1,1,1,6oct2024
-sp-3-156,male,18,1,3,0,0,1,1,1,1,1,0,15,1,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,3,0,0,0,0,1,2,6oct2024
-sp-2-157,female,18,1,2,1,,,1,,,,,9,1,1,1,1,1,1,1,1,2,1,2,1,2,1,2,0,0,1,1,0,0,0,0,1,3,6oct2024
-sp-2-158,male,17,1,2,1,0,1,1,1,,1,0,15,1,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,0,0,1,3,1,1,0,0,6oct2024
-sp-1-159,female,16,1,1,1,,1,,,,1,,15,1,1,1,1,1,1,1,0,1,0,0,0,0,0,0,0,0,1,1,0,0,0,0,1,1,6oct2024
-sp-1-160,male,17,1,1,0,0,0,1,1,0,,,,1,0,0,1,2,1,2,1,3,1,1,1,3,1,2,0,0,1,1,1,3,0,0,0,0,
-sp-2-161,female,19,1,2,1,0,0,1,,0,1,0,15,1,1,2,0,0,0,0,1,1,1,2,1,1,1,2,1,1,1,1,0,0,0,0,1,1,6oct2024
-sp-3-162,female,18,1,3,0,0,0,1,1,0,,0,,1,1,2,1,1,,,1,2,1,2,1,2,1,2,0,0,0,0,1,3,0,0,1,3,
-sp-1-163,female,16,1,1,,,,,,,,,,1,0,0,0,0,0,0,1,2,1,2,0,0,1,1,0,0,1,1,1,3,1,1,0,0,
-sp-1-164,female,16,1,1,1,0,1,1,1,0,1,0,15,1,1,1,1,2,1,1,1,1,1,2,1,2,1,2,1,2,0,0,1,2,0,0,1,3,7oct2024
-sp-3-165,male,18,1,3,1,,1,1,1,1,0,0,3,1,1,2,1,2,1,2,1,3,1,3,1,3,1,2,0,0,1,3,1,3,0,0,0,0,6oct2024
-sp-1-166,female,16,1,1,0,0,1,1,1,,1,0,15,1,1,2,1,2,1,2,1,3,1,2,1,2,1,2,1,1,1,1,,,,,1,3,6oct2024
-sp-2-167,female,16,1,2,,,,,,,,,,1,1,2,1,1,0,0,1,,1,2,1,2,1,2,,,1,1,1,2,0,0,1,2,
-sp-3-168,female,19,1,3,1,0,0,1,1,,0,0,21,1,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,1,0,0,0,0,1,1,5oct2024
-sp-3-169,female,18,1,3,,,,,,,,,,1,1,1,1,2,1,1,1,3,1,3,1,2,1,2,1,2,1,3,1,2,0,0,1,1,
-sp-3-170,female,19,1,3,,,,,,,,,,1,0,0,0,0,0,0,1,2,1,3,1,3,1,2,1,3,1,1,0,0,0,0,1,3,
-sp-2-172,male,17,1,2,,,,,,,,,,1,1,1,1,2,1,2,1,2,1,2,1,2,1,2,0,0,1,1,1,3,0,0,1,2,
-sp-2-173,female,17,1,2,,,,,,,,,,1,0,0,0,0,0,0,0,2,0,0,1,2,1,2,0,0,0,0,0,0,0,0,1,2,
-sp-1-174,female,15,1,1,1,0,0,1,1,0,1,0,15,1,1,3,1,2,0,0,0,3,0,0,0,0,1,2,0,0,1,1,1,2,0,0,1,1,6oct2024
-sp-1-175,female,16,1,1,,,,,,,,,,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,1,3,1,1,0,0,1,,
-sp-1-176,male,17,1,1,,,,,,,,,,1,1,3,1,3,1,2,1,2,1,1,1,1,1,2,1,1,1,1,1,2,0,0,1,1,
-sp-3-177,female,18,1,3,,,,,,,,,,1,0,0,0,0,0,0,0,0,0,0,1,1,1,1,0,0,1,2,1,3,,,1,1,
-sp-1-178,male,16,1,1,,,,,,,,,,1,1,3,1,3,1,3,1,3,1,3,1,3,1,3,1,3,1,1,1,1,0,0,1,3,
-sp-2-179,female,18,1,2,,,,,,,,,,1,0,0,0,0,1,2,1,2,1,2,1,2,1,2,0,0,0,0,1,3,1,1,1,2,
-sp-1-180,male,16,1,1,1,0,,1,1,1,1,0,15,1,0,0,1,3,1,3,1,3,1,3,1,3,1,1,0,0,1,1,1,3,0,0,1,3,6oct2024
-sp-0-181,female,17,1,,,,,,,,,,,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,1,1,3,0,0,1,3,
-sp-1-182,female,17,1,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-sp-0-183,female,16,1,,1,0,1,1,1,0,1,0,9,1,0,0,1,2,0,0,1,2,1,3,0,0,1,2,,,1,1,0,0,0,0,0,0,6oct2024
-sp-1-184,male,16,1,1,1,0,0,1,,0,0,0,15,1,1,2,1,2,0,0,1,3,1,2,1,1,1,2,1,3,1,1,1,3,1,2,1,1,6oct2024
-sp-3-185,male,18,1,3,,,,,,,,,,1,1,2,1,2,1,2,1,2,1,2,1,2,0,0,1,2,1,2,1,3,1,2,0,0,
-sp-1-186,female,16,1,1,1,0,0,1,1,0,1,0,21,1,1,1,1,2,0,0,1,2,1,2,0,0,1,2,0,0,0,0,1,2,0,0,1,2,6oct2024
-sp-1-187,female,17,1,1,,,,,,,,,,1,1,1,0,0,0,0,1,1,1,1,1,1,1,1,,,0,0,1,2,0,0,1,1,
-sp-1-188,female,17,1,1,1,,,1,,1,1,1,9,1,1,3,1,2,1,3,1,1,1,3,1,2,1,2,1,2,1,1,1,2,,,1,3,6oct2024
-sp-2-189,male,17,1,2,,,1,1,,,1,,15,1,1,2,0,0,1,2,1,2,1,2,1,2,1,3,,,1,3,1,3,1,2,,,6oct2024
-sp-3-190,female,19,1,3,1,0,1,1,1,0,0,0,15,1,1,1,1,2,1,2,1,2,1,2,0,0,1,1,0,0,1,3,1,3,0,0,1,3,6oct2024
-sp-1-191,male,17,1,1,,,,,,,,,,1,1,2,1,2,1,2,1,2,1,3,0,0,1,2,0,0,1,3,1,3,0,0,1,,
-sp-3-192,male,19,1,3,0,0,0,1,0,0,1,0,,1,0,0,1,2,0,0,1,2,1,2,1,2,1,1,0,0,1,3,1,3,0,0,0,0,
-sp-2-193,female,17,1,2,,,,,,,,,,1,0,0,0,0,0,0,0,2,0,0,0,0,1,2,0,0,1,1,0,0,0,0,1,3,
-sp-1-194,female,16,1,1,1,0,0,1,1,,,,15,1,1,2,1,2,1,3,1,3,1,3,1,3,1,3,1,3,0,0,1,3,0,0,1,3,6oct2024
-sp-3-195,male,17,1,3,1,0,1,1,1,1,1,0,15,1,1,2,1,2,0,0,1,2,1,2,0,0,1,2,1,2,1,3,1,3,0,0,1,3,6oct2024
-sp-2-196,female,17,1,2,,,,,,,,,,1,1,1,0,0,1,2,1,2,1,2,0,0,1,2,0,0,1,1,0,0,0,0,1,3,
-sp-2-197,female,17,1,2,,,,,,,,,,0,,,,,,,,,,,,,,,,,,,,,,,,,
-sp-3-198,female,19,1,3,0,0,1,1,1,0,1,0,3,1,0,0,0,0,0,0,1,1,1,1,0,0,1,1,0,0,0,0,0,0,0,0,0,0,7oct2024
-sp-2-199,female,17,1,2,0,0,1,1,1,,1,0,15,1,1,1,0,0,0,0,1,2,1,1,0,0,1,2,0,0,1,1,0,0,0,0,0,0,6oct2024
-sp-1-200,male,16,1,1,,,,,,,,,,1,0,0,0,0,0,0,1,2,1,1,0,0,1,2,0,0,0,0,1,3,0,0,0,0,
-sp-3-201,female,18,1,3,1,,1,1,1,,1,,15,1,0,0,0,0,0,0,1,3,1,2,1,3,0,0,0,0,,,,,,,1,1,6oct2024
-sp-1-202,female,17,1,1,1,,,,,,,,9,,,,,,,,,,,,,,,,,,,,,,,,,,6oct2024
-sp-2-203,female,17,1,2,1,0,0,1,1,1,1,0,9,1,0,0,0,0,0,0,1,2,1,3,1,2,1,2,0,0,0,0,1,3,1,1,1,2,6oct2024
-sp-1-204,female,16,1,1,0,0,0,1,1,1,1,0,,1,1,3,1,2,1,3,1,3,1,3,1,3,1,2,0,0,1,1,1,1,0,0,1,1,
-sp-1-205,male,16,1,1,1,,1,,1,,,,9,1,0,0,1,2,0,0,1,3,1,3,1,2,1,2,0,0,1,1,1,2,0,0,1,3,6oct2024
-sp-3-206,female,18,1,3,1,,,,,,,,15,1,0,0,,,,,1,2,1,2,,,1,2,1,2,1,1,1,1,,,,,6oct2024
-sp-1-207,female,16,1,1,,,,,,,,,,1,0,0,0,0,0,0,0,0,0,0,0,0,1,3,0,0,1,1,0,0,0,0,1,3,
-sp-1-208,female,16,1,1,,,,,,,,,,0,1,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,1,1,1,3,0,0,1,3,
-sp-1-209,male,15,1,1,1,0,1,1,1,,1,0,21,1,1,3,1,3,1,3,1,3,1,3,1,2,1,2,1,3,1,2,,,,,1,1,5oct2024
-sp-1-210,female,16,1,1,,,,,,,,,,1,0,0,0,0,0,0,1,2,1,2,1,2,1,1,0,0,0,0,0,0,0,0,0,0,
-sp-3-211,female,19,1,3,,,,,,,,,,1,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,3,1,3,0,0,1,2,
-sp-1-213,female,16,1,1,,,,,,,,,,1,1,1,1,1,1,1,0,0,0,0,0,0,1,1,0,0,1,1,1,3,1,1,1,3,
-sp-2-214,male,17,1,2,,,,,,,,,,1,1,1,0,0,0,0,1,2,1,2,0,0,1,2,1,3,0,0,1,3,0,0,1,2,
-sp-2-215,male,18,1,2,,,,,,,,,,1,0,0,0,0,0,0,1,2,1,2,0,0,0,0,0,0,1,3,0,0,1,3,1,3,
-sp-2-216,male,18,1,2,1,0,1,1,1,0,1,0,9,1,1,3,0,0,1,2,1,2,1,3,0,0,1,2,0,0,1,1,1,3,1,1,1,3,6oct2024
-sp-1-217,female,16,1,1,1,0,1,1,1,1,0,0,9,1,1,1,0,0,1,2,1,2,1,2,0,0,1,2,0,0,1,1,0,0,0,0,1,3,6oct2024
-sp-1-219,male,16,1,1,1,0,0,1,1,0,1,0,9,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,3,0,0,1,2,6oct2024
-sp-2-220,male,18,1,2,0,0,0,1,0,0,1,0,,1,1,2,1,2,1,1,1,3,1,3,1,2,1,2,1,2,1,1,1,3,0,0,0,0,
-sp-2-221,female,17,1,2,,,,,,,1,,,1,1,3,1,3,1,3,1,3,1,3,1,3,1,3,1,3,1,1,1,3,0,0,1,2,
-sp-1-222,male,16,1,1,1,,,1,,,,,9,1,1,2,0,0,1,2,1,2,1,3,0,0,1,1,0,0,1,1,1,2,0,0,1,2,6oct2024
-sp-2-223,male,17,1,2,1,0,0,1,0,1,1,,9,1,1,1,1,2,0,0,1,3,1,2,0,0,1,2,0,0,1,1,1,3,1,2,1,2,6oct2024
-sp-3-224,female,18,1,3,1,1,0,1,1,0,1,1,,,,,,,,,,,,,,,,,,,,,,,,,,,
-sp-2-225,male,18,1,2,1,0,0,1,0,0,1,0,9,1,0,0,0,0,0,0,1,0,1,2,0,0,1,2,0,0,1,1,0,0,1,1,1,2,6oct2024
-sp-3-226,male,19,1,3,1,,1,,,,,,9,1,1,2,1,2,1,2,1,3,1,3,1,2,1,2,0,0,1,1,1,3,0,0,1,3,6oct2024
-sp-1-227,male,16,1,1,1,0,0,1,0,0,0,0,9,1,1,1,1,2,0,0,1,1,1,1,0,0,1,1,1,1,1,2,1,3,0,0,0,0,6oct2024
-sp-2-228,female,18,1,2,,,,,,,,,,1,1,2,0,0,0,0,0,0,0,0,0,0,1,2,0,0,1,1,1,2,0,0,1,3,
-sp-3-229,female,18,1,3,1,0,0,1,0,1,0,,15,1,1,1,1,2,0,0,1,2,1,3,0,0,1,2,1,2,1,2,0,0,0,0,1,1,6oct2024
-sp-2-230,female,18,1,2,0,0,0,1,1,0,0,0,,1,1,1,1,2,0,0,1,2,1,1,1,1,1,2,0,0,1,1,1,2,0,0,1,2,
-sp-1-231,female,16,1,1,,,,,,,,,,1,1,1,1,3,0,0,1,2,1,1,0,0,1,2,0,0,1,1,1,3,0,0,1,2,
-sp-0-232,female,18,1,,1,0,1,1,1,1,0,0,15,1,0,0,0,0,0,0,1,1,1,2,1,1,1,2,0,0,0,0,1,3,0,0,1,2,6oct2024
-sp-2-233,female,17,1,2,1,,,1,,,,,21,1,0,0,1,1,0,0,1,3,1,3,1,2,1,2,0,0,1,2,1,1,0,0,1,3,6oct2024
-sp-2-234,male,17,1,2,1,0,0,0,0,0,0,0,21,1,1,2,1,2,1,2,1,2,1,2,0,0,1,2,1,2,1,1,1,1,0,0,0,0,6oct2024
-sp-2-235,female,17,1,2,0,0,0,1,,0,1,0,,1,1,2,1,2,0,0,0,1,0,0,0,0,1,2,0,0,1,1,1,3,1,3,0,0,
-sp-2-236,male,18,1,2,1,0,0,0,0,0,1,0,9,1,0,0,0,0,0,0,1,2,1,2,0,0,1,2,0,0,,,1,3,,,1,1,6oct2024
-sp-1-237,male,17,1,1,,,,,,,,,,1,1,2,1,2,1,2,1,1,1,1,0,0,1,2,0,0,1,1,1,3,1,2,0,0,
-sp-0-238,female,59,0,,,,,,,,,,,1,1,2,1,2,,,1,3,1,3,1,3,1,2,1,2,1,1,1,2,1,2,,,
-sp-3-239,female,18,1,3,,,,,1,,,,,1,0,0,0,0,0,0,1,1,1,2,1,1,1,1,1,1,1,1,1,3,0,0,1,3,
-sp-3-240,male,18,1,3,1,0,0,1,1,0,0,0,9,1,1,3,1,3,1,3,1,3,1,3,1,3,,,1,3,1,2,1,,0,0,0,0,6oct2024
-sp-1-241,male,16,1,1,1,0,0,0,0,0,0,0,9,1,1,2,1,2,1,2,1,2,1,2,1,1,1,2,0,0,1,1,0,0,0,0,0,0,7oct2024
-sp-1-242,female,16,1,1,0,0,0,1,1,1,1,1,,1,1,2,1,3,1,2,1,3,1,2,0,0,1,2,0,0,0,0,0,0,0,0,0,0,
-sp-3-243,female,18,1,3,1,0,1,1,1,1,1,0,21,1,1,2,1,2,1,2,1,0,1,1,0,0,1,1,0,0,1,1,0,0,0,0,0,0,6oct2024
-sp-1-244,female,17,1,1,1,,,1,1,,,,21,1,1,1,1,1,0,0,1,2,1,2,1,1,1,2,1,1,1,1,1,3,0,0,1,3,6oct2024
-sp-3-245,female,18,1,3,,,,,,,,,,1,1,1,0,0,0,0,0,1,0,0,0,0,1,2,0,0,1,3,1,2,0,0,1,2,
-sp-3-246,female,8,1,3,1,0,0,1,1,,1,0,15,1,1,2,0,0,0,0,1,3,1,2,1,2,1,2,0,0,1,1,0,0,0,0,0,0,6oct2024
-sp-3-247,female,18,1,3,,,,,,,,,,1,1,2,1,2,1,2,1,0,1,1,0,0,1,2,0,0,1,1,0,0,0,0,1,3,5oct2024
-sp-2-248,male,17,1,2,1,,,,,,,,3,1,1,3,1,3,,,1,3,1,3,1,3,1,2,,,1,2,1,3,1,2,1,2,6oct2024
-sp-0-249,male,17,1,,,,,,,,,,,1,,,,,1,2,0,2,0,0,0,0,1,2,0,0,0,0,1,3,1,1,1,2,
-sp-3-250,female,18,1,3,1,0,1,1,1,1,1,0,15,1,1,1,1,2,0,0,1,2,1,2,0,0,1,2,0,0,1,1,0,0,0,0,1,2,6oct2024
-sp-2-251,female,18,1,2,,,,,,,,,,1,1,1,1,2,1,1,1,2,1,1,1,2,1,2,0,0,1,1,1,1,0,0,1,3,
-sp-0-252,male,17,1,,1,,,1,1,0,,0,9,1,1,2,1,2,1,2,1,1,1,1,1,1,1,1,0,0,1,1,1,1,0,0,1,1,6oct2024
-sp-3-253,male,19,1,3,1,,,,1,,,,15,1,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,1,1,3,1,3,0,0,7oct2024
-sp-1-254,male,17,1,1,1,0,0,0,1,1,1,0,21,1,1,2,1,2,1,2,1,3,1,3,1,3,1,2,1,3,1,1,1,1,1,1,1,3,5oct2024
-sp-2-255,female,18,1,2,,,,,,,,,,1,0,0,0,0,0,0,1,3,1,3,0,0,1,3,0,0,0,0,1,2,0,0,1,3,
-sp-3-256,female,18,1,3,1,0,0,1,1,0,1,0,15,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,3,0,0,1,3,0,0,6oct2024
-sp-1-257,female,16,1,1,,,,,,,,,,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,,,1,1,1,1,,,1,1,
-sp-3-258,female,19,1,3,,,,,,,,,,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,1,3,
-sp-3-259,male,18,1,3,1,0,0,1,1,0,1,0,15,1,1,2,1,2,1,2,1,2,1,1,1,1,1,2,1,1,1,1,1,,0,0,0,0,7oct2024
-sp-3-260,female,19,1,3,0,0,0,1,1,,1,,,1,1,2,1,2,1,2,1,2,1,2,0,0,1,2,1,1,1,1,0,0,0,0,0,0,
-sp-0-261,male,17,1,,1,,,,,,,,21,1,1,2,1,2,1,2,0,2,0,0,0,0,1,2,1,1,1,1,1,1,0,0,1,3,6oct2024
-sp-2-262,female,18,1,2,1,0,1,1,1,0,0,0,9,1,0,0,1,3,0,0,1,3,1,3,1,3,0,0,1,3,1,2,1,2,1,1,1,3,6oct2024
-sp-0-263,female,26,0,,1,0,0,1,1,,0,0,9,1,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,0,0,0,0,1,1,7oct2024
-sp-3-264,female,17,1,3,1,0,1,1,1,1,0,0,9,1,0,0,1,1,,,1,1,1,1,1,1,1,1,0,0,1,1,1,3,0,0,0,0,6oct2024
-sp-1-265,female,16,1,1,0,0,0,1,0,,1,0,,1,1,2,1,2,1,2,1,1,1,1,1,1,1,2,0,0,0,0,0,0,0,0,0,0,
-sp-0-267,male,17,1,,1,0,0,0,1,1,1,,15,1,1,3,1,3,1,3,1,3,1,1,0,0,1,3,0,0,1,2,1,3,1,3,1,,6oct2024
-sp-1-268,male,16,1,1,,,,,,,,,,1,1,1,1,2,1,2,1,3,1,2,0,0,1,3,0,0,1,1,1,3,0,0,1,3,
-sp-2-269,male,18,1,2,,,,,,,,,,1,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,0,0,1,3,0,0,0,0,
-sp-3-270,female,18,1,3,1,,0,1,1,0,1,0,21,1,1,2,1,2,0,0,1,2,1,2,1,1,1,2,0,0,1,3,1,2,0,0,1,2,6oct2024
-sp-2-271,female,18,1,2,1,0,1,1,1,,0,0,9,1,1,2,1,2,1,2,1,2,1,2,1,1,1,2,1,2,1,1,1,2,0,0,1,3,6oct2024
-sp-3-272,female,18,1,3,1,0,0,0,0,0,0,0,9,1,1,2,1,2,1,2,1,1,1,1,0,0,1,2,0,0,1,2,1,2,0,0,1,3,6oct2024
-sp-3-273,male,18,1,3,1,0,0,1,1,0,1,0,9,1,1,1,1,1,1,2,1,2,1,2,1,1,1,2,1,1,1,3,1,2,1,1,1,3,6oct2024
-sp-1-274,female,16,1,1,1,0,0,1,1,1,1,0,15,1,1,1,1,2,0,0,1,0,1,2,0,0,1,2,1,2,1,1,1,1,0,0,1,2,6oct2024
-sp-0-275,female,33,0,,1,,1,1,1,,1,,15,1,1,2,1,2,1,2,1,2,1,3,1,3,1,2,1,2,0,0,0,0,0,0,1,1,6oct2024
-sp-0-276,male,58,0,,,,,,,,,,,1,1,1,1,2,1,1,0,1,0,0,1,1,1,2,,,,,,,1,1,1,1,
-sp-1-277,male,18,1,1,1,,,,1,,,,9,1,0,0,1,2,0,0,1,3,1,2,0,0,1,2,0,0,0,0,1,3,0,0,1,3,6oct2024
-sp-1-278,female,16,1,1,,,,,,,,,,1,1,1,1,2,1,2,1,0,1,1,1,1,1,2,0,0,1,1,0,0,0,0,1,2,
-sp-3-279,female,18,1,3,,0,0,1,1,0,,0,,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,0,0,1,2,0,0,1,1,0,0,
-sp-0-280,female,54,0,,,,,,,,,,,1,1,2,1,2,1,2,1,2,1,2,1,2,1,2,0,0,1,1,,,1,1,1,1,
-sp-1-281,female,17,1,1,1,,1,1,1,1,1,1,21,1,1,2,1,2,1,2,1,2,1,2,0,0,1,2,0,0,1,1,1,,1,,1,3,6oct2024
-sp-3-282,female,18,1,3,0,0,0,1,0,0,0,0,,1,1,3,1,2,1,3,0,2,0,0,0,0,1,2,1,3,1,3,1,2,0,0,1,2,
-sp-1-283,female,17,1,1,1,0,0,1,1,0,1,,9,1,0,0,0,0,0,0,1,2,1,2,0,0,1,2,0,0,,,1,3,,,1,2,6oct2024
-sp-1-284,female,16,1,1,,,,,,,,,,1,1,3,1,1,0,0,1,0,1,1,0,0,1,2,0,0,1,1,0,0,0,0,0,0,
-sp-3-285,female,18,1,3,,,,1,1,1,1,1,15,1,1,2,1,2,1,2,1,0,1,2,1,2,1,2,1,2,1,3,0,0,0,0,1,1,6oct2024
-sp-3-286,male,19,1,3,,,,,,,,,,1,0,0,1,2,1,1,1,2,1,1,0,0,1,2,0,0,1,3,1,3,0,0,1,3,
-sp-3-287,female,19,1,3,1,0,0,0,0,0,1,0,9,1,1,2,1,2,0,0,1,1,1,1,1,1,1,2,0,0,1,3,1,3,0,0,1,1,6oct2024
-sp-3-288,male,18,1,3,1,0,0,1,0,0,1,0,21,1,1,2,1,2,1,2,1,1,1,1,0,0,0,0,0,0,1,3,1,3,1,3,1,3,5oct2024
-sp-2-289,female,18,1,2,,,,,,,,,,1,1,2,1,2,1,2,1,1,1,2,1,2,1,2,0,0,1,1,1,3,0,0,1,2,
-sp-1-290,female,16,1,1,,,,,,,,,,1,1,1,0,0,1,2,1,1,1,1,1,1,1,1,1,1,1,1,1,3,0,0,1,1,
-sp-1-291,male,17,1,1,,,,,,,,,,1,,,,,,,,2,,,,,1,2,,,1,1,1,3,1,3,1,3,
-sp-1-292,female,16,1,1,,,,,,,,,,1,1,2,1,2,1,1,1,2,1,2,0,0,1,2,0,0,1,1,1,3,0,0,0,0,
-sp-2-293,male,16,1,2,0,0,0,1,1,0,0,0,,1,1,1,1,2,1,2,1,2,1,3,0,0,,,1,2,1,1,1,2,1,1,1,3,
-sp-3-294,female,19,1,3,0,0,0,1,1,0,1,0,,1,1,2,0,0,1,2,1,1,1,2,1,2,1,2,0,0,1,3,1,3,1,2,0,0,
-sp-3-295,female,18,1,3,0,0,0,1,1,1,1,1,,1,1,2,0,0,1,2,1,1,1,1,0,0,0,0,0,0,0,0,1,1,0,0,1,2,
-sp-1-296,female,16,1,1,1,0,0,0,1,0,1,0,21,1,1,1,1,2,1,2,1,1,1,1,0,0,1,1,1,1,1,1,1,2,0,0,1,3,6oct2024
-sp-1-297,female,15,1,1,1,0,0,0,0,0,0,0,9,1,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,1,1,1,1,3,0,0,1,1,6oct2024
-sp-3-298,male,18,1,3,1,0,0,1,0,1,0,0,9,1,1,2,1,2,1,2,1,1,1,2,1,1,1,2,1,1,1,3,1,3,0,0,0,0,6oct2024
-sp-1-299,female,15,1,1,1,0,0,1,1,0,0,0,15,1,1,1,1,1,0,0,1,2,1,2,1,2,1,2,1,2,1,1,1,3,0,0,1,1,6oct2024
-sp-3-300,female,18,1,3,1,0,1,1,1,0,1,0,15,1,1,2,0,0,1,1,1,2,1,2,1,2,1,2,0,0,1,1,1,3,0,0,1,1,6oct2024
-sp-3-301,male,18,1,3,,,,,1,1,,,,1,0,0,1,2,,,,2,,,,,0,0,,,1,3,1,,0,0,1,1,
-sp-3-302,female,19,1,3,1,0,0,1,1,0,1,1,15,1,1,2,1,2,0,0,1,2,1,2,0,0,1,2,0,0,1,3,0,0,0,0,1,3,6oct2024
-sp-2-303,female,17,1,2,1,0,0,1,1,,1,1,15,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,0,0,1,3,6oct2024
-sp-1-304,female,17,1,1,0,0,0,1,1,,1,1,,1,0,0,1,3,0,0,1,2,1,2,0,0,1,2,1,1,1,1,1,3,0,0,1,1,
-sp-3-305,female,18,1,3,1,,,,,,,,3,1,1,2,0,0,1,2,1,1,1,3,1,2,1,2,1,2,0,0,0,0,0,0,1,1,6oct2024
-sp-2-307,male,18,1,2,1,,1,1,1,,1,,21,1,1,2,1,2,1,2,1,3,1,3,1,3,1,2,0,0,1,1,1,3,0,0,1,3,5oct2024
-sp-1-308,male,16,1,1,1,0,0,1,1,0,0,0,15,1,1,2,1,2,0,0,1,3,1,3,0,0,1,2,1,2,1,1,1,3,0,0,1,2,6oct2024
-sp-1-310,female,16,1,1,1,0,1,,,0,1,1,9,1,1,2,1,2,1,2,1,0,1,2,0,0,1,2,0,0,1,1,0,0,0,0,1,3,6oct2024
-sp-3-311,female,18,1,3,0,0,0,1,1,0,1,0,,1,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,3,0,0,0,0,
-sp-3-312,male,19,1,3,0,0,0,1,1,0,1,0,,1,1,2,1,2,1,2,1,3,1,2,1,2,1,2,0,0,1,3,1,3,,,1,3,
-sp-1-313,male,16,1,1,,,,,,,,,,1,0,0,1,3,0,0,1,2,1,1,0,0,1,2,0,0,1,3,1,2,1,3,1,,
-sp-1-314,female,17,1,1,,,,,,,,,,1,1,2,1,2,1,2,0,2,0,0,1,2,1,2,1,2,1,1,1,1,0,0,1,3,
-sp-3-315,male,19,1,3,1,0,1,1,1,0,0,0,9,1,1,2,1,2,0,0,1,3,1,3,0,0,1,2,1,3,1,3,1,3,0,0,1,3,6oct2024
-sp-2-317,male,18,1,2,,,,,,,,,,1,1,3,1,3,1,2,1,2,1,2,1,2,1,2,1,2,1,1,0,0,1,2,1,1,
-sp-2-318,female,17,1,2,0,0,0,1,1,0,1,0,,1,1,2,1,2,0,0,1,0,1,2,0,0,1,2,1,1,0,0,0,0,0,0,0,0,
-sp-3-319,male,18,1,3,1,0,0,0,1,0,0,0,9,1,1,2,1,2,1,2,1,2,1,2,,,1,2,,,1,3,1,3,0,0,0,0,6oct2024
-sp-2-320,male,17,1,2,1,0,0,1,1,,1,,9,1,1,3,1,3,1,3,1,2,1,2,0,0,1,2,1,2,0,0,1,3,0,0,1,3,6oct2024
-sp-3-323,male,17,1,3,1,,1,1,1,1,1,,21,1,1,1,1,2,1,1,1,2,1,3,0,0,1,2,0,0,1,3,1,3,,,1,3,5oct2024
-sp-2-324,male,17,1,2,1,0,1,1,1,0,1,,15,1,1,2,1,2,1,2,1,3,1,3,0,0,1,2,0,0,1,1,1,3,1,3,1,3,6oct2024
-sp-2-325,male,17,1,2,1,0,0,0,1,1,0,1,9,1,1,1,1,2,1,2,1,3,1,3,0,0,1,3,0,0,1,1,1,2,1,3,0,0,6oct2024
-sp-2-326,male,17,1,2,1,0,0,1,1,,1,0,9,1,0,0,1,2,0,0,0,3,0,0,0,0,1,2,0,0,0,0,1,3,1,3,0,0,6oct2024
-sp-1-329,male,16,1,1,,,,,,,,,,1,1,2,1,2,0,0,1,3,1,2,0,0,1,2,0,0,1,1,1,3,1,2,0,0,
-sp-3-330,male,18,1,3,1,0,0,0,0,,1,1,15,1,1,3,1,3,1,3,1,2,1,1,1,1,1,2,1,2,1,3,1,3,1,3,1,3,6oct2024
-sp-2-331,male,17,1,2,1,,,1,1,,,,9,1,0,0,0,0,0,0,1,2,1,2,1,2,1,2,,,1,3,1,2,1,3,,,6oct2024
-sp-3-332,female,19,1,3,1,0,1,1,1,,0,0,9,1,0,0,0,0,0,0,1,3,1,3,0,0,1,2,1,2,1,1,1,3,0,0,0,0,6oct2024
-sp-1-334,male,16,1,1,1,0,0,0,1,0,0,0,15,1,1,2,1,2,1,2,1,1,1,1,1,3,1,1,,,1,1,0,0,0,0,1,1,6oct2024
-sp-0-335,male,65,0,,1,0,0,0,0,0,0,0,9,1,1,2,1,2,1,1,1,3,1,2,1,2,1,2,1,2,1,1,,,1,2,,,6oct2024
-sp-2-336,male,17,1,2,1,0,0,0,0,1,1,1,9,1,0,0,1,2,1,2,1,2,1,2,0,0,1,3,0,0,1,2,1,3,0,0,1,,6oct2024
-sp-3-337,male,17,1,3,1,0,1,1,1,1,1,1,21,1,0,0,0,0,1,1,1,3,1,3,1,3,1,2,1,3,1,2,1,3,0,0,1,1,6oct2024
-sp-3-338,female,18,1,3,,0,0,0,1,0,,,,1,1,2,1,2,1,1,1,2,1,3,1,2,1,1,0,0,1,1,0,0,0,0,1,3,
-sp-2-339,female,17,1,2,,,,,,,,,,1,1,2,1,2,1,2,1,1,1,2,1,1,1,1,0,0,0,0,0,0,0,0,1,3,
-sp-3-340,female,18,1,3,1,,,1,,,,,9,1,1,1,0,0,1,1,1,2,1,1,1,2,1,2,1,1,1,1,1,3,0,0,0,0,6oct2024
-sp-3-341,male,18,1,3,1,0,0,0,1,0,1,0,21,1,0,0,1,2,0,0,1,3,1,3,0,0,1,2,0,0,1,3,1,3,1,3,0,0,6oct2024
-sp-0-342,male,18,1,,1,0,1,0,0,0,0,0,21,1,1,3,1,3,1,3,1,2,1,1,0,0,1,2,1,2,1,2,1,3,0,0,1,2,6oct2024
-sp-0-343,female,32,0,,,,,,,,,,,1,1,2,1,2,1,2,1,3,1,3,1,3,1,3,1,3,1,1,0,0,1,1,1,2,
-sp-3-344,female,18,1,3,1,0,0,0,1,0,0,0,15,1,0,0,0,0,0,0,0,1,0,0,0,0,1,1,0,0,,,,,,,1,3,6oct2024
-sp-2-345,male,17,1,2,1,,1,1,1,,1,,3,1,1,2,1,2,1,2,1,2,1,2,0,0,1,2,1,2,1,1,1,3,1,2,0,0,6oct2024
-sp-3-346,male,19,1,3,1,,1,1,,,,,3,1,1,1,1,1,1,1,1,2,1,2,1,2,,,,,1,2,1,3,,,,,6oct2024
-sp-1-347,male,17,1,1,1,0,0,1,1,0,0,0,9,1,1,1,1,2,0,0,1,3,1,3,0,0,1,2,1,2,1,3,1,3,0,0,0,0,6oct2024
-sp-3-348,female,18,1,3,1,0,0,0,1,0,0,0,9,1,1,1,0,0,1,2,1,0,1,2,0,0,1,2,0,0,1,3,0,0,0,0,1,1,6oct2024
-sp-3-349,female,18,1,3,1,,,1,,,,,9,1,1,3,1,3,1,3,1,3,1,3,1,3,1,3,,,1,1,,,,,1,3,6oct2024
-sp-0-351,male,30,0,,,,,,,,,,,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
-sp-1-352,female,16,1,1,1,0,0,1,0,0,0,0,9,1,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,1,1,2,0,0,0,0,6oct2024
-sp-3-353,female,19,1,3,1,0,0,0,0,0,1,0,15,1,0,0,0,0,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,3,,,1,2,6oct2024
-sp-2-354,female,17,1,2,0,0,0,1,1,0,1,1,,1,1,2,0,0,0,0,1,0,1,3,1,1,1,3,1,2,1,1,0,0,0,0,1,2,
-sp-1-355,female,16,1,1,,,,,,,,,,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,
-sp-3-356,female,19,1,3,1,0,0,1,0,,0,0,9,1,0,0,0,0,0,0,1,0,1,2,1,2,1,2,0,0,1,3,1,3,0,0,1,3,6oct2024
-sp-1-357,female,16,1,1,1,0,1,,,1,1,,9,1,1,2,1,2,1,2,1,2,1,2,0,0,1,2,1,2,1,1,1,3,0,0,1,3,6oct2024
-sp-3-358,female,18,1,3,0,0,0,1,1,0,1,1,,1,1,2,1,2,1,2,1,2,1,2,1,2,1,2,0,0,1,3,0,0,0,0,1,2,
-sp-1-359,female,16,1,1,1,,,1,,,1,,3,1,1,1,1,2,0,0,1,2,1,2,1,2,1,2,1,2,1,1,0,0,0,0,1,3,6oct2024
-sp-1-360,female,16,1,1,,,,,,,,,,1,1,3,1,3,1,2,1,0,1,2,1,2,1,3,1,1,,,,,1,3,1,3,
-sp-2-361,male,16,1,2,1,0,1,1,1,1,1,0,15,1,1,3,1,3,1,1,1,1,1,1,0,0,1,1,0,0,1,1,1,3,1,3,1,3,6oct2024
-sp-2-362,male,17,1,2,,,,,,,,,,0,,,,,,,,,,,,,,,,,,,,,,,,,
-sp-1-363,male,17,1,1,,,,,,,,,,1,1,1,1,2,0,0,1,1,1,1,1,2,1,2,0,0,1,1,1,3,1,3,1,1,
-sp-1-364,female,17,1,1,,,,,,,,,,1,0,0,0,0,0,0,1,3,1,2,1,3,1,2,0,0,1,1,1,2,,,1,3,
-sp-1-365,female,16,1,1,1,,1,1,1,,,,9,1,0,0,0,0,0,0,1,3,1,3,0,0,1,2,,,0,0,1,,0,0,1,3,6oct2024
-sp-2-366,female,17,1,2,1,0,0,1,1,0,1,0,15,1,0,0,0,0,0,0,1,2,1,2,1,1,1,1,0,0,1,2,1,1,0,0,1,2,6oct2024
-sp-1-367,female,15,1,1,1,0,1,1,1,1,1,0,9,1,0,0,0,0,0,0,1,2,1,3,0,0,1,1,0,0,1,1,1,2,0,0,1,3,7oct2024
-sp-1-368,female,16,1,1,1,0,0,1,0,0,0,0,9,1,1,2,0,0,1,2,1,1,1,3,1,1,1,1,0,0,0,0,1,2,0,0,0,0,6oct2024
-sp-0-369,male,17,1,,,,,,,,,,,1,0,0,1,1,0,0,1,3,1,2,0,0,1,2,1,2,1,1,1,3,1,,1,2,
-sp-1-370,female,17,1,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-sp-3-372,female,19,1,3,,,,1,1,,1,,,1,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,1,2,1,2,0,0,0,0,
-sp-3-373,female,18,1,3,1,0,0,1,0,0,1,1,15,1,0,0,1,3,1,2,1,1,1,2,0,0,1,2,0,0,1,1,1,2,0,0,0,0,6oct2024
-sp-3-374,male,18,1,3,1,0,0,1,1,,1,0,9,1,1,2,1,3,1,2,1,2,1,2,1,2,1,3,1,2,1,3,1,3,0,0,1,3,6oct2024
-sp-2-375,female,17,1,2,1,0,0,1,1,0,1,0,9,1,1,2,1,2,1,2,1,2,1,2,0,0,1,2,0,0,1,1,0,0,0,0,1,3,6oct2024
-sp-2-376,female,17,1,2,1,,,1,1,,,,9,1,1,2,1,2,1,2,1,2,1,2,1,2,1,2,0,0,1,2,1,2,,,1,1,6oct2024
-sp-2-377,male,17,1,2,,,,,,,,,,1,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,3,1,3,0,0,1,1,
-sp-1-378,female,17,1,1,1,,,1,1,,1,,15,1,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,1,0,0,,,1,2,6oct2024
-sp-2-379,female,18,1,2,1,0,0,1,0,0,0,0,15,1,0,0,0,0,0,0,1,1,1,2,0,0,0,0,0,0,1,1,0,0,1,2,1,1,6oct2024
-sp-1-381,female,16,1,1,,,,,,,,,,1,,,0,0,,,1,2,1,2,0,0,1,1,0,0,1,1,0,0,0,0,0,0,
-sp-3-382,female,18,1,3,,,,,,,,,,1,1,2,1,2,1,2,1,2,1,2,1,2,1,2,0,0,1,1,1,3,0,0,0,0,
-sp-3-383,female,18,1,3,1,,,,,,,,9,1,0,0,1,1,1,1,1,2,1,2,1,2,0,0,0,0,1,3,0,0,0,0,0,0,6oct2024
-sp-2-385,male,17,1,2,,,,,,,,,,1,1,1,1,1,1,1,1,2,1,2,1,2,1,1,0,0,1,3,1,2,0,0,1,3,
-sp-1-386,female,16,1,1,1,,,1,,,,,9,1,1,2,1,2,1,2,1,2,1,2,1,2,1,2,,,,,1,3,,,1,1,6oct2024
-sp-2-387,female,17,1,2,1,0,0,1,0,0,0,0,9,1,0,0,0,0,0,0,1,1,1,1,0,0,1,1,0,0,1,2,0,0,0,0,1,1,6oct2024
-sp-2-388,male,17,1,2,,,,,,,,,,1,1,3,1,3,1,2,1,3,1,2,0,0,0,0,0,0,1,2,1,3,0,0,1,3,
-sp-0-389,female,18,1,,1,0,0,1,0,0,0,0,9,1,0,0,1,1,1,2,1,2,1,2,0,0,1,2,0,0,1,1,0,0,0,0,1,2,6oct2024
-sp-3-390,male,18,1,3,1,,,1,0,0,0,0,9,1,0,0,0,0,0,0,1,2,1,2,0,0,1,2,0,0,1,1,1,3,,,1,1,6oct2024
-sp-2-391,male,18,1,2,1,1,1,1,1,1,1,0,15,1,0,0,0,0,0,0,1,1,1,1,0,0,1,1,0,0,0,0,1,3,0,0,0,0,6oct2024
-sp-2-392,male,17,1,2,1,0,0,1,1,0,1,1,9,,,,,,,,,,,,,,,,,,,,,,,,,,6oct2024
-sp-3-393,female,19,1,3,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-sp-2-394,female,17,1,2,,,1,1,1,1,1,,9,1,1,1,0,0,0,0,1,1,1,3,0,0,1,2,1,1,1,1,1,1,0,0,0,0,6oct2024
-sp-1-395,male,16,1,1,1,,1,,,,,,9,1,1,3,0,0,1,3,1,3,1,3,0,0,1,2,0,0,1,1,1,3,1,2,0,0,6oct2024
-sp-0-396,male,16,1,,,,,,,,,,,1,1,3,1,3,1,3,1,2,1,2,1,2,1,3,1,2,1,1,1,3,1,3,1,3,
-sp-0-398,male,34,0,,0,0,0,1,0,0,1,0,,1,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,,,,,1,3,
-sp-2-399,male,17,1,2,0,0,0,0,1,0,1,0,,1,1,3,1,3,1,3,1,3,1,2,1,3,1,3,1,2,,,1,3,,,,,
-sp-2-400,female,17,1,2,,,,,,,,,,1,0,0,0,0,0,0,1,1,1,1,1,1,1,2,0,0,1,1,0,0,0,0,1,3,
-sp-2-401,male,17,1,2,1,0,0,0,1,1,0,0,21,1,0,0,1,2,1,2,1,3,1,3,0,0,1,2,0,0,,,1,3,1,3,,,5oct2024
-sp-2-402,male,17,1,2,1,0,0,1,0,,0,0,9,1,1,2,1,2,1,1,1,2,1,2,1,2,1,2,1,1,1,1,1,2,1,1,1,3,6oct2024
-sp-1-403,female,17,1,1,,,,,,,,,,1,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,1,1,1,,,,,
-sp-3-404,male,19,1,3,1,0,1,1,1,,0,0,3,1,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,3,1,2,1,1,1,2,6oct2024
-sp-0-405,male,31,0,,,,,,,,,,,1,1,2,0,0,,,1,2,1,2,1,2,1,2,,,0,0,1,3,1,2,1,2,
-sp-1-406,male,16,1,1,,,,,,,,,,1,1,2,0,0,1,1,1,0,1,2,1,2,1,2,1,2,1,1,1,3,0,0,1,2,
-sp-3-408,female,17,1,3,,,,,,,,,,1,1,1,0,0,0,0,1,2,1,1,0,0,1,2,1,2,0,0,1,1,1,3,1,3,
-sp-1-409,female,16,1,1,1,,,,1,,1,,15,1,1,2,1,2,1,2,1,2,1,2,1,2,1,2,,,1,1,1,2,,,,,6oct2024
-sp-1-410,female,16,1,1,,,,,,,,,,1,1,1,1,1,0,0,0,1,1,1,1,1,1,1,0,0,1,1,1,3,,,,,
-sp-3-411,male,19,1,3,1,,,,,,,,15,1,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,3,1,3,,,,,7oct2024
-sp-1-412,male,17,1,1,1,0,0,1,,0,0,0,9,1,1,2,1,2,1,2,1,2,1,2,0,0,0,0,1,2,1,2,1,3,0,0,1,3,6oct2024
-sp-1-413,female,16,1,1,,,,,,,,,,1,0,0,0,0,0,0,0,1,1,1,1,1,1,2,0,0,1,1,0,0,0,0,1,1,
-sp-3-414,female,18,1,3,1,,,1,,,,,9,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,1,3,1,1,0,0,1,1,6oct2024
-sp-3-415,female,18,1,3,1,,,,,,1,1,9,1,1,2,1,2,1,2,1,2,1,2,0,0,1,2,1,2,1,1,1,2,,,1,1,6oct2024
-sp-0-417,female,29,0,,,,1,,,0,0,0,9,1,1,1,1,1,,,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,1,2,6oct2024
-sp-2-418,male,18,1,2,,,,,,,,,,1,1,2,1,2,1,2,0,0,0,0,0,0,1,2,0,0,1,1,1,3,0,0,1,3,
-sp-1-421,female,16,1,1,1,0,1,1,1,,,,3,1,1,1,1,2,1,2,1,2,1,2,0,0,1,2,0,0,1,1,1,3,0,0,1,3,6oct2024
-sp-2-422,male,17,1,2,,,,,,,,,,1,0,0,0,0,0,0,0,3,0,0,1,3,1,2,0,0,1,2,1,3,0,0,0,0,
-sp-2-423,male,18,1,2,,,,,,,,,,1,0,0,0,0,0,0,0,2,1,2,0,0,1,2,0,0,0,0,1,3,0,0,1,1,
-sp-1-424,male,15,1,1,1,0,0,0,0,,,,21,1,0,0,0,0,0,0,1,2,1,2,1,2,1,1,0,0,1,1,1,2,0,0,1,3,6oct2024
-sp-3-425,female,18,1,3,,,,,,,,,,1,1,1,0,0,1,1,0,0,0,0,0,0,0,0,0,0,1,3,1,2,0,0,1,2,
-sp-2-426,male,17,1,2,1,0,0,0,0,0,0,0,21,1,1,1,0,0,0,0,0,0,0,0,,,0,0,0,0,1,1,1,3,1,2,1,3,5oct2024
-sp-1-427,male,16,1,1,1,0,0,1,0,0,0,0,9,1,1,1,1,1,1,1,1,1,1,1,0,0,1,1,0,0,1,2,1,1,,,,,6oct2024
-sp-1-429,female,15,1,1,1,0,1,1,1,1,0,1,9,1,1,2,1,2,1,1,0,3,1,3,0,0,1,2,0,0,1,1,0,0,0,0,0,0,6oct2024
-sp-1-430,female,16,1,1,1,,,1,,,,,21,1,0,0,1,2,1,2,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6oct2024
-sp-3-432,female,18,1,3,1,,,1,,,,,21,1,0,0,0,0,1,1,0,2,1,3,0,0,1,2,1,2,1,3,0,0,0,0,1,3,6oct2024
-sp-3-433,female,18,1,3,1,0,1,1,1,0,0,0,21,1,1,2,1,2,0,0,0,2,1,1,1,1,1,2,0,0,1,3,1,1,0,0,1,3,6oct2024
-sp-3-434,male,19,1,3,1,,,1,,1,,,9,1,1,3,1,3,1,3,0,2,1,2,1,2,1,2,,,1,1,1,3,0,0,1,2,6oct2024
-sp-3-435,female,19,1,3,,,,,,,,,,1,1,2,1,2,1,2,0,1,1,1,0,0,1,2,0,0,1,1,1,2,0,0,1,2,
+id,sex,age,group,class,ill,diarrhoea,bloody,vomiting,abdo,nausea,fever,headache,jointpain,starthour,meal,feta,fetaD,sardines,sardinesD,eggplant,eggplantD,veal,vealD,pasta,pastaD,tomsal,tomsalD,bread,breadD,dessert,dessertD,champagne,champagneD,beer,beerD,redwine,redwineD,whitewine,whitewineD,dayonset
+sp-2-001,male,18,1,2,1,1,0,0,1,0,,0,0,9,1,1,2,1,2,0,0,1,2,1,3,1,1,1,2,1,2,1,1,1,3,0,0,0,0,6oct2024
+sp-3-003,female,18,1,3,0,,,,,,,,,,1,0,0,0,0,0,0,1,1,1,3,1,3,1,3,1,3,1,1,0,0,1,3,0,0,""
+sp-1-005,female,17,1,1,1,,,,1,1,,1,,,1,,,,,,,1,0,1,1,,,1,1,,,0,0,0,0,0,0,0,0,""
+sp-2-006,male,17,1,2,0,,,,,,,,,,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,3,1,3,1,3,1,3,""
+sp-3-007,female,18,1,3,1,1,0,0,1,1,0,1,0,15,1,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,1,1,2,0,0,1,3,6oct2024
+sp-2-008,male,180,1,2,1,1,0,0,1,0,0,0,0,15,1,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,1,1,3,0,0,1,2,7oct2024
+sp-0-009,male,16,0,,0,,,,,,,,,,1,1,2,1,2,1,2,1,1,1,2,1,2,1,2,0,0,1,2,,,1,2,1,1,""
+sp-1-010,female,15,1,1,1,0,0,0,0,1,0,1,0,,1,1,1,0,0,0,0,1,3,1,3,1,2,1,2,1,1,1,1,1,1,0,0,1,3,""
+sp-0-011,female,43,0,,1,1,,,,1,,1,1,3,1,1,2,1,2,,,1,2,1,2,1,2,1,2,,,1,1,,,1,2,,,7oct2024
+sp-1-012,male,16,1,1,0,,,,,,,,,,1,1,2,1,2,1,2,1,2,1,3,1,2,1,2,,,1,1,1,2,0,0,0,0,""
+sp-2-013,male,18,1,2,0,,,,,,,,,,1,0,0,0,0,0,0,1,1,1,1,0,0,0,0,0,0,0,0,1,3,0,0,1,3,""
+sp-3-015,male,18,1,3,1,1,0,0,1,1,0,1,1,21,1,0,0,0,0,0,0,1,3,1,2,0,0,1,2,0,0,1,3,1,3,0,0,0,0,6oct2024
+sp-3-016,male,20,1,3,1,1,0,0,1,0,0,0,,9,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,3,1,3,0,0,0,0,6oct2024
+sp-0-017,male,58,0,,1,1,,,,1,,,,21,1,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,,,,,1,3,,,6oct2024
+sp-3-018,male,19,1,3,1,1,0,0,1,1,0,0,0,9,1,0,0,0,0,0,0,1,2,1,3,1,1,1,2,1,2,1,3,1,3,0,0,0,0,7oct2024
+sp-0-019,male,17,1,,0,,,,,,,,,,1,0,0,0,0,0,0,1,2,1,2,0,0,1,2,0,0,1,1,1,3,1,1,1,1,""
+sp-0-020,female,39,0,,1,0,0,0,1,1,0,1,,,1,1,2,1,2,1,2,1,3,1,3,1,2,1,2,1,1,1,1,,,1,2,1,1,""
+sp-2-021,male,18,1,2,1,1,1,0,1,1,,1,1,21,1,0,0,1,1,1,2,1,3,1,3,1,2,1,2,1,3,1,3,1,3,1,3,1,1,6oct2024
+sp-2-022,male,17,1,2,0,,,,,,,,,,1,1,2,0,0,1,2,1,2,1,2,1,2,1,2,1,2,1,3,1,3,1,1,1,3,""
+sp-2-023,female,17,1,2,1,1,0,1,1,1,0,1,0,9,1,0,0,0,0,0,0,1,2,1,2,0,0,1,2,0,0,1,1,1,1,0,0,1,2,6oct2024
+sp-1-024,female,16,1,1,0,,,,,,,,,,1,1,2,1,2,1,2,1,2,1,2,1,2,1,2,0,0,1,1,1,1,1,1,1,3,""
+sp-1-025,female,17,1,1,0,,,,,,,,,,1,1,2,1,2,1,2,1,3,1,3,1,2,1,1,0,0,1,1,1,1,1,1,1,3,""
+sp-3-026,female,19,1,3,1,0,0,0,1,1,0,1,0,,1,1,3,1,3,1,3,1,2,1,2,1,2,1,2,1,2,0,0,0,0,1,1,1,1,""
+sp-2-027,male,17,1,2,1,1,,,,,,,,15,1,0,0,0,0,,,1,2,1,2,,,1,2,0,0,,,1,3,1,1,1,1,6oct2024
+sp-3-028,female,18,1,3,1,0,0,0,1,1,1,1,,,1,0,0,1,2,0,0,1,2,1,2,0,0,1,1,0,0,1,2,0,0,0,0,0,0,""
+sp-1-029,male,16,1,1,1,1,0,0,1,1,1,1,0,15,1,1,2,1,2,1,2,1,2,1,3,1,3,1,2,1,2,0,0,1,3,0,0,1,1,6oct2024
+sp-1-030,male,16,1,1,1,1,,,,1,,1,,9,1,1,1,0,0,1,2,1,3,1,2,0,0,1,2,0,0,0,0,1,2,0,0,1,1,6oct2024
+sp-2-031,male,18,1,2,1,1,0,0,1,1,0,1,0,15,1,1,2,0,0,1,2,1,1,1,2,0,0,,,1,1,1,1,1,3,0,0,0,0,6oct2024
+sp-2-032,female,18,1,2,1,1,0,0,1,1,,0,0,9,1,1,2,0,0,0,0,1,3,1,3,0,0,1,2,1,3,1,1,1,3,,,,,6oct2024
+sp-3-033,female,19,1,3,1,1,,,1,1,0,0,0,21,1,0,0,1,1,0,0,1,1,1,1,,,0,0,,,1,2,1,3,0,0,0,0,6oct2024
+sp-2-034,female,17,1,2,1,1,,0,1,1,1,1,1,9,1,1,1,1,1,0,0,1,1,1,2,1,2,1,2,1,2,1,2,0,0,0,0,1,3,6oct2024
+sp-1-036,male,16,1,1,0,,,,,,,,,,1,1,3,0,0,0,0,1,0,1,2,1,2,1,2,0,0,1,1,1,3,0,0,0,0,""
+sp-2-037,female,17,1,2,1,0,0,0,1,0,0,0,0,,1,0,0,0,0,0,0,1,2,1,2,0,0,0,0,1,2,1,1,1,3,0,0,1,3,""
+sp-1-038,female,17,1,1,1,1,0,0,1,1,0,1,0,15,1,0,0,0,0,0,0,1,2,1,1,0,0,1,2,0,0,1,1,1,3,0,0,0,0,6oct2024
+sp-1-039,female,17,1,1,1,1,,,1,1,,,,15,1,1,1,1,2,,,1,0,1,2,0,0,1,1,0,0,1,1,1,3,0,0,1,3,6oct2024
+sp-3-040,male,19,1,3,1,1,0,1,1,1,,0,0,9,1,1,2,1,2,1,2,1,3,1,2,1,2,1,2,1,2,1,3,1,3,0,0,0,0,6oct2024
+sp-2-041,female,16,1,2,1,0,0,0,1,0,0,0,0,,1,1,1,1,2,0,0,1,2,1,2,1,2,1,2,0,0,1,1,1,1,0,0,1,1,""
+sp-2-042,male,17,1,2,1,1,0,0,0,1,1,0,0,9,1,1,1,0,0,0,0,1,3,1,2,1,2,1,2,0,0,1,1,1,3,0,0,0,0,6oct2024
+sp-0-045,female,18,1,,1,1,0,0,1,1,0,0,0,3,1,0,0,0,0,0,0,1,1,0,0,0,0,1,2,0,0,1,1,1,3,0,0,0,0,6oct2024
+sp-2-046,male,17,1,2,1,1,0,0,1,0,0,0,1,9,1,1,2,1,2,1,2,,2,,,,,1,2,1,2,1,2,1,2,1,2,1,2,6oct2024
+sp-2-047,male,18,1,2,0,,,,,,,,,,1,0,0,0,0,0,0,1,3,1,3,0,0,1,2,0,0,0,0,1,2,0,0,0,0,""
+sp-1-048,female,17,1,1,1,,,,1,,,,,,1,1,2,1,2,1,2,1,2,1,2,1,2,1,1,0,0,1,1,0,0,0,0,0,0,""
+sp-1-049,female,16,1,1,1,1,0,1,1,1,0,1,1,9,1,0,0,0,0,0,0,1,2,1,1,0,0,1,2,1,2,1,1,1,2,0,0,1,3,6oct2024
+sp-2-050,female,19,1,2,1,,1,,1,1,,,,9,1,1,2,1,2,0,0,1,2,1,2,1,3,1,2,,,1,1,1,3,1,1,1,3,6oct2024
+sp-3-051,female,18,1,3,1,0,0,0,1,1,0,0,0,,1,1,3,1,2,1,2,1,2,1,3,1,3,1,2,1,3,1,1,1,1,0,0,1,2,""
+sp-0-052,male,56,0,,0,,,,,,,,,,1,1,3,1,3,1,3,1,3,1,3,1,3,1,3,1,3,1,2,,,1,2,,,""
+sp-2-053,female,17,1,2,0,,,,,,,,,,1,1,1,0,0,0,0,1,0,1,2,0,0,1,2,0,0,1,1,1,3,0,0,1,2,""
+sp-1-054,female,16,1,1,1,1,0,0,1,1,0,1,0,21,1,0,0,0,0,0,0,1,0,1,1,1,1,1,2,0,0,1,1,0,0,0,0,0,0,6oct2024
+sp-0-055,male,18,1,,1,1,,,1,,,1,,15,1,1,2,1,2,1,2,1,2,1,2,0,0,1,2,1,1,1,3,1,3,0,0,0,0,6oct2024
+sp-2-056,female,18,1,2,0,,,,,,,,,,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,2,1,3,0,0,1,1,""
+sp-1-057,male,16,1,1,1,1,0,1,1,1,0,0,0,15,1,0,0,0,0,1,1,1,2,1,2,1,1,1,2,,,1,1,1,3,0,0,1,2,6oct2024
+sp-3-058,male,20,1,3,1,,,,1,,,,,21,1,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,3,1,3,1,3,,,6oct2024
+sp-3-059,female,18,1,3,1,1,0,1,1,1,,1,,9,1,1,2,1,2,1,2,1,1,1,2,1,2,1,2,0,0,1,1,1,2,0,0,1,1,6oct2024
+sp-2-060,female,18,1,2,1,1,0,0,0,1,0,1,0,15,1,1,1,0,0,1,1,1,1,1,1,1,2,1,2,1,1,1,1,1,3,0,0,1,1,7oct2024
+sp-1-062,male,16,1,1,1,1,0,0,1,1,0,1,0,15,1,1,2,1,2,1,2,1,2,1,2,,,1,2,,,0,0,1,3,0,0,1,3,6oct2024
+sp-3-063,female,19,1,3,1,1,,0,1,1,,1,0,9,1,1,1,0,0,0,0,1,2,1,2,1,2,1,2,0,0,1,3,1,1,0,0,1,2,6oct2024
+sp-1-064,male,15,1,1,0,,,,,,,,,,1,0,0,1,1,0,0,1,3,0,0,0,0,1,2,0,0,1,1,,,,,1,3,""
+sp-1-066,male,16,1,1,0,,,,,,,,,,1,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,1,1,3,0,0,1,3,""
+sp-1-067,female,17,1,1,1,0,0,0,0,1,1,1,0,,1,1,2,1,2,1,2,1,1,1,2,1,1,1,2,0,0,1,1,1,1,,,1,1,""
+sp-1-068,female,17,1,1,0,,,,,,,,,,1,1,2,1,2,1,2,1,2,0,0,1,2,0,0,1,2,1,1,1,2,0,0,1,1,""
+sp-2-069,female,16,1,2,0,,,,,,,,,,1,0,0,0,0,0,0,1,1,1,2,0,0,1,1,0,0,1,1,1,3,0,0,1,1,""
+sp-1-070,male,16,1,1,0,,,,,,,,,,1,1,2,1,2,1,3,1,3,0,0,0,0,1,2,0,0,0,0,1,3,0,0,1,3,""
+sp-1-072,female,17,1,1,1,1,1,0,1,1,1,1,1,9,1,1,2,1,2,1,2,1,2,1,2,1,2,1,2,,,1,1,,,,,1,3,6oct2024
+sp-2-073,male,18,1,2,1,0,0,0,1,1,0,1,0,,1,0,0,0,0,0,0,1,2,0,0,0,0,1,3,0,0,1,1,1,3,0,0,0,0,""
+sp-1-075,male,15,1,1,0,,,,,,,,,,1,1,2,1,2,1,2,1,0,1,2,1,2,1,2,1,2,0,0,0,0,0,0,0,0,""
+sp-1-076,female,17,1,1,1,1,,,,,,1,,9,1,0,0,0,0,0,0,1,3,1,1,0,0,1,1,0,0,1,2,1,1,,,1,3,6oct2024
+sp-1-077,female,17,1,1,1,,,,1,1,,1,,,1,0,0,0,0,0,0,1,1,1,1,0,0,0,0,0,0,1,1,0,0,0,0,1,3,""
+sp-3-078,female,18,1,3,0,,,,,,,,,,1,1,1,1,1,0,0,1,2,1,2,1,2,1,2,1,2,1,1,1,3,0,0,1,2,""
+sp-2-079,female,18,1,2,1,1,0,1,1,1,,,0,9,1,1,1,0,0,0,0,1,2,1,2,1,2,1,2,0,0,1,2,1,2,0,0,1,2,6oct2024
+sp-1-080,female,16,1,1,1,1,0,0,1,1,0,1,0,9,1,0,0,1,1,1,2,1,1,1,1,0,0,1,2,0,0,1,2,1,3,0,0,1,3,6oct2024
+sp-3-081,male,18,1,3,1,1,0,0,0,1,0,0,0,9,1,1,2,1,2,1,2,1,2,1,3,1,2,1,2,1,2,1,3,1,3,0,0,1,2,6oct2024
+sp-2-082,female,16,1,2,0,,,,,,,,,,1,0,0,0,0,0,0,1,2,1,3,1,1,0,0,0,0,1,1,1,3,0,0,1,1,""
+sp-1-083,male,16,1,1,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,""
+sp-2-084,male,17,1,2,1,1,0,1,1,1,0,1,0,15,1,1,1,0,0,1,2,1,2,1,2,1,2,1,2,1,2,1,1,1,3,0,0,0,0,6oct2024
+sp-1-085,female,16,1,1,0,,,,,,,,,,1,1,2,1,2,1,2,1,1,1,1,0,0,1,1,1,1,1,1,1,2,0,0,1,3,""
+sp-2-086,female,17,1,2,1,1,0,1,1,1,0,0,0,9,1,1,1,1,2,1,2,1,2,1,2,0,0,1,2,0,0,1,1,1,1,0,0,1,2,6oct2024
+sp-1-087,female,16,1,1,0,,,,,,,,,,1,1,2,1,2,0,0,1,2,1,2,1,2,1,2,0,0,1,1,1,3,0,0,0,0,""
+sp-1-088,female,17,1,1,0,,,,,,,,,,1,1,1,1,2,1,1,1,2,1,1,1,1,0,0,0,0,1,1,0,0,0,0,1,3,""
+sp-2-089,female,18,1,2,1,,,,1,,,,,,1,1,1,1,1,0,0,1,2,0,0,0,0,1,2,0,0,1,1,1,3,0,0,0,0,""
+sp-0-090,male,16,1,,0,,,,,,,,,,1,1,2,1,2,1,2,1,3,1,3,0,0,1,2,1,3,1,1,0,0,0,0,0,0,""
+sp-3-091,female,19,1,3,1,0,0,0,1,0,0,0,0,,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,3,0,0,0,0,0,0,""
+sp-2-092,female,16,1,2,1,1,,,1,,,,,15,1,0,0,0,0,0,0,1,1,1,1,1,1,1,1,0,0,1,1,0,0,0,0,1,3,6oct2024
+sp-3-093,female,19,1,3,1,0,0,1,1,1,,1,0,21,1,1,1,1,1,1,1,1,1,0,0,1,1,1,2,1,1,1,3,0,0,0,0,0,0,5oct2024
+sp-2-094,female,16,1,2,1,,,,1,,,1,,,1,1,2,,,,,1,2,1,2,,,1,2,1,2,,,,,,,1,3,""
+sp-1-095,female,16,1,1,1,1,0,0,1,0,0,1,0,15,1,1,2,1,2,1,2,1,2,1,2,1,2,1,2,,,1,2,1,3,1,3,0,0,6oct2024
+sp-1-096,male,16,1,1,0,,,,,,,,,,1,1,3,1,3,1,3,1,3,1,3,1,3,,,1,3,,,1,2,,,,,""
+sp-3-097,female,17,1,3,1,1,0,0,1,1,0,1,0,9,1,1,3,0,0,1,2,1,2,1,3,1,2,1,2,1,3,1,3,1,3,0,0,1,1,6oct2024
+sp-1-099,female,17,1,1,0,,,,,,,,,,1,1,2,1,2,1,1,1,2,1,2,1,2,1,2,1,2,1,1,1,1,1,1,1,3,""
+sp-2-101,male,17,1,2,1,1,0,0,1,0,0,0,0,15,1,1,2,1,3,0,0,1,3,1,3,0,0,1,2,1,2,1,1,1,3,0,0,0,0,6oct2024
+sp-2-102,female,18,1,2,1,1,0,0,0,1,,1,,15,1,0,0,1,2,0,0,1,2,1,2,1,2,1,2,0,0,1,3,1,3,0,0,1,1,6oct2024
+sp-0-103,male,16,1,,0,,,,,,,,,,1,1,1,1,1,1,1,1,1,1,1,,,1,1,1,1,0,0,1,1,0,0,1,2,""
+sp-3-104,female,18,1,3,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,""
+sp-0-105,male,18,1,,1,1,,,,1,,1,,15,1,1,2,1,2,1,2,1,3,1,3,1,2,,,1,2,1,1,1,3,0,0,0,0,6oct2024
+sp-1-106,female,16,1,1,1,1,0,0,1,1,,1,,9,1,1,2,1,2,0,0,1,2,1,3,1,3,1,1,1,3,0,0,1,1,0,0,1,3,6oct2024
+sp-1-107,female,17,1,1,0,,,,,,,,,,0,,,,,,,,,,,,,,,,,,,,,,,,,""
+sp-1-108,male,16,1,1,1,1,0,0,0,0,0,0,0,21,1,0,0,0,0,1,2,1,2,1,2,0,0,1,2,0,0,1,1,1,3,0,0,1,1,6oct2024
+sp-0-109,female,17,1,,1,1,,,1,,,,1,9,1,1,2,1,2,1,2,1,2,1,2,1,2,1,2,,,1,1,1,1,0,0,1,3,6oct2024
+sp-2-110,female,17,1,2,1,1,,,1,,,1,,9,1,1,1,1,1,0,0,1,0,1,1,1,1,1,1,0,0,1,1,1,1,,,1,2,6oct2024
+sp-1-111,male,17,1,1,1,1,0,1,1,1,0,1,1,15,1,1,1,1,3,0,0,1,3,1,2,0,0,1,2,1,2,1,3,1,3,1,1,1,3,6oct2024
+sp-2-112,male,17,1,2,1,0,0,0,1,0,0,0,0,,1,1,2,1,2,1,2,1,2,1,2,1,2,1,2,,,1,1,1,3,0,0,1,2,""
+sp-0-113,female,17,1,,0,,,,,,,,,,1,1,2,1,2,0,0,1,2,1,2,1,2,1,2,1,2,1,2,1,3,0,0,1,2,""
+sp-1-114,male,16,1,1,1,1,0,0,1,0,0,0,0,9,1,1,2,1,2,1,2,1,2,1,2,0,0,1,2,1,2,1,3,1,3,1,3,1,1,7oct2024
+sp-3-115,male,18,1,3,1,0,0,0,1,1,0,1,0,,1,1,3,1,3,1,3,1,3,1,3,1,3,1,3,0,0,1,3,1,3,0,0,0,0,""
+sp-1-116,male,17,1,1,1,1,,1,,1,,,,9,1,1,2,0,0,1,2,1,3,1,3,0,0,1,3,0,0,1,3,1,3,0,0,1,2,6oct2024
+sp-3-117,male,18,1,3,0,0,0,0,0,0,0,0,0,,1,0,0,1,2,1,1,1,3,1,2,1,1,1,2,0,0,1,3,1,3,0,0,1,1,""
+sp-1-118,male,16,1,1,1,0,0,0,1,,0,0,0,,1,1,2,1,2,1,2,1,3,1,3,1,1,1,2,1,3,1,1,1,3,1,1,1,1,""
+sp-1-119,male,17,1,1,1,1,0,0,1,0,0,0,1,21,1,1,1,1,2,,,1,3,1,3,0,0,1,2,0,0,1,3,1,2,0,0,1,3,6oct2024
+sp-0-120,male,17,1,,1,1,0,1,1,1,,1,,9,1,1,2,1,2,1,2,1,3,1,1,0,0,1,1,1,3,1,1,1,3,0,0,1,1,6oct2024
+sp-1-121,male,16,1,1,1,0,0,0,1,0,,1,0,,1,0,0,0,0,0,0,1,3,1,1,0,0,1,3,0,0,1,1,1,3,0,0,0,0,""
+sp-3-122,female,18,1,3,1,0,0,0,1,1,1,1,0,,1,1,1,0,0,1,2,1,2,1,3,0,0,1,2,1,3,1,3,1,3,0,0,0,0,""
+sp-1-123,male,16,1,1,0,,,,,,,,,,1,0,0,0,0,1,2,1,2,1,2,1,2,1,2,0,0,0,0,1,3,0,0,1,3,""
+sp-1-124,female,16,1,1,1,1,,1,1,,,1,,9,1,0,0,0,0,0,0,1,1,0,0,0,0,1,2,0,0,1,1,0,0,1,1,1,1,7oct2024
+sp-2-125,female,18,1,2,1,1,0,0,1,1,0,0,0,9,1,1,2,1,2,,,1,0,1,1,1,1,1,2,0,0,1,2,1,3,0,0,1,3,6oct2024
+sp-1-126,male,17,1,1,1,0,0,0,0,0,0,1,0,,1,1,2,1,2,1,2,1,2,1,2,0,0,1,2,0,0,1,1,1,3,0,0,0,0,""
+sp-3-127,female,18,1,3,1,1,0,0,1,1,,,,15,1,1,2,1,2,,,1,2,1,2,1,2,1,2,1,2,1,2,1,2,0,0,0,0,6oct2024
+sp-2-128,female,18,1,2,1,0,0,0,1,1,0,0,0,,1,1,1,0,0,1,1,1,1,1,2,1,2,1,2,0,0,1,1,1,2,0,0,1,2,""
+sp-1-129,female,17,1,1,1,1,0,0,1,1,0,1,0,9,1,1,3,1,3,1,3,1,3,1,3,1,2,1,2,0,0,1,1,1,1,0,0,1,1,6oct2024
+sp-1-131,female,16,1,1,1,1,0,0,1,1,0,0,0,9,1,0,0,0,0,1,1,1,0,1,3,1,2,0,0,1,1,1,1,0,0,0,0,1,3,6oct2024
+sp-3-132,male,19,1,3,0,,,,,,,,,,1,0,0,0,0,1,2,1,2,1,2,1,2,1,2,1,2,1,3,1,3,0,0,0,0,""
+sp-1-134,female,16,1,1,0,,,,,,,,,,1,1,2,1,2,1,2,1,2,1,2,1,2,1,2,0,0,1,1,1,3,0,0,1,1,""
+sp-3-135,male,20,1,3,1,1,,0,1,0,,0,0,15,1,1,3,1,2,0,0,1,2,1,1,1,1,1,2,1,2,1,3,1,3,0,0,1,3,6oct2024
+sp-2-136,female,17,1,2,1,0,0,1,1,0,0,0,0,9,1,0,0,0,0,0,0,1,2,1,2,0,0,1,2,0,0,1,2,1,3,0,0,1,3,6oct2024
+sp-1-137,female,16,1,1,1,1,,,1,1,,,,21,1,1,1,0,0,0,0,1,1,1,3,0,0,1,2,0,0,1,1,1,3,0,0,1,1,6oct2024
+sp-3-138,female,19,1,3,1,0,0,0,0,1,1,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,""
+sp-1-139,male,16,1,1,0,,,,,,,,,,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,,,1,3,5oct2024
+sp-1-140,male,16,1,1,0,,,,,,,,,,1,1,2,1,2,0,0,1,3,1,3,1,2,1,2,1,2,1,1,1,2,,,1,3,""
+sp-1-141,female,16,1,1,0,,,,,,,,,,1,1,1,1,2,0,0,0,1,0,0,0,0,1,1,0,0,1,1,1,3,0,0,0,0,""
+sp-3-142,male,18,1,3,1,1,0,0,0,1,0,1,0,15,1,1,2,1,2,0,0,1,2,1,2,1,2,1,2,0,0,1,1,1,3,0,0,1,2,6oct2024
+sp-3-143,male,18,1,3,1,1,0,0,1,1,0,0,0,9,1,1,3,1,3,1,3,1,2,1,2,1,2,1,3,1,2,1,1,1,3,0,0,0,0,6oct2024
+sp-3-144,male,18,1,3,1,1,1,0,1,0,1,0,0,15,1,0,0,1,2,0,0,1,3,1,3,0,0,0,0,0,0,1,1,1,3,0,0,1,2,6oct2024
+sp-1-145,female,15,1,1,0,,,,,,,,,,1,1,3,1,2,1,3,1,2,1,2,0,0,1,2,0,0,0,0,0,0,0,0,1,1,""
+sp-2-146,female,18,1,2,1,1,0,0,1,,0,0,0,15,1,1,2,1,2,0,0,1,2,1,2,1,2,1,2,0,0,1,1,1,1,0,0,1,2,6oct2024
+sp-1-147,male,16,1,1,1,1,0,1,1,1,0,0,0,21,1,1,2,1,2,0,0,1,3,1,3,1,2,0,0,0,0,1,1,1,3,0,0,1,3,6oct2024
+sp-2-148,female,17,1,2,0,,,,,,,,,,1,0,0,0,0,0,0,1,2,1,2,1,2,1,2,0,0,1,1,1,3,1,1,1,3,""
+sp-3-149,female,18,1,3,1,1,0,1,1,1,0,1,0,15,1,1,3,1,3,1,3,1,2,1,2,1,1,1,3,0,0,1,2,1,1,0,0,1,2,6oct2024
+sp-1-150,male,16,1,1,0,,,,,,,,,,1,1,3,1,3,1,3,1,3,1,2,1,2,1,2,1,3,1,1,0,0,0,0,0,0,""
+sp-3-151,female,18,1,3,1,1,0,0,1,1,,1,1,9,1,1,2,1,2,1,2,1,,1,1,0,0,1,2,0,0,1,1,1,3,0,0,1,2,6oct2024
+sp-3-152,female,19,1,3,1,1,0,0,1,0,0,0,0,9,1,1,2,1,2,1,2,1,1,1,3,1,3,1,2,1,3,1,2,0,0,0,0,0,0,6oct2024
+sp-3-154,male,18,1,3,1,1,0,0,1,0,1,1,0,15,1,1,2,1,2,0,0,1,3,1,1,0,0,1,2,0,0,1,1,1,3,0,0,0,0,6oct2024
+sp-3-155,male,19,1,3,1,1,,1,1,1,,1,0,3,1,1,2,1,2,1,2,1,3,1,3,1,3,1,3,1,3,1,1,1,2,1,1,1,1,6oct2024
+sp-3-156,male,18,1,3,1,0,0,1,1,1,1,1,0,15,1,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,3,0,0,0,0,1,2,6oct2024
+sp-2-157,female,18,1,2,1,1,,,1,,,,,9,1,1,1,1,1,1,1,1,2,1,2,1,2,1,2,0,0,1,1,0,0,0,0,1,3,6oct2024
+sp-2-158,male,17,1,2,1,1,0,1,1,1,,1,0,15,1,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,0,0,1,3,1,1,0,0,6oct2024
+sp-1-159,female,16,1,1,1,1,,1,,,,1,,15,1,1,1,1,1,1,1,0,1,0,0,0,0,0,0,0,0,1,1,0,0,0,0,1,1,6oct2024
+sp-1-160,male,17,1,1,1,0,0,0,1,1,0,,,,1,0,0,1,2,1,2,1,3,1,1,1,3,1,2,0,0,1,1,1,3,0,0,0,0,""
+sp-2-161,female,19,1,2,1,1,0,0,1,,0,1,0,15,1,1,2,0,0,0,0,1,1,1,2,1,1,1,2,1,1,1,1,0,0,0,0,1,1,6oct2024
+sp-3-162,female,18,1,3,1,0,0,0,1,1,0,,0,,1,1,2,1,1,,,1,2,1,2,1,2,1,2,0,0,0,0,1,3,0,0,1,3,""
+sp-1-163,female,16,1,1,0,,,,,,,,,,1,0,0,0,0,0,0,1,2,1,2,0,0,1,1,0,0,1,1,1,3,1,1,0,0,""
+sp-1-164,female,16,1,1,1,1,0,1,1,1,0,1,0,15,1,1,1,1,2,1,1,1,1,1,2,1,2,1,2,1,2,0,0,1,2,0,0,1,3,7oct2024
+sp-3-165,male,18,1,3,1,1,,1,1,1,1,0,0,3,1,1,2,1,2,1,2,1,3,1,3,1,3,1,2,0,0,1,3,1,3,0,0,0,0,6oct2024
+sp-1-166,female,16,1,1,1,0,0,1,1,1,,1,0,15,1,1,2,1,2,1,2,1,3,1,2,1,2,1,2,1,1,1,1,,,,,1,3,6oct2024
+sp-2-167,female,16,1,2,0,,,,,,,,,,1,1,2,1,1,0,0,1,,1,2,1,2,1,2,,,1,1,1,2,0,0,1,2,""
+sp-3-168,female,19,1,3,1,1,0,0,1,1,,0,0,21,1,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,1,0,0,0,0,1,1,5oct2024
+sp-3-169,female,18,1,3,0,,,,,,,,,,1,1,1,1,2,1,1,1,3,1,3,1,2,1,2,1,2,1,3,1,2,0,0,1,1,""
+sp-3-170,female,19,1,3,0,,,,,,,,,,1,0,0,0,0,0,0,1,2,1,3,1,3,1,2,1,3,1,1,0,0,0,0,1,3,""
+sp-2-172,male,17,1,2,0,,,,,,,,,,1,1,1,1,2,1,2,1,2,1,2,1,2,1,2,0,0,1,1,1,3,0,0,1,2,""
+sp-2-173,female,17,1,2,0,,,,,,,,,,1,0,0,0,0,0,0,0,2,0,0,1,2,1,2,0,0,0,0,0,0,0,0,1,2,""
+sp-1-174,female,15,1,1,1,1,0,0,1,1,0,1,0,15,1,1,3,1,2,0,0,0,3,0,0,0,0,1,2,0,0,1,1,1,2,0,0,1,1,6oct2024
+sp-1-175,female,16,1,1,0,,,,,,,,,,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,1,3,1,1,0,0,1,,""
+sp-1-176,male,17,1,1,0,,,,,,,,,,1,1,3,1,3,1,2,1,2,1,1,1,1,1,2,1,1,1,1,1,2,0,0,1,1,""
+sp-3-177,female,18,1,3,0,,,,,,,,,,1,0,0,0,0,0,0,0,0,0,0,1,1,1,1,0,0,1,2,1,3,,,1,1,""
+sp-1-178,male,16,1,1,0,,,,,,,,,,1,1,3,1,3,1,3,1,3,1,3,1,3,1,3,1,3,1,1,1,1,0,0,1,3,""
+sp-2-179,female,18,1,2,0,,,,,,,,,,1,0,0,0,0,1,2,1,2,1,2,1,2,1,2,0,0,0,0,1,3,1,1,1,2,""
+sp-1-180,male,16,1,1,1,1,0,,1,1,1,1,0,15,1,0,0,1,3,1,3,1,3,1,3,1,3,1,1,0,0,1,1,1,3,0,0,1,3,6oct2024
+sp-0-181,female,17,1,,0,,,,,,,,,,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,1,1,3,0,0,1,3,""
+sp-1-182,female,17,1,1,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,""
+sp-0-183,female,16,1,,1,1,0,1,1,1,0,1,0,9,1,0,0,1,2,0,0,1,2,1,3,0,0,1,2,,,1,1,0,0,0,0,0,0,6oct2024
+sp-1-184,male,16,1,1,1,1,0,0,1,,0,0,0,15,1,1,2,1,2,0,0,1,3,1,2,1,1,1,2,1,3,1,1,1,3,1,2,1,1,6oct2024
+sp-3-185,male,18,1,3,0,,,,,,,,,,1,1,2,1,2,1,2,1,2,1,2,1,2,0,0,1,2,1,2,1,3,1,2,0,0,""
+sp-1-186,female,16,1,1,1,1,0,0,1,1,0,1,0,21,1,1,1,1,2,0,0,1,2,1,2,0,0,1,2,0,0,0,0,1,2,0,0,1,2,6oct2024
+sp-1-187,female,17,1,1,0,,,,,,,,,,1,1,1,0,0,0,0,1,1,1,1,1,1,1,1,,,0,0,1,2,0,0,1,1,""
+sp-1-188,female,17,1,1,1,1,,,1,,1,1,1,9,1,1,3,1,2,1,3,1,1,1,3,1,2,1,2,1,2,1,1,1,2,,,1,3,6oct2024
+sp-2-189,male,17,1,2,1,,,1,1,,,1,,15,1,1,2,0,0,1,2,1,2,1,2,1,2,1,3,,,1,3,1,3,1,2,,,6oct2024
+sp-3-190,female,19,1,3,1,1,0,1,1,1,0,0,0,15,1,1,1,1,2,1,2,1,2,1,2,0,0,1,1,0,0,1,3,1,3,0,0,1,3,6oct2024
+sp-1-191,male,17,1,1,0,,,,,,,,,,1,1,2,1,2,1,2,1,2,1,3,0,0,1,2,0,0,1,3,1,3,0,0,1,,""
+sp-3-192,male,19,1,3,1,0,0,0,1,0,0,1,0,,1,0,0,1,2,0,0,1,2,1,2,1,2,1,1,0,0,1,3,1,3,0,0,0,0,""
+sp-2-193,female,17,1,2,0,,,,,,,,,,1,0,0,0,0,0,0,0,2,0,0,0,0,1,2,0,0,1,1,0,0,0,0,1,3,""
+sp-1-194,female,16,1,1,1,1,0,0,1,1,,,,15,1,1,2,1,2,1,3,1,3,1,3,1,3,1,3,1,3,0,0,1,3,0,0,1,3,6oct2024
+sp-3-195,male,17,1,3,1,1,0,1,1,1,1,1,0,15,1,1,2,1,2,0,0,1,2,1,2,0,0,1,2,1,2,1,3,1,3,0,0,1,3,6oct2024
+sp-2-196,female,17,1,2,0,,,,,,,,,,1,1,1,0,0,1,2,1,2,1,2,0,0,1,2,0,0,1,1,0,0,0,0,1,3,""
+sp-2-197,female,17,1,2,0,,,,,,,,,,0,,,,,,,,,,,,,,,,,,,,,,,,,""
+sp-3-198,female,19,1,3,1,0,0,1,1,1,0,1,0,3,1,0,0,0,0,0,0,1,1,1,1,0,0,1,1,0,0,0,0,0,0,0,0,0,0,7oct2024
+sp-2-199,female,17,1,2,1,0,0,1,1,1,,1,0,15,1,1,1,0,0,0,0,1,2,1,1,0,0,1,2,0,0,1,1,0,0,0,0,0,0,6oct2024
+sp-1-200,male,16,1,1,0,,,,,,,,,,1,0,0,0,0,0,0,1,2,1,1,0,0,1,2,0,0,0,0,1,3,0,0,0,0,""
+sp-3-201,female,18,1,3,1,1,,1,1,1,,1,,15,1,0,0,0,0,0,0,1,3,1,2,1,3,0,0,0,0,,,,,,,1,1,6oct2024
+sp-1-202,female,17,1,1,1,1,,,,,,,,9,,,,,,,,,,,,,,,,,,,,,,,,,,6oct2024
+sp-2-203,female,17,1,2,1,1,0,0,1,1,1,1,0,9,1,0,0,0,0,0,0,1,2,1,3,1,2,1,2,0,0,0,0,1,3,1,1,1,2,6oct2024
+sp-1-204,female,16,1,1,1,0,0,0,1,1,1,1,0,,1,1,3,1,2,1,3,1,3,1,3,1,3,1,2,0,0,1,1,1,1,0,0,1,1,""
+sp-1-205,male,16,1,1,1,1,,1,,1,,,,9,1,0,0,1,2,0,0,1,3,1,3,1,2,1,2,0,0,1,1,1,2,0,0,1,3,6oct2024
+sp-3-206,female,18,1,3,1,1,,,,,,,,15,1,0,0,,,,,1,2,1,2,,,1,2,1,2,1,1,1,1,,,,,6oct2024
+sp-1-207,female,16,1,1,0,,,,,,,,,,1,0,0,0,0,0,0,0,0,0,0,0,0,1,3,0,0,1,1,0,0,0,0,1,3,""
+sp-1-208,female,16,1,1,0,,,,,,,,,,0,1,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,1,1,1,3,0,0,1,3,""
+sp-1-209,male,15,1,1,1,1,0,1,1,1,,1,0,21,1,1,3,1,3,1,3,1,3,1,3,1,2,1,2,1,3,1,2,,,,,1,1,5oct2024
+sp-1-210,female,16,1,1,0,,,,,,,,,,1,0,0,0,0,0,0,1,2,1,2,1,2,1,1,0,0,0,0,0,0,0,0,0,0,""
+sp-3-211,female,19,1,3,0,,,,,,,,,,1,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,3,1,3,0,0,1,2,""
+sp-1-213,female,16,1,1,0,,,,,,,,,,1,1,1,1,1,1,1,0,0,0,0,0,0,1,1,0,0,1,1,1,3,1,1,1,3,""
+sp-2-214,male,17,1,2,0,,,,,,,,,,1,1,1,0,0,0,0,1,2,1,2,0,0,1,2,1,3,0,0,1,3,0,0,1,2,""
+sp-2-215,male,18,1,2,0,,,,,,,,,,1,0,0,0,0,0,0,1,2,1,2,0,0,0,0,0,0,1,3,0,0,1,3,1,3,""
+sp-2-216,male,18,1,2,1,1,0,1,1,1,0,1,0,9,1,1,3,0,0,1,2,1,2,1,3,0,0,1,2,0,0,1,1,1,3,1,1,1,3,6oct2024
+sp-1-217,female,16,1,1,1,1,0,1,1,1,1,0,0,9,1,1,1,0,0,1,2,1,2,1,2,0,0,1,2,0,0,1,1,0,0,0,0,1,3,6oct2024
+sp-1-219,male,16,1,1,1,1,0,0,1,1,0,1,0,9,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,3,0,0,1,2,6oct2024
+sp-2-220,male,18,1,2,1,0,0,0,1,0,0,1,0,,1,1,2,1,2,1,1,1,3,1,3,1,2,1,2,1,2,1,1,1,3,0,0,0,0,""
+sp-2-221,female,17,1,2,1,,,,,,,1,,,1,1,3,1,3,1,3,1,3,1,3,1,3,1,3,1,3,1,1,1,3,0,0,1,2,""
+sp-1-222,male,16,1,1,1,1,,,1,,,,,9,1,1,2,0,0,1,2,1,2,1,3,0,0,1,1,0,0,1,1,1,2,0,0,1,2,6oct2024
+sp-2-223,male,17,1,2,1,1,0,0,1,0,1,1,,9,1,1,1,1,2,0,0,1,3,1,2,0,0,1,2,0,0,1,1,1,3,1,2,1,2,6oct2024
+sp-3-224,female,18,1,3,1,1,1,0,1,1,0,1,1,,,,,,,,,,,,,,,,,,,,,,,,,,,""
+sp-2-225,male,18,1,2,1,1,0,0,1,0,0,1,0,9,1,0,0,0,0,0,0,1,0,1,2,0,0,1,2,0,0,1,1,0,0,1,1,1,2,6oct2024
+sp-3-226,male,19,1,3,1,1,,1,,,,,,9,1,1,2,1,2,1,2,1,3,1,3,1,2,1,2,0,0,1,1,1,3,0,0,1,3,6oct2024
+sp-1-227,male,16,1,1,1,1,0,0,1,0,0,0,0,9,1,1,1,1,2,0,0,1,1,1,1,0,0,1,1,1,1,1,2,1,3,0,0,0,0,6oct2024
+sp-2-228,female,18,1,2,0,,,,,,,,,,1,1,2,0,0,0,0,0,0,0,0,0,0,1,2,0,0,1,1,1,2,0,0,1,3,""
+sp-3-229,female,18,1,3,1,1,0,0,1,0,1,0,,15,1,1,1,1,2,0,0,1,2,1,3,0,0,1,2,1,2,1,2,0,0,0,0,1,1,6oct2024
+sp-2-230,female,18,1,2,1,0,0,0,1,1,0,0,0,,1,1,1,1,2,0,0,1,2,1,1,1,1,1,2,0,0,1,1,1,2,0,0,1,2,""
+sp-1-231,female,16,1,1,0,,,,,,,,,,1,1,1,1,3,0,0,1,2,1,1,0,0,1,2,0,0,1,1,1,3,0,0,1,2,""
+sp-0-232,female,18,1,,1,1,0,1,1,1,1,0,0,15,1,0,0,0,0,0,0,1,1,1,2,1,1,1,2,0,0,0,0,1,3,0,0,1,2,6oct2024
+sp-2-233,female,17,1,2,1,1,,,1,,,,,21,1,0,0,1,1,0,0,1,3,1,3,1,2,1,2,0,0,1,2,1,1,0,0,1,3,6oct2024
+sp-2-234,male,17,1,2,1,1,0,0,0,0,0,0,0,21,1,1,2,1,2,1,2,1,2,1,2,0,0,1,2,1,2,1,1,1,1,0,0,0,0,6oct2024
+sp-2-235,female,17,1,2,1,0,0,0,1,,0,1,0,,1,1,2,1,2,0,0,0,1,0,0,0,0,1,2,0,0,1,1,1,3,1,3,0,0,""
+sp-2-236,male,18,1,2,1,1,0,0,0,0,0,1,0,9,1,0,0,0,0,0,0,1,2,1,2,0,0,1,2,0,0,,,1,3,,,1,1,6oct2024
+sp-1-237,male,17,1,1,0,,,,,,,,,,1,1,2,1,2,1,2,1,1,1,1,0,0,1,2,0,0,1,1,1,3,1,2,0,0,""
+sp-0-238,female,59,0,,0,,,,,,,,,,1,1,2,1,2,,,1,3,1,3,1,3,1,2,1,2,1,1,1,2,1,2,,,""
+sp-3-239,female,18,1,3,1,,,,,1,,,,,1,0,0,0,0,0,0,1,1,1,2,1,1,1,1,1,1,1,1,1,3,0,0,1,3,""
+sp-3-240,male,18,1,3,1,1,0,0,1,1,0,0,0,9,1,1,3,1,3,1,3,1,3,1,3,1,3,,,1,3,1,2,1,,0,0,0,0,6oct2024
+sp-1-241,male,16,1,1,1,1,0,0,0,0,0,0,0,9,1,1,2,1,2,1,2,1,2,1,2,1,1,1,2,0,0,1,1,0,0,0,0,0,0,7oct2024
+sp-1-242,female,16,1,1,1,0,0,0,1,1,1,1,1,,1,1,2,1,3,1,2,1,3,1,2,0,0,1,2,0,0,0,0,0,0,0,0,0,0,""
+sp-3-243,female,18,1,3,1,1,0,1,1,1,1,1,0,21,1,1,2,1,2,1,2,1,0,1,1,0,0,1,1,0,0,1,1,0,0,0,0,0,0,6oct2024
+sp-1-244,female,17,1,1,1,1,,,1,1,,,,21,1,1,1,1,1,0,0,1,2,1,2,1,1,1,2,1,1,1,1,1,3,0,0,1,3,6oct2024
+sp-3-245,female,18,1,3,0,,,,,,,,,,1,1,1,0,0,0,0,0,1,0,0,0,0,1,2,0,0,1,3,1,2,0,0,1,2,""
+sp-3-246,female,8,1,3,1,1,0,0,1,1,,1,0,15,1,1,2,0,0,0,0,1,3,1,2,1,2,1,2,0,0,1,1,0,0,0,0,0,0,6oct2024
+sp-3-247,female,18,1,3,0,,,,,,,,,,1,1,2,1,2,1,2,1,0,1,1,0,0,1,2,0,0,1,1,0,0,0,0,1,3,5oct2024
+sp-2-248,male,17,1,2,1,1,,,,,,,,3,1,1,3,1,3,,,1,3,1,3,1,3,1,2,,,1,2,1,3,1,2,1,2,6oct2024
+sp-0-249,male,17,1,,0,,,,,,,,,,1,,,,,1,2,0,2,0,0,0,0,1,2,0,0,0,0,1,3,1,1,1,2,""
+sp-3-250,female,18,1,3,1,1,0,1,1,1,1,1,0,15,1,1,1,1,2,0,0,1,2,1,2,0,0,1,2,0,0,1,1,0,0,0,0,1,2,6oct2024
+sp-2-251,female,18,1,2,0,,,,,,,,,,1,1,1,1,2,1,1,1,2,1,1,1,2,1,2,0,0,1,1,1,1,0,0,1,3,""
+sp-0-252,male,17,1,,1,1,,,1,1,0,,0,9,1,1,2,1,2,1,2,1,1,1,1,1,1,1,1,0,0,1,1,1,1,0,0,1,1,6oct2024
+sp-3-253,male,19,1,3,1,1,,,,1,,,,15,1,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,1,1,3,1,3,0,0,7oct2024
+sp-1-254,male,17,1,1,1,1,0,0,0,1,1,1,0,21,1,1,2,1,2,1,2,1,3,1,3,1,3,1,2,1,3,1,1,1,1,1,1,1,3,5oct2024
+sp-2-255,female,18,1,2,0,,,,,,,,,,1,0,0,0,0,0,0,1,3,1,3,0,0,1,3,0,0,0,0,1,2,0,0,1,3,""
+sp-3-256,female,18,1,3,1,1,0,0,1,1,0,1,0,15,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,3,0,0,1,3,0,0,6oct2024
+sp-1-257,female,16,1,1,0,,,,,,,,,,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,,,1,1,1,1,,,1,1,""
+sp-3-258,female,19,1,3,0,,,,,,,,,,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,1,3,""
+sp-3-259,male,18,1,3,1,1,0,0,1,1,0,1,0,15,1,1,2,1,2,1,2,1,2,1,1,1,1,1,2,1,1,1,1,1,,0,0,0,0,7oct2024
+sp-3-260,female,19,1,3,1,0,0,0,1,1,,1,,,1,1,2,1,2,1,2,1,2,1,2,0,0,1,2,1,1,1,1,0,0,0,0,0,0,""
+sp-0-261,male,17,1,,1,1,,,,,,,,21,1,1,2,1,2,1,2,0,2,0,0,0,0,1,2,1,1,1,1,1,1,0,0,1,3,6oct2024
+sp-2-262,female,18,1,2,1,1,0,1,1,1,0,0,0,9,1,0,0,1,3,0,0,1,3,1,3,1,3,0,0,1,3,1,2,1,2,1,1,1,3,6oct2024
+sp-0-263,female,26,0,,1,1,0,0,1,1,,0,0,9,1,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,0,0,0,0,1,1,7oct2024
+sp-3-264,female,17,1,3,1,1,0,1,1,1,1,0,0,9,1,0,0,1,1,,,1,1,1,1,1,1,1,1,0,0,1,1,1,3,0,0,0,0,6oct2024
+sp-1-265,female,16,1,1,1,0,0,0,1,0,,1,0,,1,1,2,1,2,1,2,1,1,1,1,1,1,1,2,0,0,0,0,0,0,0,0,0,0,""
+sp-0-267,male,17,1,,1,1,0,0,0,1,1,1,,15,1,1,3,1,3,1,3,1,3,1,1,0,0,1,3,0,0,1,2,1,3,1,3,1,,6oct2024
+sp-1-268,male,16,1,1,0,,,,,,,,,,1,1,1,1,2,1,2,1,3,1,2,0,0,1,3,0,0,1,1,1,3,0,0,1,3,""
+sp-2-269,male,18,1,2,0,,,,,,,,,,1,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,0,0,1,3,0,0,0,0,""
+sp-3-270,female,18,1,3,1,1,,0,1,1,0,1,0,21,1,1,2,1,2,0,0,1,2,1,2,1,1,1,2,0,0,1,3,1,2,0,0,1,2,6oct2024
+sp-2-271,female,18,1,2,1,1,0,1,1,1,,0,0,9,1,1,2,1,2,1,2,1,2,1,2,1,1,1,2,1,2,1,1,1,2,0,0,1,3,6oct2024
+sp-3-272,female,18,1,3,1,1,0,0,0,0,0,0,0,9,1,1,2,1,2,1,2,1,1,1,1,0,0,1,2,0,0,1,2,1,2,0,0,1,3,6oct2024
+sp-3-273,male,18,1,3,1,1,0,0,1,1,0,1,0,9,1,1,1,1,1,1,2,1,2,1,2,1,1,1,2,1,1,1,3,1,2,1,1,1,3,6oct2024
+sp-1-274,female,16,1,1,1,1,0,0,1,1,1,1,0,15,1,1,1,1,2,0,0,1,0,1,2,0,0,1,2,1,2,1,1,1,1,0,0,1,2,6oct2024
+sp-0-275,female,33,0,,1,1,,1,1,1,,1,,15,1,1,2,1,2,1,2,1,2,1,3,1,3,1,2,1,2,0,0,0,0,0,0,1,1,6oct2024
+sp-0-276,male,58,0,,0,,,,,,,,,,1,1,1,1,2,1,1,0,1,0,0,1,1,1,2,,,,,,,1,1,1,1,""
+sp-1-277,male,18,1,1,1,1,,,,1,,,,9,1,0,0,1,2,0,0,1,3,1,2,0,0,1,2,0,0,0,0,1,3,0,0,1,3,6oct2024
+sp-1-278,female,16,1,1,0,,,,,,,,,,1,1,1,1,2,1,2,1,0,1,1,1,1,1,2,0,0,1,1,0,0,0,0,1,2,""
+sp-3-279,female,18,1,3,1,,0,0,1,1,0,,0,,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,0,0,1,2,0,0,1,1,0,0,""
+sp-0-280,female,54,0,,0,,,,,,,,,,1,1,2,1,2,1,2,1,2,1,2,1,2,1,2,0,0,1,1,,,1,1,1,1,""
+sp-1-281,female,17,1,1,1,1,,1,1,1,1,1,1,21,1,1,2,1,2,1,2,1,2,1,2,0,0,1,2,0,0,1,1,1,,1,,1,3,6oct2024
+sp-3-282,female,18,1,3,1,0,0,0,1,0,0,0,0,,1,1,3,1,2,1,3,0,2,0,0,0,0,1,2,1,3,1,3,1,2,0,0,1,2,""
+sp-1-283,female,17,1,1,1,1,0,0,1,1,0,1,,9,1,0,0,0,0,0,0,1,2,1,2,0,0,1,2,0,0,,,1,3,,,1,2,6oct2024
+sp-1-284,female,16,1,1,0,,,,,,,,,,1,1,3,1,1,0,0,1,0,1,1,0,0,1,2,0,0,1,1,0,0,0,0,0,0,""
+sp-3-285,female,18,1,3,1,,,,1,1,1,1,1,15,1,1,2,1,2,1,2,1,0,1,2,1,2,1,2,1,2,1,3,0,0,0,0,1,1,6oct2024
+sp-3-286,male,19,1,3,0,,,,,,,,,,1,0,0,1,2,1,1,1,2,1,1,0,0,1,2,0,0,1,3,1,3,0,0,1,3,""
+sp-3-287,female,19,1,3,1,1,0,0,0,0,0,1,0,9,1,1,2,1,2,0,0,1,1,1,1,1,1,1,2,0,0,1,3,1,3,0,0,1,1,6oct2024
+sp-3-288,male,18,1,3,1,1,0,0,1,0,0,1,0,21,1,1,2,1,2,1,2,1,1,1,1,0,0,0,0,0,0,1,3,1,3,1,3,1,3,5oct2024
+sp-2-289,female,18,1,2,0,,,,,,,,,,1,1,2,1,2,1,2,1,1,1,2,1,2,1,2,0,0,1,1,1,3,0,0,1,2,""
+sp-1-290,female,16,1,1,0,,,,,,,,,,1,1,1,0,0,1,2,1,1,1,1,1,1,1,1,1,1,1,1,1,3,0,0,1,1,""
+sp-1-291,male,17,1,1,0,,,,,,,,,,1,,,,,,,,2,,,,,1,2,,,1,1,1,3,1,3,1,3,""
+sp-1-292,female,16,1,1,0,,,,,,,,,,1,1,2,1,2,1,1,1,2,1,2,0,0,1,2,0,0,1,1,1,3,0,0,0,0,""
+sp-2-293,male,16,1,2,1,0,0,0,1,1,0,0,0,,1,1,1,1,2,1,2,1,2,1,3,0,0,,,1,2,1,1,1,2,1,1,1,3,""
+sp-3-294,female,19,1,3,1,0,0,0,1,1,0,1,0,,1,1,2,0,0,1,2,1,1,1,2,1,2,1,2,0,0,1,3,1,3,1,2,0,0,""
+sp-3-295,female,18,1,3,1,0,0,0,1,1,1,1,1,,1,1,2,0,0,1,2,1,1,1,1,0,0,0,0,0,0,0,0,1,1,0,0,1,2,""
+sp-1-296,female,16,1,1,1,1,0,0,0,1,0,1,0,21,1,1,1,1,2,1,2,1,1,1,1,0,0,1,1,1,1,1,1,1,2,0,0,1,3,6oct2024
+sp-1-297,female,15,1,1,1,1,0,0,0,0,0,0,0,9,1,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,1,1,1,1,3,0,0,1,1,6oct2024
+sp-3-298,male,18,1,3,1,1,0,0,1,0,1,0,0,9,1,1,2,1,2,1,2,1,1,1,2,1,1,1,2,1,1,1,3,1,3,0,0,0,0,6oct2024
+sp-1-299,female,15,1,1,1,1,0,0,1,1,0,0,0,15,1,1,1,1,1,0,0,1,2,1,2,1,2,1,2,1,2,1,1,1,3,0,0,1,1,6oct2024
+sp-3-300,female,18,1,3,1,1,0,1,1,1,0,1,0,15,1,1,2,0,0,1,1,1,2,1,2,1,2,1,2,0,0,1,1,1,3,0,0,1,1,6oct2024
+sp-3-301,male,18,1,3,1,,,,,1,1,,,,1,0,0,1,2,,,,2,,,,,0,0,,,1,3,1,,0,0,1,1,""
+sp-3-302,female,19,1,3,1,1,0,0,1,1,0,1,1,15,1,1,2,1,2,0,0,1,2,1,2,0,0,1,2,0,0,1,3,0,0,0,0,1,3,6oct2024
+sp-2-303,female,17,1,2,1,1,0,0,1,1,,1,1,15,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,0,0,1,3,6oct2024
+sp-1-304,female,17,1,1,1,0,0,0,1,1,,1,1,,1,0,0,1,3,0,0,1,2,1,2,0,0,1,2,1,1,1,1,1,3,0,0,1,1,""
+sp-3-305,female,18,1,3,1,1,,,,,,,,3,1,1,2,0,0,1,2,1,1,1,3,1,2,1,2,1,2,0,0,0,0,0,0,1,1,6oct2024
+sp-2-307,male,18,1,2,1,1,,1,1,1,,1,,21,1,1,2,1,2,1,2,1,3,1,3,1,3,1,2,0,0,1,1,1,3,0,0,1,3,5oct2024
+sp-1-308,male,16,1,1,1,1,0,0,1,1,0,0,0,15,1,1,2,1,2,0,0,1,3,1,3,0,0,1,2,1,2,1,1,1,3,0,0,1,2,6oct2024
+sp-1-310,female,16,1,1,1,1,0,1,,,0,1,1,9,1,1,2,1,2,1,2,1,0,1,2,0,0,1,2,0,0,1,1,0,0,0,0,1,3,6oct2024
+sp-3-311,female,18,1,3,1,0,0,0,1,1,0,1,0,,1,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,3,0,0,0,0,""
+sp-3-312,male,19,1,3,1,0,0,0,1,1,0,1,0,,1,1,2,1,2,1,2,1,3,1,2,1,2,1,2,0,0,1,3,1,3,,,1,3,""
+sp-1-313,male,16,1,1,0,,,,,,,,,,1,0,0,1,3,0,0,1,2,1,1,0,0,1,2,0,0,1,3,1,2,1,3,1,,""
+sp-1-314,female,17,1,1,0,,,,,,,,,,1,1,2,1,2,1,2,0,2,0,0,1,2,1,2,1,2,1,1,1,1,0,0,1,3,""
+sp-3-315,male,19,1,3,1,1,0,1,1,1,0,0,0,9,1,1,2,1,2,0,0,1,3,1,3,0,0,1,2,1,3,1,3,1,3,0,0,1,3,6oct2024
+sp-2-317,male,18,1,2,0,,,,,,,,,,1,1,3,1,3,1,2,1,2,1,2,1,2,1,2,1,2,1,1,0,0,1,2,1,1,""
+sp-2-318,female,17,1,2,1,0,0,0,1,1,0,1,0,,1,1,2,1,2,0,0,1,0,1,2,0,0,1,2,1,1,0,0,0,0,0,0,0,0,""
+sp-3-319,male,18,1,3,1,1,0,0,0,1,0,0,0,9,1,1,2,1,2,1,2,1,2,1,2,,,1,2,,,1,3,1,3,0,0,0,0,6oct2024
+sp-2-320,male,17,1,2,1,1,0,0,1,1,,1,,9,1,1,3,1,3,1,3,1,2,1,2,0,0,1,2,1,2,0,0,1,3,0,0,1,3,6oct2024
+sp-3-323,male,17,1,3,1,1,,1,1,1,1,1,,21,1,1,1,1,2,1,1,1,2,1,3,0,0,1,2,0,0,1,3,1,3,,,1,3,5oct2024
+sp-2-324,male,17,1,2,1,1,0,1,1,1,0,1,,15,1,1,2,1,2,1,2,1,3,1,3,0,0,1,2,0,0,1,1,1,3,1,3,1,3,6oct2024
+sp-2-325,male,17,1,2,1,1,0,0,0,1,1,0,1,9,1,1,1,1,2,1,2,1,3,1,3,0,0,1,3,0,0,1,1,1,2,1,3,0,0,6oct2024
+sp-2-326,male,17,1,2,1,1,0,0,1,1,,1,0,9,1,0,0,1,2,0,0,0,3,0,0,0,0,1,2,0,0,0,0,1,3,1,3,0,0,6oct2024
+sp-1-329,male,16,1,1,0,,,,,,,,,,1,1,2,1,2,0,0,1,3,1,2,0,0,1,2,0,0,1,1,1,3,1,2,0,0,""
+sp-3-330,male,18,1,3,1,1,0,0,0,0,,1,1,15,1,1,3,1,3,1,3,1,2,1,1,1,1,1,2,1,2,1,3,1,3,1,3,1,3,6oct2024
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     "search-matching-documents-text": "matching documents",
@@ -90,16 +96,18 @@
     "search-more-match-text": "more match in this document",
     "search-more-matches-text": "more matches in this document",
     "search-clear-button-title": "Clear",
+    "search-text-placeholder": "",
     "search-detached-cancel-button-title": "Cancel",
     "search-submit-button-title": "Submit",
     "search-label": "Search"
   }
 }</script>
-
 <link href="site_libs/pagedtable-1.1/css/pagedtable.css" rel="stylesheet">
+
 <script src="site_libs/pagedtable-1.1/js/pagedtable.js"></script>
 
 
+
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 </head>
 
@@ -107,55 +115,56 @@
 
 <div id="quarto-search-results"></div>
   <header id="quarto-header" class="headroom fixed-top">
-    <nav class="navbar navbar-expand-lg navbar-dark ">
+    <nav class="navbar navbar-expand-lg " data-bs-theme="dark">
       <div class="navbar-container container-fluid">
-      <div class="navbar-brand-container">
+      <div class="navbar-brand-container mx-auto">
     <a class="navbar-brand" href="./index.html">
     <span class="navbar-title">Spetses Introductory Course</span>
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   </div>
             <div id="quarto-search" class="" title="Search"></div>
-          <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbarCollapse" aria-controls="navbarCollapse" aria-expanded="false" aria-label="Toggle navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
+          <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbarCollapse" aria-controls="navbarCollapse" role="menu" aria-expanded="false" aria-label="Toggle navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
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-    <a class="nav-link" href="./index.html" rel="" target="">
- <span class="menu-text">What is this?</span></a>
+    <a class="nav-link" href="./index.html"> 
+<span class="menu-text">What is this?</span></a>
   </li>  
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-    <a class="nav-link" href="./Data_import_cleaning.html" rel="" target="">
- <span class="menu-text">Data import and cleaning</span></a>
+    <a class="nav-link" href="./Data_import_cleaning.html"> 
+<span class="menu-text">Data import and cleaning</span></a>
   </li>  
   <li class="nav-item">
-    <a class="nav-link active" href="./Case_definition.html" rel="" target="" aria-current="page">
- <span class="menu-text">Case Definition</span></a>
+    <a class="nav-link active" href="./Case_definition.html" aria-current="page"> 
+<span class="menu-text">Case Definition</span></a>
   </li>  
   <li class="nav-item">
-    <a class="nav-link" href="./Lab_and_Descriptive.html" rel="" target="">
- <span class="menu-text">Laboratory data and descriptive analysis</span></a>
+    <a class="nav-link" href="./Lab_and_Descriptive.html"> 
+<span class="menu-text">Laboratory data and descriptive analysis</span></a>
   </li>  
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-    <a class="nav-link" href="./Univariate.html" rel="" target="">
- <span class="menu-text">Univariable analysis</span></a>
+    <a class="nav-link" href="./Univariate.html"> 
+<span class="menu-text">Univariable analysis</span></a>
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-    <a class="nav-link" href="./Stratified.html" rel="" target="">
- <span class="menu-text">Stratified analysis</span></a>
+    <a class="nav-link" href="./Stratified.html"> 
+<span class="menu-text">Stratified analysis</span></a>
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-            <div class="quarto-navbar-tools ms-auto">
+          </div> <!-- /navcollapse -->
+            <div class="quarto-navbar-tools">
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-      <a href="" title="" id="quarto-navigation-tool-dropdown-0" class="quarto-navigation-tool dropdown-toggle px-1" data-bs-toggle="dropdown" aria-expanded="false" aria-label=""><i class="bi bi-github"></i></a>
-      <ul class="dropdown-menu dropdown-menu-end" aria-labelledby="quarto-navigation-tool-dropdown-0">
+      <a href="" title="" id="quarto-navigation-tool-dropdown-0" class="quarto-navigation-tool dropdown-toggle px-1" data-bs-toggle="dropdown" aria-expanded="false" role="link" aria-label=""><i class="bi bi-github"></i></a>
+      <ul class="dropdown-menu" aria-labelledby="quarto-navigation-tool-dropdown-0">
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-            <a class="dropdown-item quarto-navbar-tools ms-auto-item" href="https://github.com/EPIET/IntroCourse_inCop/tree/main">
+            <a class="dropdown-item quarto-navbar-tools-item" href="https://github.com/EPIET/IntroCourse_inCop/tree/main">
             Source Code
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-            <a class="dropdown-item quarto-navbar-tools ms-auto-item" href="https://github.com/EPIET/IntroCourse_inCop/issues">
+            <a class="dropdown-item quarto-navbar-tools-item" href="https://github.com/EPIET/IntroCourse_inCop/issues">
             Report a code issue
             </a>
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@@ -163,7 +172,6 @@
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   <a href="" class="quarto-color-scheme-toggle quarto-navigation-tool  px-1" onclick="window.quartoToggleColorScheme(); return false;" title="Toggle dark mode"><i class="bi"></i></a>
 </div>
-          </div> <!-- /navcollapse -->
       </div> <!-- /container-fluid -->
     </nav>
 </header>
@@ -187,7 +195,7 @@ <h2 id="toc-title">On this page</h2>
   <li><a href="#a-ate-a-meal-at-the-school-dinner" id="toc-a-ate-a-meal-at-the-school-dinner" class="nav-link" data-scroll-target="#a-ate-a-meal-at-the-school-dinner">a) Ate a meal at the school dinner</a></li>
   <li><a href="#b-fell-ill-after-the-start-of-the-meal" id="toc-b-fell-ill-after-the-start-of-the-meal" class="nav-link" data-scroll-target="#b-fell-ill-after-the-start-of-the-meal">b) Fell ill after the start of the meal</a></li>
   <li><a href="#c-fell-ill-within-the-time-period-of-interest" id="toc-c-fell-ill-within-the-time-period-of-interest" class="nav-link" data-scroll-target="#c-fell-ill-within-the-time-period-of-interest">c) Fell ill within the time period of interest</a></li>
-  <li><a href="#d-finally-with-this-information-you-can-create-a-new-case-column-to-hold-the-binary-truefalse-case-definition-variable." id="toc-d-finally-with-this-information-you-can-create-a-new-case-column-to-hold-the-binary-truefalse-case-definition-variable." class="nav-link" data-scroll-target="#d-finally-with-this-information-you-can-create-a-new-case-column-to-hold-the-binary-truefalse-case-definition-variable.">d) Finally, with this information you can create a new <code>case</code> column to hold the binary (<code>TRUE</code>/<code>FALSE</code>) case definition variable.</a></li>
+  <li><a href="#d-finally-with-this-information-you-can-create-a-new-case-column-to-hold-the-binary-10-case-definition-variable." id="toc-d-finally-with-this-information-you-can-create-a-new-case-column-to-hold-the-binary-10-case-definition-variable." class="nav-link" data-scroll-target="#d-finally-with-this-information-you-can-create-a-new-case-column-to-hold-the-binary-10-case-definition-variable.">d) Finally, with this information you can create a new <code>case</code> column to hold the binary (<code>1</code>/<code>0</code>) case definition variable.</a></li>
   </ul></li>
   <li><a href="#export-clean-data" id="toc-export-clean-data" class="nav-link" data-scroll-target="#export-clean-data">4. Export clean data</a></li>
   </ul>
@@ -211,8 +219,10 @@ <h2 id="toc-title">On this page</h2>
   </div>
   
 
+
 </header>
 
+
 <section id="learning-outcomes" class="level2">
 <h2 class="anchored" data-anchor-id="learning-outcomes">1. Learning outcomes</h2>
 <p>At the end of the session, participants will be able to:</p>
@@ -222,32 +232,32 @@ <h2 class="anchored" data-anchor-id="learning-outcomes">1. Learning outcomes</h2
 </section>
 <section id="storyplot-description" class="level2">
 <h2 class="anchored" data-anchor-id="storyplot-description">2. Story/plot description</h2>
-<p>You will now create a new column in the data set to hold the case definition you decided in a previous step during your investigation. You can call this column <code>case</code> and set it to <code>TRUE</code> if the individual meets the case definition criteria and <code>FALSE</code> if not. You will use this column later on for any calculations needed (descriptive statistics, two-by-two tables to compute measures of association, etc.) to figure out the culprit of this outbreak.</p>
+<p>You will now create a new column in the data set to hold the case definition you decided in a previous step during your investigation. You can call this column <code>case</code> and set it to <code>1</code> if the individual meets the case definition criteria and <code>0</code> if not. You will use this column later on for any calculations needed (descriptive statistics, two-by-two tables to compute measures of association, etc.) to figure out the culprit of this outbreak.</p>
 </section>
 <section id="questionsassignments" class="level2">
 <h2 class="anchored" data-anchor-id="questionsassignments">3. Questions/Assignments</h2>
 <p>If you closed your R project, please open it again and load the clean dataset. We strongly recommend you use the <code>Spetses_clean1_2024.rds</code>. This is just to be sure all needed changes in that file for the code to work have been done (as you may have changed the file in a slightly different way than we have).</p>
 <p>To be sure we all start from the same case definition, let’s agree that a case was defined as a person who:</p>
 <ul class="task-list">
-<li><input type="checkbox" checked="">attended the school dinner on 11 November 2006 (i.e.&nbsp;is on the linelist)</li>
-<li><input type="checkbox" checked="">ate a meal at the school dinner (i.e.&nbsp;was exposed)</li>
-<li><input type="checkbox" checked="">fell ill after the start of the meal</li>
-<li><input type="checkbox" checked="">fell ill within the time period of interest after the school dinner</li>
-<li><input type="checkbox" checked="">suffered from diarrhoea with or without blood, or vomiting</li>
+<li><label><input type="checkbox" checked="">attended the school dinner on 11 November 2006 (i.e.&nbsp;is on the linelist)</label></li>
+<li><label><input type="checkbox" checked="">ate a meal at the school dinner (i.e.&nbsp;was exposed)</label></li>
+<li><label><input type="checkbox" checked="">fell ill after the start of the meal</label></li>
+<li><label><input type="checkbox" checked="">fell ill within the time period of interest after the school dinner</label></li>
+<li><label><input type="checkbox" checked="">suffered from diarrhoea with or without blood, or vomiting</label></li>
 </ul>
 <p>Non cases (not-ill) were defined as people who:</p>
 <ul class="task-list">
-<li><input type="checkbox" checked="">attended the school dinner on 11 November 2006 (i.e.&nbsp;are on the linelist)</li>
-<li><input type="checkbox" checked="">ate a meal at the school dinner (i.e.&nbsp;were exposed)</li>
-<li><input type="checkbox" checked="">did not fall ill within the time period of interest</li>
-<li><input type="checkbox" checked="">did not develop diarrhoea (with or without blood) or vomiting</li>
+<li><label><input type="checkbox" checked="">attended the school dinner on 11 November 2006 (i.e.&nbsp;are on the linelist)</label></li>
+<li><label><input type="checkbox" checked="">ate a meal at the school dinner (i.e.&nbsp;were exposed)</label></li>
+<li><label><input type="checkbox" checked="">did not fall ill within the time period of interest</label></li>
+<li><label><input type="checkbox" checked="">did not develop diarrhoea (with or without blood) or vomiting</label></li>
 </ul>
 </section>
 <section id="install-packages-if-needed-and-load-libraries" class="level2">
 <h2 class="anchored" data-anchor-id="install-packages-if-needed-and-load-libraries">3.1 Install packages (if needed) and load libraries</h2>
 <div class="cell">
-<details open="">
-<summary>Code</summary>
+<details open="" class="code-fold">
+<summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb1"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Load the required libraries into the current R session:</span></span>
 <span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a>pacman<span class="sc">::</span><span class="fu">p_load</span>(rio, </span>
 <span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a>               here, </span>
@@ -268,10 +278,10 @@ <h2 class="anchored" data-anchor-id="install-packages-if-needed-and-load-librari
 <section id="import-your-data" class="level2">
 <h2 class="anchored" data-anchor-id="import-your-data">3.2 Import your data</h2>
 <div class="cell">
-<details open="">
-<summary>Code</summary>
+<details open="" class="code-fold">
+<summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb2"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Import the clean data set:</span></span>
-<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a>copdata <span class="ot">&lt;-</span> rio<span class="sc">::</span><span class="fu">import</span>(here<span class="sc">::</span><span class="fu">here</span>(<span class="st">"data"</span>, <span class="st">"Spetses_clean1_2024.rds"</span>))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a>copdata <span class="ot">&lt;-</span> rio<span class="sc">::</span><span class="fu">import</span>(here<span class="sc">::</span><span class="fu">here</span>(<span class="st">"data"</span>, <span class="st">"Spetses_clean1_2024.rds"</span>), <span class="at">trust =</span> <span class="cn">TRUE</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
 </div>
 </section>
@@ -289,7 +299,7 @@ <h2 class="anchored" data-anchor-id="identify-the-variables-you-need-to-apply-th
 </div>
 <div id="callout-1" class="callout-1-contents callout-collapse collapse">
 <div class="callout-body-container callout-body">
-<p>The variables we need from the dataset to apply the above case definition are: <code>meal</code>, <code>onset_datetime</code>, <code>diarrhoea</code>, <code>bloody</code> and <code>vomiting</code>.</p>
+<p>The variables we need from the dataset to apply the above case definition are: <code>meal</code>, <code>ill</code>, <code>onset_datetime</code>, <code>diarrhoea</code>, <code>bloody</code> and <code>vomiting</code>.</p>
 </div>
 </div>
 </div>
@@ -311,7 +321,7 @@ <h3 class="anchored" data-anchor-id="a-ate-a-meal-at-the-school-dinner">a) Ate a
 </div>
 <div id="callout-2" class="callout-2-contents callout-collapse collapse">
 <div class="callout-body-container callout-body">
-<p><code>filter</code> by those with <code>meal == TRUE</code>.</p>
+<p><code>filter</code> by those with <code>meal == 1</code>.</p>
 </div>
 </div>
 </div>
@@ -332,10 +342,10 @@ <h3 class="anchored" data-anchor-id="a-ate-a-meal-at-the-school-dinner">a) Ate a
 </div>
 </div>
 <div class="cell">
-<details open="">
-<summary>Code</summary>
+<details open="" class="code-fold">
+<summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb3"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a>copdata <span class="ot">&lt;-</span> copdata <span class="sc">%&gt;%</span> </span>
-<span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(meal <span class="sc">==</span> <span class="cn">TRUE</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(meal <span class="sc">==</span> <span class="dv">1</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
 </div>
 <div class="callout callout-style-default callout-note callout-titled" title="Once you've thought about the above, have a look here">
@@ -352,7 +362,7 @@ <h3 class="anchored" data-anchor-id="a-ate-a-meal-at-the-school-dinner">a) Ate a
 <div class="callout-body-container callout-body">
 <p>Seven of the respondents actually said they did not eat the meal, but when it came to the questions about which food items they ate, they provided answers! This issue could have been minimised by:</p>
 <p>- At the survey state, one could adjust the design of an electronic questionnaire to prevent key questions from being skipped. This can come with both pros and cons. Allow fellows to discuss if time allows.</p>
-<p>- Explore your data further, realise this is the case, and recode the <code>meal</code> variable for these individuals as <code>TRUE.</code> =&gt; This would be the way to go, but is not what we did in our example because we tried to keep it simple, and also because it is good to show that you may not always clean the data perfectly, and that has consequences: You can highlight the importance or really explore your data in depth.</p>
+<p>- Explore your data further, realise this is the case, and recode the <code>meal</code> variable for these individuals as <code>1.</code> =&gt; This would be the way to go, but is not what we did in our example because we tried to keep it simple, and also because it is good to show that you may not always clean the data perfectly, and that has consequences: You can highlight the importance or really explore your data in depth.</p>
 <p>By making the above decision, we may be missing cases and non-cases people, and thus, modifying the final estimate of our measure of association. =&gt; It is very important to know your data, explore it deeply and try to clean it as well as possible. Every step one makes when cleaning the data may have a consequence, and we should be aware of it when making the data cleaning decisions and when interpreting the results.</p>
 </div>
 </div>
@@ -360,7 +370,7 @@ <h3 class="anchored" data-anchor-id="a-ate-a-meal-at-the-school-dinner">a) Ate a
 </section>
 <section id="b-fell-ill-after-the-start-of-the-meal" class="level3">
 <h3 class="anchored" data-anchor-id="b-fell-ill-after-the-start-of-the-meal">b) Fell ill after the start of the meal</h3>
-<p>We define “fell ill” as any person having had diarrhoea with OR without blood, OR vomiting. To capture this information easily, you will create a new <code>gastrosymptoms</code> variable. This variable will indicate that the person had one OR (“or” is R is achieved by using <code>|</code>) more of the clinical symptoms in your definition.</p>
+<p>The questionnaire included a question on whether the person fell ill after the school dinner party (column <code>ill</code>). However, the person may have experience symptoms that are very unspecific to this outbreak (e.g., headache, joint pain…). We may want to only include those who develop gastrointestinal symptoms. That is any person having had diarrhoea with OR without blood, OR vomiting. To capture this information easily, you will create a new <code>gastrosymptoms</code> variable. This variable will indicate that the person had one OR (“or” is R is achieved by using <code>|</code>) more of the clinical symptoms in your definition.</p>
 <p>Note that we the concept of having eaten a meal is already included as per one of the steps above.</p>
 <div class="callout callout-style-default callout-tip callout-titled" title="Need a little bit of help?">
 <div class="callout-header d-flex align-content-center" data-bs-toggle="collapse" data-bs-target=".callout-5-contents" aria-controls="callout-5" aria-expanded="false" aria-label="Toggle callout">
@@ -406,24 +416,24 @@ <h3 class="anchored" data-anchor-id="b-fell-ill-after-the-start-of-the-meal">b)
 </div>
 <div id="callout-7" class="callout-7-contents callout-collapse collapse">
 <div class="callout-body-container callout-body">
-<p>Having one clinical symptom enough to be considered a potential case at this point may be considered too unspecific (low specificity). For example, a person who ate at the dinner party and developed diarrhoea for other reasons other than food poisoning (say, they recently started on antibiotics known for unbalancing the intestinal flora and causing diarrhoea) could be misclassified as a potential case. (Note we talk about <strong>potential case</strong>, and not <strong>case</strong>; that is because here we are not talking about cases per-se yet, but this decision has implications for when applying the case definition below).</p>
+<p>Having one gastrointestinal symptom is enough to be considered a potential case at this point, which may still be considered too unspecific (low specificity). For example, a person who ate at the dinner party and developed diarrhoea for other reasons other than food poisoning (say, they recently started on antibiotics known for unbalancing the intestinal flora and causing diarrhoea) could be misclassified as a potential case. (Note we talk about <strong>potential case</strong>, and not <strong>case</strong>; that is because here we are not talking about cases per-se yet, but this decision has implications for when applying the case definition below).</p>
 <p>Moreover, those who did not report clinical symptoms will be defined as non-cases. Thus, we are assuming that these individuals did not develop symptoms because they didn’t report them. The missing values could be due to, for example, them skipping the questions in the questionnaire. Some individuals may be reluctant to report symptoms, due to shame, fear of repercussion, or others. It is important to think ahead, before the interview, about ways to minimise these situations. For example, through questionnaire design, you may impede skipping questions; you could promote trust by using the right interviewers (in some cases this will be someone from the community, in others someone form specific NGOs, someone of a specific race or gender, etc); choose to carry out online questionnaires vs in person (or vice versa, depending on the situation), etc.</p>
 </div>
 </div>
 </div>
 <div class="cell">
-<details open="">
-<summary>Code</summary>
+<details open="" class="code-fold">
+<summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb4"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a>copdata <span class="ot">&lt;-</span> copdata <span class="sc">%&gt;%</span> </span>
 <span id="cb4-2"><a href="#cb4-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">gastrosymptoms =</span> <span class="fu">case_when</span>(</span>
 <span id="cb4-3"><a href="#cb4-3" aria-hidden="true" tabindex="-1"></a>    <span class="co"># Those had diarrhoea...</span></span>
-<span id="cb4-4"><a href="#cb4-4" aria-hidden="true" tabindex="-1"></a>    diarrhoea <span class="sc">==</span> <span class="cn">TRUE</span> <span class="sc">|</span></span>
+<span id="cb4-4"><a href="#cb4-4" aria-hidden="true" tabindex="-1"></a>    diarrhoea <span class="sc">==</span> <span class="st">"1"</span> <span class="sc">|</span></span>
 <span id="cb4-5"><a href="#cb4-5" aria-hidden="true" tabindex="-1"></a>      <span class="co">#or bloody diarrhoea...</span></span>
-<span id="cb4-6"><a href="#cb4-6" aria-hidden="true" tabindex="-1"></a>    bloody <span class="sc">==</span> <span class="cn">TRUE</span> <span class="sc">|</span></span>
-<span id="cb4-7"><a href="#cb4-7" aria-hidden="true" tabindex="-1"></a>      <span class="co"># or vomiting, are marked as TRUE (fell ill after the meal)</span></span>
-<span id="cb4-8"><a href="#cb4-8" aria-hidden="true" tabindex="-1"></a>    vomiting <span class="sc">==</span> <span class="cn">TRUE</span> <span class="sc">~</span> <span class="cn">TRUE</span>,</span>
-<span id="cb4-9"><a href="#cb4-9" aria-hidden="true" tabindex="-1"></a>    <span class="co"># The rest are FALSE. This includes those who ate a meal but had no symptoms (did not fell ill after the meal)</span></span>
-<span id="cb4-10"><a href="#cb4-10" aria-hidden="true" tabindex="-1"></a>    <span class="at">.default =</span> <span class="cn">FALSE</span>)</span>
+<span id="cb4-6"><a href="#cb4-6" aria-hidden="true" tabindex="-1"></a>    bloody <span class="sc">==</span> <span class="st">"1"</span> <span class="sc">|</span></span>
+<span id="cb4-7"><a href="#cb4-7" aria-hidden="true" tabindex="-1"></a>      <span class="co"># or vomiting, are marked as 1 (fell ill after the meal)</span></span>
+<span id="cb4-8"><a href="#cb4-8" aria-hidden="true" tabindex="-1"></a>    vomiting <span class="sc">==</span> <span class="st">"1"</span> <span class="sc">~</span> <span class="dv">1</span>,</span>
+<span id="cb4-9"><a href="#cb4-9" aria-hidden="true" tabindex="-1"></a>    <span class="co"># The rest are 0 This includes those who ate a meal but had no symptoms (did not fell ill after the meal)</span></span>
+<span id="cb4-10"><a href="#cb4-10" aria-hidden="true" tabindex="-1"></a>    <span class="at">.default =</span> <span class="dv">0</span>)</span>
 <span id="cb4-11"><a href="#cb4-11" aria-hidden="true" tabindex="-1"></a>    )</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
 </div>
@@ -435,8 +445,8 @@ <h3 class="anchored" data-anchor-id="c-fell-ill-within-the-time-period-of-intere
 <li>Dinner was served at 18:00 roughly for everyone. Create a new <code>meal_datetime</code> variable with this information for all people in your dataset (as per 05 Oct 2024, at 18:00h).</li>
 </ul>
 <div class="cell">
-<details open="">
-<summary>Code</summary>
+<details open="" class="code-fold">
+<summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb5"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Start with copdata:</span></span>
 <span id="cb5-2"><a href="#cb5-2" aria-hidden="true" tabindex="-1"></a>copdata <span class="ot">&lt;-</span> copdata <span class="sc">%&gt;%</span> </span>
 <span id="cb5-3"><a href="#cb5-3" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Create new column for meal date and time:</span></span>
@@ -480,8 +490,8 @@ <h3 class="anchored" data-anchor-id="c-fell-ill-within-the-time-period-of-intere
 </div>
 </div>
 <div class="cell">
-<details open="">
-<summary>Code</summary>
+<details open="" class="code-fold">
+<summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb6"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a>copdata <span class="ot">&lt;-</span> copdata <span class="sc">%&gt;%</span> </span>
 <span id="cb6-2"><a href="#cb6-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">incubation =</span> onset_datetime <span class="sc">-</span> meal_datetime,</span>
 <span id="cb6-3"><a href="#cb6-3" aria-hidden="true" tabindex="-1"></a>         <span class="at">incubation =</span> <span class="fu">as.numeric</span> (incubation))</span>
@@ -544,22 +554,22 @@ <h3 class="anchored" data-anchor-id="c-fell-ill-within-the-time-period-of-intere
 </div>
 </div>
 </section>
-<section id="d-finally-with-this-information-you-can-create-a-new-case-column-to-hold-the-binary-truefalse-case-definition-variable." class="level3">
-<h3 class="anchored" data-anchor-id="d-finally-with-this-information-you-can-create-a-new-case-column-to-hold-the-binary-truefalse-case-definition-variable.">d) Finally, with this information you can create a new <code>case</code> column to hold the binary (<code>TRUE</code>/<code>FALSE</code>) case definition variable.</h3>
+<section id="d-finally-with-this-information-you-can-create-a-new-case-column-to-hold-the-binary-10-case-definition-variable." class="level3">
+<h3 class="anchored" data-anchor-id="d-finally-with-this-information-you-can-create-a-new-case-column-to-hold-the-binary-10-case-definition-variable.">d) Finally, with this information you can create a new <code>case</code> column to hold the binary (<code>1</code>/<code>0</code>) case definition variable.</h3>
 <div class="cell">
-<details open="">
-<summary>Code</summary>
+<details open="" class="code-fold">
+<summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb8"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a>copdata <span class="ot">&lt;-</span> copdata <span class="sc">%&gt;%</span> </span>
 <span id="cb8-2"><a href="#cb8-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">case =</span> <span class="fu">case_when</span>(</span>
-<span id="cb8-3"><a href="#cb8-3" aria-hidden="true" tabindex="-1"></a>    <span class="co"># Those who had symptoms &lt;48h from the meal are cases (TRUE)</span></span>
-<span id="cb8-4"><a href="#cb8-4" aria-hidden="true" tabindex="-1"></a>    gastrosymptoms <span class="sc">==</span> <span class="cn">TRUE</span> <span class="sc">&amp;</span> </span>
+<span id="cb8-3"><a href="#cb8-3" aria-hidden="true" tabindex="-1"></a>    <span class="co"># Those who had symptoms &lt;48h from the meal are cases (1)</span></span>
+<span id="cb8-4"><a href="#cb8-4" aria-hidden="true" tabindex="-1"></a>    gastrosymptoms <span class="sc">==</span> <span class="dv">1</span> <span class="sc">&amp;</span> </span>
 <span id="cb8-5"><a href="#cb8-5" aria-hidden="true" tabindex="-1"></a>      onset_datetime <span class="sc">&gt;=</span> meal_datetime <span class="sc">&amp;</span></span>
-<span id="cb8-6"><a href="#cb8-6" aria-hidden="true" tabindex="-1"></a>      onset_datetime <span class="sc">&lt;=</span> (meal_datetime <span class="sc">+</span> <span class="fu">days</span>(<span class="dv">2</span>)) <span class="sc">~</span> <span class="cn">TRUE</span>,</span>
+<span id="cb8-6"><a href="#cb8-6" aria-hidden="true" tabindex="-1"></a>      onset_datetime <span class="sc">&lt;=</span> (meal_datetime <span class="sc">+</span> <span class="fu">days</span>(<span class="dv">2</span>)) <span class="sc">~</span> <span class="dv">1</span>,</span>
 <span id="cb8-7"><a href="#cb8-7" aria-hidden="true" tabindex="-1"></a>    <span class="co"># Those who had symptoms &gt;48h from the meal are non-cases (FALSE)</span></span>
-<span id="cb8-8"><a href="#cb8-8" aria-hidden="true" tabindex="-1"></a>    gastrosymptoms <span class="sc">==</span> <span class="cn">TRUE</span> <span class="sc">&amp;</span> </span>
-<span id="cb8-9"><a href="#cb8-9" aria-hidden="true" tabindex="-1"></a>      onset_datetime <span class="sc">&gt;</span> (meal_datetime <span class="sc">+</span> <span class="fu">days</span>(<span class="dv">2</span>)) <span class="sc">~</span> <span class="cn">FALSE</span>,</span>
+<span id="cb8-8"><a href="#cb8-8" aria-hidden="true" tabindex="-1"></a>    gastrosymptoms <span class="sc">==</span> <span class="dv">1</span> <span class="sc">&amp;</span> </span>
+<span id="cb8-9"><a href="#cb8-9" aria-hidden="true" tabindex="-1"></a>      onset_datetime <span class="sc">&gt;</span> (meal_datetime <span class="sc">+</span> <span class="fu">days</span>(<span class="dv">2</span>)) <span class="sc">~</span> <span class="dv">0</span>,</span>
 <span id="cb8-10"><a href="#cb8-10" aria-hidden="true" tabindex="-1"></a>    <span class="co"># The rest are considered non-cases. Including, those who had no symptoms at all, who have missing data on the onset_datetime variable, or who had symptoms before eating the meal </span></span>
-<span id="cb8-11"><a href="#cb8-11" aria-hidden="true" tabindex="-1"></a>    <span class="at">.default =</span> <span class="cn">FALSE</span>)</span>
+<span id="cb8-11"><a href="#cb8-11" aria-hidden="true" tabindex="-1"></a>    <span class="at">.default =</span> <span class="dv">0</span>)</span>
 <span id="cb8-12"><a href="#cb8-12" aria-hidden="true" tabindex="-1"></a>  )</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
 </div>
@@ -599,32 +609,30 @@ <h3 class="anchored" data-anchor-id="d-finally-with-this-information-you-can-cre
 </div>
 </div>
 <div class="cell">
-<details open="">
-<summary>Code</summary>
+<details open="" class="code-fold">
+<summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb9"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Tabulate cases:</span></span>
 <span id="cb9-2"><a href="#cb9-2" aria-hidden="true" tabindex="-1"></a>janitor<span class="sc">::</span><span class="fu">tabyl</span>(<span class="at">dat =</span> copdata, case)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
 <div class="cell-output-display">
-
 <div data-pagedtable="false">
   <script data-pagedtable-source="" type="application/json">
-{"columns":[{"label":["case"],"name":[1],"type":["lgl"],"align":["right"]},{"label":["n"],"name":[2],"type":["int"],"align":["right"]},{"label":["percent"],"name":[3],"type":["dbl"],"align":["right"]}],"data":[{"1":"FALSE","2":"161","3":"0.4270557"},{"1":"TRUE","2":"216","3":"0.5729443"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}}
+{"columns":[{"label":["case"],"name":[1],"type":["dbl"],"align":["right"]},{"label":["n"],"name":[2],"type":["int"],"align":["right"]},{"label":["percent"],"name":[3],"type":["dbl"],"align":["right"]}],"data":[{"1":"0","2":"161","3":"0.4270557"},{"1":"1","2":"216","3":"0.5729443"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}}
   </script>
 </div>
 </div>
 </div>
 <p>Let’s have a look at how many people ate a meal, had symptoms, and were considered as cases after applying our case definition:</p>
 <div class="cell">
-<details open="">
-<summary>Code</summary>
+<details open="" class="code-fold">
+<summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb10"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb10-1"><a href="#cb10-1" aria-hidden="true" tabindex="-1"></a>copdata <span class="sc">%&gt;%</span> </span>
-<span id="cb10-2"><a href="#cb10-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarise</span>(<span class="at">atemeal =</span> <span class="fu">sum</span>(meal <span class="sc">==</span> <span class="cn">TRUE</span>),</span>
-<span id="cb10-3"><a href="#cb10-3" aria-hidden="true" tabindex="-1"></a>            <span class="at">hadsympt =</span> <span class="fu">sum</span>(gastrosymptoms <span class="sc">==</span> <span class="cn">TRUE</span>),</span>
-<span id="cb10-4"><a href="#cb10-4" aria-hidden="true" tabindex="-1"></a>            <span class="at">nb_cases =</span> <span class="fu">sum</span>(case <span class="sc">==</span> <span class="cn">TRUE</span>)</span>
+<span id="cb10-2"><a href="#cb10-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarise</span>(<span class="at">atemeal =</span> <span class="fu">sum</span>(meal <span class="sc">==</span> <span class="dv">1</span>),</span>
+<span id="cb10-3"><a href="#cb10-3" aria-hidden="true" tabindex="-1"></a>            <span class="at">hadsympt =</span> <span class="fu">sum</span>(gastrosymptoms <span class="sc">==</span> <span class="dv">1</span>),</span>
+<span id="cb10-4"><a href="#cb10-4" aria-hidden="true" tabindex="-1"></a>            <span class="at">nb_cases =</span> <span class="fu">sum</span>(case <span class="sc">==</span> <span class="dv">1</span>)</span>
 <span id="cb10-5"><a href="#cb10-5" aria-hidden="true" tabindex="-1"></a>            )</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
 <div class="cell-output-display">
-
 <div data-pagedtable="false">
   <script data-pagedtable-source="" type="application/json">
 {"columns":[{"label":["atemeal"],"name":[1],"type":["int"],"align":["right"]},{"label":["hadsympt"],"name":[2],"type":["int"],"align":["right"]},{"label":["nb_cases"],"name":[3],"type":["int"],"align":["right"]}],"data":[{"1":"377","2":"216","3":"216"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}}
@@ -638,8 +646,8 @@ <h3 class="anchored" data-anchor-id="d-finally-with-this-information-you-can-cre
 <h1>4. Export clean data</h1>
 <p>Finally, we can save the cleaned data set as a new file <code>Spetses_clean2_YOURINITIALS</code>, under the data folder, before we proceeding with descriptive analysis.</p>
 <div class="cell">
-<details open="">
-<summary>Code</summary>
+<details open="" class="code-fold">
+<summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb11"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb11-1"><a href="#cb11-1" aria-hidden="true" tabindex="-1"></a>rio<span class="sc">::</span><span class="fu">export</span>(<span class="at">x =</span> copdata, </span>
 <span id="cb11-2"><a href="#cb11-2" aria-hidden="true" tabindex="-1"></a>            <span class="at">file =</span> here<span class="sc">::</span><span class="fu">here</span>(<span class="st">"data"</span>, <span class="st">"Spetses_clean2_2024.rds"</span>))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
@@ -694,6 +702,33 @@ <h1>4. Export clean data</h1>
       }
     }
   }
+  const toggleGiscusIfUsed = (isAlternate, darkModeDefault) => {
+    const baseTheme = document.querySelector('#giscus-base-theme')?.value ?? 'light';
+    const alternateTheme = document.querySelector('#giscus-alt-theme')?.value ?? 'dark';
+    let newTheme = '';
+    if(darkModeDefault) {
+      newTheme = isAlternate ? baseTheme : alternateTheme;
+    } else {
+      newTheme = isAlternate ? alternateTheme : baseTheme;
+    }
+    const changeGiscusTheme = () => {
+      // From: https://github.com/giscus/giscus/issues/336
+      const sendMessage = (message) => {
+        const iframe = document.querySelector('iframe.giscus-frame');
+        if (!iframe) return;
+        iframe.contentWindow.postMessage({ giscus: message }, 'https://giscus.app');
+      }
+      sendMessage({
+        setConfig: {
+          theme: newTheme
+        }
+      });
+    }
+    const isGiscussLoaded = window.document.querySelector('iframe.giscus-frame') !== null;
+    if (isGiscussLoaded) {
+      changeGiscusTheme();
+    }
+  }
   const toggleColorMode = (alternate) => {
     // Switch the stylesheets
     const alternateStylesheets = window.document.querySelectorAll('link.quarto-color-scheme.quarto-color-alternate');
@@ -760,13 +795,15 @@ <h1>4. Export clean data</h1>
       return localAlternateSentinel;
     }
   }
-  let localAlternateSentinel = 'default';
+  const darkModeDefault = false;
+  let localAlternateSentinel = darkModeDefault ? 'alternate' : 'default';
   // Dark / light mode switch
   window.quartoToggleColorScheme = () => {
     // Read the current dark / light value 
     let toAlternate = !hasAlternateSentinel();
     toggleColorMode(toAlternate);
     setStyleSentinel(toAlternate);
+    toggleGiscusIfUsed(toAlternate, darkModeDefault);
   };
   // Ensure there is a toggle, if there isn't float one in the top right
   if (window.document.querySelector('.quarto-color-scheme-toggle') === null) {
@@ -801,18 +838,7 @@ <h1>4. Export clean data</h1>
     }
     return false;
   }
-  const clipboard = new window.ClipboardJS('.code-copy-button', {
-    text: function(trigger) {
-      const codeEl = trigger.previousElementSibling.cloneNode(true);
-      for (const childEl of codeEl.children) {
-        if (isCodeAnnotation(childEl)) {
-          childEl.remove();
-        }
-      }
-      return codeEl.innerText;
-    }
-  });
-  clipboard.on('success', function(e) {
+  const onCopySuccess = function(e) {
     // button target
     const button = e.trigger;
     // don't keep focus
@@ -844,7 +870,29 @@ <h1>4. Export clean data</h1>
     }, 1000);
     // clear code selection
     e.clearSelection();
+  }
+  const getTextToCopy = function(trigger) {
+      const codeEl = trigger.previousElementSibling.cloneNode(true);
+      for (const childEl of codeEl.children) {
+        if (isCodeAnnotation(childEl)) {
+          childEl.remove();
+        }
+      }
+      return codeEl.innerText;
+  }
+  const clipboard = new window.ClipboardJS('.code-copy-button:not([data-in-quarto-modal])', {
+    text: getTextToCopy
   });
+  clipboard.on('success', onCopySuccess);
+  if (window.document.getElementById('quarto-embedded-source-code-modal')) {
+    // For code content inside modals, clipBoardJS needs to be initialized with a container option
+    // TODO: Check when it could be a function (https://github.com/zenorocha/clipboard.js/issues/860)
+    const clipboardModal = new window.ClipboardJS('.code-copy-button[data-in-quarto-modal]', {
+      text: getTextToCopy,
+      container: window.document.getElementById('quarto-embedded-source-code-modal')
+    });
+    clipboardModal.on('success', onCopySuccess);
+  }
   const viewSource = window.document.getElementById('quarto-view-source') ||
                      window.document.getElementById('quarto-code-tools-source');
   if (viewSource) {
@@ -896,10 +944,27 @@ <h1>4. Export clean data</h1>
   if (showAllCode) {
     showAllCode.addEventListener("click", toggleCodeHandler(true));
   }
-  function tippyHover(el, contentFn) {
+    var localhostRegex = new RegExp(/^(?:http|https):\/\/localhost\:?[0-9]*\//);
+    var mailtoRegex = new RegExp(/^mailto:/);
+      var filterRegex = new RegExp('/' + window.location.host + '/');
+    var isInternal = (href) => {
+        return filterRegex.test(href) || localhostRegex.test(href) || mailtoRegex.test(href);
+    }
+    // Inspect non-navigation links and adorn them if external
+ 	var links = window.document.querySelectorAll('a[href]:not(.nav-link):not(.navbar-brand):not(.toc-action):not(.sidebar-link):not(.sidebar-item-toggle):not(.pagination-link):not(.no-external):not([aria-hidden]):not(.dropdown-item):not(.quarto-navigation-tool):not(.about-link)');
+    for (var i=0; i<links.length; i++) {
+      const link = links[i];
+      if (!isInternal(link.href)) {
+        // undo the damage that might have been done by quarto-nav.js in the case of
+        // links that we want to consider external
+        if (link.dataset.originalHref !== undefined) {
+          link.href = link.dataset.originalHref;
+        }
+      }
+    }
+  function tippyHover(el, contentFn, onTriggerFn, onUntriggerFn) {
     const config = {
       allowHTML: true,
-      content: contentFn,
       maxWidth: 500,
       delay: 100,
       arrow: false,
@@ -909,8 +974,17 @@ <h1>4. Export clean data</h1>
       interactive: true,
       interactiveBorder: 10,
       theme: 'quarto',
-      placement: 'bottom-start'
+      placement: 'bottom-start',
     };
+    if (contentFn) {
+      config.content = contentFn;
+    }
+    if (onTriggerFn) {
+      config.onTrigger = onTriggerFn;
+    }
+    if (onUntriggerFn) {
+      config.onUntrigger = onUntriggerFn;
+    }
     window.tippy(el, config); 
   }
   const noterefs = window.document.querySelectorAll('a[role="doc-noteref"]');
@@ -922,7 +996,130 @@ <h1>4. Export clean data</h1>
       try { href = new URL(href).hash; } catch {}
       const id = href.replace(/^#\/?/, "");
       const note = window.document.getElementById(id);
-      return note.innerHTML;
+      if (note) {
+        return note.innerHTML;
+      } else {
+        return "";
+      }
+    });
+  }
+  const xrefs = window.document.querySelectorAll('a.quarto-xref');
+  const processXRef = (id, note) => {
+    // Strip column container classes
+    const stripColumnClz = (el) => {
+      el.classList.remove("page-full", "page-columns");
+      if (el.children) {
+        for (const child of el.children) {
+          stripColumnClz(child);
+        }
+      }
+    }
+    stripColumnClz(note)
+    if (id === null || id.startsWith('sec-')) {
+      // Special case sections, only their first couple elements
+      const container = document.createElement("div");
+      if (note.children && note.children.length > 2) {
+        container.appendChild(note.children[0].cloneNode(true));
+        for (let i = 1; i < note.children.length; i++) {
+          const child = note.children[i];
+          if (child.tagName === "P" && child.innerText === "") {
+            continue;
+          } else {
+            container.appendChild(child.cloneNode(true));
+            break;
+          }
+        }
+        if (window.Quarto?.typesetMath) {
+          window.Quarto.typesetMath(container);
+        }
+        return container.innerHTML
+      } else {
+        if (window.Quarto?.typesetMath) {
+          window.Quarto.typesetMath(note);
+        }
+        return note.innerHTML;
+      }
+    } else {
+      // Remove any anchor links if they are present
+      const anchorLink = note.querySelector('a.anchorjs-link');
+      if (anchorLink) {
+        anchorLink.remove();
+      }
+      if (window.Quarto?.typesetMath) {
+        window.Quarto.typesetMath(note);
+      }
+      // TODO in 1.5, we should make sure this works without a callout special case
+      if (note.classList.contains("callout")) {
+        return note.outerHTML;
+      } else {
+        return note.innerHTML;
+      }
+    }
+  }
+  for (var i=0; i<xrefs.length; i++) {
+    const xref = xrefs[i];
+    tippyHover(xref, undefined, function(instance) {
+      instance.disable();
+      let url = xref.getAttribute('href');
+      let hash = undefined; 
+      if (url.startsWith('#')) {
+        hash = url;
+      } else {
+        try { hash = new URL(url).hash; } catch {}
+      }
+      if (hash) {
+        const id = hash.replace(/^#\/?/, "");
+        const note = window.document.getElementById(id);
+        if (note !== null) {
+          try {
+            const html = processXRef(id, note.cloneNode(true));
+            instance.setContent(html);
+          } finally {
+            instance.enable();
+            instance.show();
+          }
+        } else {
+          // See if we can fetch this
+          fetch(url.split('#')[0])
+          .then(res => res.text())
+          .then(html => {
+            const parser = new DOMParser();
+            const htmlDoc = parser.parseFromString(html, "text/html");
+            const note = htmlDoc.getElementById(id);
+            if (note !== null) {
+              const html = processXRef(id, note);
+              instance.setContent(html);
+            } 
+          }).finally(() => {
+            instance.enable();
+            instance.show();
+          });
+        }
+      } else {
+        // See if we can fetch a full url (with no hash to target)
+        // This is a special case and we should probably do some content thinning / targeting
+        fetch(url)
+        .then(res => res.text())
+        .then(html => {
+          const parser = new DOMParser();
+          const htmlDoc = parser.parseFromString(html, "text/html");
+          const note = htmlDoc.querySelector('main.content');
+          if (note !== null) {
+            // This should only happen for chapter cross references
+            // (since there is no id in the URL)
+            // remove the first header
+            if (note.children.length > 0 && note.children[0].tagName === "HEADER") {
+              note.children[0].remove();
+            }
+            const html = processXRef(null, note);
+            instance.setContent(html);
+          } 
+        }).finally(() => {
+          instance.enable();
+          instance.show();
+        });
+      }
+    }, function(instance) {
     });
   }
       let selectedAnnoteEl;
@@ -966,6 +1163,7 @@ <h1>4. Export clean data</h1>
             }
             div.style.top = top - 2 + "px";
             div.style.height = height + 4 + "px";
+            div.style.left = 0;
             let gutterDiv = window.document.getElementById("code-annotation-line-highlight-gutter");
             if (gutterDiv === null) {
               gutterDiv = window.document.createElement("div");
@@ -991,6 +1189,32 @@ <h1>4. Export clean data</h1>
         });
         selectedAnnoteEl = undefined;
       };
+        // Handle positioning of the toggle
+    window.addEventListener(
+      "resize",
+      throttle(() => {
+        elRect = undefined;
+        if (selectedAnnoteEl) {
+          selectCodeLines(selectedAnnoteEl);
+        }
+      }, 10)
+    );
+    function throttle(fn, ms) {
+    let throttle = false;
+    let timer;
+      return (...args) => {
+        if(!throttle) { // first call gets through
+            fn.apply(this, args);
+            throttle = true;
+        } else { // all the others get throttled
+            if(timer) clearTimeout(timer); // cancel #2
+            timer = setTimeout(() => {
+              fn.apply(this, args);
+              timer = throttle = false;
+            }, ms);
+        }
+      };
+    }
       // Attach click handler to the DT
       const annoteDls = window.document.querySelectorAll('dt[data-target-cell]');
       for (const annoteDlNode of annoteDls) {
@@ -1053,243 +1277,246 @@ <h1>4. Export clean data</h1>
 <div class="sourceCode" id="cb12" data-shortcodes="false"><pre class="sourceCode markdown code-with-copy"><code class="sourceCode markdown"><span id="cb12-1"><a href="#cb12-1" aria-hidden="true" tabindex="-1"></a><span class="co">---</span></span>
 <span id="cb12-2"><a href="#cb12-2" aria-hidden="true" tabindex="-1"></a><span class="an">title:</span><span class="co"> "Case Definition"</span></span>
 <span id="cb12-3"><a href="#cb12-3" aria-hidden="true" tabindex="-1"></a><span class="an">editor:</span><span class="co"> visual</span></span>
-<span id="cb12-4"><a href="#cb12-4" aria-hidden="true" tabindex="-1"></a><span class="co">---</span></span>
-<span id="cb12-5"><a href="#cb12-5" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-6"><a href="#cb12-6" aria-hidden="true" tabindex="-1"></a><span class="fu">## 1. Learning outcomes</span></span>
+<span id="cb12-4"><a href="#cb12-4" aria-hidden="true" tabindex="-1"></a><span class="an">editor_options:</span><span class="co"> </span></span>
+<span id="cb12-5"><a href="#cb12-5" aria-hidden="true" tabindex="-1"></a><span class="co">  chunk_output_type: console</span></span>
+<span id="cb12-6"><a href="#cb12-6" aria-hidden="true" tabindex="-1"></a><span class="co">---</span></span>
 <span id="cb12-7"><a href="#cb12-7" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-8"><a href="#cb12-8" aria-hidden="true" tabindex="-1"></a>At the end of the session, participants will be able to:</span>
+<span id="cb12-8"><a href="#cb12-8" aria-hidden="true" tabindex="-1"></a><span class="fu">## 1. Learning outcomes</span></span>
 <span id="cb12-9"><a href="#cb12-9" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-10"><a href="#cb12-10" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span>Apply case definition criteria to a dataset using logical arguments in R</span>
+<span id="cb12-10"><a href="#cb12-10" aria-hidden="true" tabindex="-1"></a>At the end of the session, participants will be able to:</span>
 <span id="cb12-11"><a href="#cb12-11" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-12"><a href="#cb12-12" aria-hidden="true" tabindex="-1"></a><span class="fu">## 2. Story/plot description</span></span>
+<span id="cb12-12"><a href="#cb12-12" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span>Apply case definition criteria to a dataset using logical arguments in R</span>
 <span id="cb12-13"><a href="#cb12-13" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-14"><a href="#cb12-14" aria-hidden="true" tabindex="-1"></a>You will now create a new column in the data set to hold the case definition you decided in a previous step during your investigation. You can call this column <span class="in">`case`</span> and set it to <span class="in">`TRUE`</span> if the individual meets the case definition criteria and <span class="in">`FALSE`</span> if not. You will use this column later on for any calculations needed (descriptive statistics, two-by-two tables to compute measures of association, etc.) to figure out the culprit of this outbreak.</span>
+<span id="cb12-14"><a href="#cb12-14" aria-hidden="true" tabindex="-1"></a><span class="fu">## 2. Story/plot description</span></span>
 <span id="cb12-15"><a href="#cb12-15" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-16"><a href="#cb12-16" aria-hidden="true" tabindex="-1"></a><span class="fu">## 3. Questions/Assignments</span></span>
+<span id="cb12-16"><a href="#cb12-16" aria-hidden="true" tabindex="-1"></a>You will now create a new column in the data set to hold the case definition you decided in a previous step during your investigation. You can call this column <span class="in">`case`</span> and set it to <span class="in">`1`</span> if the individual meets the case definition criteria and <span class="in">`0`</span> if not. You will use this column later on for any calculations needed (descriptive statistics, two-by-two tables to compute measures of association, etc.) to figure out the culprit of this outbreak.</span>
 <span id="cb12-17"><a href="#cb12-17" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-18"><a href="#cb12-18" aria-hidden="true" tabindex="-1"></a>If you closed your R project, please open it again and load the clean dataset. We strongly recommend you use the <span class="in">`Spetses_clean1_2024.rds`</span>. This is just to be sure all needed changes in that file for the code to work have been done (as you may have changed the file in a slightly different way than we have).</span>
+<span id="cb12-18"><a href="#cb12-18" aria-hidden="true" tabindex="-1"></a><span class="fu">## 3. Questions/Assignments</span></span>
 <span id="cb12-19"><a href="#cb12-19" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-20"><a href="#cb12-20" aria-hidden="true" tabindex="-1"></a>To be sure we all start from the same case definition, let's agree that a case was defined as a person who:</span>
+<span id="cb12-20"><a href="#cb12-20" aria-hidden="true" tabindex="-1"></a>If you closed your R project, please open it again and load the clean dataset. We strongly recommend you use the <span class="in">`Spetses_clean1_2024.rds`</span>. This is just to be sure all needed changes in that file for the code to work have been done (as you may have changed the file in a slightly different way than we have).</span>
 <span id="cb12-21"><a href="#cb12-21" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-22"><a href="#cb12-22" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span><span class="va">[x]</span> attended the school dinner on 11 November 2006 (i.e. is on the linelist)</span>
-<span id="cb12-23"><a href="#cb12-23" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span><span class="va">[x]</span> ate a meal at the school dinner (i.e. was exposed)</span>
-<span id="cb12-24"><a href="#cb12-24" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span><span class="va">[x]</span> fell ill after the start of the meal</span>
-<span id="cb12-25"><a href="#cb12-25" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span><span class="va">[x]</span> fell ill within the time period of interest after the school dinner</span>
-<span id="cb12-26"><a href="#cb12-26" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span><span class="va">[x]</span> suffered from diarrhoea with or without blood, or vomiting</span>
-<span id="cb12-27"><a href="#cb12-27" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-28"><a href="#cb12-28" aria-hidden="true" tabindex="-1"></a>Non cases (not-ill) were defined as people who:</span>
+<span id="cb12-22"><a href="#cb12-22" aria-hidden="true" tabindex="-1"></a>To be sure we all start from the same case definition, let's agree that a case was defined as a person who:</span>
+<span id="cb12-23"><a href="#cb12-23" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb12-24"><a href="#cb12-24" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span><span class="va">[x]</span> attended the school dinner on 11 November 2006 (i.e. is on the linelist)</span>
+<span id="cb12-25"><a href="#cb12-25" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span><span class="va">[x]</span> ate a meal at the school dinner (i.e. was exposed)</span>
+<span id="cb12-26"><a href="#cb12-26" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span><span class="va">[x]</span> fell ill after the start of the meal</span>
+<span id="cb12-27"><a href="#cb12-27" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span><span class="va">[x]</span> fell ill within the time period of interest after the school dinner</span>
+<span id="cb12-28"><a href="#cb12-28" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span><span class="va">[x]</span> suffered from diarrhoea with or without blood, or vomiting</span>
 <span id="cb12-29"><a href="#cb12-29" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-30"><a href="#cb12-30" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span><span class="va">[x]</span> attended the school dinner on 11 November 2006 (i.e. are on the linelist)</span>
-<span id="cb12-31"><a href="#cb12-31" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span><span class="va">[x]</span> ate a meal at the school dinner (i.e. were exposed)</span>
-<span id="cb12-32"><a href="#cb12-32" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span><span class="va">[x]</span> did not fall ill within the time period of interest</span>
-<span id="cb12-33"><a href="#cb12-33" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span><span class="va">[x]</span> did not develop diarrhoea (with or without blood) or vomiting</span>
-<span id="cb12-34"><a href="#cb12-34" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-35"><a href="#cb12-35" aria-hidden="true" tabindex="-1"></a><span class="fu">## 3.1 Install packages (if needed) and load libraries</span></span>
+<span id="cb12-30"><a href="#cb12-30" aria-hidden="true" tabindex="-1"></a>Non cases (not-ill) were defined as people who:</span>
+<span id="cb12-31"><a href="#cb12-31" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb12-32"><a href="#cb12-32" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span><span class="va">[x]</span> attended the school dinner on 11 November 2006 (i.e. are on the linelist)</span>
+<span id="cb12-33"><a href="#cb12-33" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span><span class="va">[x]</span> ate a meal at the school dinner (i.e. were exposed)</span>
+<span id="cb12-34"><a href="#cb12-34" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span><span class="va">[x]</span> did not fall ill within the time period of interest</span>
+<span id="cb12-35"><a href="#cb12-35" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span><span class="va">[x]</span> did not develop diarrhoea (with or without blood) or vomiting</span>
 <span id="cb12-36"><a href="#cb12-36" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-37"><a href="#cb12-37" aria-hidden="true" tabindex="-1"></a><span class="in">```{r, Packages}</span></span>
-<span id="cb12-38"><a href="#cb12-38" aria-hidden="true" tabindex="-1"></a><span class="co"># Load the required libraries into the current R session:</span></span>
-<span id="cb12-39"><a href="#cb12-39" aria-hidden="true" tabindex="-1"></a>pacman<span class="sc">::</span><span class="fu">p_load</span>(rio, </span>
-<span id="cb12-40"><a href="#cb12-40" aria-hidden="true" tabindex="-1"></a>               here, </span>
-<span id="cb12-41"><a href="#cb12-41" aria-hidden="true" tabindex="-1"></a>               tidyverse, </span>
-<span id="cb12-42"><a href="#cb12-42" aria-hidden="true" tabindex="-1"></a>               skimr,</span>
-<span id="cb12-43"><a href="#cb12-43" aria-hidden="true" tabindex="-1"></a>               plyr,</span>
-<span id="cb12-44"><a href="#cb12-44" aria-hidden="true" tabindex="-1"></a>               janitor,</span>
-<span id="cb12-45"><a href="#cb12-45" aria-hidden="true" tabindex="-1"></a>               lubridate,</span>
-<span id="cb12-46"><a href="#cb12-46" aria-hidden="true" tabindex="-1"></a>               gtsummary, </span>
-<span id="cb12-47"><a href="#cb12-47" aria-hidden="true" tabindex="-1"></a>               flextable,</span>
-<span id="cb12-48"><a href="#cb12-48" aria-hidden="true" tabindex="-1"></a>               officer,</span>
-<span id="cb12-49"><a href="#cb12-49" aria-hidden="true" tabindex="-1"></a>               epikit, </span>
-<span id="cb12-50"><a href="#cb12-50" aria-hidden="true" tabindex="-1"></a>               apyramid, </span>
-<span id="cb12-51"><a href="#cb12-51" aria-hidden="true" tabindex="-1"></a>               scales)</span>
-<span id="cb12-52"><a href="#cb12-52" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-53"><a href="#cb12-53" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb12-37"><a href="#cb12-37" aria-hidden="true" tabindex="-1"></a><span class="fu">## 3.1 Install packages (if needed) and load libraries</span></span>
+<span id="cb12-38"><a href="#cb12-38" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb12-39"><a href="#cb12-39" aria-hidden="true" tabindex="-1"></a><span class="in">```{r, Packages}</span></span>
+<span id="cb12-40"><a href="#cb12-40" aria-hidden="true" tabindex="-1"></a><span class="in"># Load the required libraries into the current R session:</span></span>
+<span id="cb12-41"><a href="#cb12-41" aria-hidden="true" tabindex="-1"></a><span class="in">pacman::p_load(rio, </span></span>
+<span id="cb12-42"><a href="#cb12-42" aria-hidden="true" tabindex="-1"></a><span class="in">               here, </span></span>
+<span id="cb12-43"><a href="#cb12-43" aria-hidden="true" tabindex="-1"></a><span class="in">               tidyverse, </span></span>
+<span id="cb12-44"><a href="#cb12-44" aria-hidden="true" tabindex="-1"></a><span class="in">               skimr,</span></span>
+<span id="cb12-45"><a href="#cb12-45" aria-hidden="true" tabindex="-1"></a><span class="in">               plyr,</span></span>
+<span id="cb12-46"><a href="#cb12-46" aria-hidden="true" tabindex="-1"></a><span class="in">               janitor,</span></span>
+<span id="cb12-47"><a href="#cb12-47" aria-hidden="true" tabindex="-1"></a><span class="in">               lubridate,</span></span>
+<span id="cb12-48"><a href="#cb12-48" aria-hidden="true" tabindex="-1"></a><span class="in">               gtsummary, </span></span>
+<span id="cb12-49"><a href="#cb12-49" aria-hidden="true" tabindex="-1"></a><span class="in">               flextable,</span></span>
+<span id="cb12-50"><a href="#cb12-50" aria-hidden="true" tabindex="-1"></a><span class="in">               officer,</span></span>
+<span id="cb12-51"><a href="#cb12-51" aria-hidden="true" tabindex="-1"></a><span class="in">               epikit, </span></span>
+<span id="cb12-52"><a href="#cb12-52" aria-hidden="true" tabindex="-1"></a><span class="in">               apyramid, </span></span>
+<span id="cb12-53"><a href="#cb12-53" aria-hidden="true" tabindex="-1"></a><span class="in">               scales)</span></span>
 <span id="cb12-54"><a href="#cb12-54" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-55"><a href="#cb12-55" aria-hidden="true" tabindex="-1"></a><span class="fu">## 3.2 Import your data</span></span>
+<span id="cb12-55"><a href="#cb12-55" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
 <span id="cb12-56"><a href="#cb12-56" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-57"><a href="#cb12-57" aria-hidden="true" tabindex="-1"></a><span class="in">```{r, Import_data}</span></span>
-<span id="cb12-58"><a href="#cb12-58" aria-hidden="true" tabindex="-1"></a><span class="co"># Import the clean data set:</span></span>
-<span id="cb12-59"><a href="#cb12-59" aria-hidden="true" tabindex="-1"></a>copdata <span class="ot">&lt;-</span> rio<span class="sc">::</span><span class="fu">import</span>(here<span class="sc">::</span><span class="fu">here</span>(<span class="st">"data"</span>, <span class="st">"Spetses_clean1_2024.rds"</span>))</span>
-<span id="cb12-60"><a href="#cb12-60" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb12-61"><a href="#cb12-61" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-62"><a href="#cb12-62" aria-hidden="true" tabindex="-1"></a><span class="fu">## 3.3 Identify the variables you need to apply the case definition criteria.</span></span>
+<span id="cb12-57"><a href="#cb12-57" aria-hidden="true" tabindex="-1"></a><span class="fu">## 3.2 Import your data</span></span>
+<span id="cb12-58"><a href="#cb12-58" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb12-59"><a href="#cb12-59" aria-hidden="true" tabindex="-1"></a><span class="in">```{r, Import_data}</span></span>
+<span id="cb12-60"><a href="#cb12-60" aria-hidden="true" tabindex="-1"></a><span class="in"># Import the clean data set:</span></span>
+<span id="cb12-61"><a href="#cb12-61" aria-hidden="true" tabindex="-1"></a><span class="in">copdata &lt;- rio::import(here::here("data", "Spetses_clean1_2024.rds"), trust = TRUE)</span></span>
+<span id="cb12-62"><a href="#cb12-62" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
 <span id="cb12-63"><a href="#cb12-63" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-64"><a href="#cb12-64" aria-hidden="true" tabindex="-1"></a>::: {.callout-tip title="Need a little bit of help?" collapse="true"}</span>
-<span id="cb12-65"><a href="#cb12-65" aria-hidden="true" tabindex="-1"></a>The variables we need from the dataset to apply the above case definition are: <span class="in">`meal`</span>, <span class="in">`onset_datetime`</span>, <span class="in">`diarrhoea`</span>, <span class="in">`bloody`</span> and <span class="in">`vomiting`</span>.</span>
-<span id="cb12-66"><a href="#cb12-66" aria-hidden="true" tabindex="-1"></a>:::</span>
-<span id="cb12-67"><a href="#cb12-67" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-68"><a href="#cb12-68" aria-hidden="true" tabindex="-1"></a><span class="fu">## 3.4 Create a new `case` column to hold the binary case definition variable. Let's think about how to do this little by little:</span></span>
+<span id="cb12-64"><a href="#cb12-64" aria-hidden="true" tabindex="-1"></a><span class="fu">## 3.3 Identify the variables you need to apply the case definition criteria.</span></span>
+<span id="cb12-65"><a href="#cb12-65" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb12-66"><a href="#cb12-66" aria-hidden="true" tabindex="-1"></a>::: {.callout-tip title="Need a little bit of help?" collapse="true"}</span>
+<span id="cb12-67"><a href="#cb12-67" aria-hidden="true" tabindex="-1"></a>The variables we need from the dataset to apply the above case definition are: <span class="in">`meal`</span>, <span class="in">`ill`</span>, <span class="in">`onset_datetime`</span>, <span class="in">`diarrhoea`</span>, <span class="in">`bloody`</span> and <span class="in">`vomiting`</span>.</span>
+<span id="cb12-68"><a href="#cb12-68" aria-hidden="true" tabindex="-1"></a>:::</span>
 <span id="cb12-69"><a href="#cb12-69" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-70"><a href="#cb12-70" aria-hidden="true" tabindex="-1"></a><span class="fu">### a) Ate a meal at the school dinner</span></span>
+<span id="cb12-70"><a href="#cb12-70" aria-hidden="true" tabindex="-1"></a><span class="fu">## 3.4 Create a new `case` column to hold the binary case definition variable. Let's think about how to do this little by little:</span></span>
 <span id="cb12-71"><a href="#cb12-71" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-72"><a href="#cb12-72" aria-hidden="true" tabindex="-1"></a>You decide to exclude any people from the cohort who didn't eat at the dinner, because we specifically hypothesised a food item to be the vehicle of infection in this outbreak. Thus, filter your dataset to those who ate a meal: Keep in your dataset only those who ate a meal.</span>
+<span id="cb12-72"><a href="#cb12-72" aria-hidden="true" tabindex="-1"></a><span class="fu">### a) Ate a meal at the school dinner</span></span>
 <span id="cb12-73"><a href="#cb12-73" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-74"><a href="#cb12-74" aria-hidden="true" tabindex="-1"></a>::: {.callout-tip title="Need a little bit of help?" collapse="true"}</span>
-<span id="cb12-75"><a href="#cb12-75" aria-hidden="true" tabindex="-1"></a><span class="in">`filter`</span> by those with <span class="in">`meal == TRUE`</span>.</span>
-<span id="cb12-76"><a href="#cb12-76" aria-hidden="true" tabindex="-1"></a>:::</span>
-<span id="cb12-77"><a href="#cb12-77" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-78"><a href="#cb12-78" aria-hidden="true" tabindex="-1"></a>::: {.callout-warning title="Let's stop and... think!" collapse="true"}</span>
-<span id="cb12-79"><a href="#cb12-79" aria-hidden="true" tabindex="-1"></a>What are some of the implications this decision may lead to? (excluding any people from the cohort who didn't eat at the dinner)</span>
-<span id="cb12-80"><a href="#cb12-80" aria-hidden="true" tabindex="-1"></a>:::</span>
-<span id="cb12-81"><a href="#cb12-81" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-84"><a href="#cb12-84" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
-<span id="cb12-85"><a href="#cb12-85" aria-hidden="true" tabindex="-1"></a>copdata <span class="ot">&lt;-</span> copdata <span class="sc">%&gt;%</span> </span>
-<span id="cb12-86"><a href="#cb12-86" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(meal <span class="sc">==</span> <span class="cn">TRUE</span>)</span>
-<span id="cb12-87"><a href="#cb12-87" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb12-88"><a href="#cb12-88" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-89"><a href="#cb12-89" aria-hidden="true" tabindex="-1"></a>::: {.callout-note title="Once you've thought about the above, have a look here" collapse="true"}</span>
-<span id="cb12-90"><a href="#cb12-90" aria-hidden="true" tabindex="-1"></a>Seven of the respondents actually said they did not eat the meal, but when it came to the questions about which food items they ate, they provided answers! This issue could have been minimised by:</span>
-<span id="cb12-91"><a href="#cb12-91" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-92"><a href="#cb12-92" aria-hidden="true" tabindex="-1"></a><span class="sc">\-</span> At the survey state, one could adjust the design of an electronic questionnaire to prevent key questions from being skipped. This can come with both pros and cons. Allow fellows to discuss if time allows.</span>
+<span id="cb12-74"><a href="#cb12-74" aria-hidden="true" tabindex="-1"></a>You decide to exclude any people from the cohort who didn't eat at the dinner, because we specifically hypothesised a food item to be the vehicle of infection in this outbreak. Thus, filter your dataset to those who ate a meal: Keep in your dataset only those who ate a meal.</span>
+<span id="cb12-75"><a href="#cb12-75" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb12-76"><a href="#cb12-76" aria-hidden="true" tabindex="-1"></a>::: {.callout-tip title="Need a little bit of help?" collapse="true"}</span>
+<span id="cb12-77"><a href="#cb12-77" aria-hidden="true" tabindex="-1"></a><span class="in">`filter`</span> by those with <span class="in">`meal == 1`</span>.</span>
+<span id="cb12-78"><a href="#cb12-78" aria-hidden="true" tabindex="-1"></a>:::</span>
+<span id="cb12-79"><a href="#cb12-79" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb12-80"><a href="#cb12-80" aria-hidden="true" tabindex="-1"></a>::: {.callout-warning title="Let's stop and... think!" collapse="true"}</span>
+<span id="cb12-81"><a href="#cb12-81" aria-hidden="true" tabindex="-1"></a>What are some of the implications this decision may lead to? (excluding any people from the cohort who didn't eat at the dinner)</span>
+<span id="cb12-82"><a href="#cb12-82" aria-hidden="true" tabindex="-1"></a>:::</span>
+<span id="cb12-83"><a href="#cb12-83" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb12-86"><a href="#cb12-86" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb12-87"><a href="#cb12-87" aria-hidden="true" tabindex="-1"></a>copdata <span class="ot">&lt;-</span> copdata <span class="sc">%&gt;%</span> </span>
+<span id="cb12-88"><a href="#cb12-88" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(meal <span class="sc">==</span> <span class="dv">1</span>)</span>
+<span id="cb12-89"><a href="#cb12-89" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb12-90"><a href="#cb12-90" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb12-91"><a href="#cb12-91" aria-hidden="true" tabindex="-1"></a>::: {.callout-note title="Once you've thought about the above, have a look here" collapse="true"}</span>
+<span id="cb12-92"><a href="#cb12-92" aria-hidden="true" tabindex="-1"></a>Seven of the respondents actually said they did not eat the meal, but when it came to the questions about which food items they ate, they provided answers! This issue could have been minimised by:</span>
 <span id="cb12-93"><a href="#cb12-93" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-94"><a href="#cb12-94" aria-hidden="true" tabindex="-1"></a><span class="sc">\-</span> Explore your data further, realise this is the case, and recode the <span class="in">`meal`</span> variable for these individuals as <span class="in">`TRUE.`</span> =<span class="sc">\&gt;</span> This would be the way to go, but is not what we did in our example because we tried to keep it simple, and also because it is good to show that you may not always clean the data perfectly, and that has consequences: You can highlight the importance or really explore your data in depth.</span>
+<span id="cb12-94"><a href="#cb12-94" aria-hidden="true" tabindex="-1"></a><span class="sc">\-</span> At the survey state, one could adjust the design of an electronic questionnaire to prevent key questions from being skipped. This can come with both pros and cons. Allow fellows to discuss if time allows.</span>
 <span id="cb12-95"><a href="#cb12-95" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-96"><a href="#cb12-96" aria-hidden="true" tabindex="-1"></a>By making the above decision, we may be missing cases and non-cases people, and thus, modifying the final estimate of our measure of association. =<span class="sc">\&gt;</span> It is very important to know your data, explore it deeply and try to clean it as well as possible. Every step one makes when cleaning the data may have a consequence, and we should be aware of it when making the data cleaning decisions and when interpreting the results.</span>
-<span id="cb12-97"><a href="#cb12-97" aria-hidden="true" tabindex="-1"></a>:::</span>
-<span id="cb12-98"><a href="#cb12-98" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-99"><a href="#cb12-99" aria-hidden="true" tabindex="-1"></a><span class="fu">### b) Fell ill after the start of the meal</span></span>
+<span id="cb12-96"><a href="#cb12-96" aria-hidden="true" tabindex="-1"></a><span class="sc">\-</span> Explore your data further, realise this is the case, and recode the <span class="in">`meal`</span> variable for these individuals as <span class="in">`1.`</span> =<span class="sc">\&gt;</span> This would be the way to go, but is not what we did in our example because we tried to keep it simple, and also because it is good to show that you may not always clean the data perfectly, and that has consequences: You can highlight the importance or really explore your data in depth.</span>
+<span id="cb12-97"><a href="#cb12-97" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb12-98"><a href="#cb12-98" aria-hidden="true" tabindex="-1"></a>By making the above decision, we may be missing cases and non-cases people, and thus, modifying the final estimate of our measure of association. =<span class="sc">\&gt;</span> It is very important to know your data, explore it deeply and try to clean it as well as possible. Every step one makes when cleaning the data may have a consequence, and we should be aware of it when making the data cleaning decisions and when interpreting the results.</span>
+<span id="cb12-99"><a href="#cb12-99" aria-hidden="true" tabindex="-1"></a>:::</span>
 <span id="cb12-100"><a href="#cb12-100" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-101"><a href="#cb12-101" aria-hidden="true" tabindex="-1"></a>We define "fell ill" as any person having had diarrhoea with OR without blood, OR vomiting. To capture this information easily, you will create a new <span class="in">`gastrosymptoms`</span> variable. This variable will indicate that the person had one OR ("or" is R is achieved by using <span class="in">`|`</span>) more of the clinical symptoms in your definition.</span>
+<span id="cb12-101"><a href="#cb12-101" aria-hidden="true" tabindex="-1"></a><span class="fu">### b) Fell ill after the start of the meal</span></span>
 <span id="cb12-102"><a href="#cb12-102" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-103"><a href="#cb12-103" aria-hidden="true" tabindex="-1"></a>Note that we the concept of having eaten a meal is already included as per one of the steps above.</span>
+<span id="cb12-103"><a href="#cb12-103" aria-hidden="true" tabindex="-1"></a>The questionnaire included a question on whether the person fell ill after the school dinner party (column <span class="in">`ill`</span>). However, the person may have experience symptoms that are very unspecific to this outbreak (e.g., headache, joint pain...). We may want to only include those who develop gastrointestinal symptoms. That is any person having had diarrhoea with OR without blood, OR vomiting. To capture this information easily, you will create a new <span class="in">`gastrosymptoms`</span> variable. This variable will indicate that the person had one OR ("or" is R is achieved by using <span class="in">`|`</span>) more of the clinical symptoms in your definition.</span>
 <span id="cb12-104"><a href="#cb12-104" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-105"><a href="#cb12-105" aria-hidden="true" tabindex="-1"></a>::: {.callout-tip title="Need a little bit of help?" collapse="true"}</span>
-<span id="cb12-106"><a href="#cb12-106" aria-hidden="true" tabindex="-1"></a>Create a new <span class="in">`gastrosymptoms`</span> column in your line list with <span class="in">`mutate()`</span> and case_when<span class="in">`()`</span>.</span>
-<span id="cb12-107"><a href="#cb12-107" aria-hidden="true" tabindex="-1"></a>:::</span>
-<span id="cb12-108"><a href="#cb12-108" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-109"><a href="#cb12-109" aria-hidden="true" tabindex="-1"></a>::: {.callout-warning title="Let's stop and... think!" collapse="true"}</span>
-<span id="cb12-110"><a href="#cb12-110" aria-hidden="true" tabindex="-1"></a>What are some of the implications this decision may lead to? (defining "fell ill" as any person who reported having diarrhoea with OR without blood, OR vomiting)</span>
-<span id="cb12-111"><a href="#cb12-111" aria-hidden="true" tabindex="-1"></a>:::</span>
-<span id="cb12-112"><a href="#cb12-112" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-113"><a href="#cb12-113" aria-hidden="true" tabindex="-1"></a>::: {.callout-note title="If you've thought about the above, have a look here" collapse="true"}</span>
-<span id="cb12-114"><a href="#cb12-114" aria-hidden="true" tabindex="-1"></a>Having one clinical symptom enough to be considered a potential case at this point may be considered too unspecific (low specificity). For example, a person who ate at the dinner party and developed diarrhoea for other reasons other than food poisoning (say, they recently started on antibiotics known for unbalancing the intestinal flora and causing diarrhoea) could be misclassified as a potential case. (Note we talk about **potential case**, and not **case**; that is because here we are not talking about cases per-se yet, but this decision has implications for when applying the case definition below).</span>
-<span id="cb12-115"><a href="#cb12-115" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-116"><a href="#cb12-116" aria-hidden="true" tabindex="-1"></a>Moreover, those who did not report clinical symptoms will be defined as non-cases. Thus, we are assuming that these individuals did not develop symptoms because they didn't report them. The missing values could be due to, for example, them skipping the questions in the questionnaire. Some individuals may be reluctant to report symptoms, due to shame, fear of repercussion, or others. It is important to think ahead, before the interview, about ways to minimise these situations. For example, through questionnaire design, you may impede skipping questions; you could promote trust by using the right interviewers (in some cases this will be someone from the community, in others someone form specific NGOs, someone of a specific race or gender, etc); choose to carry out online questionnaires vs in person (or vice versa, depending on the situation), etc.</span>
-<span id="cb12-117"><a href="#cb12-117" aria-hidden="true" tabindex="-1"></a>:::</span>
-<span id="cb12-118"><a href="#cb12-118" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-121"><a href="#cb12-121" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
-<span id="cb12-122"><a href="#cb12-122" aria-hidden="true" tabindex="-1"></a>copdata <span class="ot">&lt;-</span> copdata <span class="sc">%&gt;%</span> </span>
-<span id="cb12-123"><a href="#cb12-123" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">gastrosymptoms =</span> <span class="fu">case_when</span>(</span>
-<span id="cb12-124"><a href="#cb12-124" aria-hidden="true" tabindex="-1"></a>    <span class="co"># Those had diarrhoea...</span></span>
-<span id="cb12-125"><a href="#cb12-125" aria-hidden="true" tabindex="-1"></a>    diarrhoea <span class="sc">==</span> <span class="cn">TRUE</span> <span class="sc">|</span></span>
-<span id="cb12-126"><a href="#cb12-126" aria-hidden="true" tabindex="-1"></a>      <span class="co">#or bloody diarrhoea...</span></span>
-<span id="cb12-127"><a href="#cb12-127" aria-hidden="true" tabindex="-1"></a>    bloody <span class="sc">==</span> <span class="cn">TRUE</span> <span class="sc">|</span></span>
-<span id="cb12-128"><a href="#cb12-128" aria-hidden="true" tabindex="-1"></a>      <span class="co"># or vomiting, are marked as TRUE (fell ill after the meal)</span></span>
-<span id="cb12-129"><a href="#cb12-129" aria-hidden="true" tabindex="-1"></a>    vomiting <span class="sc">==</span> <span class="cn">TRUE</span> <span class="sc">~</span> <span class="cn">TRUE</span>,</span>
-<span id="cb12-130"><a href="#cb12-130" aria-hidden="true" tabindex="-1"></a>    <span class="co"># The rest are FALSE. This includes those who ate a meal but had no symptoms (did not fell ill after the meal)</span></span>
-<span id="cb12-131"><a href="#cb12-131" aria-hidden="true" tabindex="-1"></a>    <span class="at">.default =</span> <span class="cn">FALSE</span>)</span>
-<span id="cb12-132"><a href="#cb12-132" aria-hidden="true" tabindex="-1"></a>    )</span>
-<span id="cb12-133"><a href="#cb12-133" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb12-134"><a href="#cb12-134" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-135"><a href="#cb12-135" aria-hidden="true" tabindex="-1"></a><span class="fu">### c) Fell ill within the time period of interest</span></span>
+<span id="cb12-105"><a href="#cb12-105" aria-hidden="true" tabindex="-1"></a>Note that we the concept of having eaten a meal is already included as per one of the steps above.</span>
+<span id="cb12-106"><a href="#cb12-106" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb12-107"><a href="#cb12-107" aria-hidden="true" tabindex="-1"></a>::: {.callout-tip title="Need a little bit of help?" collapse="true"}</span>
+<span id="cb12-108"><a href="#cb12-108" aria-hidden="true" tabindex="-1"></a>Create a new <span class="in">`gastrosymptoms`</span> column in your line list with <span class="in">`mutate()`</span> and case_when<span class="in">`()`</span>.</span>
+<span id="cb12-109"><a href="#cb12-109" aria-hidden="true" tabindex="-1"></a>:::</span>
+<span id="cb12-110"><a href="#cb12-110" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb12-111"><a href="#cb12-111" aria-hidden="true" tabindex="-1"></a>::: {.callout-warning title="Let's stop and... think!" collapse="true"}</span>
+<span id="cb12-112"><a href="#cb12-112" aria-hidden="true" tabindex="-1"></a>What are some of the implications this decision may lead to? (defining "fell ill" as any person who reported having diarrhoea with OR without blood, OR vomiting)</span>
+<span id="cb12-113"><a href="#cb12-113" aria-hidden="true" tabindex="-1"></a>:::</span>
+<span id="cb12-114"><a href="#cb12-114" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb12-115"><a href="#cb12-115" aria-hidden="true" tabindex="-1"></a>::: {.callout-note title="If you've thought about the above, have a look here" collapse="true"}</span>
+<span id="cb12-116"><a href="#cb12-116" aria-hidden="true" tabindex="-1"></a>Having one gastrointestinal symptom is enough to be considered a potential case at this point, which may still be considered too unspecific (low specificity). For example, a person who ate at the dinner party and developed diarrhoea for other reasons other than food poisoning (say, they recently started on antibiotics known for unbalancing the intestinal flora and causing diarrhoea) could be misclassified as a potential case. (Note we talk about **potential case**, and not **case**; that is because here we are not talking about cases per-se yet, but this decision has implications for when applying the case definition below).</span>
+<span id="cb12-117"><a href="#cb12-117" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb12-118"><a href="#cb12-118" aria-hidden="true" tabindex="-1"></a>Moreover, those who did not report clinical symptoms will be defined as non-cases. Thus, we are assuming that these individuals did not develop symptoms because they didn't report them. The missing values could be due to, for example, them skipping the questions in the questionnaire. Some individuals may be reluctant to report symptoms, due to shame, fear of repercussion, or others. It is important to think ahead, before the interview, about ways to minimise these situations. For example, through questionnaire design, you may impede skipping questions; you could promote trust by using the right interviewers (in some cases this will be someone from the community, in others someone form specific NGOs, someone of a specific race or gender, etc); choose to carry out online questionnaires vs in person (or vice versa, depending on the situation), etc.</span>
+<span id="cb12-119"><a href="#cb12-119" aria-hidden="true" tabindex="-1"></a>:::</span>
+<span id="cb12-120"><a href="#cb12-120" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb12-123"><a href="#cb12-123" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb12-124"><a href="#cb12-124" aria-hidden="true" tabindex="-1"></a>copdata <span class="ot">&lt;-</span> copdata <span class="sc">%&gt;%</span> </span>
+<span id="cb12-125"><a href="#cb12-125" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">gastrosymptoms =</span> <span class="fu">case_when</span>(</span>
+<span id="cb12-126"><a href="#cb12-126" aria-hidden="true" tabindex="-1"></a>    <span class="co"># Those had diarrhoea...</span></span>
+<span id="cb12-127"><a href="#cb12-127" aria-hidden="true" tabindex="-1"></a>    diarrhoea <span class="sc">==</span> <span class="st">"1"</span> <span class="sc">|</span></span>
+<span id="cb12-128"><a href="#cb12-128" aria-hidden="true" tabindex="-1"></a>      <span class="co">#or bloody diarrhoea...</span></span>
+<span id="cb12-129"><a href="#cb12-129" aria-hidden="true" tabindex="-1"></a>    bloody <span class="sc">==</span> <span class="st">"1"</span> <span class="sc">|</span></span>
+<span id="cb12-130"><a href="#cb12-130" aria-hidden="true" tabindex="-1"></a>      <span class="co"># or vomiting, are marked as 1 (fell ill after the meal)</span></span>
+<span id="cb12-131"><a href="#cb12-131" aria-hidden="true" tabindex="-1"></a>    vomiting <span class="sc">==</span> <span class="st">"1"</span> <span class="sc">~</span> <span class="dv">1</span>,</span>
+<span id="cb12-132"><a href="#cb12-132" aria-hidden="true" tabindex="-1"></a>    <span class="co"># The rest are 0 This includes those who ate a meal but had no symptoms (did not fell ill after the meal)</span></span>
+<span id="cb12-133"><a href="#cb12-133" aria-hidden="true" tabindex="-1"></a>    <span class="at">.default =</span> <span class="dv">0</span>)</span>
+<span id="cb12-134"><a href="#cb12-134" aria-hidden="true" tabindex="-1"></a>    )</span>
+<span id="cb12-135"><a href="#cb12-135" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
 <span id="cb12-136"><a href="#cb12-136" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-137"><a href="#cb12-137" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span>Hmmm... what is the time period of interest? You could start calculating the incubation period, which can be defined by calculating the time between exposure (the meal) and onset of symptoms, and then looking at the distribution of these time differences. In this outbreak, incubation periods are easy to calculate, because everyone was exposed at (roughly) the same time and on the same day (eating the meal at the school dinner party).</span>
-<span id="cb12-138"><a href="#cb12-138" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span>Dinner was served at 18:00 roughly for everyone. Create a new <span class="in">`meal_datetime`</span> variable with this information for all people in your dataset (as per 05 Oct 2024, at 18:00h).</span>
-<span id="cb12-139"><a href="#cb12-139" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-142"><a href="#cb12-142" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
-<span id="cb12-143"><a href="#cb12-143" aria-hidden="true" tabindex="-1"></a><span class="co"># Start with copdata:</span></span>
-<span id="cb12-144"><a href="#cb12-144" aria-hidden="true" tabindex="-1"></a>copdata <span class="ot">&lt;-</span> copdata <span class="sc">%&gt;%</span> </span>
-<span id="cb12-145"><a href="#cb12-145" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Create new column for meal date and time:</span></span>
-<span id="cb12-146"><a href="#cb12-146" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">meal_datetime =</span> lubridate<span class="sc">::</span><span class="fu">ymd_hm</span>(<span class="st">"2024-10-05 18:00"</span>))</span>
-<span id="cb12-147"><a href="#cb12-147" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb12-148"><a href="#cb12-148" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-149"><a href="#cb12-149" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span>You can calculate the incubation period for each participant by subtracting <span class="in">`onset_datetime`</span> - <span class="in">`meal_datetime.`</span></span>
-<span id="cb12-150"><a href="#cb12-150" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span>Then, take the <span class="in">`median()`</span> of that column to calculate a median incubation period, this will help you start having some hypothesis about the type of pathogen we are dealing with until the lab results come back.</span>
-<span id="cb12-151"><a href="#cb12-151" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-152"><a href="#cb12-152" aria-hidden="true" tabindex="-1"></a>::: {.callout-tip title="Need a little bit of help?" collapse="true"}</span>
-<span id="cb12-153"><a href="#cb12-153" aria-hidden="true" tabindex="-1"></a>Be aware of the type of variable <span class="in">`incubation`</span> is (check <span class="in">`class()`</span>), as well as of missing values<span class="in">`NA`</span>.</span>
-<span id="cb12-154"><a href="#cb12-154" aria-hidden="true" tabindex="-1"></a>:::</span>
-<span id="cb12-155"><a href="#cb12-155" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-156"><a href="#cb12-156" aria-hidden="true" tabindex="-1"></a>::: {.callout-note title="Note" collapse="true"}</span>
-<span id="cb12-157"><a href="#cb12-157" aria-hidden="true" tabindex="-1"></a>The **incubation period** of a disease is the time from an exposure resulting in infection until the onset of disease. Because infection usually occurs immediately after exposure, the incubation period is generally the duration of the period from the onset of infection to the onset of disease -- Rothman, Greenland, Lash (2017): Modern Epidemiology, 3rd edition</span>
-<span id="cb12-158"><a href="#cb12-158" aria-hidden="true" tabindex="-1"></a>:::</span>
-<span id="cb12-159"><a href="#cb12-159" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-162"><a href="#cb12-162" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
-<span id="cb12-163"><a href="#cb12-163" aria-hidden="true" tabindex="-1"></a>copdata <span class="ot">&lt;-</span> copdata <span class="sc">%&gt;%</span> </span>
-<span id="cb12-164"><a href="#cb12-164" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">incubation =</span> onset_datetime <span class="sc">-</span> meal_datetime,</span>
-<span id="cb12-165"><a href="#cb12-165" aria-hidden="true" tabindex="-1"></a>         <span class="at">incubation =</span> <span class="fu">as.numeric</span> (incubation))</span>
-<span id="cb12-166"><a href="#cb12-166" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-167"><a href="#cb12-167" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-168"><a href="#cb12-168" aria-hidden="true" tabindex="-1"></a><span class="fu">median</span>(<span class="fu">as.numeric</span>(copdata<span class="sc">$</span>incubation), <span class="at">na.rm =</span> <span class="cn">TRUE</span>)</span>
+<span id="cb12-137"><a href="#cb12-137" aria-hidden="true" tabindex="-1"></a><span class="fu">### c) Fell ill within the time period of interest</span></span>
+<span id="cb12-138"><a href="#cb12-138" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb12-139"><a href="#cb12-139" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span>Hmmm... what is the time period of interest? You could start calculating the incubation period, which can be defined by calculating the time between exposure (the meal) and onset of symptoms, and then looking at the distribution of these time differences. In this outbreak, incubation periods are easy to calculate, because everyone was exposed at (roughly) the same time and on the same day (eating the meal at the school dinner party).</span>
+<span id="cb12-140"><a href="#cb12-140" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span>Dinner was served at 18:00 roughly for everyone. Create a new <span class="in">`meal_datetime`</span> variable with this information for all people in your dataset (as per 05 Oct 2024, at 18:00h).</span>
+<span id="cb12-141"><a href="#cb12-141" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb12-144"><a href="#cb12-144" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb12-145"><a href="#cb12-145" aria-hidden="true" tabindex="-1"></a><span class="co"># Start with copdata:</span></span>
+<span id="cb12-146"><a href="#cb12-146" aria-hidden="true" tabindex="-1"></a>copdata <span class="ot">&lt;-</span> copdata <span class="sc">%&gt;%</span> </span>
+<span id="cb12-147"><a href="#cb12-147" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Create new column for meal date and time:</span></span>
+<span id="cb12-148"><a href="#cb12-148" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">meal_datetime =</span> lubridate<span class="sc">::</span><span class="fu">ymd_hm</span>(<span class="st">"2024-10-05 18:00"</span>))</span>
+<span id="cb12-149"><a href="#cb12-149" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb12-150"><a href="#cb12-150" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb12-151"><a href="#cb12-151" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span>You can calculate the incubation period for each participant by subtracting <span class="in">`onset_datetime`</span> - <span class="in">`meal_datetime.`</span></span>
+<span id="cb12-152"><a href="#cb12-152" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span>Then, take the <span class="in">`median()`</span> of that column to calculate a median incubation period, this will help you start having some hypothesis about the type of pathogen we are dealing with until the lab results come back.</span>
+<span id="cb12-153"><a href="#cb12-153" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb12-154"><a href="#cb12-154" aria-hidden="true" tabindex="-1"></a>::: {.callout-tip title="Need a little bit of help?" collapse="true"}</span>
+<span id="cb12-155"><a href="#cb12-155" aria-hidden="true" tabindex="-1"></a>Be aware of the type of variable <span class="in">`incubation`</span> is (check <span class="in">`class()`</span>), as well as of missing values<span class="in">`NA`</span>.</span>
+<span id="cb12-156"><a href="#cb12-156" aria-hidden="true" tabindex="-1"></a>:::</span>
+<span id="cb12-157"><a href="#cb12-157" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb12-158"><a href="#cb12-158" aria-hidden="true" tabindex="-1"></a>::: {.callout-note title="Note" collapse="true"}</span>
+<span id="cb12-159"><a href="#cb12-159" aria-hidden="true" tabindex="-1"></a>The **incubation period** of a disease is the time from an exposure resulting in infection until the onset of disease. Because infection usually occurs immediately after exposure, the incubation period is generally the duration of the period from the onset of infection to the onset of disease -- Rothman, Greenland, Lash (2017): Modern Epidemiology, 3rd edition</span>
+<span id="cb12-160"><a href="#cb12-160" aria-hidden="true" tabindex="-1"></a>:::</span>
+<span id="cb12-161"><a href="#cb12-161" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb12-164"><a href="#cb12-164" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb12-165"><a href="#cb12-165" aria-hidden="true" tabindex="-1"></a>copdata <span class="ot">&lt;-</span> copdata <span class="sc">%&gt;%</span> </span>
+<span id="cb12-166"><a href="#cb12-166" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">incubation =</span> onset_datetime <span class="sc">-</span> meal_datetime,</span>
+<span id="cb12-167"><a href="#cb12-167" aria-hidden="true" tabindex="-1"></a>         <span class="at">incubation =</span> <span class="fu">as.numeric</span> (incubation))</span>
+<span id="cb12-168"><a href="#cb12-168" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb12-169"><a href="#cb12-169" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-170"><a href="#cb12-170" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb12-170"><a href="#cb12-170" aria-hidden="true" tabindex="-1"></a><span class="fu">median</span>(<span class="fu">as.numeric</span>(copdata<span class="sc">$</span>incubation), <span class="at">na.rm =</span> <span class="cn">TRUE</span>)</span>
 <span id="cb12-171"><a href="#cb12-171" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-172"><a href="#cb12-172" aria-hidden="true" tabindex="-1"></a>::: {.callout-caution title="Now we are messing up with your brain..." collapse="true"}</span>
-<span id="cb12-173"><a href="#cb12-173" aria-hidden="true" tabindex="-1"></a>If you pay attention, there were two individuals who don't have a time recorded, only a date, the day of the dinner. These people probably got sick the same night they had the dinner, but the exact time was not recoded. They way we've managed the data will insert "00:00" in a missing value of a time. This means that the 2 people that got sick the day of the dinner have been recorded sick *before* the dinner (in the early morning of the day of the dinner). The two people have a negative incubation period! This should not happen. If you feel the force is with you, you can modify this and fix the error (if you do this, your results will be a bit different than your colleagues, but similar). We will continue with this "erroneous" data, for easiness. But note that in real life, as soon as you realise there is a coding error like this, you would have to go back to your cleaning code and fix the error! This may happen a couple of times during your outbreak investigation, and is totally normal. Cleaning data is not an easy job!</span>
-<span id="cb12-174"><a href="#cb12-174" aria-hidden="true" tabindex="-1"></a>:::</span>
-<span id="cb12-175"><a href="#cb12-175" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-176"><a href="#cb12-176" aria-hidden="true" tabindex="-1"></a>We see that the median incubation time is <span class="in">`r median(as.numeric(copdata$incubation), na.rm = TRUE)`</span> hours. This is useful information, as incubation periods tend to be relatively pathogen-specific.</span>
+<span id="cb12-172"><a href="#cb12-172" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb12-173"><a href="#cb12-173" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb12-174"><a href="#cb12-174" aria-hidden="true" tabindex="-1"></a>::: {.callout-caution title="Now we are messing up with your brain..." collapse="true"}</span>
+<span id="cb12-175"><a href="#cb12-175" aria-hidden="true" tabindex="-1"></a>If you pay attention, there were two individuals who don't have a time recorded, only a date, the day of the dinner. These people probably got sick the same night they had the dinner, but the exact time was not recoded. They way we've managed the data will insert "00:00" in a missing value of a time. This means that the 2 people that got sick the day of the dinner have been recorded sick *before* the dinner (in the early morning of the day of the dinner). The two people have a negative incubation period! This should not happen. If you feel the force is with you, you can modify this and fix the error (if you do this, your results will be a bit different than your colleagues, but similar). We will continue with this "erroneous" data, for easiness. But note that in real life, as soon as you realise there is a coding error like this, you would have to go back to your cleaning code and fix the error! This may happen a couple of times during your outbreak investigation, and is totally normal. Cleaning data is not an easy job!</span>
+<span id="cb12-176"><a href="#cb12-176" aria-hidden="true" tabindex="-1"></a>:::</span>
 <span id="cb12-177"><a href="#cb12-177" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-178"><a href="#cb12-178" aria-hidden="true" tabindex="-1"></a>Based on this, say you define and limit the maximum incubation period to 48 hours after the meal, as the data points to a fast-acting bacterial toxin or a virus. That is, dinner participants should have developed (<span class="in">`onset_datetime`</span>) at least one symptom (<span class="in">`diarrhoea`</span>, <span class="in">`bloody`</span> or <span class="in">`vomiting`</span>) 48h after <span class="in">`meal_datetime`</span> to become a <span class="in">`case.`</span></span>
+<span id="cb12-178"><a href="#cb12-178" aria-hidden="true" tabindex="-1"></a>We see that the median incubation time is <span class="in">`r median(as.numeric(copdata$incubation), na.rm = TRUE)`</span> hours. This is useful information, as incubation periods tend to be relatively pathogen-specific.</span>
 <span id="cb12-179"><a href="#cb12-179" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-180"><a href="#cb12-180" aria-hidden="true" tabindex="-1"></a>::: {.callout-warning title="Let's stop and... think!" collapse="true"}</span>
-<span id="cb12-181"><a href="#cb12-181" aria-hidden="true" tabindex="-1"></a>What are some of the implications this decision may lead to? (implications of this case definition)</span>
-<span id="cb12-182"><a href="#cb12-182" aria-hidden="true" tabindex="-1"></a>:::</span>
-<span id="cb12-183"><a href="#cb12-183" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-184"><a href="#cb12-184" aria-hidden="true" tabindex="-1"></a>::: {.callout-note title="If you've thought about the above, have a look here" collapse="true"}</span>
-<span id="cb12-185"><a href="#cb12-185" aria-hidden="true" tabindex="-1"></a>All those not developing at least one symptom (diarrhoea with OR without blood, OR vomiting) 48h after the dinner are considered non-cases. This could (depending on how you decide to analyse your data) include those who had no symptoms at all, those who have missing data on the <span class="in">`onset_datetime`</span> variable, and/or those who had symptoms before eating the meal. *This is a reminder that you need to be both careful and aware of the implications of your data analysis decisions.* If a person had clinical symptoms before eating the meal, they are considered as not-cases. However, it could be that a person had symptoms before the meal, and yet, still got infected by the pathogen when eating their meal (bad luck, we know...). According to this definition, we would be missing that case.</span>
-<span id="cb12-186"><a href="#cb12-186" aria-hidden="true" tabindex="-1"></a>:::</span>
-<span id="cb12-187"><a href="#cb12-187" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-188"><a href="#cb12-188" aria-hidden="true" tabindex="-1"></a><span class="fu">### d) Finally, with this information you can create a new `case` column to hold the binary (`TRUE`/`FALSE`) case definition variable.</span></span>
+<span id="cb12-180"><a href="#cb12-180" aria-hidden="true" tabindex="-1"></a>Based on this, say you define and limit the maximum incubation period to 48 hours after the meal, as the data points to a fast-acting bacterial toxin or a virus. That is, dinner participants should have developed (<span class="in">`onset_datetime`</span>) at least one symptom (<span class="in">`diarrhoea`</span>, <span class="in">`bloody`</span> or <span class="in">`vomiting`</span>) 48h after <span class="in">`meal_datetime`</span> to become a <span class="in">`case.`</span></span>
+<span id="cb12-181"><a href="#cb12-181" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb12-182"><a href="#cb12-182" aria-hidden="true" tabindex="-1"></a>::: {.callout-warning title="Let's stop and... think!" collapse="true"}</span>
+<span id="cb12-183"><a href="#cb12-183" aria-hidden="true" tabindex="-1"></a>What are some of the implications this decision may lead to? (implications of this case definition)</span>
+<span id="cb12-184"><a href="#cb12-184" aria-hidden="true" tabindex="-1"></a>:::</span>
+<span id="cb12-185"><a href="#cb12-185" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb12-186"><a href="#cb12-186" aria-hidden="true" tabindex="-1"></a>::: {.callout-note title="If you've thought about the above, have a look here" collapse="true"}</span>
+<span id="cb12-187"><a href="#cb12-187" aria-hidden="true" tabindex="-1"></a>All those not developing at least one symptom (diarrhoea with OR without blood, OR vomiting) 48h after the dinner are considered non-cases. This could (depending on how you decide to analyse your data) include those who had no symptoms at all, those who have missing data on the <span class="in">`onset_datetime`</span> variable, and/or those who had symptoms before eating the meal. *This is a reminder that you need to be both careful and aware of the implications of your data analysis decisions.* If a person had clinical symptoms before eating the meal, they are considered as not-cases. However, it could be that a person had symptoms before the meal, and yet, still got infected by the pathogen when eating their meal (bad luck, we know...). According to this definition, we would be missing that case.</span>
+<span id="cb12-188"><a href="#cb12-188" aria-hidden="true" tabindex="-1"></a>:::</span>
 <span id="cb12-189"><a href="#cb12-189" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-192"><a href="#cb12-192" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
-<span id="cb12-193"><a href="#cb12-193" aria-hidden="true" tabindex="-1"></a>copdata <span class="ot">&lt;-</span> copdata <span class="sc">%&gt;%</span> </span>
-<span id="cb12-194"><a href="#cb12-194" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">case =</span> <span class="fu">case_when</span>(</span>
-<span id="cb12-195"><a href="#cb12-195" aria-hidden="true" tabindex="-1"></a>    <span class="co"># Those who had symptoms &lt;48h from the meal are cases (TRUE)</span></span>
-<span id="cb12-196"><a href="#cb12-196" aria-hidden="true" tabindex="-1"></a>    gastrosymptoms <span class="sc">==</span> <span class="cn">TRUE</span> <span class="sc">&amp;</span> </span>
-<span id="cb12-197"><a href="#cb12-197" aria-hidden="true" tabindex="-1"></a>      onset_datetime <span class="sc">&gt;=</span> meal_datetime <span class="sc">&amp;</span></span>
-<span id="cb12-198"><a href="#cb12-198" aria-hidden="true" tabindex="-1"></a>      onset_datetime <span class="sc">&lt;=</span> (meal_datetime <span class="sc">+</span> <span class="fu">days</span>(<span class="dv">2</span>)) <span class="sc">~</span> <span class="cn">TRUE</span>,</span>
-<span id="cb12-199"><a href="#cb12-199" aria-hidden="true" tabindex="-1"></a>    <span class="co"># Those who had symptoms &gt;48h from the meal are non-cases (FALSE)</span></span>
-<span id="cb12-200"><a href="#cb12-200" aria-hidden="true" tabindex="-1"></a>    gastrosymptoms <span class="sc">==</span> <span class="cn">TRUE</span> <span class="sc">&amp;</span> </span>
-<span id="cb12-201"><a href="#cb12-201" aria-hidden="true" tabindex="-1"></a>      onset_datetime <span class="sc">&gt;</span> (meal_datetime <span class="sc">+</span> <span class="fu">days</span>(<span class="dv">2</span>)) <span class="sc">~</span> <span class="cn">FALSE</span>,</span>
-<span id="cb12-202"><a href="#cb12-202" aria-hidden="true" tabindex="-1"></a>    <span class="co"># The rest are considered non-cases. Including, those who had no symptoms at all, who have missing data on the onset_datetime variable, or who had symptoms before eating the meal </span></span>
-<span id="cb12-203"><a href="#cb12-203" aria-hidden="true" tabindex="-1"></a>    <span class="at">.default =</span> <span class="cn">FALSE</span>)</span>
-<span id="cb12-204"><a href="#cb12-204" aria-hidden="true" tabindex="-1"></a>  )</span>
-<span id="cb12-205"><a href="#cb12-205" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb12-206"><a href="#cb12-206" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-207"><a href="#cb12-207" aria-hidden="true" tabindex="-1"></a>Note that we may be incurring in misclassification bias with the code above. The last section indicates that if a person had clinical symptoms before eating the meal, they are considered as non-cases. However, it could be that a person had symptoms before the meal, and yet, still got infected by the pathogen when eating their meal (bad luck, we know...).</span>
+<span id="cb12-190"><a href="#cb12-190" aria-hidden="true" tabindex="-1"></a><span class="fu">### d) Finally, with this information you can create a new `case` column to hold the binary (`1`/`0`) case definition variable.</span></span>
+<span id="cb12-191"><a href="#cb12-191" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb12-194"><a href="#cb12-194" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb12-195"><a href="#cb12-195" aria-hidden="true" tabindex="-1"></a>copdata <span class="ot">&lt;-</span> copdata <span class="sc">%&gt;%</span> </span>
+<span id="cb12-196"><a href="#cb12-196" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">case =</span> <span class="fu">case_when</span>(</span>
+<span id="cb12-197"><a href="#cb12-197" aria-hidden="true" tabindex="-1"></a>    <span class="co"># Those who had symptoms &lt;48h from the meal are cases (1)</span></span>
+<span id="cb12-198"><a href="#cb12-198" aria-hidden="true" tabindex="-1"></a>    gastrosymptoms <span class="sc">==</span> <span class="dv">1</span> <span class="sc">&amp;</span> </span>
+<span id="cb12-199"><a href="#cb12-199" aria-hidden="true" tabindex="-1"></a>      onset_datetime <span class="sc">&gt;=</span> meal_datetime <span class="sc">&amp;</span></span>
+<span id="cb12-200"><a href="#cb12-200" aria-hidden="true" tabindex="-1"></a>      onset_datetime <span class="sc">&lt;=</span> (meal_datetime <span class="sc">+</span> <span class="fu">days</span>(<span class="dv">2</span>)) <span class="sc">~</span> <span class="dv">1</span>,</span>
+<span id="cb12-201"><a href="#cb12-201" aria-hidden="true" tabindex="-1"></a>    <span class="co"># Those who had symptoms &gt;48h from the meal are non-cases (FALSE)</span></span>
+<span id="cb12-202"><a href="#cb12-202" aria-hidden="true" tabindex="-1"></a>    gastrosymptoms <span class="sc">==</span> <span class="dv">1</span> <span class="sc">&amp;</span> </span>
+<span id="cb12-203"><a href="#cb12-203" aria-hidden="true" tabindex="-1"></a>      onset_datetime <span class="sc">&gt;</span> (meal_datetime <span class="sc">+</span> <span class="fu">days</span>(<span class="dv">2</span>)) <span class="sc">~</span> <span class="dv">0</span>,</span>
+<span id="cb12-204"><a href="#cb12-204" aria-hidden="true" tabindex="-1"></a>    <span class="co"># The rest are considered non-cases. Including, those who had no symptoms at all, who have missing data on the onset_datetime variable, or who had symptoms before eating the meal </span></span>
+<span id="cb12-205"><a href="#cb12-205" aria-hidden="true" tabindex="-1"></a>    <span class="at">.default =</span> <span class="dv">0</span>)</span>
+<span id="cb12-206"><a href="#cb12-206" aria-hidden="true" tabindex="-1"></a>  )</span>
+<span id="cb12-207"><a href="#cb12-207" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
 <span id="cb12-208"><a href="#cb12-208" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-209"><a href="#cb12-209" aria-hidden="true" tabindex="-1"></a>Moreover, if you remember, there were a couple of people with an <span class="in">`dayonset`</span>, but no <span class="in">`starthour`</span>. The code we used (<span class="in">`lubridate::ymd_h`</span> with argument <span class="in">`truncated = 2`</span>) results in dates with missing <span class="in">`starthour`</span> being converted to date-time, with the missing time being set to <span class="in">`00:00`</span> (midnight). This means that these two people don't fulfill the case definition criteria because we marked their symptoms started early in the morning of Nov 11 (at 00:00), before the meal time (18:00), and thus, they did not "fell ill within the time period of interest".</span>
+<span id="cb12-209"><a href="#cb12-209" aria-hidden="true" tabindex="-1"></a>Note that we may be incurring in misclassification bias with the code above. The last section indicates that if a person had clinical symptoms before eating the meal, they are considered as non-cases. However, it could be that a person had symptoms before the meal, and yet, still got infected by the pathogen when eating their meal (bad luck, we know...).</span>
 <span id="cb12-210"><a href="#cb12-210" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-211"><a href="#cb12-211" aria-hidden="true" tabindex="-1"></a>The two situations above are a reminder that you need to be both careful and aware of the implications of your data analysis decisions.</span>
+<span id="cb12-211"><a href="#cb12-211" aria-hidden="true" tabindex="-1"></a>Moreover, if you remember, there were a couple of people with an <span class="in">`dayonset`</span>, but no <span class="in">`starthour`</span>. The code we used (<span class="in">`lubridate::ymd_h`</span> with argument <span class="in">`truncated = 2`</span>) results in dates with missing <span class="in">`starthour`</span> being converted to date-time, with the missing time being set to <span class="in">`00:00`</span> (midnight). This means that these two people don't fulfill the case definition criteria because we marked their symptoms started early in the morning of Nov 11 (at 00:00), before the meal time (18:00), and thus, they did not "fell ill within the time period of interest".</span>
 <span id="cb12-212"><a href="#cb12-212" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-213"><a href="#cb12-213" aria-hidden="true" tabindex="-1"></a>::: {.callout-warning title="Let's stop and... think!" collapse="true"}</span>
-<span id="cb12-214"><a href="#cb12-214" aria-hidden="true" tabindex="-1"></a>What do you think are the risks of mis-classifying cases as non-cases in your analysis?</span>
-<span id="cb12-215"><a href="#cb12-215" aria-hidden="true" tabindex="-1"></a>:::</span>
-<span id="cb12-216"><a href="#cb12-216" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-217"><a href="#cb12-217" aria-hidden="true" tabindex="-1"></a>::: {.callout-note title="If you've thought about the above, have a look here" collapse="true"}</span>
-<span id="cb12-218"><a href="#cb12-218" aria-hidden="true" tabindex="-1"></a>We will have bias either towards or away from the null, depending on the proportions of subjects misclassified.</span>
-<span id="cb12-219"><a href="#cb12-219" aria-hidden="true" tabindex="-1"></a>:::</span>
-<span id="cb12-220"><a href="#cb12-220" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-223"><a href="#cb12-223" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
-<span id="cb12-224"><a href="#cb12-224" aria-hidden="true" tabindex="-1"></a><span class="co"># Tabulate cases:</span></span>
-<span id="cb12-225"><a href="#cb12-225" aria-hidden="true" tabindex="-1"></a>janitor<span class="sc">::</span><span class="fu">tabyl</span>(<span class="at">dat =</span> copdata, case)</span>
-<span id="cb12-226"><a href="#cb12-226" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb12-227"><a href="#cb12-227" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-228"><a href="#cb12-228" aria-hidden="true" tabindex="-1"></a>Let's have a look at how many people ate a meal, had symptoms, and were considered as cases after applying our case definition:</span>
+<span id="cb12-213"><a href="#cb12-213" aria-hidden="true" tabindex="-1"></a>The two situations above are a reminder that you need to be both careful and aware of the implications of your data analysis decisions.</span>
+<span id="cb12-214"><a href="#cb12-214" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb12-215"><a href="#cb12-215" aria-hidden="true" tabindex="-1"></a>::: {.callout-warning title="Let's stop and... think!" collapse="true"}</span>
+<span id="cb12-216"><a href="#cb12-216" aria-hidden="true" tabindex="-1"></a>What do you think are the risks of mis-classifying cases as non-cases in your analysis?</span>
+<span id="cb12-217"><a href="#cb12-217" aria-hidden="true" tabindex="-1"></a>:::</span>
+<span id="cb12-218"><a href="#cb12-218" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb12-219"><a href="#cb12-219" aria-hidden="true" tabindex="-1"></a>::: {.callout-note title="If you've thought about the above, have a look here" collapse="true"}</span>
+<span id="cb12-220"><a href="#cb12-220" aria-hidden="true" tabindex="-1"></a>We will have bias either towards or away from the null, depending on the proportions of subjects misclassified.</span>
+<span id="cb12-221"><a href="#cb12-221" aria-hidden="true" tabindex="-1"></a>:::</span>
+<span id="cb12-222"><a href="#cb12-222" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb12-225"><a href="#cb12-225" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb12-226"><a href="#cb12-226" aria-hidden="true" tabindex="-1"></a><span class="co"># Tabulate cases:</span></span>
+<span id="cb12-227"><a href="#cb12-227" aria-hidden="true" tabindex="-1"></a>janitor<span class="sc">::</span><span class="fu">tabyl</span>(<span class="at">dat =</span> copdata, case)</span>
+<span id="cb12-228"><a href="#cb12-228" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
 <span id="cb12-229"><a href="#cb12-229" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-230"><a href="#cb12-230" aria-hidden="true" tabindex="-1"></a><span class="in">```{r overview}</span></span>
-<span id="cb12-231"><a href="#cb12-231" aria-hidden="true" tabindex="-1"></a>copdata <span class="sc">%&gt;%</span> </span>
-<span id="cb12-232"><a href="#cb12-232" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarise</span>(<span class="at">atemeal =</span> <span class="fu">sum</span>(meal <span class="sc">==</span> <span class="cn">TRUE</span>),</span>
-<span id="cb12-233"><a href="#cb12-233" aria-hidden="true" tabindex="-1"></a>            <span class="at">hadsympt =</span> <span class="fu">sum</span>(gastrosymptoms <span class="sc">==</span> <span class="cn">TRUE</span>),</span>
-<span id="cb12-234"><a href="#cb12-234" aria-hidden="true" tabindex="-1"></a>            <span class="at">nb_cases =</span> <span class="fu">sum</span>(case <span class="sc">==</span> <span class="cn">TRUE</span>)</span>
-<span id="cb12-235"><a href="#cb12-235" aria-hidden="true" tabindex="-1"></a>            )</span>
-<span id="cb12-236"><a href="#cb12-236" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb12-237"><a href="#cb12-237" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-238"><a href="#cb12-238" aria-hidden="true" tabindex="-1"></a><span class="fu"># 4. Export clean data</span></span>
+<span id="cb12-230"><a href="#cb12-230" aria-hidden="true" tabindex="-1"></a>Let's have a look at how many people ate a meal, had symptoms, and were considered as cases after applying our case definition:</span>
+<span id="cb12-231"><a href="#cb12-231" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb12-232"><a href="#cb12-232" aria-hidden="true" tabindex="-1"></a><span class="in">```{r overview}</span></span>
+<span id="cb12-233"><a href="#cb12-233" aria-hidden="true" tabindex="-1"></a><span class="in">copdata %&gt;% </span></span>
+<span id="cb12-234"><a href="#cb12-234" aria-hidden="true" tabindex="-1"></a><span class="in">  summarise(atemeal = sum(meal == 1),</span></span>
+<span id="cb12-235"><a href="#cb12-235" aria-hidden="true" tabindex="-1"></a><span class="in">            hadsympt = sum(gastrosymptoms == 1),</span></span>
+<span id="cb12-236"><a href="#cb12-236" aria-hidden="true" tabindex="-1"></a><span class="in">            nb_cases = sum(case == 1)</span></span>
+<span id="cb12-237"><a href="#cb12-237" aria-hidden="true" tabindex="-1"></a><span class="in">            )</span></span>
+<span id="cb12-238"><a href="#cb12-238" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
 <span id="cb12-239"><a href="#cb12-239" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-240"><a href="#cb12-240" aria-hidden="true" tabindex="-1"></a>Finally, we can save the cleaned data set as a new file <span class="in">`Spetses_clean2_YOURINITIALS`</span>, under the data folder, before we proceeding with descriptive analysis.</span>
+<span id="cb12-240"><a href="#cb12-240" aria-hidden="true" tabindex="-1"></a><span class="fu"># 4. Export clean data</span></span>
 <span id="cb12-241"><a href="#cb12-241" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-242"><a href="#cb12-242" aria-hidden="true" tabindex="-1"></a><span class="in">```{r export_clean_data}</span></span>
+<span id="cb12-242"><a href="#cb12-242" aria-hidden="true" tabindex="-1"></a>Finally, we can save the cleaned data set as a new file <span class="in">`Spetses_clean2_YOURINITIALS`</span>, under the data folder, before we proceeding with descriptive analysis.</span>
 <span id="cb12-243"><a href="#cb12-243" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-244"><a href="#cb12-244" aria-hidden="true" tabindex="-1"></a>rio<span class="sc">::</span><span class="fu">export</span>(<span class="at">x =</span> copdata, </span>
-<span id="cb12-245"><a href="#cb12-245" aria-hidden="true" tabindex="-1"></a>            <span class="at">file =</span> here<span class="sc">::</span><span class="fu">here</span>(<span class="st">"data"</span>, <span class="st">"Spetses_clean2_2024.rds"</span>))</span>
-<span id="cb12-246"><a href="#cb12-246" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-247"><a href="#cb12-247" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-248"><a href="#cb12-248" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-</code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<span id="cb12-244"><a href="#cb12-244" aria-hidden="true" tabindex="-1"></a><span class="in">```{r export_clean_data}</span></span>
+<span id="cb12-245"><a href="#cb12-245" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb12-246"><a href="#cb12-246" aria-hidden="true" tabindex="-1"></a><span class="in">rio::export(x = copdata, </span></span>
+<span id="cb12-247"><a href="#cb12-247" aria-hidden="true" tabindex="-1"></a><span class="in">            file = here::here("data", "Spetses_clean2_2024.rds"))</span></span>
+<span id="cb12-248"><a href="#cb12-248" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb12-249"><a href="#cb12-249" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb12-250"><a href="#cb12-250" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+</code><button title="Copy to Clipboard" class="code-copy-button" data-in-quarto-modal=""><i class="bi"></i></button></pre></div>
 </div></div></div></div></div>
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-<title>Spetses Introductory Course - Data import and cleaning</title>
+<title>Data import and cleaning – Spetses Introductory Course</title>
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@@ -33,7 +33,7 @@
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-pre > code.sourceCode > span { text-indent: -5em; padding-left: 5em; }
+pre > code.sourceCode > span { display: inline-block; text-indent: -5em; padding-left: 5em; }
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@@ -81,7 +81,13 @@
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+  "limit": 50,
+  "keyboard-shortcut": [
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- <span class="menu-text">Data import and cleaning</span></a>
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- <span class="menu-text">Case Definition</span></a>
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+<span class="menu-text">Case Definition</span></a>
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-    <a class="nav-link" href="./Lab_and_Descriptive.html" rel="" target="">
- <span class="menu-text">Laboratory data and descriptive analysis</span></a>
+    <a class="nav-link" href="./Lab_and_Descriptive.html"> 
+<span class="menu-text">Laboratory data and descriptive analysis</span></a>
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- <span class="menu-text">Univariable analysis</span></a>
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@@ -207,8 +215,10 @@ <h2 id="toc-title">On this page</h2>
   </div>
   
 
+
 </header>
 
+
 <section id="learning-outcomes" class="level1">
 <h1><strong>1. Learning outcomes</strong></h1>
 <p>At the end of the session, participants will be able to:</p>
@@ -234,25 +244,26 @@ <h2 class="anchored" data-anchor-id="install-packages-and-load-libraries">3.1. I
 <li>Load the R packages you will be using. Update this list of packages as you dig into the code: You may realise you need packages you didn’t think about at the beginning. Remember that it is a good practice to load tydiverse at the end of your package list, to avoid <a href="https://www.ucl.ac.uk/~uctqiax/PUBLG100/2015/faq/masking.html">masking functions</a>.</li>
 </ul>
 <div class="cell">
-<details open="">
-<summary>Code</summary>
+<details open="" class="code-fold">
+<summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb1"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Check if the 'pacman' package is installed, if not install it:</span></span>
 <span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="cf">if</span> (<span class="sc">!</span><span class="fu">requireNamespace</span>(<span class="st">"pacman"</span>, <span class="at">quietly =</span> <span class="cn">TRUE</span>)) <span class="fu">install.packages</span>(<span class="st">"pacman"</span>)</span>
 <span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a><span class="co"># Load the required libraries into the current R session:</span></span>
 <span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a>pacman<span class="sc">::</span><span class="fu">p_load</span>(rio, </span>
 <span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a>               here, </span>
-<span id="cb1-7"><a href="#cb1-7" aria-hidden="true" tabindex="-1"></a>               tidyverse, </span>
-<span id="cb1-8"><a href="#cb1-8" aria-hidden="true" tabindex="-1"></a>               skimr,</span>
-<span id="cb1-9"><a href="#cb1-9" aria-hidden="true" tabindex="-1"></a>               plyr,</span>
-<span id="cb1-10"><a href="#cb1-10" aria-hidden="true" tabindex="-1"></a>               janitor,</span>
-<span id="cb1-11"><a href="#cb1-11" aria-hidden="true" tabindex="-1"></a>               lubridate,</span>
-<span id="cb1-12"><a href="#cb1-12" aria-hidden="true" tabindex="-1"></a>               gtsummary, </span>
-<span id="cb1-13"><a href="#cb1-13" aria-hidden="true" tabindex="-1"></a>               flextable,</span>
-<span id="cb1-14"><a href="#cb1-14" aria-hidden="true" tabindex="-1"></a>               officer,</span>
-<span id="cb1-15"><a href="#cb1-15" aria-hidden="true" tabindex="-1"></a>               epikit, </span>
-<span id="cb1-16"><a href="#cb1-16" aria-hidden="true" tabindex="-1"></a>               apyramid, </span>
-<span id="cb1-17"><a href="#cb1-17" aria-hidden="true" tabindex="-1"></a>               scales)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<span id="cb1-7"><a href="#cb1-7" aria-hidden="true" tabindex="-1"></a>               skimr,</span>
+<span id="cb1-8"><a href="#cb1-8" aria-hidden="true" tabindex="-1"></a>               plyr,</span>
+<span id="cb1-9"><a href="#cb1-9" aria-hidden="true" tabindex="-1"></a>               janitor,</span>
+<span id="cb1-10"><a href="#cb1-10" aria-hidden="true" tabindex="-1"></a>               lubridate,</span>
+<span id="cb1-11"><a href="#cb1-11" aria-hidden="true" tabindex="-1"></a>               gtsummary, </span>
+<span id="cb1-12"><a href="#cb1-12" aria-hidden="true" tabindex="-1"></a>               flextable,</span>
+<span id="cb1-13"><a href="#cb1-13" aria-hidden="true" tabindex="-1"></a>               officer,</span>
+<span id="cb1-14"><a href="#cb1-14" aria-hidden="true" tabindex="-1"></a>               epikit, </span>
+<span id="cb1-15"><a href="#cb1-15" aria-hidden="true" tabindex="-1"></a>               apyramid, </span>
+<span id="cb1-16"><a href="#cb1-16" aria-hidden="true" tabindex="-1"></a>               scales,</span>
+<span id="cb1-17"><a href="#cb1-17" aria-hidden="true" tabindex="-1"></a>               tidyverse </span>
+<span id="cb1-18"><a href="#cb1-18" aria-hidden="true" tabindex="-1"></a>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
 </div>
 </section>
@@ -262,10 +273,10 @@ <h2 class="anchored" data-anchor-id="import-your-data">3.2. Import your data</h2
 <li>You can find the <code>spetses_school.csv</code> file in EVA. Feel free to explore the <strong>codebook</strong> as well.</li>
 </ul>
 <div class="cell">
-<details open="">
-<summary>Code</summary>
+<details open="" class="code-fold">
+<summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb2"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Import the raw data set:</span></span>
-<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a>copdata <span class="ot">&lt;-</span> rio<span class="sc">::</span><span class="fu">import</span>(here<span class="sc">::</span><span class="fu">here</span>(<span class="st">"data"</span>, <span class="st">"spetses_school.csv"</span>))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a>copdata <span class="ot">&lt;-</span> <span class="fu">import</span>(here<span class="sc">::</span><span class="fu">here</span>(<span class="st">"data"</span>, <span class="st">"spetses_school.csv"</span>))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
 </div>
 </section>
@@ -297,36 +308,36 @@ <h2 class="anchored" data-anchor-id="explore-and-clean-your-data">3.3. Explore a
 </div>
 </div>
 <div class="cell">
-<details open="">
-<summary>Code</summary>
+<details open="" class="code-fold">
+<summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb3"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a><span class="fu">head</span>(copdata)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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   </script>
 </div>
 </div>
-<details open="">
-<summary>Code</summary>
+<details open="" class="code-fold">
+<summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb4"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a><span class="fu">dim</span>(copdata)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
 <div class="cell-output cell-output-stdout">
-<pre><code>[1] 397  40</code></pre>
+<pre><code>[1] 397  41</code></pre>
 </div>
-<details open="">
-<summary>Code</summary>
+<details open="" class="code-fold">
+<summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb6"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a><span class="fu">str</span>(copdata)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
 <div class="cell-output cell-output-stdout">
-<pre><code>'data.frame':   397 obs. of  40 variables:
+<pre><code>'data.frame':   397 obs. of  41 variables:
  $ id        : chr  "sp-2-001" "sp-3-003" "sp-1-005" "sp-2-006" ...
  $ sex       : chr  "male" "female" "female" "male" ...
  $ age       : int  18 18 17 17 18 180 16 15 43 16 ...
  $ group     : int  1 1 1 1 1 1 0 1 0 1 ...
  $ class     : int  2 3 1 2 3 2 NA 1 NA 1 ...
+ $ ill       : int  1 0 1 0 1 1 0 1 1 0 ...
  $ diarrhoea : int  1 NA NA NA 1 1 NA 0 1 NA ...
  $ bloody    : int  0 NA NA NA 0 0 NA 0 NA NA ...
  $ vomiting  : int  0 NA NA NA 0 0 NA 0 NA NA ...
@@ -343,12 +354,12 @@ <h2 class="anchored" data-anchor-id="explore-and-clean-your-data">3.3. Explore a
  $ sardinesD : int  2 0 NA 0 2 2 2 0 2 2 ...
  $ eggplant  : int  0 0 NA 0 1 1 1 0 NA 1 ...
  $ eggplantD : int  0 0 NA 0 2 2 2 0 NA 2 ...
- $ moussaka  : int  1 1 1 1 1 1 1 1 1 1 ...
- $ moussakaD : int  2 1 0 0 2 2 1 3 2 2 ...
- $ orzo      : int  1 1 1 0 1 1 1 1 1 1 ...
- $ orzoD     : int  3 3 1 0 2 2 2 3 2 3 ...
- $ greeksal  : int  1 1 NA 0 1 1 1 1 1 1 ...
- $ greeksalD : int  1 3 NA 0 2 2 2 2 2 2 ...
+ $ veal      : int  1 1 1 1 1 1 1 1 1 1 ...
+ $ vealD     : int  2 1 0 0 2 2 1 3 2 2 ...
+ $ pasta     : int  1 1 1 0 1 1 1 1 1 1 ...
+ $ pastaD    : int  3 3 1 0 2 2 2 3 2 3 ...
+ $ tomsal    : int  1 1 NA 0 1 1 1 1 1 1 ...
+ $ tomsalD   : int  1 3 NA 0 2 2 2 2 2 2 ...
  $ bread     : int  1 1 1 0 1 1 1 1 1 1 ...
  $ breadD    : int  2 3 1 0 2 2 2 2 2 2 ...
  $ dessert   : int  1 1 NA 0 1 1 0 1 NA NA ...
@@ -363,12 +374,12 @@ <h2 class="anchored" data-anchor-id="explore-and-clean-your-data">3.3. Explore a
  $ whitewineD: int  0 0 0 3 3 2 1 3 NA 0 ...
  $ dayonset  : chr  "6oct2024" "" "" "" ...</code></pre>
 </div>
-<details open="">
-<summary>Code</summary>
-<div class="sourceCode cell-code" id="cb8"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a>skimr<span class="sc">::</span><span class="fu">skim</span>(copdata)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<details open="" class="code-fold">
+<summary>Show the code</summary>
+<div class="sourceCode cell-code" id="cb8"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a><span class="fu">skim</span>(copdata)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
 <div class="cell-output-display">
-<table class="table table-sm table-striped small">
+<table class="caption-top table table-sm table-striped small">
 <caption>Data summary</caption>
 <tbody>
 <tr class="odd">
@@ -381,7 +392,7 @@ <h2 class="anchored" data-anchor-id="explore-and-clean-your-data">3.3. Explore a
 </tr>
 <tr class="odd">
 <td style="text-align: left;">Number of columns</td>
-<td style="text-align: left;">40</td>
+<td style="text-align: left;">41</td>
 </tr>
 <tr class="even">
 <td style="text-align: left;">_______________________</td>
@@ -397,7 +408,7 @@ <h2 class="anchored" data-anchor-id="explore-and-clean-your-data">3.3. Explore a
 </tr>
 <tr class="odd">
 <td style="text-align: left;">numeric</td>
-<td style="text-align: left;">37</td>
+<td style="text-align: left;">38</td>
 </tr>
 <tr class="even">
 <td style="text-align: left;">________________________</td>
@@ -410,7 +421,7 @@ <h2 class="anchored" data-anchor-id="explore-and-clean-your-data">3.3. Explore a
 </tbody>
 </table>
 <p><strong>Variable type: character</strong></p>
-<table class="table table-sm table-striped small">
+<table class="caption-top table table-sm table-striped small">
 <colgroup>
 <col style="width: 19%">
 <col style="width: 13%">
@@ -467,7 +478,7 @@ <h2 class="anchored" data-anchor-id="explore-and-clean-your-data">3.3. Explore a
 </tbody>
 </table>
 <p><strong>Variable type: numeric</strong></p>
-<table class="table table-sm table-striped small">
+<table class="caption-top table table-sm table-striped small">
 <colgroup>
 <col style="width: 18%">
 <col style="width: 13%">
@@ -537,6 +548,19 @@ <h2 class="anchored" data-anchor-id="explore-and-clean-your-data">3.3. Explore a
 <td style="text-align: left;">▇▁▆▁▇</td>
 </tr>
 <tr class="even">
+<td style="text-align: left;">ill</td>
+<td style="text-align: right;">0</td>
+<td style="text-align: right;">1.00</td>
+<td style="text-align: right;">0.69</td>
+<td style="text-align: right;">0.47</td>
+<td style="text-align: right;">0</td>
+<td style="text-align: right;">0</td>
+<td style="text-align: right;">1</td>
+<td style="text-align: right;">1</td>
+<td style="text-align: right;">1</td>
+<td style="text-align: left;">▃▁▁▁▇</td>
+</tr>
+<tr class="odd">
 <td style="text-align: left;">diarrhoea</td>
 <td style="text-align: right;">141</td>
 <td style="text-align: right;">0.64</td>
@@ -549,7 +573,7 @@ <h2 class="anchored" data-anchor-id="explore-and-clean-your-data">3.3. Explore a
 <td style="text-align: right;">1</td>
 <td style="text-align: left;">▂▁▁▁▇</td>
 </tr>
-<tr class="odd">
+<tr class="even">
 <td style="text-align: left;">bloody</td>
 <td style="text-align: right;">200</td>
 <td style="text-align: right;">0.50</td>
@@ -562,7 +586,7 @@ <h2 class="anchored" data-anchor-id="explore-and-clean-your-data">3.3. Explore a
 <td style="text-align: right;">1</td>
 <td style="text-align: left;">▇▁▁▁▁</td>
 </tr>
-<tr class="even">
+<tr class="odd">
 <td style="text-align: left;">vomiting</td>
 <td style="text-align: right;">179</td>
 <td style="text-align: right;">0.55</td>
@@ -575,7 +599,7 @@ <h2 class="anchored" data-anchor-id="explore-and-clean-your-data">3.3. Explore a
 <td style="text-align: right;">1</td>
 <td style="text-align: left;">▇▁▁▁▃</td>
 </tr>
-<tr class="odd">
+<tr class="even">
 <td style="text-align: left;">abdo</td>
 <td style="text-align: right;">152</td>
 <td style="text-align: right;">0.62</td>
@@ -588,7 +612,7 @@ <h2 class="anchored" data-anchor-id="explore-and-clean-your-data">3.3. Explore a
 <td style="text-align: right;">1</td>
 <td style="text-align: left;">▂▁▁▁▇</td>
 </tr>
-<tr class="even">
+<tr class="odd">
 <td style="text-align: left;">nausea</td>
 <td style="text-align: right;">170</td>
 <td style="text-align: right;">0.57</td>
@@ -601,7 +625,7 @@ <h2 class="anchored" data-anchor-id="explore-and-clean-your-data">3.3. Explore a
 <td style="text-align: right;">1</td>
 <td style="text-align: left;">▂▁▁▁▇</td>
 </tr>
-<tr class="odd">
+<tr class="even">
 <td style="text-align: left;">fever</td>
 <td style="text-align: right;">223</td>
 <td style="text-align: right;">0.44</td>
@@ -614,7 +638,7 @@ <h2 class="anchored" data-anchor-id="explore-and-clean-your-data">3.3. Explore a
 <td style="text-align: right;">1</td>
 <td style="text-align: left;">▇▁▁▁▃</td>
 </tr>
-<tr class="even">
+<tr class="odd">
 <td style="text-align: left;">headache</td>
 <td style="text-align: right;">174</td>
 <td style="text-align: right;">0.56</td>
@@ -627,7 +651,7 @@ <h2 class="anchored" data-anchor-id="explore-and-clean-your-data">3.3. Explore a
 <td style="text-align: right;">1</td>
 <td style="text-align: left;">▅▁▁▁▇</td>
 </tr>
-<tr class="odd">
+<tr class="even">
 <td style="text-align: left;">jointpain</td>
 <td style="text-align: right;">207</td>
 <td style="text-align: right;">0.48</td>
@@ -640,7 +664,7 @@ <h2 class="anchored" data-anchor-id="explore-and-clean-your-data">3.3. Explore a
 <td style="text-align: right;">1</td>
 <td style="text-align: left;">▇▁▁▁▂</td>
 </tr>
-<tr class="even">
+<tr class="odd">
 <td style="text-align: left;">starthour</td>
 <td style="text-align: right;">177</td>
 <td style="text-align: right;">0.55</td>
@@ -653,7 +677,7 @@ <h2 class="anchored" data-anchor-id="explore-and-clean-your-data">3.3. Explore a
 <td style="text-align: right;">21</td>
 <td style="text-align: left;">▁▇▁▆▃</td>
 </tr>
-<tr class="odd">
+<tr class="even">
 <td style="text-align: left;">meal</td>
 <td style="text-align: right;">9</td>
 <td style="text-align: right;">0.98</td>
@@ -666,7 +690,7 @@ <h2 class="anchored" data-anchor-id="explore-and-clean-your-data">3.3. Explore a
 <td style="text-align: right;">1</td>
 <td style="text-align: left;">▁▁▁▁▇</td>
 </tr>
-<tr class="even">
+<tr class="odd">
 <td style="text-align: left;">feta</td>
 <td style="text-align: right;">16</td>
 <td style="text-align: right;">0.96</td>
@@ -679,7 +703,7 @@ <h2 class="anchored" data-anchor-id="explore-and-clean-your-data">3.3. Explore a
 <td style="text-align: right;">1</td>
 <td style="text-align: left;">▃▁▁▁▇</td>
 </tr>
-<tr class="odd">
+<tr class="even">
 <td style="text-align: left;">fetaD</td>
 <td style="text-align: right;">16</td>
 <td style="text-align: right;">0.96</td>
@@ -692,7 +716,7 @@ <h2 class="anchored" data-anchor-id="explore-and-clean-your-data">3.3. Explore a
 <td style="text-align: right;">3</td>
 <td style="text-align: left;">▆▅▁▇▂</td>
 </tr>
-<tr class="even">
+<tr class="odd">
 <td style="text-align: left;">sardines</td>
 <td style="text-align: right;">17</td>
 <td style="text-align: right;">0.96</td>
@@ -705,7 +729,7 @@ <h2 class="anchored" data-anchor-id="explore-and-clean-your-data">3.3. Explore a
 <td style="text-align: right;">1</td>
 <td style="text-align: left;">▃▁▁▁▇</td>
 </tr>
-<tr class="odd">
+<tr class="even">
 <td style="text-align: left;">sardinesD</td>
 <td style="text-align: right;">17</td>
 <td style="text-align: right;">0.96</td>
@@ -718,7 +742,7 @@ <h2 class="anchored" data-anchor-id="explore-and-clean-your-data">3.3. Explore a
 <td style="text-align: right;">3</td>
 <td style="text-align: left;">▆▂▁▇▂</td>
 </tr>
-<tr class="even">
+<tr class="odd">
 <td style="text-align: left;">eggplant</td>
 <td style="text-align: right;">30</td>
 <td style="text-align: right;">0.92</td>
@@ -731,7 +755,7 @@ <h2 class="anchored" data-anchor-id="explore-and-clean-your-data">3.3. Explore a
 <td style="text-align: right;">1</td>
 <td style="text-align: left;">▆▁▁▁▇</td>
 </tr>
-<tr class="odd">
+<tr class="even">
 <td style="text-align: left;">eggplantD</td>
 <td style="text-align: right;">30</td>
 <td style="text-align: right;">0.92</td>
@@ -744,8 +768,8 @@ <h2 class="anchored" data-anchor-id="explore-and-clean-your-data">3.3. Explore a
 <td style="text-align: right;">3</td>
 <td style="text-align: left;">▇▂▁▇▂</td>
 </tr>
-<tr class="even">
-<td style="text-align: left;">moussaka</td>
+<tr class="odd">
+<td style="text-align: left;">veal</td>
 <td style="text-align: right;">15</td>
 <td style="text-align: right;">0.96</td>
 <td style="text-align: right;">0.89</td>
@@ -757,8 +781,8 @@ <h2 class="anchored" data-anchor-id="explore-and-clean-your-data">3.3. Explore a
 <td style="text-align: right;">1</td>
 <td style="text-align: left;">▁▁▁▁▇</td>
 </tr>
-<tr class="odd">
-<td style="text-align: left;">moussakaD</td>
+<tr class="even">
+<td style="text-align: left;">vealD</td>
 <td style="text-align: right;">14</td>
 <td style="text-align: right;">0.96</td>
 <td style="text-align: right;">1.83</td>
@@ -770,8 +794,8 @@ <h2 class="anchored" data-anchor-id="explore-and-clean-your-data">3.3. Explore a
 <td style="text-align: right;">3</td>
 <td style="text-align: left;">▂▃▁▇▃</td>
 </tr>
-<tr class="even">
-<td style="text-align: left;">orzo</td>
+<tr class="odd">
+<td style="text-align: left;">pasta</td>
 <td style="text-align: right;">15</td>
 <td style="text-align: right;">0.96</td>
 <td style="text-align: right;">0.88</td>
@@ -783,8 +807,8 @@ <h2 class="anchored" data-anchor-id="explore-and-clean-your-data">3.3. Explore a
 <td style="text-align: right;">1</td>
 <td style="text-align: left;">▁▁▁▁▇</td>
 </tr>
-<tr class="odd">
-<td style="text-align: left;">orzoD</td>
+<tr class="even">
+<td style="text-align: left;">pastaD</td>
 <td style="text-align: right;">15</td>
 <td style="text-align: right;">0.96</td>
 <td style="text-align: right;">1.81</td>
@@ -796,8 +820,8 @@ <h2 class="anchored" data-anchor-id="explore-and-clean-your-data">3.3. Explore a
 <td style="text-align: right;">3</td>
 <td style="text-align: left;">▂▃▁▇▃</td>
 </tr>
-<tr class="even">
-<td style="text-align: left;">greeksal</td>
+<tr class="odd">
+<td style="text-align: left;">tomsal</td>
 <td style="text-align: right;">24</td>
 <td style="text-align: right;">0.94</td>
 <td style="text-align: right;">0.57</td>
@@ -809,8 +833,8 @@ <h2 class="anchored" data-anchor-id="explore-and-clean-your-data">3.3. Explore a
 <td style="text-align: right;">1</td>
 <td style="text-align: left;">▆▁▁▁▇</td>
 </tr>
-<tr class="odd">
-<td style="text-align: left;">greeksalD</td>
+<tr class="even">
+<td style="text-align: left;">tomsalD</td>
 <td style="text-align: right;">24</td>
 <td style="text-align: right;">0.94</td>
 <td style="text-align: right;">1.08</td>
@@ -822,7 +846,7 @@ <h2 class="anchored" data-anchor-id="explore-and-clean-your-data">3.3. Explore a
 <td style="text-align: right;">3</td>
 <td style="text-align: left;">▇▂▁▆▂</td>
 </tr>
-<tr class="even">
+<tr class="odd">
 <td style="text-align: left;">bread</td>
 <td style="text-align: right;">18</td>
 <td style="text-align: right;">0.95</td>
@@ -835,7 +859,7 @@ <h2 class="anchored" data-anchor-id="explore-and-clean-your-data">3.3. Explore a
 <td style="text-align: right;">1</td>
 <td style="text-align: left;">▁▁▁▁▇</td>
 </tr>
-<tr class="odd">
+<tr class="even">
 <td style="text-align: left;">breadD</td>
 <td style="text-align: right;">18</td>
 <td style="text-align: right;">0.95</td>
@@ -848,7 +872,7 @@ <h2 class="anchored" data-anchor-id="explore-and-clean-your-data">3.3. Explore a
 <td style="text-align: right;">3</td>
 <td style="text-align: left;">▁▂▁▇▁</td>
 </tr>
-<tr class="even">
+<tr class="odd">
 <td style="text-align: left;">dessert</td>
 <td style="text-align: right;">42</td>
 <td style="text-align: right;">0.89</td>
@@ -861,7 +885,7 @@ <h2 class="anchored" data-anchor-id="explore-and-clean-your-data">3.3. Explore a
 <td style="text-align: right;">1</td>
 <td style="text-align: left;">▇▁▁▁▆</td>
 </tr>
-<tr class="odd">
+<tr class="even">
 <td style="text-align: left;">dessertD</td>
 <td style="text-align: right;">42</td>
 <td style="text-align: right;">0.89</td>
@@ -874,7 +898,7 @@ <h2 class="anchored" data-anchor-id="explore-and-clean-your-data">3.3. Explore a
 <td style="text-align: right;">3</td>
 <td style="text-align: left;">▇▁▁▃▁</td>
 </tr>
-<tr class="even">
+<tr class="odd">
 <td style="text-align: left;">champagne</td>
 <td style="text-align: right;">25</td>
 <td style="text-align: right;">0.94</td>
@@ -887,7 +911,7 @@ <h2 class="anchored" data-anchor-id="explore-and-clean-your-data">3.3. Explore a
 <td style="text-align: right;">1</td>
 <td style="text-align: left;">▁▁▁▁▇</td>
 </tr>
-<tr class="odd">
+<tr class="even">
 <td style="text-align: left;">champagneD</td>
 <td style="text-align: right;">25</td>
 <td style="text-align: right;">0.94</td>
@@ -900,7 +924,7 @@ <h2 class="anchored" data-anchor-id="explore-and-clean-your-data">3.3. Explore a
 <td style="text-align: right;">3</td>
 <td style="text-align: left;">▂▇▁▂▃</td>
 </tr>
-<tr class="even">
+<tr class="odd">
 <td style="text-align: left;">beer</td>
 <td style="text-align: right;">30</td>
 <td style="text-align: right;">0.92</td>
@@ -913,7 +937,7 @@ <h2 class="anchored" data-anchor-id="explore-and-clean-your-data">3.3. Explore a
 <td style="text-align: right;">1</td>
 <td style="text-align: left;">▂▁▁▁▇</td>
 </tr>
-<tr class="odd">
+<tr class="even">
 <td style="text-align: left;">beerD</td>
 <td style="text-align: right;">35</td>
 <td style="text-align: right;">0.91</td>
@@ -926,7 +950,7 @@ <h2 class="anchored" data-anchor-id="explore-and-clean-your-data">3.3. Explore a
 <td style="text-align: right;">3</td>
 <td style="text-align: left;">▃▂▁▂▇</td>
 </tr>
-<tr class="even">
+<tr class="odd">
 <td style="text-align: left;">redwine</td>
 <td style="text-align: right;">50</td>
 <td style="text-align: right;">0.87</td>
@@ -939,7 +963,7 @@ <h2 class="anchored" data-anchor-id="explore-and-clean-your-data">3.3. Explore a
 <td style="text-align: right;">1</td>
 <td style="text-align: left;">▇▁▁▁▂</td>
 </tr>
-<tr class="odd">
+<tr class="even">
 <td style="text-align: left;">redwineD</td>
 <td style="text-align: right;">52</td>
 <td style="text-align: right;">0.87</td>
@@ -952,7 +976,7 @@ <h2 class="anchored" data-anchor-id="explore-and-clean-your-data">3.3. Explore a
 <td style="text-align: right;">3</td>
 <td style="text-align: left;">▇▁▁▁▁</td>
 </tr>
-<tr class="even">
+<tr class="odd">
 <td style="text-align: left;">whitewine</td>
 <td style="text-align: right;">31</td>
 <td style="text-align: right;">0.92</td>
@@ -965,7 +989,7 @@ <h2 class="anchored" data-anchor-id="explore-and-clean-your-data">3.3. Explore a
 <td style="text-align: right;">1</td>
 <td style="text-align: left;">▃▁▁▁▇</td>
 </tr>
-<tr class="odd">
+<tr class="even">
 <td style="text-align: left;">whitewineD</td>
 <td style="text-align: right;">36</td>
 <td style="text-align: right;">0.91</td>
@@ -981,19 +1005,20 @@ <h2 class="anchored" data-anchor-id="explore-and-clean-your-data">3.3. Explore a
 </tbody>
 </table>
 </div>
-<details open="">
-<summary>Code</summary>
+<details open="" class="code-fold">
+<summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb9"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a><span class="fu">names</span>(copdata)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
 <div class="cell-output cell-output-stdout">
 <pre><code> [1] "id"         "sex"        "age"        "group"      "class"     
- [6] "diarrhoea"  "bloody"     "vomiting"   "abdo"       "nausea"    
-[11] "fever"      "headache"   "jointpain"  "starthour"  "meal"      
-[16] "feta"       "fetaD"      "sardines"   "sardinesD"  "eggplant"  
-[21] "eggplantD"  "moussaka"   "moussakaD"  "orzo"       "orzoD"     
-[26] "greeksal"   "greeksalD"  "bread"      "breadD"     "dessert"   
-[31] "dessertD"   "champagne"  "champagneD" "beer"       "beerD"     
-[36] "redwine"    "redwineD"   "whitewine"  "whitewineD" "dayonset"  </code></pre>
+ [6] "ill"        "diarrhoea"  "bloody"     "vomiting"   "abdo"      
+[11] "nausea"     "fever"      "headache"   "jointpain"  "starthour" 
+[16] "meal"       "feta"       "fetaD"      "sardines"   "sardinesD" 
+[21] "eggplant"   "eggplantD"  "veal"       "vealD"      "pasta"     
+[26] "pastaD"     "tomsal"     "tomsalD"    "bread"      "breadD"    
+[31] "dessert"    "dessertD"   "champagne"  "champagneD" "beer"      
+[36] "beerD"      "redwine"    "redwineD"   "whitewine"  "whitewineD"
+[41] "dayonset"  </code></pre>
 </div>
 </div>
 <p>Let’s explore and manage some variables in detail.</p>
@@ -1001,23 +1026,26 @@ <h2 class="anchored" data-anchor-id="explore-and-clean-your-data">3.3. Explore a
 <h4 class="anchored" data-anchor-id="age">Age</h4>
 <p>Through visual exploration of the <code>age</code> histogram we see that there is at least one very high value, likely implausible. You can then create a cross-tabulation of variables age and group to have a better idea of how your data looks like.</p>
 <div class="cell">
-<details open="">
-<summary>Code</summary>
+<details open="" class="code-fold">
+<summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb11"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb11-1"><a href="#cb11-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Have a look at the histogram  </span></span>
 <span id="cb11-2"><a href="#cb11-2" aria-hidden="true" tabindex="-1"></a><span class="fu">hist</span>(copdata<span class="sc">$</span>age)   </span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
 <div class="cell-output-display">
-<p><img src="Data_import_cleaning_files/figure-html/age_exploration-1.png" class="img-fluid" width="672"></p>
+<div>
+<figure class="figure">
+<p><img src="Data_import_cleaning_files/figure-html/age_exploration-1.png" class="img-fluid figure-img" width="672"></p>
+</figure>
+</div>
 </div>
-<details open="">
-<summary>Code</summary>
+<details open="" class="code-fold">
+<summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb12"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb12-1"><a href="#cb12-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Create cross-tab with the group variable:  </span></span>
-<span id="cb12-2"><a href="#cb12-2" aria-hidden="true" tabindex="-1"></a>janitor<span class="sc">::</span><span class="fu">tabyl</span>(<span class="at">dat =</span> copdata, </span>
+<span id="cb12-2"><a href="#cb12-2" aria-hidden="true" tabindex="-1"></a><span class="fu">tabyl</span>(<span class="at">dat =</span> copdata, </span>
 <span id="cb12-3"><a href="#cb12-3" aria-hidden="true" tabindex="-1"></a>               <span class="at">var1 =</span> age, </span>
 <span id="cb12-4"><a href="#cb12-4" aria-hidden="true" tabindex="-1"></a>               <span class="at">var2 =</span> group)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
 <div class="cell-output-display">
-
 <div data-pagedtable="false">
   <script data-pagedtable-source="" type="application/json">
 {"columns":[{"label":["age"],"name":[1],"type":["int"],"align":["right"]},{"label":["0"],"name":[2],"type":["dbl"],"align":["right"]},{"label":["1"],"name":[3],"type":["dbl"],"align":["right"]}],"data":[{"1":"8","2":"0","3":"1"},{"1":"15","2":"0","3":"11"},{"1":"16","2":"1","3":"99"},{"1":"17","2":"0","3":"115"},{"1":"18","2":"0","3":"112"},{"1":"19","2":"0","3":"39"},{"1":"20","2":"0","3":"3"},{"1":"26","2":"1","3":"0"},{"1":"29","2":"1","3":"0"},{"1":"30","2":"1","3":"0"},{"1":"31","2":"1","3":"0"},{"1":"32","2":"1","3":"0"},{"1":"33","2":"1","3":"0"},{"1":"34","2":"1","3":"0"},{"1":"39","2":"1","3":"0"},{"1":"43","2":"1","3":"0"},{"1":"54","2":"1","3":"0"},{"1":"56","2":"1","3":"0"},{"1":"58","2":"2","3":"0"},{"1":"59","2":"1","3":"0"},{"1":"65","2":"1","3":"0"},{"1":"180","2":"0","3":"1"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}}
@@ -1027,8 +1055,8 @@ <h4 class="anchored" data-anchor-id="age">Age</h4>
 </div>
 <p>Note that <code>group</code> is coded as 0 and 1, and these may be difficult to interpret when they mean something other than “no” and “yes”, respectively. From the codebook, you know that teachers are represented by 0, and students by 1. Let’s change this to make our lives easier:</p>
 <div class="cell">
-<details open="">
-<summary>Code</summary>
+<details open="" class="code-fold">
+<summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb13"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb13-1"><a href="#cb13-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Convert group to a factor and label 0 as teacher, 1 as student:</span></span>
 <span id="cb13-2"><a href="#cb13-2" aria-hidden="true" tabindex="-1"></a>copdata <span class="ot">&lt;-</span> copdata <span class="sc">%&gt;%</span> </span>
 <span id="cb13-3"><a href="#cb13-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">group =</span> <span class="fu">factor</span>(group, </span>
@@ -1037,14 +1065,13 @@ <h4 class="anchored" data-anchor-id="age">Age</h4>
 </div>
 <p>Now, have a look at your cross-tab again:</p>
 <div class="cell">
-<details open="">
-<summary>Code</summary>
-<div class="sourceCode cell-code" id="cb14"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb14-1"><a href="#cb14-1" aria-hidden="true" tabindex="-1"></a>janitor<span class="sc">::</span><span class="fu">tabyl</span>(<span class="at">dat =</span> copdata, </span>
+<details open="" class="code-fold">
+<summary>Show the code</summary>
+<div class="sourceCode cell-code" id="cb14"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb14-1"><a href="#cb14-1" aria-hidden="true" tabindex="-1"></a><span class="fu">tabyl</span>(<span class="at">dat =</span> copdata, </span>
 <span id="cb14-2"><a href="#cb14-2" aria-hidden="true" tabindex="-1"></a>               <span class="at">var1 =</span> age, </span>
 <span id="cb14-3"><a href="#cb14-3" aria-hidden="true" tabindex="-1"></a>               <span class="at">var2 =</span> group) </span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
 <div class="cell-output-display">
-
 <div data-pagedtable="false">
   <script data-pagedtable-source="" type="application/json">
 {"columns":[{"label":["age"],"name":[1],"type":["int"],"align":["right"]},{"label":["teacher"],"name":[2],"type":["dbl"],"align":["right"]},{"label":["student"],"name":[3],"type":["dbl"],"align":["right"]}],"data":[{"1":"8","2":"0","3":"1"},{"1":"15","2":"0","3":"11"},{"1":"16","2":"1","3":"99"},{"1":"17","2":"0","3":"115"},{"1":"18","2":"0","3":"112"},{"1":"19","2":"0","3":"39"},{"1":"20","2":"0","3":"3"},{"1":"26","2":"1","3":"0"},{"1":"29","2":"1","3":"0"},{"1":"30","2":"1","3":"0"},{"1":"31","2":"1","3":"0"},{"1":"32","2":"1","3":"0"},{"1":"33","2":"1","3":"0"},{"1":"34","2":"1","3":"0"},{"1":"39","2":"1","3":"0"},{"1":"43","2":"1","3":"0"},{"1":"54","2":"1","3":"0"},{"1":"56","2":"1","3":"0"},{"1":"58","2":"2","3":"0"},{"1":"59","2":"1","3":"0"},{"1":"65","2":"1","3":"0"},{"1":"180","2":"0","3":"1"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}}
@@ -1060,8 +1087,8 @@ <h4 class="anchored" data-anchor-id="age">Age</h4>
 </ul>
 <p>Assuming you have contacted the school to make sure your suspicions about the actual ages are correct, we can now correct them, using <code>case_when()</code>. We create logical conditions to identify the incorrect ages, combining the values for age with the group they belong to:</p>
 <div class="cell">
-<details open="">
-<summary>Code</summary>
+<details open="" class="code-fold">
+<summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb15"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb15-1"><a href="#cb15-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Update incorrect ages to the correct values with case_when:  </span></span>
 <span id="cb15-2"><a href="#cb15-2" aria-hidden="true" tabindex="-1"></a>copdata <span class="ot">&lt;-</span> copdata <span class="sc">%&gt;%</span>       </span>
 <span id="cb15-3"><a href="#cb15-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">age =</span>                         </span>
@@ -1081,7 +1108,7 @@ <h4 class="anchored" data-anchor-id="age">Age</h4>
 </div>
 <div class="quarto-figure quarto-figure-center">
 <figure class="figure">
-<p><img src="https://cdn.myportfolio.com/45214904-6a61-4e23-98d6-b140f8654a40/6ffcd6d6-c783-4087-ae76-99aa851663ed_rw_1920.png?h=5f67935d69c92e1fda9074d00dfea972" class="img-fluid figure-img" style="width:80.0%" alt="Cartoon showing a table with creature type (kraken, dragon, or cyclops) and age (baby, teen, or adult). The three creatures listed are adding a new column named “danger”, which contains the word “extreme!” if the type is “kraken”, or “high” for any other type. Stylized text reads “dplyr::case_when() - IF ELSE...but you love it? An example of code is shown: mutate(danger = case_when(type == “kraken” ~ “extreme!”, TRUE ~ “high”). Learn more about case_when."></p>
+<p><img src="https://cdn.myportfolio.com/45214904-6a61-4e23-98d6-b140f8654a40/6ffcd6d6-c783-4087-ae76-99aa851663ed_rw_1920.png?h=5f67935d69c92e1fda9074d00dfea972" class="img-fluid quarto-figure quarto-figure-center figure-img" style="width:80.0%" alt="Cartoon showing a table with creature type (kraken, dragon, or cyclops) and age (baby, teen, or adult). The three creatures listed are adding a new column named “danger”, which contains the word “extreme!” if the type is “kraken”, or “high” for any other type. Stylized text reads “dplyr::case_when() - IF ELSE...but you love it? An example of code is shown: mutate(danger = case_when(type == “kraken” ~ “extreme!”, TRUE ~ “high”). Learn more about case_when."></p>
 </figure>
 </div>
 <ul>
@@ -1104,8 +1131,8 @@ <h4 class="anchored" data-anchor-id="age">Age</h4>
 </div>
 </div>
 <div class="cell">
-<details open="">
-<summary>Code</summary>
+<details open="" class="code-fold">
+<summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb16"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb16-1"><a href="#cb16-1" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: check_dose_cols</span></span>
 <span id="cb16-2"><a href="#cb16-2" aria-hidden="true" tabindex="-1"></a><span class="co">#| tbl-cap: no caption</span></span>
 <span id="cb16-3"><a href="#cb16-3" aria-hidden="true" tabindex="-1"></a></span>
@@ -1114,30 +1141,29 @@ <h4 class="anchored" data-anchor-id="age">Age</h4>
 <span id="cb16-6"><a href="#cb16-6" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Select all the columns with column names that end in upper case 'D':   </span></span>
 <span id="cb16-7"><a href="#cb16-7" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(<span class="fu">ends_with</span>(<span class="st">"D"</span>, <span class="at">ignore.case =</span> <span class="cn">FALSE</span>)) <span class="sc">%&gt;%</span>       </span>
 <span id="cb16-8"><a href="#cb16-8" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Create the summary table, excluding missing values:   </span></span>
-<span id="cb16-9"><a href="#cb16-9" aria-hidden="true" tabindex="-1"></a>  gtsummary<span class="sc">::</span><span class="fu">tbl_summary</span>(<span class="at">missing =</span> <span class="st">"no"</span>) </span>
+<span id="cb16-9"><a href="#cb16-9" aria-hidden="true" tabindex="-1"></a>  <span class="fu">tbl_summary</span>(<span class="at">missing =</span> <span class="st">"no"</span>) </span>
 <span id="cb16-10"><a href="#cb16-10" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb16-11"><a href="#cb16-11" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Print the summary table: </span></span>
 <span id="cb16-12"><a href="#cb16-12" aria-hidden="true" tabindex="-1"></a>drtable</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
 <div class="cell-output-display">
-
-<div id="dnfydbrirg" style="padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;">
-<style>#dnfydbrirg table {
+<div id="itdxjkwwpn" style="padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;">
+<style>#itdxjkwwpn table {
   font-family: system-ui, 'Segoe UI', Roboto, Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol', 'Noto Color Emoji';
   -webkit-font-smoothing: antialiased;
   -moz-osx-font-smoothing: grayscale;
 }
 
-#dnfydbrirg thead, #dnfydbrirg tbody, #dnfydbrirg tfoot, #dnfydbrirg tr, #dnfydbrirg td, #dnfydbrirg th {
+#itdxjkwwpn thead, #itdxjkwwpn tbody, #itdxjkwwpn tfoot, #itdxjkwwpn tr, #itdxjkwwpn td, #itdxjkwwpn th {
   border-style: none;
 }
 
-#dnfydbrirg p {
+#itdxjkwwpn p {
   margin: 0;
   padding: 0;
 }
 
-#dnfydbrirg .gt_table {
+#itdxjkwwpn .gt_table {
   display: table;
   border-collapse: collapse;
   line-height: normal;
@@ -1163,12 +1189,12 @@ <h4 class="anchored" data-anchor-id="age">Age</h4>
   border-left-color: #D3D3D3;
 }
 
-#dnfydbrirg .gt_caption {
+#itdxjkwwpn .gt_caption {
   padding-top: 4px;
   padding-bottom: 4px;
 }
 
-#dnfydbrirg .gt_title {
+#itdxjkwwpn .gt_title {
   color: #333333;
   font-size: 125%;
   font-weight: initial;
@@ -1180,7 +1206,7 @@ <h4 class="anchored" data-anchor-id="age">Age</h4>
   border-bottom-width: 0;
 }
 
-#dnfydbrirg .gt_subtitle {
+#itdxjkwwpn .gt_subtitle {
   color: #333333;
   font-size: 85%;
   font-weight: initial;
@@ -1192,7 +1218,7 @@ <h4 class="anchored" data-anchor-id="age">Age</h4>
   border-top-width: 0;
 }
 
-#dnfydbrirg .gt_heading {
+#itdxjkwwpn .gt_heading {
   background-color: #FFFFFF;
   text-align: center;
   border-bottom-color: #FFFFFF;
@@ -1204,13 +1230,13 @@ <h4 class="anchored" data-anchor-id="age">Age</h4>
   border-right-color: #D3D3D3;
 }
 
-#dnfydbrirg .gt_bottom_border {
+#itdxjkwwpn .gt_bottom_border {
   border-bottom-style: solid;
   border-bottom-width: 2px;
   border-bottom-color: #D3D3D3;
 }
 
-#dnfydbrirg .gt_col_headings {
+#itdxjkwwpn .gt_col_headings {
   border-top-style: solid;
   border-top-width: 2px;
   border-top-color: #D3D3D3;
@@ -1225,7 +1251,7 @@ <h4 class="anchored" data-anchor-id="age">Age</h4>
   border-right-color: #D3D3D3;
 }
 
-#dnfydbrirg .gt_col_heading {
+#itdxjkwwpn .gt_col_heading {
   color: #333333;
   background-color: #FFFFFF;
   font-size: 100%;
@@ -1245,7 +1271,7 @@ <h4 class="anchored" data-anchor-id="age">Age</h4>
   overflow-x: hidden;
 }
 
-#dnfydbrirg .gt_column_spanner_outer {
+#itdxjkwwpn .gt_column_spanner_outer {
   color: #333333;
   background-color: #FFFFFF;
   font-size: 100%;
@@ -1257,15 +1283,15 @@ <h4 class="anchored" data-anchor-id="age">Age</h4>
   padding-right: 4px;
 }
 
-#dnfydbrirg .gt_column_spanner_outer:first-child {
+#itdxjkwwpn .gt_column_spanner_outer:first-child {
   padding-left: 0;
 }
 
-#dnfydbrirg .gt_column_spanner_outer:last-child {
+#itdxjkwwpn .gt_column_spanner_outer:last-child {
   padding-right: 0;
 }
 
-#dnfydbrirg .gt_column_spanner {
+#itdxjkwwpn .gt_column_spanner {
   border-bottom-style: solid;
   border-bottom-width: 2px;
   border-bottom-color: #D3D3D3;
@@ -1277,11 +1303,11 @@ <h4 class="anchored" data-anchor-id="age">Age</h4>
   width: 100%;
 }
 
-#dnfydbrirg .gt_spanner_row {
+#itdxjkwwpn .gt_spanner_row {
   border-bottom-style: hidden;
 }
 
-#dnfydbrirg .gt_group_heading {
+#itdxjkwwpn .gt_group_heading {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -1307,7 +1333,7 @@ <h4 class="anchored" data-anchor-id="age">Age</h4>
   text-align: left;
 }
 
-#dnfydbrirg .gt_empty_group_heading {
+#itdxjkwwpn .gt_empty_group_heading {
   padding: 0.5px;
   color: #333333;
   background-color: #FFFFFF;
@@ -1322,15 +1348,15 @@ <h4 class="anchored" data-anchor-id="age">Age</h4>
   vertical-align: middle;
 }
 
-#dnfydbrirg .gt_from_md > :first-child {
+#itdxjkwwpn .gt_from_md > :first-child {
   margin-top: 0;
 }
 
-#dnfydbrirg .gt_from_md > :last-child {
+#itdxjkwwpn .gt_from_md > :last-child {
   margin-bottom: 0;
 }
 
-#dnfydbrirg .gt_row {
+#itdxjkwwpn .gt_row {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -1349,7 +1375,7 @@ <h4 class="anchored" data-anchor-id="age">Age</h4>
   overflow-x: hidden;
 }
 
-#dnfydbrirg .gt_stub {
+#itdxjkwwpn .gt_stub {
   color: #333333;
   background-color: #FFFFFF;
   font-size: 100%;
@@ -1362,7 +1388,7 @@ <h4 class="anchored" data-anchor-id="age">Age</h4>
   padding-right: 5px;
 }
 
-#dnfydbrirg .gt_stub_row_group {
+#itdxjkwwpn .gt_stub_row_group {
   color: #333333;
   background-color: #FFFFFF;
   font-size: 100%;
@@ -1376,15 +1402,15 @@ <h4 class="anchored" data-anchor-id="age">Age</h4>
   vertical-align: top;
 }
 
-#dnfydbrirg .gt_row_group_first td {
+#itdxjkwwpn .gt_row_group_first td {
   border-top-width: 2px;
 }
 
-#dnfydbrirg .gt_row_group_first th {
+#itdxjkwwpn .gt_row_group_first th {
   border-top-width: 2px;
 }
 
-#dnfydbrirg .gt_summary_row {
+#itdxjkwwpn .gt_summary_row {
   color: #333333;
   background-color: #FFFFFF;
   text-transform: inherit;
@@ -1394,16 +1420,16 @@ <h4 class="anchored" data-anchor-id="age">Age</h4>
   padding-right: 5px;
 }
 
-#dnfydbrirg .gt_first_summary_row {
+#itdxjkwwpn .gt_first_summary_row {
   border-top-style: solid;
   border-top-color: #D3D3D3;
 }
 
-#dnfydbrirg .gt_first_summary_row.thick {
+#itdxjkwwpn .gt_first_summary_row.thick {
   border-top-width: 2px;
 }
 
-#dnfydbrirg .gt_last_summary_row {
+#itdxjkwwpn .gt_last_summary_row {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -1413,7 +1439,7 @@ <h4 class="anchored" data-anchor-id="age">Age</h4>
   border-bottom-color: #D3D3D3;
 }
 
-#dnfydbrirg .gt_grand_summary_row {
+#itdxjkwwpn .gt_grand_summary_row {
   color: #333333;
   background-color: #FFFFFF;
   text-transform: inherit;
@@ -1423,7 +1449,7 @@ <h4 class="anchored" data-anchor-id="age">Age</h4>
   padding-right: 5px;
 }
 
-#dnfydbrirg .gt_first_grand_summary_row {
+#itdxjkwwpn .gt_first_grand_summary_row {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -1433,7 +1459,7 @@ <h4 class="anchored" data-anchor-id="age">Age</h4>
   border-top-color: #D3D3D3;
 }
 
-#dnfydbrirg .gt_last_grand_summary_row_top {
+#itdxjkwwpn .gt_last_grand_summary_row_top {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -1443,11 +1469,11 @@ <h4 class="anchored" data-anchor-id="age">Age</h4>
   border-bottom-color: #D3D3D3;
 }
 
-#dnfydbrirg .gt_striped {
+#itdxjkwwpn .gt_striped {
   background-color: rgba(128, 128, 128, 0.05);
 }
 
-#dnfydbrirg .gt_table_body {
+#itdxjkwwpn .gt_table_body {
   border-top-style: solid;
   border-top-width: 2px;
   border-top-color: #D3D3D3;
@@ -1456,7 +1482,7 @@ <h4 class="anchored" data-anchor-id="age">Age</h4>
   border-bottom-color: #D3D3D3;
 }
 
-#dnfydbrirg .gt_footnotes {
+#itdxjkwwpn .gt_footnotes {
   color: #333333;
   background-color: #FFFFFF;
   border-bottom-style: none;
@@ -1470,7 +1496,7 @@ <h4 class="anchored" data-anchor-id="age">Age</h4>
   border-right-color: #D3D3D3;
 }
 
-#dnfydbrirg .gt_footnote {
+#itdxjkwwpn .gt_footnote {
   margin: 0px;
   font-size: 90%;
   padding-top: 4px;
@@ -1479,7 +1505,7 @@ <h4 class="anchored" data-anchor-id="age">Age</h4>
   padding-right: 5px;
 }
 
-#dnfydbrirg .gt_sourcenotes {
+#itdxjkwwpn .gt_sourcenotes {
   color: #333333;
   background-color: #FFFFFF;
   border-bottom-style: none;
@@ -1493,7 +1519,7 @@ <h4 class="anchored" data-anchor-id="age">Age</h4>
   border-right-color: #D3D3D3;
 }
 
-#dnfydbrirg .gt_sourcenote {
+#itdxjkwwpn .gt_sourcenote {
   font-size: 90%;
   padding-top: 4px;
   padding-bottom: 4px;
@@ -1501,203 +1527,348 @@ <h4 class="anchored" data-anchor-id="age">Age</h4>
   padding-right: 5px;
 }
 
-#dnfydbrirg .gt_left {
+#itdxjkwwpn .gt_left {
   text-align: left;
 }
 
-#dnfydbrirg .gt_center {
+#itdxjkwwpn .gt_center {
   text-align: center;
 }
 
-#dnfydbrirg .gt_right {
+#itdxjkwwpn .gt_right {
   text-align: right;
   font-variant-numeric: tabular-nums;
 }
 
-#dnfydbrirg .gt_font_normal {
+#itdxjkwwpn .gt_font_normal {
   font-weight: normal;
 }
 
-#dnfydbrirg .gt_font_bold {
+#itdxjkwwpn .gt_font_bold {
   font-weight: bold;
 }
 
-#dnfydbrirg .gt_font_italic {
+#itdxjkwwpn .gt_font_italic {
   font-style: italic;
 }
 
-#dnfydbrirg .gt_super {
+#itdxjkwwpn .gt_super {
   font-size: 65%;
 }
 
-#dnfydbrirg .gt_footnote_marks {
+#itdxjkwwpn .gt_footnote_marks {
   font-size: 75%;
   vertical-align: 0.4em;
   position: initial;
 }
 
-#dnfydbrirg .gt_asterisk {
+#itdxjkwwpn .gt_asterisk {
   font-size: 100%;
   vertical-align: 0;
 }
 
-#dnfydbrirg .gt_indent_1 {
+#itdxjkwwpn .gt_indent_1 {
   text-indent: 5px;
 }
 
-#dnfydbrirg .gt_indent_2 {
+#itdxjkwwpn .gt_indent_2 {
   text-indent: 10px;
 }
 
-#dnfydbrirg .gt_indent_3 {
+#itdxjkwwpn .gt_indent_3 {
   text-indent: 15px;
 }
 
-#dnfydbrirg .gt_indent_4 {
+#itdxjkwwpn .gt_indent_4 {
   text-indent: 20px;
 }
 
-#dnfydbrirg .gt_indent_5 {
+#itdxjkwwpn .gt_indent_5 {
   text-indent: 25px;
 }
+
+#itdxjkwwpn .katex-display {
+  display: inline-flex !important;
+  margin-bottom: 0.75em !important;
+}
+
+#itdxjkwwpn div.Reactable > div.rt-table > div.rt-thead > div.rt-tr.rt-tr-group-header > div.rt-th-group:after {
+  height: 0px !important;
+}
 </style>
-<table class="gt_table" data-quarto-disable-processing="false" data-quarto-bootstrap="false">
-  <thead>
-    
-    <tr class="gt_col_headings">
-      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="<strong>Characteristic</strong>"><strong>Characteristic</strong></th>
-      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="<strong>N = 397</strong><span class=&quot;gt_footnote_marks&quot; style=&quot;white-space:nowrap;font-style:italic;font-weight:normal;&quot;><sup>1</sup></span>"><strong>N = 397</strong><span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;"><sup>1</sup></span></th>
-    </tr>
-  </thead>
-  <tbody class="gt_table_body">
-    <tr><td headers="label" class="gt_row gt_left">fetaD</td>
-<td headers="stat_0" class="gt_row gt_center"></td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;0</td>
-<td headers="stat_0" class="gt_row gt_center">110 (29%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;1</td>
-<td headers="stat_0" class="gt_row gt_center">78 (20%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;2</td>
-<td headers="stat_0" class="gt_row gt_center">155 (41%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;3</td>
-<td headers="stat_0" class="gt_row gt_center">38 (10.0%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">sardinesD</td>
-<td headers="stat_0" class="gt_row gt_center"></td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;0</td>
-<td headers="stat_0" class="gt_row gt_center">125 (33%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;1</td>
-<td headers="stat_0" class="gt_row gt_center">35 (9.2%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;2</td>
-<td headers="stat_0" class="gt_row gt_center">184 (48%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;3</td>
-<td headers="stat_0" class="gt_row gt_center">36 (9.5%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">eggplantD</td>
-<td headers="stat_0" class="gt_row gt_center"></td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;0</td>
-<td headers="stat_0" class="gt_row gt_center">151 (41%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;1</td>
-<td headers="stat_0" class="gt_row gt_center">41 (11%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;2</td>
-<td headers="stat_0" class="gt_row gt_center">147 (40%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;3</td>
-<td headers="stat_0" class="gt_row gt_center">28 (7.6%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">moussakaD</td>
-<td headers="stat_0" class="gt_row gt_center"></td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;0</td>
-<td headers="stat_0" class="gt_row gt_center">42 (11%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;1</td>
-<td headers="stat_0" class="gt_row gt_center">70 (18%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;2</td>
-<td headers="stat_0" class="gt_row gt_center">184 (48%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;3</td>
-<td headers="stat_0" class="gt_row gt_center">87 (23%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">orzoD</td>
-<td headers="stat_0" class="gt_row gt_center"></td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;0</td>
-<td headers="stat_0" class="gt_row gt_center">44 (12%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;1</td>
-<td headers="stat_0" class="gt_row gt_center">70 (18%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;2</td>
-<td headers="stat_0" class="gt_row gt_center">183 (48%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;3</td>
-<td headers="stat_0" class="gt_row gt_center">85 (22%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">greeksalD</td>
-<td headers="stat_0" class="gt_row gt_center"></td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;0</td>
-<td headers="stat_0" class="gt_row gt_center">162 (43%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;1</td>
-<td headers="stat_0" class="gt_row gt_center">52 (14%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;2</td>
-<td headers="stat_0" class="gt_row gt_center">126 (34%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;3</td>
-<td headers="stat_0" class="gt_row gt_center">33 (8.8%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">breadD</td>
-<td headers="stat_0" class="gt_row gt_center"></td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;0</td>
-<td headers="stat_0" class="gt_row gt_center">35 (9.2%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;1</td>
-<td headers="stat_0" class="gt_row gt_center">51 (13%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;2</td>
-<td headers="stat_0" class="gt_row gt_center">267 (70%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;3</td>
-<td headers="stat_0" class="gt_row gt_center">26 (6.9%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">dessertD</td>
-<td headers="stat_0" class="gt_row gt_center"></td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;0</td>
-<td headers="stat_0" class="gt_row gt_center">206 (58%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;1</td>
-<td headers="stat_0" class="gt_row gt_center">33 (9.3%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;2</td>
-<td headers="stat_0" class="gt_row gt_center">87 (25%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;3</td>
-<td headers="stat_0" class="gt_row gt_center">29 (8.2%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">champagneD</td>
-<td headers="stat_0" class="gt_row gt_center"></td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;0</td>
-<td headers="stat_0" class="gt_row gt_center">49 (13%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;1</td>
-<td headers="stat_0" class="gt_row gt_center">208 (56%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;2</td>
-<td headers="stat_0" class="gt_row gt_center">45 (12%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;3</td>
-<td headers="stat_0" class="gt_row gt_center">70 (19%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">beerD</td>
-<td headers="stat_0" class="gt_row gt_center"></td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;0</td>
-<td headers="stat_0" class="gt_row gt_center">81 (22%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;1</td>
-<td headers="stat_0" class="gt_row gt_center">41 (11%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;2</td>
-<td headers="stat_0" class="gt_row gt_center">57 (16%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;3</td>
-<td headers="stat_0" class="gt_row gt_center">183 (51%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">redwineD</td>
-<td headers="stat_0" class="gt_row gt_center"></td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;0</td>
-<td headers="stat_0" class="gt_row gt_center">266 (77%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;1</td>
-<td headers="stat_0" class="gt_row gt_center">31 (9.0%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;2</td>
-<td headers="stat_0" class="gt_row gt_center">21 (6.1%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;3</td>
-<td headers="stat_0" class="gt_row gt_center">27 (7.8%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">whitewineD</td>
-<td headers="stat_0" class="gt_row gt_center"></td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;0</td>
-<td headers="stat_0" class="gt_row gt_center">100 (28%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;1</td>
-<td headers="stat_0" class="gt_row gt_center">73 (20%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;2</td>
-<td headers="stat_0" class="gt_row gt_center">67 (19%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;3</td>
-<td headers="stat_0" class="gt_row gt_center">121 (34%)</td></tr>
-  </tbody>
-  
-  <tfoot class="gt_footnotes">
-    <tr>
-      <td class="gt_footnote" colspan="2"><span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;"><sup>1</sup></span> n (%)</td>
-    </tr>
-  </tfoot>
+
+<table class="gt_table caption-top table table-sm table-striped small" data-quarto-postprocess="true" data-quarto-disable-processing="false" data-quarto-bootstrap="false">
+<colgroup>
+<col style="width: 50%">
+<col style="width: 50%">
+</colgroup>
+<thead>
+<tr class="header gt_col_headings">
+<th id="label" class="gt_col_heading gt_columns_bottom_border gt_left" data-quarto-table-cell-role="th" scope="col"><strong>Characteristic</strong></th>
+<th id="stat_0" class="gt_col_heading gt_columns_bottom_border gt_center" data-quarto-table-cell-role="th" scope="col"><strong>N = 397</strong><span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;line-height:0;"><sup>1</sup></span></th>
+</tr>
+</thead>
+<tbody class="gt_table_body">
+<tr class="odd">
+<td class="gt_row gt_left" headers="label">fetaD</td>
+<td class="gt_row gt_center" headers="stat_0"><br>
+</td>
+</tr>
+<tr class="even">
+<td class="gt_row gt_left" headers="label">&nbsp;&nbsp;&nbsp;&nbsp;0</td>
+<td class="gt_row gt_center" headers="stat_0">110 (29%)</td>
+</tr>
+<tr class="odd">
+<td class="gt_row gt_left" headers="label">&nbsp;&nbsp;&nbsp;&nbsp;1</td>
+<td class="gt_row gt_center" headers="stat_0">78 (20%)</td>
+</tr>
+<tr class="even">
+<td class="gt_row gt_left" headers="label">&nbsp;&nbsp;&nbsp;&nbsp;2</td>
+<td class="gt_row gt_center" headers="stat_0">155 (41%)</td>
+</tr>
+<tr class="odd">
+<td class="gt_row gt_left" headers="label">&nbsp;&nbsp;&nbsp;&nbsp;3</td>
+<td class="gt_row gt_center" headers="stat_0">38 (10.0%)</td>
+</tr>
+<tr class="even">
+<td class="gt_row gt_left" headers="label">sardinesD</td>
+<td class="gt_row gt_center" headers="stat_0"><br>
+</td>
+</tr>
+<tr class="odd">
+<td class="gt_row gt_left" headers="label">&nbsp;&nbsp;&nbsp;&nbsp;0</td>
+<td class="gt_row gt_center" headers="stat_0">125 (33%)</td>
+</tr>
+<tr class="even">
+<td class="gt_row gt_left" headers="label">&nbsp;&nbsp;&nbsp;&nbsp;1</td>
+<td class="gt_row gt_center" headers="stat_0">35 (9.2%)</td>
+</tr>
+<tr class="odd">
+<td class="gt_row gt_left" headers="label">&nbsp;&nbsp;&nbsp;&nbsp;2</td>
+<td class="gt_row gt_center" headers="stat_0">184 (48%)</td>
+</tr>
+<tr class="even">
+<td class="gt_row gt_left" headers="label">&nbsp;&nbsp;&nbsp;&nbsp;3</td>
+<td class="gt_row gt_center" headers="stat_0">36 (9.5%)</td>
+</tr>
+<tr class="odd">
+<td class="gt_row gt_left" headers="label">eggplantD</td>
+<td class="gt_row gt_center" headers="stat_0"><br>
+</td>
+</tr>
+<tr class="even">
+<td class="gt_row gt_left" headers="label">&nbsp;&nbsp;&nbsp;&nbsp;0</td>
+<td class="gt_row gt_center" headers="stat_0">151 (41%)</td>
+</tr>
+<tr class="odd">
+<td class="gt_row gt_left" headers="label">&nbsp;&nbsp;&nbsp;&nbsp;1</td>
+<td class="gt_row gt_center" headers="stat_0">41 (11%)</td>
+</tr>
+<tr class="even">
+<td class="gt_row gt_left" headers="label">&nbsp;&nbsp;&nbsp;&nbsp;2</td>
+<td class="gt_row gt_center" headers="stat_0">147 (40%)</td>
+</tr>
+<tr class="odd">
+<td class="gt_row gt_left" headers="label">&nbsp;&nbsp;&nbsp;&nbsp;3</td>
+<td class="gt_row gt_center" headers="stat_0">28 (7.6%)</td>
+</tr>
+<tr class="even">
+<td class="gt_row gt_left" headers="label">vealD</td>
+<td class="gt_row gt_center" headers="stat_0"><br>
+</td>
+</tr>
+<tr class="odd">
+<td class="gt_row gt_left" headers="label">&nbsp;&nbsp;&nbsp;&nbsp;0</td>
+<td class="gt_row gt_center" headers="stat_0">42 (11%)</td>
+</tr>
+<tr class="even">
+<td class="gt_row gt_left" headers="label">&nbsp;&nbsp;&nbsp;&nbsp;1</td>
+<td class="gt_row gt_center" headers="stat_0">70 (18%)</td>
+</tr>
+<tr class="odd">
+<td class="gt_row gt_left" headers="label">&nbsp;&nbsp;&nbsp;&nbsp;2</td>
+<td class="gt_row gt_center" headers="stat_0">184 (48%)</td>
+</tr>
+<tr class="even">
+<td class="gt_row gt_left" headers="label">&nbsp;&nbsp;&nbsp;&nbsp;3</td>
+<td class="gt_row gt_center" headers="stat_0">87 (23%)</td>
+</tr>
+<tr class="odd">
+<td class="gt_row gt_left" headers="label">pastaD</td>
+<td class="gt_row gt_center" headers="stat_0"><br>
+</td>
+</tr>
+<tr class="even">
+<td class="gt_row gt_left" headers="label">&nbsp;&nbsp;&nbsp;&nbsp;0</td>
+<td class="gt_row gt_center" headers="stat_0">44 (12%)</td>
+</tr>
+<tr class="odd">
+<td class="gt_row gt_left" headers="label">&nbsp;&nbsp;&nbsp;&nbsp;1</td>
+<td class="gt_row gt_center" headers="stat_0">70 (18%)</td>
+</tr>
+<tr class="even">
+<td class="gt_row gt_left" headers="label">&nbsp;&nbsp;&nbsp;&nbsp;2</td>
+<td class="gt_row gt_center" headers="stat_0">183 (48%)</td>
+</tr>
+<tr class="odd">
+<td class="gt_row gt_left" headers="label">&nbsp;&nbsp;&nbsp;&nbsp;3</td>
+<td class="gt_row gt_center" headers="stat_0">85 (22%)</td>
+</tr>
+<tr class="even">
+<td class="gt_row gt_left" headers="label">tomsalD</td>
+<td class="gt_row gt_center" headers="stat_0"><br>
+</td>
+</tr>
+<tr class="odd">
+<td class="gt_row gt_left" headers="label">&nbsp;&nbsp;&nbsp;&nbsp;0</td>
+<td class="gt_row gt_center" headers="stat_0">162 (43%)</td>
+</tr>
+<tr class="even">
+<td class="gt_row gt_left" headers="label">&nbsp;&nbsp;&nbsp;&nbsp;1</td>
+<td class="gt_row gt_center" headers="stat_0">52 (14%)</td>
+</tr>
+<tr class="odd">
+<td class="gt_row gt_left" headers="label">&nbsp;&nbsp;&nbsp;&nbsp;2</td>
+<td class="gt_row gt_center" headers="stat_0">126 (34%)</td>
+</tr>
+<tr class="even">
+<td class="gt_row gt_left" headers="label">&nbsp;&nbsp;&nbsp;&nbsp;3</td>
+<td class="gt_row gt_center" headers="stat_0">33 (8.8%)</td>
+</tr>
+<tr class="odd">
+<td class="gt_row gt_left" headers="label">breadD</td>
+<td class="gt_row gt_center" headers="stat_0"><br>
+</td>
+</tr>
+<tr class="even">
+<td class="gt_row gt_left" headers="label">&nbsp;&nbsp;&nbsp;&nbsp;0</td>
+<td class="gt_row gt_center" headers="stat_0">35 (9.2%)</td>
+</tr>
+<tr class="odd">
+<td class="gt_row gt_left" headers="label">&nbsp;&nbsp;&nbsp;&nbsp;1</td>
+<td class="gt_row gt_center" headers="stat_0">51 (13%)</td>
+</tr>
+<tr class="even">
+<td class="gt_row gt_left" headers="label">&nbsp;&nbsp;&nbsp;&nbsp;2</td>
+<td class="gt_row gt_center" headers="stat_0">267 (70%)</td>
+</tr>
+<tr class="odd">
+<td class="gt_row gt_left" headers="label">&nbsp;&nbsp;&nbsp;&nbsp;3</td>
+<td class="gt_row gt_center" headers="stat_0">26 (6.9%)</td>
+</tr>
+<tr class="even">
+<td class="gt_row gt_left" headers="label">dessertD</td>
+<td class="gt_row gt_center" headers="stat_0"><br>
+</td>
+</tr>
+<tr class="odd">
+<td class="gt_row gt_left" headers="label">&nbsp;&nbsp;&nbsp;&nbsp;0</td>
+<td class="gt_row gt_center" headers="stat_0">206 (58%)</td>
+</tr>
+<tr class="even">
+<td class="gt_row gt_left" headers="label">&nbsp;&nbsp;&nbsp;&nbsp;1</td>
+<td class="gt_row gt_center" headers="stat_0">33 (9.3%)</td>
+</tr>
+<tr class="odd">
+<td class="gt_row gt_left" headers="label">&nbsp;&nbsp;&nbsp;&nbsp;2</td>
+<td class="gt_row gt_center" headers="stat_0">87 (25%)</td>
+</tr>
+<tr class="even">
+<td class="gt_row gt_left" headers="label">&nbsp;&nbsp;&nbsp;&nbsp;3</td>
+<td class="gt_row gt_center" headers="stat_0">29 (8.2%)</td>
+</tr>
+<tr class="odd">
+<td class="gt_row gt_left" headers="label">champagneD</td>
+<td class="gt_row gt_center" headers="stat_0"><br>
+</td>
+</tr>
+<tr class="even">
+<td class="gt_row gt_left" headers="label">&nbsp;&nbsp;&nbsp;&nbsp;0</td>
+<td class="gt_row gt_center" headers="stat_0">49 (13%)</td>
+</tr>
+<tr class="odd">
+<td class="gt_row gt_left" headers="label">&nbsp;&nbsp;&nbsp;&nbsp;1</td>
+<td class="gt_row gt_center" headers="stat_0">208 (56%)</td>
+</tr>
+<tr class="even">
+<td class="gt_row gt_left" headers="label">&nbsp;&nbsp;&nbsp;&nbsp;2</td>
+<td class="gt_row gt_center" headers="stat_0">45 (12%)</td>
+</tr>
+<tr class="odd">
+<td class="gt_row gt_left" headers="label">&nbsp;&nbsp;&nbsp;&nbsp;3</td>
+<td class="gt_row gt_center" headers="stat_0">70 (19%)</td>
+</tr>
+<tr class="even">
+<td class="gt_row gt_left" headers="label">beerD</td>
+<td class="gt_row gt_center" headers="stat_0"><br>
+</td>
+</tr>
+<tr class="odd">
+<td class="gt_row gt_left" headers="label">&nbsp;&nbsp;&nbsp;&nbsp;0</td>
+<td class="gt_row gt_center" headers="stat_0">81 (22%)</td>
+</tr>
+<tr class="even">
+<td class="gt_row gt_left" headers="label">&nbsp;&nbsp;&nbsp;&nbsp;1</td>
+<td class="gt_row gt_center" headers="stat_0">41 (11%)</td>
+</tr>
+<tr class="odd">
+<td class="gt_row gt_left" headers="label">&nbsp;&nbsp;&nbsp;&nbsp;2</td>
+<td class="gt_row gt_center" headers="stat_0">57 (16%)</td>
+</tr>
+<tr class="even">
+<td class="gt_row gt_left" headers="label">&nbsp;&nbsp;&nbsp;&nbsp;3</td>
+<td class="gt_row gt_center" headers="stat_0">183 (51%)</td>
+</tr>
+<tr class="odd">
+<td class="gt_row gt_left" headers="label">redwineD</td>
+<td class="gt_row gt_center" headers="stat_0"><br>
+</td>
+</tr>
+<tr class="even">
+<td class="gt_row gt_left" headers="label">&nbsp;&nbsp;&nbsp;&nbsp;0</td>
+<td class="gt_row gt_center" headers="stat_0">266 (77%)</td>
+</tr>
+<tr class="odd">
+<td class="gt_row gt_left" headers="label">&nbsp;&nbsp;&nbsp;&nbsp;1</td>
+<td class="gt_row gt_center" headers="stat_0">31 (9.0%)</td>
+</tr>
+<tr class="even">
+<td class="gt_row gt_left" headers="label">&nbsp;&nbsp;&nbsp;&nbsp;2</td>
+<td class="gt_row gt_center" headers="stat_0">21 (6.1%)</td>
+</tr>
+<tr class="odd">
+<td class="gt_row gt_left" headers="label">&nbsp;&nbsp;&nbsp;&nbsp;3</td>
+<td class="gt_row gt_center" headers="stat_0">27 (7.8%)</td>
+</tr>
+<tr class="even">
+<td class="gt_row gt_left" headers="label">whitewineD</td>
+<td class="gt_row gt_center" headers="stat_0"><br>
+</td>
+</tr>
+<tr class="odd">
+<td class="gt_row gt_left" headers="label">&nbsp;&nbsp;&nbsp;&nbsp;0</td>
+<td class="gt_row gt_center" headers="stat_0">100 (28%)</td>
+</tr>
+<tr class="even">
+<td class="gt_row gt_left" headers="label">&nbsp;&nbsp;&nbsp;&nbsp;1</td>
+<td class="gt_row gt_center" headers="stat_0">73 (20%)</td>
+</tr>
+<tr class="odd">
+<td class="gt_row gt_left" headers="label">&nbsp;&nbsp;&nbsp;&nbsp;2</td>
+<td class="gt_row gt_center" headers="stat_0">67 (19%)</td>
+</tr>
+<tr class="even">
+<td class="gt_row gt_left" headers="label">&nbsp;&nbsp;&nbsp;&nbsp;3</td>
+<td class="gt_row gt_center" headers="stat_0">121 (34%)</td>
+</tr>
+</tbody><tfoot class="gt_footnotes">
+<tr class="odd">
+<td colspan="2" class="gt_footnote"><span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;line-height:0;"><sup>1</sup></span> n (%)</td>
+</tr>
+</tfoot>
+
 </table>
+
 </div>
 </div>
 </div>
@@ -1708,11 +1879,11 @@ <h2 class="anchored" data-anchor-id="modify-variables-format">3.4. Modify variab
 <p>There are some “column types” you would like to modify because either R needs some specific types of data when working with certain variables, or because it will be easier for you to visualise and interpret the data. For example, the variable “group” is encoded as 0 and 1, when you create a table later on, you don’t want to see 0’s and 1’s because that doesn’t mean anything. It is better to change this variable to a factor, with explicit labels, so they actually mean something when they come out in your table.</p>
 <div class="quarto-figure quarto-figure-center">
 <figure class="figure">
-<p><img src="https://cdn.myportfolio.com/45214904-6a61-4e23-98d6-b140f8654a40/bd4ae264-ae51-4d18-bd60-7a058ab42fba_rw_1920.png?h=a3757d1f46f418c59e7e7946d026344e" class="img-fluid figure-img" style="width:50.0%" alt="Cartoon of cute fuzzy monsters dressed up as different X-men characters, working together to add a new column to an existing data frame. Stylized title text reads “dplyr::mutate - add columns, keep existing.” Learn more about dplyr::mutate."></p>
+<p><img src="https://cdn.myportfolio.com/45214904-6a61-4e23-98d6-b140f8654a40/bd4ae264-ae51-4d18-bd60-7a058ab42fba_rw_1920.png?h=a3757d1f46f418c59e7e7946d026344e" class="img-fluid quarto-figure quarto-figure-center figure-img" style="width:50.0%" alt="Cartoon of cute fuzzy monsters dressed up as different X-men characters, working together to add a new column to an existing data frame. Stylized title text reads “dplyr::mutate - add columns, keep existing.” Learn more about dplyr::mutate."></p>
 </figure>
 </div>
 <p>You can use <code>mutate</code> to modify the type of the following variables:</p>
-<table class="table">
+<table class="caption-top table">
 <caption>Table 1: Variable types to modify</caption>
 <colgroup>
 <col style="width: 46%">
@@ -1742,24 +1913,30 @@ <h2 class="anchored" data-anchor-id="modify-variables-format">3.4. Modify variab
 <td>mutate(), as.factor()</td>
 </tr>
 <tr class="odd">
-<td>All the clinical symptom variables</td>
+<td>ill</td>
 <td>integer</td>
-<td>logical</td>
-<td>mutate(across()), as.logical()</td>
+<td>factor</td>
+<td>mutate(), as.factor()</td>
 </tr>
 <tr class="even">
-<td>All the food variables representing the amount of specific foods eaten (those finishing with a capital “D”)</td>
+<td>All the clinical symptom variables</td>
 <td>integer</td>
 <td>factor</td>
 <td>mutate(across()), as.factor()</td>
 </tr>
 <tr class="odd">
+<td>All the food variables representing the amount of specific foods eaten (those finishing with a capital “D”)</td>
+<td>integer</td>
+<td>factor</td>
+<td>mutate(across()), as.factor()</td>
+</tr>
+<tr class="even">
 <td>dayonset</td>
 <td>character</td>
 <td>date</td>
 <td>lubridate::dmy()</td>
 </tr>
-<tr class="even">
+<tr class="odd">
 <td>starthour and dayonset together</td>
 <td><p>integer (starthour)</p>
 <p>date (dayonset)</p></td>
@@ -1768,16 +1945,17 @@ <h2 class="anchored" data-anchor-id="modify-variables-format">3.4. Modify variab
 </tr>
 </tbody>
 </table>
-<section id="sex-group-and-class" class="level4">
-<h4 class="anchored" data-anchor-id="sex-group-and-class">Sex, group and class</h4>
-<p>Let’s start transforming one-by-one the first two variables in the table: <code>sex</code>, and <code>class</code>.</p>
+<section id="sex-class-and-ill" class="level4">
+<h4 class="anchored" data-anchor-id="sex-class-and-ill">Sex, class and ill</h4>
+<p>Let’s start transforming one-by-one the first three variables in the table: <code>sex</code>, <code>class</code> and <code>ill</code>.</p>
 <div class="cell">
-<details open="">
-<summary>Code</summary>
+<details open="" class="code-fold">
+<summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb17"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb17-1"><a href="#cb17-1" aria-hidden="true" tabindex="-1"></a>copdata <span class="ot">&lt;-</span> copdata <span class="sc">%&gt;%</span> </span>
-<span id="cb17-2"><a href="#cb17-2" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">mutate</span>(</span>
+<span id="cb17-2"><a href="#cb17-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(</span>
 <span id="cb17-3"><a href="#cb17-3" aria-hidden="true" tabindex="-1"></a>    <span class="at">sex =</span> <span class="fu">as.factor</span>(sex),</span>
-<span id="cb17-4"><a href="#cb17-4" aria-hidden="true" tabindex="-1"></a>    <span class="at">class =</span> <span class="fu">as.factor</span>(class))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<span id="cb17-4"><a href="#cb17-4" aria-hidden="true" tabindex="-1"></a>    <span class="at">class =</span> <span class="fu">as.factor</span>(class),</span>
+<span id="cb17-5"><a href="#cb17-5" aria-hidden="true" tabindex="-1"></a>    <span class="at">ill =</span> <span class="fu">as.factor</span>(ill))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
 </div>
 </section>
@@ -1788,14 +1966,14 @@ <h4 class="anchored" data-anchor-id="symptoms-and-food-variables">Symptoms and F
 <li>For the variables that are clinical symptoms, we will list them one by one and show you the use of <code>mutate(across( ))</code>.</li>
 </ol>
 <div class="cell">
-<details open="">
-<summary>Code</summary>
+<details open="" class="code-fold">
+<summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb18"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb18-1"><a href="#cb18-1" aria-hidden="true" tabindex="-1"></a>copdata <span class="ot">&lt;-</span> copdata <span class="sc">%&gt;%</span> </span>
-<span id="cb18-2"><a href="#cb18-2" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">mutate</span>(</span>
+<span id="cb18-2"><a href="#cb18-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(</span>
 <span id="cb18-3"><a href="#cb18-3" aria-hidden="true" tabindex="-1"></a>    <span class="co"># clinical symptoms</span></span>
 <span id="cb18-4"><a href="#cb18-4" aria-hidden="true" tabindex="-1"></a>    <span class="fu">across</span>(<span class="at">.cols =</span> <span class="fu">c</span>(diarrhoea, bloody, vomiting,</span>
 <span id="cb18-5"><a href="#cb18-5" aria-hidden="true" tabindex="-1"></a>             abdo, nausea, fever,headache, jointpain), </span>
-<span id="cb18-6"><a href="#cb18-6" aria-hidden="true" tabindex="-1"></a>           <span class="at">.fns =</span> <span class="sc">~</span> <span class="fu">as.logical</span>(.)</span>
+<span id="cb18-6"><a href="#cb18-6" aria-hidden="true" tabindex="-1"></a>           <span class="at">.fns =</span> <span class="sc">~</span> <span class="fu">as.factor</span>(.)</span>
 <span id="cb18-7"><a href="#cb18-7" aria-hidden="true" tabindex="-1"></a>           )</span>
 <span id="cb18-8"><a href="#cb18-8" aria-hidden="true" tabindex="-1"></a>    )</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
@@ -1804,23 +1982,23 @@ <h4 class="anchored" data-anchor-id="symptoms-and-food-variables">Symptoms and F
 <li>For the variables that are food doses, we will show you how to first create a vector of names, following by using <code>mutate(across(all_of( )))</code> on this vector.</li>
 </ol>
 <div class="cell">
-<details open="">
-<summary>Code</summary>
+<details open="" class="code-fold">
+<summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb19"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb19-1"><a href="#cb19-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Create a vector with all the food variables representing the amount of specific foods items eaten (those finishing with a capital "D")</span></span>
 <span id="cb19-2"><a href="#cb19-2" aria-hidden="true" tabindex="-1"></a><span class="co"># One way of doing it:</span></span>
 <span id="cb19-3"><a href="#cb19-3" aria-hidden="true" tabindex="-1"></a>food_dose <span class="ot">&lt;-</span> copdata <span class="sc">%&gt;%</span> </span>
-<span id="cb19-4"><a href="#cb19-4" aria-hidden="true" tabindex="-1"></a>    dplyr<span class="sc">::</span><span class="fu">select</span>(</span>
+<span id="cb19-4"><a href="#cb19-4" aria-hidden="true" tabindex="-1"></a>    <span class="fu">select</span>(</span>
 <span id="cb19-5"><a href="#cb19-5" aria-hidden="true" tabindex="-1"></a>      <span class="fu">ends_with</span>(<span class="st">"D"</span>, <span class="at">ignore.case =</span> <span class="cn">FALSE</span>)) <span class="sc">%&gt;%</span> </span>
 <span id="cb19-6"><a href="#cb19-6" aria-hidden="true" tabindex="-1"></a>    <span class="fu">names</span>()</span>
 <span id="cb19-7"><a href="#cb19-7" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb19-8"><a href="#cb19-8" aria-hidden="true" tabindex="-1"></a><span class="co"># Another way of doing it:</span></span>
-<span id="cb19-9"><a href="#cb19-9" aria-hidden="true" tabindex="-1"></a><span class="co"># food_dose &lt;- c("fetaD", "sardinesD", "eggplantD", "moussakaD", </span></span>
-<span id="cb19-10"><a href="#cb19-10" aria-hidden="true" tabindex="-1"></a><span class="co">#                 "orzoD", "greeksalD", "dessertD", "breadD", </span></span>
+<span id="cb19-9"><a href="#cb19-9" aria-hidden="true" tabindex="-1"></a><span class="co"># food_dose &lt;- c("fetaD", "sardinesD", "eggplantD", "pastaD", </span></span>
+<span id="cb19-10"><a href="#cb19-10" aria-hidden="true" tabindex="-1"></a><span class="co">#                 "vealD", "greeksalD", "dessertD", "breadD", </span></span>
 <span id="cb19-11"><a href="#cb19-11" aria-hidden="true" tabindex="-1"></a><span class="co">#                 "champagneD", "beerD", "redwineD", "whitewineD")</span></span>
 <span id="cb19-12"><a href="#cb19-12" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb19-13"><a href="#cb19-13" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb19-14"><a href="#cb19-14" aria-hidden="true" tabindex="-1"></a>copdata <span class="ot">&lt;-</span> copdata <span class="sc">%&gt;%</span> </span>
-<span id="cb19-15"><a href="#cb19-15" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">mutate</span>(</span>
+<span id="cb19-15"><a href="#cb19-15" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(</span>
 <span id="cb19-16"><a href="#cb19-16" aria-hidden="true" tabindex="-1"></a>    <span class="co"># food dose variables</span></span>
 <span id="cb19-17"><a href="#cb19-17" aria-hidden="true" tabindex="-1"></a>    <span class="fu">across</span>(<span class="at">.cols =</span> <span class="fu">all_of</span>(food_dose), </span>
 <span id="cb19-18"><a href="#cb19-18" aria-hidden="true" tabindex="-1"></a>           <span class="at">.fns =</span> <span class="sc">~</span><span class="fu">as.factor</span>(.))) </span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
@@ -1838,7 +2016,7 @@ <h4 class="anchored" data-anchor-id="symptoms-and-food-variables">Symptoms and F
 </div>
 <div id="callout-3" class="callout-3-contents callout-collapse collapse">
 <div class="callout-body-container callout-body">
-<p>The tilde (<code>~</code>) above is used to apply the transformation <code>as.logical(.)</code> to each selected column, which in our case is either all columns included in <code>food_items</code> and <code>food_dose.</code></p>
+<p>The tilde (<code>~</code>) above is used to apply the transformation <code>as.factor(.)</code> to each selected column, which in our case is either all columns included in <code>food_items</code> and <code>food_dose.</code></p>
 </div>
 </div>
 </div>
@@ -1863,32 +2041,32 @@ <h4 class="anchored" data-anchor-id="date-and-time-variables">Date and time vari
 </div>
 <div class="quarto-figure quarto-figure-center">
 <figure class="figure">
-<p><img src="https://cdn.myportfolio.com/45214904-6a61-4e23-98d6-b140f8654a40/3017cf57-d7d9-4f73-9b66-f2be0ebf2489_rw_1920.png?h=2cdd179bc087c56b43f8aa6ea849ca1a" class="img-fluid figure-img" style="width:50.0%" alt="Fuzzy cartoon monsters in construction hats sorting a pile of “Y” “M” and “D”s into separate piles labeled “Years”, “Months” and “Days”. Title text reads “Lubridate: wrangle times and dates!” Learn more about date-time wrangling with lubridate."></p>
+<p><img src="https://cdn.myportfolio.com/45214904-6a61-4e23-98d6-b140f8654a40/3017cf57-d7d9-4f73-9b66-f2be0ebf2489_rw_1920.png?h=2cdd179bc087c56b43f8aa6ea849ca1a" class="img-fluid quarto-figure quarto-figure-center figure-img" style="width:50.0%" alt="Fuzzy cartoon monsters in construction hats sorting a pile of “Y” “M” and “D”s into separate piles labeled “Years”, “Months” and “Days”. Title text reads “Lubridate: wrangle times and dates!” Learn more about date-time wrangling with lubridate."></p>
 </figure>
 </div>
 <p>You can use <code>lubridate::dmy()</code> to mutate the <code>dayonset</code> variable into a date variable. Note that we are using the function <code>dmy()</code> because dates are formatted as day, then month (abbreviated character string), then year (i.e.&nbsp;“12nov2006”).</p>
 <div class="cell">
-<details open="">
-<summary>Code</summary>
+<details open="" class="code-fold">
+<summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb20"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb20-1"><a href="#cb20-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Have a look at how the data is stored</span></span>
 <span id="cb20-2"><a href="#cb20-2" aria-hidden="true" tabindex="-1"></a><span class="fu">head</span>(copdata<span class="sc">$</span>dayonset)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
 <div class="cell-output cell-output-stdout">
 <pre><code>[1] "6oct2024" ""         ""         ""         "6oct2024" "7oct2024"</code></pre>
 </div>
-<details open="">
-<summary>Code</summary>
+<details open="" class="code-fold">
+<summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb22"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb22-1"><a href="#cb22-1" aria-hidden="true" tabindex="-1"></a><span class="fu">class</span>(copdata<span class="sc">$</span>dayonset)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
 <div class="cell-output cell-output-stdout">
 <pre><code>[1] "character"</code></pre>
 </div>
-<details open="">
-<summary>Code</summary>
+<details open="" class="code-fold">
+<summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb24"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb24-1"><a href="#cb24-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Update copdata:</span></span>
 <span id="cb24-2"><a href="#cb24-2" aria-hidden="true" tabindex="-1"></a>copdata <span class="ot">&lt;-</span> copdata <span class="sc">%&gt;%</span> </span>
 <span id="cb24-3"><a href="#cb24-3" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Change column to date class:</span></span>
-<span id="cb24-4"><a href="#cb24-4" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">mutate</span>(</span>
+<span id="cb24-4"><a href="#cb24-4" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(</span>
 <span id="cb24-5"><a href="#cb24-5" aria-hidden="true" tabindex="-1"></a>    <span class="at">dayonset =</span> lubridate<span class="sc">::</span><span class="fu">dmy</span>(dayonset))</span>
 <span id="cb24-6"><a href="#cb24-6" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb24-7"><a href="#cb24-7" aria-hidden="true" tabindex="-1"></a><span class="co"># Check class of updated column:</span></span>
@@ -1897,8 +2075,8 @@ <h4 class="anchored" data-anchor-id="date-and-time-variables">Date and time vari
 <div class="cell-output cell-output-stdout">
 <pre><code>[1] "Date"</code></pre>
 </div>
-<details open="">
-<summary>Code</summary>
+<details open="" class="code-fold">
+<summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb26"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb26-1"><a href="#cb26-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Have a look at your data now:</span></span>
 <span id="cb26-2"><a href="#cb26-2" aria-hidden="true" tabindex="-1"></a><span class="fu">head</span>(copdata<span class="sc">$</span>dayonset)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
@@ -1911,15 +2089,14 @@ <h4 class="anchored" data-anchor-id="date-and-time-variables">Date and time vari
 <p>Before we proceed, it would be wise to check if any respondents have a value for <code>dayonset</code> but not <code>starthour</code>, or vice versa. The <code>lubridate</code> date-time conversion functions do not have an explicit argument for dealing with missing values, but the <code>truncated = …</code> argument can help prevent spurious date-times being derived from a date-time combination where one value is missing.</p>
 <p>We can check if we have any missing values by cross-tabulating <code>starthour</code> with <code>dayonset</code>:</p>
 <div class="cell">
-<details open="">
-<summary>Code</summary>
+<details open="" class="code-fold">
+<summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb28"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb28-1"><a href="#cb28-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Cross-tabulate dayonset with starthour:</span></span>
-<span id="cb28-2"><a href="#cb28-2" aria-hidden="true" tabindex="-1"></a>janitor<span class="sc">::</span><span class="fu">tabyl</span>(<span class="at">dat =</span> copdata, </span>
+<span id="cb28-2"><a href="#cb28-2" aria-hidden="true" tabindex="-1"></a><span class="fu">tabyl</span>(<span class="at">dat =</span> copdata, </span>
 <span id="cb28-3"><a href="#cb28-3" aria-hidden="true" tabindex="-1"></a>               <span class="at">var1 =</span> starthour, </span>
 <span id="cb28-4"><a href="#cb28-4" aria-hidden="true" tabindex="-1"></a>               <span class="at">var2 =</span> dayonset)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
 <div class="cell-output-display">
-
 <div data-pagedtable="false">
   <script data-pagedtable-source="" type="application/json">
 {"columns":[{"label":["starthour"],"name":[1],"type":["int"],"align":["right"]},{"label":["2024-10-05"],"name":[2],"type":["dbl"],"align":["right"]},{"label":["2024-10-06"],"name":[3],"type":["dbl"],"align":["right"]},{"label":["2024-10-07"],"name":[4],"type":["dbl"],"align":["right"]},{"label":["NA_"],"name":[5],"type":["dbl"],"align":["right"]}],"data":[{"1":"3","2":"0","3":"10","4":"2","5":"0"},{"1":"9","2":"0","3":"97","4":"6","5":"0"},{"1":"15","2":"0","3":"64","4":"6","5":"0"},{"1":"21","2":"9","3":"26","4":"0","5":"0"},{"1":"NA","2":"2","3":"0","4":"0","5":"175"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}}
@@ -1929,20 +2106,20 @@ <h4 class="anchored" data-anchor-id="date-and-time-variables">Date and time vari
 </div>
 <p>This shows us that there are two respondents who had an onset date, but are missing onset time (<code>starthour</code>). Since <code>starthour</code> is represented by 1 - 2 digits, we can specify that we want <code>lubridate</code> to also parse date-time combinations that are truncated by up to two digits:</p>
 <div class="cell">
-<details open="">
-<summary>Code</summary>
+<details open="" class="code-fold">
+<summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb29"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb29-1"><a href="#cb29-1" aria-hidden="true" tabindex="-1"></a>copdata <span class="ot">&lt;-</span> copdata <span class="sc">%&gt;%</span> </span>
 <span id="cb29-2"><a href="#cb29-2" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Combine dayonset and starthour in a new date time variable:</span></span>
 <span id="cb29-3"><a href="#cb29-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">onset_datetime =</span> </span>
-<span id="cb29-4"><a href="#cb29-4" aria-hidden="true" tabindex="-1"></a>           lubridate<span class="sc">::</span><span class="fu">ymd_h</span>(</span>
+<span id="cb29-4"><a href="#cb29-4" aria-hidden="true" tabindex="-1"></a>           <span class="fu">ymd_h</span>(</span>
 <span id="cb29-5"><a href="#cb29-5" aria-hidden="true" tabindex="-1"></a>             <span class="fu">str_glue</span>(<span class="st">"{dayonset}, {starthour}"</span>), </span>
 <span id="cb29-6"><a href="#cb29-6" aria-hidden="true" tabindex="-1"></a>                                           <span class="co"># Deal with missing starthour:</span></span>
 <span id="cb29-7"><a href="#cb29-7" aria-hidden="true" tabindex="-1"></a>                                           <span class="at">truncated =</span> <span class="dv">2</span>))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
 <div class="cell-output cell-output-stderr">
 <pre><code>Warning: There was 1 warning in `mutate()`.
-ℹ In argument: `onset_datetime = lubridate::ymd_h(str_glue("{dayonset},
-  {starthour}"), truncated = 2)`.
+ℹ In argument: `onset_datetime = ymd_h(str_glue("{dayonset}, {starthour}"),
+  truncated = 2)`.
 Caused by warning:
 !  175 failed to parse.</code></pre>
 </div>
@@ -1951,23 +2128,23 @@ <h4 class="anchored" data-anchor-id="date-and-time-variables">Date and time vari
 <p>The argument <code>truncated = 2</code> will result in dates with missing <code>starthour</code> still being converted to date-time, with the missing time being set to <code>00:00</code> (midnight). Whether you want to deal with missing <code>starthour</code> in this way or prefer to code these date-times as <code>NA</code> will depend on how you want them to be represented in your analysis.</p>
 <p>Now we can check that everything in the new combined date-time variable has parsed correctly:</p>
 <div class="cell">
-<details open="">
-<summary>Code</summary>
+<details open="" class="code-fold">
+<summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb31"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb31-1"><a href="#cb31-1" aria-hidden="true" tabindex="-1"></a><span class="fu">head</span>(copdata<span class="sc">$</span>dayonset)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
 <div class="cell-output cell-output-stdout">
 <pre><code>[1] "2024-10-06" NA           NA           NA           "2024-10-06"
 [6] "2024-10-07"</code></pre>
 </div>
-<details open="">
-<summary>Code</summary>
+<details open="" class="code-fold">
+<summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb33"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb33-1"><a href="#cb33-1" aria-hidden="true" tabindex="-1"></a><span class="fu">head</span>(copdata<span class="sc">$</span>starthour)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
 <div class="cell-output cell-output-stdout">
 <pre><code>[1]  9 NA NA NA 15 15</code></pre>
 </div>
-<details open="">
-<summary>Code</summary>
+<details open="" class="code-fold">
+<summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb35"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb35-1"><a href="#cb35-1" aria-hidden="true" tabindex="-1"></a><span class="fu">head</span>(copdata<span class="sc">$</span>onset_datetime)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
 <div class="cell-output cell-output-stdout">
@@ -1999,15 +2176,15 @@ <h2 class="anchored" data-anchor-id="export-clean-data">3.5. Export clean data</
 </div>
 </div>
 <div class="cell">
-<details open="">
-<summary>Code</summary>
-<div class="sourceCode cell-code" id="cb37"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb37-1"><a href="#cb37-1" aria-hidden="true" tabindex="-1"></a>rio<span class="sc">::</span><span class="fu">export</span>(<span class="at">x =</span> copdata, </span>
+<details open="" class="code-fold">
+<summary>Show the code</summary>
+<div class="sourceCode cell-code" id="cb37"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb37-1"><a href="#cb37-1" aria-hidden="true" tabindex="-1"></a><span class="fu">export</span>(<span class="at">x =</span> copdata, </span>
 <span id="cb37-2"><a href="#cb37-2" aria-hidden="true" tabindex="-1"></a>            <span class="at">file =</span> here<span class="sc">::</span><span class="fu">here</span>(<span class="st">"data"</span>, <span class="st">"Spetses_clean1_2024.rds"</span>))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
 </div>
 <div class="quarto-figure quarto-figure-center">
 <figure class="figure">
-<p><img src="https://cdn.myportfolio.com/45214904-6a61-4e23-98d6-b140f8654a40/e4a11b1f-f235-461f-ab73-ce6128c59e49_rw_1920.png?h=a34a4173c717bfbc600a8682d419288e" class="img-fluid figure-img" style="width:80.0%"></p>
+<p><img src="https://cdn.myportfolio.com/45214904-6a61-4e23-98d6-b140f8654a40/e4a11b1f-f235-461f-ab73-ce6128c59e49_rw_1920.png?h=a34a4173c717bfbc600a8682d419288e" class="img-fluid quarto-figure quarto-figure-center figure-img" style="width:80.0%"></p>
 </figure>
 </div>
 
@@ -2061,6 +2238,33 @@ <h2 class="anchored" data-anchor-id="export-clean-data">3.5. Export clean data</
       }
     }
   }
+  const toggleGiscusIfUsed = (isAlternate, darkModeDefault) => {
+    const baseTheme = document.querySelector('#giscus-base-theme')?.value ?? 'light';
+    const alternateTheme = document.querySelector('#giscus-alt-theme')?.value ?? 'dark';
+    let newTheme = '';
+    if(darkModeDefault) {
+      newTheme = isAlternate ? baseTheme : alternateTheme;
+    } else {
+      newTheme = isAlternate ? alternateTheme : baseTheme;
+    }
+    const changeGiscusTheme = () => {
+      // From: https://github.com/giscus/giscus/issues/336
+      const sendMessage = (message) => {
+        const iframe = document.querySelector('iframe.giscus-frame');
+        if (!iframe) return;
+        iframe.contentWindow.postMessage({ giscus: message }, 'https://giscus.app');
+      }
+      sendMessage({
+        setConfig: {
+          theme: newTheme
+        }
+      });
+    }
+    const isGiscussLoaded = window.document.querySelector('iframe.giscus-frame') !== null;
+    if (isGiscussLoaded) {
+      changeGiscusTheme();
+    }
+  }
   const toggleColorMode = (alternate) => {
     // Switch the stylesheets
     const alternateStylesheets = window.document.querySelectorAll('link.quarto-color-scheme.quarto-color-alternate');
@@ -2127,13 +2331,15 @@ <h2 class="anchored" data-anchor-id="export-clean-data">3.5. Export clean data</
       return localAlternateSentinel;
     }
   }
-  let localAlternateSentinel = 'default';
+  const darkModeDefault = false;
+  let localAlternateSentinel = darkModeDefault ? 'alternate' : 'default';
   // Dark / light mode switch
   window.quartoToggleColorScheme = () => {
     // Read the current dark / light value 
     let toAlternate = !hasAlternateSentinel();
     toggleColorMode(toAlternate);
     setStyleSentinel(toAlternate);
+    toggleGiscusIfUsed(toAlternate, darkModeDefault);
   };
   // Ensure there is a toggle, if there isn't float one in the top right
   if (window.document.querySelector('.quarto-color-scheme-toggle') === null) {
@@ -2168,18 +2374,7 @@ <h2 class="anchored" data-anchor-id="export-clean-data">3.5. Export clean data</
     }
     return false;
   }
-  const clipboard = new window.ClipboardJS('.code-copy-button', {
-    text: function(trigger) {
-      const codeEl = trigger.previousElementSibling.cloneNode(true);
-      for (const childEl of codeEl.children) {
-        if (isCodeAnnotation(childEl)) {
-          childEl.remove();
-        }
-      }
-      return codeEl.innerText;
-    }
-  });
-  clipboard.on('success', function(e) {
+  const onCopySuccess = function(e) {
     // button target
     const button = e.trigger;
     // don't keep focus
@@ -2211,7 +2406,29 @@ <h2 class="anchored" data-anchor-id="export-clean-data">3.5. Export clean data</
     }, 1000);
     // clear code selection
     e.clearSelection();
+  }
+  const getTextToCopy = function(trigger) {
+      const codeEl = trigger.previousElementSibling.cloneNode(true);
+      for (const childEl of codeEl.children) {
+        if (isCodeAnnotation(childEl)) {
+          childEl.remove();
+        }
+      }
+      return codeEl.innerText;
+  }
+  const clipboard = new window.ClipboardJS('.code-copy-button:not([data-in-quarto-modal])', {
+    text: getTextToCopy
   });
+  clipboard.on('success', onCopySuccess);
+  if (window.document.getElementById('quarto-embedded-source-code-modal')) {
+    // For code content inside modals, clipBoardJS needs to be initialized with a container option
+    // TODO: Check when it could be a function (https://github.com/zenorocha/clipboard.js/issues/860)
+    const clipboardModal = new window.ClipboardJS('.code-copy-button[data-in-quarto-modal]', {
+      text: getTextToCopy,
+      container: window.document.getElementById('quarto-embedded-source-code-modal')
+    });
+    clipboardModal.on('success', onCopySuccess);
+  }
   const viewSource = window.document.getElementById('quarto-view-source') ||
                      window.document.getElementById('quarto-code-tools-source');
   if (viewSource) {
@@ -2263,10 +2480,27 @@ <h2 class="anchored" data-anchor-id="export-clean-data">3.5. Export clean data</
   if (showAllCode) {
     showAllCode.addEventListener("click", toggleCodeHandler(true));
   }
-  function tippyHover(el, contentFn) {
+    var localhostRegex = new RegExp(/^(?:http|https):\/\/localhost\:?[0-9]*\//);
+    var mailtoRegex = new RegExp(/^mailto:/);
+      var filterRegex = new RegExp('/' + window.location.host + '/');
+    var isInternal = (href) => {
+        return filterRegex.test(href) || localhostRegex.test(href) || mailtoRegex.test(href);
+    }
+    // Inspect non-navigation links and adorn them if external
+ 	var links = window.document.querySelectorAll('a[href]:not(.nav-link):not(.navbar-brand):not(.toc-action):not(.sidebar-link):not(.sidebar-item-toggle):not(.pagination-link):not(.no-external):not([aria-hidden]):not(.dropdown-item):not(.quarto-navigation-tool):not(.about-link)');
+    for (var i=0; i<links.length; i++) {
+      const link = links[i];
+      if (!isInternal(link.href)) {
+        // undo the damage that might have been done by quarto-nav.js in the case of
+        // links that we want to consider external
+        if (link.dataset.originalHref !== undefined) {
+          link.href = link.dataset.originalHref;
+        }
+      }
+    }
+  function tippyHover(el, contentFn, onTriggerFn, onUntriggerFn) {
     const config = {
       allowHTML: true,
-      content: contentFn,
       maxWidth: 500,
       delay: 100,
       arrow: false,
@@ -2276,8 +2510,17 @@ <h2 class="anchored" data-anchor-id="export-clean-data">3.5. Export clean data</
       interactive: true,
       interactiveBorder: 10,
       theme: 'quarto',
-      placement: 'bottom-start'
+      placement: 'bottom-start',
     };
+    if (contentFn) {
+      config.content = contentFn;
+    }
+    if (onTriggerFn) {
+      config.onTrigger = onTriggerFn;
+    }
+    if (onUntriggerFn) {
+      config.onUntrigger = onUntriggerFn;
+    }
     window.tippy(el, config); 
   }
   const noterefs = window.document.querySelectorAll('a[role="doc-noteref"]');
@@ -2289,7 +2532,130 @@ <h2 class="anchored" data-anchor-id="export-clean-data">3.5. Export clean data</
       try { href = new URL(href).hash; } catch {}
       const id = href.replace(/^#\/?/, "");
       const note = window.document.getElementById(id);
-      return note.innerHTML;
+      if (note) {
+        return note.innerHTML;
+      } else {
+        return "";
+      }
+    });
+  }
+  const xrefs = window.document.querySelectorAll('a.quarto-xref');
+  const processXRef = (id, note) => {
+    // Strip column container classes
+    const stripColumnClz = (el) => {
+      el.classList.remove("page-full", "page-columns");
+      if (el.children) {
+        for (const child of el.children) {
+          stripColumnClz(child);
+        }
+      }
+    }
+    stripColumnClz(note)
+    if (id === null || id.startsWith('sec-')) {
+      // Special case sections, only their first couple elements
+      const container = document.createElement("div");
+      if (note.children && note.children.length > 2) {
+        container.appendChild(note.children[0].cloneNode(true));
+        for (let i = 1; i < note.children.length; i++) {
+          const child = note.children[i];
+          if (child.tagName === "P" && child.innerText === "") {
+            continue;
+          } else {
+            container.appendChild(child.cloneNode(true));
+            break;
+          }
+        }
+        if (window.Quarto?.typesetMath) {
+          window.Quarto.typesetMath(container);
+        }
+        return container.innerHTML
+      } else {
+        if (window.Quarto?.typesetMath) {
+          window.Quarto.typesetMath(note);
+        }
+        return note.innerHTML;
+      }
+    } else {
+      // Remove any anchor links if they are present
+      const anchorLink = note.querySelector('a.anchorjs-link');
+      if (anchorLink) {
+        anchorLink.remove();
+      }
+      if (window.Quarto?.typesetMath) {
+        window.Quarto.typesetMath(note);
+      }
+      // TODO in 1.5, we should make sure this works without a callout special case
+      if (note.classList.contains("callout")) {
+        return note.outerHTML;
+      } else {
+        return note.innerHTML;
+      }
+    }
+  }
+  for (var i=0; i<xrefs.length; i++) {
+    const xref = xrefs[i];
+    tippyHover(xref, undefined, function(instance) {
+      instance.disable();
+      let url = xref.getAttribute('href');
+      let hash = undefined; 
+      if (url.startsWith('#')) {
+        hash = url;
+      } else {
+        try { hash = new URL(url).hash; } catch {}
+      }
+      if (hash) {
+        const id = hash.replace(/^#\/?/, "");
+        const note = window.document.getElementById(id);
+        if (note !== null) {
+          try {
+            const html = processXRef(id, note.cloneNode(true));
+            instance.setContent(html);
+          } finally {
+            instance.enable();
+            instance.show();
+          }
+        } else {
+          // See if we can fetch this
+          fetch(url.split('#')[0])
+          .then(res => res.text())
+          .then(html => {
+            const parser = new DOMParser();
+            const htmlDoc = parser.parseFromString(html, "text/html");
+            const note = htmlDoc.getElementById(id);
+            if (note !== null) {
+              const html = processXRef(id, note);
+              instance.setContent(html);
+            } 
+          }).finally(() => {
+            instance.enable();
+            instance.show();
+          });
+        }
+      } else {
+        // See if we can fetch a full url (with no hash to target)
+        // This is a special case and we should probably do some content thinning / targeting
+        fetch(url)
+        .then(res => res.text())
+        .then(html => {
+          const parser = new DOMParser();
+          const htmlDoc = parser.parseFromString(html, "text/html");
+          const note = htmlDoc.querySelector('main.content');
+          if (note !== null) {
+            // This should only happen for chapter cross references
+            // (since there is no id in the URL)
+            // remove the first header
+            if (note.children.length > 0 && note.children[0].tagName === "HEADER") {
+              note.children[0].remove();
+            }
+            const html = processXRef(null, note);
+            instance.setContent(html);
+          } 
+        }).finally(() => {
+          instance.enable();
+          instance.show();
+        });
+      }
+    }, function(instance) {
     });
   }
       let selectedAnnoteEl;
@@ -2333,6 +2699,7 @@ <h2 class="anchored" data-anchor-id="export-clean-data">3.5. Export clean data</
             }
             div.style.top = top - 2 + "px";
             div.style.height = height + 4 + "px";
+            div.style.left = 0;
             let gutterDiv = window.document.getElementById("code-annotation-line-highlight-gutter");
             if (gutterDiv === null) {
               gutterDiv = window.document.createElement("div");
@@ -2358,6 +2725,32 @@ <h2 class="anchored" data-anchor-id="export-clean-data">3.5. Export clean data</
         });
         selectedAnnoteEl = undefined;
       };
+        // Handle positioning of the toggle
+    window.addEventListener(
+      "resize",
+      throttle(() => {
+        elRect = undefined;
+        if (selectedAnnoteEl) {
+          selectCodeLines(selectedAnnoteEl);
+        }
+      }, 10)
+    );
+    function throttle(fn, ms) {
+    let throttle = false;
+    let timer;
+      return (...args) => {
+        if(!throttle) { // first call gets through
+            fn.apply(this, args);
+            throttle = true;
+        } else { // all the others get throttled
+            if(timer) clearTimeout(timer); // cancel #2
+            timer = setTimeout(() => {
+              fn.apply(this, args);
+              timer = throttle = false;
+            }, ms);
+        }
+      };
+    }
       // Attach click handler to the DT
       const annoteDls = window.document.querySelectorAll('dt[data-target-cell]');
       for (const annoteDlNode of annoteDls) {
@@ -2420,347 +2813,354 @@ <h2 class="anchored" data-anchor-id="export-clean-data">3.5. Export clean data</
 <div class="sourceCode" id="cb38" data-shortcodes="false"><pre class="sourceCode markdown code-with-copy"><code class="sourceCode markdown"><span id="cb38-1"><a href="#cb38-1" aria-hidden="true" tabindex="-1"></a><span class="co">---</span></span>
 <span id="cb38-2"><a href="#cb38-2" aria-hidden="true" tabindex="-1"></a><span class="an">title:</span><span class="co"> "Data import and cleaning"</span></span>
 <span id="cb38-3"><a href="#cb38-3" aria-hidden="true" tabindex="-1"></a><span class="an">editor:</span><span class="co"> visual</span></span>
-<span id="cb38-4"><a href="#cb38-4" aria-hidden="true" tabindex="-1"></a><span class="co">---</span></span>
-<span id="cb38-5"><a href="#cb38-5" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-6"><a href="#cb38-6" aria-hidden="true" tabindex="-1"></a><span class="fu"># **1. Learning outcomes**</span></span>
+<span id="cb38-4"><a href="#cb38-4" aria-hidden="true" tabindex="-1"></a><span class="an">editor_options:</span><span class="co"> </span></span>
+<span id="cb38-5"><a href="#cb38-5" aria-hidden="true" tabindex="-1"></a><span class="co">  chunk_output_type: console</span></span>
+<span id="cb38-6"><a href="#cb38-6" aria-hidden="true" tabindex="-1"></a><span class="co">---</span></span>
 <span id="cb38-7"><a href="#cb38-7" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-8"><a href="#cb38-8" aria-hidden="true" tabindex="-1"></a>At the end of the session, participants will be able to:</span>
+<span id="cb38-8"><a href="#cb38-8" aria-hidden="true" tabindex="-1"></a><span class="fu"># **1. Learning outcomes**</span></span>
 <span id="cb38-9"><a href="#cb38-9" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-10"><a href="#cb38-10" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span>Import datasets in R</span>
-<span id="cb38-11"><a href="#cb38-11" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span>Apply basic data management techniques to explore and familiarise themselves with the dataset</span>
-<span id="cb38-12"><a href="#cb38-12" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span>Apply strategies for data cleaning</span>
-<span id="cb38-13"><a href="#cb38-13" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-14"><a href="#cb38-14" aria-hidden="true" tabindex="-1"></a><span class="fu"># **2. Story/plot description**</span></span>
+<span id="cb38-10"><a href="#cb38-10" aria-hidden="true" tabindex="-1"></a>At the end of the session, participants will be able to:</span>
+<span id="cb38-11"><a href="#cb38-11" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb38-12"><a href="#cb38-12" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span>Import datasets in R</span>
+<span id="cb38-13"><a href="#cb38-13" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span>Apply basic data management techniques to explore and familiarise themselves with the dataset</span>
+<span id="cb38-14"><a href="#cb38-14" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span>Apply strategies for data cleaning</span>
 <span id="cb38-15"><a href="#cb38-15" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-16"><a href="#cb38-16" aria-hidden="true" tabindex="-1"></a>You and your team have carried out hundreds of interviews in a very short time. Well done!</span>
+<span id="cb38-16"><a href="#cb38-16" aria-hidden="true" tabindex="-1"></a><span class="fu"># **2. Story/plot description**</span></span>
 <span id="cb38-17"><a href="#cb38-17" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-18"><a href="#cb38-18" aria-hidden="true" tabindex="-1"></a>You will now have your first look at the data. Note that in real life you would have ideally done a pilot of the questionnaire and you would have prepared in advance a codebook and be familiar with the names and format of all variables.</span>
+<span id="cb38-18"><a href="#cb38-18" aria-hidden="true" tabindex="-1"></a>You and your team have carried out hundreds of interviews in a very short time. Well done!</span>
 <span id="cb38-19"><a href="#cb38-19" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-20"><a href="#cb38-20" aria-hidden="true" tabindex="-1"></a>Explore and clean the data you have collected and save a new version of the cleaned dataset in your computer. Be sure to make use of the R code that was provided to you previously, during the R practicals. You can also use the code you wrote during the Applied Epi course. You are expected to look at the help functions and google as well. Try to understand what the functions are doing and what their arguments are.</span>
+<span id="cb38-20"><a href="#cb38-20" aria-hidden="true" tabindex="-1"></a>You will now have your first look at the data. Note that in real life you would have ideally done a pilot of the questionnaire and you would have prepared in advance a codebook and be familiar with the names and format of all variables.</span>
 <span id="cb38-21"><a href="#cb38-21" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-22"><a href="#cb38-22" aria-hidden="true" tabindex="-1"></a>Work on your own computer and help your fellow colleagues as needed. The lead of the outbreak will be in charge of keeping the time (unless you designate another person, but be sure that someone keeps track of time).</span>
+<span id="cb38-22"><a href="#cb38-22" aria-hidden="true" tabindex="-1"></a>Explore and clean the data you have collected and save a new version of the cleaned dataset in your computer. Be sure to make use of the R code that was provided to you previously, during the R practicals. You can also use the code you wrote during the Applied Epi course. You are expected to look at the help functions and google as well. Try to understand what the functions are doing and what their arguments are.</span>
 <span id="cb38-23"><a href="#cb38-23" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-24"><a href="#cb38-24" aria-hidden="true" tabindex="-1"></a>Open R Studio and set up a new R project, and create a <span class="in">`data`</span> and a scripts <span class="in">`folder`</span>.</span>
+<span id="cb38-24"><a href="#cb38-24" aria-hidden="true" tabindex="-1"></a>Work on your own computer and help your fellow colleagues as needed. The lead of the outbreak will be in charge of keeping the time (unless you designate another person, but be sure that someone keeps track of time).</span>
 <span id="cb38-25"><a href="#cb38-25" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-26"><a href="#cb38-26" aria-hidden="true" tabindex="-1"></a><span class="fu"># **3. Tips you could follow**</span></span>
+<span id="cb38-26"><a href="#cb38-26" aria-hidden="true" tabindex="-1"></a>Open R Studio and set up a new R project, and create a <span class="in">`data`</span> and a scripts <span class="in">`folder`</span>.</span>
 <span id="cb38-27"><a href="#cb38-27" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-28"><a href="#cb38-28" aria-hidden="true" tabindex="-1"></a><span class="fu">## 3.1. Install packages and load libraries</span></span>
+<span id="cb38-28"><a href="#cb38-28" aria-hidden="true" tabindex="-1"></a><span class="fu"># **3. Tips you could follow**</span></span>
 <span id="cb38-29"><a href="#cb38-29" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-30"><a href="#cb38-30" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span>Load the R packages you will be using. Update this list of packages as you dig into the code: You may realise you need packages you didn't think about at the beginning. Remember that it is a good practice to load tydiverse at the end of your package list, to avoid <span class="co">[</span><span class="ot">masking functions</span><span class="co">](https://www.ucl.ac.uk/~uctqiax/PUBLG100/2015/faq/masking.html)</span>.</span>
+<span id="cb38-30"><a href="#cb38-30" aria-hidden="true" tabindex="-1"></a><span class="fu">## 3.1. Install packages and load libraries</span></span>
 <span id="cb38-31"><a href="#cb38-31" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-32"><a href="#cb38-32" aria-hidden="true" tabindex="-1"></a><span class="in">```{r, Packages}</span></span>
-<span id="cb38-33"><a href="#cb38-33" aria-hidden="true" tabindex="-1"></a><span class="co"># Check if the 'pacman' package is installed, if not install it:</span></span>
-<span id="cb38-34"><a href="#cb38-34" aria-hidden="true" tabindex="-1"></a><span class="cf">if</span> (<span class="sc">!</span><span class="fu">requireNamespace</span>(<span class="st">"pacman"</span>, <span class="at">quietly =</span> <span class="cn">TRUE</span>)) <span class="fu">install.packages</span>(<span class="st">"pacman"</span>)</span>
-<span id="cb38-35"><a href="#cb38-35" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-36"><a href="#cb38-36" aria-hidden="true" tabindex="-1"></a><span class="co"># Load the required libraries into the current R session:</span></span>
-<span id="cb38-37"><a href="#cb38-37" aria-hidden="true" tabindex="-1"></a>pacman<span class="sc">::</span><span class="fu">p_load</span>(rio, </span>
-<span id="cb38-38"><a href="#cb38-38" aria-hidden="true" tabindex="-1"></a>               here, </span>
-<span id="cb38-39"><a href="#cb38-39" aria-hidden="true" tabindex="-1"></a>               tidyverse, </span>
-<span id="cb38-40"><a href="#cb38-40" aria-hidden="true" tabindex="-1"></a>               skimr,</span>
-<span id="cb38-41"><a href="#cb38-41" aria-hidden="true" tabindex="-1"></a>               plyr,</span>
-<span id="cb38-42"><a href="#cb38-42" aria-hidden="true" tabindex="-1"></a>               janitor,</span>
-<span id="cb38-43"><a href="#cb38-43" aria-hidden="true" tabindex="-1"></a>               lubridate,</span>
-<span id="cb38-44"><a href="#cb38-44" aria-hidden="true" tabindex="-1"></a>               gtsummary, </span>
-<span id="cb38-45"><a href="#cb38-45" aria-hidden="true" tabindex="-1"></a>               flextable,</span>
-<span id="cb38-46"><a href="#cb38-46" aria-hidden="true" tabindex="-1"></a>               officer,</span>
-<span id="cb38-47"><a href="#cb38-47" aria-hidden="true" tabindex="-1"></a>               epikit, </span>
-<span id="cb38-48"><a href="#cb38-48" aria-hidden="true" tabindex="-1"></a>               apyramid, </span>
-<span id="cb38-49"><a href="#cb38-49" aria-hidden="true" tabindex="-1"></a>               scales)</span>
-<span id="cb38-50"><a href="#cb38-50" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-51"><a href="#cb38-51" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb38-52"><a href="#cb38-52" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-53"><a href="#cb38-53" aria-hidden="true" tabindex="-1"></a><span class="fu">## 3.2. Import your data</span></span>
-<span id="cb38-54"><a href="#cb38-54" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-55"><a href="#cb38-55" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span>You can find the <span class="in">`spetses_school.csv`</span> file in EVA. Feel free to explore the **codebook** as well.</span>
-<span id="cb38-56"><a href="#cb38-56" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-57"><a href="#cb38-57" aria-hidden="true" tabindex="-1"></a><span class="in">```{r, Import_data}</span></span>
-<span id="cb38-58"><a href="#cb38-58" aria-hidden="true" tabindex="-1"></a><span class="co"># Import the raw data set:</span></span>
-<span id="cb38-59"><a href="#cb38-59" aria-hidden="true" tabindex="-1"></a>copdata <span class="ot">&lt;-</span> rio<span class="sc">::</span><span class="fu">import</span>(here<span class="sc">::</span><span class="fu">here</span>(<span class="st">"data"</span>, <span class="st">"spetses_school.csv"</span>))</span>
-<span id="cb38-60"><a href="#cb38-60" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb38-61"><a href="#cb38-61" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-62"><a href="#cb38-62" aria-hidden="true" tabindex="-1"></a><span class="fu">## 3.3. Explore and clean your data</span></span>
-<span id="cb38-63"><a href="#cb38-63" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-64"><a href="#cb38-64" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span>Have a look at the structure of your data. Here you have some questions that may help you get started:</span>
-<span id="cb38-65"><a href="#cb38-65" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-66"><a href="#cb38-66" aria-hidden="true" tabindex="-1"></a><span class="ss">    -   </span>How many observations and variables does the dataset contain?</span>
-<span id="cb38-67"><a href="#cb38-67" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-68"><a href="#cb38-68" aria-hidden="true" tabindex="-1"></a><span class="ss">    -   </span>What types of variables do you have and what types of values are recorded?</span>
-<span id="cb38-69"><a href="#cb38-69" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-70"><a href="#cb38-70" aria-hidden="true" tabindex="-1"></a><span class="ss">    -   </span>Do any of the values of the other variables look implausible? Which ones and why? What will you do about it?</span>
-<span id="cb38-71"><a href="#cb38-71" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-72"><a href="#cb38-72" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span>Go ahead and clean your data as you see fit.</span>
-<span id="cb38-73"><a href="#cb38-73" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-74"><a href="#cb38-74" aria-hidden="true" tabindex="-1"></a>::: {.callout-tip title="Need a little bit of help?" collapse="true"}</span>
-<span id="cb38-75"><a href="#cb38-75" aria-hidden="true" tabindex="-1"></a>You could use <span class="in">`head()`</span>, <span class="in">`dim()`</span>, <span class="in">`str()`</span>, or <span class="in">`skim()`</span> to have a quick look at the data and get an idea of which variables you want to explore further. You can also do some tables</span>
-<span id="cb38-76"><a href="#cb38-76" aria-hidden="true" tabindex="-1"></a>:::</span>
-<span id="cb38-77"><a href="#cb38-77" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-78"><a href="#cb38-78" aria-hidden="true" tabindex="-1"></a><span class="in">```{r, Explore_data}</span></span>
-<span id="cb38-79"><a href="#cb38-79" aria-hidden="true" tabindex="-1"></a><span class="fu">head</span>(copdata)</span>
-<span id="cb38-80"><a href="#cb38-80" aria-hidden="true" tabindex="-1"></a><span class="fu">dim</span>(copdata)</span>
-<span id="cb38-81"><a href="#cb38-81" aria-hidden="true" tabindex="-1"></a><span class="fu">str</span>(copdata)</span>
-<span id="cb38-82"><a href="#cb38-82" aria-hidden="true" tabindex="-1"></a>skimr<span class="sc">::</span><span class="fu">skim</span>(copdata)</span>
-<span id="cb38-83"><a href="#cb38-83" aria-hidden="true" tabindex="-1"></a><span class="fu">names</span>(copdata)</span>
-<span id="cb38-84"><a href="#cb38-84" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-85"><a href="#cb38-85" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb38-86"><a href="#cb38-86" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-87"><a href="#cb38-87" aria-hidden="true" tabindex="-1"></a>Let's explore and manage some variables in detail.</span>
-<span id="cb38-88"><a href="#cb38-88" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-89"><a href="#cb38-89" aria-hidden="true" tabindex="-1"></a><span class="fu">#### Age</span></span>
-<span id="cb38-90"><a href="#cb38-90" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-91"><a href="#cb38-91" aria-hidden="true" tabindex="-1"></a>Through visual exploration of the <span class="in">`age`</span> histogram we see that there is at least one very high value, likely implausible. You can then create a cross-tabulation of variables age and group to have a better idea of how your data looks like.</span>
-<span id="cb38-92"><a href="#cb38-92" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-93"><a href="#cb38-93" aria-hidden="true" tabindex="-1"></a><span class="in">```{r age_exploration}</span></span>
-<span id="cb38-94"><a href="#cb38-94" aria-hidden="true" tabindex="-1"></a><span class="co"># Have a look at the histogram  </span></span>
-<span id="cb38-95"><a href="#cb38-95" aria-hidden="true" tabindex="-1"></a><span class="fu">hist</span>(copdata<span class="sc">$</span>age)   </span>
-<span id="cb38-96"><a href="#cb38-96" aria-hidden="true" tabindex="-1"></a><span class="co"># Create cross-tab with the group variable:  </span></span>
-<span id="cb38-97"><a href="#cb38-97" aria-hidden="true" tabindex="-1"></a>janitor<span class="sc">::</span><span class="fu">tabyl</span>(<span class="at">dat =</span> copdata, </span>
-<span id="cb38-98"><a href="#cb38-98" aria-hidden="true" tabindex="-1"></a>               <span class="at">var1 =</span> age, </span>
-<span id="cb38-99"><a href="#cb38-99" aria-hidden="true" tabindex="-1"></a>               <span class="at">var2 =</span> group)</span>
-<span id="cb38-100"><a href="#cb38-100" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb38-101"><a href="#cb38-101" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-102"><a href="#cb38-102" aria-hidden="true" tabindex="-1"></a>Note that <span class="in">`group`</span> is coded as 0 and 1, and these may be difficult to interpret when they mean something other than "no" and "yes", respectively. From the codebook, you know that teachers are represented by 0, and students by 1. Let's change this to make our lives easier:</span>
-<span id="cb38-103"><a href="#cb38-103" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-104"><a href="#cb38-104" aria-hidden="true" tabindex="-1"></a><span class="in">```{r recode_group}</span></span>
-<span id="cb38-105"><a href="#cb38-105" aria-hidden="true" tabindex="-1"></a><span class="co"># Convert group to a factor and label 0 as teacher, 1 as student:</span></span>
-<span id="cb38-106"><a href="#cb38-106" aria-hidden="true" tabindex="-1"></a>copdata <span class="ot">&lt;-</span> copdata <span class="sc">%&gt;%</span> </span>
-<span id="cb38-107"><a href="#cb38-107" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">group =</span> <span class="fu">factor</span>(group, </span>
-<span id="cb38-108"><a href="#cb38-108" aria-hidden="true" tabindex="-1"></a>                        <span class="at">labels =</span> <span class="fu">c</span>(<span class="st">"teacher"</span>, <span class="st">"student"</span>)))</span>
-<span id="cb38-109"><a href="#cb38-109" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-110"><a href="#cb38-110" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb38-111"><a href="#cb38-111" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-112"><a href="#cb38-112" aria-hidden="true" tabindex="-1"></a>Now, have a look at your cross-tab again:</span>
-<span id="cb38-113"><a href="#cb38-113" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-114"><a href="#cb38-114" aria-hidden="true" tabindex="-1"></a><span class="in">```{r cross-tab_age_group}</span></span>
-<span id="cb38-115"><a href="#cb38-115" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-116"><a href="#cb38-116" aria-hidden="true" tabindex="-1"></a>janitor<span class="sc">::</span><span class="fu">tabyl</span>(<span class="at">dat =</span> copdata, </span>
-<span id="cb38-117"><a href="#cb38-117" aria-hidden="true" tabindex="-1"></a>               <span class="at">var1 =</span> age, </span>
-<span id="cb38-118"><a href="#cb38-118" aria-hidden="true" tabindex="-1"></a>               <span class="at">var2 =</span> group) </span>
-<span id="cb38-119"><a href="#cb38-119" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb38-120"><a href="#cb38-120" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-121"><a href="#cb38-121" aria-hidden="true" tabindex="-1"></a>With this table, we can more easily identify ages that are likely to be typographic errors. Specifically:</span>
-<span id="cb38-122"><a href="#cb38-122" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-123"><a href="#cb38-123" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span>There is one teacher aged 16 (likely digit reversal - should be 61)</span>
-<span id="cb38-124"><a href="#cb38-124" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-125"><a href="#cb38-125" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span>There is one student aged 8 (likely missing a digit - should be 18)</span>
-<span id="cb38-126"><a href="#cb38-126" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-127"><a href="#cb38-127" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span>There is one student aged 180 (likely has an extra digit - should be 18)</span>
-<span id="cb38-128"><a href="#cb38-128" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-129"><a href="#cb38-129" aria-hidden="true" tabindex="-1"></a>Assuming you have contacted the school to make sure your suspicions about the actual ages are correct, we can now correct them, using <span class="in">`case_when()`</span>. We create logical conditions to identify the incorrect ages, combining the values for age with the group they belong to:</span>
-<span id="cb38-130"><a href="#cb38-130" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-131"><a href="#cb38-131" aria-hidden="true" tabindex="-1"></a><span class="in">```{r age_cleaning}</span></span>
-<span id="cb38-132"><a href="#cb38-132" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-133"><a href="#cb38-133" aria-hidden="true" tabindex="-1"></a><span class="co"># Update incorrect ages to the correct values with case_when:  </span></span>
-<span id="cb38-134"><a href="#cb38-134" aria-hidden="true" tabindex="-1"></a>copdata <span class="ot">&lt;-</span> copdata <span class="sc">%&gt;%</span>       </span>
-<span id="cb38-135"><a href="#cb38-135" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">age =</span>                         </span>
-<span id="cb38-136"><a href="#cb38-136" aria-hidden="true" tabindex="-1"></a>           <span class="fu">case_when</span>(                            </span>
-<span id="cb38-137"><a href="#cb38-137" aria-hidden="true" tabindex="-1"></a>             <span class="co"># Where respondent is 16 and a teacher, change age to 61:</span></span>
-<span id="cb38-138"><a href="#cb38-138" aria-hidden="true" tabindex="-1"></a>             age <span class="sc">==</span> <span class="dv">16</span> <span class="sc">&amp;</span> group <span class="sc">==</span> <span class="st">"teacher"</span> <span class="sc">~</span> <span class="dv">61</span>,              </span>
-<span id="cb38-139"><a href="#cb38-139" aria-hidden="true" tabindex="-1"></a>             <span class="co"># where respondent is 8 or 180 and a student, change age to 18:</span></span>
-<span id="cb38-140"><a href="#cb38-140" aria-hidden="true" tabindex="-1"></a>             age <span class="sc">==</span> <span class="dv">8</span> <span class="sc">&amp;</span> group <span class="sc">==</span> <span class="st">"student"</span> <span class="sc">~</span> <span class="dv">18</span>,              </span>
-<span id="cb38-141"><a href="#cb38-141" aria-hidden="true" tabindex="-1"></a>             age <span class="sc">==</span> <span class="dv">180</span> <span class="sc">&amp;</span> group <span class="sc">==</span> <span class="st">"student"</span> <span class="sc">~</span> <span class="dv">18</span>,              </span>
-<span id="cb38-142"><a href="#cb38-142" aria-hidden="true" tabindex="-1"></a>             <span class="co"># Keep remaining values as is:              </span></span>
-<span id="cb38-143"><a href="#cb38-143" aria-hidden="true" tabindex="-1"></a>             <span class="at">.default =</span> <span class="fu">as.numeric</span>(age)   </span>
-<span id="cb38-144"><a href="#cb38-144" aria-hidden="true" tabindex="-1"></a>             <span class="co"># if .default is not working, try:</span></span>
-<span id="cb38-145"><a href="#cb38-145" aria-hidden="true" tabindex="-1"></a>             <span class="co"># TRUE ~ age</span></span>
-<span id="cb38-146"><a href="#cb38-146" aria-hidden="true" tabindex="-1"></a>             )                    </span>
-<span id="cb38-147"><a href="#cb38-147" aria-hidden="true" tabindex="-1"></a>         ) </span>
-<span id="cb38-148"><a href="#cb38-148" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb38-149"><a href="#cb38-149" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-150"><a href="#cb38-150" aria-hidden="true" tabindex="-1"></a><span class="al">![](https://cdn.myportfolio.com/45214904-6a61-4e23-98d6-b140f8654a40/6ffcd6d6-c783-4087-ae76-99aa851663ed_rw_1920.png?h=5f67935d69c92e1fda9074d00dfea972)</span>{fig-alt="Cartoon showing a table with creature type (kraken, dragon, or cyclops) and age (baby, teen, or adult). The three creatures listed are adding a new column named “danger”, which contains the word “extreme!” if the type is “kraken”, or “high” for any other type. Stylized text reads “dplyr::case_when() - IF ELSE...but you love it? An example of code is shown: mutate(danger = case_when(type == “kraken” ~ “extreme!”, TRUE ~ “high”). Learn more about case_when." width="80%" fig-align="center"}</span>
-<span id="cb38-151"><a href="#cb38-151" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-152"><a href="#cb38-152" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span>Create summary table for the dose response columns (those finishing with a capital "D") to have a look at their distribution.</span>
-<span id="cb38-153"><a href="#cb38-153" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-154"><a href="#cb38-154" aria-hidden="true" tabindex="-1"></a>::: {.callout-tip title="Need a little bit of help?" collapse="true"}</span>
-<span id="cb38-155"><a href="#cb38-155" aria-hidden="true" tabindex="-1"></a>Use <span class="in">`gtsummary::tbl_summary()`</span>.</span>
-<span id="cb38-156"><a href="#cb38-156" aria-hidden="true" tabindex="-1"></a>:::</span>
-<span id="cb38-157"><a href="#cb38-157" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-158"><a href="#cb38-158" aria-hidden="true" tabindex="-1"></a><span class="in">```{r }</span></span>
-<span id="cb38-159"><a href="#cb38-159" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-160"><a href="#cb38-160" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: check_dose_cols</span></span>
-<span id="cb38-161"><a href="#cb38-161" aria-hidden="true" tabindex="-1"></a><span class="co">#| tbl-cap: no caption</span></span>
+<span id="cb38-32"><a href="#cb38-32" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span>Load the R packages you will be using. Update this list of packages as you dig into the code: You may realise you need packages you didn't think about at the beginning. Remember that it is a good practice to load tydiverse at the end of your package list, to avoid <span class="co">[</span><span class="ot">masking functions</span><span class="co">](https://www.ucl.ac.uk/~uctqiax/PUBLG100/2015/faq/masking.html)</span>.</span>
+<span id="cb38-33"><a href="#cb38-33" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb38-34"><a href="#cb38-34" aria-hidden="true" tabindex="-1"></a><span class="in">```{r, Packages}</span></span>
+<span id="cb38-35"><a href="#cb38-35" aria-hidden="true" tabindex="-1"></a><span class="in"># Check if the 'pacman' package is installed, if not install it:</span></span>
+<span id="cb38-36"><a href="#cb38-36" aria-hidden="true" tabindex="-1"></a><span class="in">if (!requireNamespace("pacman", quietly = TRUE)) install.packages("pacman")</span></span>
+<span id="cb38-37"><a href="#cb38-37" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb38-38"><a href="#cb38-38" aria-hidden="true" tabindex="-1"></a><span class="in"># Load the required libraries into the current R session:</span></span>
+<span id="cb38-39"><a href="#cb38-39" aria-hidden="true" tabindex="-1"></a><span class="in">pacman::p_load(rio, </span></span>
+<span id="cb38-40"><a href="#cb38-40" aria-hidden="true" tabindex="-1"></a><span class="in">               here, </span></span>
+<span id="cb38-41"><a href="#cb38-41" aria-hidden="true" tabindex="-1"></a><span class="in">               skimr,</span></span>
+<span id="cb38-42"><a href="#cb38-42" aria-hidden="true" tabindex="-1"></a><span class="in">               plyr,</span></span>
+<span id="cb38-43"><a href="#cb38-43" aria-hidden="true" tabindex="-1"></a><span class="in">               janitor,</span></span>
+<span id="cb38-44"><a href="#cb38-44" aria-hidden="true" tabindex="-1"></a><span class="in">               lubridate,</span></span>
+<span id="cb38-45"><a href="#cb38-45" aria-hidden="true" tabindex="-1"></a><span class="in">               gtsummary, </span></span>
+<span id="cb38-46"><a href="#cb38-46" aria-hidden="true" tabindex="-1"></a><span class="in">               flextable,</span></span>
+<span id="cb38-47"><a href="#cb38-47" aria-hidden="true" tabindex="-1"></a><span class="in">               officer,</span></span>
+<span id="cb38-48"><a href="#cb38-48" aria-hidden="true" tabindex="-1"></a><span class="in">               epikit, </span></span>
+<span id="cb38-49"><a href="#cb38-49" aria-hidden="true" tabindex="-1"></a><span class="in">               apyramid, </span></span>
+<span id="cb38-50"><a href="#cb38-50" aria-hidden="true" tabindex="-1"></a><span class="in">               scales,</span></span>
+<span id="cb38-51"><a href="#cb38-51" aria-hidden="true" tabindex="-1"></a><span class="in">               tidyverse </span></span>
+<span id="cb38-52"><a href="#cb38-52" aria-hidden="true" tabindex="-1"></a><span class="in">)</span></span>
+<span id="cb38-53"><a href="#cb38-53" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb38-54"><a href="#cb38-54" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb38-55"><a href="#cb38-55" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb38-56"><a href="#cb38-56" aria-hidden="true" tabindex="-1"></a><span class="fu">## 3.2. Import your data</span></span>
+<span id="cb38-57"><a href="#cb38-57" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb38-58"><a href="#cb38-58" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span>You can find the <span class="in">`spetses_school.csv`</span> file in EVA. Feel free to explore the **codebook** as well.</span>
+<span id="cb38-59"><a href="#cb38-59" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb38-60"><a href="#cb38-60" aria-hidden="true" tabindex="-1"></a><span class="in">```{r, Import_data}</span></span>
+<span id="cb38-61"><a href="#cb38-61" aria-hidden="true" tabindex="-1"></a><span class="in"># Import the raw data set:</span></span>
+<span id="cb38-62"><a href="#cb38-62" aria-hidden="true" tabindex="-1"></a><span class="in">copdata &lt;- import(here::here("data", "spetses_school.csv"))</span></span>
+<span id="cb38-63"><a href="#cb38-63" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb38-64"><a href="#cb38-64" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb38-65"><a href="#cb38-65" aria-hidden="true" tabindex="-1"></a><span class="fu">## 3.3. Explore and clean your data</span></span>
+<span id="cb38-66"><a href="#cb38-66" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb38-67"><a href="#cb38-67" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span>Have a look at the structure of your data. Here you have some questions that may help you get started:</span>
+<span id="cb38-68"><a href="#cb38-68" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb38-69"><a href="#cb38-69" aria-hidden="true" tabindex="-1"></a><span class="ss">    -   </span>How many observations and variables does the dataset contain?</span>
+<span id="cb38-70"><a href="#cb38-70" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb38-71"><a href="#cb38-71" aria-hidden="true" tabindex="-1"></a><span class="ss">    -   </span>What types of variables do you have and what types of values are recorded?</span>
+<span id="cb38-72"><a href="#cb38-72" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb38-73"><a href="#cb38-73" aria-hidden="true" tabindex="-1"></a><span class="ss">    -   </span>Do any of the values of the other variables look implausible? Which ones and why? What will you do about it?</span>
+<span id="cb38-74"><a href="#cb38-74" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb38-75"><a href="#cb38-75" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span>Go ahead and clean your data as you see fit.</span>
+<span id="cb38-76"><a href="#cb38-76" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb38-77"><a href="#cb38-77" aria-hidden="true" tabindex="-1"></a>::: {.callout-tip title="Need a little bit of help?" collapse="true"}</span>
+<span id="cb38-78"><a href="#cb38-78" aria-hidden="true" tabindex="-1"></a>You could use <span class="in">`head()`</span>, <span class="in">`dim()`</span>, <span class="in">`str()`</span>, or <span class="in">`skim()`</span> to have a quick look at the data and get an idea of which variables you want to explore further. You can also do some tables</span>
+<span id="cb38-79"><a href="#cb38-79" aria-hidden="true" tabindex="-1"></a>:::</span>
+<span id="cb38-80"><a href="#cb38-80" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb38-81"><a href="#cb38-81" aria-hidden="true" tabindex="-1"></a><span class="in">```{r, Explore_data}</span></span>
+<span id="cb38-82"><a href="#cb38-82" aria-hidden="true" tabindex="-1"></a><span class="in">head(copdata)</span></span>
+<span id="cb38-83"><a href="#cb38-83" aria-hidden="true" tabindex="-1"></a><span class="in">dim(copdata)</span></span>
+<span id="cb38-84"><a href="#cb38-84" aria-hidden="true" tabindex="-1"></a><span class="in">str(copdata)</span></span>
+<span id="cb38-85"><a href="#cb38-85" aria-hidden="true" tabindex="-1"></a><span class="in">skim(copdata)</span></span>
+<span id="cb38-86"><a href="#cb38-86" aria-hidden="true" tabindex="-1"></a><span class="in">names(copdata)</span></span>
+<span id="cb38-87"><a href="#cb38-87" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb38-88"><a href="#cb38-88" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb38-89"><a href="#cb38-89" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb38-90"><a href="#cb38-90" aria-hidden="true" tabindex="-1"></a>Let's explore and manage some variables in detail.</span>
+<span id="cb38-91"><a href="#cb38-91" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb38-92"><a href="#cb38-92" aria-hidden="true" tabindex="-1"></a><span class="fu">#### Age</span></span>
+<span id="cb38-93"><a href="#cb38-93" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb38-94"><a href="#cb38-94" aria-hidden="true" tabindex="-1"></a>Through visual exploration of the <span class="in">`age`</span> histogram we see that there is at least one very high value, likely implausible. You can then create a cross-tabulation of variables age and group to have a better idea of how your data looks like.</span>
+<span id="cb38-95"><a href="#cb38-95" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb38-96"><a href="#cb38-96" aria-hidden="true" tabindex="-1"></a><span class="in">```{r age_exploration}</span></span>
+<span id="cb38-97"><a href="#cb38-97" aria-hidden="true" tabindex="-1"></a><span class="in"># Have a look at the histogram  </span></span>
+<span id="cb38-98"><a href="#cb38-98" aria-hidden="true" tabindex="-1"></a><span class="in">hist(copdata$age)   </span></span>
+<span id="cb38-99"><a href="#cb38-99" aria-hidden="true" tabindex="-1"></a><span class="in"># Create cross-tab with the group variable:  </span></span>
+<span id="cb38-100"><a href="#cb38-100" aria-hidden="true" tabindex="-1"></a><span class="in">tabyl(dat = copdata, </span></span>
+<span id="cb38-101"><a href="#cb38-101" aria-hidden="true" tabindex="-1"></a><span class="in">               var1 = age, </span></span>
+<span id="cb38-102"><a href="#cb38-102" aria-hidden="true" tabindex="-1"></a><span class="in">               var2 = group)</span></span>
+<span id="cb38-103"><a href="#cb38-103" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb38-104"><a href="#cb38-104" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb38-105"><a href="#cb38-105" aria-hidden="true" tabindex="-1"></a>Note that <span class="in">`group`</span> is coded as 0 and 1, and these may be difficult to interpret when they mean something other than "no" and "yes", respectively. From the codebook, you know that teachers are represented by 0, and students by 1. Let's change this to make our lives easier:</span>
+<span id="cb38-106"><a href="#cb38-106" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb38-107"><a href="#cb38-107" aria-hidden="true" tabindex="-1"></a><span class="in">```{r recode_group}</span></span>
+<span id="cb38-108"><a href="#cb38-108" aria-hidden="true" tabindex="-1"></a><span class="in"># Convert group to a factor and label 0 as teacher, 1 as student:</span></span>
+<span id="cb38-109"><a href="#cb38-109" aria-hidden="true" tabindex="-1"></a><span class="in">copdata &lt;- copdata %&gt;% </span></span>
+<span id="cb38-110"><a href="#cb38-110" aria-hidden="true" tabindex="-1"></a><span class="in">  mutate(group = factor(group, </span></span>
+<span id="cb38-111"><a href="#cb38-111" aria-hidden="true" tabindex="-1"></a><span class="in">                        labels = c("teacher", "student")))</span></span>
+<span id="cb38-112"><a href="#cb38-112" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb38-113"><a href="#cb38-113" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb38-114"><a href="#cb38-114" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb38-115"><a href="#cb38-115" aria-hidden="true" tabindex="-1"></a>Now, have a look at your cross-tab again:</span>
+<span id="cb38-116"><a href="#cb38-116" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb38-117"><a href="#cb38-117" aria-hidden="true" tabindex="-1"></a><span class="in">```{r cross-tab_age_group}</span></span>
+<span id="cb38-118"><a href="#cb38-118" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb38-119"><a href="#cb38-119" aria-hidden="true" tabindex="-1"></a><span class="in">tabyl(dat = copdata, </span></span>
+<span id="cb38-120"><a href="#cb38-120" aria-hidden="true" tabindex="-1"></a><span class="in">               var1 = age, </span></span>
+<span id="cb38-121"><a href="#cb38-121" aria-hidden="true" tabindex="-1"></a><span class="in">               var2 = group) </span></span>
+<span id="cb38-122"><a href="#cb38-122" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb38-123"><a href="#cb38-123" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb38-124"><a href="#cb38-124" aria-hidden="true" tabindex="-1"></a>With this table, we can more easily identify ages that are likely to be typographic errors. Specifically:</span>
+<span id="cb38-125"><a href="#cb38-125" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb38-126"><a href="#cb38-126" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span>There is one teacher aged 16 (likely digit reversal - should be 61)</span>
+<span id="cb38-127"><a href="#cb38-127" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb38-128"><a href="#cb38-128" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span>There is one student aged 8 (likely missing a digit - should be 18)</span>
+<span id="cb38-129"><a href="#cb38-129" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb38-130"><a href="#cb38-130" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span>There is one student aged 180 (likely has an extra digit - should be 18)</span>
+<span id="cb38-131"><a href="#cb38-131" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb38-132"><a href="#cb38-132" aria-hidden="true" tabindex="-1"></a>Assuming you have contacted the school to make sure your suspicions about the actual ages are correct, we can now correct them, using <span class="in">`case_when()`</span>. We create logical conditions to identify the incorrect ages, combining the values for age with the group they belong to:</span>
+<span id="cb38-133"><a href="#cb38-133" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb38-134"><a href="#cb38-134" aria-hidden="true" tabindex="-1"></a><span class="in">```{r age_cleaning}</span></span>
+<span id="cb38-135"><a href="#cb38-135" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb38-136"><a href="#cb38-136" aria-hidden="true" tabindex="-1"></a><span class="in"># Update incorrect ages to the correct values with case_when:  </span></span>
+<span id="cb38-137"><a href="#cb38-137" aria-hidden="true" tabindex="-1"></a><span class="in">copdata &lt;- copdata %&gt;%       </span></span>
+<span id="cb38-138"><a href="#cb38-138" aria-hidden="true" tabindex="-1"></a><span class="in">  mutate(age =                         </span></span>
+<span id="cb38-139"><a href="#cb38-139" aria-hidden="true" tabindex="-1"></a><span class="in">           case_when(                            </span></span>
+<span id="cb38-140"><a href="#cb38-140" aria-hidden="true" tabindex="-1"></a><span class="in">             # Where respondent is 16 and a teacher, change age to 61:</span></span>
+<span id="cb38-141"><a href="#cb38-141" aria-hidden="true" tabindex="-1"></a><span class="in">             age == 16 &amp; group == "teacher" ~ 61,              </span></span>
+<span id="cb38-142"><a href="#cb38-142" aria-hidden="true" tabindex="-1"></a><span class="in">             # where respondent is 8 or 180 and a student, change age to 18:</span></span>
+<span id="cb38-143"><a href="#cb38-143" aria-hidden="true" tabindex="-1"></a><span class="in">             age == 8 &amp; group == "student" ~ 18,              </span></span>
+<span id="cb38-144"><a href="#cb38-144" aria-hidden="true" tabindex="-1"></a><span class="in">             age == 180 &amp; group == "student" ~ 18,              </span></span>
+<span id="cb38-145"><a href="#cb38-145" aria-hidden="true" tabindex="-1"></a><span class="in">             # Keep remaining values as is:              </span></span>
+<span id="cb38-146"><a href="#cb38-146" aria-hidden="true" tabindex="-1"></a><span class="in">             .default = as.numeric(age)   </span></span>
+<span id="cb38-147"><a href="#cb38-147" aria-hidden="true" tabindex="-1"></a><span class="in">             # if .default is not working, try:</span></span>
+<span id="cb38-148"><a href="#cb38-148" aria-hidden="true" tabindex="-1"></a><span class="in">             # TRUE ~ age</span></span>
+<span id="cb38-149"><a href="#cb38-149" aria-hidden="true" tabindex="-1"></a><span class="in">             )                    </span></span>
+<span id="cb38-150"><a href="#cb38-150" aria-hidden="true" tabindex="-1"></a><span class="in">         ) </span></span>
+<span id="cb38-151"><a href="#cb38-151" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb38-152"><a href="#cb38-152" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb38-153"><a href="#cb38-153" aria-hidden="true" tabindex="-1"></a><span class="al">![](https://cdn.myportfolio.com/45214904-6a61-4e23-98d6-b140f8654a40/6ffcd6d6-c783-4087-ae76-99aa851663ed_rw_1920.png?h=5f67935d69c92e1fda9074d00dfea972)</span>{fig-alt="Cartoon showing a table with creature type (kraken, dragon, or cyclops) and age (baby, teen, or adult). The three creatures listed are adding a new column named “danger”, which contains the word “extreme!” if the type is “kraken”, or “high” for any other type. Stylized text reads “dplyr::case_when() - IF ELSE...but you love it? An example of code is shown: mutate(danger = case_when(type == “kraken” ~ “extreme!”, TRUE ~ “high”). Learn more about case_when." width="80%" fig-align="center"}</span>
+<span id="cb38-154"><a href="#cb38-154" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb38-155"><a href="#cb38-155" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span>Create summary table for the dose response columns (those finishing with a capital "D") to have a look at their distribution.</span>
+<span id="cb38-156"><a href="#cb38-156" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb38-157"><a href="#cb38-157" aria-hidden="true" tabindex="-1"></a>::: {.callout-tip title="Need a little bit of help?" collapse="true"}</span>
+<span id="cb38-158"><a href="#cb38-158" aria-hidden="true" tabindex="-1"></a>Use <span class="in">`gtsummary::tbl_summary()`</span>.</span>
+<span id="cb38-159"><a href="#cb38-159" aria-hidden="true" tabindex="-1"></a>:::</span>
+<span id="cb38-160"><a href="#cb38-160" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb38-161"><a href="#cb38-161" aria-hidden="true" tabindex="-1"></a><span class="in">```{r }</span></span>
 <span id="cb38-162"><a href="#cb38-162" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-163"><a href="#cb38-163" aria-hidden="true" tabindex="-1"></a><span class="co"># Create summary table for dose response columns: </span></span>
-<span id="cb38-164"><a href="#cb38-164" aria-hidden="true" tabindex="-1"></a>drtable <span class="ot">&lt;-</span> copdata <span class="sc">%&gt;%</span>       </span>
-<span id="cb38-165"><a href="#cb38-165" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Select all the columns with column names that end in upper case 'D':   </span></span>
-<span id="cb38-166"><a href="#cb38-166" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(<span class="fu">ends_with</span>(<span class="st">"D"</span>, <span class="at">ignore.case =</span> <span class="cn">FALSE</span>)) <span class="sc">%&gt;%</span>       </span>
-<span id="cb38-167"><a href="#cb38-167" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Create the summary table, excluding missing values:   </span></span>
-<span id="cb38-168"><a href="#cb38-168" aria-hidden="true" tabindex="-1"></a>  gtsummary<span class="sc">::</span><span class="fu">tbl_summary</span>(<span class="at">missing =</span> <span class="st">"no"</span>) </span>
-<span id="cb38-169"><a href="#cb38-169" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-170"><a href="#cb38-170" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Print the summary table: </span></span>
-<span id="cb38-171"><a href="#cb38-171" aria-hidden="true" tabindex="-1"></a>drtable</span>
-<span id="cb38-172"><a href="#cb38-172" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb38-173"><a href="#cb38-173" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-174"><a href="#cb38-174" aria-hidden="true" tabindex="-1"></a><span class="fu">## 3.4. Modify variables format</span></span>
-<span id="cb38-175"><a href="#cb38-175" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-176"><a href="#cb38-176" aria-hidden="true" tabindex="-1"></a>There are some "column types" you would like to modify because either R needs some specific types of data when working with certain variables, or because it will be easier for you to visualise and interpret the data. For example, the variable "group" is encoded as 0 and 1, when you create a table later on, you don't want to see 0's and 1's because that doesn't mean anything. It is better to change this variable to a factor, with explicit labels, so they actually mean something when they come out in your table.</span>
-<span id="cb38-177"><a href="#cb38-177" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-178"><a href="#cb38-178" aria-hidden="true" tabindex="-1"></a><span class="al">![](https://cdn.myportfolio.com/45214904-6a61-4e23-98d6-b140f8654a40/bd4ae264-ae51-4d18-bd60-7a058ab42fba_rw_1920.png?h=a3757d1f46f418c59e7e7946d026344e)</span>{fig-alt="Cartoon of cute fuzzy monsters dressed up as different X-men characters, working together to add a new column to an existing data frame. Stylized title text reads “dplyr::mutate - add columns, keep existing.” Learn more about dplyr::mutate." width="50%" fig-align="center"}</span>
-<span id="cb38-179"><a href="#cb38-179" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-180"><a href="#cb38-180" aria-hidden="true" tabindex="-1"></a>You can use <span class="in">`mutate`</span> to modify the type of the following variables:</span>
-<span id="cb38-181"><a href="#cb38-181" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-182"><a href="#cb38-182" aria-hidden="true" tabindex="-1"></a>+-------------------------------------------------------------------------------------------------------------+---------------------+-----------------+--------------------------------------------------------------------------------------+</span>
-<span id="cb38-183"><a href="#cb38-183" aria-hidden="true" tabindex="-1"></a>| Variable name                                                                                               | Original            | Desired         | Hint                                                                                 |</span>
-<span id="cb38-184"><a href="#cb38-184" aria-hidden="true" tabindex="-1"></a>+=============================================================================================================+=====================+=================+======================================================================================+</span>
-<span id="cb38-185"><a href="#cb38-185" aria-hidden="true" tabindex="-1"></a>| sex                                                                                                         | character           | factor          | mutate(), as.factor()                                                                |</span>
-<span id="cb38-186"><a href="#cb38-186" aria-hidden="true" tabindex="-1"></a>+-------------------------------------------------------------------------------------------------------------+---------------------+-----------------+--------------------------------------------------------------------------------------+</span>
-<span id="cb38-187"><a href="#cb38-187" aria-hidden="true" tabindex="-1"></a>| class                                                                                                       | integer             | factor          | mutate(), as.factor()                                                                |</span>
-<span id="cb38-188"><a href="#cb38-188" aria-hidden="true" tabindex="-1"></a>+-------------------------------------------------------------------------------------------------------------+---------------------+-----------------+--------------------------------------------------------------------------------------+</span>
-<span id="cb38-189"><a href="#cb38-189" aria-hidden="true" tabindex="-1"></a>| All the clinical symptom variables                                                                          | integer             | logical         | mutate(across()), as.logical()                                                       |</span>
-<span id="cb38-190"><a href="#cb38-190" aria-hidden="true" tabindex="-1"></a>+-------------------------------------------------------------------------------------------------------------+---------------------+-----------------+--------------------------------------------------------------------------------------+</span>
-<span id="cb38-191"><a href="#cb38-191" aria-hidden="true" tabindex="-1"></a>| All the food variables representing the amount of specific foods eaten (those finishing with a capital "D") | integer             | factor          | mutate(across()), as.factor()                                                        |</span>
-<span id="cb38-192"><a href="#cb38-192" aria-hidden="true" tabindex="-1"></a>+-------------------------------------------------------------------------------------------------------------+---------------------+-----------------+--------------------------------------------------------------------------------------+</span>
-<span id="cb38-193"><a href="#cb38-193" aria-hidden="true" tabindex="-1"></a>| dayonset                                                                                                    | character           | date            | lubridate::dmy()                                                                     |</span>
-<span id="cb38-194"><a href="#cb38-194" aria-hidden="true" tabindex="-1"></a>+-------------------------------------------------------------------------------------------------------------+---------------------+-----------------+--------------------------------------------------------------------------------------+</span>
-<span id="cb38-195"><a href="#cb38-195" aria-hidden="true" tabindex="-1"></a>| starthour and dayonset together                                                                             | integer (starthour) | POSIXct, POSIXt | lubridate::ymd_h() could have inside stringr::str_glue() with dayonset and starthour |</span>
-<span id="cb38-196"><a href="#cb38-196" aria-hidden="true" tabindex="-1"></a>|                                                                                                             |                     |                 |                                                                                      |</span>
-<span id="cb38-197"><a href="#cb38-197" aria-hidden="true" tabindex="-1"></a>|                                                                                                             | date (dayonset)     |                 |                                                                                      |</span>
-<span id="cb38-198"><a href="#cb38-198" aria-hidden="true" tabindex="-1"></a>+-------------------------------------------------------------------------------------------------------------+---------------------+-----------------+--------------------------------------------------------------------------------------+</span>
-<span id="cb38-199"><a href="#cb38-199" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-200"><a href="#cb38-200" aria-hidden="true" tabindex="-1"></a>: Table 1: Variable types to modify</span>
-<span id="cb38-201"><a href="#cb38-201" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-202"><a href="#cb38-202" aria-hidden="true" tabindex="-1"></a><span class="fu">#### Sex, group and class</span></span>
-<span id="cb38-203"><a href="#cb38-203" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-204"><a href="#cb38-204" aria-hidden="true" tabindex="-1"></a>Let's start transforming one-by-one the first two variables in the table: <span class="in">`sex`</span>, and <span class="in">`class`</span>.</span>
-<span id="cb38-205"><a href="#cb38-205" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-206"><a href="#cb38-206" aria-hidden="true" tabindex="-1"></a><span class="in">```{r, mutate_simple}</span></span>
-<span id="cb38-207"><a href="#cb38-207" aria-hidden="true" tabindex="-1"></a>copdata <span class="ot">&lt;-</span> copdata <span class="sc">%&gt;%</span> </span>
-<span id="cb38-208"><a href="#cb38-208" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">mutate</span>(</span>
-<span id="cb38-209"><a href="#cb38-209" aria-hidden="true" tabindex="-1"></a>    <span class="at">sex =</span> <span class="fu">as.factor</span>(sex),</span>
-<span id="cb38-210"><a href="#cb38-210" aria-hidden="true" tabindex="-1"></a>    <span class="at">class =</span> <span class="fu">as.factor</span>(class))</span>
-<span id="cb38-211"><a href="#cb38-211" aria-hidden="true" tabindex="-1"></a> </span>
-<span id="cb38-212"><a href="#cb38-212" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb38-213"><a href="#cb38-213" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-214"><a href="#cb38-214" aria-hidden="true" tabindex="-1"></a><span class="fu">#### Symptoms and Food variables</span></span>
-<span id="cb38-215"><a href="#cb38-215" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-216"><a href="#cb38-216" aria-hidden="true" tabindex="-1"></a>For these variables, we are going to show you a couple of different ways to carry out the same variable type transformation in a *set* of variables, so you don't need to do one variable at a time. We are showing you these ways so you see alternative ways to do the same thing.</span>
-<span id="cb38-217"><a href="#cb38-217" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-218"><a href="#cb38-218" aria-hidden="true" tabindex="-1"></a><span class="ss">1.  </span>For the variables that are clinical symptoms, we will list them one by one and show you the use of <span class="in">`mutate(across( ))`</span>.</span>
+<span id="cb38-163"><a href="#cb38-163" aria-hidden="true" tabindex="-1"></a><span class="in">#| label: check_dose_cols</span></span>
+<span id="cb38-164"><a href="#cb38-164" aria-hidden="true" tabindex="-1"></a><span class="in">#| tbl-cap: no caption</span></span>
+<span id="cb38-165"><a href="#cb38-165" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb38-166"><a href="#cb38-166" aria-hidden="true" tabindex="-1"></a><span class="in"># Create summary table for dose response columns: </span></span>
+<span id="cb38-167"><a href="#cb38-167" aria-hidden="true" tabindex="-1"></a><span class="in">drtable &lt;- copdata %&gt;%       </span></span>
+<span id="cb38-168"><a href="#cb38-168" aria-hidden="true" tabindex="-1"></a><span class="in">  # Select all the columns with column names that end in upper case 'D':   </span></span>
+<span id="cb38-169"><a href="#cb38-169" aria-hidden="true" tabindex="-1"></a><span class="in">  select(ends_with("D", ignore.case = FALSE)) %&gt;%       </span></span>
+<span id="cb38-170"><a href="#cb38-170" aria-hidden="true" tabindex="-1"></a><span class="in">  # Create the summary table, excluding missing values:   </span></span>
+<span id="cb38-171"><a href="#cb38-171" aria-hidden="true" tabindex="-1"></a><span class="in">  tbl_summary(missing = "no") </span></span>
+<span id="cb38-172"><a href="#cb38-172" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb38-173"><a href="#cb38-173" aria-hidden="true" tabindex="-1"></a><span class="in">  # Print the summary table: </span></span>
+<span id="cb38-174"><a href="#cb38-174" aria-hidden="true" tabindex="-1"></a><span class="in">drtable</span></span>
+<span id="cb38-175"><a href="#cb38-175" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb38-176"><a href="#cb38-176" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb38-177"><a href="#cb38-177" aria-hidden="true" tabindex="-1"></a><span class="fu">## 3.4. Modify variables format</span></span>
+<span id="cb38-178"><a href="#cb38-178" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb38-179"><a href="#cb38-179" aria-hidden="true" tabindex="-1"></a>There are some "column types" you would like to modify because either R needs some specific types of data when working with certain variables, or because it will be easier for you to visualise and interpret the data. For example, the variable "group" is encoded as 0 and 1, when you create a table later on, you don't want to see 0's and 1's because that doesn't mean anything. It is better to change this variable to a factor, with explicit labels, so they actually mean something when they come out in your table.</span>
+<span id="cb38-180"><a href="#cb38-180" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb38-181"><a href="#cb38-181" aria-hidden="true" tabindex="-1"></a><span class="al">![](https://cdn.myportfolio.com/45214904-6a61-4e23-98d6-b140f8654a40/bd4ae264-ae51-4d18-bd60-7a058ab42fba_rw_1920.png?h=a3757d1f46f418c59e7e7946d026344e)</span>{fig-alt="Cartoon of cute fuzzy monsters dressed up as different X-men characters, working together to add a new column to an existing data frame. Stylized title text reads “dplyr::mutate - add columns, keep existing.” Learn more about dplyr::mutate." width="50%" fig-align="center"}</span>
+<span id="cb38-182"><a href="#cb38-182" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb38-183"><a href="#cb38-183" aria-hidden="true" tabindex="-1"></a>You can use <span class="in">`mutate`</span> to modify the type of the following variables:</span>
+<span id="cb38-184"><a href="#cb38-184" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb38-185"><a href="#cb38-185" aria-hidden="true" tabindex="-1"></a>+-------------------------------------------------------------------------------------------------------------+---------------------+-----------------+--------------------------------------------------------------------------------------+</span>
+<span id="cb38-186"><a href="#cb38-186" aria-hidden="true" tabindex="-1"></a>| Variable name                                                                                               | Original            | Desired         | Hint                                                                                 |</span>
+<span id="cb38-187"><a href="#cb38-187" aria-hidden="true" tabindex="-1"></a>+=============================================================================================================+=====================+=================+======================================================================================+</span>
+<span id="cb38-188"><a href="#cb38-188" aria-hidden="true" tabindex="-1"></a>| sex                                                                                                         | character           | factor          | mutate(), as.factor()                                                                |</span>
+<span id="cb38-189"><a href="#cb38-189" aria-hidden="true" tabindex="-1"></a>+-------------------------------------------------------------------------------------------------------------+---------------------+-----------------+--------------------------------------------------------------------------------------+</span>
+<span id="cb38-190"><a href="#cb38-190" aria-hidden="true" tabindex="-1"></a>| class                                                                                                       | integer             | factor          | mutate(), as.factor()                                                                |</span>
+<span id="cb38-191"><a href="#cb38-191" aria-hidden="true" tabindex="-1"></a>+-------------------------------------------------------------------------------------------------------------+---------------------+-----------------+--------------------------------------------------------------------------------------+</span>
+<span id="cb38-192"><a href="#cb38-192" aria-hidden="true" tabindex="-1"></a>| ill                                                                                                         | integer             | factor          | mutate(), as.factor()                                                                |</span>
+<span id="cb38-193"><a href="#cb38-193" aria-hidden="true" tabindex="-1"></a>+-------------------------------------------------------------------------------------------------------------+---------------------+-----------------+--------------------------------------------------------------------------------------+</span>
+<span id="cb38-194"><a href="#cb38-194" aria-hidden="true" tabindex="-1"></a>| All the clinical symptom variables                                                                          | integer             | factor          | mutate(across()), as.factor()                                                        |</span>
+<span id="cb38-195"><a href="#cb38-195" aria-hidden="true" tabindex="-1"></a>+-------------------------------------------------------------------------------------------------------------+---------------------+-----------------+--------------------------------------------------------------------------------------+</span>
+<span id="cb38-196"><a href="#cb38-196" aria-hidden="true" tabindex="-1"></a>| All the food variables representing the amount of specific foods eaten (those finishing with a capital "D") | integer             | factor          | mutate(across()), as.factor()                                                        |</span>
+<span id="cb38-197"><a href="#cb38-197" aria-hidden="true" tabindex="-1"></a>+-------------------------------------------------------------------------------------------------------------+---------------------+-----------------+--------------------------------------------------------------------------------------+</span>
+<span id="cb38-198"><a href="#cb38-198" aria-hidden="true" tabindex="-1"></a>| dayonset                                                                                                    | character           | date            | lubridate::dmy()                                                                     |</span>
+<span id="cb38-199"><a href="#cb38-199" aria-hidden="true" tabindex="-1"></a>+-------------------------------------------------------------------------------------------------------------+---------------------+-----------------+--------------------------------------------------------------------------------------+</span>
+<span id="cb38-200"><a href="#cb38-200" aria-hidden="true" tabindex="-1"></a>| starthour and dayonset together                                                                             | integer (starthour) | POSIXct, POSIXt | lubridate::ymd_h() could have inside stringr::str_glue() with dayonset and starthour |</span>
+<span id="cb38-201"><a href="#cb38-201" aria-hidden="true" tabindex="-1"></a>|                                                                                                             |                     |                 |                                                                                      |</span>
+<span id="cb38-202"><a href="#cb38-202" aria-hidden="true" tabindex="-1"></a>|                                                                                                             | date (dayonset)     |                 |                                                                                      |</span>
+<span id="cb38-203"><a href="#cb38-203" aria-hidden="true" tabindex="-1"></a>+-------------------------------------------------------------------------------------------------------------+---------------------+-----------------+--------------------------------------------------------------------------------------+</span>
+<span id="cb38-204"><a href="#cb38-204" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb38-205"><a href="#cb38-205" aria-hidden="true" tabindex="-1"></a>: Table 1: Variable types to modify</span>
+<span id="cb38-206"><a href="#cb38-206" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb38-207"><a href="#cb38-207" aria-hidden="true" tabindex="-1"></a><span class="fu">#### Sex, class and ill</span></span>
+<span id="cb38-208"><a href="#cb38-208" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb38-209"><a href="#cb38-209" aria-hidden="true" tabindex="-1"></a>Let's start transforming one-by-one the first three variables in the table: <span class="in">`sex`</span>, <span class="in">`class`</span> and <span class="in">`ill`</span>.</span>
+<span id="cb38-210"><a href="#cb38-210" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb38-211"><a href="#cb38-211" aria-hidden="true" tabindex="-1"></a><span class="in">```{r, mutate_simple}</span></span>
+<span id="cb38-212"><a href="#cb38-212" aria-hidden="true" tabindex="-1"></a><span class="in">copdata &lt;- copdata %&gt;% </span></span>
+<span id="cb38-213"><a href="#cb38-213" aria-hidden="true" tabindex="-1"></a><span class="in">  mutate(</span></span>
+<span id="cb38-214"><a href="#cb38-214" aria-hidden="true" tabindex="-1"></a><span class="in">    sex = as.factor(sex),</span></span>
+<span id="cb38-215"><a href="#cb38-215" aria-hidden="true" tabindex="-1"></a><span class="in">    class = as.factor(class),</span></span>
+<span id="cb38-216"><a href="#cb38-216" aria-hidden="true" tabindex="-1"></a><span class="in">    ill = as.factor(ill))</span></span>
+<span id="cb38-217"><a href="#cb38-217" aria-hidden="true" tabindex="-1"></a><span class="in"> </span></span>
+<span id="cb38-218"><a href="#cb38-218" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
 <span id="cb38-219"><a href="#cb38-219" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-220"><a href="#cb38-220" aria-hidden="true" tabindex="-1"></a><span class="in">```{r, mutate_cs}</span></span>
-<span id="cb38-221"><a href="#cb38-221" aria-hidden="true" tabindex="-1"></a>copdata <span class="ot">&lt;-</span> copdata <span class="sc">%&gt;%</span> </span>
-<span id="cb38-222"><a href="#cb38-222" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">mutate</span>(</span>
-<span id="cb38-223"><a href="#cb38-223" aria-hidden="true" tabindex="-1"></a>    <span class="co"># clinical symptoms</span></span>
-<span id="cb38-224"><a href="#cb38-224" aria-hidden="true" tabindex="-1"></a>    <span class="fu">across</span>(<span class="at">.cols =</span> <span class="fu">c</span>(diarrhoea, bloody, vomiting,</span>
-<span id="cb38-225"><a href="#cb38-225" aria-hidden="true" tabindex="-1"></a>             abdo, nausea, fever,headache, jointpain), </span>
-<span id="cb38-226"><a href="#cb38-226" aria-hidden="true" tabindex="-1"></a>           <span class="at">.fns =</span> <span class="sc">~</span> <span class="fu">as.logical</span>(.)</span>
-<span id="cb38-227"><a href="#cb38-227" aria-hidden="true" tabindex="-1"></a>           )</span>
-<span id="cb38-228"><a href="#cb38-228" aria-hidden="true" tabindex="-1"></a>    )</span>
-<span id="cb38-229"><a href="#cb38-229" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb38-230"><a href="#cb38-230" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-231"><a href="#cb38-231" aria-hidden="true" tabindex="-1"></a><span class="ss">2.  </span>For the variables that are food doses, we will show you how to first create a vector of names, following by using <span class="in">`mutate(across(all_of( )))`</span> on this vector.</span>
-<span id="cb38-232"><a href="#cb38-232" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-233"><a href="#cb38-233" aria-hidden="true" tabindex="-1"></a><span class="in">```{r, mutate_food}</span></span>
-<span id="cb38-234"><a href="#cb38-234" aria-hidden="true" tabindex="-1"></a><span class="co"># Create a vector with all the food variables representing the amount of specific foods items eaten (those finishing with a capital "D")</span></span>
-<span id="cb38-235"><a href="#cb38-235" aria-hidden="true" tabindex="-1"></a><span class="co"># One way of doing it:</span></span>
-<span id="cb38-236"><a href="#cb38-236" aria-hidden="true" tabindex="-1"></a>food_dose <span class="ot">&lt;-</span> copdata <span class="sc">%&gt;%</span> </span>
-<span id="cb38-237"><a href="#cb38-237" aria-hidden="true" tabindex="-1"></a>    dplyr<span class="sc">::</span><span class="fu">select</span>(</span>
-<span id="cb38-238"><a href="#cb38-238" aria-hidden="true" tabindex="-1"></a>      <span class="fu">ends_with</span>(<span class="st">"D"</span>, <span class="at">ignore.case =</span> <span class="cn">FALSE</span>)) <span class="sc">%&gt;%</span> </span>
-<span id="cb38-239"><a href="#cb38-239" aria-hidden="true" tabindex="-1"></a>    <span class="fu">names</span>()</span>
-<span id="cb38-240"><a href="#cb38-240" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-241"><a href="#cb38-241" aria-hidden="true" tabindex="-1"></a><span class="co"># Another way of doing it:</span></span>
-<span id="cb38-242"><a href="#cb38-242" aria-hidden="true" tabindex="-1"></a><span class="co"># food_dose &lt;- c("fetaD", "sardinesD", "eggplantD", "moussakaD", </span></span>
-<span id="cb38-243"><a href="#cb38-243" aria-hidden="true" tabindex="-1"></a><span class="co">#                 "orzoD", "greeksalD", "dessertD", "breadD", </span></span>
-<span id="cb38-244"><a href="#cb38-244" aria-hidden="true" tabindex="-1"></a><span class="co">#                 "champagneD", "beerD", "redwineD", "whitewineD")</span></span>
-<span id="cb38-245"><a href="#cb38-245" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb38-220"><a href="#cb38-220" aria-hidden="true" tabindex="-1"></a><span class="fu">#### Symptoms and Food variables</span></span>
+<span id="cb38-221"><a href="#cb38-221" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb38-222"><a href="#cb38-222" aria-hidden="true" tabindex="-1"></a>For these variables, we are going to show you a couple of different ways to carry out the same variable type transformation in a *set* of variables, so you don't need to do one variable at a time. We are showing you these ways so you see alternative ways to do the same thing.</span>
+<span id="cb38-223"><a href="#cb38-223" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb38-224"><a href="#cb38-224" aria-hidden="true" tabindex="-1"></a><span class="ss">1.  </span>For the variables that are clinical symptoms, we will list them one by one and show you the use of <span class="in">`mutate(across( ))`</span>.</span>
+<span id="cb38-225"><a href="#cb38-225" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb38-226"><a href="#cb38-226" aria-hidden="true" tabindex="-1"></a><span class="in">```{r, mutate_cs}</span></span>
+<span id="cb38-227"><a href="#cb38-227" aria-hidden="true" tabindex="-1"></a><span class="in">copdata &lt;- copdata %&gt;% </span></span>
+<span id="cb38-228"><a href="#cb38-228" aria-hidden="true" tabindex="-1"></a><span class="in">  mutate(</span></span>
+<span id="cb38-229"><a href="#cb38-229" aria-hidden="true" tabindex="-1"></a><span class="in">    # clinical symptoms</span></span>
+<span id="cb38-230"><a href="#cb38-230" aria-hidden="true" tabindex="-1"></a><span class="in">    across(.cols = c(diarrhoea, bloody, vomiting,</span></span>
+<span id="cb38-231"><a href="#cb38-231" aria-hidden="true" tabindex="-1"></a><span class="in">             abdo, nausea, fever,headache, jointpain), </span></span>
+<span id="cb38-232"><a href="#cb38-232" aria-hidden="true" tabindex="-1"></a><span class="in">           .fns = ~ as.factor(.)</span></span>
+<span id="cb38-233"><a href="#cb38-233" aria-hidden="true" tabindex="-1"></a><span class="in">           )</span></span>
+<span id="cb38-234"><a href="#cb38-234" aria-hidden="true" tabindex="-1"></a><span class="in">    )</span></span>
+<span id="cb38-235"><a href="#cb38-235" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb38-236"><a href="#cb38-236" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb38-237"><a href="#cb38-237" aria-hidden="true" tabindex="-1"></a><span class="ss">2.  </span>For the variables that are food doses, we will show you how to first create a vector of names, following by using <span class="in">`mutate(across(all_of( )))`</span> on this vector.</span>
+<span id="cb38-238"><a href="#cb38-238" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb38-239"><a href="#cb38-239" aria-hidden="true" tabindex="-1"></a><span class="in">```{r, mutate_food}</span></span>
+<span id="cb38-240"><a href="#cb38-240" aria-hidden="true" tabindex="-1"></a><span class="in"># Create a vector with all the food variables representing the amount of specific foods items eaten (those finishing with a capital "D")</span></span>
+<span id="cb38-241"><a href="#cb38-241" aria-hidden="true" tabindex="-1"></a><span class="in"># One way of doing it:</span></span>
+<span id="cb38-242"><a href="#cb38-242" aria-hidden="true" tabindex="-1"></a><span class="in">food_dose &lt;- copdata %&gt;% </span></span>
+<span id="cb38-243"><a href="#cb38-243" aria-hidden="true" tabindex="-1"></a><span class="in">    select(</span></span>
+<span id="cb38-244"><a href="#cb38-244" aria-hidden="true" tabindex="-1"></a><span class="in">      ends_with("D", ignore.case = FALSE)) %&gt;% </span></span>
+<span id="cb38-245"><a href="#cb38-245" aria-hidden="true" tabindex="-1"></a><span class="in">    names()</span></span>
 <span id="cb38-246"><a href="#cb38-246" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-247"><a href="#cb38-247" aria-hidden="true" tabindex="-1"></a>copdata <span class="ot">&lt;-</span> copdata <span class="sc">%&gt;%</span> </span>
-<span id="cb38-248"><a href="#cb38-248" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">mutate</span>(</span>
-<span id="cb38-249"><a href="#cb38-249" aria-hidden="true" tabindex="-1"></a>    <span class="co"># food dose variables</span></span>
-<span id="cb38-250"><a href="#cb38-250" aria-hidden="true" tabindex="-1"></a>    <span class="fu">across</span>(<span class="at">.cols =</span> <span class="fu">all_of</span>(food_dose), </span>
-<span id="cb38-251"><a href="#cb38-251" aria-hidden="true" tabindex="-1"></a>           <span class="at">.fns =</span> <span class="sc">~</span><span class="fu">as.factor</span>(.))) </span>
-<span id="cb38-252"><a href="#cb38-252" aria-hidden="true" tabindex="-1"></a>  </span>
-<span id="cb38-253"><a href="#cb38-253" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb38-254"><a href="#cb38-254" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-255"><a href="#cb38-255" aria-hidden="true" tabindex="-1"></a>::: {.callout-note title="Note" collapse="true"}</span>
-<span id="cb38-256"><a href="#cb38-256" aria-hidden="true" tabindex="-1"></a>The tilde (<span class="in">`~`</span>) above is used to apply the transformation <span class="in">`as.logical(.)`</span> to each selected column, which in our case is either all columns included in <span class="in">`food_items`</span> and <span class="in">`food_dose.`</span></span>
-<span id="cb38-257"><a href="#cb38-257" aria-hidden="true" tabindex="-1"></a>:::</span>
-<span id="cb38-258"><a href="#cb38-258" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-259"><a href="#cb38-259" aria-hidden="true" tabindex="-1"></a><span class="fu">#### Date and time variables</span></span>
+<span id="cb38-247"><a href="#cb38-247" aria-hidden="true" tabindex="-1"></a><span class="in"># Another way of doing it:</span></span>
+<span id="cb38-248"><a href="#cb38-248" aria-hidden="true" tabindex="-1"></a><span class="in"># food_dose &lt;- c("fetaD", "sardinesD", "eggplantD", "pastaD", </span></span>
+<span id="cb38-249"><a href="#cb38-249" aria-hidden="true" tabindex="-1"></a><span class="in">#                 "vealD", "greeksalD", "dessertD", "breadD", </span></span>
+<span id="cb38-250"><a href="#cb38-250" aria-hidden="true" tabindex="-1"></a><span class="in">#                 "champagneD", "beerD", "redwineD", "whitewineD")</span></span>
+<span id="cb38-251"><a href="#cb38-251" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb38-252"><a href="#cb38-252" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb38-253"><a href="#cb38-253" aria-hidden="true" tabindex="-1"></a><span class="in">copdata &lt;- copdata %&gt;% </span></span>
+<span id="cb38-254"><a href="#cb38-254" aria-hidden="true" tabindex="-1"></a><span class="in">  mutate(</span></span>
+<span id="cb38-255"><a href="#cb38-255" aria-hidden="true" tabindex="-1"></a><span class="in">    # food dose variables</span></span>
+<span id="cb38-256"><a href="#cb38-256" aria-hidden="true" tabindex="-1"></a><span class="in">    across(.cols = all_of(food_dose), </span></span>
+<span id="cb38-257"><a href="#cb38-257" aria-hidden="true" tabindex="-1"></a><span class="in">           .fns = ~as.factor(.))) </span></span>
+<span id="cb38-258"><a href="#cb38-258" aria-hidden="true" tabindex="-1"></a><span class="in">  </span></span>
+<span id="cb38-259"><a href="#cb38-259" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
 <span id="cb38-260"><a href="#cb38-260" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-261"><a href="#cb38-261" aria-hidden="true" tabindex="-1"></a>::: {.callout-warning title="Let's stop and... think!" collapse="true"}</span>
-<span id="cb38-262"><a href="#cb38-262" aria-hidden="true" tabindex="-1"></a>Why may you want to transform the variable starthour to date-time?</span>
+<span id="cb38-261"><a href="#cb38-261" aria-hidden="true" tabindex="-1"></a>::: {.callout-note title="Note" collapse="true"}</span>
+<span id="cb38-262"><a href="#cb38-262" aria-hidden="true" tabindex="-1"></a>The tilde (<span class="in">`~`</span>) above is used to apply the transformation <span class="in">`as.factor(.)`</span> to each selected column, which in our case is either all columns included in <span class="in">`food_items`</span> and <span class="in">`food_dose.`</span></span>
 <span id="cb38-263"><a href="#cb38-263" aria-hidden="true" tabindex="-1"></a>:::</span>
 <span id="cb38-264"><a href="#cb38-264" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-265"><a href="#cb38-265" aria-hidden="true" tabindex="-1"></a><span class="al">![](https://cdn.myportfolio.com/45214904-6a61-4e23-98d6-b140f8654a40/3017cf57-d7d9-4f73-9b66-f2be0ebf2489_rw_1920.png?h=2cdd179bc087c56b43f8aa6ea849ca1a)</span>{fig-alt="Fuzzy cartoon monsters in construction hats sorting a pile of “Y” “M” and “D”s into separate piles labeled “Years”, “Months” and “Days”. Title text reads “Lubridate: wrangle times and dates!” Learn more about date-time wrangling with lubridate." width="50%" fig-align="center"}</span>
+<span id="cb38-265"><a href="#cb38-265" aria-hidden="true" tabindex="-1"></a><span class="fu">#### Date and time variables</span></span>
 <span id="cb38-266"><a href="#cb38-266" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-267"><a href="#cb38-267" aria-hidden="true" tabindex="-1"></a>You can use <span class="in">`lubridate::dmy()`</span> to mutate the <span class="in">`dayonset`</span> variable into a date variable. Note that we are using the function <span class="in">`dmy()`</span> because dates are formatted as day, then month (abbreviated character string), then year (i.e. "12nov2006").</span>
-<span id="cb38-268"><a href="#cb38-268" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-269"><a href="#cb38-269" aria-hidden="true" tabindex="-1"></a><span class="in">```{r correct_date_format}</span></span>
-<span id="cb38-270"><a href="#cb38-270" aria-hidden="true" tabindex="-1"></a><span class="co"># Have a look at how the data is stored</span></span>
-<span id="cb38-271"><a href="#cb38-271" aria-hidden="true" tabindex="-1"></a><span class="fu">head</span>(copdata<span class="sc">$</span>dayonset)</span>
-<span id="cb38-272"><a href="#cb38-272" aria-hidden="true" tabindex="-1"></a><span class="fu">class</span>(copdata<span class="sc">$</span>dayonset)</span>
-<span id="cb38-273"><a href="#cb38-273" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-274"><a href="#cb38-274" aria-hidden="true" tabindex="-1"></a><span class="co"># Update copdata:</span></span>
-<span id="cb38-275"><a href="#cb38-275" aria-hidden="true" tabindex="-1"></a>copdata <span class="ot">&lt;-</span> copdata <span class="sc">%&gt;%</span> </span>
-<span id="cb38-276"><a href="#cb38-276" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Change column to date class:</span></span>
-<span id="cb38-277"><a href="#cb38-277" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">mutate</span>(</span>
-<span id="cb38-278"><a href="#cb38-278" aria-hidden="true" tabindex="-1"></a>    <span class="at">dayonset =</span> lubridate<span class="sc">::</span><span class="fu">dmy</span>(dayonset))</span>
+<span id="cb38-267"><a href="#cb38-267" aria-hidden="true" tabindex="-1"></a>::: {.callout-warning title="Let's stop and... think!" collapse="true"}</span>
+<span id="cb38-268"><a href="#cb38-268" aria-hidden="true" tabindex="-1"></a>Why may you want to transform the variable starthour to date-time?</span>
+<span id="cb38-269"><a href="#cb38-269" aria-hidden="true" tabindex="-1"></a>:::</span>
+<span id="cb38-270"><a href="#cb38-270" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb38-271"><a href="#cb38-271" aria-hidden="true" tabindex="-1"></a><span class="al">![](https://cdn.myportfolio.com/45214904-6a61-4e23-98d6-b140f8654a40/3017cf57-d7d9-4f73-9b66-f2be0ebf2489_rw_1920.png?h=2cdd179bc087c56b43f8aa6ea849ca1a)</span>{fig-alt="Fuzzy cartoon monsters in construction hats sorting a pile of “Y” “M” and “D”s into separate piles labeled “Years”, “Months” and “Days”. Title text reads “Lubridate: wrangle times and dates!” Learn more about date-time wrangling with lubridate." width="50%" fig-align="center"}</span>
+<span id="cb38-272"><a href="#cb38-272" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb38-273"><a href="#cb38-273" aria-hidden="true" tabindex="-1"></a>You can use <span class="in">`lubridate::dmy()`</span> to mutate the <span class="in">`dayonset`</span> variable into a date variable. Note that we are using the function <span class="in">`dmy()`</span> because dates are formatted as day, then month (abbreviated character string), then year (i.e. "12nov2006").</span>
+<span id="cb38-274"><a href="#cb38-274" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb38-275"><a href="#cb38-275" aria-hidden="true" tabindex="-1"></a><span class="in">```{r correct_date_format}</span></span>
+<span id="cb38-276"><a href="#cb38-276" aria-hidden="true" tabindex="-1"></a><span class="in"># Have a look at how the data is stored</span></span>
+<span id="cb38-277"><a href="#cb38-277" aria-hidden="true" tabindex="-1"></a><span class="in">head(copdata$dayonset)</span></span>
+<span id="cb38-278"><a href="#cb38-278" aria-hidden="true" tabindex="-1"></a><span class="in">class(copdata$dayonset)</span></span>
 <span id="cb38-279"><a href="#cb38-279" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-280"><a href="#cb38-280" aria-hidden="true" tabindex="-1"></a><span class="co"># Check class of updated column:</span></span>
-<span id="cb38-281"><a href="#cb38-281" aria-hidden="true" tabindex="-1"></a><span class="fu">class</span>(copdata<span class="sc">$</span>dayonset)</span>
-<span id="cb38-282"><a href="#cb38-282" aria-hidden="true" tabindex="-1"></a><span class="co"># Have a look at your data now:</span></span>
-<span id="cb38-283"><a href="#cb38-283" aria-hidden="true" tabindex="-1"></a><span class="fu">head</span>(copdata<span class="sc">$</span>dayonset)</span>
-<span id="cb38-284"><a href="#cb38-284" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb38-280"><a href="#cb38-280" aria-hidden="true" tabindex="-1"></a><span class="in"># Update copdata:</span></span>
+<span id="cb38-281"><a href="#cb38-281" aria-hidden="true" tabindex="-1"></a><span class="in">copdata &lt;- copdata %&gt;% </span></span>
+<span id="cb38-282"><a href="#cb38-282" aria-hidden="true" tabindex="-1"></a><span class="in">  # Change column to date class:</span></span>
+<span id="cb38-283"><a href="#cb38-283" aria-hidden="true" tabindex="-1"></a><span class="in">  mutate(</span></span>
+<span id="cb38-284"><a href="#cb38-284" aria-hidden="true" tabindex="-1"></a><span class="in">    dayonset = lubridate::dmy(dayonset))</span></span>
 <span id="cb38-285"><a href="#cb38-285" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-286"><a href="#cb38-286" aria-hidden="true" tabindex="-1"></a>Having a variable that defines "time" in an outbreak investigation can be very useful when creating a case definition. An hour of the day, without a date associated with it doesn't help you much, thus, you should merge together day and time of onset of symptoms into a single variable. Moreover, you will be using this combined variable later on to estimate an incubation period and create your epicurve. We can combine these two variables by using the <span class="in">`lubridate::ymd_h()`</span> function.</span>
-<span id="cb38-287"><a href="#cb38-287" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-288"><a href="#cb38-288" aria-hidden="true" tabindex="-1"></a>Before we proceed, it would be wise to check if any respondents have a value for <span class="in">`dayonset`</span> but not <span class="in">`starthour`</span>, or vice versa. The <span class="in">`lubridate`</span> date-time conversion functions do not have an explicit argument for dealing with missing values, but the <span class="in">`truncated = …`</span> argument can help prevent spurious date-times being derived from a date-time combination where one value is missing.</span>
-<span id="cb38-289"><a href="#cb38-289" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-290"><a href="#cb38-290" aria-hidden="true" tabindex="-1"></a>We can check if we have any missing values by cross-tabulating <span class="in">`starthour`</span> with <span class="in">`dayonset`</span>:</span>
+<span id="cb38-286"><a href="#cb38-286" aria-hidden="true" tabindex="-1"></a><span class="in"># Check class of updated column:</span></span>
+<span id="cb38-287"><a href="#cb38-287" aria-hidden="true" tabindex="-1"></a><span class="in">class(copdata$dayonset)</span></span>
+<span id="cb38-288"><a href="#cb38-288" aria-hidden="true" tabindex="-1"></a><span class="in"># Have a look at your data now:</span></span>
+<span id="cb38-289"><a href="#cb38-289" aria-hidden="true" tabindex="-1"></a><span class="in">head(copdata$dayonset)</span></span>
+<span id="cb38-290"><a href="#cb38-290" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
 <span id="cb38-291"><a href="#cb38-291" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-292"><a href="#cb38-292" aria-hidden="true" tabindex="-1"></a><span class="in">```{r crosstab_dayonset_starthour}</span></span>
-<span id="cb38-293"><a href="#cb38-293" aria-hidden="true" tabindex="-1"></a><span class="co"># Cross-tabulate dayonset with starthour:</span></span>
-<span id="cb38-294"><a href="#cb38-294" aria-hidden="true" tabindex="-1"></a>janitor<span class="sc">::</span><span class="fu">tabyl</span>(<span class="at">dat =</span> copdata, </span>
-<span id="cb38-295"><a href="#cb38-295" aria-hidden="true" tabindex="-1"></a>               <span class="at">var1 =</span> starthour, </span>
-<span id="cb38-296"><a href="#cb38-296" aria-hidden="true" tabindex="-1"></a>               <span class="at">var2 =</span> dayonset)</span>
-<span id="cb38-297"><a href="#cb38-297" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb38-298"><a href="#cb38-298" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-299"><a href="#cb38-299" aria-hidden="true" tabindex="-1"></a>This shows us that there are two respondents who had an onset date, but are missing onset time (<span class="in">`starthour`</span>). Since <span class="in">`starthour`</span> is represented by 1 - 2 digits, we can specify that we want <span class="in">`lubridate`</span> to also parse date-time combinations that are truncated by up to two digits:</span>
-<span id="cb38-300"><a href="#cb38-300" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-301"><a href="#cb38-301" aria-hidden="true" tabindex="-1"></a><span class="in">```{r combine_date_time}</span></span>
-<span id="cb38-302"><a href="#cb38-302" aria-hidden="true" tabindex="-1"></a>copdata <span class="ot">&lt;-</span> copdata <span class="sc">%&gt;%</span> </span>
-<span id="cb38-303"><a href="#cb38-303" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Combine dayonset and starthour in a new date time variable:</span></span>
-<span id="cb38-304"><a href="#cb38-304" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">onset_datetime =</span> </span>
-<span id="cb38-305"><a href="#cb38-305" aria-hidden="true" tabindex="-1"></a>           lubridate<span class="sc">::</span><span class="fu">ymd_h</span>(</span>
-<span id="cb38-306"><a href="#cb38-306" aria-hidden="true" tabindex="-1"></a>             <span class="fu">str_glue</span>(<span class="st">"{dayonset}, {starthour}"</span>), </span>
-<span id="cb38-307"><a href="#cb38-307" aria-hidden="true" tabindex="-1"></a>                                           <span class="co"># Deal with missing starthour:</span></span>
-<span id="cb38-308"><a href="#cb38-308" aria-hidden="true" tabindex="-1"></a>                                           <span class="at">truncated =</span> <span class="dv">2</span>))</span>
-<span id="cb38-309"><a href="#cb38-309" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb38-310"><a href="#cb38-310" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-311"><a href="#cb38-311" aria-hidden="true" tabindex="-1"></a>Note that we needed to use <span class="in">`str_glue()`</span> to concatenate <span class="in">`dayonset`</span> and <span class="in">`starthour`</span> together before we could convert the variable to a date-time object. This is because the <span class="in">`ymd_h()`</span> function expects a single character string, containing both the date and the time, as input.</span>
-<span id="cb38-312"><a href="#cb38-312" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-313"><a href="#cb38-313" aria-hidden="true" tabindex="-1"></a>The argument <span class="in">`truncated = 2`</span> will result in dates with missing <span class="in">`starthour`</span> still being converted to date-time, with the missing time being set to <span class="in">`00:00`</span> (midnight). Whether you want to deal with missing <span class="in">`starthour`</span> in this way or prefer to code these date-times as <span class="in">`NA`</span> will depend on how you want them to be represented in your analysis.</span>
-<span id="cb38-314"><a href="#cb38-314" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-315"><a href="#cb38-315" aria-hidden="true" tabindex="-1"></a>Now we can check that everything in the new combined date-time variable has parsed correctly:</span>
+<span id="cb38-292"><a href="#cb38-292" aria-hidden="true" tabindex="-1"></a>Having a variable that defines "time" in an outbreak investigation can be very useful when creating a case definition. An hour of the day, without a date associated with it doesn't help you much, thus, you should merge together day and time of onset of symptoms into a single variable. Moreover, you will be using this combined variable later on to estimate an incubation period and create your epicurve. We can combine these two variables by using the <span class="in">`lubridate::ymd_h()`</span> function.</span>
+<span id="cb38-293"><a href="#cb38-293" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb38-294"><a href="#cb38-294" aria-hidden="true" tabindex="-1"></a>Before we proceed, it would be wise to check if any respondents have a value for <span class="in">`dayonset`</span> but not <span class="in">`starthour`</span>, or vice versa. The <span class="in">`lubridate`</span> date-time conversion functions do not have an explicit argument for dealing with missing values, but the <span class="in">`truncated = …`</span> argument can help prevent spurious date-times being derived from a date-time combination where one value is missing.</span>
+<span id="cb38-295"><a href="#cb38-295" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb38-296"><a href="#cb38-296" aria-hidden="true" tabindex="-1"></a>We can check if we have any missing values by cross-tabulating <span class="in">`starthour`</span> with <span class="in">`dayonset`</span>:</span>
+<span id="cb38-297"><a href="#cb38-297" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb38-298"><a href="#cb38-298" aria-hidden="true" tabindex="-1"></a><span class="in">```{r crosstab_dayonset_starthour}</span></span>
+<span id="cb38-299"><a href="#cb38-299" aria-hidden="true" tabindex="-1"></a><span class="in"># Cross-tabulate dayonset with starthour:</span></span>
+<span id="cb38-300"><a href="#cb38-300" aria-hidden="true" tabindex="-1"></a><span class="in">tabyl(dat = copdata, </span></span>
+<span id="cb38-301"><a href="#cb38-301" aria-hidden="true" tabindex="-1"></a><span class="in">               var1 = starthour, </span></span>
+<span id="cb38-302"><a href="#cb38-302" aria-hidden="true" tabindex="-1"></a><span class="in">               var2 = dayonset)</span></span>
+<span id="cb38-303"><a href="#cb38-303" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb38-304"><a href="#cb38-304" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb38-305"><a href="#cb38-305" aria-hidden="true" tabindex="-1"></a>This shows us that there are two respondents who had an onset date, but are missing onset time (<span class="in">`starthour`</span>). Since <span class="in">`starthour`</span> is represented by 1 - 2 digits, we can specify that we want <span class="in">`lubridate`</span> to also parse date-time combinations that are truncated by up to two digits:</span>
+<span id="cb38-306"><a href="#cb38-306" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb38-307"><a href="#cb38-307" aria-hidden="true" tabindex="-1"></a><span class="in">```{r combine_date_time}</span></span>
+<span id="cb38-308"><a href="#cb38-308" aria-hidden="true" tabindex="-1"></a><span class="in">copdata &lt;- copdata %&gt;% </span></span>
+<span id="cb38-309"><a href="#cb38-309" aria-hidden="true" tabindex="-1"></a><span class="in">  # Combine dayonset and starthour in a new date time variable:</span></span>
+<span id="cb38-310"><a href="#cb38-310" aria-hidden="true" tabindex="-1"></a><span class="in">  mutate(onset_datetime = </span></span>
+<span id="cb38-311"><a href="#cb38-311" aria-hidden="true" tabindex="-1"></a><span class="in">           ymd_h(</span></span>
+<span id="cb38-312"><a href="#cb38-312" aria-hidden="true" tabindex="-1"></a><span class="in">             str_glue("{dayonset}, {starthour}"), </span></span>
+<span id="cb38-313"><a href="#cb38-313" aria-hidden="true" tabindex="-1"></a><span class="in">                                           # Deal with missing starthour:</span></span>
+<span id="cb38-314"><a href="#cb38-314" aria-hidden="true" tabindex="-1"></a><span class="in">                                           truncated = 2))</span></span>
+<span id="cb38-315"><a href="#cb38-315" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
 <span id="cb38-316"><a href="#cb38-316" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-317"><a href="#cb38-317" aria-hidden="true" tabindex="-1"></a><span class="in">```{r check_onset_datetime}</span></span>
+<span id="cb38-317"><a href="#cb38-317" aria-hidden="true" tabindex="-1"></a>Note that we needed to use <span class="in">`str_glue()`</span> to concatenate <span class="in">`dayonset`</span> and <span class="in">`starthour`</span> together before we could convert the variable to a date-time object. This is because the <span class="in">`ymd_h()`</span> function expects a single character string, containing both the date and the time, as input.</span>
 <span id="cb38-318"><a href="#cb38-318" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-319"><a href="#cb38-319" aria-hidden="true" tabindex="-1"></a><span class="fu">head</span>(copdata<span class="sc">$</span>dayonset)</span>
-<span id="cb38-320"><a href="#cb38-320" aria-hidden="true" tabindex="-1"></a><span class="fu">head</span>(copdata<span class="sc">$</span>starthour)</span>
-<span id="cb38-321"><a href="#cb38-321" aria-hidden="true" tabindex="-1"></a><span class="fu">head</span>(copdata<span class="sc">$</span>onset_datetime)</span>
-<span id="cb38-322"><a href="#cb38-322" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb38-323"><a href="#cb38-323" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-324"><a href="#cb38-324" aria-hidden="true" tabindex="-1"></a><span class="fu">## 3.5. Export clean data</span></span>
-<span id="cb38-325"><a href="#cb38-325" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-326"><a href="#cb38-326" aria-hidden="true" tabindex="-1"></a>Save the cleaned data set before proceeding with using your case definition to identify cases in your dataset. Use the .rds format, as it preserves column classes. This ensures you will have only minimal cleaning to do after importing the data into R at the next inject.</span>
-<span id="cb38-327"><a href="#cb38-327" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-328"><a href="#cb38-328" aria-hidden="true" tabindex="-1"></a>Save your clean data as a new file "Spetses_clean1_YOURINITIALS.rds", under the data folder of your Rproject.</span>
+<span id="cb38-319"><a href="#cb38-319" aria-hidden="true" tabindex="-1"></a>The argument <span class="in">`truncated = 2`</span> will result in dates with missing <span class="in">`starthour`</span> still being converted to date-time, with the missing time being set to <span class="in">`00:00`</span> (midnight). Whether you want to deal with missing <span class="in">`starthour`</span> in this way or prefer to code these date-times as <span class="in">`NA`</span> will depend on how you want them to be represented in your analysis.</span>
+<span id="cb38-320"><a href="#cb38-320" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb38-321"><a href="#cb38-321" aria-hidden="true" tabindex="-1"></a>Now we can check that everything in the new combined date-time variable has parsed correctly:</span>
+<span id="cb38-322"><a href="#cb38-322" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb38-323"><a href="#cb38-323" aria-hidden="true" tabindex="-1"></a><span class="in">```{r check_onset_datetime}</span></span>
+<span id="cb38-324"><a href="#cb38-324" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb38-325"><a href="#cb38-325" aria-hidden="true" tabindex="-1"></a><span class="in">head(copdata$dayonset)</span></span>
+<span id="cb38-326"><a href="#cb38-326" aria-hidden="true" tabindex="-1"></a><span class="in">head(copdata$starthour)</span></span>
+<span id="cb38-327"><a href="#cb38-327" aria-hidden="true" tabindex="-1"></a><span class="in">head(copdata$onset_datetime)</span></span>
+<span id="cb38-328"><a href="#cb38-328" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
 <span id="cb38-329"><a href="#cb38-329" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-330"><a href="#cb38-330" aria-hidden="true" tabindex="-1"></a>::: {.callout-tip title="Need a little bit of help?" collapse="true"}</span>
-<span id="cb38-331"><a href="#cb38-331" aria-hidden="true" tabindex="-1"></a>Use <span class="in">`rio::export()`</span>.</span>
-<span id="cb38-332"><a href="#cb38-332" aria-hidden="true" tabindex="-1"></a>:::</span>
+<span id="cb38-330"><a href="#cb38-330" aria-hidden="true" tabindex="-1"></a><span class="fu">## 3.5. Export clean data</span></span>
+<span id="cb38-331"><a href="#cb38-331" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb38-332"><a href="#cb38-332" aria-hidden="true" tabindex="-1"></a>Save the cleaned data set before proceeding with using your case definition to identify cases in your dataset. Use the .rds format, as it preserves column classes. This ensures you will have only minimal cleaning to do after importing the data into R at the next inject.</span>
 <span id="cb38-333"><a href="#cb38-333" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-334"><a href="#cb38-334" aria-hidden="true" tabindex="-1"></a><span class="in">```{r export_clean_data}</span></span>
+<span id="cb38-334"><a href="#cb38-334" aria-hidden="true" tabindex="-1"></a>Save your clean data as a new file "Spetses_clean1_YOURINITIALS.rds", under the data folder of your Rproject.</span>
 <span id="cb38-335"><a href="#cb38-335" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-336"><a href="#cb38-336" aria-hidden="true" tabindex="-1"></a>rio<span class="sc">::</span><span class="fu">export</span>(<span class="at">x =</span> copdata, </span>
-<span id="cb38-337"><a href="#cb38-337" aria-hidden="true" tabindex="-1"></a>            <span class="at">file =</span> here<span class="sc">::</span><span class="fu">here</span>(<span class="st">"data"</span>, <span class="st">"Spetses_clean1_2024.rds"</span>))</span>
-<span id="cb38-338"><a href="#cb38-338" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-339"><a href="#cb38-339" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb38-340"><a href="#cb38-340" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-341"><a href="#cb38-341" aria-hidden="true" tabindex="-1"></a><span class="al">![](https://cdn.myportfolio.com/45214904-6a61-4e23-98d6-b140f8654a40/e4a11b1f-f235-461f-ab73-ce6128c59e49_rw_1920.png?h=a34a4173c717bfbc600a8682d419288e)</span>{width="80%" fig-align="center"}</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<span id="cb38-336"><a href="#cb38-336" aria-hidden="true" tabindex="-1"></a>::: {.callout-tip title="Need a little bit of help?" collapse="true"}</span>
+<span id="cb38-337"><a href="#cb38-337" aria-hidden="true" tabindex="-1"></a>Use <span class="in">`rio::export()`</span>.</span>
+<span id="cb38-338"><a href="#cb38-338" aria-hidden="true" tabindex="-1"></a>:::</span>
+<span id="cb38-339"><a href="#cb38-339" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb38-340"><a href="#cb38-340" aria-hidden="true" tabindex="-1"></a><span class="in">```{r export_clean_data}</span></span>
+<span id="cb38-341"><a href="#cb38-341" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb38-342"><a href="#cb38-342" aria-hidden="true" tabindex="-1"></a><span class="in">export(x = copdata, </span></span>
+<span id="cb38-343"><a href="#cb38-343" aria-hidden="true" tabindex="-1"></a><span class="in">            file = here::here("data", "Spetses_clean1_2024.rds"))</span></span>
+<span id="cb38-344"><a href="#cb38-344" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb38-345"><a href="#cb38-345" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb38-346"><a href="#cb38-346" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb38-347"><a href="#cb38-347" aria-hidden="true" tabindex="-1"></a><span class="al">![](https://cdn.myportfolio.com/45214904-6a61-4e23-98d6-b140f8654a40/e4a11b1f-f235-461f-ab73-ce6128c59e49_rw_1920.png?h=a34a4173c717bfbc600a8682d419288e)</span>{width="80%" fig-align="center"}</span></code><button title="Copy to Clipboard" class="code-copy-button" data-in-quarto-modal=""><i class="bi"></i></button></pre></div>
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diff --git a/docs/Lab_and_Descriptive.html b/docs/Lab_and_Descriptive.html
index 04a22a6..29969bf 100644
--- a/docs/Lab_and_Descriptive.html
+++ b/docs/Lab_and_Descriptive.html
@@ -2,12 +2,12 @@
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+<meta name="generator" content="quarto-1.5.57">
 
 <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">
 
 
-<title>Spetses Introductory Course - Laboratory data and descriptive analysis</title>
+<title>Laboratory data and descriptive analysis – Spetses Introductory Course</title>
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@@ -22,7 +22,7 @@
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@@ -33,7 +33,7 @@
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+pre > code.sourceCode > span { display: inline-block; text-indent: -5em; padding-left: 5em; }
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@@ -81,7 +81,13 @@
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@@ -90,16 +96,18 @@
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+    "search-text-placeholder": "",
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+
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+
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@@ -107,55 +115,56 @@
 
 <div id="quarto-search-results"></div>
   <header id="quarto-header" class="headroom fixed-top">
-    <nav class="navbar navbar-expand-lg navbar-dark ">
+    <nav class="navbar navbar-expand-lg " data-bs-theme="dark">
       <div class="navbar-container container-fluid">
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+      <div class="navbar-brand-container mx-auto">
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-    <a class="nav-link" href="./index.html" rel="" target="">
- <span class="menu-text">What is this?</span></a>
+    <a class="nav-link" href="./index.html"> 
+<span class="menu-text">What is this?</span></a>
   </li>  
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-    <a class="nav-link" href="./Data_import_cleaning.html" rel="" target="">
- <span class="menu-text">Data import and cleaning</span></a>
+    <a class="nav-link" href="./Data_import_cleaning.html"> 
+<span class="menu-text">Data import and cleaning</span></a>
   </li>  
   <li class="nav-item">
-    <a class="nav-link" href="./Case_definition.html" rel="" target="">
- <span class="menu-text">Case Definition</span></a>
+    <a class="nav-link" href="./Case_definition.html"> 
+<span class="menu-text">Case Definition</span></a>
   </li>  
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- <span class="menu-text">Laboratory data and descriptive analysis</span></a>
+    <a class="nav-link active" href="./Lab_and_Descriptive.html" aria-current="page"> 
+<span class="menu-text">Laboratory data and descriptive analysis</span></a>
   </li>  
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-    <a class="nav-link" href="./Univariate.html" rel="" target="">
- <span class="menu-text">Univariable analysis</span></a>
+    <a class="nav-link" href="./Univariate.html"> 
+<span class="menu-text">Univariable analysis</span></a>
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- <span class="menu-text">Stratified analysis</span></a>
+    <a class="nav-link" href="./Stratified.html"> 
+<span class="menu-text">Stratified analysis</span></a>
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+            <div class="quarto-navbar-tools">
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+            <a class="dropdown-item quarto-navbar-tools-item" href="https://github.com/EPIET/IntroCourse_inCop/tree/main">
             Source Code
             </a>
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           <li>
-            <a class="dropdown-item quarto-navbar-tools ms-auto-item" href="https://github.com/EPIET/IntroCourse_inCop/issues">
+            <a class="dropdown-item quarto-navbar-tools-item" href="https://github.com/EPIET/IntroCourse_inCop/issues">
             Report a code issue
             </a>
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@@ -163,7 +172,6 @@
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-          </div> <!-- /navcollapse -->
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@@ -225,8 +233,10 @@ <h2 id="toc-title">On this page</h2>
   </div>
   
 
+
 </header>
 
+
 <section id="learning-outcomes" class="level2">
 <h2 class="anchored" data-anchor-id="learning-outcomes">1. Learning outcomes</h2>
 <p>At the end of the session, participants will be able to:</p>
@@ -249,8 +259,8 @@ <h2 class="anchored" data-anchor-id="questionsassignments">3. Questions/Assignme
 <section id="install-packages-if-needed-and-load-libraries" class="level2">
 <h2 class="anchored" data-anchor-id="install-packages-if-needed-and-load-libraries">3.1 Install packages (if needed) and load libraries</h2>
 <div class="cell">
-<details open="">
-<summary>Code</summary>
+<details open="" class="code-fold">
+<summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb1"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Load the required libraries into the current R session:</span></span>
 <span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a>pacman<span class="sc">::</span><span class="fu">p_load</span>(rio, </span>
 <span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a>               here, </span>
@@ -271,10 +281,10 @@ <h2 class="anchored" data-anchor-id="install-packages-if-needed-and-load-librari
 <section id="import-your-data" class="level2">
 <h2 class="anchored" data-anchor-id="import-your-data">3.2 Import your data</h2>
 <div class="cell">
-<details open="">
-<summary>Code</summary>
+<details open="" class="code-fold">
+<summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb2"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Import the clean data set:</span></span>
-<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a>copdata <span class="ot">&lt;-</span> rio<span class="sc">::</span><span class="fu">import</span>(here<span class="sc">::</span><span class="fu">here</span>(<span class="st">"data"</span>, <span class="st">"Spetses_clean2_2024.rds"</span>)) </span>
+<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a>copdata <span class="ot">&lt;-</span> rio<span class="sc">::</span><span class="fu">import</span>(here<span class="sc">::</span><span class="fu">here</span>(<span class="st">"data"</span>, <span class="st">"Spetses_clean2_2024.rds"</span>), <span class="at">trust =</span> T) </span>
 <span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a>lab <span class="ot">&lt;-</span> rio<span class="sc">::</span><span class="fu">import</span>(here<span class="sc">::</span><span class="fu">here</span>(<span class="st">"data"</span>, <span class="st">"Lab data.xlsx"</span>),</span>
 <span id="cb2-4"><a href="#cb2-4" aria-hidden="true" tabindex="-1"></a>                   <span class="at">skip =</span> <span class="dv">1</span>) <span class="co"># skip the first row.</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
@@ -283,15 +293,14 @@ <h2 class="anchored" data-anchor-id="import-your-data">3.2 Import your data</h2>
 <section id="merge-and-explore-the-lab-data.-explore-it-as-you-wish" class="level2">
 <h2 class="anchored" data-anchor-id="merge-and-explore-the-lab-data.-explore-it-as-you-wish">3.3 Merge and explore the lab data. Explore it as you wish!</h2>
 <div class="cell">
-<details open="">
-<summary>Code</summary>
+<details open="" class="code-fold">
+<summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb3"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a>copdatalab <span class="ot">&lt;-</span> <span class="fu">left_join</span>(copdata, lab, </span>
 <span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a>                        <span class="at">by =</span> <span class="st">"id"</span>)</span>
 <span id="cb3-3"><a href="#cb3-3" aria-hidden="true" tabindex="-1"></a><span class="co"># Tabulate:</span></span>
 <span id="cb3-4"><a href="#cb3-4" aria-hidden="true" tabindex="-1"></a>janitor<span class="sc">::</span><span class="fu">tabyl</span>(<span class="at">dat =</span> copdatalab, <span class="st">"RT-PCR_ecoli_etec"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
 <div class="cell-output-display">
-
 <div data-pagedtable="false">
   <script data-pagedtable-source="" type="application/json">
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@@ -322,13 +331,13 @@ <h3 class="anchored" data-anchor-id="a-create-a-histogram-to-visualize-the-incub
 </div>
 </div>
 <div class="cell">
-<details open="">
-<summary>Code</summary>
+<details open="" class="code-fold">
+<summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb4"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: inc_time</span></span>
 <span id="cb4-2"><a href="#cb4-2" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb4-3"><a href="#cb4-3" aria-hidden="true" tabindex="-1"></a><span class="co"># Create a dataset with only cases</span></span>
 <span id="cb4-4"><a href="#cb4-4" aria-hidden="true" tabindex="-1"></a>cases <span class="ot">&lt;-</span> copdata <span class="sc">%&gt;%</span> </span>
-<span id="cb4-5"><a href="#cb4-5" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(case <span class="sc">==</span> <span class="cn">TRUE</span>)</span>
+<span id="cb4-5"><a href="#cb4-5" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(case <span class="sc">==</span> <span class="dv">1</span>)</span>
 <span id="cb4-6"><a href="#cb4-6" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb4-7"><a href="#cb4-7" aria-hidden="true" tabindex="-1"></a>incplot <span class="ot">&lt;-</span> cases <span class="sc">%&gt;%</span> </span>
 <span id="cb4-8"><a href="#cb4-8" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Create an empty ggplot frame:</span></span>
@@ -348,7 +357,11 @@ <h3 class="anchored" data-anchor-id="a-create-a-histogram-to-visualize-the-incub
 <span id="cb4-22"><a href="#cb4-22" aria-hidden="true" tabindex="-1"></a>incplot</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
 <div class="cell-output-display">
-<p><img src="Lab_and_Descriptive_files/figure-html/unnamed-chunk-2-1.png" class="img-fluid" width="672"></p>
+<div>
+<figure class="figure">
+<p><img src="Lab_and_Descriptive_files/figure-html/unnamed-chunk-2-1.png" class="img-fluid figure-img" width="672"></p>
+</figure>
+</div>
 </div>
 </div>
 </section>
@@ -375,8 +388,8 @@ <h3 class="anchored" data-anchor-id="b-create-an-epicurve-for-the-date-and-time-
 </div>
 </div>
 <div class="cell">
-<details open="">
-<summary>Code</summary>
+<details open="" class="code-fold">
+<summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb5"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Create a vector with sequences every 6h from the first to the last case</span></span>
 <span id="cb5-2"><a href="#cb5-2" aria-hidden="true" tabindex="-1"></a>breaks_6h <span class="ot">&lt;-</span> <span class="fu">seq</span>(<span class="at">from =</span> <span class="fu">min</span>(cases<span class="sc">$</span>onset_datetime, <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
 <span id="cb5-3"><a href="#cb5-3" aria-hidden="true" tabindex="-1"></a>                 <span class="at">to =</span> <span class="fu">max</span>(cases<span class="sc">$</span>onset_datetime, <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
@@ -405,7 +418,11 @@ <h3 class="anchored" data-anchor-id="b-create-an-epicurve-for-the-date-and-time-
 <span id="cb5-26"><a href="#cb5-26" aria-hidden="true" tabindex="-1"></a>epicurve_datetime</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
 <div class="cell-output-display">
-<p><img src="Lab_and_Descriptive_files/figure-html/epicurve date-time-1.png" class="img-fluid" width="672"></p>
+<div>
+<figure class="figure">
+<p><img src="Lab_and_Descriptive_files/figure-html/epicurve date-time-1.png" class="img-fluid figure-img" width="672"></p>
+</figure>
+</div>
 </div>
 </div>
 <ol start="3" type="a">
@@ -448,8 +465,8 @@ <h3 class="anchored" data-anchor-id="b-create-an-epicurve-for-the-date-and-time-
 </div>
 </div>
 <div class="cell">
-<details open="">
-<summary>Code</summary>
+<details open="" class="code-fold">
+<summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb6"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a>epicurve_strata <span class="ot">&lt;-</span> cases <span class="sc">%&gt;%</span> </span>
 <span id="cb6-2"><a href="#cb6-2" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Add factor onset_day to ggplot aesthetic:</span></span>
 <span id="cb6-3"><a href="#cb6-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggplot</span>(</span>
@@ -480,7 +497,11 @@ <h3 class="anchored" data-anchor-id="b-create-an-epicurve-for-the-date-and-time-
 <span id="cb6-28"><a href="#cb6-28" aria-hidden="true" tabindex="-1"></a>epicurve_strata </span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
 <div class="cell-output-display">
-<p><img src="Lab_and_Descriptive_files/figure-html/epicurve strata-1.png" class="img-fluid" width="672"></p>
+<div>
+<figure class="figure">
+<p><img src="Lab_and_Descriptive_files/figure-html/epicurve strata-1.png" class="img-fluid figure-img" width="672"></p>
+</figure>
+</div>
 </div>
 </div>
 <div class="callout callout-style-default callout-warning callout-titled" title="Let's stop and... think!">
@@ -526,8 +547,8 @@ <h2 class="anchored" data-anchor-id="describe-the-outbreak-in-terms-of-person.">
 <section id="a-create-a-cross-tabulation-of-case-with-group." class="level3">
 <h3 class="anchored" data-anchor-id="a-create-a-cross-tabulation-of-case-with-group.">a) Create a cross-tabulation of <code>case</code> with <code>group</code>.</h3>
 <div class="cell">
-<details open="">
-<summary>Code</summary>
+<details open="" class="code-fold">
+<summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb7"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a>copdata <span class="sc">%&gt;%</span> </span>
 <span id="cb7-2"><a href="#cb7-2" aria-hidden="true" tabindex="-1"></a>  janitor<span class="sc">::</span><span class="fu">tabyl</span>(case, group) <span class="sc">%&gt;%</span> </span>
 <span id="cb7-3"><a href="#cb7-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">adorn_totals</span>() <span class="sc">%&gt;%</span> </span>
@@ -535,10 +556,9 @@ <h3 class="anchored" data-anchor-id="a-create-a-cross-tabulation-of-case-with-gr
 <span id="cb7-5"><a href="#cb7-5" aria-hidden="true" tabindex="-1"></a>  <span class="fu">adorn_pct_formatting</span>() </span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
 <div class="cell-output-display">
-
 <div data-pagedtable="false">
   <script data-pagedtable-source="" type="application/json">
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+{"columns":[{"label":[""],"name":["_rn_"],"type":[""],"align":["left"]},{"label":["case"],"name":[1],"type":["chr"],"align":["left"]},{"label":["teacher"],"name":[2],"type":["chr"],"align":["left"]},{"label":["student"],"name":[3],"type":["chr"],"align":["left"]}],"data":[{"1":"0","2":"5.6%","3":"94.4%","_rn_":"1"},{"1":"1","2":"2.8%","3":"97.2%","_rn_":"2"},{"1":"Total","2":"4.0%","3":"96.0%","_rn_":"3"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}}
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@@ -547,8 +567,8 @@ <h3 class="anchored" data-anchor-id="a-create-a-cross-tabulation-of-case-with-gr
 <section id="b-create-a-cross-tabulation-of-case-with-sex." class="level3">
 <h3 class="anchored" data-anchor-id="b-create-a-cross-tabulation-of-case-with-sex.">b) Create a cross-tabulation of <code>case</code> with <code>sex</code>.</h3>
 <div class="cell">
-<details open="">
-<summary>Code</summary>
+<details open="" class="code-fold">
+<summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb8"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a>copdata <span class="sc">%&gt;%</span> </span>
 <span id="cb8-2"><a href="#cb8-2" aria-hidden="true" tabindex="-1"></a>  janitor<span class="sc">::</span><span class="fu">tabyl</span>(case, sex) <span class="sc">%&gt;%</span> </span>
 <span id="cb8-3"><a href="#cb8-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">adorn_totals</span>() <span class="sc">%&gt;%</span> </span>
@@ -556,10 +576,9 @@ <h3 class="anchored" data-anchor-id="b-create-a-cross-tabulation-of-case-with-se
 <span id="cb8-5"><a href="#cb8-5" aria-hidden="true" tabindex="-1"></a>  <span class="fu">adorn_pct_formatting</span>() </span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
 <div class="cell-output-display">
-
 <div data-pagedtable="false">
   <script data-pagedtable-source="" type="application/json">
-{"columns":[{"label":[""],"name":["_rn_"],"type":[""],"align":["left"]},{"label":["case"],"name":[1],"type":["chr"],"align":["left"]},{"label":["female"],"name":[2],"type":["chr"],"align":["left"]},{"label":["male"],"name":[3],"type":["chr"],"align":["left"]}],"data":[{"1":"FALSE","2":"59.6%","3":"40.4%","_rn_":"1"},{"1":"TRUE","2":"54.2%","3":"45.8%","_rn_":"2"},{"1":"Total","2":"56.5%","3":"43.5%","_rn_":"3"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}}
+{"columns":[{"label":[""],"name":["_rn_"],"type":[""],"align":["left"]},{"label":["case"],"name":[1],"type":["chr"],"align":["left"]},{"label":["female"],"name":[2],"type":["chr"],"align":["left"]},{"label":["male"],"name":[3],"type":["chr"],"align":["left"]}],"data":[{"1":"0","2":"59.6%","3":"40.4%","_rn_":"1"},{"1":"1","2":"54.2%","3":"45.8%","_rn_":"2"},{"1":"Total","2":"56.5%","3":"43.5%","_rn_":"3"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}}
   </script>
 </div>
 </div>
@@ -617,8 +636,8 @@ <h3 class="anchored" data-anchor-id="b-create-a-cross-tabulation-of-case-with-se
 <h3 class="anchored" data-anchor-id="c-extra---age-sex-pyramid-of-cases.">c) Extra - Age-sex pyramid of cases.</h3>
 <p>Hint: Change <code>show_midpoint = FALSE</code> to <code>TRUE</code> to see skewedness in the data patterns more easily.</p>
 <div class="cell">
-<details open="">
-<summary>Code</summary>
+<details open="" class="code-fold">
+<summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb9"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a>copdata <span class="ot">&lt;-</span> copdata <span class="sc">%&gt;%</span> </span>
 <span id="cb9-2"><a href="#cb9-2" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Create age categories:</span></span>
 <span id="cb9-3"><a href="#cb9-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">age_cat =</span> epikit<span class="sc">::</span><span class="fu">age_categories</span>(</span>
@@ -634,15 +653,14 @@ <h3 class="anchored" data-anchor-id="c-extra---age-sex-pyramid-of-cases.">c) Ext
 <span id="cb9-13"><a href="#cb9-13" aria-hidden="true" tabindex="-1"></a>janitor<span class="sc">::</span><span class="fu">tabyl</span>(copdata, age_cat)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
 <div class="cell-output-display">
-
 <div data-pagedtable="false">
   <script data-pagedtable-source="" type="application/json">
 {"columns":[{"label":["age_cat"],"name":[1],"type":["fct"],"align":["left"]},{"label":["n"],"name":[2],"type":["int"],"align":["right"]},{"label":["percent"],"name":[3],"type":["dbl"],"align":["right"]}],"data":[{"1":"0-9","2":"0","3":"0.00000000"},{"1":"10-15","2":"11","3":"0.02917772"},{"1":"16-17","2":"201","3":"0.53315650"},{"1":"18-19","2":"147","3":"0.38992042"},{"1":"20-49","2":"11","3":"0.02917772"},{"1":"50-69","2":"7","3":"0.01856764"},{"1":"70+","2":"0","3":"0.00000000"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}}
   </script>
 </div>
 </div>
-<details open="">
-<summary>Code</summary>
+<details open="" class="code-fold">
+<summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb10"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb10-1"><a href="#cb10-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Pipe copdata:</span></span>
 <span id="cb10-2"><a href="#cb10-2" aria-hidden="true" tabindex="-1"></a>agesex <span class="ot">&lt;-</span> copdata <span class="sc">%&gt;%</span> </span>
 <span id="cb10-3"><a href="#cb10-3" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Filter for cases only:</span></span>
@@ -661,7 +679,11 @@ <h3 class="anchored" data-anchor-id="c-extra---age-sex-pyramid-of-cases.">c) Ext
 <span id="cb10-16"><a href="#cb10-16" aria-hidden="true" tabindex="-1"></a>agesex</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
 <div class="cell-output-display">
-<p><img src="Lab_and_Descriptive_files/figure-html/unnamed-chunk-7-1.png" class="img-fluid" width="672"></p>
+<div>
+<figure class="figure">
+<p><img src="Lab_and_Descriptive_files/figure-html/unnamed-chunk-4-1.png" class="img-fluid figure-img" width="672"></p>
+</figure>
+</div>
 </div>
 </div>
 </section>
@@ -671,8 +693,8 @@ <h2 class="anchored" data-anchor-id="explore-the-distribution-of-all-clinical-si
 <section id="a-create-a-summary-table-with-symptoms-stratified-by-case-definition-and-present-an-overall-column-as-well.-you-could-use-tabyl-or-gtsummarytbl_summary.-you-can-find-further-information-about-gtsummary-in-the-epidemiologist-r-handbook-section-on-gtsummary." class="level3">
 <h3 class="anchored" data-anchor-id="a-create-a-summary-table-with-symptoms-stratified-by-case-definition-and-present-an-overall-column-as-well.-you-could-use-tabyl-or-gtsummarytbl_summary.-you-can-find-further-information-about-gtsummary-in-the-epidemiologist-r-handbook-section-on-gtsummary.">a) Create a summary table with symptoms stratified by case definition, and present an overall column as well. You could use <code>tabyl()</code> or <code>gtsummary::tbl_summary()</code>. You can find further information about <code>gtsummary</code> in <a href="https://www.epirhandbook.com/en/descriptive-tables.html#tbl_gt">The Epidemiologist R handbook section on gtsummary</a>.</h3>
 <div class="cell">
-<details open="">
-<summary>Code</summary>
+<details open="" class="code-fold">
+<summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb11"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb11-1"><a href="#cb11-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Create summary table:</span></span>
 <span id="cb11-2"><a href="#cb11-2" aria-hidden="true" tabindex="-1"></a>tabsymptoms <span class="ot">&lt;-</span> copdata <span class="sc">%&gt;%</span> </span>
 <span id="cb11-3"><a href="#cb11-3" aria-hidden="true" tabindex="-1"></a>    <span class="co"># Select person characteristics to summarise:</span></span>
@@ -728,24 +750,23 @@ <h3 class="anchored" data-anchor-id="a-create-a-summary-table-with-symptoms-stra
 <span id="cb11-53"><a href="#cb11-53" aria-hidden="true" tabindex="-1"></a>tabsymptoms</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
 <div class="cell-output-display">
-
-<div id="nqohqaehfv" style="padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;">
-<style>#nqohqaehfv table {
+<div id="tlxvherrbd" style="padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;">
+<style>#tlxvherrbd table {
   font-family: system-ui, 'Segoe UI', Roboto, Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol', 'Noto Color Emoji';
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+#tlxvherrbd thead, #tlxvherrbd tbody, #tlxvherrbd tfoot, #tlxvherrbd tr, #tlxvherrbd td, #tlxvherrbd th {
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+#tlxvherrbd p {
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-#nqohqaehfv .gt_table {
+#tlxvherrbd .gt_table {
   display: table;
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@@ -771,12 +792,12 @@ <h3 class="anchored" data-anchor-id="a-create-a-summary-table-with-symptoms-stra
   border-left-color: #D3D3D3;
 }
 
-#nqohqaehfv .gt_caption {
+#tlxvherrbd .gt_caption {
   padding-top: 4px;
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 }
 
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+#tlxvherrbd .gt_title {
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@@ -788,7 +809,7 @@ <h3 class="anchored" data-anchor-id="a-create-a-summary-table-with-symptoms-stra
   border-bottom-width: 0;
 }
 
-#nqohqaehfv .gt_subtitle {
+#tlxvherrbd .gt_subtitle {
   color: #333333;
   font-size: 85%;
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@@ -800,7 +821,7 @@ <h3 class="anchored" data-anchor-id="a-create-a-summary-table-with-symptoms-stra
   border-top-width: 0;
 }
 
-#nqohqaehfv .gt_heading {
+#tlxvherrbd .gt_heading {
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@@ -812,13 +833,13 @@ <h3 class="anchored" data-anchor-id="a-create-a-summary-table-with-symptoms-stra
   border-right-color: #D3D3D3;
 }
 
-#nqohqaehfv .gt_bottom_border {
+#tlxvherrbd .gt_bottom_border {
   border-bottom-style: solid;
   border-bottom-width: 2px;
   border-bottom-color: #D3D3D3;
 }
 
-#nqohqaehfv .gt_col_headings {
+#tlxvherrbd .gt_col_headings {
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@@ -833,7 +854,7 @@ <h3 class="anchored" data-anchor-id="a-create-a-summary-table-with-symptoms-stra
   border-right-color: #D3D3D3;
 }
 
-#nqohqaehfv .gt_col_heading {
+#tlxvherrbd .gt_col_heading {
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@@ -853,7 +874,7 @@ <h3 class="anchored" data-anchor-id="a-create-a-summary-table-with-symptoms-stra
   overflow-x: hidden;
 }
 
-#nqohqaehfv .gt_column_spanner_outer {
+#tlxvherrbd .gt_column_spanner_outer {
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@@ -865,15 +886,15 @@ <h3 class="anchored" data-anchor-id="a-create-a-summary-table-with-symptoms-stra
   padding-right: 4px;
 }
 
-#nqohqaehfv .gt_column_spanner_outer:first-child {
+#tlxvherrbd .gt_column_spanner_outer:first-child {
   padding-left: 0;
 }
 
-#nqohqaehfv .gt_column_spanner_outer:last-child {
+#tlxvherrbd .gt_column_spanner_outer:last-child {
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 }
 
-#nqohqaehfv .gt_column_spanner {
+#tlxvherrbd .gt_column_spanner {
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@@ -885,11 +906,11 @@ <h3 class="anchored" data-anchor-id="a-create-a-summary-table-with-symptoms-stra
   width: 100%;
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+#tlxvherrbd .gt_group_heading {
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@@ -915,7 +936,7 @@ <h3 class="anchored" data-anchor-id="a-create-a-summary-table-with-symptoms-stra
   text-align: left;
 }
 
-#nqohqaehfv .gt_empty_group_heading {
+#tlxvherrbd .gt_empty_group_heading {
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@@ -930,15 +951,15 @@ <h3 class="anchored" data-anchor-id="a-create-a-summary-table-with-symptoms-stra
   vertical-align: middle;
 }
 
-#nqohqaehfv .gt_from_md > :first-child {
+#tlxvherrbd .gt_from_md > :first-child {
   margin-top: 0;
 }
 
-#nqohqaehfv .gt_from_md > :last-child {
+#tlxvherrbd .gt_from_md > :last-child {
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+#tlxvherrbd .gt_row {
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@@ -957,7 +978,7 @@ <h3 class="anchored" data-anchor-id="a-create-a-summary-table-with-symptoms-stra
   overflow-x: hidden;
 }
 
-#nqohqaehfv .gt_stub {
+#tlxvherrbd .gt_stub {
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@@ -970,7 +991,7 @@ <h3 class="anchored" data-anchor-id="a-create-a-summary-table-with-symptoms-stra
   padding-right: 5px;
 }
 
-#nqohqaehfv .gt_stub_row_group {
+#tlxvherrbd .gt_stub_row_group {
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@@ -984,15 +1005,15 @@ <h3 class="anchored" data-anchor-id="a-create-a-summary-table-with-symptoms-stra
   vertical-align: top;
 }
 
-#nqohqaehfv .gt_row_group_first td {
+#tlxvherrbd .gt_row_group_first td {
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+#tlxvherrbd .gt_row_group_first th {
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-#nqohqaehfv .gt_summary_row {
+#tlxvherrbd .gt_summary_row {
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@@ -1002,16 +1023,16 @@ <h3 class="anchored" data-anchor-id="a-create-a-summary-table-with-symptoms-stra
   padding-right: 5px;
 }
 
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+#tlxvherrbd .gt_last_summary_row {
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@@ -1021,7 +1042,7 @@ <h3 class="anchored" data-anchor-id="a-create-a-summary-table-with-symptoms-stra
   border-bottom-color: #D3D3D3;
 }
 
-#nqohqaehfv .gt_grand_summary_row {
+#tlxvherrbd .gt_grand_summary_row {
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@@ -1031,7 +1052,7 @@ <h3 class="anchored" data-anchor-id="a-create-a-summary-table-with-symptoms-stra
   padding-right: 5px;
 }
 
-#nqohqaehfv .gt_first_grand_summary_row {
+#tlxvherrbd .gt_first_grand_summary_row {
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@@ -1041,7 +1062,7 @@ <h3 class="anchored" data-anchor-id="a-create-a-summary-table-with-symptoms-stra
   border-top-color: #D3D3D3;
 }
 
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+#tlxvherrbd .gt_last_grand_summary_row_top {
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@@ -1051,11 +1072,11 @@ <h3 class="anchored" data-anchor-id="a-create-a-summary-table-with-symptoms-stra
   border-bottom-color: #D3D3D3;
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+#tlxvherrbd .gt_striped {
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+#tlxvherrbd .gt_table_body {
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@@ -1064,7 +1085,7 @@ <h3 class="anchored" data-anchor-id="a-create-a-summary-table-with-symptoms-stra
   border-bottom-color: #D3D3D3;
 }
 
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+#tlxvherrbd .gt_footnotes {
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@@ -1078,7 +1099,7 @@ <h3 class="anchored" data-anchor-id="a-create-a-summary-table-with-symptoms-stra
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 }
 
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+#tlxvherrbd .gt_footnote {
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   padding-right: 5px;
 }
 
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+#tlxvherrbd .gt_sourcenotes {
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   border-right-color: #D3D3D3;
 }
 
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+#tlxvherrbd .gt_sourcenote {
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   padding-right: 5px;
 }
 
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+#tlxvherrbd .gt_left {
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+#tlxvherrbd .gt_indent_5 {
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+
+#tlxvherrbd .katex-display {
+  display: inline-flex !important;
+  margin-bottom: 0.75em !important;
+}
+
+#tlxvherrbd div.Reactable > div.rt-table > div.rt-thead > div.rt-tr.rt-tr-group-header > div.rt-th-group:after {
+  height: 0px !important;
+}
 </style>
-<table class="gt_table" data-quarto-disable-processing="false" data-quarto-bootstrap="false">
-  <thead>
-    
-    <tr class="gt_col_headings">
-      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="<strong>Characteristic</strong>"><strong>Characteristic</strong></th>
-      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="<strong>Overall</strong>
-<strong>N</strong> = 377<span class=&quot;gt_footnote_marks&quot; style=&quot;white-space:nowrap;font-style:italic;font-weight:normal;&quot;><sup>1</sup></span>"><strong>Overall</strong>
-<strong>N</strong> = 377<span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;"><sup>1</sup></span></th>
-      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="<strong>Non-case</strong>
-<strong>N</strong> = 161<span class=&quot;gt_footnote_marks&quot; style=&quot;white-space:nowrap;font-style:italic;font-weight:normal;&quot;><sup>1</sup></span>"><strong>Non-case</strong>
-<strong>N</strong> = 161<span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;"><sup>1</sup></span></th>
-      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="<strong>Case</strong>
-<strong>N</strong> = 216<span class=&quot;gt_footnote_marks&quot; style=&quot;white-space:nowrap;font-style:italic;font-weight:normal;&quot;><sup>1</sup></span>"><strong>Case</strong>
-<strong>N</strong> = 216<span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;"><sup>1</sup></span></th>
-    </tr>
-  </thead>
-  <tbody class="gt_table_body">
-    <tr><td headers="label" class="gt_row gt_left" style="font-weight: bold; font-style: italic;">Diarrhoea</td>
-<td headers="stat_0" class="gt_row gt_center"></td>
-<td headers="stat_1" class="gt_row gt_center"></td>
-<td headers="stat_2" class="gt_row gt_center"></td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;FALSE</td>
-<td headers="stat_0" class="gt_row gt_center">46 (18%)</td>
-<td headers="stat_1" class="gt_row gt_center">40 (100%)</td>
-<td headers="stat_2" class="gt_row gt_center">6 (2.8%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;TRUE</td>
-<td headers="stat_0" class="gt_row gt_center">206 (82%)</td>
-<td headers="stat_1" class="gt_row gt_center">0 (0%)</td>
-<td headers="stat_2" class="gt_row gt_center">206 (97%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left" style="font-weight: bold; font-style: italic;">Dysentary</td>
-<td headers="stat_0" class="gt_row gt_center"></td>
-<td headers="stat_1" class="gt_row gt_center"></td>
-<td headers="stat_2" class="gt_row gt_center"></td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;FALSE</td>
-<td headers="stat_0" class="gt_row gt_center">189 (97%)</td>
-<td headers="stat_1" class="gt_row gt_center">42 (100%)</td>
-<td headers="stat_2" class="gt_row gt_center">147 (97%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;TRUE</td>
-<td headers="stat_0" class="gt_row gt_center">5 (2.6%)</td>
-<td headers="stat_1" class="gt_row gt_center">0 (0%)</td>
-<td headers="stat_2" class="gt_row gt_center">5 (3.3%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left" style="font-weight: bold; font-style: italic;">Vomiting</td>
-<td headers="stat_0" class="gt_row gt_center"></td>
-<td headers="stat_1" class="gt_row gt_center"></td>
-<td headers="stat_2" class="gt_row gt_center"></td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;FALSE</td>
-<td headers="stat_0" class="gt_row gt_center">149 (69%)</td>
-<td headers="stat_1" class="gt_row gt_center">42 (100%)</td>
-<td headers="stat_2" class="gt_row gt_center">107 (62%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;TRUE</td>
-<td headers="stat_0" class="gt_row gt_center">66 (31%)</td>
-<td headers="stat_1" class="gt_row gt_center">0 (0%)</td>
-<td headers="stat_2" class="gt_row gt_center">66 (38%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left" style="font-weight: bold; font-style: italic;">Abdominal pain</td>
-<td headers="stat_0" class="gt_row gt_center"></td>
-<td headers="stat_1" class="gt_row gt_center"></td>
-<td headers="stat_2" class="gt_row gt_center"></td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;FALSE</td>
-<td headers="stat_0" class="gt_row gt_center">35 (14%)</td>
-<td headers="stat_1" class="gt_row gt_center">6 (12%)</td>
-<td headers="stat_2" class="gt_row gt_center">29 (15%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;TRUE</td>
-<td headers="stat_0" class="gt_row gt_center">207 (86%)</td>
-<td headers="stat_1" class="gt_row gt_center">44 (88%)</td>
-<td headers="stat_2" class="gt_row gt_center">163 (85%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left" style="font-weight: bold; font-style: italic;">Nausea</td>
-<td headers="stat_0" class="gt_row gt_center"></td>
-<td headers="stat_1" class="gt_row gt_center"></td>
-<td headers="stat_2" class="gt_row gt_center"></td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;FALSE</td>
-<td headers="stat_0" class="gt_row gt_center">55 (25%)</td>
-<td headers="stat_1" class="gt_row gt_center">12 (26%)</td>
-<td headers="stat_2" class="gt_row gt_center">43 (24%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;TRUE</td>
-<td headers="stat_0" class="gt_row gt_center">169 (75%)</td>
-<td headers="stat_1" class="gt_row gt_center">34 (74%)</td>
-<td headers="stat_2" class="gt_row gt_center">135 (76%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left" style="font-weight: bold; font-style: italic;">Fever</td>
-<td headers="stat_0" class="gt_row gt_center"></td>
-<td headers="stat_1" class="gt_row gt_center"></td>
-<td headers="stat_2" class="gt_row gt_center"></td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;FALSE</td>
-<td headers="stat_0" class="gt_row gt_center">127 (74%)</td>
-<td headers="stat_1" class="gt_row gt_center">32 (80%)</td>
-<td headers="stat_2" class="gt_row gt_center">95 (73%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;TRUE</td>
-<td headers="stat_0" class="gt_row gt_center">44 (26%)</td>
-<td headers="stat_1" class="gt_row gt_center">8 (20%)</td>
-<td headers="stat_2" class="gt_row gt_center">36 (27%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left" style="font-weight: bold; font-style: italic;">Headache</td>
-<td headers="stat_0" class="gt_row gt_center"></td>
-<td headers="stat_1" class="gt_row gt_center"></td>
-<td headers="stat_2" class="gt_row gt_center"></td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;FALSE</td>
-<td headers="stat_0" class="gt_row gt_center">83 (38%)</td>
-<td headers="stat_1" class="gt_row gt_center">11 (25%)</td>
-<td headers="stat_2" class="gt_row gt_center">72 (41%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;TRUE</td>
-<td headers="stat_0" class="gt_row gt_center">137 (62%)</td>
-<td headers="stat_1" class="gt_row gt_center">33 (75%)</td>
-<td headers="stat_2" class="gt_row gt_center">104 (59%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left" style="font-weight: bold; font-style: italic;">Joint pain</td>
-<td headers="stat_0" class="gt_row gt_center"></td>
-<td headers="stat_1" class="gt_row gt_center"></td>
-<td headers="stat_2" class="gt_row gt_center"></td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;FALSE</td>
-<td headers="stat_0" class="gt_row gt_center">159 (85%)</td>
-<td headers="stat_1" class="gt_row gt_center">32 (84%)</td>
-<td headers="stat_2" class="gt_row gt_center">127 (85%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;TRUE</td>
-<td headers="stat_0" class="gt_row gt_center">29 (15%)</td>
-<td headers="stat_1" class="gt_row gt_center">6 (16%)</td>
-<td headers="stat_2" class="gt_row gt_center">23 (15%)</td></tr>
-  </tbody>
-  
-  <tfoot class="gt_footnotes">
-    <tr>
-      <td class="gt_footnote" colspan="4"><span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;"><sup>1</sup></span> n (%)</td>
-    </tr>
-  </tfoot>
+
+<table class="gt_table caption-top table table-sm table-striped small" data-quarto-postprocess="true" data-quarto-disable-processing="false" data-quarto-bootstrap="false">
+<colgroup>
+<col style="width: 25%">
+<col style="width: 25%">
+<col style="width: 25%">
+<col style="width: 25%">
+</colgroup>
+<thead>
+<tr class="header gt_col_headings">
+<th id="label" class="gt_col_heading gt_columns_bottom_border gt_left" data-quarto-table-cell-role="th" scope="col"><strong>Characteristic</strong></th>
+<th id="stat_0" class="gt_col_heading gt_columns_bottom_border gt_center" data-quarto-table-cell-role="th" scope="col"><strong>Overall</strong> <strong>N</strong> = 377<span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;line-height:0;"><sup>1</sup></span></th>
+<th id="stat_1" class="gt_col_heading gt_columns_bottom_border gt_center" data-quarto-table-cell-role="th" scope="col"><strong>Non-case</strong> <strong>N</strong> = 161<span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;line-height:0;"><sup>1</sup></span></th>
+<th id="stat_2" class="gt_col_heading gt_columns_bottom_border gt_center" data-quarto-table-cell-role="th" scope="col"><strong>Case</strong> <strong>N</strong> = 216<span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;line-height:0;"><sup>1</sup></span></th>
+</tr>
+</thead>
+<tbody class="gt_table_body">
+<tr class="odd">
+<td class="gt_row gt_left" headers="label" style="font-weight: bold; font-style: italic">Diarrhoea</td>
+<td class="gt_row gt_center" headers="stat_0"><br>
+</td>
+<td class="gt_row gt_center" headers="stat_1"><br>
+</td>
+<td class="gt_row gt_center" headers="stat_2"><br>
+</td>
+</tr>
+<tr class="even">
+<td class="gt_row gt_left" headers="label">&nbsp;&nbsp;&nbsp;&nbsp;0</td>
+<td class="gt_row gt_center" headers="stat_0">46 (18%)</td>
+<td class="gt_row gt_center" headers="stat_1">40 (100%)</td>
+<td class="gt_row gt_center" headers="stat_2">6 (2.8%)</td>
+</tr>
+<tr class="odd">
+<td class="gt_row gt_left" headers="label">&nbsp;&nbsp;&nbsp;&nbsp;1</td>
+<td class="gt_row gt_center" headers="stat_0">206 (82%)</td>
+<td class="gt_row gt_center" headers="stat_1">0 (0%)</td>
+<td class="gt_row gt_center" headers="stat_2">206 (97%)</td>
+</tr>
+<tr class="even">
+<td class="gt_row gt_left" headers="label" style="font-weight: bold; font-style: italic">Dysentary</td>
+<td class="gt_row gt_center" headers="stat_0"><br>
+</td>
+<td class="gt_row gt_center" headers="stat_1"><br>
+</td>
+<td class="gt_row gt_center" headers="stat_2"><br>
+</td>
+</tr>
+<tr class="odd">
+<td class="gt_row gt_left" headers="label">&nbsp;&nbsp;&nbsp;&nbsp;0</td>
+<td class="gt_row gt_center" headers="stat_0">189 (97%)</td>
+<td class="gt_row gt_center" headers="stat_1">42 (100%)</td>
+<td class="gt_row gt_center" headers="stat_2">147 (97%)</td>
+</tr>
+<tr class="even">
+<td class="gt_row gt_left" headers="label">&nbsp;&nbsp;&nbsp;&nbsp;1</td>
+<td class="gt_row gt_center" headers="stat_0">5 (2.6%)</td>
+<td class="gt_row gt_center" headers="stat_1">0 (0%)</td>
+<td class="gt_row gt_center" headers="stat_2">5 (3.3%)</td>
+</tr>
+<tr class="odd">
+<td class="gt_row gt_left" headers="label" style="font-weight: bold; font-style: italic">Vomiting</td>
+<td class="gt_row gt_center" headers="stat_0"><br>
+</td>
+<td class="gt_row gt_center" headers="stat_1"><br>
+</td>
+<td class="gt_row gt_center" headers="stat_2"><br>
+</td>
+</tr>
+<tr class="even">
+<td class="gt_row gt_left" headers="label">&nbsp;&nbsp;&nbsp;&nbsp;0</td>
+<td class="gt_row gt_center" headers="stat_0">149 (69%)</td>
+<td class="gt_row gt_center" headers="stat_1">42 (100%)</td>
+<td class="gt_row gt_center" headers="stat_2">107 (62%)</td>
+</tr>
+<tr class="odd">
+<td class="gt_row gt_left" headers="label">&nbsp;&nbsp;&nbsp;&nbsp;1</td>
+<td class="gt_row gt_center" headers="stat_0">66 (31%)</td>
+<td class="gt_row gt_center" headers="stat_1">0 (0%)</td>
+<td class="gt_row gt_center" headers="stat_2">66 (38%)</td>
+</tr>
+<tr class="even">
+<td class="gt_row gt_left" headers="label" style="font-weight: bold; font-style: italic">Abdominal pain</td>
+<td class="gt_row gt_center" headers="stat_0"><br>
+</td>
+<td class="gt_row gt_center" headers="stat_1"><br>
+</td>
+<td class="gt_row gt_center" headers="stat_2"><br>
+</td>
+</tr>
+<tr class="odd">
+<td class="gt_row gt_left" headers="label">&nbsp;&nbsp;&nbsp;&nbsp;0</td>
+<td class="gt_row gt_center" headers="stat_0">35 (14%)</td>
+<td class="gt_row gt_center" headers="stat_1">6 (12%)</td>
+<td class="gt_row gt_center" headers="stat_2">29 (15%)</td>
+</tr>
+<tr class="even">
+<td class="gt_row gt_left" headers="label">&nbsp;&nbsp;&nbsp;&nbsp;1</td>
+<td class="gt_row gt_center" headers="stat_0">207 (86%)</td>
+<td class="gt_row gt_center" headers="stat_1">44 (88%)</td>
+<td class="gt_row gt_center" headers="stat_2">163 (85%)</td>
+</tr>
+<tr class="odd">
+<td class="gt_row gt_left" headers="label" style="font-weight: bold; font-style: italic">Nausea</td>
+<td class="gt_row gt_center" headers="stat_0"><br>
+</td>
+<td class="gt_row gt_center" headers="stat_1"><br>
+</td>
+<td class="gt_row gt_center" headers="stat_2"><br>
+</td>
+</tr>
+<tr class="even">
+<td class="gt_row gt_left" headers="label">&nbsp;&nbsp;&nbsp;&nbsp;0</td>
+<td class="gt_row gt_center" headers="stat_0">55 (25%)</td>
+<td class="gt_row gt_center" headers="stat_1">12 (26%)</td>
+<td class="gt_row gt_center" headers="stat_2">43 (24%)</td>
+</tr>
+<tr class="odd">
+<td class="gt_row gt_left" headers="label">&nbsp;&nbsp;&nbsp;&nbsp;1</td>
+<td class="gt_row gt_center" headers="stat_0">169 (75%)</td>
+<td class="gt_row gt_center" headers="stat_1">34 (74%)</td>
+<td class="gt_row gt_center" headers="stat_2">135 (76%)</td>
+</tr>
+<tr class="even">
+<td class="gt_row gt_left" headers="label" style="font-weight: bold; font-style: italic">Fever</td>
+<td class="gt_row gt_center" headers="stat_0"><br>
+</td>
+<td class="gt_row gt_center" headers="stat_1"><br>
+</td>
+<td class="gt_row gt_center" headers="stat_2"><br>
+</td>
+</tr>
+<tr class="odd">
+<td class="gt_row gt_left" headers="label">&nbsp;&nbsp;&nbsp;&nbsp;0</td>
+<td class="gt_row gt_center" headers="stat_0">127 (74%)</td>
+<td class="gt_row gt_center" headers="stat_1">32 (80%)</td>
+<td class="gt_row gt_center" headers="stat_2">95 (73%)</td>
+</tr>
+<tr class="even">
+<td class="gt_row gt_left" headers="label">&nbsp;&nbsp;&nbsp;&nbsp;1</td>
+<td class="gt_row gt_center" headers="stat_0">44 (26%)</td>
+<td class="gt_row gt_center" headers="stat_1">8 (20%)</td>
+<td class="gt_row gt_center" headers="stat_2">36 (27%)</td>
+</tr>
+<tr class="odd">
+<td class="gt_row gt_left" headers="label" style="font-weight: bold; font-style: italic">Headache</td>
+<td class="gt_row gt_center" headers="stat_0"><br>
+</td>
+<td class="gt_row gt_center" headers="stat_1"><br>
+</td>
+<td class="gt_row gt_center" headers="stat_2"><br>
+</td>
+</tr>
+<tr class="even">
+<td class="gt_row gt_left" headers="label">&nbsp;&nbsp;&nbsp;&nbsp;0</td>
+<td class="gt_row gt_center" headers="stat_0">83 (38%)</td>
+<td class="gt_row gt_center" headers="stat_1">11 (25%)</td>
+<td class="gt_row gt_center" headers="stat_2">72 (41%)</td>
+</tr>
+<tr class="odd">
+<td class="gt_row gt_left" headers="label">&nbsp;&nbsp;&nbsp;&nbsp;1</td>
+<td class="gt_row gt_center" headers="stat_0">137 (62%)</td>
+<td class="gt_row gt_center" headers="stat_1">33 (75%)</td>
+<td class="gt_row gt_center" headers="stat_2">104 (59%)</td>
+</tr>
+<tr class="even">
+<td class="gt_row gt_left" headers="label" style="font-weight: bold; font-style: italic">Joint pain</td>
+<td class="gt_row gt_center" headers="stat_0"><br>
+</td>
+<td class="gt_row gt_center" headers="stat_1"><br>
+</td>
+<td class="gt_row gt_center" headers="stat_2"><br>
+</td>
+</tr>
+<tr class="odd">
+<td class="gt_row gt_left" headers="label">&nbsp;&nbsp;&nbsp;&nbsp;0</td>
+<td class="gt_row gt_center" headers="stat_0">159 (85%)</td>
+<td class="gt_row gt_center" headers="stat_1">32 (84%)</td>
+<td class="gt_row gt_center" headers="stat_2">127 (85%)</td>
+</tr>
+<tr class="even">
+<td class="gt_row gt_left" headers="label">&nbsp;&nbsp;&nbsp;&nbsp;1</td>
+<td class="gt_row gt_center" headers="stat_0">29 (15%)</td>
+<td class="gt_row gt_center" headers="stat_1">6 (16%)</td>
+<td class="gt_row gt_center" headers="stat_2">23 (15%)</td>
+</tr>
+</tbody><tfoot class="gt_footnotes">
+<tr class="odd">
+<td colspan="4" class="gt_footnote"><span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;line-height:0;"><sup>1</sup></span> n (%)</td>
+</tr>
+</tfoot>
+
 </table>
+
 </div>
 </div>
 </div>
@@ -1329,8 +1431,8 @@ <h3 class="anchored" data-anchor-id="a-create-a-summary-table-with-symptoms-stra
 <section id="b-extra---bar-plot-of-symptoms-stratified-by-case-definition." class="level3">
 <h3 class="anchored" data-anchor-id="b-extra---bar-plot-of-symptoms-stratified-by-case-definition.">b) Extra - Bar plot of symptoms stratified by case definition.</h3>
 <div class="cell">
-<details open="">
-<summary>Code</summary>
+<details open="" class="code-fold">
+<summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb12"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb12-1"><a href="#cb12-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Create list of symptom variables:</span></span>
 <span id="cb12-2"><a href="#cb12-2" aria-hidden="true" tabindex="-1"></a>symptoms <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">"diarrhoea"</span>, </span>
 <span id="cb12-3"><a href="#cb12-3" aria-hidden="true" tabindex="-1"></a>              <span class="st">"bloody"</span>, </span>
@@ -1342,8 +1444,8 @@ <h3 class="anchored" data-anchor-id="b-extra---bar-plot-of-symptoms-stratified-b
 <span id="cb12-9"><a href="#cb12-9" aria-hidden="true" tabindex="-1"></a>              <span class="st">"jointpain"</span>)</span>
 <span id="cb12-10"><a href="#cb12-10" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb12-11"><a href="#cb12-11" aria-hidden="true" tabindex="-1"></a><span class="co"># Create nice labels for case definition:</span></span>
-<span id="cb12-12"><a href="#cb12-12" aria-hidden="true" tabindex="-1"></a>caselabs <span class="ot">&lt;-</span> ggplot2<span class="sc">::</span><span class="fu">as_labeller</span>(<span class="fu">c</span>(<span class="st">`</span><span class="at">FALSE</span><span class="st">`</span> <span class="ot">=</span> <span class="st">"Non-case"</span>, </span>
-<span id="cb12-13"><a href="#cb12-13" aria-hidden="true" tabindex="-1"></a>                                   <span class="st">`</span><span class="at">TRUE</span><span class="st">`</span> <span class="ot">=</span> <span class="st">"Case"</span>))</span>
+<span id="cb12-12"><a href="#cb12-12" aria-hidden="true" tabindex="-1"></a>caselabs <span class="ot">&lt;-</span> ggplot2<span class="sc">::</span><span class="fu">as_labeller</span>(<span class="fu">c</span>(<span class="st">"0"</span> <span class="ot">=</span> <span class="st">"Non-case"</span>, </span>
+<span id="cb12-13"><a href="#cb12-13" aria-hidden="true" tabindex="-1"></a>                                   <span class="st">"1"</span> <span class="ot">=</span> <span class="st">"Case"</span>))</span>
 <span id="cb12-14"><a href="#cb12-14" aria-hidden="true" tabindex="-1"></a><span class="co"># Select variables and cases:</span></span>
 <span id="cb12-15"><a href="#cb12-15" aria-hidden="true" tabindex="-1"></a>symptom_bar <span class="ot">&lt;-</span> copdata <span class="sc">%&gt;%</span> </span>
 <span id="cb12-16"><a href="#cb12-16" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Select symptom columns:</span></span>
@@ -1355,7 +1457,7 @@ <h3 class="anchored" data-anchor-id="b-extra---bar-plot-of-symptoms-stratified-b
 <span id="cb12-22"><a href="#cb12-22" aria-hidden="true" tabindex="-1"></a>               <span class="at">names_to =</span> <span class="st">"Symptoms"</span>, </span>
 <span id="cb12-23"><a href="#cb12-23" aria-hidden="true" tabindex="-1"></a>               <span class="at">values_drop_na =</span> <span class="cn">TRUE</span>) <span class="sc">%&gt;%</span> </span>
 <span id="cb12-24"><a href="#cb12-24" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Keep only TRUE values:</span></span>
-<span id="cb12-25"><a href="#cb12-25" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(value <span class="sc">==</span> <span class="cn">TRUE</span>) <span class="sc">%&gt;%</span> </span>
+<span id="cb12-25"><a href="#cb12-25" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(value <span class="sc">==</span> <span class="st">"1"</span>) <span class="sc">%&gt;%</span> </span>
 <span id="cb12-26"><a href="#cb12-26" aria-hidden="true" tabindex="-1"></a> </span>
 <span id="cb12-27"><a href="#cb12-27" aria-hidden="true" tabindex="-1"></a>   <span class="co"># Group by symptoms and case:</span></span>
 <span id="cb12-28"><a href="#cb12-28" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(Symptoms, case) <span class="sc">%&gt;%</span> </span>
@@ -1384,13 +1486,17 @@ <h3 class="anchored" data-anchor-id="b-extra---bar-plot-of-symptoms-stratified-b
 <pre><code>`summarise()` has grouped output by 'Symptoms'. You can override using the
 `.groups` argument.</code></pre>
 </div>
-<details open="">
-<summary>Code</summary>
+<details open="" class="code-fold">
+<summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb14"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb14-1"><a href="#cb14-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Print plot:</span></span>
 <span id="cb14-2"><a href="#cb14-2" aria-hidden="true" tabindex="-1"></a>symptom_bar</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
 <div class="cell-output-display">
-<p><img src="Lab_and_Descriptive_files/figure-html/symptom_barplot-1.png" class="img-fluid" width="672"></p>
+<div>
+<figure class="figure">
+<p><img src="Lab_and_Descriptive_files/figure-html/symptom_barplot-1.png" class="img-fluid figure-img" width="672"></p>
+</figure>
+</div>
 </div>
 </div>
 </section>
@@ -1400,8 +1506,8 @@ <h2 class="anchored" data-anchor-id="attack-proportions">3.7 Attack proportions<
 <section id="a-calculate-the-overall-attack-proportions-percentage-of-cases-among-the-total-observed-individuals.-you-could-use-tabyl." class="level3">
 <h3 class="anchored" data-anchor-id="a-calculate-the-overall-attack-proportions-percentage-of-cases-among-the-total-observed-individuals.-you-could-use-tabyl.">a) Calculate the overall attack proportions (percentage of cases among the total observed individuals). You could use <code>tabyl()</code>.</h3>
 <div class="cell">
-<details open="">
-<summary>Code</summary>
+<details open="" class="code-fold">
+<summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb15"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb15-1"><a href="#cb15-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Create table of case status:</span></span>
 <span id="cb15-2"><a href="#cb15-2" aria-hidden="true" tabindex="-1"></a>total_ap <span class="ot">&lt;-</span> <span class="fu">tabyl</span>(copdata, case) <span class="sc">%&gt;%</span> </span>
 <span id="cb15-3"><a href="#cb15-3" aria-hidden="true" tabindex="-1"></a> <span class="co"># Add row totals:</span></span>
@@ -1409,7 +1515,7 @@ <h3 class="anchored" data-anchor-id="a-calculate-the-overall-attack-proportions-
 <span id="cb15-5"><a href="#cb15-5" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Add percentages with 1 digit after the decimal point:</span></span>
 <span id="cb15-6"><a href="#cb15-6" aria-hidden="true" tabindex="-1"></a>  <span class="fu">adorn_pct_formatting</span>(<span class="at">digits =</span> <span class="dv">1</span>) <span class="sc">%&gt;%</span> </span>
 <span id="cb15-7"><a href="#cb15-7" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Filter to rows where case is TRUE:</span></span>
-<span id="cb15-8"><a href="#cb15-8" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(case <span class="sc">==</span> <span class="cn">TRUE</span>) <span class="sc">%&gt;%</span> </span>
+<span id="cb15-8"><a href="#cb15-8" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(case <span class="sc">==</span> <span class="st">"1"</span>) <span class="sc">%&gt;%</span> </span>
 <span id="cb15-9"><a href="#cb15-9" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Select the column percent:</span></span>
 <span id="cb15-10"><a href="#cb15-10" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(percent) <span class="sc">%&gt;%</span> </span>
 <span id="cb15-11"><a href="#cb15-11" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Extract (pull) the value from this cell:</span></span>
@@ -1458,8 +1564,8 @@ <h3 class="anchored" data-anchor-id="a-calculate-the-overall-attack-proportions-
 <section id="b-calculate-attack-proportions-for-group-class-and-sex-by-case-status.-you-could-use-tabyl-as-you-did-in-3.4-or-gtsummarytbl_summary." class="level3">
 <h3 class="anchored" data-anchor-id="b-calculate-attack-proportions-for-group-class-and-sex-by-case-status.-you-could-use-tabyl-as-you-did-in-3.4-or-gtsummarytbl_summary.">b) Calculate attack proportions for <code>group</code>, <code>class</code> and <code>sex</code> by case status. You could use <code>tabyl()</code> (as you did in 3.4) or <code>gtsummary::tbl_summary</code>.</h3>
 <div class="cell">
-<details open="">
-<summary>Code</summary>
+<details open="" class="code-fold">
+<summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb17"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb17-1"><a href="#cb17-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Table to calculate attack proportions:</span></span>
 <span id="cb17-2"><a href="#cb17-2" aria-hidden="true" tabindex="-1"></a>attack_prop <span class="ot">&lt;-</span> copdata <span class="sc">%&gt;%</span> </span>
 <span id="cb17-3"><a href="#cb17-3" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Select columns:</span></span>
@@ -1492,24 +1598,23 @@ <h3 class="anchored" data-anchor-id="b-calculate-attack-proportions-for-group-cl
 <span id="cb17-30"><a href="#cb17-30" aria-hidden="true" tabindex="-1"></a>attack_prop</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
 <div class="cell-output-display">
-
-<div id="ripasjqfit" style="padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;">
-<style>#ripasjqfit table {
+<div id="wmlwfnvvuq" style="padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;">
+<style>#wmlwfnvvuq table {
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-#ripasjqfit thead, #ripasjqfit tbody, #ripasjqfit tfoot, #ripasjqfit tr, #ripasjqfit td, #ripasjqfit th {
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+#wmlwfnvvuq .gt_table {
   display: table;
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@@ -1535,12 +1640,12 @@ <h3 class="anchored" data-anchor-id="b-calculate-attack-proportions-for-group-cl
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+#wmlwfnvvuq .gt_subtitle {
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-#ripasjqfit .gt_heading {
+#wmlwfnvvuq .gt_heading {
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   border-right-color: #D3D3D3;
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+#wmlwfnvvuq .gt_bottom_border {
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-#ripasjqfit .gt_col_headings {
+#wmlwfnvvuq .gt_col_headings {
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   border-right-color: #D3D3D3;
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-#ripasjqfit .gt_col_heading {
+#wmlwfnvvuq .gt_col_heading {
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-#ripasjqfit .gt_column_spanner_outer {
+#wmlwfnvvuq .gt_column_spanner_outer {
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+#wmlwfnvvuq .gt_column_spanner_outer:first-child {
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+#wmlwfnvvuq .gt_column_spanner {
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+#wmlwfnvvuq .gt_empty_group_heading {
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@@ -1694,15 +1799,15 @@ <h3 class="anchored" data-anchor-id="b-calculate-attack-proportions-for-group-cl
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-#ripasjqfit .gt_from_md > :first-child {
+#wmlwfnvvuq .gt_from_md > :first-child {
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+#wmlwfnvvuq .gt_stub {
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+#wmlwfnvvuq .gt_stub_row_group {
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+#wmlwfnvvuq .gt_row_group_first td {
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+#wmlwfnvvuq .gt_summary_row {
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+#wmlwfnvvuq .gt_grand_summary_row {
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+#wmlwfnvvuq .gt_sourcenotes {
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-    <tr class="gt_col_headings">
-      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="<strong>Characteristic</strong>"><strong>Characteristic</strong></th>
-      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="<strong>Overall</strong> <strong>N</strong> = 377<span class=&quot;gt_footnote_marks&quot; style=&quot;white-space:nowrap;font-style:italic;font-weight:normal;&quot;><sup>1</sup></span>"><strong>Overall</strong> <strong>N</strong> = 377<span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;"><sup>1</sup></span></th>
-      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="<strong>Non-case</strong> <strong>N</strong> = 161<span class=&quot;gt_footnote_marks&quot; style=&quot;white-space:nowrap;font-style:italic;font-weight:normal;&quot;><sup>1</sup></span>"><strong>Non-case</strong> <strong>N</strong> = 161<span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;"><sup>1</sup></span></th>
-      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="<strong>Case</strong> <strong>N</strong> = 216<span class=&quot;gt_footnote_marks&quot; style=&quot;white-space:nowrap;font-style:italic;font-weight:normal;&quot;><sup>1</sup></span>"><strong>Case</strong> <strong>N</strong> = 216<span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;"><sup>1</sup></span></th>
-    </tr>
-  </thead>
-  <tbody class="gt_table_body">
-    <tr><td headers="label" class="gt_row gt_left" style="font-weight: bold; font-style: italic;">class</td>
-<td headers="stat_0" class="gt_row gt_center"></td>
-<td headers="stat_1" class="gt_row gt_center"></td>
-<td headers="stat_2" class="gt_row gt_center"></td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;1</td>
-<td headers="stat_0" class="gt_row gt_center">131 (100%)</td>
-<td headers="stat_1" class="gt_row gt_center">63 (48%)</td>
-<td headers="stat_2" class="gt_row gt_center">68 (52%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;2</td>
-<td headers="stat_0" class="gt_row gt_center">101 (100%)</td>
-<td headers="stat_1" class="gt_row gt_center">44 (44%)</td>
-<td headers="stat_2" class="gt_row gt_center">57 (56%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;3</td>
-<td headers="stat_0" class="gt_row gt_center">111 (100%)</td>
-<td headers="stat_1" class="gt_row gt_center">38 (34%)</td>
-<td headers="stat_2" class="gt_row gt_center">73 (66%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;Unknown</td>
-<td headers="stat_0" class="gt_row gt_center">34</td>
-<td headers="stat_1" class="gt_row gt_center">16</td>
-<td headers="stat_2" class="gt_row gt_center">18</td></tr>
-    <tr><td headers="label" class="gt_row gt_left" style="font-weight: bold; font-style: italic;">group</td>
-<td headers="stat_0" class="gt_row gt_center"></td>
-<td headers="stat_1" class="gt_row gt_center"></td>
-<td headers="stat_2" class="gt_row gt_center"></td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;teacher</td>
-<td headers="stat_0" class="gt_row gt_center">15 (100%)</td>
-<td headers="stat_1" class="gt_row gt_center">9 (60%)</td>
-<td headers="stat_2" class="gt_row gt_center">6 (40%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;student</td>
-<td headers="stat_0" class="gt_row gt_center">362 (100%)</td>
-<td headers="stat_1" class="gt_row gt_center">152 (42%)</td>
-<td headers="stat_2" class="gt_row gt_center">210 (58%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left" style="font-weight: bold; font-style: italic;">sex</td>
-<td headers="stat_0" class="gt_row gt_center"></td>
-<td headers="stat_1" class="gt_row gt_center"></td>
-<td headers="stat_2" class="gt_row gt_center"></td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;female</td>
-<td headers="stat_0" class="gt_row gt_center">213 (100%)</td>
-<td headers="stat_1" class="gt_row gt_center">96 (45%)</td>
-<td headers="stat_2" class="gt_row gt_center">117 (55%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;male</td>
-<td headers="stat_0" class="gt_row gt_center">164 (100%)</td>
-<td headers="stat_1" class="gt_row gt_center">65 (40%)</td>
-<td headers="stat_2" class="gt_row gt_center">99 (60%)</td></tr>
-  </tbody>
-  
-  <tfoot class="gt_footnotes">
-    <tr>
-      <td class="gt_footnote" colspan="4"><span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;"><sup>1</sup></span> n (%)</td>
-    </tr>
-  </tfoot>
+
+<table class="gt_table caption-top table table-sm table-striped small" data-quarto-postprocess="true" data-quarto-disable-processing="false" data-quarto-bootstrap="false">
+<colgroup>
+<col style="width: 25%">
+<col style="width: 25%">
+<col style="width: 25%">
+<col style="width: 25%">
+</colgroup>
+<thead>
+<tr class="header gt_col_headings">
+<th id="label" class="gt_col_heading gt_columns_bottom_border gt_left" data-quarto-table-cell-role="th" scope="col"><strong>Characteristic</strong></th>
+<th id="stat_0" class="gt_col_heading gt_columns_bottom_border gt_center" data-quarto-table-cell-role="th" scope="col"><strong>Overall</strong> <strong>N</strong> = 377<span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;line-height:0;"><sup>1</sup></span></th>
+<th id="stat_1" class="gt_col_heading gt_columns_bottom_border gt_center" data-quarto-table-cell-role="th" scope="col"><strong>Non-case</strong> <strong>N</strong> = 161<span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;line-height:0;"><sup>1</sup></span></th>
+<th id="stat_2" class="gt_col_heading gt_columns_bottom_border gt_center" data-quarto-table-cell-role="th" scope="col"><strong>Case</strong> <strong>N</strong> = 216<span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;line-height:0;"><sup>1</sup></span></th>
+</tr>
+</thead>
+<tbody class="gt_table_body">
+<tr class="odd">
+<td class="gt_row gt_left" headers="label" style="font-weight: bold; font-style: italic">class</td>
+<td class="gt_row gt_center" headers="stat_0"><br>
+</td>
+<td class="gt_row gt_center" headers="stat_1"><br>
+</td>
+<td class="gt_row gt_center" headers="stat_2"><br>
+</td>
+</tr>
+<tr class="even">
+<td class="gt_row gt_left" headers="label">&nbsp;&nbsp;&nbsp;&nbsp;1</td>
+<td class="gt_row gt_center" headers="stat_0">131 (100%)</td>
+<td class="gt_row gt_center" headers="stat_1">63 (48%)</td>
+<td class="gt_row gt_center" headers="stat_2">68 (52%)</td>
+</tr>
+<tr class="odd">
+<td class="gt_row gt_left" headers="label">&nbsp;&nbsp;&nbsp;&nbsp;2</td>
+<td class="gt_row gt_center" headers="stat_0">101 (100%)</td>
+<td class="gt_row gt_center" headers="stat_1">44 (44%)</td>
+<td class="gt_row gt_center" headers="stat_2">57 (56%)</td>
+</tr>
+<tr class="even">
+<td class="gt_row gt_left" headers="label">&nbsp;&nbsp;&nbsp;&nbsp;3</td>
+<td class="gt_row gt_center" headers="stat_0">111 (100%)</td>
+<td class="gt_row gt_center" headers="stat_1">38 (34%)</td>
+<td class="gt_row gt_center" headers="stat_2">73 (66%)</td>
+</tr>
+<tr class="odd">
+<td class="gt_row gt_left" headers="label">&nbsp;&nbsp;&nbsp;&nbsp;Unknown</td>
+<td class="gt_row gt_center" headers="stat_0">34</td>
+<td class="gt_row gt_center" headers="stat_1">16</td>
+<td class="gt_row gt_center" headers="stat_2">18</td>
+</tr>
+<tr class="even">
+<td class="gt_row gt_left" headers="label" style="font-weight: bold; font-style: italic">group</td>
+<td class="gt_row gt_center" headers="stat_0"><br>
+</td>
+<td class="gt_row gt_center" headers="stat_1"><br>
+</td>
+<td class="gt_row gt_center" headers="stat_2"><br>
+</td>
+</tr>
+<tr class="odd">
+<td class="gt_row gt_left" headers="label">&nbsp;&nbsp;&nbsp;&nbsp;teacher</td>
+<td class="gt_row gt_center" headers="stat_0">15 (100%)</td>
+<td class="gt_row gt_center" headers="stat_1">9 (60%)</td>
+<td class="gt_row gt_center" headers="stat_2">6 (40%)</td>
+</tr>
+<tr class="even">
+<td class="gt_row gt_left" headers="label">&nbsp;&nbsp;&nbsp;&nbsp;student</td>
+<td class="gt_row gt_center" headers="stat_0">362 (100%)</td>
+<td class="gt_row gt_center" headers="stat_1">152 (42%)</td>
+<td class="gt_row gt_center" headers="stat_2">210 (58%)</td>
+</tr>
+<tr class="odd">
+<td class="gt_row gt_left" headers="label" style="font-weight: bold; font-style: italic">sex</td>
+<td class="gt_row gt_center" headers="stat_0"><br>
+</td>
+<td class="gt_row gt_center" headers="stat_1"><br>
+</td>
+<td class="gt_row gt_center" headers="stat_2"><br>
+</td>
+</tr>
+<tr class="even">
+<td class="gt_row gt_left" headers="label">&nbsp;&nbsp;&nbsp;&nbsp;female</td>
+<td class="gt_row gt_center" headers="stat_0">213 (100%)</td>
+<td class="gt_row gt_center" headers="stat_1">96 (45%)</td>
+<td class="gt_row gt_center" headers="stat_2">117 (55%)</td>
+</tr>
+<tr class="odd">
+<td class="gt_row gt_left" headers="label">&nbsp;&nbsp;&nbsp;&nbsp;male</td>
+<td class="gt_row gt_center" headers="stat_0">164 (100%)</td>
+<td class="gt_row gt_center" headers="stat_1">65 (40%)</td>
+<td class="gt_row gt_center" headers="stat_2">99 (60%)</td>
+</tr>
+</tbody><tfoot class="gt_footnotes">
+<tr class="even">
+<td colspan="4" class="gt_footnote"><span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;line-height:0;"><sup>1</sup></span> n (%)</td>
+</tr>
+</tfoot>
+
 </table>
+
 </div>
 </div>
 </div>
@@ -2069,6 +2220,33 @@ <h2 class="anchored" data-anchor-id="draft-the-relevant-paragraphs-and-tables-an
       }
     }
   }
+  const toggleGiscusIfUsed = (isAlternate, darkModeDefault) => {
+    const baseTheme = document.querySelector('#giscus-base-theme')?.value ?? 'light';
+    const alternateTheme = document.querySelector('#giscus-alt-theme')?.value ?? 'dark';
+    let newTheme = '';
+    if(darkModeDefault) {
+      newTheme = isAlternate ? baseTheme : alternateTheme;
+    } else {
+      newTheme = isAlternate ? alternateTheme : baseTheme;
+    }
+    const changeGiscusTheme = () => {
+      // From: https://github.com/giscus/giscus/issues/336
+      const sendMessage = (message) => {
+        const iframe = document.querySelector('iframe.giscus-frame');
+        if (!iframe) return;
+        iframe.contentWindow.postMessage({ giscus: message }, 'https://giscus.app');
+      }
+      sendMessage({
+        setConfig: {
+          theme: newTheme
+        }
+      });
+    }
+    const isGiscussLoaded = window.document.querySelector('iframe.giscus-frame') !== null;
+    if (isGiscussLoaded) {
+      changeGiscusTheme();
+    }
+  }
   const toggleColorMode = (alternate) => {
     // Switch the stylesheets
     const alternateStylesheets = window.document.querySelectorAll('link.quarto-color-scheme.quarto-color-alternate');
@@ -2135,13 +2313,15 @@ <h2 class="anchored" data-anchor-id="draft-the-relevant-paragraphs-and-tables-an
       return localAlternateSentinel;
     }
   }
-  let localAlternateSentinel = 'default';
+  const darkModeDefault = false;
+  let localAlternateSentinel = darkModeDefault ? 'alternate' : 'default';
   // Dark / light mode switch
   window.quartoToggleColorScheme = () => {
     // Read the current dark / light value 
     let toAlternate = !hasAlternateSentinel();
     toggleColorMode(toAlternate);
     setStyleSentinel(toAlternate);
+    toggleGiscusIfUsed(toAlternate, darkModeDefault);
   };
   // Ensure there is a toggle, if there isn't float one in the top right
   if (window.document.querySelector('.quarto-color-scheme-toggle') === null) {
@@ -2176,18 +2356,7 @@ <h2 class="anchored" data-anchor-id="draft-the-relevant-paragraphs-and-tables-an
     }
     return false;
   }
-  const clipboard = new window.ClipboardJS('.code-copy-button', {
-    text: function(trigger) {
-      const codeEl = trigger.previousElementSibling.cloneNode(true);
-      for (const childEl of codeEl.children) {
-        if (isCodeAnnotation(childEl)) {
-          childEl.remove();
-        }
-      }
-      return codeEl.innerText;
-    }
-  });
-  clipboard.on('success', function(e) {
+  const onCopySuccess = function(e) {
     // button target
     const button = e.trigger;
     // don't keep focus
@@ -2219,7 +2388,29 @@ <h2 class="anchored" data-anchor-id="draft-the-relevant-paragraphs-and-tables-an
     }, 1000);
     // clear code selection
     e.clearSelection();
+  }
+  const getTextToCopy = function(trigger) {
+      const codeEl = trigger.previousElementSibling.cloneNode(true);
+      for (const childEl of codeEl.children) {
+        if (isCodeAnnotation(childEl)) {
+          childEl.remove();
+        }
+      }
+      return codeEl.innerText;
+  }
+  const clipboard = new window.ClipboardJS('.code-copy-button:not([data-in-quarto-modal])', {
+    text: getTextToCopy
   });
+  clipboard.on('success', onCopySuccess);
+  if (window.document.getElementById('quarto-embedded-source-code-modal')) {
+    // For code content inside modals, clipBoardJS needs to be initialized with a container option
+    // TODO: Check when it could be a function (https://github.com/zenorocha/clipboard.js/issues/860)
+    const clipboardModal = new window.ClipboardJS('.code-copy-button[data-in-quarto-modal]', {
+      text: getTextToCopy,
+      container: window.document.getElementById('quarto-embedded-source-code-modal')
+    });
+    clipboardModal.on('success', onCopySuccess);
+  }
   const viewSource = window.document.getElementById('quarto-view-source') ||
                      window.document.getElementById('quarto-code-tools-source');
   if (viewSource) {
@@ -2271,10 +2462,27 @@ <h2 class="anchored" data-anchor-id="draft-the-relevant-paragraphs-and-tables-an
   if (showAllCode) {
     showAllCode.addEventListener("click", toggleCodeHandler(true));
   }
-  function tippyHover(el, contentFn) {
+    var localhostRegex = new RegExp(/^(?:http|https):\/\/localhost\:?[0-9]*\//);
+    var mailtoRegex = new RegExp(/^mailto:/);
+      var filterRegex = new RegExp('/' + window.location.host + '/');
+    var isInternal = (href) => {
+        return filterRegex.test(href) || localhostRegex.test(href) || mailtoRegex.test(href);
+    }
+    // Inspect non-navigation links and adorn them if external
+ 	var links = window.document.querySelectorAll('a[href]:not(.nav-link):not(.navbar-brand):not(.toc-action):not(.sidebar-link):not(.sidebar-item-toggle):not(.pagination-link):not(.no-external):not([aria-hidden]):not(.dropdown-item):not(.quarto-navigation-tool):not(.about-link)');
+    for (var i=0; i<links.length; i++) {
+      const link = links[i];
+      if (!isInternal(link.href)) {
+        // undo the damage that might have been done by quarto-nav.js in the case of
+        // links that we want to consider external
+        if (link.dataset.originalHref !== undefined) {
+          link.href = link.dataset.originalHref;
+        }
+      }
+    }
+  function tippyHover(el, contentFn, onTriggerFn, onUntriggerFn) {
     const config = {
       allowHTML: true,
-      content: contentFn,
       maxWidth: 500,
       delay: 100,
       arrow: false,
@@ -2284,8 +2492,17 @@ <h2 class="anchored" data-anchor-id="draft-the-relevant-paragraphs-and-tables-an
       interactive: true,
       interactiveBorder: 10,
       theme: 'quarto',
-      placement: 'bottom-start'
+      placement: 'bottom-start',
     };
+    if (contentFn) {
+      config.content = contentFn;
+    }
+    if (onTriggerFn) {
+      config.onTrigger = onTriggerFn;
+    }
+    if (onUntriggerFn) {
+      config.onUntrigger = onUntriggerFn;
+    }
     window.tippy(el, config); 
   }
   const noterefs = window.document.querySelectorAll('a[role="doc-noteref"]');
@@ -2297,7 +2514,130 @@ <h2 class="anchored" data-anchor-id="draft-the-relevant-paragraphs-and-tables-an
       try { href = new URL(href).hash; } catch {}
       const id = href.replace(/^#\/?/, "");
       const note = window.document.getElementById(id);
-      return note.innerHTML;
+      if (note) {
+        return note.innerHTML;
+      } else {
+        return "";
+      }
+    });
+  }
+  const xrefs = window.document.querySelectorAll('a.quarto-xref');
+  const processXRef = (id, note) => {
+    // Strip column container classes
+    const stripColumnClz = (el) => {
+      el.classList.remove("page-full", "page-columns");
+      if (el.children) {
+        for (const child of el.children) {
+          stripColumnClz(child);
+        }
+      }
+    }
+    stripColumnClz(note)
+    if (id === null || id.startsWith('sec-')) {
+      // Special case sections, only their first couple elements
+      const container = document.createElement("div");
+      if (note.children && note.children.length > 2) {
+        container.appendChild(note.children[0].cloneNode(true));
+        for (let i = 1; i < note.children.length; i++) {
+          const child = note.children[i];
+          if (child.tagName === "P" && child.innerText === "") {
+            continue;
+          } else {
+            container.appendChild(child.cloneNode(true));
+            break;
+          }
+        }
+        if (window.Quarto?.typesetMath) {
+          window.Quarto.typesetMath(container);
+        }
+        return container.innerHTML
+      } else {
+        if (window.Quarto?.typesetMath) {
+          window.Quarto.typesetMath(note);
+        }
+        return note.innerHTML;
+      }
+    } else {
+      // Remove any anchor links if they are present
+      const anchorLink = note.querySelector('a.anchorjs-link');
+      if (anchorLink) {
+        anchorLink.remove();
+      }
+      if (window.Quarto?.typesetMath) {
+        window.Quarto.typesetMath(note);
+      }
+      // TODO in 1.5, we should make sure this works without a callout special case
+      if (note.classList.contains("callout")) {
+        return note.outerHTML;
+      } else {
+        return note.innerHTML;
+      }
+    }
+  }
+  for (var i=0; i<xrefs.length; i++) {
+    const xref = xrefs[i];
+    tippyHover(xref, undefined, function(instance) {
+      instance.disable();
+      let url = xref.getAttribute('href');
+      let hash = undefined; 
+      if (url.startsWith('#')) {
+        hash = url;
+      } else {
+        try { hash = new URL(url).hash; } catch {}
+      }
+      if (hash) {
+        const id = hash.replace(/^#\/?/, "");
+        const note = window.document.getElementById(id);
+        if (note !== null) {
+          try {
+            const html = processXRef(id, note.cloneNode(true));
+            instance.setContent(html);
+          } finally {
+            instance.enable();
+            instance.show();
+          }
+        } else {
+          // See if we can fetch this
+          fetch(url.split('#')[0])
+          .then(res => res.text())
+          .then(html => {
+            const parser = new DOMParser();
+            const htmlDoc = parser.parseFromString(html, "text/html");
+            const note = htmlDoc.getElementById(id);
+            if (note !== null) {
+              const html = processXRef(id, note);
+              instance.setContent(html);
+            } 
+          }).finally(() => {
+            instance.enable();
+            instance.show();
+          });
+        }
+      } else {
+        // See if we can fetch a full url (with no hash to target)
+        // This is a special case and we should probably do some content thinning / targeting
+        fetch(url)
+        .then(res => res.text())
+        .then(html => {
+          const parser = new DOMParser();
+          const htmlDoc = parser.parseFromString(html, "text/html");
+          const note = htmlDoc.querySelector('main.content');
+          if (note !== null) {
+            // This should only happen for chapter cross references
+            // (since there is no id in the URL)
+            // remove the first header
+            if (note.children.length > 0 && note.children[0].tagName === "HEADER") {
+              note.children[0].remove();
+            }
+            const html = processXRef(null, note);
+            instance.setContent(html);
+          } 
+        }).finally(() => {
+          instance.enable();
+          instance.show();
+        });
+      }
+    }, function(instance) {
     });
   }
       let selectedAnnoteEl;
@@ -2341,6 +2681,7 @@ <h2 class="anchored" data-anchor-id="draft-the-relevant-paragraphs-and-tables-an
             }
             div.style.top = top - 2 + "px";
             div.style.height = height + 4 + "px";
+            div.style.left = 0;
             let gutterDiv = window.document.getElementById("code-annotation-line-highlight-gutter");
             if (gutterDiv === null) {
               gutterDiv = window.document.createElement("div");
@@ -2366,6 +2707,32 @@ <h2 class="anchored" data-anchor-id="draft-the-relevant-paragraphs-and-tables-an
         });
         selectedAnnoteEl = undefined;
       };
+        // Handle positioning of the toggle
+    window.addEventListener(
+      "resize",
+      throttle(() => {
+        elRect = undefined;
+        if (selectedAnnoteEl) {
+          selectCodeLines(selectedAnnoteEl);
+        }
+      }, 10)
+    );
+    function throttle(fn, ms) {
+    let throttle = false;
+    let timer;
+      return (...args) => {
+        if(!throttle) { // first call gets through
+            fn.apply(this, args);
+            throttle = true;
+        } else { // all the others get throttled
+            if(timer) clearTimeout(timer); // cancel #2
+            timer = setTimeout(() => {
+              fn.apply(this, args);
+              timer = throttle = false;
+            }, ms);
+        }
+      };
+    }
       // Attach click handler to the DT
       const annoteDls = window.document.querySelectorAll('dt[data-target-cell]');
       for (const annoteDlNode of annoteDls) {
@@ -2472,19 +2839,19 @@ <h2 class="anchored" data-anchor-id="draft-the-relevant-paragraphs-and-tables-an
 <span id="cb18-47"><a href="#cb18-47" aria-hidden="true" tabindex="-1"></a><span class="fu">## 3.2 Import your data</span></span>
 <span id="cb18-48"><a href="#cb18-48" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb18-49"><a href="#cb18-49" aria-hidden="true" tabindex="-1"></a><span class="in">```{r, Import_data}</span></span>
-<span id="cb18-50"><a href="#cb18-50" aria-hidden="true" tabindex="-1"></a><span class="co"># Import the clean data set:</span></span>
-<span id="cb18-51"><a href="#cb18-51" aria-hidden="true" tabindex="-1"></a>copdata <span class="ot">&lt;-</span> rio<span class="sc">::</span><span class="fu">import</span>(here<span class="sc">::</span><span class="fu">here</span>(<span class="st">"data"</span>, <span class="st">"Spetses_clean2_2024.rds"</span>)) </span>
-<span id="cb18-52"><a href="#cb18-52" aria-hidden="true" tabindex="-1"></a>lab <span class="ot">&lt;-</span> rio<span class="sc">::</span><span class="fu">import</span>(here<span class="sc">::</span><span class="fu">here</span>(<span class="st">"data"</span>, <span class="st">"Lab data.xlsx"</span>),</span>
-<span id="cb18-53"><a href="#cb18-53" aria-hidden="true" tabindex="-1"></a>                   <span class="at">skip =</span> <span class="dv">1</span>) <span class="co"># skip the first row.</span></span>
+<span id="cb18-50"><a href="#cb18-50" aria-hidden="true" tabindex="-1"></a><span class="in"># Import the clean data set:</span></span>
+<span id="cb18-51"><a href="#cb18-51" aria-hidden="true" tabindex="-1"></a><span class="in">copdata &lt;- rio::import(here::here("data", "Spetses_clean2_2024.rds"), trust = T) </span></span>
+<span id="cb18-52"><a href="#cb18-52" aria-hidden="true" tabindex="-1"></a><span class="in">lab &lt;- rio::import(here::here("data", "Lab data.xlsx"),</span></span>
+<span id="cb18-53"><a href="#cb18-53" aria-hidden="true" tabindex="-1"></a><span class="in">                   skip = 1) # skip the first row.</span></span>
 <span id="cb18-54"><a href="#cb18-54" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
 <span id="cb18-55"><a href="#cb18-55" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb18-56"><a href="#cb18-56" aria-hidden="true" tabindex="-1"></a><span class="fu">## 3.3 Merge and explore the lab data. Explore it as you wish!</span></span>
 <span id="cb18-57"><a href="#cb18-57" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb18-58"><a href="#cb18-58" aria-hidden="true" tabindex="-1"></a><span class="in">```{r, merge_lab_data}</span></span>
-<span id="cb18-59"><a href="#cb18-59" aria-hidden="true" tabindex="-1"></a>copdatalab <span class="ot">&lt;-</span> <span class="fu">left_join</span>(copdata, lab, </span>
-<span id="cb18-60"><a href="#cb18-60" aria-hidden="true" tabindex="-1"></a>                        <span class="at">by =</span> <span class="st">"id"</span>)</span>
-<span id="cb18-61"><a href="#cb18-61" aria-hidden="true" tabindex="-1"></a><span class="co"># Tabulate:</span></span>
-<span id="cb18-62"><a href="#cb18-62" aria-hidden="true" tabindex="-1"></a>janitor<span class="sc">::</span><span class="fu">tabyl</span>(<span class="at">dat =</span> copdatalab, <span class="st">"RT-PCR_ecoli_etec"</span>)</span>
+<span id="cb18-59"><a href="#cb18-59" aria-hidden="true" tabindex="-1"></a><span class="in">copdatalab &lt;- left_join(copdata, lab, </span></span>
+<span id="cb18-60"><a href="#cb18-60" aria-hidden="true" tabindex="-1"></a><span class="in">                        by = "id")</span></span>
+<span id="cb18-61"><a href="#cb18-61" aria-hidden="true" tabindex="-1"></a><span class="in"># Tabulate:</span></span>
+<span id="cb18-62"><a href="#cb18-62" aria-hidden="true" tabindex="-1"></a><span class="in">janitor::tabyl(dat = copdatalab, "RT-PCR_ecoli_etec")</span></span>
 <span id="cb18-63"><a href="#cb18-63" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
 <span id="cb18-64"><a href="#cb18-64" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb18-65"><a href="#cb18-65" aria-hidden="true" tabindex="-1"></a><span class="fu">## 3.4 Describe the outbreak in terms of time.</span></span>
@@ -2502,7 +2869,7 @@ <h2 class="anchored" data-anchor-id="draft-the-relevant-paragraphs-and-tables-an
 <span id="cb18-79"><a href="#cb18-79" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb18-80"><a href="#cb18-80" aria-hidden="true" tabindex="-1"></a><span class="co"># Create a dataset with only cases</span></span>
 <span id="cb18-81"><a href="#cb18-81" aria-hidden="true" tabindex="-1"></a>cases <span class="ot">&lt;-</span> copdata <span class="sc">%&gt;%</span> </span>
-<span id="cb18-82"><a href="#cb18-82" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(case <span class="sc">==</span> <span class="cn">TRUE</span>)</span>
+<span id="cb18-82"><a href="#cb18-82" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(case <span class="sc">==</span> <span class="dv">1</span>)</span>
 <span id="cb18-83"><a href="#cb18-83" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb18-84"><a href="#cb18-84" aria-hidden="true" tabindex="-1"></a>incplot <span class="ot">&lt;-</span> cases <span class="sc">%&gt;%</span> </span>
 <span id="cb18-85"><a href="#cb18-85" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Create an empty ggplot frame:</span></span>
@@ -2771,57 +3138,57 @@ <h2 class="anchored" data-anchor-id="draft-the-relevant-paragraphs-and-tables-an
 <span id="cb18-360"><a href="#cb18-360" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb18-361"><a href="#cb18-361" aria-hidden="true" tabindex="-1"></a><span class="in">```{r symptom_barplot}</span></span>
 <span id="cb18-362"><a href="#cb18-362" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb18-363"><a href="#cb18-363" aria-hidden="true" tabindex="-1"></a><span class="co"># Create list of symptom variables:</span></span>
-<span id="cb18-364"><a href="#cb18-364" aria-hidden="true" tabindex="-1"></a>symptoms <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">"diarrhoea"</span>, </span>
-<span id="cb18-365"><a href="#cb18-365" aria-hidden="true" tabindex="-1"></a>              <span class="st">"bloody"</span>, </span>
-<span id="cb18-366"><a href="#cb18-366" aria-hidden="true" tabindex="-1"></a>              <span class="st">"vomiting"</span>, </span>
-<span id="cb18-367"><a href="#cb18-367" aria-hidden="true" tabindex="-1"></a>              <span class="st">"abdo"</span>, </span>
-<span id="cb18-368"><a href="#cb18-368" aria-hidden="true" tabindex="-1"></a>              <span class="st">"nausea"</span>, </span>
-<span id="cb18-369"><a href="#cb18-369" aria-hidden="true" tabindex="-1"></a>              <span class="st">"fever"</span>, </span>
-<span id="cb18-370"><a href="#cb18-370" aria-hidden="true" tabindex="-1"></a>              <span class="st">"headache"</span>, </span>
-<span id="cb18-371"><a href="#cb18-371" aria-hidden="true" tabindex="-1"></a>              <span class="st">"jointpain"</span>)</span>
+<span id="cb18-363"><a href="#cb18-363" aria-hidden="true" tabindex="-1"></a><span class="in"># Create list of symptom variables:</span></span>
+<span id="cb18-364"><a href="#cb18-364" aria-hidden="true" tabindex="-1"></a><span class="in">symptoms &lt;- c("diarrhoea", </span></span>
+<span id="cb18-365"><a href="#cb18-365" aria-hidden="true" tabindex="-1"></a><span class="in">              "bloody", </span></span>
+<span id="cb18-366"><a href="#cb18-366" aria-hidden="true" tabindex="-1"></a><span class="in">              "vomiting", </span></span>
+<span id="cb18-367"><a href="#cb18-367" aria-hidden="true" tabindex="-1"></a><span class="in">              "abdo", </span></span>
+<span id="cb18-368"><a href="#cb18-368" aria-hidden="true" tabindex="-1"></a><span class="in">              "nausea", </span></span>
+<span id="cb18-369"><a href="#cb18-369" aria-hidden="true" tabindex="-1"></a><span class="in">              "fever", </span></span>
+<span id="cb18-370"><a href="#cb18-370" aria-hidden="true" tabindex="-1"></a><span class="in">              "headache", </span></span>
+<span id="cb18-371"><a href="#cb18-371" aria-hidden="true" tabindex="-1"></a><span class="in">              "jointpain")</span></span>
 <span id="cb18-372"><a href="#cb18-372" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb18-373"><a href="#cb18-373" aria-hidden="true" tabindex="-1"></a><span class="co"># Create nice labels for case definition:</span></span>
-<span id="cb18-374"><a href="#cb18-374" aria-hidden="true" tabindex="-1"></a>caselabs <span class="ot">&lt;-</span> ggplot2<span class="sc">::</span><span class="fu">as_labeller</span>(<span class="fu">c</span>(<span class="st">`</span><span class="at">FALSE</span><span class="st">`</span> <span class="ot">=</span> <span class="st">"Non-case"</span>, </span>
-<span id="cb18-375"><a href="#cb18-375" aria-hidden="true" tabindex="-1"></a>                                   <span class="st">`</span><span class="at">TRUE</span><span class="st">`</span> <span class="ot">=</span> <span class="st">"Case"</span>))</span>
-<span id="cb18-376"><a href="#cb18-376" aria-hidden="true" tabindex="-1"></a><span class="co"># Select variables and cases:</span></span>
-<span id="cb18-377"><a href="#cb18-377" aria-hidden="true" tabindex="-1"></a>symptom_bar <span class="ot">&lt;-</span> copdata <span class="sc">%&gt;%</span> </span>
-<span id="cb18-378"><a href="#cb18-378" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Select symptom columns:</span></span>
-<span id="cb18-379"><a href="#cb18-379" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(case, <span class="fu">c</span>(<span class="fu">all_of</span>(symptoms))) <span class="sc">%&gt;%</span></span>
-<span id="cb18-380"><a href="#cb18-380" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Drop NAs:</span></span>
-<span id="cb18-381"><a href="#cb18-381" aria-hidden="true" tabindex="-1"></a>  <span class="fu">drop_na</span>() <span class="sc">%&gt;%</span> </span>
-<span id="cb18-382"><a href="#cb18-382" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Reshape (pivot longer):</span></span>
-<span id="cb18-383"><a href="#cb18-383" aria-hidden="true" tabindex="-1"></a>  <span class="fu">pivot_longer</span>(<span class="sc">!</span>case, </span>
-<span id="cb18-384"><a href="#cb18-384" aria-hidden="true" tabindex="-1"></a>               <span class="at">names_to =</span> <span class="st">"Symptoms"</span>, </span>
-<span id="cb18-385"><a href="#cb18-385" aria-hidden="true" tabindex="-1"></a>               <span class="at">values_drop_na =</span> <span class="cn">TRUE</span>) <span class="sc">%&gt;%</span> </span>
-<span id="cb18-386"><a href="#cb18-386" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Keep only TRUE values:</span></span>
-<span id="cb18-387"><a href="#cb18-387" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(value <span class="sc">==</span> <span class="cn">TRUE</span>) <span class="sc">%&gt;%</span> </span>
-<span id="cb18-388"><a href="#cb18-388" aria-hidden="true" tabindex="-1"></a> </span>
-<span id="cb18-389"><a href="#cb18-389" aria-hidden="true" tabindex="-1"></a>   <span class="co"># Group by symptoms and case:</span></span>
-<span id="cb18-390"><a href="#cb18-390" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(Symptoms, case) <span class="sc">%&gt;%</span> </span>
-<span id="cb18-391"><a href="#cb18-391" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Count for each symptom by case:</span></span>
-<span id="cb18-392"><a href="#cb18-392" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">summarise</span>(<span class="at">count =</span> <span class="fu">n</span>()) <span class="sc">%&gt;%</span> </span>
-<span id="cb18-393"><a href="#cb18-393" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Create plot:</span></span>
-<span id="cb18-394"><a href="#cb18-394" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggplot</span>(</span>
-<span id="cb18-395"><a href="#cb18-395" aria-hidden="true" tabindex="-1"></a>    <span class="at">mapping =</span> <span class="fu">aes</span>(</span>
-<span id="cb18-396"><a href="#cb18-396" aria-hidden="true" tabindex="-1"></a>    <span class="co"># Order symptom bars so most common ones are ontop:</span></span>
-<span id="cb18-397"><a href="#cb18-397" aria-hidden="true" tabindex="-1"></a>    <span class="at">x =</span> <span class="fu">reorder</span>(Symptoms, <span class="fu">desc</span>(count), <span class="at">decreasing =</span> <span class="cn">TRUE</span>), </span>
-<span id="cb18-398"><a href="#cb18-398" aria-hidden="true" tabindex="-1"></a>    <span class="at">y =</span> count)) <span class="sc">+</span></span>
-<span id="cb18-399"><a href="#cb18-399" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Display bars as proportions</span></span>
-<span id="cb18-400"><a href="#cb18-400" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_bar</span>(<span class="at">stat =</span> <span class="st">"identity"</span>) <span class="sc">+</span></span>
-<span id="cb18-401"><a href="#cb18-401" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Update x axis label:</span></span>
-<span id="cb18-402"><a href="#cb18-402" aria-hidden="true" tabindex="-1"></a>  <span class="fu">xlab</span>(<span class="st">"Symptoms"</span>) <span class="sc">+</span></span>
-<span id="cb18-403"><a href="#cb18-403" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Update y axis label:</span></span>
-<span id="cb18-404"><a href="#cb18-404" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ylab</span>(<span class="st">"Proportion of respondents"</span>) <span class="sc">+</span></span>
-<span id="cb18-405"><a href="#cb18-405" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Flip plot on its side so symptom labels are clear:</span></span>
-<span id="cb18-406"><a href="#cb18-406" aria-hidden="true" tabindex="-1"></a>  <span class="fu">coord_flip</span>() <span class="sc">+</span></span>
-<span id="cb18-407"><a href="#cb18-407" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Facet the plot by (labelled) case:</span></span>
-<span id="cb18-408"><a href="#cb18-408" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_wrap</span>(<span class="at">facets =</span> <span class="st">"case"</span>,</span>
-<span id="cb18-409"><a href="#cb18-409" aria-hidden="true" tabindex="-1"></a>             <span class="at">labeller =</span> caselabs,</span>
-<span id="cb18-410"><a href="#cb18-410" aria-hidden="true" tabindex="-1"></a>             <span class="at">ncol =</span> <span class="dv">2</span>)</span>
+<span id="cb18-373"><a href="#cb18-373" aria-hidden="true" tabindex="-1"></a><span class="in"># Create nice labels for case definition:</span></span>
+<span id="cb18-374"><a href="#cb18-374" aria-hidden="true" tabindex="-1"></a><span class="in">caselabs &lt;- ggplot2::as_labeller(c("0" = "Non-case", </span></span>
+<span id="cb18-375"><a href="#cb18-375" aria-hidden="true" tabindex="-1"></a><span class="in">                                   "1" = "Case"))</span></span>
+<span id="cb18-376"><a href="#cb18-376" aria-hidden="true" tabindex="-1"></a><span class="in"># Select variables and cases:</span></span>
+<span id="cb18-377"><a href="#cb18-377" aria-hidden="true" tabindex="-1"></a><span class="in">symptom_bar &lt;- copdata %&gt;% </span></span>
+<span id="cb18-378"><a href="#cb18-378" aria-hidden="true" tabindex="-1"></a><span class="in">  # Select symptom columns:</span></span>
+<span id="cb18-379"><a href="#cb18-379" aria-hidden="true" tabindex="-1"></a><span class="in">  select(case, c(all_of(symptoms))) %&gt;%</span></span>
+<span id="cb18-380"><a href="#cb18-380" aria-hidden="true" tabindex="-1"></a><span class="in">  # Drop NAs:</span></span>
+<span id="cb18-381"><a href="#cb18-381" aria-hidden="true" tabindex="-1"></a><span class="in">  drop_na() %&gt;% </span></span>
+<span id="cb18-382"><a href="#cb18-382" aria-hidden="true" tabindex="-1"></a><span class="in">  # Reshape (pivot longer):</span></span>
+<span id="cb18-383"><a href="#cb18-383" aria-hidden="true" tabindex="-1"></a><span class="in">  pivot_longer(!case, </span></span>
+<span id="cb18-384"><a href="#cb18-384" aria-hidden="true" tabindex="-1"></a><span class="in">               names_to = "Symptoms", </span></span>
+<span id="cb18-385"><a href="#cb18-385" aria-hidden="true" tabindex="-1"></a><span class="in">               values_drop_na = TRUE) %&gt;% </span></span>
+<span id="cb18-386"><a href="#cb18-386" aria-hidden="true" tabindex="-1"></a><span class="in">  # Keep only TRUE values:</span></span>
+<span id="cb18-387"><a href="#cb18-387" aria-hidden="true" tabindex="-1"></a><span class="in">  filter(value == "1") %&gt;% </span></span>
+<span id="cb18-388"><a href="#cb18-388" aria-hidden="true" tabindex="-1"></a><span class="in"> </span></span>
+<span id="cb18-389"><a href="#cb18-389" aria-hidden="true" tabindex="-1"></a><span class="in">   # Group by symptoms and case:</span></span>
+<span id="cb18-390"><a href="#cb18-390" aria-hidden="true" tabindex="-1"></a><span class="in">  group_by(Symptoms, case) %&gt;% </span></span>
+<span id="cb18-391"><a href="#cb18-391" aria-hidden="true" tabindex="-1"></a><span class="in">  # Count for each symptom by case:</span></span>
+<span id="cb18-392"><a href="#cb18-392" aria-hidden="true" tabindex="-1"></a><span class="in">  dplyr::summarise(count = n()) %&gt;% </span></span>
+<span id="cb18-393"><a href="#cb18-393" aria-hidden="true" tabindex="-1"></a><span class="in">  # Create plot:</span></span>
+<span id="cb18-394"><a href="#cb18-394" aria-hidden="true" tabindex="-1"></a><span class="in">  ggplot(</span></span>
+<span id="cb18-395"><a href="#cb18-395" aria-hidden="true" tabindex="-1"></a><span class="in">    mapping = aes(</span></span>
+<span id="cb18-396"><a href="#cb18-396" aria-hidden="true" tabindex="-1"></a><span class="in">    # Order symptom bars so most common ones are ontop:</span></span>
+<span id="cb18-397"><a href="#cb18-397" aria-hidden="true" tabindex="-1"></a><span class="in">    x = reorder(Symptoms, desc(count), decreasing = TRUE), </span></span>
+<span id="cb18-398"><a href="#cb18-398" aria-hidden="true" tabindex="-1"></a><span class="in">    y = count)) +</span></span>
+<span id="cb18-399"><a href="#cb18-399" aria-hidden="true" tabindex="-1"></a><span class="in">  # Display bars as proportions</span></span>
+<span id="cb18-400"><a href="#cb18-400" aria-hidden="true" tabindex="-1"></a><span class="in">  geom_bar(stat = "identity") +</span></span>
+<span id="cb18-401"><a href="#cb18-401" aria-hidden="true" tabindex="-1"></a><span class="in">  # Update x axis label:</span></span>
+<span id="cb18-402"><a href="#cb18-402" aria-hidden="true" tabindex="-1"></a><span class="in">  xlab("Symptoms") +</span></span>
+<span id="cb18-403"><a href="#cb18-403" aria-hidden="true" tabindex="-1"></a><span class="in">  # Update y axis label:</span></span>
+<span id="cb18-404"><a href="#cb18-404" aria-hidden="true" tabindex="-1"></a><span class="in">  ylab("Proportion of respondents") +</span></span>
+<span id="cb18-405"><a href="#cb18-405" aria-hidden="true" tabindex="-1"></a><span class="in">  # Flip plot on its side so symptom labels are clear:</span></span>
+<span id="cb18-406"><a href="#cb18-406" aria-hidden="true" tabindex="-1"></a><span class="in">  coord_flip() +</span></span>
+<span id="cb18-407"><a href="#cb18-407" aria-hidden="true" tabindex="-1"></a><span class="in">  # Facet the plot by (labelled) case:</span></span>
+<span id="cb18-408"><a href="#cb18-408" aria-hidden="true" tabindex="-1"></a><span class="in">  facet_wrap(facets = "case",</span></span>
+<span id="cb18-409"><a href="#cb18-409" aria-hidden="true" tabindex="-1"></a><span class="in">             labeller = caselabs,</span></span>
+<span id="cb18-410"><a href="#cb18-410" aria-hidden="true" tabindex="-1"></a><span class="in">             ncol = 2)</span></span>
 <span id="cb18-411"><a href="#cb18-411" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb18-412"><a href="#cb18-412" aria-hidden="true" tabindex="-1"></a><span class="co"># Print plot:</span></span>
-<span id="cb18-413"><a href="#cb18-413" aria-hidden="true" tabindex="-1"></a>symptom_bar</span>
+<span id="cb18-412"><a href="#cb18-412" aria-hidden="true" tabindex="-1"></a><span class="in"># Print plot:</span></span>
+<span id="cb18-413"><a href="#cb18-413" aria-hidden="true" tabindex="-1"></a><span class="in">symptom_bar</span></span>
 <span id="cb18-414"><a href="#cb18-414" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
 <span id="cb18-415"><a href="#cb18-415" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb18-416"><a href="#cb18-416" aria-hidden="true" tabindex="-1"></a><span class="fu">## 3.7 Attack proportions</span></span>
@@ -2836,7 +3203,7 @@ <h2 class="anchored" data-anchor-id="draft-the-relevant-paragraphs-and-tables-an
 <span id="cb18-427"><a href="#cb18-427" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Add percentages with 1 digit after the decimal point:</span></span>
 <span id="cb18-428"><a href="#cb18-428" aria-hidden="true" tabindex="-1"></a>  <span class="fu">adorn_pct_formatting</span>(<span class="at">digits =</span> <span class="dv">1</span>) <span class="sc">%&gt;%</span> </span>
 <span id="cb18-429"><a href="#cb18-429" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Filter to rows where case is TRUE:</span></span>
-<span id="cb18-430"><a href="#cb18-430" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(case <span class="sc">==</span> <span class="cn">TRUE</span>) <span class="sc">%&gt;%</span> </span>
+<span id="cb18-430"><a href="#cb18-430" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(case <span class="sc">==</span> <span class="st">"1"</span>) <span class="sc">%&gt;%</span> </span>
 <span id="cb18-431"><a href="#cb18-431" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Select the column percent:</span></span>
 <span id="cb18-432"><a href="#cb18-432" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(percent) <span class="sc">%&gt;%</span> </span>
 <span id="cb18-433"><a href="#cb18-433" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Extract (pull) the value from this cell:</span></span>
@@ -2896,10 +3263,11 @@ <h2 class="anchored" data-anchor-id="draft-the-relevant-paragraphs-and-tables-an
 <span id="cb18-489"><a href="#cb18-489" aria-hidden="true" tabindex="-1"></a>::: {.callout-tip title="Need a little bit of help?" collapse="true"}</span>
 <span id="cb18-490"><a href="#cb18-490" aria-hidden="true" tabindex="-1"></a>To save your tabyl table as a .docx, you could convert your tabyl to flextable (<span class="in">`as_flex_table()`</span>), ensure only one line per row (<span class="in">`flextable::autofit()`</span>) and save as .docx (<span class="in">`flextable::save_as_docx(path = "nameoftable.docx"`</span>)</span>
 <span id="cb18-491"><a href="#cb18-491" aria-hidden="true" tabindex="-1"></a>:::</span>
-</code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</code><button title="Copy to Clipboard" class="code-copy-button" data-in-quarto-modal=""><i class="bi"></i></button></pre></div>
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-  <li><a href="#tips-you-could-follow" id="toc-tips-you-could-follow" class="nav-link" data-scroll-target="#tips-you-could-follow"><strong>3. Tips you could follow</strong></a>
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-  <li><a href="#install-packages-and-load-libraries" id="toc-install-packages-and-load-libraries" class="nav-link" data-scroll-target="#install-packages-and-load-libraries">3.1. Install packages and load libraries</a></li>
-  <li><a href="#import-your-data" id="toc-import-your-data" class="nav-link" data-scroll-target="#import-your-data">3.2. Import your data</a></li>
-  <li><a href="#explore-and-clean-your-data" id="toc-explore-and-clean-your-data" class="nav-link" data-scroll-target="#explore-and-clean-your-data">3.3. Explore and clean your data</a></li>
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-  <li><a href="#export-clean-data" id="toc-export-clean-data" class="nav-link" data-scroll-target="#export-clean-data">3.5. Export clean data</a></li>
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-<div class="quarto-title-block"><div><h1 class="title">Data import and cleaning</h1><button type="button" class="btn code-tools-button dropdown-toggle" id="quarto-code-tools-menu" data-bs-toggle="dropdown" aria-expanded="false"><i class="bi"></i> Code</button><ul class="dropdown-menu dropdown-menu-end" aria-labelelledby="quarto-code-tools-menu"><li><a id="quarto-show-all-code" class="dropdown-item" href="javascript:void(0)" role="button">Show All Code</a></li><li><a id="quarto-hide-all-code" class="dropdown-item" href="javascript:void(0)" role="button">Hide All Code</a></li><li><hr class="dropdown-divider"></li><li><a id="quarto-view-source" class="dropdown-item" href="javascript:void(0)" role="button">View Source</a></li></ul></div></div>
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-    
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-  
-
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-
-<section id="learning-outcomes" class="level1">
-<h1><strong>1. Learning outcomes</strong></h1>
-<p>At the end of the session, participants will be able to:</p>
-<ul>
-<li>Import datasets in R</li>
-<li>Apply basic data management techniques to explore and familiarise themselves with the dataset</li>
-<li>Apply strategies for data cleaning</li>
-</ul>
-</section>
-<section id="storyplot-description" class="level1">
-<h1><strong>2. Story/plot description</strong></h1>
-<p>You and your team have carried out hundreds of interviews in a very short time. Well done!</p>
-<p>You will now have your first look at the data. Note that in real life you would have ideally done a pilot of the questionnaire and you would have prepared in advance a codebook and be familiar with the names and format of all variables.</p>
-<p>Explore and clean the data you have collected and save a new version of the cleaned dataset in your computer. Be sure to make use of the R code that was provided to you previously, during the R practicals. You can also use the code you wrote during the Applied Epi course. You are expected to look at the help functions and google as well. Try to understand what the functions are doing and what their arguments are.</p>
-<p>Work on your own computer and help your fellow colleagues as needed. The lead of the outbreak will be in charge of keeping the time (unless you designate another person, but be sure that someone keeps track of time).</p>
-<p>Open R Studio and set up a new R project, and create a <code>data</code> and a scripts <code>folder</code>.</p>
-</section>
-<section id="tips-you-could-follow" class="level1">
-<h1><strong>3. Tips you could follow</strong></h1>
-<section id="install-packages-and-load-libraries" class="level2">
-<h2 class="anchored" data-anchor-id="install-packages-and-load-libraries">3.1. Install packages and load libraries</h2>
-<ul>
-<li>Load the R packages you will be using. Update this list of packages as you dig into the code: You may realise you need packages you didn’t think about at the beginning. Remember that it is a good practice to load tydiverse at the end of your package list, to avoid <a href="https://www.ucl.ac.uk/~uctqiax/PUBLG100/2015/faq/masking.html">masking functions</a>.</li>
-</ul>
-<div class="cell">
-<details open="">
-<summary>Code</summary>
-<div class="sourceCode cell-code" id="cb1"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Check if the 'pacman' package is installed, if not install it:</span></span>
-<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="cf">if</span> (<span class="sc">!</span><span class="fu">requireNamespace</span>(<span class="st">"pacman"</span>, <span class="at">quietly =</span> <span class="cn">TRUE</span>)) <span class="fu">install.packages</span>(<span class="st">"pacman"</span>)</span>
-<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a><span class="co"># Load the required libraries into the current R session:</span></span>
-<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a>pacman<span class="sc">::</span><span class="fu">p_load</span>(rio, </span>
-<span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a>               here, </span>
-<span id="cb1-7"><a href="#cb1-7" aria-hidden="true" tabindex="-1"></a>               tidyverse, </span>
-<span id="cb1-8"><a href="#cb1-8" aria-hidden="true" tabindex="-1"></a>               skimr,</span>
-<span id="cb1-9"><a href="#cb1-9" aria-hidden="true" tabindex="-1"></a>               plyr,</span>
-<span id="cb1-10"><a href="#cb1-10" aria-hidden="true" tabindex="-1"></a>               janitor,</span>
-<span id="cb1-11"><a href="#cb1-11" aria-hidden="true" tabindex="-1"></a>               lubridate,</span>
-<span id="cb1-12"><a href="#cb1-12" aria-hidden="true" tabindex="-1"></a>               gtsummary, </span>
-<span id="cb1-13"><a href="#cb1-13" aria-hidden="true" tabindex="-1"></a>               flextable,</span>
-<span id="cb1-14"><a href="#cb1-14" aria-hidden="true" tabindex="-1"></a>               officer,</span>
-<span id="cb1-15"><a href="#cb1-15" aria-hidden="true" tabindex="-1"></a>               epikit, </span>
-<span id="cb1-16"><a href="#cb1-16" aria-hidden="true" tabindex="-1"></a>               apyramid, </span>
-<span id="cb1-17"><a href="#cb1-17" aria-hidden="true" tabindex="-1"></a>               scales)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-</details>
-</div>
-</section>
-<section id="import-your-data" class="level2">
-<h2 class="anchored" data-anchor-id="import-your-data">3.2. Import your data</h2>
-<ul>
-<li>You can find the <code>spetses_school.csv</code> file in EVA. Feel free to explore the <strong>codebook</strong> as well.</li>
-</ul>
-<div class="cell">
-<details open="">
-<summary>Code</summary>
-<div class="sourceCode cell-code" id="cb2"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Import the raw data set:</span></span>
-<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a>copdata <span class="ot">&lt;-</span> rio<span class="sc">::</span><span class="fu">import</span>(here<span class="sc">::</span><span class="fu">here</span>(<span class="st">"data"</span>, <span class="st">"spetses_school.csv"</span>))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-</details>
-</div>
-</section>
-<section id="explore-and-clean-your-data" class="level2">
-<h2 class="anchored" data-anchor-id="explore-and-clean-your-data">3.3. Explore and clean your data</h2>
-<ul>
-<li><p>Have a look at the structure of your data. Here you have some questions that may help you get started:</p>
-<ul>
-<li><p>How many observations and variables does the dataset contain?</p></li>
-<li><p>What types of variables do you have and what types of values are recorded?</p></li>
-<li><p>Do any of the values of the other variables look implausible? Which ones and why? What will you do about it?</p></li>
-</ul></li>
-<li><p>Go ahead and clean your data as you see fit.</p></li>
-</ul>
-<div class="callout callout-style-default callout-tip callout-titled" title="Need a little bit of help?">
-<div class="callout-header d-flex align-content-center" data-bs-toggle="collapse" data-bs-target=".callout-1-contents" aria-controls="callout-1" aria-expanded="false" aria-label="Toggle callout">
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-Need a little bit of help?
-</div>
-<div class="callout-btn-toggle d-inline-block border-0 py-1 ps-1 pe-0 float-end"><i class="callout-toggle"></i></div>
-</div>
-<div id="callout-1" class="callout-1-contents callout-collapse collapse">
-<div class="callout-body-container callout-body">
-<p>You could use <code>head()</code>, <code>dim()</code>, <code>str()</code>, or <code>skim()</code> to have a quick look at the data and get an idea of which variables you want to explore further. You can also do some tables</p>
-</div>
-</div>
-</div>
-<div class="cell">
-<details open="">
-<summary>Code</summary>
-<div class="sourceCode cell-code" id="cb3"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a><span class="fu">head</span>(copdata)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-</details>
-<div class="cell-output-display">
-
-<div data-pagedtable="false">
-  <script data-pagedtable-source="" type="application/json">
-{"columns":[{"label":[""],"name":["_rn_"],"type":[""],"align":["left"]},{"label":["id"],"name":[1],"type":["chr"],"align":["left"]},{"label":["sex"],"name":[2],"type":["chr"],"align":["left"]},{"label":["age"],"name":[3],"type":["int"],"align":["right"]},{"label":["group"],"name":[4],"type":["int"],"align":["right"]},{"label":["class"],"name":[5],"type":["int"],"align":["right"]},{"label":["diarrhoea"],"name":[6],"type":["int"],"align":["right"]},{"label":["bloody"],"name":[7],"type":["int"],"align":["right"]},{"label":["vomiting"],"name":[8],"type":["int"],"align":["right"]},{"label":["abdo"],"name":[9],"type":["int"],"align":["right"]},{"label":["nausea"],"name":[10],"type":["int"],"align":["right"]},{"label":["fever"],"name":[11],"type":["int"],"align":["right"]},{"label":["headache"],"name":[12],"type":["int"],"align":["right"]},{"label":["jointpain"],"name":[13],"type":["int"],"align":["right"]},{"label":["starthour"],"name":[14],"type":["int"],"align":["right"]},{"label":["meal"],"name":[15],"type":["int"],"align":["right"]},{"label":["feta"],"name":[16],"type":["int"],"align":["right"]},{"label":["fetaD"],"name":[17],"type":["int"],"align":["right"]},{"label":["sardines"],"name":[18],"type":["int"],"align":["right"]},{"label":["sardinesD"],"name":[19],"type":["int"],"align":["right"]},{"label":["eggplant"],"name":[20],"type":["int"],"align":["right"]},{"label":["eggplantD"],"name":[21],"type":["int"],"align":["right"]},{"label":["moussaka"],"name":[22],"type":["int"],"align":["right"]},{"label":["moussakaD"],"name":[23],"type":["int"],"align":["right"]},{"label":["orzo"],"name":[24],"type":["int"],"align":["right"]},{"label":["orzoD"],"name":[25],"type":["int"],"align":["right"]},{"label":["greeksal"],"name":[26],"type":["int"],"align":["right"]},{"label":["greeksalD"],"name":[27],"type":["int"],"align":["right"]},{"label":["bread"],"name":[28],"type":["int"],"align":["right"]},{"label":["breadD"],"name":[29],"type":["int"],"align":["right"]},{"label":["dessert"],"name":[30],"type":["int"],"align":["right"]},{"label":["dessertD"],"name":[31],"type":["int"],"align":["right"]},{"label":["champagne"],"name":[32],"type":["int"],"align":["right"]},{"label":["champagneD"],"name":[33],"type":["int"],"align":["right"]},{"label":["beer"],"name":[34],"type":["int"],"align":["right"]},{"label":["beerD"],"name":[35],"type":["int"],"align":["right"]},{"label":["redwine"],"name":[36],"type":["int"],"align":["right"]},{"label":["redwineD"],"name":[37],"type":["int"],"align":["right"]},{"label":["whitewine"],"name":[38],"type":["int"],"align":["right"]},{"label":["whitewineD"],"name":[39],"type":["int"],"align":["right"]},{"label":["dayonset"],"name":[40],"type":["chr"],"align":["left"]}],"data":[{"1":"sp-2-001","2":"male","3":"18","4":"1","5":"2","6":"1","7":"0","8":"0","9":"1","10":"0","11":"NA","12":"0","13":"0","14":"9","15":"1","16":"1","17":"2","18":"1","19":"2","20":"0","21":"0","22":"1","23":"2","24":"1","25":"3","26":"1","27":"1","28":"1","29":"2","30":"1","31":"2","32":"1","33":"1","34":"1","35":"3","36":"0","37":"0","38":"0","39":"0","40":"6oct2024","_rn_":"1"},{"1":"sp-3-003","2":"female","3":"18","4":"1","5":"3","6":"NA","7":"NA","8":"NA","9":"NA","10":"NA","11":"NA","12":"NA","13":"NA","14":"NA","15":"1","16":"0","17":"0","18":"0","19":"0","20":"0","21":"0","22":"1","23":"1","24":"1","25":"3","26":"1","27":"3","28":"1","29":"3","30":"1","31":"3","32":"1","33":"1","34":"0","35":"0","36":"1","37":"3","38":"0","39":"0","40":"","_rn_":"2"},{"1":"sp-1-005","2":"female","3":"17","4":"1","5":"1","6":"NA","7":"NA","8":"NA","9":"1","10":"1","11":"NA","12":"1","13":"NA","14":"NA","15":"1","16":"NA","17":"NA","18":"NA","19":"NA","20":"NA","21":"NA","22":"1","23":"0","24":"1","25":"1","26":"NA","27":"NA","28":"1","29":"1","30":"NA","31":"NA","32":"0","33":"0","34":"0","35":"0","36":"0","37":"0","38":"0","39":"0","40":"","_rn_":"3"},{"1":"sp-2-006","2":"male","3":"17","4":"1","5":"2","6":"NA","7":"NA","8":"NA","9":"NA","10":"NA","11":"NA","12":"NA","13":"NA","14":"NA","15":"0","16":"0","17":"0","18":"0","19":"0","20":"0","21":"0","22":"1","23":"0","24":"0","25":"0","26":"0","27":"0","28":"0","29":"0","30":"0","31":"0","32":"1","33":"3","34":"1","35":"3","36":"1","37":"3","38":"1","39":"3","40":"","_rn_":"4"},{"1":"sp-3-007","2":"female","3":"18","4":"1","5":"3","6":"1","7":"0","8":"0","9":"1","10":"1","11":"0","12":"1","13":"0","14":"15","15":"1","16":"1","17":"2","18":"1","19":"2","20":"1","21":"2","22":"1","23":"2","24":"1","25":"2","26":"1","27":"2","28":"1","29":"2","30":"1","31":"2","32":"1","33":"1","34":"1","35":"2","36":"0","37":"0","38":"1","39":"3","40":"6oct2024","_rn_":"5"},{"1":"sp-2-008","2":"male","3":"180","4":"1","5":"2","6":"1","7":"0","8":"0","9":"1","10":"0","11":"0","12":"0","13":"0","14":"15","15":"1","16":"1","17":"2","18":"1","19":"2","20":"1","21":"2","22":"1","23":"2","24":"1","25":"2","26":"1","27":"2","28":"1","29":"2","30":"1","31":"2","32":"1","33":"1","34":"1","35":"3","36":"0","37":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-  </script>
-</div>
-</div>
-<details open="">
-<summary>Code</summary>
-<div class="sourceCode cell-code" id="cb4"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a><span class="fu">dim</span>(copdata)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-</details>
-<div class="cell-output cell-output-stdout">
-<pre><code>[1] 397  40</code></pre>
-</div>
-<details open="">
-<summary>Code</summary>
-<div class="sourceCode cell-code" id="cb6"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a><span class="fu">str</span>(copdata)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-</details>
-<div class="cell-output cell-output-stdout">
-<pre><code>'data.frame':   397 obs. of  40 variables:
- $ id        : chr  "sp-2-001" "sp-3-003" "sp-1-005" "sp-2-006" ...
- $ sex       : chr  "male" "female" "female" "male" ...
- $ age       : int  18 18 17 17 18 180 16 15 43 16 ...
- $ group     : int  1 1 1 1 1 1 0 1 0 1 ...
- $ class     : int  2 3 1 2 3 2 NA 1 NA 1 ...
- $ diarrhoea : int  1 NA NA NA 1 1 NA 0 1 NA ...
- $ bloody    : int  0 NA NA NA 0 0 NA 0 NA NA ...
- $ vomiting  : int  0 NA NA NA 0 0 NA 0 NA NA ...
- $ abdo      : int  1 NA 1 NA 1 1 NA 0 NA NA ...
- $ nausea    : int  0 NA 1 NA 1 0 NA 1 1 NA ...
- $ fever     : int  NA NA NA NA 0 0 NA 0 NA NA ...
- $ headache  : int  0 NA 1 NA 1 0 NA 1 1 NA ...
- $ jointpain : int  0 NA NA NA 0 0 NA 0 1 NA ...
- $ starthour : int  9 NA NA NA 15 15 NA NA 3 NA ...
- $ meal      : int  1 1 1 0 1 1 1 1 1 1 ...
- $ feta      : int  1 0 NA 0 1 1 1 1 1 1 ...
- $ fetaD     : int  2 0 NA 0 2 2 2 1 2 2 ...
- $ sardines  : int  1 0 NA 0 1 1 1 0 1 1 ...
- $ sardinesD : int  2 0 NA 0 2 2 2 0 2 2 ...
- $ eggplant  : int  0 0 NA 0 1 1 1 0 NA 1 ...
- $ eggplantD : int  0 0 NA 0 2 2 2 0 NA 2 ...
- $ moussaka  : int  1 1 1 1 1 1 1 1 1 1 ...
- $ moussakaD : int  2 1 0 0 2 2 1 3 2 2 ...
- $ orzo      : int  1 1 1 0 1 1 1 1 1 1 ...
- $ orzoD     : int  3 3 1 0 2 2 2 3 2 3 ...
- $ greeksal  : int  1 1 NA 0 1 1 1 1 1 1 ...
- $ greeksalD : int  1 3 NA 0 2 2 2 2 2 2 ...
- $ bread     : int  1 1 1 0 1 1 1 1 1 1 ...
- $ breadD    : int  2 3 1 0 2 2 2 2 2 2 ...
- $ dessert   : int  1 1 NA 0 1 1 0 1 NA NA ...
- $ dessertD  : int  2 3 NA 0 2 2 0 1 NA NA ...
- $ champagne : int  1 1 0 1 1 1 1 1 1 1 ...
- $ champagneD: int  1 1 0 3 1 1 2 1 1 1 ...
- $ beer      : int  1 0 0 1 1 1 NA 1 NA 1 ...
- $ beerD     : int  3 0 0 3 2 3 NA 1 NA 2 ...
- $ redwine   : int  0 1 0 1 0 0 1 0 1 0 ...
- $ redwineD  : int  0 3 0 3 0 0 2 0 2 0 ...
- $ whitewine : int  0 0 0 1 1 1 1 1 NA 0 ...
- $ whitewineD: int  0 0 0 3 3 2 1 3 NA 0 ...
- $ dayonset  : chr  "6oct2024" "" "" "" ...</code></pre>
-</div>
-<details open="">
-<summary>Code</summary>
-<div class="sourceCode cell-code" id="cb8"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a>skimr<span class="sc">::</span><span class="fu">skim</span>(copdata)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-</details>
-<div class="cell-output-display">
-<table class="table table-sm table-striped small">
-<caption>Data summary</caption>
-<tbody>
-<tr class="odd">
-<td style="text-align: left;">Name</td>
-<td style="text-align: left;">copdata</td>
-</tr>
-<tr class="even">
-<td style="text-align: left;">Number of rows</td>
-<td style="text-align: left;">397</td>
-</tr>
-<tr class="odd">
-<td style="text-align: left;">Number of columns</td>
-<td style="text-align: left;">40</td>
-</tr>
-<tr class="even">
-<td style="text-align: left;">_______________________</td>
-<td style="text-align: left;"></td>
-</tr>
-<tr class="odd">
-<td style="text-align: left;">Column type frequency:</td>
-<td style="text-align: left;"></td>
-</tr>
-<tr class="even">
-<td style="text-align: left;">character</td>
-<td style="text-align: left;">3</td>
-</tr>
-<tr class="odd">
-<td style="text-align: left;">numeric</td>
-<td style="text-align: left;">37</td>
-</tr>
-<tr class="even">
-<td style="text-align: left;">________________________</td>
-<td style="text-align: left;"></td>
-</tr>
-<tr class="odd">
-<td style="text-align: left;">Group variables</td>
-<td style="text-align: left;">None</td>
-</tr>
-</tbody>
-</table>
-<p><strong>Variable type: character</strong></p>
-<table class="table table-sm table-striped small">
-<colgroup>
-<col style="width: 19%">
-<col style="width: 13%">
-<col style="width: 19%">
-<col style="width: 5%">
-<col style="width: 5%">
-<col style="width: 8%">
-<col style="width: 12%">
-<col style="width: 15%">
-</colgroup>
-<thead>
-<tr class="header">
-<th style="text-align: left;">skim_variable</th>
-<th style="text-align: right;">n_missing</th>
-<th style="text-align: right;">complete_rate</th>
-<th style="text-align: right;">min</th>
-<th style="text-align: right;">max</th>
-<th style="text-align: right;">empty</th>
-<th style="text-align: right;">n_unique</th>
-<th style="text-align: right;">whitespace</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<td style="text-align: left;">id</td>
-<td style="text-align: right;">0</td>
-<td style="text-align: right;">1</td>
-<td style="text-align: right;">8</td>
-<td style="text-align: right;">8</td>
-<td style="text-align: right;">0</td>
-<td style="text-align: right;">397</td>
-<td style="text-align: right;">0</td>
-</tr>
-<tr class="even">
-<td style="text-align: left;">sex</td>
-<td style="text-align: right;">0</td>
-<td style="text-align: right;">1</td>
-<td style="text-align: right;">4</td>
-<td style="text-align: right;">6</td>
-<td style="text-align: right;">0</td>
-<td style="text-align: right;">2</td>
-<td style="text-align: right;">0</td>
-</tr>
-<tr class="odd">
-<td style="text-align: left;">dayonset</td>
-<td style="text-align: right;">0</td>
-<td style="text-align: right;">1</td>
-<td style="text-align: right;">0</td>
-<td style="text-align: right;">8</td>
-<td style="text-align: right;">175</td>
-<td style="text-align: right;">4</td>
-<td style="text-align: right;">0</td>
-</tr>
-</tbody>
-</table>
-<p><strong>Variable type: numeric</strong></p>
-<table class="table table-sm table-striped small">
-<colgroup>
-<col style="width: 18%">
-<col style="width: 13%">
-<col style="width: 18%">
-<col style="width: 8%">
-<col style="width: 6%">
-<col style="width: 4%">
-<col style="width: 5%">
-<col style="width: 5%">
-<col style="width: 5%">
-<col style="width: 6%">
-<col style="width: 8%">
-</colgroup>
-<thead>
-<tr class="header">
-<th style="text-align: left;">skim_variable</th>
-<th style="text-align: right;">n_missing</th>
-<th style="text-align: right;">complete_rate</th>
-<th style="text-align: right;">mean</th>
-<th style="text-align: right;">sd</th>
-<th style="text-align: right;">p0</th>
-<th style="text-align: right;">p25</th>
-<th style="text-align: right;">p50</th>
-<th style="text-align: right;">p75</th>
-<th style="text-align: right;">p100</th>
-<th style="text-align: left;">hist</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<td style="text-align: left;">age</td>
-<td style="text-align: right;">0</td>
-<td style="text-align: right;">1.00</td>
-<td style="text-align: right;">18.57</td>
-<td style="text-align: right;">9.92</td>
-<td style="text-align: right;">8</td>
-<td style="text-align: right;">16</td>
-<td style="text-align: right;">17</td>
-<td style="text-align: right;">18</td>
-<td style="text-align: right;">180</td>
-<td style="text-align: left;">▇▁▁▁▁</td>
-</tr>
-<tr class="even">
-<td style="text-align: left;">group</td>
-<td style="text-align: right;">0</td>
-<td style="text-align: right;">1.00</td>
-<td style="text-align: right;">0.96</td>
-<td style="text-align: right;">0.20</td>
-<td style="text-align: right;">0</td>
-<td style="text-align: right;">1</td>
-<td style="text-align: right;">1</td>
-<td style="text-align: right;">1</td>
-<td style="text-align: right;">1</td>
-<td style="text-align: left;">▁▁▁▁▇</td>
-</tr>
-<tr class="odd">
-<td style="text-align: left;">class</td>
-<td style="text-align: right;">36</td>
-<td style="text-align: right;">0.91</td>
-<td style="text-align: right;">1.94</td>
-<td style="text-align: right;">0.84</td>
-<td style="text-align: right;">1</td>
-<td style="text-align: right;">1</td>
-<td style="text-align: right;">2</td>
-<td style="text-align: right;">3</td>
-<td style="text-align: right;">3</td>
-<td style="text-align: left;">▇▁▆▁▇</td>
-</tr>
-<tr class="even">
-<td style="text-align: left;">diarrhoea</td>
-<td style="text-align: right;">141</td>
-<td style="text-align: right;">0.64</td>
-<td style="text-align: right;">0.82</td>
-<td style="text-align: right;">0.39</td>
-<td style="text-align: right;">0</td>
-<td style="text-align: right;">1</td>
-<td style="text-align: right;">1</td>
-<td style="text-align: right;">1</td>
-<td style="text-align: right;">1</td>
-<td style="text-align: left;">▂▁▁▁▇</td>
-</tr>
-<tr class="odd">
-<td style="text-align: left;">bloody</td>
-<td style="text-align: right;">200</td>
-<td style="text-align: right;">0.50</td>
-<td style="text-align: right;">0.03</td>
-<td style="text-align: right;">0.17</td>
-<td style="text-align: right;">0</td>
-<td style="text-align: right;">0</td>
-<td style="text-align: right;">0</td>
-<td style="text-align: right;">0</td>
-<td style="text-align: right;">1</td>
-<td style="text-align: left;">▇▁▁▁▁</td>
-</tr>
-<tr class="even">
-<td style="text-align: left;">vomiting</td>
-<td style="text-align: right;">179</td>
-<td style="text-align: right;">0.55</td>
-<td style="text-align: right;">0.30</td>
-<td style="text-align: right;">0.46</td>
-<td style="text-align: right;">0</td>
-<td style="text-align: right;">0</td>
-<td style="text-align: right;">0</td>
-<td style="text-align: right;">1</td>
-<td style="text-align: right;">1</td>
-<td style="text-align: left;">▇▁▁▁▃</td>
-</tr>
-<tr class="odd">
-<td style="text-align: left;">abdo</td>
-<td style="text-align: right;">152</td>
-<td style="text-align: right;">0.62</td>
-<td style="text-align: right;">0.85</td>
-<td style="text-align: right;">0.35</td>
-<td style="text-align: right;">0</td>
-<td style="text-align: right;">1</td>
-<td style="text-align: right;">1</td>
-<td style="text-align: right;">1</td>
-<td style="text-align: right;">1</td>
-<td style="text-align: left;">▂▁▁▁▇</td>
-</tr>
-<tr class="even">
-<td style="text-align: left;">nausea</td>
-<td style="text-align: right;">170</td>
-<td style="text-align: right;">0.57</td>
-<td style="text-align: right;">0.76</td>
-<td style="text-align: right;">0.43</td>
-<td style="text-align: right;">0</td>
-<td style="text-align: right;">1</td>
-<td style="text-align: right;">1</td>
-<td style="text-align: right;">1</td>
-<td style="text-align: right;">1</td>
-<td style="text-align: left;">▂▁▁▁▇</td>
-</tr>
-<tr class="odd">
-<td style="text-align: left;">fever</td>
-<td style="text-align: right;">223</td>
-<td style="text-align: right;">0.44</td>
-<td style="text-align: right;">0.26</td>
-<td style="text-align: right;">0.44</td>
-<td style="text-align: right;">0</td>
-<td style="text-align: right;">0</td>
-<td style="text-align: right;">0</td>
-<td style="text-align: right;">1</td>
-<td style="text-align: right;">1</td>
-<td style="text-align: left;">▇▁▁▁▃</td>
-</tr>
-<tr class="even">
-<td style="text-align: left;">headache</td>
-<td style="text-align: right;">174</td>
-<td style="text-align: right;">0.56</td>
-<td style="text-align: right;">0.63</td>
-<td style="text-align: right;">0.48</td>
-<td style="text-align: right;">0</td>
-<td style="text-align: right;">0</td>
-<td style="text-align: right;">1</td>
-<td style="text-align: right;">1</td>
-<td style="text-align: right;">1</td>
-<td style="text-align: left;">▅▁▁▁▇</td>
-</tr>
-<tr class="odd">
-<td style="text-align: left;">jointpain</td>
-<td style="text-align: right;">207</td>
-<td style="text-align: right;">0.48</td>
-<td style="text-align: right;">0.16</td>
-<td style="text-align: right;">0.37</td>
-<td style="text-align: right;">0</td>
-<td style="text-align: right;">0</td>
-<td style="text-align: right;">0</td>
-<td style="text-align: right;">0</td>
-<td style="text-align: right;">1</td>
-<td style="text-align: left;">▇▁▁▁▂</td>
-</tr>
-<tr class="even">
-<td style="text-align: left;">starthour</td>
-<td style="text-align: right;">177</td>
-<td style="text-align: right;">0.55</td>
-<td style="text-align: right;">12.49</td>
-<td style="text-align: right;">4.92</td>
-<td style="text-align: right;">3</td>
-<td style="text-align: right;">9</td>
-<td style="text-align: right;">9</td>
-<td style="text-align: right;">15</td>
-<td style="text-align: right;">21</td>
-<td style="text-align: left;">▁▇▁▆▃</td>
-</tr>
-<tr class="odd">
-<td style="text-align: left;">meal</td>
-<td style="text-align: right;">9</td>
-<td style="text-align: right;">0.98</td>
-<td style="text-align: right;">0.97</td>
-<td style="text-align: right;">0.17</td>
-<td style="text-align: right;">0</td>
-<td style="text-align: right;">1</td>
-<td style="text-align: right;">1</td>
-<td style="text-align: right;">1</td>
-<td style="text-align: right;">1</td>
-<td style="text-align: left;">▁▁▁▁▇</td>
-</tr>
-<tr class="even">
-<td style="text-align: left;">feta</td>
-<td style="text-align: right;">16</td>
-<td style="text-align: right;">0.96</td>
-<td style="text-align: right;">0.71</td>
-<td style="text-align: right;">0.45</td>
-<td style="text-align: right;">0</td>
-<td style="text-align: right;">0</td>
-<td style="text-align: right;">1</td>
-<td style="text-align: right;">1</td>
-<td style="text-align: right;">1</td>
-<td style="text-align: left;">▃▁▁▁▇</td>
-</tr>
-<tr class="odd">
-<td style="text-align: left;">fetaD</td>
-<td style="text-align: right;">16</td>
-<td style="text-align: right;">0.96</td>
-<td style="text-align: right;">1.32</td>
-<td style="text-align: right;">1.00</td>
-<td style="text-align: right;">0</td>
-<td style="text-align: right;">0</td>
-<td style="text-align: right;">2</td>
-<td style="text-align: right;">2</td>
-<td style="text-align: right;">3</td>
-<td style="text-align: left;">▆▅▁▇▂</td>
-</tr>
-<tr class="even">
-<td style="text-align: left;">sardines</td>
-<td style="text-align: right;">17</td>
-<td style="text-align: right;">0.96</td>
-<td style="text-align: right;">0.67</td>
-<td style="text-align: right;">0.47</td>
-<td style="text-align: right;">0</td>
-<td style="text-align: right;">0</td>
-<td style="text-align: right;">1</td>
-<td style="text-align: right;">1</td>
-<td style="text-align: right;">1</td>
-<td style="text-align: left;">▃▁▁▁▇</td>
-</tr>
-<tr class="odd">
-<td style="text-align: left;">sardinesD</td>
-<td style="text-align: right;">17</td>
-<td style="text-align: right;">0.96</td>
-<td style="text-align: right;">1.34</td>
-<td style="text-align: right;">1.04</td>
-<td style="text-align: right;">0</td>
-<td style="text-align: right;">0</td>
-<td style="text-align: right;">2</td>
-<td style="text-align: right;">2</td>
-<td style="text-align: right;">3</td>
-<td style="text-align: left;">▆▂▁▇▂</td>
-</tr>
-<tr class="even">
-<td style="text-align: left;">eggplant</td>
-<td style="text-align: right;">30</td>
-<td style="text-align: right;">0.92</td>
-<td style="text-align: right;">0.59</td>
-<td style="text-align: right;">0.49</td>
-<td style="text-align: right;">0</td>
-<td style="text-align: right;">0</td>
-<td style="text-align: right;">1</td>
-<td style="text-align: right;">1</td>
-<td style="text-align: right;">1</td>
-<td style="text-align: left;">▆▁▁▁▇</td>
-</tr>
-<tr class="odd">
-<td style="text-align: left;">eggplantD</td>
-<td style="text-align: right;">30</td>
-<td style="text-align: right;">0.92</td>
-<td style="text-align: right;">1.14</td>
-<td style="text-align: right;">1.05</td>
-<td style="text-align: right;">0</td>
-<td style="text-align: right;">0</td>
-<td style="text-align: right;">1</td>
-<td style="text-align: right;">2</td>
-<td style="text-align: right;">3</td>
-<td style="text-align: left;">▇▂▁▇▂</td>
-</tr>
-<tr class="even">
-<td style="text-align: left;">moussaka</td>
-<td style="text-align: right;">15</td>
-<td style="text-align: right;">0.96</td>
-<td style="text-align: right;">0.89</td>
-<td style="text-align: right;">0.31</td>
-<td style="text-align: right;">0</td>
-<td style="text-align: right;">1</td>
-<td style="text-align: right;">1</td>
-<td style="text-align: right;">1</td>
-<td style="text-align: right;">1</td>
-<td style="text-align: left;">▁▁▁▁▇</td>
-</tr>
-<tr class="odd">
-<td style="text-align: left;">moussakaD</td>
-<td style="text-align: right;">14</td>
-<td style="text-align: right;">0.96</td>
-<td style="text-align: right;">1.83</td>
-<td style="text-align: right;">0.91</td>
-<td style="text-align: right;">0</td>
-<td style="text-align: right;">1</td>
-<td style="text-align: right;">2</td>
-<td style="text-align: right;">2</td>
-<td style="text-align: right;">3</td>
-<td style="text-align: left;">▂▃▁▇▃</td>
-</tr>
-<tr class="even">
-<td style="text-align: left;">orzo</td>
-<td style="text-align: right;">15</td>
-<td style="text-align: right;">0.96</td>
-<td style="text-align: right;">0.88</td>
-<td style="text-align: right;">0.32</td>
-<td style="text-align: right;">0</td>
-<td style="text-align: right;">1</td>
-<td style="text-align: right;">1</td>
-<td style="text-align: right;">1</td>
-<td style="text-align: right;">1</td>
-<td style="text-align: left;">▁▁▁▁▇</td>
-</tr>
-<tr class="odd">
-<td style="text-align: left;">orzoD</td>
-<td style="text-align: right;">15</td>
-<td style="text-align: right;">0.96</td>
-<td style="text-align: right;">1.81</td>
-<td style="text-align: right;">0.91</td>
-<td style="text-align: right;">0</td>
-<td style="text-align: right;">1</td>
-<td style="text-align: right;">2</td>
-<td style="text-align: right;">2</td>
-<td style="text-align: right;">3</td>
-<td style="text-align: left;">▂▃▁▇▃</td>
-</tr>
-<tr class="even">
-<td style="text-align: left;">greeksal</td>
-<td style="text-align: right;">24</td>
-<td style="text-align: right;">0.94</td>
-<td style="text-align: right;">0.57</td>
-<td style="text-align: right;">0.50</td>
-<td style="text-align: right;">0</td>
-<td style="text-align: right;">0</td>
-<td style="text-align: right;">1</td>
-<td style="text-align: right;">1</td>
-<td style="text-align: right;">1</td>
-<td style="text-align: left;">▆▁▁▁▇</td>
-</tr>
-<tr class="odd">
-<td style="text-align: left;">greeksalD</td>
-<td style="text-align: right;">24</td>
-<td style="text-align: right;">0.94</td>
-<td style="text-align: right;">1.08</td>
-<td style="text-align: right;">1.06</td>
-<td style="text-align: right;">0</td>
-<td style="text-align: right;">0</td>
-<td style="text-align: right;">1</td>
-<td style="text-align: right;">2</td>
-<td style="text-align: right;">3</td>
-<td style="text-align: left;">▇▂▁▆▂</td>
-</tr>
-<tr class="even">
-<td style="text-align: left;">bread</td>
-<td style="text-align: right;">18</td>
-<td style="text-align: right;">0.95</td>
-<td style="text-align: right;">0.91</td>
-<td style="text-align: right;">0.29</td>
-<td style="text-align: right;">0</td>
-<td style="text-align: right;">1</td>
-<td style="text-align: right;">1</td>
-<td style="text-align: right;">1</td>
-<td style="text-align: right;">1</td>
-<td style="text-align: left;">▁▁▁▁▇</td>
-</tr>
-<tr class="odd">
-<td style="text-align: left;">breadD</td>
-<td style="text-align: right;">18</td>
-<td style="text-align: right;">0.95</td>
-<td style="text-align: right;">1.75</td>
-<td style="text-align: right;">0.71</td>
-<td style="text-align: right;">0</td>
-<td style="text-align: right;">2</td>
-<td style="text-align: right;">2</td>
-<td style="text-align: right;">2</td>
-<td style="text-align: right;">3</td>
-<td style="text-align: left;">▁▂▁▇▁</td>
-</tr>
-<tr class="even">
-<td style="text-align: left;">dessert</td>
-<td style="text-align: right;">42</td>
-<td style="text-align: right;">0.89</td>
-<td style="text-align: right;">0.42</td>
-<td style="text-align: right;">0.49</td>
-<td style="text-align: right;">0</td>
-<td style="text-align: right;">0</td>
-<td style="text-align: right;">0</td>
-<td style="text-align: right;">1</td>
-<td style="text-align: right;">1</td>
-<td style="text-align: left;">▇▁▁▁▆</td>
-</tr>
-<tr class="odd">
-<td style="text-align: left;">dessertD</td>
-<td style="text-align: right;">42</td>
-<td style="text-align: right;">0.89</td>
-<td style="text-align: right;">0.83</td>
-<td style="text-align: right;">1.06</td>
-<td style="text-align: right;">0</td>
-<td style="text-align: right;">0</td>
-<td style="text-align: right;">0</td>
-<td style="text-align: right;">2</td>
-<td style="text-align: right;">3</td>
-<td style="text-align: left;">▇▁▁▃▁</td>
-</tr>
-<tr class="even">
-<td style="text-align: left;">champagne</td>
-<td style="text-align: right;">25</td>
-<td style="text-align: right;">0.94</td>
-<td style="text-align: right;">0.87</td>
-<td style="text-align: right;">0.34</td>
-<td style="text-align: right;">0</td>
-<td style="text-align: right;">1</td>
-<td style="text-align: right;">1</td>
-<td style="text-align: right;">1</td>
-<td style="text-align: right;">1</td>
-<td style="text-align: left;">▁▁▁▁▇</td>
-</tr>
-<tr class="odd">
-<td style="text-align: left;">champagneD</td>
-<td style="text-align: right;">25</td>
-<td style="text-align: right;">0.94</td>
-<td style="text-align: right;">1.37</td>
-<td style="text-align: right;">0.93</td>
-<td style="text-align: right;">0</td>
-<td style="text-align: right;">1</td>
-<td style="text-align: right;">1</td>
-<td style="text-align: right;">2</td>
-<td style="text-align: right;">3</td>
-<td style="text-align: left;">▂▇▁▂▃</td>
-</tr>
-<tr class="even">
-<td style="text-align: left;">beer</td>
-<td style="text-align: right;">30</td>
-<td style="text-align: right;">0.92</td>
-<td style="text-align: right;">0.78</td>
-<td style="text-align: right;">0.42</td>
-<td style="text-align: right;">0</td>
-<td style="text-align: right;">1</td>
-<td style="text-align: right;">1</td>
-<td style="text-align: right;">1</td>
-<td style="text-align: right;">1</td>
-<td style="text-align: left;">▂▁▁▁▇</td>
-</tr>
-<tr class="odd">
-<td style="text-align: left;">beerD</td>
-<td style="text-align: right;">35</td>
-<td style="text-align: right;">0.91</td>
-<td style="text-align: right;">1.94</td>
-<td style="text-align: right;">1.23</td>
-<td style="text-align: right;">0</td>
-<td style="text-align: right;">1</td>
-<td style="text-align: right;">3</td>
-<td style="text-align: right;">3</td>
-<td style="text-align: right;">3</td>
-<td style="text-align: left;">▃▂▁▂▇</td>
-</tr>
-<tr class="even">
-<td style="text-align: left;">redwine</td>
-<td style="text-align: right;">50</td>
-<td style="text-align: right;">0.87</td>
-<td style="text-align: right;">0.23</td>
-<td style="text-align: right;">0.42</td>
-<td style="text-align: right;">0</td>
-<td style="text-align: right;">0</td>
-<td style="text-align: right;">0</td>
-<td style="text-align: right;">0</td>
-<td style="text-align: right;">1</td>
-<td style="text-align: left;">▇▁▁▁▂</td>
-</tr>
-<tr class="odd">
-<td style="text-align: left;">redwineD</td>
-<td style="text-align: right;">52</td>
-<td style="text-align: right;">0.87</td>
-<td style="text-align: right;">0.45</td>
-<td style="text-align: right;">0.92</td>
-<td style="text-align: right;">0</td>
-<td style="text-align: right;">0</td>
-<td style="text-align: right;">0</td>
-<td style="text-align: right;">0</td>
-<td style="text-align: right;">3</td>
-<td style="text-align: left;">▇▁▁▁▁</td>
-</tr>
-<tr class="even">
-<td style="text-align: left;">whitewine</td>
-<td style="text-align: right;">31</td>
-<td style="text-align: right;">0.92</td>
-<td style="text-align: right;">0.73</td>
-<td style="text-align: right;">0.45</td>
-<td style="text-align: right;">0</td>
-<td style="text-align: right;">0</td>
-<td style="text-align: right;">1</td>
-<td style="text-align: right;">1</td>
-<td style="text-align: right;">1</td>
-<td style="text-align: left;">▃▁▁▁▇</td>
-</tr>
-<tr class="odd">
-<td style="text-align: left;">whitewineD</td>
-<td style="text-align: right;">36</td>
-<td style="text-align: right;">0.91</td>
-<td style="text-align: right;">1.58</td>
-<td style="text-align: right;">1.21</td>
-<td style="text-align: right;">0</td>
-<td style="text-align: right;">0</td>
-<td style="text-align: right;">2</td>
-<td style="text-align: right;">3</td>
-<td style="text-align: right;">3</td>
-<td style="text-align: left;">▆▅▁▅▇</td>
-</tr>
-</tbody>
-</table>
-</div>
-<details open="">
-<summary>Code</summary>
-<div class="sourceCode cell-code" id="cb9"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a><span class="fu">names</span>(copdata)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-</details>
-<div class="cell-output cell-output-stdout">
-<pre><code> [1] "id"         "sex"        "age"        "group"      "class"     
- [6] "diarrhoea"  "bloody"     "vomiting"   "abdo"       "nausea"    
-[11] "fever"      "headache"   "jointpain"  "starthour"  "meal"      
-[16] "feta"       "fetaD"      "sardines"   "sardinesD"  "eggplant"  
-[21] "eggplantD"  "moussaka"   "moussakaD"  "orzo"       "orzoD"     
-[26] "greeksal"   "greeksalD"  "bread"      "breadD"     "dessert"   
-[31] "dessertD"   "champagne"  "champagneD" "beer"       "beerD"     
-[36] "redwine"    "redwineD"   "whitewine"  "whitewineD" "dayonset"  </code></pre>
-</div>
-</div>
-<p>Let’s explore and manage some variables in detail.</p>
-<section id="age" class="level4">
-<h4 class="anchored" data-anchor-id="age">Age</h4>
-<p>Through visual exploration of the <code>age</code> histogram we see that there is at least one very high value, likely implausible. You can then create a cross-tabulation of variables age and group to have a better idea of how your data looks like.</p>
-<div class="cell">
-<details open="">
-<summary>Code</summary>
-<div class="sourceCode cell-code" id="cb11"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb11-1"><a href="#cb11-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Have a look at the histogram  </span></span>
-<span id="cb11-2"><a href="#cb11-2" aria-hidden="true" tabindex="-1"></a><span class="fu">hist</span>(copdata<span class="sc">$</span>age)   </span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-</details>
-<div class="cell-output-display">
-<p><img src="SimOb_RInject09_files/figure-html/age_exploration-1.png" class="img-fluid" width="672"></p>
-</div>
-<details open="">
-<summary>Code</summary>
-<div class="sourceCode cell-code" id="cb12"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb12-1"><a href="#cb12-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Create cross-tab with the group variable:  </span></span>
-<span id="cb12-2"><a href="#cb12-2" aria-hidden="true" tabindex="-1"></a>janitor<span class="sc">::</span><span class="fu">tabyl</span>(<span class="at">dat =</span> copdata, </span>
-<span id="cb12-3"><a href="#cb12-3" aria-hidden="true" tabindex="-1"></a>               <span class="at">var1 =</span> age, </span>
-<span id="cb12-4"><a href="#cb12-4" aria-hidden="true" tabindex="-1"></a>               <span class="at">var2 =</span> group)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-</details>
-<div class="cell-output-display">
-
-<div data-pagedtable="false">
-  <script data-pagedtable-source="" type="application/json">
-{"columns":[{"label":["age"],"name":[1],"type":["int"],"align":["right"]},{"label":["0"],"name":[2],"type":["dbl"],"align":["right"]},{"label":["1"],"name":[3],"type":["dbl"],"align":["right"]}],"data":[{"1":"8","2":"0","3":"1"},{"1":"15","2":"0","3":"11"},{"1":"16","2":"1","3":"99"},{"1":"17","2":"0","3":"115"},{"1":"18","2":"0","3":"112"},{"1":"19","2":"0","3":"39"},{"1":"20","2":"0","3":"3"},{"1":"26","2":"1","3":"0"},{"1":"29","2":"1","3":"0"},{"1":"30","2":"1","3":"0"},{"1":"31","2":"1","3":"0"},{"1":"32","2":"1","3":"0"},{"1":"33","2":"1","3":"0"},{"1":"34","2":"1","3":"0"},{"1":"39","2":"1","3":"0"},{"1":"43","2":"1","3":"0"},{"1":"54","2":"1","3":"0"},{"1":"56","2":"1","3":"0"},{"1":"58","2":"2","3":"0"},{"1":"59","2":"1","3":"0"},{"1":"65","2":"1","3":"0"},{"1":"180","2":"0","3":"1"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}}
-  </script>
-</div>
-</div>
-</div>
-<p>Note that <code>group</code> is coded as 0 and 1, and these may be difficult to interpret when they mean something other than “no” and “yes”, respectively. From the codebook, you know that teachers are represented by 0, and students by 1. Let’s change this to make our lives easier:</p>
-<div class="cell">
-<details open="">
-<summary>Code</summary>
-<div class="sourceCode cell-code" id="cb13"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb13-1"><a href="#cb13-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Convert group to a factor and label 0 as teacher, 1 as student:</span></span>
-<span id="cb13-2"><a href="#cb13-2" aria-hidden="true" tabindex="-1"></a>copdata <span class="ot">&lt;-</span> copdata <span class="sc">%&gt;%</span> </span>
-<span id="cb13-3"><a href="#cb13-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">group =</span> <span class="fu">factor</span>(group, </span>
-<span id="cb13-4"><a href="#cb13-4" aria-hidden="true" tabindex="-1"></a>                        <span class="at">labels =</span> <span class="fu">c</span>(<span class="st">"teacher"</span>, <span class="st">"student"</span>)))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-</details>
-</div>
-<p>Now, have a look at your cross-tab again:</p>
-<div class="cell">
-<details open="">
-<summary>Code</summary>
-<div class="sourceCode cell-code" id="cb14"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb14-1"><a href="#cb14-1" aria-hidden="true" tabindex="-1"></a>janitor<span class="sc">::</span><span class="fu">tabyl</span>(<span class="at">dat =</span> copdata, </span>
-<span id="cb14-2"><a href="#cb14-2" aria-hidden="true" tabindex="-1"></a>               <span class="at">var1 =</span> age, </span>
-<span id="cb14-3"><a href="#cb14-3" aria-hidden="true" tabindex="-1"></a>               <span class="at">var2 =</span> group) </span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-</details>
-<div class="cell-output-display">
-
-<div data-pagedtable="false">
-  <script data-pagedtable-source="" type="application/json">
-{"columns":[{"label":["age"],"name":[1],"type":["int"],"align":["right"]},{"label":["teacher"],"name":[2],"type":["dbl"],"align":["right"]},{"label":["student"],"name":[3],"type":["dbl"],"align":["right"]}],"data":[{"1":"8","2":"0","3":"1"},{"1":"15","2":"0","3":"11"},{"1":"16","2":"1","3":"99"},{"1":"17","2":"0","3":"115"},{"1":"18","2":"0","3":"112"},{"1":"19","2":"0","3":"39"},{"1":"20","2":"0","3":"3"},{"1":"26","2":"1","3":"0"},{"1":"29","2":"1","3":"0"},{"1":"30","2":"1","3":"0"},{"1":"31","2":"1","3":"0"},{"1":"32","2":"1","3":"0"},{"1":"33","2":"1","3":"0"},{"1":"34","2":"1","3":"0"},{"1":"39","2":"1","3":"0"},{"1":"43","2":"1","3":"0"},{"1":"54","2":"1","3":"0"},{"1":"56","2":"1","3":"0"},{"1":"58","2":"2","3":"0"},{"1":"59","2":"1","3":"0"},{"1":"65","2":"1","3":"0"},{"1":"180","2":"0","3":"1"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}}
-  </script>
-</div>
-</div>
-</div>
-<p>With this table, we can more easily identify ages that are likely to be typographic errors. Specifically:</p>
-<ul>
-<li><p>There is one teacher aged 16 (likely digit reversal - should be 61)</p></li>
-<li><p>There is one student aged 8 (likely missing a digit - should be 18)</p></li>
-<li><p>There is one student aged 180 (likely has an extra digit - should be 18)</p></li>
-</ul>
-<p>Assuming you have contacted the school to make sure your suspicions about the actual ages are correct, we can now correct them, using <code>case_when()</code>. We create logical conditions to identify the incorrect ages, combining the values for age with the group they belong to:</p>
-<div class="cell">
-<details open="">
-<summary>Code</summary>
-<div class="sourceCode cell-code" id="cb15"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb15-1"><a href="#cb15-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Update incorrect ages to the correct values with case_when:  </span></span>
-<span id="cb15-2"><a href="#cb15-2" aria-hidden="true" tabindex="-1"></a>copdata <span class="ot">&lt;-</span> copdata <span class="sc">%&gt;%</span>       </span>
-<span id="cb15-3"><a href="#cb15-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">age =</span>                         </span>
-<span id="cb15-4"><a href="#cb15-4" aria-hidden="true" tabindex="-1"></a>           <span class="fu">case_when</span>(                            </span>
-<span id="cb15-5"><a href="#cb15-5" aria-hidden="true" tabindex="-1"></a>             <span class="co"># Where respondent is 16 and a teacher, change age to 61:</span></span>
-<span id="cb15-6"><a href="#cb15-6" aria-hidden="true" tabindex="-1"></a>             age <span class="sc">==</span> <span class="dv">16</span> <span class="sc">&amp;</span> group <span class="sc">==</span> <span class="st">"teacher"</span> <span class="sc">~</span> <span class="dv">61</span>,              </span>
-<span id="cb15-7"><a href="#cb15-7" aria-hidden="true" tabindex="-1"></a>             <span class="co"># where respondent is 8 or 180 and a student, change age to 18:</span></span>
-<span id="cb15-8"><a href="#cb15-8" aria-hidden="true" tabindex="-1"></a>             age <span class="sc">==</span> <span class="dv">8</span> <span class="sc">&amp;</span> group <span class="sc">==</span> <span class="st">"student"</span> <span class="sc">~</span> <span class="dv">18</span>,              </span>
-<span id="cb15-9"><a href="#cb15-9" aria-hidden="true" tabindex="-1"></a>             age <span class="sc">==</span> <span class="dv">180</span> <span class="sc">&amp;</span> group <span class="sc">==</span> <span class="st">"student"</span> <span class="sc">~</span> <span class="dv">18</span>,              </span>
-<span id="cb15-10"><a href="#cb15-10" aria-hidden="true" tabindex="-1"></a>             <span class="co"># Keep remaining values as is:              </span></span>
-<span id="cb15-11"><a href="#cb15-11" aria-hidden="true" tabindex="-1"></a>             <span class="at">.default =</span> <span class="fu">as.numeric</span>(age)   </span>
-<span id="cb15-12"><a href="#cb15-12" aria-hidden="true" tabindex="-1"></a>             <span class="co"># if .default is not working, try:</span></span>
-<span id="cb15-13"><a href="#cb15-13" aria-hidden="true" tabindex="-1"></a>             <span class="co"># TRUE ~ age</span></span>
-<span id="cb15-14"><a href="#cb15-14" aria-hidden="true" tabindex="-1"></a>             )                    </span>
-<span id="cb15-15"><a href="#cb15-15" aria-hidden="true" tabindex="-1"></a>         ) </span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-</details>
-</div>
-<div class="quarto-figure quarto-figure-center">
-<figure class="figure">
-<p><img src="https://cdn.myportfolio.com/45214904-6a61-4e23-98d6-b140f8654a40/6ffcd6d6-c783-4087-ae76-99aa851663ed_rw_1920.png?h=5f67935d69c92e1fda9074d00dfea972" class="img-fluid figure-img" style="width:80.0%" alt="Cartoon showing a table with creature type (kraken, dragon, or cyclops) and age (baby, teen, or adult). The three creatures listed are adding a new column named “danger”, which contains the word “extreme!” if the type is “kraken”, or “high” for any other type. Stylized text reads “dplyr::case_when() - IF ELSE...but you love it? An example of code is shown: mutate(danger = case_when(type == “kraken” ~ “extreme!”, TRUE ~ “high”). Learn more about case_when."></p>
-</figure>
-</div>
-<ul>
-<li>Create summary table for the dose response columns (those finishing with a capital “D”) to have a look at their distribution.</li>
-</ul>
-<div class="callout callout-style-default callout-tip callout-titled" title="Need a little bit of help?">
-<div class="callout-header d-flex align-content-center" data-bs-toggle="collapse" data-bs-target=".callout-2-contents" aria-controls="callout-2" aria-expanded="false" aria-label="Toggle callout">
-<div class="callout-icon-container">
-<i class="callout-icon"></i>
-</div>
-<div class="callout-title-container flex-fill">
-Need a little bit of help?
-</div>
-<div class="callout-btn-toggle d-inline-block border-0 py-1 ps-1 pe-0 float-end"><i class="callout-toggle"></i></div>
-</div>
-<div id="callout-2" class="callout-2-contents callout-collapse collapse">
-<div class="callout-body-container callout-body">
-<p>Use <code>gtsummary::tbl_summary()</code>.</p>
-</div>
-</div>
-</div>
-<div class="cell">
-<details open="">
-<summary>Code</summary>
-<div class="sourceCode cell-code" id="cb16"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb16-1"><a href="#cb16-1" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: check_dose_cols</span></span>
-<span id="cb16-2"><a href="#cb16-2" aria-hidden="true" tabindex="-1"></a><span class="co">#| tbl-cap: no caption</span></span>
-<span id="cb16-3"><a href="#cb16-3" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb16-4"><a href="#cb16-4" aria-hidden="true" tabindex="-1"></a><span class="co"># Create summary table for dose response columns: </span></span>
-<span id="cb16-5"><a href="#cb16-5" aria-hidden="true" tabindex="-1"></a>drtable <span class="ot">&lt;-</span> copdata <span class="sc">%&gt;%</span>       </span>
-<span id="cb16-6"><a href="#cb16-6" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Select all the columns with column names that end in upper case 'D':   </span></span>
-<span id="cb16-7"><a href="#cb16-7" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(<span class="fu">ends_with</span>(<span class="st">"D"</span>, <span class="at">ignore.case =</span> <span class="cn">FALSE</span>)) <span class="sc">%&gt;%</span>       </span>
-<span id="cb16-8"><a href="#cb16-8" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Create the summary table, excluding missing values:   </span></span>
-<span id="cb16-9"><a href="#cb16-9" aria-hidden="true" tabindex="-1"></a>  gtsummary<span class="sc">::</span><span class="fu">tbl_summary</span>(<span class="at">missing =</span> <span class="st">"no"</span>) </span>
-<span id="cb16-10"><a href="#cb16-10" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb16-11"><a href="#cb16-11" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Print the summary table: </span></span>
-<span id="cb16-12"><a href="#cb16-12" aria-hidden="true" tabindex="-1"></a>drtable</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-</details>
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-    <tr class="gt_col_headings">
-      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="<strong>Characteristic</strong>"><strong>Characteristic</strong></th>
-      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="<strong>N = 397</strong><span class=&quot;gt_footnote_marks&quot; style=&quot;white-space:nowrap;font-style:italic;font-weight:normal;&quot;><sup>1</sup></span>"><strong>N = 397</strong><span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;"><sup>1</sup></span></th>
-    </tr>
-  </thead>
-  <tbody class="gt_table_body">
-    <tr><td headers="label" class="gt_row gt_left">fetaD</td>
-<td headers="stat_0" class="gt_row gt_center"></td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;0</td>
-<td headers="stat_0" class="gt_row gt_center">110 (29%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;1</td>
-<td headers="stat_0" class="gt_row gt_center">78 (20%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;2</td>
-<td headers="stat_0" class="gt_row gt_center">155 (41%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;3</td>
-<td headers="stat_0" class="gt_row gt_center">38 (10.0%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">sardinesD</td>
-<td headers="stat_0" class="gt_row gt_center"></td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;0</td>
-<td headers="stat_0" class="gt_row gt_center">125 (33%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;1</td>
-<td headers="stat_0" class="gt_row gt_center">35 (9.2%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;2</td>
-<td headers="stat_0" class="gt_row gt_center">184 (48%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;3</td>
-<td headers="stat_0" class="gt_row gt_center">36 (9.5%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">eggplantD</td>
-<td headers="stat_0" class="gt_row gt_center"></td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;0</td>
-<td headers="stat_0" class="gt_row gt_center">151 (41%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;1</td>
-<td headers="stat_0" class="gt_row gt_center">41 (11%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;2</td>
-<td headers="stat_0" class="gt_row gt_center">147 (40%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;3</td>
-<td headers="stat_0" class="gt_row gt_center">28 (7.6%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">moussakaD</td>
-<td headers="stat_0" class="gt_row gt_center"></td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;0</td>
-<td headers="stat_0" class="gt_row gt_center">42 (11%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;1</td>
-<td headers="stat_0" class="gt_row gt_center">70 (18%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;2</td>
-<td headers="stat_0" class="gt_row gt_center">184 (48%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;3</td>
-<td headers="stat_0" class="gt_row gt_center">87 (23%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">orzoD</td>
-<td headers="stat_0" class="gt_row gt_center"></td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;0</td>
-<td headers="stat_0" class="gt_row gt_center">44 (12%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;1</td>
-<td headers="stat_0" class="gt_row gt_center">70 (18%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;2</td>
-<td headers="stat_0" class="gt_row gt_center">183 (48%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;3</td>
-<td headers="stat_0" class="gt_row gt_center">85 (22%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">greeksalD</td>
-<td headers="stat_0" class="gt_row gt_center"></td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;0</td>
-<td headers="stat_0" class="gt_row gt_center">162 (43%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;1</td>
-<td headers="stat_0" class="gt_row gt_center">52 (14%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;2</td>
-<td headers="stat_0" class="gt_row gt_center">126 (34%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;3</td>
-<td headers="stat_0" class="gt_row gt_center">33 (8.8%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">breadD</td>
-<td headers="stat_0" class="gt_row gt_center"></td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;0</td>
-<td headers="stat_0" class="gt_row gt_center">35 (9.2%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;1</td>
-<td headers="stat_0" class="gt_row gt_center">51 (13%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;2</td>
-<td headers="stat_0" class="gt_row gt_center">267 (70%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;3</td>
-<td headers="stat_0" class="gt_row gt_center">26 (6.9%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">dessertD</td>
-<td headers="stat_0" class="gt_row gt_center"></td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;0</td>
-<td headers="stat_0" class="gt_row gt_center">206 (58%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;1</td>
-<td headers="stat_0" class="gt_row gt_center">33 (9.3%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;2</td>
-<td headers="stat_0" class="gt_row gt_center">87 (25%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;3</td>
-<td headers="stat_0" class="gt_row gt_center">29 (8.2%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">champagneD</td>
-<td headers="stat_0" class="gt_row gt_center"></td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;0</td>
-<td headers="stat_0" class="gt_row gt_center">49 (13%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;1</td>
-<td headers="stat_0" class="gt_row gt_center">208 (56%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;2</td>
-<td headers="stat_0" class="gt_row gt_center">45 (12%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;3</td>
-<td headers="stat_0" class="gt_row gt_center">70 (19%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">beerD</td>
-<td headers="stat_0" class="gt_row gt_center"></td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;0</td>
-<td headers="stat_0" class="gt_row gt_center">81 (22%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;1</td>
-<td headers="stat_0" class="gt_row gt_center">41 (11%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;2</td>
-<td headers="stat_0" class="gt_row gt_center">57 (16%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;3</td>
-<td headers="stat_0" class="gt_row gt_center">183 (51%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">redwineD</td>
-<td headers="stat_0" class="gt_row gt_center"></td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;0</td>
-<td headers="stat_0" class="gt_row gt_center">266 (77%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;1</td>
-<td headers="stat_0" class="gt_row gt_center">31 (9.0%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;2</td>
-<td headers="stat_0" class="gt_row gt_center">21 (6.1%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;3</td>
-<td headers="stat_0" class="gt_row gt_center">27 (7.8%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">whitewineD</td>
-<td headers="stat_0" class="gt_row gt_center"></td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;0</td>
-<td headers="stat_0" class="gt_row gt_center">100 (28%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;1</td>
-<td headers="stat_0" class="gt_row gt_center">73 (20%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;2</td>
-<td headers="stat_0" class="gt_row gt_center">67 (19%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;3</td>
-<td headers="stat_0" class="gt_row gt_center">121 (34%)</td></tr>
-  </tbody>
-  
-  <tfoot class="gt_footnotes">
-    <tr>
-      <td class="gt_footnote" colspan="2"><span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;"><sup>1</sup></span> n (%)</td>
-    </tr>
-  </tfoot>
-</table>
-</div>
-</div>
-</div>
-</section>
-</section>
-<section id="modify-variables-format" class="level2">
-<h2 class="anchored" data-anchor-id="modify-variables-format">3.4. Modify variables format</h2>
-<p>There are some “column types” you would like to modify because either R needs some specific types of data when working with certain variables, or because it will be easier for you to visualise and interpret the data. For example, the variable “group” is encoded as 0 and 1, when you create a table later on, you don’t want to see 0’s and 1’s because that doesn’t mean anything. It is better to change this variable to a factor, with explicit labels, so they actually mean something when they come out in your table.</p>
-<div class="quarto-figure quarto-figure-center">
-<figure class="figure">
-<p><img src="https://cdn.myportfolio.com/45214904-6a61-4e23-98d6-b140f8654a40/bd4ae264-ae51-4d18-bd60-7a058ab42fba_rw_1920.png?h=a3757d1f46f418c59e7e7946d026344e" class="img-fluid figure-img" style="width:50.0%" alt="Cartoon of cute fuzzy monsters dressed up as different X-men characters, working together to add a new column to an existing data frame. Stylized title text reads “dplyr::mutate - add columns, keep existing.” Learn more about dplyr::mutate."></p>
-</figure>
-</div>
-<p>You can use <code>mutate</code> to modify the type of the following variables:</p>
-<table class="table">
-<caption>Table 1: Variable types to modify</caption>
-<colgroup>
-<col style="width: 46%">
-<col style="width: 9%">
-<col style="width: 7%">
-<col style="width: 36%">
-</colgroup>
-<thead>
-<tr class="header">
-<th>Variable name</th>
-<th>Original</th>
-<th>Desired</th>
-<th>Hint</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<td>sex</td>
-<td>character</td>
-<td>factor</td>
-<td>mutate(), as.factor()</td>
-</tr>
-<tr class="even">
-<td>class</td>
-<td>integer</td>
-<td>factor</td>
-<td>mutate(), as.factor()</td>
-</tr>
-<tr class="odd">
-<td>All the clinical symptom variables</td>
-<td>integer</td>
-<td>logical</td>
-<td>mutate(across()), as.logical()</td>
-</tr>
-<tr class="even">
-<td>All the food variables representing the amount of specific foods eaten (those finishing with a capital “D”)</td>
-<td>integer</td>
-<td>factor</td>
-<td>mutate(across()), as.factor()</td>
-</tr>
-<tr class="odd">
-<td>dayonset</td>
-<td>character</td>
-<td>date</td>
-<td>lubridate::dmy()</td>
-</tr>
-<tr class="even">
-<td>starthour and dayonset together</td>
-<td><p>integer (starthour)</p>
-<p>date (dayonset)</p></td>
-<td>POSIXct, POSIXt</td>
-<td>lubridate::ymd_h() could have inside stringr::str_glue() with dayonset and starthour</td>
-</tr>
-</tbody>
-</table>
-<section id="sex-group-and-class" class="level4">
-<h4 class="anchored" data-anchor-id="sex-group-and-class">Sex, group and class</h4>
-<p>Let’s start transforming one-by-one the first two variables in the table: <code>sex</code>, and <code>class</code>.</p>
-<div class="cell">
-<details open="">
-<summary>Code</summary>
-<div class="sourceCode cell-code" id="cb17"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb17-1"><a href="#cb17-1" aria-hidden="true" tabindex="-1"></a>copdata <span class="ot">&lt;-</span> copdata <span class="sc">%&gt;%</span> </span>
-<span id="cb17-2"><a href="#cb17-2" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">mutate</span>(</span>
-<span id="cb17-3"><a href="#cb17-3" aria-hidden="true" tabindex="-1"></a>    <span class="at">sex =</span> <span class="fu">as.factor</span>(sex),</span>
-<span id="cb17-4"><a href="#cb17-4" aria-hidden="true" tabindex="-1"></a>    <span class="at">class =</span> <span class="fu">as.factor</span>(class))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-</details>
-</div>
-</section>
-<section id="symptoms-and-food-variables" class="level4">
-<h4 class="anchored" data-anchor-id="symptoms-and-food-variables">Symptoms and Food variables</h4>
-<p>For these variables, we are going to show you a couple of different ways to carry out the same variable type transformation in a <em>set</em> of variables, so you don’t need to do one variable at a time. We are showing you these ways so you see alternative ways to do the same thing.</p>
-<ol type="1">
-<li>For the variables that are clinical symptoms, we will list them one by one and show you the use of <code>mutate(across( ))</code>.</li>
-</ol>
-<div class="cell">
-<details open="">
-<summary>Code</summary>
-<div class="sourceCode cell-code" id="cb18"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb18-1"><a href="#cb18-1" aria-hidden="true" tabindex="-1"></a>copdata <span class="ot">&lt;-</span> copdata <span class="sc">%&gt;%</span> </span>
-<span id="cb18-2"><a href="#cb18-2" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">mutate</span>(</span>
-<span id="cb18-3"><a href="#cb18-3" aria-hidden="true" tabindex="-1"></a>    <span class="co"># clinical symptoms</span></span>
-<span id="cb18-4"><a href="#cb18-4" aria-hidden="true" tabindex="-1"></a>    <span class="fu">across</span>(<span class="at">.cols =</span> <span class="fu">c</span>(diarrhoea, bloody, vomiting,</span>
-<span id="cb18-5"><a href="#cb18-5" aria-hidden="true" tabindex="-1"></a>             abdo, nausea, fever,headache, jointpain), </span>
-<span id="cb18-6"><a href="#cb18-6" aria-hidden="true" tabindex="-1"></a>           <span class="at">.fns =</span> <span class="sc">~</span> <span class="fu">as.logical</span>(.)</span>
-<span id="cb18-7"><a href="#cb18-7" aria-hidden="true" tabindex="-1"></a>           )</span>
-<span id="cb18-8"><a href="#cb18-8" aria-hidden="true" tabindex="-1"></a>    )</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-</details>
-</div>
-<ol start="2" type="1">
-<li>For the variables that are food doses, we will show you how to first create a vector of names, following by using <code>mutate(across(all_of( )))</code> on this vector.</li>
-</ol>
-<div class="cell">
-<details open="">
-<summary>Code</summary>
-<div class="sourceCode cell-code" id="cb19"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb19-1"><a href="#cb19-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Create a vector with all the food variables representing the amount of specific foods items eaten (those finishing with a capital "D")</span></span>
-<span id="cb19-2"><a href="#cb19-2" aria-hidden="true" tabindex="-1"></a><span class="co"># One way of doing it:</span></span>
-<span id="cb19-3"><a href="#cb19-3" aria-hidden="true" tabindex="-1"></a>food_dose <span class="ot">&lt;-</span> copdata <span class="sc">%&gt;%</span> </span>
-<span id="cb19-4"><a href="#cb19-4" aria-hidden="true" tabindex="-1"></a>    dplyr<span class="sc">::</span><span class="fu">select</span>(</span>
-<span id="cb19-5"><a href="#cb19-5" aria-hidden="true" tabindex="-1"></a>      <span class="fu">ends_with</span>(<span class="st">"D"</span>, <span class="at">ignore.case =</span> <span class="cn">FALSE</span>)) <span class="sc">%&gt;%</span> </span>
-<span id="cb19-6"><a href="#cb19-6" aria-hidden="true" tabindex="-1"></a>    <span class="fu">names</span>()</span>
-<span id="cb19-7"><a href="#cb19-7" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb19-8"><a href="#cb19-8" aria-hidden="true" tabindex="-1"></a><span class="co"># Another way of doing it:</span></span>
-<span id="cb19-9"><a href="#cb19-9" aria-hidden="true" tabindex="-1"></a><span class="co"># food_dose &lt;- c("fetaD", "sardinesD", "eggplantD", "moussakaD", </span></span>
-<span id="cb19-10"><a href="#cb19-10" aria-hidden="true" tabindex="-1"></a><span class="co">#                 "orzoD", "greeksalD", "dessertD", "breadD", </span></span>
-<span id="cb19-11"><a href="#cb19-11" aria-hidden="true" tabindex="-1"></a><span class="co">#                 "champagneD", "beerD", "redwineD", "whitewineD")</span></span>
-<span id="cb19-12"><a href="#cb19-12" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb19-13"><a href="#cb19-13" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb19-14"><a href="#cb19-14" aria-hidden="true" tabindex="-1"></a>copdata <span class="ot">&lt;-</span> copdata <span class="sc">%&gt;%</span> </span>
-<span id="cb19-15"><a href="#cb19-15" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">mutate</span>(</span>
-<span id="cb19-16"><a href="#cb19-16" aria-hidden="true" tabindex="-1"></a>    <span class="co"># food dose variables</span></span>
-<span id="cb19-17"><a href="#cb19-17" aria-hidden="true" tabindex="-1"></a>    <span class="fu">across</span>(<span class="at">.cols =</span> <span class="fu">all_of</span>(food_dose), </span>
-<span id="cb19-18"><a href="#cb19-18" aria-hidden="true" tabindex="-1"></a>           <span class="at">.fns =</span> <span class="sc">~</span><span class="fu">as.factor</span>(.))) </span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-</details>
-</div>
-<div class="callout callout-style-default callout-note callout-titled" title="Note">
-<div class="callout-header d-flex align-content-center" data-bs-toggle="collapse" data-bs-target=".callout-3-contents" aria-controls="callout-3" aria-expanded="false" aria-label="Toggle callout">
-<div class="callout-icon-container">
-<i class="callout-icon"></i>
-</div>
-<div class="callout-title-container flex-fill">
-Note
-</div>
-<div class="callout-btn-toggle d-inline-block border-0 py-1 ps-1 pe-0 float-end"><i class="callout-toggle"></i></div>
-</div>
-<div id="callout-3" class="callout-3-contents callout-collapse collapse">
-<div class="callout-body-container callout-body">
-<p>The tilde (<code>~</code>) above is used to apply the transformation <code>as.logical(.)</code> to each selected column, which in our case is either all columns included in <code>food_items</code> and <code>food_dose.</code></p>
-</div>
-</div>
-</div>
-</section>
-<section id="date-and-time-variables" class="level4">
-<h4 class="anchored" data-anchor-id="date-and-time-variables">Date and time variables</h4>
-<div class="callout callout-style-default callout-warning callout-titled" title="Let's stop and... think!">
-<div class="callout-header d-flex align-content-center" data-bs-toggle="collapse" data-bs-target=".callout-4-contents" aria-controls="callout-4" aria-expanded="false" aria-label="Toggle callout">
-<div class="callout-icon-container">
-<i class="callout-icon"></i>
-</div>
-<div class="callout-title-container flex-fill">
-Let’s stop and… think!
-</div>
-<div class="callout-btn-toggle d-inline-block border-0 py-1 ps-1 pe-0 float-end"><i class="callout-toggle"></i></div>
-</div>
-<div id="callout-4" class="callout-4-contents callout-collapse collapse">
-<div class="callout-body-container callout-body">
-<p>Why may you want to transform the variable starthour to date-time?</p>
-</div>
-</div>
-</div>
-<div class="quarto-figure quarto-figure-center">
-<figure class="figure">
-<p><img src="https://cdn.myportfolio.com/45214904-6a61-4e23-98d6-b140f8654a40/3017cf57-d7d9-4f73-9b66-f2be0ebf2489_rw_1920.png?h=2cdd179bc087c56b43f8aa6ea849ca1a" class="img-fluid figure-img" style="width:50.0%" alt="Fuzzy cartoon monsters in construction hats sorting a pile of “Y” “M” and “D”s into separate piles labeled “Years”, “Months” and “Days”. Title text reads “Lubridate: wrangle times and dates!” Learn more about date-time wrangling with lubridate."></p>
-</figure>
-</div>
-<p>You can use <code>lubridate::dmy()</code> to mutate the <code>dayonset</code> variable into a date variable. Note that we are using the function <code>dmy()</code> because dates are formatted as day, then month (abbreviated character string), then year (i.e.&nbsp;“12nov2006”).</p>
-<div class="cell">
-<details open="">
-<summary>Code</summary>
-<div class="sourceCode cell-code" id="cb20"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb20-1"><a href="#cb20-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Have a look at how the data is stored</span></span>
-<span id="cb20-2"><a href="#cb20-2" aria-hidden="true" tabindex="-1"></a><span class="fu">head</span>(copdata<span class="sc">$</span>dayonset)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-</details>
-<div class="cell-output cell-output-stdout">
-<pre><code>[1] "6oct2024" ""         ""         ""         "6oct2024" "7oct2024"</code></pre>
-</div>
-<details open="">
-<summary>Code</summary>
-<div class="sourceCode cell-code" id="cb22"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb22-1"><a href="#cb22-1" aria-hidden="true" tabindex="-1"></a><span class="fu">class</span>(copdata<span class="sc">$</span>dayonset)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-</details>
-<div class="cell-output cell-output-stdout">
-<pre><code>[1] "character"</code></pre>
-</div>
-<details open="">
-<summary>Code</summary>
-<div class="sourceCode cell-code" id="cb24"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb24-1"><a href="#cb24-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Update copdata:</span></span>
-<span id="cb24-2"><a href="#cb24-2" aria-hidden="true" tabindex="-1"></a>copdata <span class="ot">&lt;-</span> copdata <span class="sc">%&gt;%</span> </span>
-<span id="cb24-3"><a href="#cb24-3" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Change column to date class:</span></span>
-<span id="cb24-4"><a href="#cb24-4" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">mutate</span>(</span>
-<span id="cb24-5"><a href="#cb24-5" aria-hidden="true" tabindex="-1"></a>    <span class="at">dayonset =</span> lubridate<span class="sc">::</span><span class="fu">dmy</span>(dayonset))</span>
-<span id="cb24-6"><a href="#cb24-6" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb24-7"><a href="#cb24-7" aria-hidden="true" tabindex="-1"></a><span class="co"># Check class of updated column:</span></span>
-<span id="cb24-8"><a href="#cb24-8" aria-hidden="true" tabindex="-1"></a><span class="fu">class</span>(copdata<span class="sc">$</span>dayonset)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-</details>
-<div class="cell-output cell-output-stdout">
-<pre><code>[1] "Date"</code></pre>
-</div>
-<details open="">
-<summary>Code</summary>
-<div class="sourceCode cell-code" id="cb26"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb26-1"><a href="#cb26-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Have a look at your data now:</span></span>
-<span id="cb26-2"><a href="#cb26-2" aria-hidden="true" tabindex="-1"></a><span class="fu">head</span>(copdata<span class="sc">$</span>dayonset)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-</details>
-<div class="cell-output cell-output-stdout">
-<pre><code>[1] "2024-10-06" NA           NA           NA           "2024-10-06"
-[6] "2024-10-07"</code></pre>
-</div>
-</div>
-<p>Having a variable that defines “time” in an outbreak investigation can be very useful when creating a case definition. An hour of the day, without a date associated with it doesn’t help you much, thus, you should merge together day and time of onset of symptoms into a single variable. Moreover, you will be using this combined variable later on to estimate an incubation period and create your epicurve. We can combine these two variables by using the <code>lubridate::ymd_h()</code> function.</p>
-<p>Before we proceed, it would be wise to check if any respondents have a value for <code>dayonset</code> but not <code>starthour</code>, or vice versa. The <code>lubridate</code> date-time conversion functions do not have an explicit argument for dealing with missing values, but the <code>truncated = …</code> argument can help prevent spurious date-times being derived from a date-time combination where one value is missing.</p>
-<p>We can check if we have any missing values by cross-tabulating <code>starthour</code> with <code>dayonset</code>:</p>
-<div class="cell">
-<details open="">
-<summary>Code</summary>
-<div class="sourceCode cell-code" id="cb28"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb28-1"><a href="#cb28-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Cross-tabulate dayonset with starthour:</span></span>
-<span id="cb28-2"><a href="#cb28-2" aria-hidden="true" tabindex="-1"></a>janitor<span class="sc">::</span><span class="fu">tabyl</span>(<span class="at">dat =</span> copdata, </span>
-<span id="cb28-3"><a href="#cb28-3" aria-hidden="true" tabindex="-1"></a>               <span class="at">var1 =</span> starthour, </span>
-<span id="cb28-4"><a href="#cb28-4" aria-hidden="true" tabindex="-1"></a>               <span class="at">var2 =</span> dayonset)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-</details>
-<div class="cell-output-display">
-
-<div data-pagedtable="false">
-  <script data-pagedtable-source="" type="application/json">
-{"columns":[{"label":["starthour"],"name":[1],"type":["int"],"align":["right"]},{"label":["2024-10-05"],"name":[2],"type":["dbl"],"align":["right"]},{"label":["2024-10-06"],"name":[3],"type":["dbl"],"align":["right"]},{"label":["2024-10-07"],"name":[4],"type":["dbl"],"align":["right"]},{"label":["NA_"],"name":[5],"type":["dbl"],"align":["right"]}],"data":[{"1":"3","2":"0","3":"10","4":"2","5":"0"},{"1":"9","2":"0","3":"97","4":"6","5":"0"},{"1":"15","2":"0","3":"64","4":"6","5":"0"},{"1":"21","2":"9","3":"26","4":"0","5":"0"},{"1":"NA","2":"2","3":"0","4":"0","5":"175"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}}
-  </script>
-</div>
-</div>
-</div>
-<p>This shows us that there are two respondents who had an onset date, but are missing onset time (<code>starthour</code>). Since <code>starthour</code> is represented by 1 - 2 digits, we can specify that we want <code>lubridate</code> to also parse date-time combinations that are truncated by up to two digits:</p>
-<div class="cell">
-<details open="">
-<summary>Code</summary>
-<div class="sourceCode cell-code" id="cb29"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb29-1"><a href="#cb29-1" aria-hidden="true" tabindex="-1"></a>copdata <span class="ot">&lt;-</span> copdata <span class="sc">%&gt;%</span> </span>
-<span id="cb29-2"><a href="#cb29-2" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Combine dayonset and starthour in a new date time variable:</span></span>
-<span id="cb29-3"><a href="#cb29-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">onset_datetime =</span> </span>
-<span id="cb29-4"><a href="#cb29-4" aria-hidden="true" tabindex="-1"></a>           lubridate<span class="sc">::</span><span class="fu">ymd_h</span>(</span>
-<span id="cb29-5"><a href="#cb29-5" aria-hidden="true" tabindex="-1"></a>             <span class="fu">str_glue</span>(<span class="st">"{dayonset}, {starthour}"</span>), </span>
-<span id="cb29-6"><a href="#cb29-6" aria-hidden="true" tabindex="-1"></a>                                           <span class="co"># Deal with missing starthour:</span></span>
-<span id="cb29-7"><a href="#cb29-7" aria-hidden="true" tabindex="-1"></a>                                           <span class="at">truncated =</span> <span class="dv">2</span>))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-</details>
-<div class="cell-output cell-output-stderr">
-<pre><code>Warning: There was 1 warning in `mutate()`.
-ℹ In argument: `onset_datetime = lubridate::ymd_h(str_glue("{dayonset},
-  {starthour}"), truncated = 2)`.
-Caused by warning:
-!  175 failed to parse.</code></pre>
-</div>
-</div>
-<p>Note that we needed to use <code>str_glue()</code> to concatenate <code>dayonset</code> and <code>starthour</code> together before we could convert the variable to a date-time object. This is because the <code>ymd_h()</code> function expects a single character string, containing both the date and the time, as input.</p>
-<p>The argument <code>truncated = 2</code> will result in dates with missing <code>starthour</code> still being converted to date-time, with the missing time being set to <code>00:00</code> (midnight). Whether you want to deal with missing <code>starthour</code> in this way or prefer to code these date-times as <code>NA</code> will depend on how you want them to be represented in your analysis.</p>
-<p>Now we can check that everything in the new combined date-time variable has parsed correctly:</p>
-<div class="cell">
-<details open="">
-<summary>Code</summary>
-<div class="sourceCode cell-code" id="cb31"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb31-1"><a href="#cb31-1" aria-hidden="true" tabindex="-1"></a><span class="fu">head</span>(copdata<span class="sc">$</span>dayonset)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-</details>
-<div class="cell-output cell-output-stdout">
-<pre><code>[1] "2024-10-06" NA           NA           NA           "2024-10-06"
-[6] "2024-10-07"</code></pre>
-</div>
-<details open="">
-<summary>Code</summary>
-<div class="sourceCode cell-code" id="cb33"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb33-1"><a href="#cb33-1" aria-hidden="true" tabindex="-1"></a><span class="fu">head</span>(copdata<span class="sc">$</span>starthour)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-</details>
-<div class="cell-output cell-output-stdout">
-<pre><code>[1]  9 NA NA NA 15 15</code></pre>
-</div>
-<details open="">
-<summary>Code</summary>
-<div class="sourceCode cell-code" id="cb35"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb35-1"><a href="#cb35-1" aria-hidden="true" tabindex="-1"></a><span class="fu">head</span>(copdata<span class="sc">$</span>onset_datetime)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-</details>
-<div class="cell-output cell-output-stdout">
-<pre><code>[1] "2024-10-06 09:00:00 UTC" NA                       
-[3] NA                        NA                       
-[5] "2024-10-06 15:00:00 UTC" "2024-10-07 15:00:00 UTC"</code></pre>
-</div>
-</div>
-</section>
-</section>
-<section id="export-clean-data" class="level2">
-<h2 class="anchored" data-anchor-id="export-clean-data">3.5. Export clean data</h2>
-<p>Save the cleaned data set before proceeding with using your case definition to identify cases in your dataset. Use the .rds format, as it preserves column classes. This ensures you will have only minimal cleaning to do after importing the data into R at the next inject.</p>
-<p>Save your clean data as a new file “Copenhagen_clean1_YOURINITIALS.rds”, under the data folder of your Rproject.</p>
-<div class="callout callout-style-default callout-tip callout-titled" title="Need a little bit of help?">
-<div class="callout-header d-flex align-content-center" data-bs-toggle="collapse" data-bs-target=".callout-5-contents" aria-controls="callout-5" aria-expanded="false" aria-label="Toggle callout">
-<div class="callout-icon-container">
-<i class="callout-icon"></i>
-</div>
-<div class="callout-title-container flex-fill">
-Need a little bit of help?
-</div>
-<div class="callout-btn-toggle d-inline-block border-0 py-1 ps-1 pe-0 float-end"><i class="callout-toggle"></i></div>
-</div>
-<div id="callout-5" class="callout-5-contents callout-collapse collapse">
-<div class="callout-body-container callout-body">
-<p>Use <code>rio::export()</code>.</p>
-</div>
-</div>
-</div>
-<div class="cell">
-<details open="">
-<summary>Code</summary>
-<div class="sourceCode cell-code" id="cb37"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb37-1"><a href="#cb37-1" aria-hidden="true" tabindex="-1"></a>rio<span class="sc">::</span><span class="fu">export</span>(<span class="at">x =</span> copdata, </span>
-<span id="cb37-2"><a href="#cb37-2" aria-hidden="true" tabindex="-1"></a>            <span class="at">file =</span> here<span class="sc">::</span><span class="fu">here</span>(<span class="st">"data"</span>, <span class="st">"Copenhagen_clean1_2024.rds"</span>))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-</details>
-</div>
-<div class="quarto-figure quarto-figure-center">
-<figure class="figure">
-<p><img src="https://cdn.myportfolio.com/45214904-6a61-4e23-98d6-b140f8654a40/e4a11b1f-f235-461f-ab73-ce6128c59e49_rw_1920.png?h=a34a4173c717bfbc600a8682d419288e" class="img-fluid figure-img" style="width:80.0%"></p>
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-</script><div class="modal fade" id="quarto-embedded-source-code-modal" tabindex="-1" aria-labelledby="quarto-embedded-source-code-modal-label" aria-hidden="true"><div class="modal-dialog modal-dialog-scrollable"><div class="modal-content"><div class="modal-header"><h5 class="modal-title" id="quarto-embedded-source-code-modal-label">Source Code</h5><button class="btn-close" data-bs-dismiss="modal"></button></div><div class="modal-body"><div class="">
-<div class="sourceCode" id="cb38" data-shortcodes="false"><pre class="sourceCode markdown code-with-copy"><code class="sourceCode markdown"><span id="cb38-1"><a href="#cb38-1" aria-hidden="true" tabindex="-1"></a><span class="co">---</span></span>
-<span id="cb38-2"><a href="#cb38-2" aria-hidden="true" tabindex="-1"></a><span class="an">title:</span><span class="co"> "Data import and cleaning"</span></span>
-<span id="cb38-3"><a href="#cb38-3" aria-hidden="true" tabindex="-1"></a><span class="an">editor:</span><span class="co"> visual</span></span>
-<span id="cb38-4"><a href="#cb38-4" aria-hidden="true" tabindex="-1"></a><span class="co">---</span></span>
-<span id="cb38-5"><a href="#cb38-5" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-6"><a href="#cb38-6" aria-hidden="true" tabindex="-1"></a><span class="fu"># **1. Learning outcomes**</span></span>
-<span id="cb38-7"><a href="#cb38-7" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-8"><a href="#cb38-8" aria-hidden="true" tabindex="-1"></a>At the end of the session, participants will be able to:</span>
-<span id="cb38-9"><a href="#cb38-9" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-10"><a href="#cb38-10" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span>Import datasets in R</span>
-<span id="cb38-11"><a href="#cb38-11" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span>Apply basic data management techniques to explore and familiarise themselves with the dataset</span>
-<span id="cb38-12"><a href="#cb38-12" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span>Apply strategies for data cleaning</span>
-<span id="cb38-13"><a href="#cb38-13" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-14"><a href="#cb38-14" aria-hidden="true" tabindex="-1"></a><span class="fu"># **2. Story/plot description**</span></span>
-<span id="cb38-15"><a href="#cb38-15" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-16"><a href="#cb38-16" aria-hidden="true" tabindex="-1"></a>You and your team have carried out hundreds of interviews in a very short time. Well done!</span>
-<span id="cb38-17"><a href="#cb38-17" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-18"><a href="#cb38-18" aria-hidden="true" tabindex="-1"></a>You will now have your first look at the data. Note that in real life you would have ideally done a pilot of the questionnaire and you would have prepared in advance a codebook and be familiar with the names and format of all variables.</span>
-<span id="cb38-19"><a href="#cb38-19" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-20"><a href="#cb38-20" aria-hidden="true" tabindex="-1"></a>Explore and clean the data you have collected and save a new version of the cleaned dataset in your computer. Be sure to make use of the R code that was provided to you previously, during the R practicals. You can also use the code you wrote during the Applied Epi course. You are expected to look at the help functions and google as well. Try to understand what the functions are doing and what their arguments are.</span>
-<span id="cb38-21"><a href="#cb38-21" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-22"><a href="#cb38-22" aria-hidden="true" tabindex="-1"></a>Work on your own computer and help your fellow colleagues as needed. The lead of the outbreak will be in charge of keeping the time (unless you designate another person, but be sure that someone keeps track of time).</span>
-<span id="cb38-23"><a href="#cb38-23" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-24"><a href="#cb38-24" aria-hidden="true" tabindex="-1"></a>Open R Studio and set up a new R project, and create a <span class="in">`data`</span> and a scripts <span class="in">`folder`</span>.</span>
-<span id="cb38-25"><a href="#cb38-25" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-26"><a href="#cb38-26" aria-hidden="true" tabindex="-1"></a><span class="fu"># **3. Tips you could follow**</span></span>
-<span id="cb38-27"><a href="#cb38-27" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-28"><a href="#cb38-28" aria-hidden="true" tabindex="-1"></a><span class="fu">## 3.1. Install packages and load libraries</span></span>
-<span id="cb38-29"><a href="#cb38-29" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-30"><a href="#cb38-30" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span>Load the R packages you will be using. Update this list of packages as you dig into the code: You may realise you need packages you didn't think about at the beginning. Remember that it is a good practice to load tydiverse at the end of your package list, to avoid <span class="co">[</span><span class="ot">masking functions</span><span class="co">](https://www.ucl.ac.uk/~uctqiax/PUBLG100/2015/faq/masking.html)</span>.</span>
-<span id="cb38-31"><a href="#cb38-31" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-32"><a href="#cb38-32" aria-hidden="true" tabindex="-1"></a><span class="in">```{r, Packages}</span></span>
-<span id="cb38-33"><a href="#cb38-33" aria-hidden="true" tabindex="-1"></a><span class="co"># Check if the 'pacman' package is installed, if not install it:</span></span>
-<span id="cb38-34"><a href="#cb38-34" aria-hidden="true" tabindex="-1"></a><span class="cf">if</span> (<span class="sc">!</span><span class="fu">requireNamespace</span>(<span class="st">"pacman"</span>, <span class="at">quietly =</span> <span class="cn">TRUE</span>)) <span class="fu">install.packages</span>(<span class="st">"pacman"</span>)</span>
-<span id="cb38-35"><a href="#cb38-35" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-36"><a href="#cb38-36" aria-hidden="true" tabindex="-1"></a><span class="co"># Load the required libraries into the current R session:</span></span>
-<span id="cb38-37"><a href="#cb38-37" aria-hidden="true" tabindex="-1"></a>pacman<span class="sc">::</span><span class="fu">p_load</span>(rio, </span>
-<span id="cb38-38"><a href="#cb38-38" aria-hidden="true" tabindex="-1"></a>               here, </span>
-<span id="cb38-39"><a href="#cb38-39" aria-hidden="true" tabindex="-1"></a>               tidyverse, </span>
-<span id="cb38-40"><a href="#cb38-40" aria-hidden="true" tabindex="-1"></a>               skimr,</span>
-<span id="cb38-41"><a href="#cb38-41" aria-hidden="true" tabindex="-1"></a>               plyr,</span>
-<span id="cb38-42"><a href="#cb38-42" aria-hidden="true" tabindex="-1"></a>               janitor,</span>
-<span id="cb38-43"><a href="#cb38-43" aria-hidden="true" tabindex="-1"></a>               lubridate,</span>
-<span id="cb38-44"><a href="#cb38-44" aria-hidden="true" tabindex="-1"></a>               gtsummary, </span>
-<span id="cb38-45"><a href="#cb38-45" aria-hidden="true" tabindex="-1"></a>               flextable,</span>
-<span id="cb38-46"><a href="#cb38-46" aria-hidden="true" tabindex="-1"></a>               officer,</span>
-<span id="cb38-47"><a href="#cb38-47" aria-hidden="true" tabindex="-1"></a>               epikit, </span>
-<span id="cb38-48"><a href="#cb38-48" aria-hidden="true" tabindex="-1"></a>               apyramid, </span>
-<span id="cb38-49"><a href="#cb38-49" aria-hidden="true" tabindex="-1"></a>               scales)</span>
-<span id="cb38-50"><a href="#cb38-50" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-51"><a href="#cb38-51" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb38-52"><a href="#cb38-52" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-53"><a href="#cb38-53" aria-hidden="true" tabindex="-1"></a><span class="fu">## 3.2. Import your data</span></span>
-<span id="cb38-54"><a href="#cb38-54" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-55"><a href="#cb38-55" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span>You can find the <span class="in">`spetses_school.csv`</span> file in EVA. Feel free to explore the **codebook** as well.</span>
-<span id="cb38-56"><a href="#cb38-56" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-57"><a href="#cb38-57" aria-hidden="true" tabindex="-1"></a><span class="in">```{r, Import_data}</span></span>
-<span id="cb38-58"><a href="#cb38-58" aria-hidden="true" tabindex="-1"></a><span class="co"># Import the raw data set:</span></span>
-<span id="cb38-59"><a href="#cb38-59" aria-hidden="true" tabindex="-1"></a>copdata <span class="ot">&lt;-</span> rio<span class="sc">::</span><span class="fu">import</span>(here<span class="sc">::</span><span class="fu">here</span>(<span class="st">"data"</span>, <span class="st">"spetses_school.csv"</span>))</span>
-<span id="cb38-60"><a href="#cb38-60" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb38-61"><a href="#cb38-61" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-62"><a href="#cb38-62" aria-hidden="true" tabindex="-1"></a><span class="fu">## 3.3. Explore and clean your data</span></span>
-<span id="cb38-63"><a href="#cb38-63" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-64"><a href="#cb38-64" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span>Have a look at the structure of your data. Here you have some questions that may help you get started:</span>
-<span id="cb38-65"><a href="#cb38-65" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-66"><a href="#cb38-66" aria-hidden="true" tabindex="-1"></a><span class="ss">    -   </span>How many observations and variables does the dataset contain?</span>
-<span id="cb38-67"><a href="#cb38-67" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-68"><a href="#cb38-68" aria-hidden="true" tabindex="-1"></a><span class="ss">    -   </span>What types of variables do you have and what types of values are recorded?</span>
-<span id="cb38-69"><a href="#cb38-69" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-70"><a href="#cb38-70" aria-hidden="true" tabindex="-1"></a><span class="ss">    -   </span>Do any of the values of the other variables look implausible? Which ones and why? What will you do about it?</span>
-<span id="cb38-71"><a href="#cb38-71" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-72"><a href="#cb38-72" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span>Go ahead and clean your data as you see fit.</span>
-<span id="cb38-73"><a href="#cb38-73" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-74"><a href="#cb38-74" aria-hidden="true" tabindex="-1"></a>::: {.callout-tip title="Need a little bit of help?" collapse="true"}</span>
-<span id="cb38-75"><a href="#cb38-75" aria-hidden="true" tabindex="-1"></a>You could use <span class="in">`head()`</span>, <span class="in">`dim()`</span>, <span class="in">`str()`</span>, or <span class="in">`skim()`</span> to have a quick look at the data and get an idea of which variables you want to explore further. You can also do some tables</span>
-<span id="cb38-76"><a href="#cb38-76" aria-hidden="true" tabindex="-1"></a>:::</span>
-<span id="cb38-77"><a href="#cb38-77" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-78"><a href="#cb38-78" aria-hidden="true" tabindex="-1"></a><span class="in">```{r, Explore_data}</span></span>
-<span id="cb38-79"><a href="#cb38-79" aria-hidden="true" tabindex="-1"></a><span class="fu">head</span>(copdata)</span>
-<span id="cb38-80"><a href="#cb38-80" aria-hidden="true" tabindex="-1"></a><span class="fu">dim</span>(copdata)</span>
-<span id="cb38-81"><a href="#cb38-81" aria-hidden="true" tabindex="-1"></a><span class="fu">str</span>(copdata)</span>
-<span id="cb38-82"><a href="#cb38-82" aria-hidden="true" tabindex="-1"></a>skimr<span class="sc">::</span><span class="fu">skim</span>(copdata)</span>
-<span id="cb38-83"><a href="#cb38-83" aria-hidden="true" tabindex="-1"></a><span class="fu">names</span>(copdata)</span>
-<span id="cb38-84"><a href="#cb38-84" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-85"><a href="#cb38-85" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb38-86"><a href="#cb38-86" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-87"><a href="#cb38-87" aria-hidden="true" tabindex="-1"></a>Let's explore and manage some variables in detail.</span>
-<span id="cb38-88"><a href="#cb38-88" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-89"><a href="#cb38-89" aria-hidden="true" tabindex="-1"></a><span class="fu">#### Age</span></span>
-<span id="cb38-90"><a href="#cb38-90" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-91"><a href="#cb38-91" aria-hidden="true" tabindex="-1"></a>Through visual exploration of the <span class="in">`age`</span> histogram we see that there is at least one very high value, likely implausible. You can then create a cross-tabulation of variables age and group to have a better idea of how your data looks like.</span>
-<span id="cb38-92"><a href="#cb38-92" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-93"><a href="#cb38-93" aria-hidden="true" tabindex="-1"></a><span class="in">```{r age_exploration}</span></span>
-<span id="cb38-94"><a href="#cb38-94" aria-hidden="true" tabindex="-1"></a><span class="co"># Have a look at the histogram  </span></span>
-<span id="cb38-95"><a href="#cb38-95" aria-hidden="true" tabindex="-1"></a><span class="fu">hist</span>(copdata<span class="sc">$</span>age)   </span>
-<span id="cb38-96"><a href="#cb38-96" aria-hidden="true" tabindex="-1"></a><span class="co"># Create cross-tab with the group variable:  </span></span>
-<span id="cb38-97"><a href="#cb38-97" aria-hidden="true" tabindex="-1"></a>janitor<span class="sc">::</span><span class="fu">tabyl</span>(<span class="at">dat =</span> copdata, </span>
-<span id="cb38-98"><a href="#cb38-98" aria-hidden="true" tabindex="-1"></a>               <span class="at">var1 =</span> age, </span>
-<span id="cb38-99"><a href="#cb38-99" aria-hidden="true" tabindex="-1"></a>               <span class="at">var2 =</span> group)</span>
-<span id="cb38-100"><a href="#cb38-100" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb38-101"><a href="#cb38-101" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-102"><a href="#cb38-102" aria-hidden="true" tabindex="-1"></a>Note that <span class="in">`group`</span> is coded as 0 and 1, and these may be difficult to interpret when they mean something other than "no" and "yes", respectively. From the codebook, you know that teachers are represented by 0, and students by 1. Let's change this to make our lives easier:</span>
-<span id="cb38-103"><a href="#cb38-103" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-104"><a href="#cb38-104" aria-hidden="true" tabindex="-1"></a><span class="in">```{r recode_group}</span></span>
-<span id="cb38-105"><a href="#cb38-105" aria-hidden="true" tabindex="-1"></a><span class="co"># Convert group to a factor and label 0 as teacher, 1 as student:</span></span>
-<span id="cb38-106"><a href="#cb38-106" aria-hidden="true" tabindex="-1"></a>copdata <span class="ot">&lt;-</span> copdata <span class="sc">%&gt;%</span> </span>
-<span id="cb38-107"><a href="#cb38-107" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">group =</span> <span class="fu">factor</span>(group, </span>
-<span id="cb38-108"><a href="#cb38-108" aria-hidden="true" tabindex="-1"></a>                        <span class="at">labels =</span> <span class="fu">c</span>(<span class="st">"teacher"</span>, <span class="st">"student"</span>)))</span>
-<span id="cb38-109"><a href="#cb38-109" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-110"><a href="#cb38-110" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb38-111"><a href="#cb38-111" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-112"><a href="#cb38-112" aria-hidden="true" tabindex="-1"></a>Now, have a look at your cross-tab again:</span>
-<span id="cb38-113"><a href="#cb38-113" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-114"><a href="#cb38-114" aria-hidden="true" tabindex="-1"></a><span class="in">```{r cross-tab_age_group}</span></span>
-<span id="cb38-115"><a href="#cb38-115" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-116"><a href="#cb38-116" aria-hidden="true" tabindex="-1"></a>janitor<span class="sc">::</span><span class="fu">tabyl</span>(<span class="at">dat =</span> copdata, </span>
-<span id="cb38-117"><a href="#cb38-117" aria-hidden="true" tabindex="-1"></a>               <span class="at">var1 =</span> age, </span>
-<span id="cb38-118"><a href="#cb38-118" aria-hidden="true" tabindex="-1"></a>               <span class="at">var2 =</span> group) </span>
-<span id="cb38-119"><a href="#cb38-119" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb38-120"><a href="#cb38-120" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-121"><a href="#cb38-121" aria-hidden="true" tabindex="-1"></a>With this table, we can more easily identify ages that are likely to be typographic errors. Specifically:</span>
-<span id="cb38-122"><a href="#cb38-122" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-123"><a href="#cb38-123" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span>There is one teacher aged 16 (likely digit reversal - should be 61)</span>
-<span id="cb38-124"><a href="#cb38-124" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-125"><a href="#cb38-125" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span>There is one student aged 8 (likely missing a digit - should be 18)</span>
-<span id="cb38-126"><a href="#cb38-126" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-127"><a href="#cb38-127" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span>There is one student aged 180 (likely has an extra digit - should be 18)</span>
-<span id="cb38-128"><a href="#cb38-128" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-129"><a href="#cb38-129" aria-hidden="true" tabindex="-1"></a>Assuming you have contacted the school to make sure your suspicions about the actual ages are correct, we can now correct them, using <span class="in">`case_when()`</span>. We create logical conditions to identify the incorrect ages, combining the values for age with the group they belong to:</span>
-<span id="cb38-130"><a href="#cb38-130" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-131"><a href="#cb38-131" aria-hidden="true" tabindex="-1"></a><span class="in">```{r age_cleaning}</span></span>
-<span id="cb38-132"><a href="#cb38-132" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-133"><a href="#cb38-133" aria-hidden="true" tabindex="-1"></a><span class="co"># Update incorrect ages to the correct values with case_when:  </span></span>
-<span id="cb38-134"><a href="#cb38-134" aria-hidden="true" tabindex="-1"></a>copdata <span class="ot">&lt;-</span> copdata <span class="sc">%&gt;%</span>       </span>
-<span id="cb38-135"><a href="#cb38-135" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">age =</span>                         </span>
-<span id="cb38-136"><a href="#cb38-136" aria-hidden="true" tabindex="-1"></a>           <span class="fu">case_when</span>(                            </span>
-<span id="cb38-137"><a href="#cb38-137" aria-hidden="true" tabindex="-1"></a>             <span class="co"># Where respondent is 16 and a teacher, change age to 61:</span></span>
-<span id="cb38-138"><a href="#cb38-138" aria-hidden="true" tabindex="-1"></a>             age <span class="sc">==</span> <span class="dv">16</span> <span class="sc">&amp;</span> group <span class="sc">==</span> <span class="st">"teacher"</span> <span class="sc">~</span> <span class="dv">61</span>,              </span>
-<span id="cb38-139"><a href="#cb38-139" aria-hidden="true" tabindex="-1"></a>             <span class="co"># where respondent is 8 or 180 and a student, change age to 18:</span></span>
-<span id="cb38-140"><a href="#cb38-140" aria-hidden="true" tabindex="-1"></a>             age <span class="sc">==</span> <span class="dv">8</span> <span class="sc">&amp;</span> group <span class="sc">==</span> <span class="st">"student"</span> <span class="sc">~</span> <span class="dv">18</span>,              </span>
-<span id="cb38-141"><a href="#cb38-141" aria-hidden="true" tabindex="-1"></a>             age <span class="sc">==</span> <span class="dv">180</span> <span class="sc">&amp;</span> group <span class="sc">==</span> <span class="st">"student"</span> <span class="sc">~</span> <span class="dv">18</span>,              </span>
-<span id="cb38-142"><a href="#cb38-142" aria-hidden="true" tabindex="-1"></a>             <span class="co"># Keep remaining values as is:              </span></span>
-<span id="cb38-143"><a href="#cb38-143" aria-hidden="true" tabindex="-1"></a>             <span class="at">.default =</span> <span class="fu">as.numeric</span>(age)   </span>
-<span id="cb38-144"><a href="#cb38-144" aria-hidden="true" tabindex="-1"></a>             <span class="co"># if .default is not working, try:</span></span>
-<span id="cb38-145"><a href="#cb38-145" aria-hidden="true" tabindex="-1"></a>             <span class="co"># TRUE ~ age</span></span>
-<span id="cb38-146"><a href="#cb38-146" aria-hidden="true" tabindex="-1"></a>             )                    </span>
-<span id="cb38-147"><a href="#cb38-147" aria-hidden="true" tabindex="-1"></a>         ) </span>
-<span id="cb38-148"><a href="#cb38-148" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb38-149"><a href="#cb38-149" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-150"><a href="#cb38-150" aria-hidden="true" tabindex="-1"></a><span class="al">![](https://cdn.myportfolio.com/45214904-6a61-4e23-98d6-b140f8654a40/6ffcd6d6-c783-4087-ae76-99aa851663ed_rw_1920.png?h=5f67935d69c92e1fda9074d00dfea972)</span>{fig-alt="Cartoon showing a table with creature type (kraken, dragon, or cyclops) and age (baby, teen, or adult). The three creatures listed are adding a new column named “danger”, which contains the word “extreme!” if the type is “kraken”, or “high” for any other type. Stylized text reads “dplyr::case_when() - IF ELSE...but you love it? An example of code is shown: mutate(danger = case_when(type == “kraken” ~ “extreme!”, TRUE ~ “high”). Learn more about case_when." width="80%" fig-align="center"}</span>
-<span id="cb38-151"><a href="#cb38-151" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-152"><a href="#cb38-152" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span>Create summary table for the dose response columns (those finishing with a capital "D") to have a look at their distribution.</span>
-<span id="cb38-153"><a href="#cb38-153" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-154"><a href="#cb38-154" aria-hidden="true" tabindex="-1"></a>::: {.callout-tip title="Need a little bit of help?" collapse="true"}</span>
-<span id="cb38-155"><a href="#cb38-155" aria-hidden="true" tabindex="-1"></a>Use <span class="in">`gtsummary::tbl_summary()`</span>.</span>
-<span id="cb38-156"><a href="#cb38-156" aria-hidden="true" tabindex="-1"></a>:::</span>
-<span id="cb38-157"><a href="#cb38-157" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-158"><a href="#cb38-158" aria-hidden="true" tabindex="-1"></a><span class="in">```{r }</span></span>
-<span id="cb38-159"><a href="#cb38-159" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-160"><a href="#cb38-160" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: check_dose_cols</span></span>
-<span id="cb38-161"><a href="#cb38-161" aria-hidden="true" tabindex="-1"></a><span class="co">#| tbl-cap: no caption</span></span>
-<span id="cb38-162"><a href="#cb38-162" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-163"><a href="#cb38-163" aria-hidden="true" tabindex="-1"></a><span class="co"># Create summary table for dose response columns: </span></span>
-<span id="cb38-164"><a href="#cb38-164" aria-hidden="true" tabindex="-1"></a>drtable <span class="ot">&lt;-</span> copdata <span class="sc">%&gt;%</span>       </span>
-<span id="cb38-165"><a href="#cb38-165" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Select all the columns with column names that end in upper case 'D':   </span></span>
-<span id="cb38-166"><a href="#cb38-166" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(<span class="fu">ends_with</span>(<span class="st">"D"</span>, <span class="at">ignore.case =</span> <span class="cn">FALSE</span>)) <span class="sc">%&gt;%</span>       </span>
-<span id="cb38-167"><a href="#cb38-167" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Create the summary table, excluding missing values:   </span></span>
-<span id="cb38-168"><a href="#cb38-168" aria-hidden="true" tabindex="-1"></a>  gtsummary<span class="sc">::</span><span class="fu">tbl_summary</span>(<span class="at">missing =</span> <span class="st">"no"</span>) </span>
-<span id="cb38-169"><a href="#cb38-169" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-170"><a href="#cb38-170" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Print the summary table: </span></span>
-<span id="cb38-171"><a href="#cb38-171" aria-hidden="true" tabindex="-1"></a>drtable</span>
-<span id="cb38-172"><a href="#cb38-172" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb38-173"><a href="#cb38-173" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-174"><a href="#cb38-174" aria-hidden="true" tabindex="-1"></a><span class="fu">## 3.4. Modify variables format</span></span>
-<span id="cb38-175"><a href="#cb38-175" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-176"><a href="#cb38-176" aria-hidden="true" tabindex="-1"></a>There are some "column types" you would like to modify because either R needs some specific types of data when working with certain variables, or because it will be easier for you to visualise and interpret the data. For example, the variable "group" is encoded as 0 and 1, when you create a table later on, you don't want to see 0's and 1's because that doesn't mean anything. It is better to change this variable to a factor, with explicit labels, so they actually mean something when they come out in your table.</span>
-<span id="cb38-177"><a href="#cb38-177" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-178"><a href="#cb38-178" aria-hidden="true" tabindex="-1"></a><span class="al">![](https://cdn.myportfolio.com/45214904-6a61-4e23-98d6-b140f8654a40/bd4ae264-ae51-4d18-bd60-7a058ab42fba_rw_1920.png?h=a3757d1f46f418c59e7e7946d026344e)</span>{fig-alt="Cartoon of cute fuzzy monsters dressed up as different X-men characters, working together to add a new column to an existing data frame. Stylized title text reads “dplyr::mutate - add columns, keep existing.” Learn more about dplyr::mutate." width="50%" fig-align="center"}</span>
-<span id="cb38-179"><a href="#cb38-179" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-180"><a href="#cb38-180" aria-hidden="true" tabindex="-1"></a>You can use <span class="in">`mutate`</span> to modify the type of the following variables:</span>
-<span id="cb38-181"><a href="#cb38-181" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-182"><a href="#cb38-182" aria-hidden="true" tabindex="-1"></a>+-------------------------------------------------------------------------------------------------------------+---------------------+-----------------+--------------------------------------------------------------------------------------+</span>
-<span id="cb38-183"><a href="#cb38-183" aria-hidden="true" tabindex="-1"></a>| Variable name                                                                                               | Original            | Desired         | Hint                                                                                 |</span>
-<span id="cb38-184"><a href="#cb38-184" aria-hidden="true" tabindex="-1"></a>+=============================================================================================================+=====================+=================+======================================================================================+</span>
-<span id="cb38-185"><a href="#cb38-185" aria-hidden="true" tabindex="-1"></a>| sex                                                                                                         | character           | factor          | mutate(), as.factor()                                                                |</span>
-<span id="cb38-186"><a href="#cb38-186" aria-hidden="true" tabindex="-1"></a>+-------------------------------------------------------------------------------------------------------------+---------------------+-----------------+--------------------------------------------------------------------------------------+</span>
-<span id="cb38-187"><a href="#cb38-187" aria-hidden="true" tabindex="-1"></a>| class                                                                                                       | integer             | factor          | mutate(), as.factor()                                                                |</span>
-<span id="cb38-188"><a href="#cb38-188" aria-hidden="true" tabindex="-1"></a>+-------------------------------------------------------------------------------------------------------------+---------------------+-----------------+--------------------------------------------------------------------------------------+</span>
-<span id="cb38-189"><a href="#cb38-189" aria-hidden="true" tabindex="-1"></a>| All the clinical symptom variables                                                                          | integer             | logical         | mutate(across()), as.logical()                                                       |</span>
-<span id="cb38-190"><a href="#cb38-190" aria-hidden="true" tabindex="-1"></a>+-------------------------------------------------------------------------------------------------------------+---------------------+-----------------+--------------------------------------------------------------------------------------+</span>
-<span id="cb38-191"><a href="#cb38-191" aria-hidden="true" tabindex="-1"></a>| All the food variables representing the amount of specific foods eaten (those finishing with a capital "D") | integer             | factor          | mutate(across()), as.factor()                                                        |</span>
-<span id="cb38-192"><a href="#cb38-192" aria-hidden="true" tabindex="-1"></a>+-------------------------------------------------------------------------------------------------------------+---------------------+-----------------+--------------------------------------------------------------------------------------+</span>
-<span id="cb38-193"><a href="#cb38-193" aria-hidden="true" tabindex="-1"></a>| dayonset                                                                                                    | character           | date            | lubridate::dmy()                                                                     |</span>
-<span id="cb38-194"><a href="#cb38-194" aria-hidden="true" tabindex="-1"></a>+-------------------------------------------------------------------------------------------------------------+---------------------+-----------------+--------------------------------------------------------------------------------------+</span>
-<span id="cb38-195"><a href="#cb38-195" aria-hidden="true" tabindex="-1"></a>| starthour and dayonset together                                                                             | integer (starthour) | POSIXct, POSIXt | lubridate::ymd_h() could have inside stringr::str_glue() with dayonset and starthour |</span>
-<span id="cb38-196"><a href="#cb38-196" aria-hidden="true" tabindex="-1"></a>|                                                                                                             |                     |                 |                                                                                      |</span>
-<span id="cb38-197"><a href="#cb38-197" aria-hidden="true" tabindex="-1"></a>|                                                                                                             | date (dayonset)     |                 |                                                                                      |</span>
-<span id="cb38-198"><a href="#cb38-198" aria-hidden="true" tabindex="-1"></a>+-------------------------------------------------------------------------------------------------------------+---------------------+-----------------+--------------------------------------------------------------------------------------+</span>
-<span id="cb38-199"><a href="#cb38-199" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-200"><a href="#cb38-200" aria-hidden="true" tabindex="-1"></a>: Table 1: Variable types to modify</span>
-<span id="cb38-201"><a href="#cb38-201" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-202"><a href="#cb38-202" aria-hidden="true" tabindex="-1"></a><span class="fu">#### Sex, group and class</span></span>
-<span id="cb38-203"><a href="#cb38-203" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-204"><a href="#cb38-204" aria-hidden="true" tabindex="-1"></a>Let's start transforming one-by-one the first two variables in the table: <span class="in">`sex`</span>, and <span class="in">`class`</span>.</span>
-<span id="cb38-205"><a href="#cb38-205" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-206"><a href="#cb38-206" aria-hidden="true" tabindex="-1"></a><span class="in">```{r, mutate_simple}</span></span>
-<span id="cb38-207"><a href="#cb38-207" aria-hidden="true" tabindex="-1"></a>copdata <span class="ot">&lt;-</span> copdata <span class="sc">%&gt;%</span> </span>
-<span id="cb38-208"><a href="#cb38-208" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">mutate</span>(</span>
-<span id="cb38-209"><a href="#cb38-209" aria-hidden="true" tabindex="-1"></a>    <span class="at">sex =</span> <span class="fu">as.factor</span>(sex),</span>
-<span id="cb38-210"><a href="#cb38-210" aria-hidden="true" tabindex="-1"></a>    <span class="at">class =</span> <span class="fu">as.factor</span>(class))</span>
-<span id="cb38-211"><a href="#cb38-211" aria-hidden="true" tabindex="-1"></a> </span>
-<span id="cb38-212"><a href="#cb38-212" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb38-213"><a href="#cb38-213" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-214"><a href="#cb38-214" aria-hidden="true" tabindex="-1"></a><span class="fu">#### Symptoms and Food variables</span></span>
-<span id="cb38-215"><a href="#cb38-215" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-216"><a href="#cb38-216" aria-hidden="true" tabindex="-1"></a>For these variables, we are going to show you a couple of different ways to carry out the same variable type transformation in a *set* of variables, so you don't need to do one variable at a time. We are showing you these ways so you see alternative ways to do the same thing.</span>
-<span id="cb38-217"><a href="#cb38-217" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-218"><a href="#cb38-218" aria-hidden="true" tabindex="-1"></a><span class="ss">1.  </span>For the variables that are clinical symptoms, we will list them one by one and show you the use of <span class="in">`mutate(across( ))`</span>.</span>
-<span id="cb38-219"><a href="#cb38-219" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-220"><a href="#cb38-220" aria-hidden="true" tabindex="-1"></a><span class="in">```{r, mutate_cs}</span></span>
-<span id="cb38-221"><a href="#cb38-221" aria-hidden="true" tabindex="-1"></a>copdata <span class="ot">&lt;-</span> copdata <span class="sc">%&gt;%</span> </span>
-<span id="cb38-222"><a href="#cb38-222" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">mutate</span>(</span>
-<span id="cb38-223"><a href="#cb38-223" aria-hidden="true" tabindex="-1"></a>    <span class="co"># clinical symptoms</span></span>
-<span id="cb38-224"><a href="#cb38-224" aria-hidden="true" tabindex="-1"></a>    <span class="fu">across</span>(<span class="at">.cols =</span> <span class="fu">c</span>(diarrhoea, bloody, vomiting,</span>
-<span id="cb38-225"><a href="#cb38-225" aria-hidden="true" tabindex="-1"></a>             abdo, nausea, fever,headache, jointpain), </span>
-<span id="cb38-226"><a href="#cb38-226" aria-hidden="true" tabindex="-1"></a>           <span class="at">.fns =</span> <span class="sc">~</span> <span class="fu">as.logical</span>(.)</span>
-<span id="cb38-227"><a href="#cb38-227" aria-hidden="true" tabindex="-1"></a>           )</span>
-<span id="cb38-228"><a href="#cb38-228" aria-hidden="true" tabindex="-1"></a>    )</span>
-<span id="cb38-229"><a href="#cb38-229" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb38-230"><a href="#cb38-230" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-231"><a href="#cb38-231" aria-hidden="true" tabindex="-1"></a><span class="ss">2.  </span>For the variables that are food doses, we will show you how to first create a vector of names, following by using <span class="in">`mutate(across(all_of( )))`</span> on this vector.</span>
-<span id="cb38-232"><a href="#cb38-232" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-233"><a href="#cb38-233" aria-hidden="true" tabindex="-1"></a><span class="in">```{r, mutate_food}</span></span>
-<span id="cb38-234"><a href="#cb38-234" aria-hidden="true" tabindex="-1"></a><span class="co"># Create a vector with all the food variables representing the amount of specific foods items eaten (those finishing with a capital "D")</span></span>
-<span id="cb38-235"><a href="#cb38-235" aria-hidden="true" tabindex="-1"></a><span class="co"># One way of doing it:</span></span>
-<span id="cb38-236"><a href="#cb38-236" aria-hidden="true" tabindex="-1"></a>food_dose <span class="ot">&lt;-</span> copdata <span class="sc">%&gt;%</span> </span>
-<span id="cb38-237"><a href="#cb38-237" aria-hidden="true" tabindex="-1"></a>    dplyr<span class="sc">::</span><span class="fu">select</span>(</span>
-<span id="cb38-238"><a href="#cb38-238" aria-hidden="true" tabindex="-1"></a>      <span class="fu">ends_with</span>(<span class="st">"D"</span>, <span class="at">ignore.case =</span> <span class="cn">FALSE</span>)) <span class="sc">%&gt;%</span> </span>
-<span id="cb38-239"><a href="#cb38-239" aria-hidden="true" tabindex="-1"></a>    <span class="fu">names</span>()</span>
-<span id="cb38-240"><a href="#cb38-240" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-241"><a href="#cb38-241" aria-hidden="true" tabindex="-1"></a><span class="co"># Another way of doing it:</span></span>
-<span id="cb38-242"><a href="#cb38-242" aria-hidden="true" tabindex="-1"></a><span class="co"># food_dose &lt;- c("fetaD", "sardinesD", "eggplantD", "moussakaD", </span></span>
-<span id="cb38-243"><a href="#cb38-243" aria-hidden="true" tabindex="-1"></a><span class="co">#                 "orzoD", "greeksalD", "dessertD", "breadD", </span></span>
-<span id="cb38-244"><a href="#cb38-244" aria-hidden="true" tabindex="-1"></a><span class="co">#                 "champagneD", "beerD", "redwineD", "whitewineD")</span></span>
-<span id="cb38-245"><a href="#cb38-245" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-246"><a href="#cb38-246" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-247"><a href="#cb38-247" aria-hidden="true" tabindex="-1"></a>copdata <span class="ot">&lt;-</span> copdata <span class="sc">%&gt;%</span> </span>
-<span id="cb38-248"><a href="#cb38-248" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">mutate</span>(</span>
-<span id="cb38-249"><a href="#cb38-249" aria-hidden="true" tabindex="-1"></a>    <span class="co"># food dose variables</span></span>
-<span id="cb38-250"><a href="#cb38-250" aria-hidden="true" tabindex="-1"></a>    <span class="fu">across</span>(<span class="at">.cols =</span> <span class="fu">all_of</span>(food_dose), </span>
-<span id="cb38-251"><a href="#cb38-251" aria-hidden="true" tabindex="-1"></a>           <span class="at">.fns =</span> <span class="sc">~</span><span class="fu">as.factor</span>(.))) </span>
-<span id="cb38-252"><a href="#cb38-252" aria-hidden="true" tabindex="-1"></a>  </span>
-<span id="cb38-253"><a href="#cb38-253" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb38-254"><a href="#cb38-254" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-255"><a href="#cb38-255" aria-hidden="true" tabindex="-1"></a>::: {.callout-note title="Note" collapse="true"}</span>
-<span id="cb38-256"><a href="#cb38-256" aria-hidden="true" tabindex="-1"></a>The tilde (<span class="in">`~`</span>) above is used to apply the transformation <span class="in">`as.logical(.)`</span> to each selected column, which in our case is either all columns included in <span class="in">`food_items`</span> and <span class="in">`food_dose.`</span></span>
-<span id="cb38-257"><a href="#cb38-257" aria-hidden="true" tabindex="-1"></a>:::</span>
-<span id="cb38-258"><a href="#cb38-258" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-259"><a href="#cb38-259" aria-hidden="true" tabindex="-1"></a><span class="fu">#### Date and time variables</span></span>
-<span id="cb38-260"><a href="#cb38-260" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-261"><a href="#cb38-261" aria-hidden="true" tabindex="-1"></a>::: {.callout-warning title="Let's stop and... think!" collapse="true"}</span>
-<span id="cb38-262"><a href="#cb38-262" aria-hidden="true" tabindex="-1"></a>Why may you want to transform the variable starthour to date-time?</span>
-<span id="cb38-263"><a href="#cb38-263" aria-hidden="true" tabindex="-1"></a>:::</span>
-<span id="cb38-264"><a href="#cb38-264" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-265"><a href="#cb38-265" aria-hidden="true" tabindex="-1"></a><span class="al">![](https://cdn.myportfolio.com/45214904-6a61-4e23-98d6-b140f8654a40/3017cf57-d7d9-4f73-9b66-f2be0ebf2489_rw_1920.png?h=2cdd179bc087c56b43f8aa6ea849ca1a)</span>{fig-alt="Fuzzy cartoon monsters in construction hats sorting a pile of “Y” “M” and “D”s into separate piles labeled “Years”, “Months” and “Days”. Title text reads “Lubridate: wrangle times and dates!” Learn more about date-time wrangling with lubridate." width="50%" fig-align="center"}</span>
-<span id="cb38-266"><a href="#cb38-266" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-267"><a href="#cb38-267" aria-hidden="true" tabindex="-1"></a>You can use <span class="in">`lubridate::dmy()`</span> to mutate the <span class="in">`dayonset`</span> variable into a date variable. Note that we are using the function <span class="in">`dmy()`</span> because dates are formatted as day, then month (abbreviated character string), then year (i.e. "12nov2006").</span>
-<span id="cb38-268"><a href="#cb38-268" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-269"><a href="#cb38-269" aria-hidden="true" tabindex="-1"></a><span class="in">```{r correct_date_format}</span></span>
-<span id="cb38-270"><a href="#cb38-270" aria-hidden="true" tabindex="-1"></a><span class="co"># Have a look at how the data is stored</span></span>
-<span id="cb38-271"><a href="#cb38-271" aria-hidden="true" tabindex="-1"></a><span class="fu">head</span>(copdata<span class="sc">$</span>dayonset)</span>
-<span id="cb38-272"><a href="#cb38-272" aria-hidden="true" tabindex="-1"></a><span class="fu">class</span>(copdata<span class="sc">$</span>dayonset)</span>
-<span id="cb38-273"><a href="#cb38-273" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-274"><a href="#cb38-274" aria-hidden="true" tabindex="-1"></a><span class="co"># Update copdata:</span></span>
-<span id="cb38-275"><a href="#cb38-275" aria-hidden="true" tabindex="-1"></a>copdata <span class="ot">&lt;-</span> copdata <span class="sc">%&gt;%</span> </span>
-<span id="cb38-276"><a href="#cb38-276" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Change column to date class:</span></span>
-<span id="cb38-277"><a href="#cb38-277" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">mutate</span>(</span>
-<span id="cb38-278"><a href="#cb38-278" aria-hidden="true" tabindex="-1"></a>    <span class="at">dayonset =</span> lubridate<span class="sc">::</span><span class="fu">dmy</span>(dayonset))</span>
-<span id="cb38-279"><a href="#cb38-279" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-280"><a href="#cb38-280" aria-hidden="true" tabindex="-1"></a><span class="co"># Check class of updated column:</span></span>
-<span id="cb38-281"><a href="#cb38-281" aria-hidden="true" tabindex="-1"></a><span class="fu">class</span>(copdata<span class="sc">$</span>dayonset)</span>
-<span id="cb38-282"><a href="#cb38-282" aria-hidden="true" tabindex="-1"></a><span class="co"># Have a look at your data now:</span></span>
-<span id="cb38-283"><a href="#cb38-283" aria-hidden="true" tabindex="-1"></a><span class="fu">head</span>(copdata<span class="sc">$</span>dayonset)</span>
-<span id="cb38-284"><a href="#cb38-284" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb38-285"><a href="#cb38-285" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-286"><a href="#cb38-286" aria-hidden="true" tabindex="-1"></a>Having a variable that defines "time" in an outbreak investigation can be very useful when creating a case definition. An hour of the day, without a date associated with it doesn't help you much, thus, you should merge together day and time of onset of symptoms into a single variable. Moreover, you will be using this combined variable later on to estimate an incubation period and create your epicurve. We can combine these two variables by using the <span class="in">`lubridate::ymd_h()`</span> function.</span>
-<span id="cb38-287"><a href="#cb38-287" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-288"><a href="#cb38-288" aria-hidden="true" tabindex="-1"></a>Before we proceed, it would be wise to check if any respondents have a value for <span class="in">`dayonset`</span> but not <span class="in">`starthour`</span>, or vice versa. The <span class="in">`lubridate`</span> date-time conversion functions do not have an explicit argument for dealing with missing values, but the <span class="in">`truncated = …`</span> argument can help prevent spurious date-times being derived from a date-time combination where one value is missing.</span>
-<span id="cb38-289"><a href="#cb38-289" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-290"><a href="#cb38-290" aria-hidden="true" tabindex="-1"></a>We can check if we have any missing values by cross-tabulating <span class="in">`starthour`</span> with <span class="in">`dayonset`</span>:</span>
-<span id="cb38-291"><a href="#cb38-291" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-292"><a href="#cb38-292" aria-hidden="true" tabindex="-1"></a><span class="in">```{r crosstab_dayonset_starthour}</span></span>
-<span id="cb38-293"><a href="#cb38-293" aria-hidden="true" tabindex="-1"></a><span class="co"># Cross-tabulate dayonset with starthour:</span></span>
-<span id="cb38-294"><a href="#cb38-294" aria-hidden="true" tabindex="-1"></a>janitor<span class="sc">::</span><span class="fu">tabyl</span>(<span class="at">dat =</span> copdata, </span>
-<span id="cb38-295"><a href="#cb38-295" aria-hidden="true" tabindex="-1"></a>               <span class="at">var1 =</span> starthour, </span>
-<span id="cb38-296"><a href="#cb38-296" aria-hidden="true" tabindex="-1"></a>               <span class="at">var2 =</span> dayonset)</span>
-<span id="cb38-297"><a href="#cb38-297" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb38-298"><a href="#cb38-298" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-299"><a href="#cb38-299" aria-hidden="true" tabindex="-1"></a>This shows us that there are two respondents who had an onset date, but are missing onset time (<span class="in">`starthour`</span>). Since <span class="in">`starthour`</span> is represented by 1 - 2 digits, we can specify that we want <span class="in">`lubridate`</span> to also parse date-time combinations that are truncated by up to two digits:</span>
-<span id="cb38-300"><a href="#cb38-300" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-301"><a href="#cb38-301" aria-hidden="true" tabindex="-1"></a><span class="in">```{r combine_date_time}</span></span>
-<span id="cb38-302"><a href="#cb38-302" aria-hidden="true" tabindex="-1"></a>copdata <span class="ot">&lt;-</span> copdata <span class="sc">%&gt;%</span> </span>
-<span id="cb38-303"><a href="#cb38-303" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Combine dayonset and starthour in a new date time variable:</span></span>
-<span id="cb38-304"><a href="#cb38-304" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">onset_datetime =</span> </span>
-<span id="cb38-305"><a href="#cb38-305" aria-hidden="true" tabindex="-1"></a>           lubridate<span class="sc">::</span><span class="fu">ymd_h</span>(</span>
-<span id="cb38-306"><a href="#cb38-306" aria-hidden="true" tabindex="-1"></a>             <span class="fu">str_glue</span>(<span class="st">"{dayonset}, {starthour}"</span>), </span>
-<span id="cb38-307"><a href="#cb38-307" aria-hidden="true" tabindex="-1"></a>                                           <span class="co"># Deal with missing starthour:</span></span>
-<span id="cb38-308"><a href="#cb38-308" aria-hidden="true" tabindex="-1"></a>                                           <span class="at">truncated =</span> <span class="dv">2</span>))</span>
-<span id="cb38-309"><a href="#cb38-309" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb38-310"><a href="#cb38-310" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-311"><a href="#cb38-311" aria-hidden="true" tabindex="-1"></a>Note that we needed to use <span class="in">`str_glue()`</span> to concatenate <span class="in">`dayonset`</span> and <span class="in">`starthour`</span> together before we could convert the variable to a date-time object. This is because the <span class="in">`ymd_h()`</span> function expects a single character string, containing both the date and the time, as input.</span>
-<span id="cb38-312"><a href="#cb38-312" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-313"><a href="#cb38-313" aria-hidden="true" tabindex="-1"></a>The argument <span class="in">`truncated = 2`</span> will result in dates with missing <span class="in">`starthour`</span> still being converted to date-time, with the missing time being set to <span class="in">`00:00`</span> (midnight). Whether you want to deal with missing <span class="in">`starthour`</span> in this way or prefer to code these date-times as <span class="in">`NA`</span> will depend on how you want them to be represented in your analysis.</span>
-<span id="cb38-314"><a href="#cb38-314" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-315"><a href="#cb38-315" aria-hidden="true" tabindex="-1"></a>Now we can check that everything in the new combined date-time variable has parsed correctly:</span>
-<span id="cb38-316"><a href="#cb38-316" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-317"><a href="#cb38-317" aria-hidden="true" tabindex="-1"></a><span class="in">```{r check_onset_datetime}</span></span>
-<span id="cb38-318"><a href="#cb38-318" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-319"><a href="#cb38-319" aria-hidden="true" tabindex="-1"></a><span class="fu">head</span>(copdata<span class="sc">$</span>dayonset)</span>
-<span id="cb38-320"><a href="#cb38-320" aria-hidden="true" tabindex="-1"></a><span class="fu">head</span>(copdata<span class="sc">$</span>starthour)</span>
-<span id="cb38-321"><a href="#cb38-321" aria-hidden="true" tabindex="-1"></a><span class="fu">head</span>(copdata<span class="sc">$</span>onset_datetime)</span>
-<span id="cb38-322"><a href="#cb38-322" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb38-323"><a href="#cb38-323" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-324"><a href="#cb38-324" aria-hidden="true" tabindex="-1"></a><span class="fu">## 3.5. Export clean data</span></span>
-<span id="cb38-325"><a href="#cb38-325" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-326"><a href="#cb38-326" aria-hidden="true" tabindex="-1"></a>Save the cleaned data set before proceeding with using your case definition to identify cases in your dataset. Use the .rds format, as it preserves column classes. This ensures you will have only minimal cleaning to do after importing the data into R at the next inject.</span>
-<span id="cb38-327"><a href="#cb38-327" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-328"><a href="#cb38-328" aria-hidden="true" tabindex="-1"></a>Save your clean data as a new file "Copenhagen_clean1_YOURINITIALS.rds", under the data folder of your Rproject.</span>
-<span id="cb38-329"><a href="#cb38-329" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-330"><a href="#cb38-330" aria-hidden="true" tabindex="-1"></a>::: {.callout-tip title="Need a little bit of help?" collapse="true"}</span>
-<span id="cb38-331"><a href="#cb38-331" aria-hidden="true" tabindex="-1"></a>Use <span class="in">`rio::export()`</span>.</span>
-<span id="cb38-332"><a href="#cb38-332" aria-hidden="true" tabindex="-1"></a>:::</span>
-<span id="cb38-333"><a href="#cb38-333" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-334"><a href="#cb38-334" aria-hidden="true" tabindex="-1"></a><span class="in">```{r export_clean_data}</span></span>
-<span id="cb38-335"><a href="#cb38-335" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-336"><a href="#cb38-336" aria-hidden="true" tabindex="-1"></a>rio<span class="sc">::</span><span class="fu">export</span>(<span class="at">x =</span> copdata, </span>
-<span id="cb38-337"><a href="#cb38-337" aria-hidden="true" tabindex="-1"></a>            <span class="at">file =</span> here<span class="sc">::</span><span class="fu">here</span>(<span class="st">"data"</span>, <span class="st">"Copenhagen_clean1_2024.rds"</span>))</span>
-<span id="cb38-338"><a href="#cb38-338" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-339"><a href="#cb38-339" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb38-340"><a href="#cb38-340" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb38-341"><a href="#cb38-341" aria-hidden="true" tabindex="-1"></a><span class="al">![](https://cdn.myportfolio.com/45214904-6a61-4e23-98d6-b140f8654a40/e4a11b1f-f235-461f-ab73-ce6128c59e49_rw_1920.png?h=a34a4173c717bfbc600a8682d419288e)</span>{width="80%" fig-align="center"}</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-</div></div></div></div></div>
-</div> <!-- /content -->
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-  <li><a href="#learning-outcomes" id="toc-learning-outcomes" class="nav-link active" data-scroll-target="#learning-outcomes">1. Learning outcomes</a></li>
-  <li><a href="#storyplot-description" id="toc-storyplot-description" class="nav-link" data-scroll-target="#storyplot-description">2. Story/plot description</a></li>
-  <li><a href="#questionsassignments" id="toc-questionsassignments" class="nav-link" data-scroll-target="#questionsassignments">3. Questions/Assignments</a></li>
-  <li><a href="#install-packages-if-needed-and-load-libraries" id="toc-install-packages-if-needed-and-load-libraries" class="nav-link" data-scroll-target="#install-packages-if-needed-and-load-libraries">3.1 Install packages (if needed) and load libraries</a></li>
-  <li><a href="#import-your-data" id="toc-import-your-data" class="nav-link" data-scroll-target="#import-your-data">3.2 Import your data</a></li>
-  <li><a href="#identify-the-variables-you-need-to-apply-the-case-definition-criteria." id="toc-identify-the-variables-you-need-to-apply-the-case-definition-criteria." class="nav-link" data-scroll-target="#identify-the-variables-you-need-to-apply-the-case-definition-criteria.">3.3 Identify the variables you need to apply the case definition criteria.</a></li>
-  <li><a href="#create-a-new-case-column-to-hold-the-binary-case-definition-variable.-lets-think-about-how-to-do-this-little-by-little" id="toc-create-a-new-case-column-to-hold-the-binary-case-definition-variable.-lets-think-about-how-to-do-this-little-by-little" class="nav-link" data-scroll-target="#create-a-new-case-column-to-hold-the-binary-case-definition-variable.-lets-think-about-how-to-do-this-little-by-little">3.4 Create a new <code>case</code> column to hold the binary case definition variable. Let’s think about how to do this little by little:</a>
-  <ul class="collapse">
-  <li><a href="#a-ate-a-meal-at-the-school-dinner" id="toc-a-ate-a-meal-at-the-school-dinner" class="nav-link" data-scroll-target="#a-ate-a-meal-at-the-school-dinner">a) Ate a meal at the school dinner</a></li>
-  <li><a href="#b-fell-ill-after-the-start-of-the-meal" id="toc-b-fell-ill-after-the-start-of-the-meal" class="nav-link" data-scroll-target="#b-fell-ill-after-the-start-of-the-meal">b) Fell ill after the start of the meal</a></li>
-  <li><a href="#c-fell-ill-within-the-time-period-of-interest" id="toc-c-fell-ill-within-the-time-period-of-interest" class="nav-link" data-scroll-target="#c-fell-ill-within-the-time-period-of-interest">c) Fell ill within the time period of interest</a></li>
-  <li><a href="#d-finally-with-this-information-you-can-create-a-new-case-column-to-hold-the-binary-truefalse-case-definition-variable." id="toc-d-finally-with-this-information-you-can-create-a-new-case-column-to-hold-the-binary-truefalse-case-definition-variable." class="nav-link" data-scroll-target="#d-finally-with-this-information-you-can-create-a-new-case-column-to-hold-the-binary-truefalse-case-definition-variable.">d) Finally, with this information you can create a new <code>case</code> column to hold the binary (<code>TRUE</code>/<code>FALSE</code>) case definition variable.</a></li>
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-<div class="quarto-title-block"><div><h1 class="title">Case Definition</h1><button type="button" class="btn code-tools-button dropdown-toggle" id="quarto-code-tools-menu" data-bs-toggle="dropdown" aria-expanded="false"><i class="bi"></i> Code</button><ul class="dropdown-menu dropdown-menu-end" aria-labelelledby="quarto-code-tools-menu"><li><a id="quarto-show-all-code" class="dropdown-item" href="javascript:void(0)" role="button">Show All Code</a></li><li><a id="quarto-hide-all-code" class="dropdown-item" href="javascript:void(0)" role="button">Hide All Code</a></li><li><hr class="dropdown-divider"></li><li><a id="quarto-view-source" class="dropdown-item" href="javascript:void(0)" role="button">View Source</a></li></ul></div></div>
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-<section id="learning-outcomes" class="level2">
-<h2 class="anchored" data-anchor-id="learning-outcomes">1. Learning outcomes</h2>
-<p>At the end of the session, participants will be able to:</p>
-<ul>
-<li>Apply case definition criteria to a dataset using logical arguments in R</li>
-</ul>
-</section>
-<section id="storyplot-description" class="level2">
-<h2 class="anchored" data-anchor-id="storyplot-description">2. Story/plot description</h2>
-<p>You will now create a new column in the data set to hold the case definition you decided in a previous step during your investigation. You can call this column <code>case</code> and set it to <code>TRUE</code> if the individual meets the case definition criteria and <code>FALSE</code> if not. You will use this column later on for any calculations needed (descriptive statistics, two-by-two tables to compute measures of association, etc.) to figure out the culprit of this outbreak.</p>
-</section>
-<section id="questionsassignments" class="level2">
-<h2 class="anchored" data-anchor-id="questionsassignments">3. Questions/Assignments</h2>
-<p>If you closed your R project, please open it again and load the clean dataset. We strongly recommend you use the <code>Copenhagen_clean1_2024.rds</code> provided by us in EVA. This is just to be sure all needed changes in that file for the code to work have been done (as you may have changed the file in a slightly different way than we have).</p>
-<p>To be sure we all start from the same case definition, let’s agree that a case was defined as a person who:</p>
-<ul class="task-list">
-<li><input type="checkbox" checked="">attended the school dinner on 11 November 2006 (i.e.&nbsp;is on the linelist)</li>
-<li><input type="checkbox" checked="">ate a meal at the school dinner (i.e.&nbsp;was exposed)</li>
-<li><input type="checkbox" checked="">fell ill after the start of the meal</li>
-<li><input type="checkbox" checked="">fell ill within the time period of interest after the school dinner</li>
-<li><input type="checkbox" checked="">suffered from diarrhoea with or without blood, or vomiting</li>
-</ul>
-<p>Non cases (not-ill) were defined as people who:</p>
-<ul class="task-list">
-<li><input type="checkbox" checked="">attended the school dinner on 11 November 2006 (i.e.&nbsp;are on the linelist)</li>
-<li><input type="checkbox" checked="">ate a meal at the school dinner (i.e.&nbsp;were exposed)</li>
-<li><input type="checkbox" checked="">did not fall ill within the time period of interest</li>
-<li><input type="checkbox" checked="">did not develop diarrhoea (with or without blood) or vomiting</li>
-</ul>
-</section>
-<section id="install-packages-if-needed-and-load-libraries" class="level2">
-<h2 class="anchored" data-anchor-id="install-packages-if-needed-and-load-libraries">3.1 Install packages (if needed) and load libraries</h2>
-<div class="cell">
-<details open="">
-<summary>Code</summary>
-<div class="sourceCode cell-code" id="cb1"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Load the required libraries into the current R session:</span></span>
-<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a>pacman<span class="sc">::</span><span class="fu">p_load</span>(rio, </span>
-<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a>               here, </span>
-<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a>               tidyverse, </span>
-<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a>               skimr,</span>
-<span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a>               plyr,</span>
-<span id="cb1-7"><a href="#cb1-7" aria-hidden="true" tabindex="-1"></a>               janitor,</span>
-<span id="cb1-8"><a href="#cb1-8" aria-hidden="true" tabindex="-1"></a>               lubridate,</span>
-<span id="cb1-9"><a href="#cb1-9" aria-hidden="true" tabindex="-1"></a>               gtsummary, </span>
-<span id="cb1-10"><a href="#cb1-10" aria-hidden="true" tabindex="-1"></a>               flextable,</span>
-<span id="cb1-11"><a href="#cb1-11" aria-hidden="true" tabindex="-1"></a>               officer,</span>
-<span id="cb1-12"><a href="#cb1-12" aria-hidden="true" tabindex="-1"></a>               epikit, </span>
-<span id="cb1-13"><a href="#cb1-13" aria-hidden="true" tabindex="-1"></a>               apyramid, </span>
-<span id="cb1-14"><a href="#cb1-14" aria-hidden="true" tabindex="-1"></a>               scales)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-</details>
-</div>
-</section>
-<section id="import-your-data" class="level2">
-<h2 class="anchored" data-anchor-id="import-your-data">3.2 Import your data</h2>
-<div class="cell">
-<details open="">
-<summary>Code</summary>
-<div class="sourceCode cell-code" id="cb2"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Import the clean data set:</span></span>
-<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a>copdata <span class="ot">&lt;-</span> rio<span class="sc">::</span><span class="fu">import</span>(here<span class="sc">::</span><span class="fu">here</span>(<span class="st">"data"</span>, <span class="st">"Copenhagen_clean1_2024.rds"</span>))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-</details>
-</div>
-</section>
-<section id="identify-the-variables-you-need-to-apply-the-case-definition-criteria." class="level2">
-<h2 class="anchored" data-anchor-id="identify-the-variables-you-need-to-apply-the-case-definition-criteria.">3.3 Identify the variables you need to apply the case definition criteria.</h2>
-<div class="callout callout-style-default callout-tip callout-titled" title="Need a little bit of help?">
-<div class="callout-header d-flex align-content-center" data-bs-toggle="collapse" data-bs-target=".callout-1-contents" aria-controls="callout-1" aria-expanded="false" aria-label="Toggle callout">
-<div class="callout-icon-container">
-<i class="callout-icon"></i>
-</div>
-<div class="callout-title-container flex-fill">
-Need a little bit of help?
-</div>
-<div class="callout-btn-toggle d-inline-block border-0 py-1 ps-1 pe-0 float-end"><i class="callout-toggle"></i></div>
-</div>
-<div id="callout-1" class="callout-1-contents callout-collapse collapse">
-<div class="callout-body-container callout-body">
-<p>The variables we need from the dataset to apply the above case definition are: <code>meal</code>, <code>onset_datetime</code>, <code>diarrhoea</code>, <code>bloody</code> and <code>vomiting</code>.</p>
-</div>
-</div>
-</div>
-</section>
-<section id="create-a-new-case-column-to-hold-the-binary-case-definition-variable.-lets-think-about-how-to-do-this-little-by-little" class="level2">
-<h2 class="anchored" data-anchor-id="create-a-new-case-column-to-hold-the-binary-case-definition-variable.-lets-think-about-how-to-do-this-little-by-little">3.4 Create a new <code>case</code> column to hold the binary case definition variable. Let’s think about how to do this little by little:</h2>
-<section id="a-ate-a-meal-at-the-school-dinner" class="level3">
-<h3 class="anchored" data-anchor-id="a-ate-a-meal-at-the-school-dinner">a) Ate a meal at the school dinner</h3>
-<p>You decide to exclude any people from the cohort who didn’t eat at the dinner, because we specifically hypothesised a food item to be the vehicle of infection in this outbreak. Thus, filter your dataset to those who ate a meal: Keep in your dataset only those who ate a meal.</p>
-<div class="callout callout-style-default callout-tip callout-titled" title="Need a little bit of help?">
-<div class="callout-header d-flex align-content-center" data-bs-toggle="collapse" data-bs-target=".callout-2-contents" aria-controls="callout-2" aria-expanded="false" aria-label="Toggle callout">
-<div class="callout-icon-container">
-<i class="callout-icon"></i>
-</div>
-<div class="callout-title-container flex-fill">
-Need a little bit of help?
-</div>
-<div class="callout-btn-toggle d-inline-block border-0 py-1 ps-1 pe-0 float-end"><i class="callout-toggle"></i></div>
-</div>
-<div id="callout-2" class="callout-2-contents callout-collapse collapse">
-<div class="callout-body-container callout-body">
-<p><code>filter</code> by those with <code>meal == TRUE</code>.</p>
-</div>
-</div>
-</div>
-<div class="callout callout-style-default callout-warning callout-titled" title="Let's stop and... think!">
-<div class="callout-header d-flex align-content-center" data-bs-toggle="collapse" data-bs-target=".callout-3-contents" aria-controls="callout-3" aria-expanded="false" aria-label="Toggle callout">
-<div class="callout-icon-container">
-<i class="callout-icon"></i>
-</div>
-<div class="callout-title-container flex-fill">
-Let’s stop and… think!
-</div>
-<div class="callout-btn-toggle d-inline-block border-0 py-1 ps-1 pe-0 float-end"><i class="callout-toggle"></i></div>
-</div>
-<div id="callout-3" class="callout-3-contents callout-collapse collapse">
-<div class="callout-body-container callout-body">
-<p>What are some of the implications this decision may lead to? (excluding any people from the cohort who didn’t eat at the dinner)</p>
-</div>
-</div>
-</div>
-<div class="cell">
-<details open="">
-<summary>Code</summary>
-<div class="sourceCode cell-code" id="cb3"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a>copdata <span class="ot">&lt;-</span> copdata <span class="sc">%&gt;%</span> </span>
-<span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(meal <span class="sc">==</span> <span class="cn">TRUE</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-</details>
-</div>
-<div class="callout callout-style-default callout-note callout-titled" title="Once you've thought about the above, have a look here">
-<div class="callout-header d-flex align-content-center" data-bs-toggle="collapse" data-bs-target=".callout-4-contents" aria-controls="callout-4" aria-expanded="false" aria-label="Toggle callout">
-<div class="callout-icon-container">
-<i class="callout-icon"></i>
-</div>
-<div class="callout-title-container flex-fill">
-Once you’ve thought about the above, have a look here
-</div>
-<div class="callout-btn-toggle d-inline-block border-0 py-1 ps-1 pe-0 float-end"><i class="callout-toggle"></i></div>
-</div>
-<div id="callout-4" class="callout-4-contents callout-collapse collapse">
-<div class="callout-body-container callout-body">
-<p>Seven of the respondents actually said they did not eat the meal, but when it came to the questions about which food items they ate, they provided answers! This issue could have been minimised by:</p>
-<p>- At the survey state, one could adjust the design of an electronic questionnaire to prevent key questions from being skipped. This can come with both pros and cons. Allow fellows to discuss if time allows.</p>
-<p>- Explore your data further, realise this is the case, and recode the <code>meal</code> variable for these individuals as <code>TRUE.</code> =&gt; This would be the way to go, but is not what we did in our example because we tried to keep it simple, and also because it is good to show that you may not always clean the data perfectly, and that has consequences: You can highlight the importance or really explore your data in depth.</p>
-<p>By making the above decision, we may be missing cases and non-cases people, and thus, modifying the final estimate of our measure of association. =&gt; It is very important to know your data, explore it deeply and try to clean it as well as possible. Every step one makes when cleaning the data may have a consequence, and we should be aware of it when making the data cleaning decisions and when interpreting the results.</p>
-</div>
-</div>
-</div>
-</section>
-<section id="b-fell-ill-after-the-start-of-the-meal" class="level3">
-<h3 class="anchored" data-anchor-id="b-fell-ill-after-the-start-of-the-meal">b) Fell ill after the start of the meal</h3>
-<p>We define “fell ill” as any person having had diarrhoea with OR without blood, OR vomiting. To capture this information easily, you will create a new <code>gastrosymptoms</code> variable. This variable will indicate that the person had one OR (“or” is R is achieved by using <code>|</code>) more of the clinical symptoms in your definition.</p>
-<p>Note that we the concept of having eaten a meal is already included as per one of the steps above.</p>
-<div class="callout callout-style-default callout-tip callout-titled" title="Need a little bit of help?">
-<div class="callout-header d-flex align-content-center" data-bs-toggle="collapse" data-bs-target=".callout-5-contents" aria-controls="callout-5" aria-expanded="false" aria-label="Toggle callout">
-<div class="callout-icon-container">
-<i class="callout-icon"></i>
-</div>
-<div class="callout-title-container flex-fill">
-Need a little bit of help?
-</div>
-<div class="callout-btn-toggle d-inline-block border-0 py-1 ps-1 pe-0 float-end"><i class="callout-toggle"></i></div>
-</div>
-<div id="callout-5" class="callout-5-contents callout-collapse collapse">
-<div class="callout-body-container callout-body">
-<p>Create a new <code>gastrosymptoms</code> column in your line list with <code>mutate()</code> and case_when<code>()</code>.</p>
-</div>
-</div>
-</div>
-<div class="callout callout-style-default callout-warning callout-titled" title="Let's stop and... think!">
-<div class="callout-header d-flex align-content-center" data-bs-toggle="collapse" data-bs-target=".callout-6-contents" aria-controls="callout-6" aria-expanded="false" aria-label="Toggle callout">
-<div class="callout-icon-container">
-<i class="callout-icon"></i>
-</div>
-<div class="callout-title-container flex-fill">
-Let’s stop and… think!
-</div>
-<div class="callout-btn-toggle d-inline-block border-0 py-1 ps-1 pe-0 float-end"><i class="callout-toggle"></i></div>
-</div>
-<div id="callout-6" class="callout-6-contents callout-collapse collapse">
-<div class="callout-body-container callout-body">
-<p>What are some of the implications this decision may lead to? (defining “fell ill” as any person who reported having diarrhoea with OR without blood, OR vomiting)</p>
-</div>
-</div>
-</div>
-<div class="callout callout-style-default callout-note callout-titled" title="If you've thought about the above, have a look here">
-<div class="callout-header d-flex align-content-center" data-bs-toggle="collapse" data-bs-target=".callout-7-contents" aria-controls="callout-7" aria-expanded="false" aria-label="Toggle callout">
-<div class="callout-icon-container">
-<i class="callout-icon"></i>
-</div>
-<div class="callout-title-container flex-fill">
-If you’ve thought about the above, have a look here
-</div>
-<div class="callout-btn-toggle d-inline-block border-0 py-1 ps-1 pe-0 float-end"><i class="callout-toggle"></i></div>
-</div>
-<div id="callout-7" class="callout-7-contents callout-collapse collapse">
-<div class="callout-body-container callout-body">
-<p>Having one clinical symptom enough to be considered a potential case at this point may be considered too unspecific (low specificity). For example, a person who ate at the dinner party and developed diarrhoea for other reasons other than food poisoning (say, they recently started on antibiotics known for unbalancing the intestinal flora and causing diarrhoea) could be misclassified as a potential case. (Note we talk about <strong>potential case</strong>, and not <strong>case</strong>; that is because here we are not talking about cases per-se yet, but this decision has implications for when applying the case definition below).</p>
-<p>Moreover, those who did not report clinical symptoms will be defined as non-cases. Thus, we are assuming that these individuals did not develop symptoms because they didn’t report them. The missing values could be due to, for example, them skipping the questions in the questionnaire. Some individuals may be reluctant to report symptoms, due to shame, fear of repercussion, or others. It is important to think ahead, before the interview, about ways to minimise these situations. For example, through questionnaire design, you may impede skipping questions; you could promote trust by using the right interviewers (in some cases this will be someone from the community, in others someone form specific NGOs, someone of a specific race or gender, etc); choose to carry out online questionnaires vs in person (or vice versa, depending on the situation), etc.</p>
-</div>
-</div>
-</div>
-<div class="cell">
-<details open="">
-<summary>Code</summary>
-<div class="sourceCode cell-code" id="cb4"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a>copdata <span class="ot">&lt;-</span> copdata <span class="sc">%&gt;%</span> </span>
-<span id="cb4-2"><a href="#cb4-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">gastrosymptoms =</span> <span class="fu">case_when</span>(</span>
-<span id="cb4-3"><a href="#cb4-3" aria-hidden="true" tabindex="-1"></a>    <span class="co"># Those had diarrhoea...</span></span>
-<span id="cb4-4"><a href="#cb4-4" aria-hidden="true" tabindex="-1"></a>    diarrhoea <span class="sc">==</span> <span class="cn">TRUE</span> <span class="sc">|</span></span>
-<span id="cb4-5"><a href="#cb4-5" aria-hidden="true" tabindex="-1"></a>      <span class="co">#or bloody diarrhoea...</span></span>
-<span id="cb4-6"><a href="#cb4-6" aria-hidden="true" tabindex="-1"></a>    bloody <span class="sc">==</span> <span class="cn">TRUE</span> <span class="sc">|</span></span>
-<span id="cb4-7"><a href="#cb4-7" aria-hidden="true" tabindex="-1"></a>      <span class="co"># or vomiting, are marked as TRUE (fell ill after the meal)</span></span>
-<span id="cb4-8"><a href="#cb4-8" aria-hidden="true" tabindex="-1"></a>    vomiting <span class="sc">==</span> <span class="cn">TRUE</span> <span class="sc">~</span> <span class="cn">TRUE</span>,</span>
-<span id="cb4-9"><a href="#cb4-9" aria-hidden="true" tabindex="-1"></a>    <span class="co"># The rest are FALSE. This includes those who ate a meal but had no symptoms (did not fell ill after the meal)</span></span>
-<span id="cb4-10"><a href="#cb4-10" aria-hidden="true" tabindex="-1"></a>    <span class="at">.default =</span> <span class="cn">FALSE</span>)</span>
-<span id="cb4-11"><a href="#cb4-11" aria-hidden="true" tabindex="-1"></a>    )</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-</details>
-</div>
-</section>
-<section id="c-fell-ill-within-the-time-period-of-interest" class="level3">
-<h3 class="anchored" data-anchor-id="c-fell-ill-within-the-time-period-of-interest">c) Fell ill within the time period of interest</h3>
-<ul>
-<li>Hmmm… what is the time period of interest? You could start calculating the incubation period, which can be defined by calculating the time between exposure (the meal) and onset of symptoms, and then looking at the distribution of these time differences. In this outbreak, incubation periods are easy to calculate, because everyone was exposed at (roughly) the same time and on the same day (eating the meal at the school dinner party).</li>
-<li>Dinner was served at 18:00 roughly for everyone. Create a new <code>meal_datetime</code> variable with this information for all people in your dataset (as per 05 Oct 2024, at 18:00h).</li>
-</ul>
-<div class="cell">
-<details open="">
-<summary>Code</summary>
-<div class="sourceCode cell-code" id="cb5"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Start with copdata:</span></span>
-<span id="cb5-2"><a href="#cb5-2" aria-hidden="true" tabindex="-1"></a>copdata <span class="ot">&lt;-</span> copdata <span class="sc">%&gt;%</span> </span>
-<span id="cb5-3"><a href="#cb5-3" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Create new column for meal date and time:</span></span>
-<span id="cb5-4"><a href="#cb5-4" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">meal_datetime =</span> lubridate<span class="sc">::</span><span class="fu">ymd_hm</span>(<span class="st">"2024-10-05 18:00"</span>))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-</details>
-</div>
-<ul>
-<li>You can calculate the incubation period for each participant by subtracting <code>onset_datetime</code> - <code>meal_datetime.</code></li>
-<li>Then, take the <code>median()</code> of that column to calculate a median incubation period, this will help you start having some hypothesis about the type of pathogen we are dealing with until the lab results come back.</li>
-</ul>
-<div class="callout callout-style-default callout-tip callout-titled" title="Need a little bit of help?">
-<div class="callout-header d-flex align-content-center" data-bs-toggle="collapse" data-bs-target=".callout-8-contents" aria-controls="callout-8" aria-expanded="false" aria-label="Toggle callout">
-<div class="callout-icon-container">
-<i class="callout-icon"></i>
-</div>
-<div class="callout-title-container flex-fill">
-Need a little bit of help?
-</div>
-<div class="callout-btn-toggle d-inline-block border-0 py-1 ps-1 pe-0 float-end"><i class="callout-toggle"></i></div>
-</div>
-<div id="callout-8" class="callout-8-contents callout-collapse collapse">
-<div class="callout-body-container callout-body">
-<p>Be aware of the type of variable <code>incubation</code> is (check <code>class()</code>), as well as of missing values<code>NA</code>.</p>
-</div>
-</div>
-</div>
-<div class="callout callout-style-default callout-note callout-titled" title="Note">
-<div class="callout-header d-flex align-content-center" data-bs-toggle="collapse" data-bs-target=".callout-9-contents" aria-controls="callout-9" aria-expanded="false" aria-label="Toggle callout">
-<div class="callout-icon-container">
-<i class="callout-icon"></i>
-</div>
-<div class="callout-title-container flex-fill">
-Note
-</div>
-<div class="callout-btn-toggle d-inline-block border-0 py-1 ps-1 pe-0 float-end"><i class="callout-toggle"></i></div>
-</div>
-<div id="callout-9" class="callout-9-contents callout-collapse collapse">
-<div class="callout-body-container callout-body">
-<p>The <strong>incubation period</strong> of a disease is the time from an exposure resulting in infection until the onset of disease. Because infection usually occurs immediately after exposure, the incubation period is generally the duration of the period from the onset of infection to the onset of disease – Rothman, Greenland, Lash (2017): Modern Epidemiology, 3rd edition</p>
-</div>
-</div>
-</div>
-<div class="cell">
-<details open="">
-<summary>Code</summary>
-<div class="sourceCode cell-code" id="cb6"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a>copdata <span class="ot">&lt;-</span> copdata <span class="sc">%&gt;%</span> </span>
-<span id="cb6-2"><a href="#cb6-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">incubation =</span> onset_datetime <span class="sc">-</span> meal_datetime,</span>
-<span id="cb6-3"><a href="#cb6-3" aria-hidden="true" tabindex="-1"></a>         <span class="at">incubation =</span> <span class="fu">as.numeric</span> (incubation))</span>
-<span id="cb6-4"><a href="#cb6-4" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb6-5"><a href="#cb6-5" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb6-6"><a href="#cb6-6" aria-hidden="true" tabindex="-1"></a><span class="fu">median</span>(<span class="fu">as.numeric</span>(copdata<span class="sc">$</span>incubation), <span class="at">na.rm =</span> <span class="cn">TRUE</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-</details>
-<div class="cell-output cell-output-stdout">
-<pre><code>[1] 15</code></pre>
-</div>
-</div>
-<div class="callout callout-style-default callout-caution callout-titled" title="Now we are messing up with your brain...">
-<div class="callout-header d-flex align-content-center" data-bs-toggle="collapse" data-bs-target=".callout-10-contents" aria-controls="callout-10" aria-expanded="false" aria-label="Toggle callout">
-<div class="callout-icon-container">
-<i class="callout-icon"></i>
-</div>
-<div class="callout-title-container flex-fill">
-Now we are messing up with your brain…
-</div>
-<div class="callout-btn-toggle d-inline-block border-0 py-1 ps-1 pe-0 float-end"><i class="callout-toggle"></i></div>
-</div>
-<div id="callout-10" class="callout-10-contents callout-collapse collapse">
-<div class="callout-body-container callout-body">
-<p>If you pay attention, there were two individuals who don’t have a time recorded, only a date, the day of the dinner. These people probably got sick the same night they had the dinner, but the exact time was not recoded. They way we’ve managed the data will insert “00:00” in a missing value of a time. This means that the 2 people that got sick the day of the dinner have been recorded sick <em>before</em> the dinner (in the early morning of the day of the dinner). The two people have a negative incubation period! This should not happen. If you feel the force is with you, you can modify this and fix the error (if you do this, your results will be a bit different than your colleagues, but similar). We will continue with this “erroneous” data, for easiness. But note that in real life, as soon as you realise there is a coding error like this, you would have to go back to your cleaning code and fix the error! This may happen a couple of times during your outbreak investigation, and is totally normal. Cleaning data is not an easy job!</p>
-</div>
-</div>
-</div>
-<p>We see that the median incubation time is 15 hours. This is useful information, as incubation periods tend to be relatively pathogen-specific.</p>
-<p>Based on this, say you define and limit the maximum incubation period to 48 hours after the meal, as the data points to a fast-acting bacterial toxin or a virus. That is, dinner participants should have developed (<code>onset_datetime</code>) at least one symptom (<code>diarrhoea</code>, <code>bloody</code> or <code>vomiting</code>) 48h after <code>meal_datetime</code> to become a <code>case.</code></p>
-<div class="callout callout-style-default callout-warning callout-titled" title="Let's stop and... think!">
-<div class="callout-header d-flex align-content-center" data-bs-toggle="collapse" data-bs-target=".callout-11-contents" aria-controls="callout-11" aria-expanded="false" aria-label="Toggle callout">
-<div class="callout-icon-container">
-<i class="callout-icon"></i>
-</div>
-<div class="callout-title-container flex-fill">
-Let’s stop and… think!
-</div>
-<div class="callout-btn-toggle d-inline-block border-0 py-1 ps-1 pe-0 float-end"><i class="callout-toggle"></i></div>
-</div>
-<div id="callout-11" class="callout-11-contents callout-collapse collapse">
-<div class="callout-body-container callout-body">
-<p>What are some of the implications this decision may lead to? (implications of this case definition)</p>
-</div>
-</div>
-</div>
-<div class="callout callout-style-default callout-note callout-titled" title="If you've thought about the above, have a look here">
-<div class="callout-header d-flex align-content-center" data-bs-toggle="collapse" data-bs-target=".callout-12-contents" aria-controls="callout-12" aria-expanded="false" aria-label="Toggle callout">
-<div class="callout-icon-container">
-<i class="callout-icon"></i>
-</div>
-<div class="callout-title-container flex-fill">
-If you’ve thought about the above, have a look here
-</div>
-<div class="callout-btn-toggle d-inline-block border-0 py-1 ps-1 pe-0 float-end"><i class="callout-toggle"></i></div>
-</div>
-<div id="callout-12" class="callout-12-contents callout-collapse collapse">
-<div class="callout-body-container callout-body">
-<p>All those not developing at least one symptom (diarrhoea with OR without blood, OR vomiting) 48h after the dinner are considered non-cases. This could (depending on how you decide to analyse your data) include those who had no symptoms at all, those who have missing data on the <code>onset_datetime</code> variable, and/or those who had symptoms before eating the meal. <em>This is a reminder that you need to be both careful and aware of the implications of your data analysis decisions.</em> If a person had clinical symptoms before eating the meal, they are considered as not-cases. However, it could be that a person had symptoms before the meal, and yet, still got infected by the pathogen when eating their meal (bad luck, we know…). According to this definition, we would be missing that case.</p>
-</div>
-</div>
-</div>
-</section>
-<section id="d-finally-with-this-information-you-can-create-a-new-case-column-to-hold-the-binary-truefalse-case-definition-variable." class="level3">
-<h3 class="anchored" data-anchor-id="d-finally-with-this-information-you-can-create-a-new-case-column-to-hold-the-binary-truefalse-case-definition-variable.">d) Finally, with this information you can create a new <code>case</code> column to hold the binary (<code>TRUE</code>/<code>FALSE</code>) case definition variable.</h3>
-<div class="cell">
-<details open="">
-<summary>Code</summary>
-<div class="sourceCode cell-code" id="cb8"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a>copdata <span class="ot">&lt;-</span> copdata <span class="sc">%&gt;%</span> </span>
-<span id="cb8-2"><a href="#cb8-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">case =</span> <span class="fu">case_when</span>(</span>
-<span id="cb8-3"><a href="#cb8-3" aria-hidden="true" tabindex="-1"></a>    <span class="co"># Those who had symptoms &lt;48h from the meal are cases (TRUE)</span></span>
-<span id="cb8-4"><a href="#cb8-4" aria-hidden="true" tabindex="-1"></a>    gastrosymptoms <span class="sc">==</span> <span class="cn">TRUE</span> <span class="sc">&amp;</span> </span>
-<span id="cb8-5"><a href="#cb8-5" aria-hidden="true" tabindex="-1"></a>      onset_datetime <span class="sc">&gt;=</span> meal_datetime <span class="sc">&amp;</span></span>
-<span id="cb8-6"><a href="#cb8-6" aria-hidden="true" tabindex="-1"></a>      onset_datetime <span class="sc">&lt;=</span> (meal_datetime <span class="sc">+</span> <span class="fu">days</span>(<span class="dv">2</span>)) <span class="sc">~</span> <span class="cn">TRUE</span>,</span>
-<span id="cb8-7"><a href="#cb8-7" aria-hidden="true" tabindex="-1"></a>    <span class="co"># Those who had symptoms &gt;48h from the meal are non-cases (FALSE)</span></span>
-<span id="cb8-8"><a href="#cb8-8" aria-hidden="true" tabindex="-1"></a>    gastrosymptoms <span class="sc">==</span> <span class="cn">TRUE</span> <span class="sc">&amp;</span> </span>
-<span id="cb8-9"><a href="#cb8-9" aria-hidden="true" tabindex="-1"></a>      onset_datetime <span class="sc">&gt;</span> (meal_datetime <span class="sc">+</span> <span class="fu">days</span>(<span class="dv">2</span>)) <span class="sc">~</span> <span class="cn">FALSE</span>,</span>
-<span id="cb8-10"><a href="#cb8-10" aria-hidden="true" tabindex="-1"></a>    <span class="co"># The rest are considered non-cases. Including, those who had no symptoms at all, who have missing data on the onset_datetime variable, or who had symptoms before eating the meal </span></span>
-<span id="cb8-11"><a href="#cb8-11" aria-hidden="true" tabindex="-1"></a>    <span class="at">.default =</span> <span class="cn">FALSE</span>)</span>
-<span id="cb8-12"><a href="#cb8-12" aria-hidden="true" tabindex="-1"></a>  )</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-</details>
-</div>
-<p>Note that we may be incurring in misclassification bias with the code above. The last section indicates that if a person had clinical symptoms before eating the meal, they are considered as non-cases. However, it could be that a person had symptoms before the meal, and yet, still got infected by the pathogen when eating their meal (bad luck, we know…).</p>
-<p>Moreover, if you remember, there were a couple of people with an <code>dayonset</code>, but no <code>starthour</code>. The code we used (<code>lubridate::ymd_h</code> with argument <code>truncated = 2</code>) results in dates with missing <code>starthour</code> being converted to date-time, with the missing time being set to <code>00:00</code> (midnight). This means that these two people don’t fulfill the case definition criteria because we marked their symptoms started early in the morning of Nov 11 (at 00:00), before the meal time (18:00), and thus, they did not “fell ill within the time period of interest”.</p>
-<p>The two situations above are a reminder that you need to be both careful and aware of the implications of your data analysis decisions.</p>
-<div class="callout callout-style-default callout-warning callout-titled" title="Let's stop and... think!">
-<div class="callout-header d-flex align-content-center" data-bs-toggle="collapse" data-bs-target=".callout-13-contents" aria-controls="callout-13" aria-expanded="false" aria-label="Toggle callout">
-<div class="callout-icon-container">
-<i class="callout-icon"></i>
-</div>
-<div class="callout-title-container flex-fill">
-Let’s stop and… think!
-</div>
-<div class="callout-btn-toggle d-inline-block border-0 py-1 ps-1 pe-0 float-end"><i class="callout-toggle"></i></div>
-</div>
-<div id="callout-13" class="callout-13-contents callout-collapse collapse">
-<div class="callout-body-container callout-body">
-<p>What do you think are the risks of mis-classifying cases as non-cases in your analysis?</p>
-</div>
-</div>
-</div>
-<div class="callout callout-style-default callout-note callout-titled" title="If you've thought about the above, have a look here">
-<div class="callout-header d-flex align-content-center" data-bs-toggle="collapse" data-bs-target=".callout-14-contents" aria-controls="callout-14" aria-expanded="false" aria-label="Toggle callout">
-<div class="callout-icon-container">
-<i class="callout-icon"></i>
-</div>
-<div class="callout-title-container flex-fill">
-If you’ve thought about the above, have a look here
-</div>
-<div class="callout-btn-toggle d-inline-block border-0 py-1 ps-1 pe-0 float-end"><i class="callout-toggle"></i></div>
-</div>
-<div id="callout-14" class="callout-14-contents callout-collapse collapse">
-<div class="callout-body-container callout-body">
-<p>We will have bias either towards or away from the null, depending on the proportions of subjects misclassified.</p>
-</div>
-</div>
-</div>
-<div class="cell">
-<details open="">
-<summary>Code</summary>
-<div class="sourceCode cell-code" id="cb9"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Tabulate cases:</span></span>
-<span id="cb9-2"><a href="#cb9-2" aria-hidden="true" tabindex="-1"></a>janitor<span class="sc">::</span><span class="fu">tabyl</span>(<span class="at">dat =</span> copdata, case)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-</details>
-<div class="cell-output-display">
-
-<div data-pagedtable="false">
-  <script data-pagedtable-source="" type="application/json">
-{"columns":[{"label":["case"],"name":[1],"type":["lgl"],"align":["right"]},{"label":["n"],"name":[2],"type":["int"],"align":["right"]},{"label":["percent"],"name":[3],"type":["dbl"],"align":["right"]}],"data":[{"1":"FALSE","2":"161","3":"0.4270557"},{"1":"TRUE","2":"216","3":"0.5729443"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}}
-  </script>
-</div>
-</div>
-</div>
-<p>Let’s have a look at how many people ate a meal, had symptoms, and were considered as cases after applying our case definition:</p>
-<div class="cell">
-<details open="">
-<summary>Code</summary>
-<div class="sourceCode cell-code" id="cb10"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb10-1"><a href="#cb10-1" aria-hidden="true" tabindex="-1"></a>copdata <span class="sc">%&gt;%</span> </span>
-<span id="cb10-2"><a href="#cb10-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarise</span>(<span class="at">atemeal =</span> <span class="fu">sum</span>(meal <span class="sc">==</span> <span class="cn">TRUE</span>),</span>
-<span id="cb10-3"><a href="#cb10-3" aria-hidden="true" tabindex="-1"></a>            <span class="at">hadsympt =</span> <span class="fu">sum</span>(gastrosymptoms <span class="sc">==</span> <span class="cn">TRUE</span>),</span>
-<span id="cb10-4"><a href="#cb10-4" aria-hidden="true" tabindex="-1"></a>            <span class="at">nb_cases =</span> <span class="fu">sum</span>(case <span class="sc">==</span> <span class="cn">TRUE</span>)</span>
-<span id="cb10-5"><a href="#cb10-5" aria-hidden="true" tabindex="-1"></a>            )</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-</details>
-<div class="cell-output-display">
-
-<div data-pagedtable="false">
-  <script data-pagedtable-source="" type="application/json">
-{"columns":[{"label":["atemeal"],"name":[1],"type":["int"],"align":["right"]},{"label":["hadsympt"],"name":[2],"type":["int"],"align":["right"]},{"label":["nb_cases"],"name":[3],"type":["int"],"align":["right"]}],"data":[{"1":"377","2":"216","3":"216"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}}
-  </script>
-</div>
-</div>
-</div>
-</section>
-</section>
-<section id="export-clean-data" class="level1">
-<h1>4. Export clean data</h1>
-<p>Finally, we can save the cleaned data set as a new file <code>Copenhagen_clean2_YOURINITIALS</code>, under the data folder, before we proceeding with descriptive analysis.</p>
-<div class="cell">
-<details open="">
-<summary>Code</summary>
-<div class="sourceCode cell-code" id="cb11"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb11-1"><a href="#cb11-1" aria-hidden="true" tabindex="-1"></a>rio<span class="sc">::</span><span class="fu">export</span>(<span class="at">x =</span> copdata, </span>
-<span id="cb11-2"><a href="#cb11-2" aria-hidden="true" tabindex="-1"></a>            <span class="at">file =</span> here<span class="sc">::</span><span class="fu">here</span>(<span class="st">"data"</span>, <span class="st">"Copenhagen_clean2_2024.rds"</span>))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-</details>
-</div>
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-</script><div class="modal fade" id="quarto-embedded-source-code-modal" tabindex="-1" aria-labelledby="quarto-embedded-source-code-modal-label" aria-hidden="true"><div class="modal-dialog modal-dialog-scrollable"><div class="modal-content"><div class="modal-header"><h5 class="modal-title" id="quarto-embedded-source-code-modal-label">Source Code</h5><button class="btn-close" data-bs-dismiss="modal"></button></div><div class="modal-body"><div class="">
-<div class="sourceCode" id="cb12" data-shortcodes="false"><pre class="sourceCode markdown code-with-copy"><code class="sourceCode markdown"><span id="cb12-1"><a href="#cb12-1" aria-hidden="true" tabindex="-1"></a><span class="co">---</span></span>
-<span id="cb12-2"><a href="#cb12-2" aria-hidden="true" tabindex="-1"></a><span class="an">title:</span><span class="co"> "Case Definition"</span></span>
-<span id="cb12-3"><a href="#cb12-3" aria-hidden="true" tabindex="-1"></a><span class="an">editor:</span><span class="co"> visual</span></span>
-<span id="cb12-4"><a href="#cb12-4" aria-hidden="true" tabindex="-1"></a><span class="co">---</span></span>
-<span id="cb12-5"><a href="#cb12-5" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-6"><a href="#cb12-6" aria-hidden="true" tabindex="-1"></a><span class="fu">## 1. Learning outcomes</span></span>
-<span id="cb12-7"><a href="#cb12-7" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-8"><a href="#cb12-8" aria-hidden="true" tabindex="-1"></a>At the end of the session, participants will be able to:</span>
-<span id="cb12-9"><a href="#cb12-9" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-10"><a href="#cb12-10" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span>Apply case definition criteria to a dataset using logical arguments in R</span>
-<span id="cb12-11"><a href="#cb12-11" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-12"><a href="#cb12-12" aria-hidden="true" tabindex="-1"></a><span class="fu">## 2. Story/plot description</span></span>
-<span id="cb12-13"><a href="#cb12-13" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-14"><a href="#cb12-14" aria-hidden="true" tabindex="-1"></a>You will now create a new column in the data set to hold the case definition you decided in a previous step during your investigation. You can call this column <span class="in">`case`</span> and set it to <span class="in">`TRUE`</span> if the individual meets the case definition criteria and <span class="in">`FALSE`</span> if not. You will use this column later on for any calculations needed (descriptive statistics, two-by-two tables to compute measures of association, etc.) to figure out the culprit of this outbreak.</span>
-<span id="cb12-15"><a href="#cb12-15" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-16"><a href="#cb12-16" aria-hidden="true" tabindex="-1"></a><span class="fu">## 3. Questions/Assignments</span></span>
-<span id="cb12-17"><a href="#cb12-17" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-18"><a href="#cb12-18" aria-hidden="true" tabindex="-1"></a>If you closed your R project, please open it again and load the clean dataset. We strongly recommend you use the <span class="in">`Copenhagen_clean1_2024.rds`</span> provided by us in EVA. This is just to be sure all needed changes in that file for the code to work have been done (as you may have changed the file in a slightly different way than we have).</span>
-<span id="cb12-19"><a href="#cb12-19" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-20"><a href="#cb12-20" aria-hidden="true" tabindex="-1"></a>To be sure we all start from the same case definition, let's agree that a case was defined as a person who:</span>
-<span id="cb12-21"><a href="#cb12-21" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-22"><a href="#cb12-22" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span><span class="va">[x]</span> attended the school dinner on 11 November 2006 (i.e. is on the linelist)</span>
-<span id="cb12-23"><a href="#cb12-23" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span><span class="va">[x]</span> ate a meal at the school dinner (i.e. was exposed)</span>
-<span id="cb12-24"><a href="#cb12-24" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span><span class="va">[x]</span> fell ill after the start of the meal</span>
-<span id="cb12-25"><a href="#cb12-25" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span><span class="va">[x]</span> fell ill within the time period of interest after the school dinner</span>
-<span id="cb12-26"><a href="#cb12-26" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span><span class="va">[x]</span> suffered from diarrhoea with or without blood, or vomiting</span>
-<span id="cb12-27"><a href="#cb12-27" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-28"><a href="#cb12-28" aria-hidden="true" tabindex="-1"></a>Non cases (not-ill) were defined as people who:</span>
-<span id="cb12-29"><a href="#cb12-29" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-30"><a href="#cb12-30" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span><span class="va">[x]</span> attended the school dinner on 11 November 2006 (i.e. are on the linelist)</span>
-<span id="cb12-31"><a href="#cb12-31" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span><span class="va">[x]</span> ate a meal at the school dinner (i.e. were exposed)</span>
-<span id="cb12-32"><a href="#cb12-32" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span><span class="va">[x]</span> did not fall ill within the time period of interest</span>
-<span id="cb12-33"><a href="#cb12-33" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span><span class="va">[x]</span> did not develop diarrhoea (with or without blood) or vomiting</span>
-<span id="cb12-34"><a href="#cb12-34" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-35"><a href="#cb12-35" aria-hidden="true" tabindex="-1"></a><span class="fu">## 3.1 Install packages (if needed) and load libraries</span></span>
-<span id="cb12-36"><a href="#cb12-36" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-37"><a href="#cb12-37" aria-hidden="true" tabindex="-1"></a><span class="in">```{r, Packages}</span></span>
-<span id="cb12-38"><a href="#cb12-38" aria-hidden="true" tabindex="-1"></a><span class="co"># Load the required libraries into the current R session:</span></span>
-<span id="cb12-39"><a href="#cb12-39" aria-hidden="true" tabindex="-1"></a>pacman<span class="sc">::</span><span class="fu">p_load</span>(rio, </span>
-<span id="cb12-40"><a href="#cb12-40" aria-hidden="true" tabindex="-1"></a>               here, </span>
-<span id="cb12-41"><a href="#cb12-41" aria-hidden="true" tabindex="-1"></a>               tidyverse, </span>
-<span id="cb12-42"><a href="#cb12-42" aria-hidden="true" tabindex="-1"></a>               skimr,</span>
-<span id="cb12-43"><a href="#cb12-43" aria-hidden="true" tabindex="-1"></a>               plyr,</span>
-<span id="cb12-44"><a href="#cb12-44" aria-hidden="true" tabindex="-1"></a>               janitor,</span>
-<span id="cb12-45"><a href="#cb12-45" aria-hidden="true" tabindex="-1"></a>               lubridate,</span>
-<span id="cb12-46"><a href="#cb12-46" aria-hidden="true" tabindex="-1"></a>               gtsummary, </span>
-<span id="cb12-47"><a href="#cb12-47" aria-hidden="true" tabindex="-1"></a>               flextable,</span>
-<span id="cb12-48"><a href="#cb12-48" aria-hidden="true" tabindex="-1"></a>               officer,</span>
-<span id="cb12-49"><a href="#cb12-49" aria-hidden="true" tabindex="-1"></a>               epikit, </span>
-<span id="cb12-50"><a href="#cb12-50" aria-hidden="true" tabindex="-1"></a>               apyramid, </span>
-<span id="cb12-51"><a href="#cb12-51" aria-hidden="true" tabindex="-1"></a>               scales)</span>
-<span id="cb12-52"><a href="#cb12-52" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-53"><a href="#cb12-53" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb12-54"><a href="#cb12-54" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-55"><a href="#cb12-55" aria-hidden="true" tabindex="-1"></a><span class="fu">## 3.2 Import your data</span></span>
-<span id="cb12-56"><a href="#cb12-56" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-57"><a href="#cb12-57" aria-hidden="true" tabindex="-1"></a><span class="in">```{r, Import_data}</span></span>
-<span id="cb12-58"><a href="#cb12-58" aria-hidden="true" tabindex="-1"></a><span class="co"># Import the clean data set:</span></span>
-<span id="cb12-59"><a href="#cb12-59" aria-hidden="true" tabindex="-1"></a>copdata <span class="ot">&lt;-</span> rio<span class="sc">::</span><span class="fu">import</span>(here<span class="sc">::</span><span class="fu">here</span>(<span class="st">"data"</span>, <span class="st">"Copenhagen_clean1_2024.rds"</span>))</span>
-<span id="cb12-60"><a href="#cb12-60" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb12-61"><a href="#cb12-61" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-62"><a href="#cb12-62" aria-hidden="true" tabindex="-1"></a><span class="fu">## 3.3 Identify the variables you need to apply the case definition criteria.</span></span>
-<span id="cb12-63"><a href="#cb12-63" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-64"><a href="#cb12-64" aria-hidden="true" tabindex="-1"></a>::: {.callout-tip title="Need a little bit of help?" collapse="true"}</span>
-<span id="cb12-65"><a href="#cb12-65" aria-hidden="true" tabindex="-1"></a>The variables we need from the dataset to apply the above case definition are: <span class="in">`meal`</span>, <span class="in">`onset_datetime`</span>, <span class="in">`diarrhoea`</span>, <span class="in">`bloody`</span> and <span class="in">`vomiting`</span>.</span>
-<span id="cb12-66"><a href="#cb12-66" aria-hidden="true" tabindex="-1"></a>:::</span>
-<span id="cb12-67"><a href="#cb12-67" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-68"><a href="#cb12-68" aria-hidden="true" tabindex="-1"></a><span class="fu">## 3.4 Create a new `case` column to hold the binary case definition variable. Let's think about how to do this little by little:</span></span>
-<span id="cb12-69"><a href="#cb12-69" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-70"><a href="#cb12-70" aria-hidden="true" tabindex="-1"></a><span class="fu">### a) Ate a meal at the school dinner</span></span>
-<span id="cb12-71"><a href="#cb12-71" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-72"><a href="#cb12-72" aria-hidden="true" tabindex="-1"></a>You decide to exclude any people from the cohort who didn't eat at the dinner, because we specifically hypothesised a food item to be the vehicle of infection in this outbreak. Thus, filter your dataset to those who ate a meal: Keep in your dataset only those who ate a meal.</span>
-<span id="cb12-73"><a href="#cb12-73" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-74"><a href="#cb12-74" aria-hidden="true" tabindex="-1"></a>::: {.callout-tip title="Need a little bit of help?" collapse="true"}</span>
-<span id="cb12-75"><a href="#cb12-75" aria-hidden="true" tabindex="-1"></a><span class="in">`filter`</span> by those with <span class="in">`meal == TRUE`</span>.</span>
-<span id="cb12-76"><a href="#cb12-76" aria-hidden="true" tabindex="-1"></a>:::</span>
-<span id="cb12-77"><a href="#cb12-77" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-78"><a href="#cb12-78" aria-hidden="true" tabindex="-1"></a>::: {.callout-warning title="Let's stop and... think!" collapse="true"}</span>
-<span id="cb12-79"><a href="#cb12-79" aria-hidden="true" tabindex="-1"></a>What are some of the implications this decision may lead to? (excluding any people from the cohort who didn't eat at the dinner)</span>
-<span id="cb12-80"><a href="#cb12-80" aria-hidden="true" tabindex="-1"></a>:::</span>
-<span id="cb12-81"><a href="#cb12-81" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-84"><a href="#cb12-84" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
-<span id="cb12-85"><a href="#cb12-85" aria-hidden="true" tabindex="-1"></a>copdata <span class="ot">&lt;-</span> copdata <span class="sc">%&gt;%</span> </span>
-<span id="cb12-86"><a href="#cb12-86" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(meal <span class="sc">==</span> <span class="cn">TRUE</span>)</span>
-<span id="cb12-87"><a href="#cb12-87" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb12-88"><a href="#cb12-88" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-89"><a href="#cb12-89" aria-hidden="true" tabindex="-1"></a>::: {.callout-note title="Once you've thought about the above, have a look here" collapse="true"}</span>
-<span id="cb12-90"><a href="#cb12-90" aria-hidden="true" tabindex="-1"></a>Seven of the respondents actually said they did not eat the meal, but when it came to the questions about which food items they ate, they provided answers! This issue could have been minimised by:</span>
-<span id="cb12-91"><a href="#cb12-91" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-92"><a href="#cb12-92" aria-hidden="true" tabindex="-1"></a><span class="sc">\-</span> At the survey state, one could adjust the design of an electronic questionnaire to prevent key questions from being skipped. This can come with both pros and cons. Allow fellows to discuss if time allows.</span>
-<span id="cb12-93"><a href="#cb12-93" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-94"><a href="#cb12-94" aria-hidden="true" tabindex="-1"></a><span class="sc">\-</span> Explore your data further, realise this is the case, and recode the <span class="in">`meal`</span> variable for these individuals as <span class="in">`TRUE.`</span> =<span class="sc">\&gt;</span> This would be the way to go, but is not what we did in our example because we tried to keep it simple, and also because it is good to show that you may not always clean the data perfectly, and that has consequences: You can highlight the importance or really explore your data in depth.</span>
-<span id="cb12-95"><a href="#cb12-95" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-96"><a href="#cb12-96" aria-hidden="true" tabindex="-1"></a>By making the above decision, we may be missing cases and non-cases people, and thus, modifying the final estimate of our measure of association. =<span class="sc">\&gt;</span> It is very important to know your data, explore it deeply and try to clean it as well as possible. Every step one makes when cleaning the data may have a consequence, and we should be aware of it when making the data cleaning decisions and when interpreting the results.</span>
-<span id="cb12-97"><a href="#cb12-97" aria-hidden="true" tabindex="-1"></a>:::</span>
-<span id="cb12-98"><a href="#cb12-98" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-99"><a href="#cb12-99" aria-hidden="true" tabindex="-1"></a><span class="fu">### b) Fell ill after the start of the meal</span></span>
-<span id="cb12-100"><a href="#cb12-100" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-101"><a href="#cb12-101" aria-hidden="true" tabindex="-1"></a>We define "fell ill" as any person having had diarrhoea with OR without blood, OR vomiting. To capture this information easily, you will create a new <span class="in">`gastrosymptoms`</span> variable. This variable will indicate that the person had one OR ("or" is R is achieved by using <span class="in">`|`</span>) more of the clinical symptoms in your definition.</span>
-<span id="cb12-102"><a href="#cb12-102" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-103"><a href="#cb12-103" aria-hidden="true" tabindex="-1"></a>Note that we the concept of having eaten a meal is already included as per one of the steps above.</span>
-<span id="cb12-104"><a href="#cb12-104" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-105"><a href="#cb12-105" aria-hidden="true" tabindex="-1"></a>::: {.callout-tip title="Need a little bit of help?" collapse="true"}</span>
-<span id="cb12-106"><a href="#cb12-106" aria-hidden="true" tabindex="-1"></a>Create a new <span class="in">`gastrosymptoms`</span> column in your line list with <span class="in">`mutate()`</span> and case_when<span class="in">`()`</span>.</span>
-<span id="cb12-107"><a href="#cb12-107" aria-hidden="true" tabindex="-1"></a>:::</span>
-<span id="cb12-108"><a href="#cb12-108" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-109"><a href="#cb12-109" aria-hidden="true" tabindex="-1"></a>::: {.callout-warning title="Let's stop and... think!" collapse="true"}</span>
-<span id="cb12-110"><a href="#cb12-110" aria-hidden="true" tabindex="-1"></a>What are some of the implications this decision may lead to? (defining "fell ill" as any person who reported having diarrhoea with OR without blood, OR vomiting)</span>
-<span id="cb12-111"><a href="#cb12-111" aria-hidden="true" tabindex="-1"></a>:::</span>
-<span id="cb12-112"><a href="#cb12-112" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-113"><a href="#cb12-113" aria-hidden="true" tabindex="-1"></a>::: {.callout-note title="If you've thought about the above, have a look here" collapse="true"}</span>
-<span id="cb12-114"><a href="#cb12-114" aria-hidden="true" tabindex="-1"></a>Having one clinical symptom enough to be considered a potential case at this point may be considered too unspecific (low specificity). For example, a person who ate at the dinner party and developed diarrhoea for other reasons other than food poisoning (say, they recently started on antibiotics known for unbalancing the intestinal flora and causing diarrhoea) could be misclassified as a potential case. (Note we talk about **potential case**, and not **case**; that is because here we are not talking about cases per-se yet, but this decision has implications for when applying the case definition below).</span>
-<span id="cb12-115"><a href="#cb12-115" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-116"><a href="#cb12-116" aria-hidden="true" tabindex="-1"></a>Moreover, those who did not report clinical symptoms will be defined as non-cases. Thus, we are assuming that these individuals did not develop symptoms because they didn't report them. The missing values could be due to, for example, them skipping the questions in the questionnaire. Some individuals may be reluctant to report symptoms, due to shame, fear of repercussion, or others. It is important to think ahead, before the interview, about ways to minimise these situations. For example, through questionnaire design, you may impede skipping questions; you could promote trust by using the right interviewers (in some cases this will be someone from the community, in others someone form specific NGOs, someone of a specific race or gender, etc); choose to carry out online questionnaires vs in person (or vice versa, depending on the situation), etc.</span>
-<span id="cb12-117"><a href="#cb12-117" aria-hidden="true" tabindex="-1"></a>:::</span>
-<span id="cb12-118"><a href="#cb12-118" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-121"><a href="#cb12-121" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
-<span id="cb12-122"><a href="#cb12-122" aria-hidden="true" tabindex="-1"></a>copdata <span class="ot">&lt;-</span> copdata <span class="sc">%&gt;%</span> </span>
-<span id="cb12-123"><a href="#cb12-123" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">gastrosymptoms =</span> <span class="fu">case_when</span>(</span>
-<span id="cb12-124"><a href="#cb12-124" aria-hidden="true" tabindex="-1"></a>    <span class="co"># Those had diarrhoea...</span></span>
-<span id="cb12-125"><a href="#cb12-125" aria-hidden="true" tabindex="-1"></a>    diarrhoea <span class="sc">==</span> <span class="cn">TRUE</span> <span class="sc">|</span></span>
-<span id="cb12-126"><a href="#cb12-126" aria-hidden="true" tabindex="-1"></a>      <span class="co">#or bloody diarrhoea...</span></span>
-<span id="cb12-127"><a href="#cb12-127" aria-hidden="true" tabindex="-1"></a>    bloody <span class="sc">==</span> <span class="cn">TRUE</span> <span class="sc">|</span></span>
-<span id="cb12-128"><a href="#cb12-128" aria-hidden="true" tabindex="-1"></a>      <span class="co"># or vomiting, are marked as TRUE (fell ill after the meal)</span></span>
-<span id="cb12-129"><a href="#cb12-129" aria-hidden="true" tabindex="-1"></a>    vomiting <span class="sc">==</span> <span class="cn">TRUE</span> <span class="sc">~</span> <span class="cn">TRUE</span>,</span>
-<span id="cb12-130"><a href="#cb12-130" aria-hidden="true" tabindex="-1"></a>    <span class="co"># The rest are FALSE. This includes those who ate a meal but had no symptoms (did not fell ill after the meal)</span></span>
-<span id="cb12-131"><a href="#cb12-131" aria-hidden="true" tabindex="-1"></a>    <span class="at">.default =</span> <span class="cn">FALSE</span>)</span>
-<span id="cb12-132"><a href="#cb12-132" aria-hidden="true" tabindex="-1"></a>    )</span>
-<span id="cb12-133"><a href="#cb12-133" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb12-134"><a href="#cb12-134" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-135"><a href="#cb12-135" aria-hidden="true" tabindex="-1"></a><span class="fu">### c) Fell ill within the time period of interest</span></span>
-<span id="cb12-136"><a href="#cb12-136" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-137"><a href="#cb12-137" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span>Hmmm... what is the time period of interest? You could start calculating the incubation period, which can be defined by calculating the time between exposure (the meal) and onset of symptoms, and then looking at the distribution of these time differences. In this outbreak, incubation periods are easy to calculate, because everyone was exposed at (roughly) the same time and on the same day (eating the meal at the school dinner party).</span>
-<span id="cb12-138"><a href="#cb12-138" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span>Dinner was served at 18:00 roughly for everyone. Create a new <span class="in">`meal_datetime`</span> variable with this information for all people in your dataset (as per 05 Oct 2024, at 18:00h).</span>
-<span id="cb12-139"><a href="#cb12-139" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-142"><a href="#cb12-142" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
-<span id="cb12-143"><a href="#cb12-143" aria-hidden="true" tabindex="-1"></a><span class="co"># Start with copdata:</span></span>
-<span id="cb12-144"><a href="#cb12-144" aria-hidden="true" tabindex="-1"></a>copdata <span class="ot">&lt;-</span> copdata <span class="sc">%&gt;%</span> </span>
-<span id="cb12-145"><a href="#cb12-145" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Create new column for meal date and time:</span></span>
-<span id="cb12-146"><a href="#cb12-146" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">meal_datetime =</span> lubridate<span class="sc">::</span><span class="fu">ymd_hm</span>(<span class="st">"2024-10-05 18:00"</span>))</span>
-<span id="cb12-147"><a href="#cb12-147" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb12-148"><a href="#cb12-148" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-149"><a href="#cb12-149" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span>You can calculate the incubation period for each participant by subtracting <span class="in">`onset_datetime`</span> - <span class="in">`meal_datetime.`</span></span>
-<span id="cb12-150"><a href="#cb12-150" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span>Then, take the <span class="in">`median()`</span> of that column to calculate a median incubation period, this will help you start having some hypothesis about the type of pathogen we are dealing with until the lab results come back.</span>
-<span id="cb12-151"><a href="#cb12-151" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-152"><a href="#cb12-152" aria-hidden="true" tabindex="-1"></a>::: {.callout-tip title="Need a little bit of help?" collapse="true"}</span>
-<span id="cb12-153"><a href="#cb12-153" aria-hidden="true" tabindex="-1"></a>Be aware of the type of variable <span class="in">`incubation`</span> is (check <span class="in">`class()`</span>), as well as of missing values<span class="in">`NA`</span>.</span>
-<span id="cb12-154"><a href="#cb12-154" aria-hidden="true" tabindex="-1"></a>:::</span>
-<span id="cb12-155"><a href="#cb12-155" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-156"><a href="#cb12-156" aria-hidden="true" tabindex="-1"></a>::: {.callout-note title="Note" collapse="true"}</span>
-<span id="cb12-157"><a href="#cb12-157" aria-hidden="true" tabindex="-1"></a>The **incubation period** of a disease is the time from an exposure resulting in infection until the onset of disease. Because infection usually occurs immediately after exposure, the incubation period is generally the duration of the period from the onset of infection to the onset of disease -- Rothman, Greenland, Lash (2017): Modern Epidemiology, 3rd edition</span>
-<span id="cb12-158"><a href="#cb12-158" aria-hidden="true" tabindex="-1"></a>:::</span>
-<span id="cb12-159"><a href="#cb12-159" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-162"><a href="#cb12-162" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
-<span id="cb12-163"><a href="#cb12-163" aria-hidden="true" tabindex="-1"></a>copdata <span class="ot">&lt;-</span> copdata <span class="sc">%&gt;%</span> </span>
-<span id="cb12-164"><a href="#cb12-164" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">incubation =</span> onset_datetime <span class="sc">-</span> meal_datetime,</span>
-<span id="cb12-165"><a href="#cb12-165" aria-hidden="true" tabindex="-1"></a>         <span class="at">incubation =</span> <span class="fu">as.numeric</span> (incubation))</span>
-<span id="cb12-166"><a href="#cb12-166" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-167"><a href="#cb12-167" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-168"><a href="#cb12-168" aria-hidden="true" tabindex="-1"></a><span class="fu">median</span>(<span class="fu">as.numeric</span>(copdata<span class="sc">$</span>incubation), <span class="at">na.rm =</span> <span class="cn">TRUE</span>)</span>
-<span id="cb12-169"><a href="#cb12-169" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-170"><a href="#cb12-170" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb12-171"><a href="#cb12-171" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-172"><a href="#cb12-172" aria-hidden="true" tabindex="-1"></a>::: {.callout-caution title="Now we are messing up with your brain..." collapse="true"}</span>
-<span id="cb12-173"><a href="#cb12-173" aria-hidden="true" tabindex="-1"></a>If you pay attention, there were two individuals who don't have a time recorded, only a date, the day of the dinner. These people probably got sick the same night they had the dinner, but the exact time was not recoded. They way we've managed the data will insert "00:00" in a missing value of a time. This means that the 2 people that got sick the day of the dinner have been recorded sick *before* the dinner (in the early morning of the day of the dinner). The two people have a negative incubation period! This should not happen. If you feel the force is with you, you can modify this and fix the error (if you do this, your results will be a bit different than your colleagues, but similar). We will continue with this "erroneous" data, for easiness. But note that in real life, as soon as you realise there is a coding error like this, you would have to go back to your cleaning code and fix the error! This may happen a couple of times during your outbreak investigation, and is totally normal. Cleaning data is not an easy job!</span>
-<span id="cb12-174"><a href="#cb12-174" aria-hidden="true" tabindex="-1"></a>:::</span>
-<span id="cb12-175"><a href="#cb12-175" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-176"><a href="#cb12-176" aria-hidden="true" tabindex="-1"></a>We see that the median incubation time is <span class="in">`r median(as.numeric(copdata$incubation), na.rm = TRUE)`</span> hours. This is useful information, as incubation periods tend to be relatively pathogen-specific.</span>
-<span id="cb12-177"><a href="#cb12-177" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-178"><a href="#cb12-178" aria-hidden="true" tabindex="-1"></a>Based on this, say you define and limit the maximum incubation period to 48 hours after the meal, as the data points to a fast-acting bacterial toxin or a virus. That is, dinner participants should have developed (<span class="in">`onset_datetime`</span>) at least one symptom (<span class="in">`diarrhoea`</span>, <span class="in">`bloody`</span> or <span class="in">`vomiting`</span>) 48h after <span class="in">`meal_datetime`</span> to become a <span class="in">`case.`</span></span>
-<span id="cb12-179"><a href="#cb12-179" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-180"><a href="#cb12-180" aria-hidden="true" tabindex="-1"></a>::: {.callout-warning title="Let's stop and... think!" collapse="true"}</span>
-<span id="cb12-181"><a href="#cb12-181" aria-hidden="true" tabindex="-1"></a>What are some of the implications this decision may lead to? (implications of this case definition)</span>
-<span id="cb12-182"><a href="#cb12-182" aria-hidden="true" tabindex="-1"></a>:::</span>
-<span id="cb12-183"><a href="#cb12-183" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-184"><a href="#cb12-184" aria-hidden="true" tabindex="-1"></a>::: {.callout-note title="If you've thought about the above, have a look here" collapse="true"}</span>
-<span id="cb12-185"><a href="#cb12-185" aria-hidden="true" tabindex="-1"></a>All those not developing at least one symptom (diarrhoea with OR without blood, OR vomiting) 48h after the dinner are considered non-cases. This could (depending on how you decide to analyse your data) include those who had no symptoms at all, those who have missing data on the <span class="in">`onset_datetime`</span> variable, and/or those who had symptoms before eating the meal. *This is a reminder that you need to be both careful and aware of the implications of your data analysis decisions.* If a person had clinical symptoms before eating the meal, they are considered as not-cases. However, it could be that a person had symptoms before the meal, and yet, still got infected by the pathogen when eating their meal (bad luck, we know...). According to this definition, we would be missing that case.</span>
-<span id="cb12-186"><a href="#cb12-186" aria-hidden="true" tabindex="-1"></a>:::</span>
-<span id="cb12-187"><a href="#cb12-187" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-188"><a href="#cb12-188" aria-hidden="true" tabindex="-1"></a><span class="fu">### d) Finally, with this information you can create a new `case` column to hold the binary (`TRUE`/`FALSE`) case definition variable.</span></span>
-<span id="cb12-189"><a href="#cb12-189" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-192"><a href="#cb12-192" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
-<span id="cb12-193"><a href="#cb12-193" aria-hidden="true" tabindex="-1"></a>copdata <span class="ot">&lt;-</span> copdata <span class="sc">%&gt;%</span> </span>
-<span id="cb12-194"><a href="#cb12-194" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">case =</span> <span class="fu">case_when</span>(</span>
-<span id="cb12-195"><a href="#cb12-195" aria-hidden="true" tabindex="-1"></a>    <span class="co"># Those who had symptoms &lt;48h from the meal are cases (TRUE)</span></span>
-<span id="cb12-196"><a href="#cb12-196" aria-hidden="true" tabindex="-1"></a>    gastrosymptoms <span class="sc">==</span> <span class="cn">TRUE</span> <span class="sc">&amp;</span> </span>
-<span id="cb12-197"><a href="#cb12-197" aria-hidden="true" tabindex="-1"></a>      onset_datetime <span class="sc">&gt;=</span> meal_datetime <span class="sc">&amp;</span></span>
-<span id="cb12-198"><a href="#cb12-198" aria-hidden="true" tabindex="-1"></a>      onset_datetime <span class="sc">&lt;=</span> (meal_datetime <span class="sc">+</span> <span class="fu">days</span>(<span class="dv">2</span>)) <span class="sc">~</span> <span class="cn">TRUE</span>,</span>
-<span id="cb12-199"><a href="#cb12-199" aria-hidden="true" tabindex="-1"></a>    <span class="co"># Those who had symptoms &gt;48h from the meal are non-cases (FALSE)</span></span>
-<span id="cb12-200"><a href="#cb12-200" aria-hidden="true" tabindex="-1"></a>    gastrosymptoms <span class="sc">==</span> <span class="cn">TRUE</span> <span class="sc">&amp;</span> </span>
-<span id="cb12-201"><a href="#cb12-201" aria-hidden="true" tabindex="-1"></a>      onset_datetime <span class="sc">&gt;</span> (meal_datetime <span class="sc">+</span> <span class="fu">days</span>(<span class="dv">2</span>)) <span class="sc">~</span> <span class="cn">FALSE</span>,</span>
-<span id="cb12-202"><a href="#cb12-202" aria-hidden="true" tabindex="-1"></a>    <span class="co"># The rest are considered non-cases. Including, those who had no symptoms at all, who have missing data on the onset_datetime variable, or who had symptoms before eating the meal </span></span>
-<span id="cb12-203"><a href="#cb12-203" aria-hidden="true" tabindex="-1"></a>    <span class="at">.default =</span> <span class="cn">FALSE</span>)</span>
-<span id="cb12-204"><a href="#cb12-204" aria-hidden="true" tabindex="-1"></a>  )</span>
-<span id="cb12-205"><a href="#cb12-205" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb12-206"><a href="#cb12-206" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-207"><a href="#cb12-207" aria-hidden="true" tabindex="-1"></a>Note that we may be incurring in misclassification bias with the code above. The last section indicates that if a person had clinical symptoms before eating the meal, they are considered as non-cases. However, it could be that a person had symptoms before the meal, and yet, still got infected by the pathogen when eating their meal (bad luck, we know...).</span>
-<span id="cb12-208"><a href="#cb12-208" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-209"><a href="#cb12-209" aria-hidden="true" tabindex="-1"></a>Moreover, if you remember, there were a couple of people with an <span class="in">`dayonset`</span>, but no <span class="in">`starthour`</span>. The code we used (<span class="in">`lubridate::ymd_h`</span> with argument <span class="in">`truncated = 2`</span>) results in dates with missing <span class="in">`starthour`</span> being converted to date-time, with the missing time being set to <span class="in">`00:00`</span> (midnight). This means that these two people don't fulfill the case definition criteria because we marked their symptoms started early in the morning of Nov 11 (at 00:00), before the meal time (18:00), and thus, they did not "fell ill within the time period of interest".</span>
-<span id="cb12-210"><a href="#cb12-210" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-211"><a href="#cb12-211" aria-hidden="true" tabindex="-1"></a>The two situations above are a reminder that you need to be both careful and aware of the implications of your data analysis decisions.</span>
-<span id="cb12-212"><a href="#cb12-212" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-213"><a href="#cb12-213" aria-hidden="true" tabindex="-1"></a>::: {.callout-warning title="Let's stop and... think!" collapse="true"}</span>
-<span id="cb12-214"><a href="#cb12-214" aria-hidden="true" tabindex="-1"></a>What do you think are the risks of mis-classifying cases as non-cases in your analysis?</span>
-<span id="cb12-215"><a href="#cb12-215" aria-hidden="true" tabindex="-1"></a>:::</span>
-<span id="cb12-216"><a href="#cb12-216" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-217"><a href="#cb12-217" aria-hidden="true" tabindex="-1"></a>::: {.callout-note title="If you've thought about the above, have a look here" collapse="true"}</span>
-<span id="cb12-218"><a href="#cb12-218" aria-hidden="true" tabindex="-1"></a>We will have bias either towards or away from the null, depending on the proportions of subjects misclassified.</span>
-<span id="cb12-219"><a href="#cb12-219" aria-hidden="true" tabindex="-1"></a>:::</span>
-<span id="cb12-220"><a href="#cb12-220" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-223"><a href="#cb12-223" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
-<span id="cb12-224"><a href="#cb12-224" aria-hidden="true" tabindex="-1"></a><span class="co"># Tabulate cases:</span></span>
-<span id="cb12-225"><a href="#cb12-225" aria-hidden="true" tabindex="-1"></a>janitor<span class="sc">::</span><span class="fu">tabyl</span>(<span class="at">dat =</span> copdata, case)</span>
-<span id="cb12-226"><a href="#cb12-226" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb12-227"><a href="#cb12-227" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-228"><a href="#cb12-228" aria-hidden="true" tabindex="-1"></a>Let's have a look at how many people ate a meal, had symptoms, and were considered as cases after applying our case definition:</span>
-<span id="cb12-229"><a href="#cb12-229" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-230"><a href="#cb12-230" aria-hidden="true" tabindex="-1"></a><span class="in">```{r overview}</span></span>
-<span id="cb12-231"><a href="#cb12-231" aria-hidden="true" tabindex="-1"></a>copdata <span class="sc">%&gt;%</span> </span>
-<span id="cb12-232"><a href="#cb12-232" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarise</span>(<span class="at">atemeal =</span> <span class="fu">sum</span>(meal <span class="sc">==</span> <span class="cn">TRUE</span>),</span>
-<span id="cb12-233"><a href="#cb12-233" aria-hidden="true" tabindex="-1"></a>            <span class="at">hadsympt =</span> <span class="fu">sum</span>(gastrosymptoms <span class="sc">==</span> <span class="cn">TRUE</span>),</span>
-<span id="cb12-234"><a href="#cb12-234" aria-hidden="true" tabindex="-1"></a>            <span class="at">nb_cases =</span> <span class="fu">sum</span>(case <span class="sc">==</span> <span class="cn">TRUE</span>)</span>
-<span id="cb12-235"><a href="#cb12-235" aria-hidden="true" tabindex="-1"></a>            )</span>
-<span id="cb12-236"><a href="#cb12-236" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb12-237"><a href="#cb12-237" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-238"><a href="#cb12-238" aria-hidden="true" tabindex="-1"></a><span class="fu"># 4. Export clean data</span></span>
-<span id="cb12-239"><a href="#cb12-239" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-240"><a href="#cb12-240" aria-hidden="true" tabindex="-1"></a>Finally, we can save the cleaned data set as a new file <span class="in">`Copenhagen_clean2_YOURINITIALS`</span>, under the data folder, before we proceeding with descriptive analysis.</span>
-<span id="cb12-241"><a href="#cb12-241" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-242"><a href="#cb12-242" aria-hidden="true" tabindex="-1"></a><span class="in">```{r export_clean_data}</span></span>
-<span id="cb12-243"><a href="#cb12-243" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-244"><a href="#cb12-244" aria-hidden="true" tabindex="-1"></a>rio<span class="sc">::</span><span class="fu">export</span>(<span class="at">x =</span> copdata, </span>
-<span id="cb12-245"><a href="#cb12-245" aria-hidden="true" tabindex="-1"></a>            <span class="at">file =</span> here<span class="sc">::</span><span class="fu">here</span>(<span class="st">"data"</span>, <span class="st">"Copenhagen_clean2_2024.rds"</span>))</span>
-<span id="cb12-246"><a href="#cb12-246" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-247"><a href="#cb12-247" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-248"><a href="#cb12-248" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-</code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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-    <h2 id="toc-title">On this page</h2>
-   
-  <ul>
-  <li><a href="#learning-outcomes" id="toc-learning-outcomes" class="nav-link active" data-scroll-target="#learning-outcomes">1. Learning outcomes</a></li>
-  <li><a href="#storyplot-description" id="toc-storyplot-description" class="nav-link" data-scroll-target="#storyplot-description">2. Story/plot description</a></li>
-  <li><a href="#questionsassignments" id="toc-questionsassignments" class="nav-link" data-scroll-target="#questionsassignments">3. Questions/Assignments</a></li>
-  <li><a href="#install-packages-if-needed-and-load-libraries" id="toc-install-packages-if-needed-and-load-libraries" class="nav-link" data-scroll-target="#install-packages-if-needed-and-load-libraries">3.1 Install packages (if needed) and load libraries</a></li>
-  <li><a href="#import-your-data" id="toc-import-your-data" class="nav-link" data-scroll-target="#import-your-data">3.2 Import your data</a></li>
-  <li><a href="#merge-and-explore-the-lab-data" id="toc-merge-and-explore-the-lab-data" class="nav-link" data-scroll-target="#merge-and-explore-the-lab-data">3.3 Merge and explore the lab data</a></li>
-  <li><a href="#describe-the-outbreak-in-terms-of-time." id="toc-describe-the-outbreak-in-terms-of-time." class="nav-link" data-scroll-target="#describe-the-outbreak-in-terms-of-time.">3.4 Describe the outbreak in terms of time.</a>
-  <ul class="collapse">
-  <li><a href="#a-create-a-histogram-to-visualize-the-incubation-period-geom_histogram-is-a-good-function-to-use-for-this." id="toc-a-create-a-histogram-to-visualize-the-incubation-period-geom_histogram-is-a-good-function-to-use-for-this." class="nav-link" data-scroll-target="#a-create-a-histogram-to-visualize-the-incubation-period-geom_histogram-is-a-good-function-to-use-for-this.">a) Create a histogram to visualize the incubation period (<code>geom_histogram</code> is a good function to use for this!).</a></li>
-  <li><a href="#b-create-an-epicurve-for-the-date-and-time-of-onset-using-onset_datetime-limiting-the-input-data-to-cases." id="toc-b-create-an-epicurve-for-the-date-and-time-of-onset-using-onset_datetime-limiting-the-input-data-to-cases." class="nav-link" data-scroll-target="#b-create-an-epicurve-for-the-date-and-time-of-onset-using-onset_datetime-limiting-the-input-data-to-cases.">b) Create an epicurve for the date and time of onset (using onset_datetime), limiting the input data to cases.</a></li>
-  </ul></li>
-  <li><a href="#describe-the-outbreak-in-terms-of-person." id="toc-describe-the-outbreak-in-terms-of-person." class="nav-link" data-scroll-target="#describe-the-outbreak-in-terms-of-person.">3.5. Describe the outbreak in terms of person.</a>
-  <ul class="collapse">
-  <li><a href="#a-create-a-cross-tabulation-of-case-with-group." id="toc-a-create-a-cross-tabulation-of-case-with-group." class="nav-link" data-scroll-target="#a-create-a-cross-tabulation-of-case-with-group.">a) Create a cross-tabulation of <code>case</code> with <code>group</code>.</a></li>
-  <li><a href="#b-create-a-cross-tabulation-of-case-with-sex." id="toc-b-create-a-cross-tabulation-of-case-with-sex." class="nav-link" data-scroll-target="#b-create-a-cross-tabulation-of-case-with-sex.">b) Create a cross-tabulation of <code>case</code> with <code>sex</code>.</a></li>
-  <li><a href="#c-extra---age-sex-pyramid-of-cases." id="toc-c-extra---age-sex-pyramid-of-cases." class="nav-link" data-scroll-target="#c-extra---age-sex-pyramid-of-cases.">c) Extra - Age-sex pyramid of cases.</a></li>
-  </ul></li>
-  <li><a href="#explore-the-distribution-of-all-clinical-signs-symptoms." id="toc-explore-the-distribution-of-all-clinical-signs-symptoms." class="nav-link" data-scroll-target="#explore-the-distribution-of-all-clinical-signs-symptoms.">3.6 Explore the distribution of all clinical signs (symptoms).</a>
-  <ul class="collapse">
-  <li><a href="#a-create-a-summary-table-with-symptoms-stratified-by-case-definition-and-present-an-overall-column-as-well.-you-could-use-tabyl-or-gtsummarytbl_summary.-you-can-find-further-information-about-gtsummary-in-the-epidemiologist-r-handbook-section-on-gtsummary." id="toc-a-create-a-summary-table-with-symptoms-stratified-by-case-definition-and-present-an-overall-column-as-well.-you-could-use-tabyl-or-gtsummarytbl_summary.-you-can-find-further-information-about-gtsummary-in-the-epidemiologist-r-handbook-section-on-gtsummary." class="nav-link" data-scroll-target="#a-create-a-summary-table-with-symptoms-stratified-by-case-definition-and-present-an-overall-column-as-well.-you-could-use-tabyl-or-gtsummarytbl_summary.-you-can-find-further-information-about-gtsummary-in-the-epidemiologist-r-handbook-section-on-gtsummary.">a) Create a summary table with symptoms stratified by case definition, and present an overall column as well. You could use <code>tabyl()</code> or <code>gtsummary::tbl_summary()</code>. You can find further information about <code>gtsummary</code> in The Epidemiologist R handbook section on gtsummary.</a></li>
-  <li><a href="#b-extra---bar-plot-of-symptoms-stratified-by-case-definition." id="toc-b-extra---bar-plot-of-symptoms-stratified-by-case-definition." class="nav-link" data-scroll-target="#b-extra---bar-plot-of-symptoms-stratified-by-case-definition.">b) Extra - Bar plot of symptoms stratified by case definition.</a></li>
-  </ul></li>
-  <li><a href="#attack-proportions" id="toc-attack-proportions" class="nav-link" data-scroll-target="#attack-proportions">3.7 Attack proportions</a>
-  <ul class="collapse">
-  <li><a href="#a-calculate-the-overall-attack-proportions-percentage-of-cases-among-the-total-observed-individuals.-you-could-use-tabyl." id="toc-a-calculate-the-overall-attack-proportions-percentage-of-cases-among-the-total-observed-individuals.-you-could-use-tabyl." class="nav-link" data-scroll-target="#a-calculate-the-overall-attack-proportions-percentage-of-cases-among-the-total-observed-individuals.-you-could-use-tabyl.">a) Calculate the overall attack proportions (percentage of cases among the total observed individuals). You could use <code>tabyl()</code>.</a></li>
-  <li><a href="#b-calculate-attack-proportions-for-group-class-and-sex-by-case-status.-you-could-use-tabyl-as-you-did-in-3.4-or-gtsummarytbl_summary." id="toc-b-calculate-attack-proportions-for-group-class-and-sex-by-case-status.-you-could-use-tabyl-as-you-did-in-3.4-or-gtsummarytbl_summary." class="nav-link" data-scroll-target="#b-calculate-attack-proportions-for-group-class-and-sex-by-case-status.-you-could-use-tabyl-as-you-did-in-3.4-or-gtsummarytbl_summary.">b) Calculate attack proportions for <code>group</code>, <code>class</code> and <code>sex</code> by case status. You could use <code>tabyl()</code> (as you did in 3.4) or <code>gtsummary::tbl_summary</code>.</a></li>
-  </ul></li>
-  <li><a href="#draft-the-relevant-paragraphs-and-tables-andor-figures-in-your-outbreak-report-methods-and-results." id="toc-draft-the-relevant-paragraphs-and-tables-andor-figures-in-your-outbreak-report-methods-and-results." class="nav-link" data-scroll-target="#draft-the-relevant-paragraphs-and-tables-andor-figures-in-your-outbreak-report-methods-and-results.">3.8 Draft the relevant paragraph(s) and table(s) and/or figure(s) in your outbreak report (Methods and results).</a></li>
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-<div class="quarto-title-block"><div><h1 class="title">Laboratory data and descriptive analysis</h1><button type="button" class="btn code-tools-button dropdown-toggle" id="quarto-code-tools-menu" data-bs-toggle="dropdown" aria-expanded="false"><i class="bi"></i> Code</button><ul class="dropdown-menu dropdown-menu-end" aria-labelelledby="quarto-code-tools-menu"><li><a id="quarto-show-all-code" class="dropdown-item" href="javascript:void(0)" role="button">Show All Code</a></li><li><a id="quarto-hide-all-code" class="dropdown-item" href="javascript:void(0)" role="button">Hide All Code</a></li><li><hr class="dropdown-divider"></li><li><a id="quarto-view-source" class="dropdown-item" href="javascript:void(0)" role="button">View Source</a></li></ul></div></div>
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-<section id="learning-outcomes" class="level2">
-<h2 class="anchored" data-anchor-id="learning-outcomes">1. Learning outcomes</h2>
-<p>At the end of the session, participants will be able to:</p>
-<ul>
-<li>Merge two datasets using a common column.</li>
-<li>Discuss which descriptive statistics would you use to describe the cases</li>
-<li>Describe the cases by person and place (this is an outbreak in a high school, so we won’t explore “place” this time!)</li>
-<li>Generate preliminary hypotheses bases on the descriptions.</li>
-</ul>
-</section>
-<section id="storyplot-description" class="level2">
-<h2 class="anchored" data-anchor-id="storyplot-description">2. Story/plot description</h2>
-<p>You received data from the laboratory regarding [<strong>WE NEED LAB DATA AND DECISIONS ON WHAT TO ADD HERE</strong>]. You will have to merge the questionnaire data with the data coming from the laboratory.</p>
-<p>Now that your dataset is clean and the lab data merged, it is time to describe the people identified as cases so far and generate a hypothesis to inform the next steps in the outbreak investigation. Note that you should try to have a data analysis plan <em>before</em> any data collection. Nevertheless, during the urgency of outbreak investigations, questionnaire development may sometimes occur before you had time to draft a proper analysis plan (as it is the case in this simulated outbreak). Drawing “dummy” tables and graphs will help you precise the type of statistics you will carry out later on.</p>
-</section>
-<section id="questionsassignments" class="level2">
-<h2 class="anchored" data-anchor-id="questionsassignments">3. Questions/Assignments</h2>
-<p>If you closed your R project, please open it again and load the clean dataset. We recommend you use the <code>Copenhagen_clean2_2024.rds</code> provided by us in EVA. This is just to be sure all needed changes in that file for the code to work have been done (as you may have changed the file in a slightly different way than we have).</p>
-</section>
-<section id="install-packages-if-needed-and-load-libraries" class="level2">
-<h2 class="anchored" data-anchor-id="install-packages-if-needed-and-load-libraries">3.1 Install packages (if needed) and load libraries</h2>
-<div class="cell">
-<details open="">
-<summary>Code</summary>
-<div class="sourceCode cell-code" id="cb1"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Load the required libraries into the current R session:</span></span>
-<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a>pacman<span class="sc">::</span><span class="fu">p_load</span>(rio, </span>
-<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a>               here, </span>
-<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a>               tidyverse, </span>
-<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a>               skimr,</span>
-<span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a>               plyr,</span>
-<span id="cb1-7"><a href="#cb1-7" aria-hidden="true" tabindex="-1"></a>               janitor,</span>
-<span id="cb1-8"><a href="#cb1-8" aria-hidden="true" tabindex="-1"></a>               lubridate,</span>
-<span id="cb1-9"><a href="#cb1-9" aria-hidden="true" tabindex="-1"></a>               gtsummary, </span>
-<span id="cb1-10"><a href="#cb1-10" aria-hidden="true" tabindex="-1"></a>               flextable,</span>
-<span id="cb1-11"><a href="#cb1-11" aria-hidden="true" tabindex="-1"></a>               officer,</span>
-<span id="cb1-12"><a href="#cb1-12" aria-hidden="true" tabindex="-1"></a>               epikit, </span>
-<span id="cb1-13"><a href="#cb1-13" aria-hidden="true" tabindex="-1"></a>               apyramid, </span>
-<span id="cb1-14"><a href="#cb1-14" aria-hidden="true" tabindex="-1"></a>               scales)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-</details>
-</div>
-</section>
-<section id="import-your-data" class="level2">
-<h2 class="anchored" data-anchor-id="import-your-data">3.2 Import your data</h2>
-<div class="cell">
-<details open="">
-<summary>Code</summary>
-<div class="sourceCode cell-code" id="cb2"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Import the clean data set:</span></span>
-<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a>copdata <span class="ot">&lt;-</span> rio<span class="sc">::</span><span class="fu">import</span>(here<span class="sc">::</span><span class="fu">here</span>(<span class="st">"data"</span>, <span class="st">"Copenhagen_clean2_2024.rds"</span>)) </span>
-<span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a>lab <span class="ot">&lt;-</span> rio<span class="sc">::</span><span class="fu">import</span>(here<span class="sc">::</span><span class="fu">here</span>(<span class="st">"data"</span>, <span class="st">"lab_pos.xlsx"</span>)) </span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-</details>
-</div>
-</section>
-<section id="merge-and-explore-the-lab-data" class="level2">
-<h2 class="anchored" data-anchor-id="merge-and-explore-the-lab-data">3.3 Merge and explore the lab data</h2>
-<div class="cell">
-<details open="">
-<summary>Code</summary>
-<div class="sourceCode cell-code" id="cb3"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a>copdatalab <span class="ot">&lt;-</span> <span class="fu">left_join</span>(copdata, lab, <span class="at">by =</span> <span class="st">"id"</span>)</span>
-<span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a><span class="co"># Tabulate:</span></span>
-<span id="cb3-3"><a href="#cb3-3" aria-hidden="true" tabindex="-1"></a>janitor<span class="sc">::</span><span class="fu">tabyl</span>(<span class="at">dat =</span> copdatalab, result)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-</details>
-<div class="cell-output-display">
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-<div data-pagedtable="false">
-  <script data-pagedtable-source="" type="application/json">
-{"columns":[{"label":[""],"name":["_rn_"],"type":[""],"align":["left"]},{"label":["result"],"name":[1],"type":["chr"],"align":["left"]},{"label":["n"],"name":[2],"type":["int"],"align":["right"]},{"label":["percent"],"name":[3],"type":["dbl"],"align":["right"]},{"label":["valid_percent"],"name":[4],"type":["dbl"],"align":["right"]}],"data":[{"1":"pos","2":"216","3":"0.5729443","4":"1","_rn_":"1"},{"1":"NA","2":"161","3":"0.4270557","4":"NA","_rn_":"2"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}}
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-</section>
-<section id="describe-the-outbreak-in-terms-of-time." class="level2">
-<h2 class="anchored" data-anchor-id="describe-the-outbreak-in-terms-of-time.">3.4 Describe the outbreak in terms of time.</h2>
-<p>You could use <code>ggplot2</code>.To learn more about <code>ggplot2</code> you can check <a href="https://extranet.ecdc.europa.eu/Training/PM/Documents/Module%20Preparations/Intro%20Course/Track%20-%20Simulated%20Outbreak/Injects/The%20Epidemiologist%20R%20Handbook:%20Chapter%20on%20ggplot">The Epidemiologist R Handbook: Chapter on ggplot</a> and <a href="https://ggplot2-book.org/index.html">ggplot2 - Elegant Graphics for Data Analysis</a>.</p>
-<section id="a-create-a-histogram-to-visualize-the-incubation-period-geom_histogram-is-a-good-function-to-use-for-this." class="level3">
-<h3 class="anchored" data-anchor-id="a-create-a-histogram-to-visualize-the-incubation-period-geom_histogram-is-a-good-function-to-use-for-this.">a) Create a histogram to visualize the incubation period (<code>geom_histogram</code> is a good function to use for this!).</h3>
-<div class="callout callout-style-default callout-warning callout-titled" title="Let's stop and... think!">
-<div class="callout-header d-flex align-content-center" data-bs-toggle="collapse" data-bs-target=".callout-1-contents" aria-controls="callout-1" aria-expanded="false" aria-label="Toggle callout">
-<div class="callout-icon-container">
-<i class="callout-icon"></i>
-</div>
-<div class="callout-title-container flex-fill">
-Let’s stop and… think!
-</div>
-<div class="callout-btn-toggle d-inline-block border-0 py-1 ps-1 pe-0 float-end"><i class="callout-toggle"></i></div>
-</div>
-<div id="callout-1" class="callout-1-contents callout-collapse collapse">
-<div class="callout-body-container callout-body">
-<p>What can you infer from the incubation periods in the histogram?</p>
-</div>
-</div>
-</div>
-<div class="cell">
-<details open="">
-<summary>Code</summary>
-<div class="sourceCode cell-code" id="cb4"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: inc_time</span></span>
-<span id="cb4-2"><a href="#cb4-2" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb4-3"><a href="#cb4-3" aria-hidden="true" tabindex="-1"></a><span class="co"># Create a dataset with only cases</span></span>
-<span id="cb4-4"><a href="#cb4-4" aria-hidden="true" tabindex="-1"></a>cases <span class="ot">&lt;-</span> copdata <span class="sc">%&gt;%</span> </span>
-<span id="cb4-5"><a href="#cb4-5" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(case <span class="sc">==</span> <span class="cn">TRUE</span>)</span>
-<span id="cb4-6"><a href="#cb4-6" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb4-7"><a href="#cb4-7" aria-hidden="true" tabindex="-1"></a>incplot <span class="ot">&lt;-</span> cases <span class="sc">%&gt;%</span> </span>
-<span id="cb4-8"><a href="#cb4-8" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Create an empty ggplot frame:</span></span>
-<span id="cb4-9"><a href="#cb4-9" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggplot</span>() <span class="sc">+</span></span>
-<span id="cb4-10"><a href="#cb4-10" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Add a histogram of incubation:</span></span>
-<span id="cb4-11"><a href="#cb4-11" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_histogram</span>(</span>
-<span id="cb4-12"><a href="#cb4-12" aria-hidden="true" tabindex="-1"></a>    <span class="at">mapping =</span> <span class="fu">aes</span>(<span class="at">x =</span> incubation), </span>
-<span id="cb4-13"><a href="#cb4-13" aria-hidden="true" tabindex="-1"></a>    <span class="co"># Set bin widths to 6 hours:</span></span>
-<span id="cb4-14"><a href="#cb4-14" aria-hidden="true" tabindex="-1"></a>    <span class="at">binwidth =</span> <span class="dv">6</span>) <span class="sc">+</span></span>
-<span id="cb4-15"><a href="#cb4-15" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Adapt scale to better fit data</span></span>
-<span id="cb4-16"><a href="#cb4-16" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_continuous</span>(<span class="at">breaks =</span> <span class="fu">seq</span>(<span class="dv">0</span>, <span class="dv">48</span>, <span class="dv">6</span>)) <span class="sc">+</span> </span>
-<span id="cb4-17"><a href="#cb4-17" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Label x and y axes:</span></span>
-<span id="cb4-18"><a href="#cb4-18" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">x =</span> <span class="st">"Incubation period in 6-hour bins"</span>,</span>
-<span id="cb4-19"><a href="#cb4-19" aria-hidden="true" tabindex="-1"></a>       <span class="at">y =</span> <span class="st">"Number of cases"</span>)</span>
-<span id="cb4-20"><a href="#cb4-20" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb4-21"><a href="#cb4-21" aria-hidden="true" tabindex="-1"></a><span class="co"># Print plot:</span></span>
-<span id="cb4-22"><a href="#cb4-22" aria-hidden="true" tabindex="-1"></a>incplot</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-</details>
-<div class="cell-output-display">
-<p><img src="SimOb_RInject11_files/figure-html/unnamed-chunk-2-1.png" class="img-fluid" width="672"></p>
-</div>
-</div>
-</section>
-<section id="b-create-an-epicurve-for-the-date-and-time-of-onset-using-onset_datetime-limiting-the-input-data-to-cases." class="level3">
-<h3 class="anchored" data-anchor-id="b-create-an-epicurve-for-the-date-and-time-of-onset-using-onset_datetime-limiting-the-input-data-to-cases.">b) Create an epicurve for the date and time of onset (using onset_datetime), limiting the input data to cases.</h3>
-<ul>
-<li>Use <code>scale_x_datetime()</code> and chose a value for <code>date_breaks()</code> based on your calculated incubation period.</li>
-<li>Label your x and y axes using <code>labs()</code>.</li>
-</ul>
-<div class="callout callout-style-default callout-tip callout-titled" title="Need a little bit of help?">
-<div class="callout-header d-flex align-content-center" data-bs-toggle="collapse" data-bs-target=".callout-2-contents" aria-controls="callout-2" aria-expanded="false" aria-label="Toggle callout">
-<div class="callout-icon-container">
-<i class="callout-icon"></i>
-</div>
-<div class="callout-title-container flex-fill">
-Need a little bit of help?
-</div>
-<div class="callout-btn-toggle d-inline-block border-0 py-1 ps-1 pe-0 float-end"><i class="callout-toggle"></i></div>
-</div>
-<div id="callout-2" class="callout-2-contents callout-collapse collapse">
-<div class="callout-body-container callout-body">
-<p>A rule of thumb is to use one third or one fourth of the average incubation period as an interval. For our investigation this means we should use approximately a 6h interval for the <code>date_breaks</code> argument.</p>
-</div>
-</div>
-</div>
-<div class="cell">
-<details open="">
-<summary>Code</summary>
-<div class="sourceCode cell-code" id="cb5"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Create a vector with sequences every 6h from the first to the last case</span></span>
-<span id="cb5-2"><a href="#cb5-2" aria-hidden="true" tabindex="-1"></a>breaks_6h <span class="ot">&lt;-</span> <span class="fu">seq</span>(<span class="at">from =</span> <span class="fu">min</span>(cases<span class="sc">$</span>onset_datetime, <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
-<span id="cb5-3"><a href="#cb5-3" aria-hidden="true" tabindex="-1"></a>                 <span class="at">to =</span> <span class="fu">max</span>(cases<span class="sc">$</span>onset_datetime, <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
-<span id="cb5-4"><a href="#cb5-4" aria-hidden="true" tabindex="-1"></a>                 <span class="at">by =</span> <span class="st">"6 hours"</span>)</span>
-<span id="cb5-5"><a href="#cb5-5" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb5-6"><a href="#cb5-6" aria-hidden="true" tabindex="-1"></a><span class="co"># Fetch cases data:</span></span>
-<span id="cb5-7"><a href="#cb5-7" aria-hidden="true" tabindex="-1"></a>epicurve_datetime <span class="ot">&lt;-</span> cases <span class="sc">%&gt;%</span>  </span>
-<span id="cb5-8"><a href="#cb5-8" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Add factor onset_datetime to ggplot aesthetic:</span></span>
-<span id="cb5-9"><a href="#cb5-9" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggplot</span>(</span>
-<span id="cb5-10"><a href="#cb5-10" aria-hidden="true" tabindex="-1"></a>    <span class="at">mapping =</span> <span class="fu">aes</span>(<span class="at">x =</span> onset_datetime)) <span class="sc">+</span> </span>
-<span id="cb5-11"><a href="#cb5-11" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Add geom_histogram:</span></span>
-<span id="cb5-12"><a href="#cb5-12" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_histogram</span>(</span>
-<span id="cb5-13"><a href="#cb5-13" aria-hidden="true" tabindex="-1"></a>    <span class="co"># Apply the vector of requences created above</span></span>
-<span id="cb5-14"><a href="#cb5-14" aria-hidden="true" tabindex="-1"></a>    <span class="at">breaks =</span> breaks_6h) <span class="sc">+</span></span>
-<span id="cb5-15"><a href="#cb5-15" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Adapt scale to data and adjust axis label angle:</span></span>
-<span id="cb5-16"><a href="#cb5-16" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_datetime</span>(</span>
-<span id="cb5-17"><a href="#cb5-17" aria-hidden="true" tabindex="-1"></a>    <span class="at">date_breaks =</span> <span class="st">"6 hours"</span>,</span>
-<span id="cb5-18"><a href="#cb5-18" aria-hidden="true" tabindex="-1"></a>    <span class="at">labels =</span> <span class="fu">label_date_short</span>()) <span class="sc">+</span></span>
-<span id="cb5-19"><a href="#cb5-19" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Update x and y axis labels:</span></span>
-<span id="cb5-20"><a href="#cb5-20" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">x =</span> <span class="st">"Date and time of onset symptoms"</span>, </span>
-<span id="cb5-21"><a href="#cb5-21" aria-hidden="true" tabindex="-1"></a>       <span class="at">y =</span> <span class="st">"Number of cases"</span>) <span class="sc">+</span></span>
-<span id="cb5-22"><a href="#cb5-22" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Remove unnecessary grid lines:</span></span>
-<span id="cb5-23"><a href="#cb5-23" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_bw</span>()</span>
-<span id="cb5-24"><a href="#cb5-24" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb5-25"><a href="#cb5-25" aria-hidden="true" tabindex="-1"></a><span class="co"># Print epicurve:</span></span>
-<span id="cb5-26"><a href="#cb5-26" aria-hidden="true" tabindex="-1"></a>epicurve_datetime</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-</details>
-<div class="cell-output-display">
-<p><img src="SimOb_RInject11_files/figure-html/epicurve date-time-1.png" class="img-fluid" width="672"></p>
-</div>
-</div>
-<ol start="3" type="a">
-<li>Building up on your previous epicurve, create another epicurve to compare between sexes and additionally investigate how teachers versus students were distributed.</li>
-</ol>
-<ul>
-<li>Use <code>fill = group</code></li>
-<li>Use sex as your facets for <code>facet_wrap()</code></li>
-</ul>
-<div class="callout callout-style-default callout-tip callout-titled" title="Need a little bit of help?">
-<div class="callout-header d-flex align-content-center" data-bs-toggle="collapse" data-bs-target=".callout-3-contents" aria-controls="callout-3" aria-expanded="false" aria-label="Toggle callout">
-<div class="callout-icon-container">
-<i class="callout-icon"></i>
-</div>
-<div class="callout-title-container flex-fill">
-Need a little bit of help?
-</div>
-<div class="callout-btn-toggle d-inline-block border-0 py-1 ps-1 pe-0 float-end"><i class="callout-toggle"></i></div>
-</div>
-<div id="callout-3" class="callout-3-contents callout-collapse collapse">
-<div class="callout-body-container callout-body">
-<p><code>fill</code> adds an additional variable to be displayed in the plot: <code>group</code> is going to determine the fill-colour of our bars. <code>facet_wrap</code> splits the graph in two: one each for the two levels of <code>sex</code>.</p>
-</div>
-</div>
-</div>
-<div class="callout callout-style-default callout-important callout-titled" title="Do you feel like a challenge?">
-<div class="callout-header d-flex align-content-center" data-bs-toggle="collapse" data-bs-target=".callout-4-contents" aria-controls="callout-4" aria-expanded="false" aria-label="Toggle callout">
-<div class="callout-icon-container">
-<i class="callout-icon"></i>
-</div>
-<div class="callout-title-container flex-fill">
-Do you feel like a challenge?
-</div>
-<div class="callout-btn-toggle d-inline-block border-0 py-1 ps-1 pe-0 float-end"><i class="callout-toggle"></i></div>
-</div>
-<div id="callout-4" class="callout-4-contents callout-collapse collapse">
-<div class="callout-body-container callout-body">
-<p>If you want, try using <code>str_glue()</code> to add the total number of cases to the sub-title of the plot. <code>str_glue()</code> is a very useful function that allows you to dynamically create a summary statistic from your data within some normal text.</p>
-</div>
-</div>
-</div>
-<div class="cell">
-<details open="">
-<summary>Code</summary>
-<div class="sourceCode cell-code" id="cb6"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a>epicurve_strata <span class="ot">&lt;-</span> cases <span class="sc">%&gt;%</span> </span>
-<span id="cb6-2"><a href="#cb6-2" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Add factor onset_day to ggplot aesthetic:</span></span>
-<span id="cb6-3"><a href="#cb6-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggplot</span>(</span>
-<span id="cb6-4"><a href="#cb6-4" aria-hidden="true" tabindex="-1"></a>    <span class="at">mapping =</span> <span class="fu">aes</span>(<span class="at">x =</span> onset_datetime, <span class="at">fill =</span> group)) <span class="sc">+</span> </span>
-<span id="cb6-5"><a href="#cb6-5" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Add nicer fill colours:</span></span>
-<span id="cb6-6"><a href="#cb6-6" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_fill_manual</span>(<span class="at">values =</span> <span class="fu">c</span>(<span class="st">"darkred"</span>, <span class="st">"lightblue"</span>)) <span class="sc">+</span></span>
-<span id="cb6-7"><a href="#cb6-7" aria-hidden="true" tabindex="-1"></a>    <span class="co"># Add geom_histogram:</span></span>
-<span id="cb6-8"><a href="#cb6-8" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_histogram</span>(</span>
-<span id="cb6-9"><a href="#cb6-9" aria-hidden="true" tabindex="-1"></a>    <span class="co"># Apply the vector of requences created above</span></span>
-<span id="cb6-10"><a href="#cb6-10" aria-hidden="true" tabindex="-1"></a>    <span class="at">breaks =</span> breaks_6h) <span class="sc">+</span></span>
-<span id="cb6-11"><a href="#cb6-11" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Adjust x axis scales to a suitable unit:</span></span>
-<span id="cb6-12"><a href="#cb6-12" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_datetime</span>(</span>
-<span id="cb6-13"><a href="#cb6-13" aria-hidden="true" tabindex="-1"></a>    <span class="at">date_breaks =</span> <span class="st">"6 hours"</span>, </span>
-<span id="cb6-14"><a href="#cb6-14" aria-hidden="true" tabindex="-1"></a>    <span class="at">labels =</span> <span class="fu">label_date_short</span>()) <span class="sc">+</span></span>
-<span id="cb6-15"><a href="#cb6-15" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Update x and y axis labels:</span></span>
-<span id="cb6-16"><a href="#cb6-16" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">x =</span> <span class="st">"Date and time of onset"</span>, </span>
-<span id="cb6-17"><a href="#cb6-17" aria-hidden="true" tabindex="-1"></a>       <span class="at">y =</span> <span class="st">"Number of cases"</span>, </span>
-<span id="cb6-18"><a href="#cb6-18" aria-hidden="true" tabindex="-1"></a>       <span class="at">fill =</span> <span class="st">"Group"</span>, </span>
-<span id="cb6-19"><a href="#cb6-19" aria-hidden="true" tabindex="-1"></a>       <span class="at">title =</span> <span class="st">"Epicurve of the outbreak, stratified by sex"</span>,</span>
-<span id="cb6-20"><a href="#cb6-20" aria-hidden="true" tabindex="-1"></a>       <span class="at">subtitle =</span> <span class="fu">str_glue</span>(<span class="st">"Spetses, October 2024, N = {sum(copdata$case)}"</span>)) <span class="sc">+</span></span>
-<span id="cb6-21"><a href="#cb6-21" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Stratify by sex:</span></span>
-<span id="cb6-22"><a href="#cb6-22" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_wrap</span>(<span class="at">facets =</span> <span class="st">"sex"</span>,</span>
-<span id="cb6-23"><a href="#cb6-23" aria-hidden="true" tabindex="-1"></a>             <span class="at">ncol =</span> <span class="dv">2</span>) <span class="sc">+</span></span>
-<span id="cb6-24"><a href="#cb6-24" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Add theme:</span></span>
-<span id="cb6-25"><a href="#cb6-25" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_bw</span>()</span>
-<span id="cb6-26"><a href="#cb6-26" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb6-27"><a href="#cb6-27" aria-hidden="true" tabindex="-1"></a><span class="co"># Print epicurve:</span></span>
-<span id="cb6-28"><a href="#cb6-28" aria-hidden="true" tabindex="-1"></a>epicurve_strata </span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-</details>
-<div class="cell-output-display">
-<p><img src="SimOb_RInject11_files/figure-html/epicurve strata-1.png" class="img-fluid" width="672"></p>
-</div>
-</div>
-<div class="callout callout-style-default callout-warning callout-titled" title="Let's stop and... think!">
-<div class="callout-header d-flex align-content-center" data-bs-toggle="collapse" data-bs-target=".callout-5-contents" aria-controls="callout-5" aria-expanded="false" aria-label="Toggle callout">
-<div class="callout-icon-container">
-<i class="callout-icon"></i>
-</div>
-<div class="callout-title-container flex-fill">
-Let’s stop and… think!
-</div>
-<div class="callout-btn-toggle d-inline-block border-0 py-1 ps-1 pe-0 float-end"><i class="callout-toggle"></i></div>
-</div>
-<div id="callout-5" class="callout-5-contents callout-collapse collapse">
-<div class="callout-body-container callout-body">
-<p>What does the stratified epicurve tell you? Does the shape of the epicurve support a viral or toxic aetiology? What other information can you obtain from it?</p>
-</div>
-</div>
-</div>
-<div class="callout callout-style-default callout-note callout-titled" title="If you've thought about the above questions, have a look here">
-<div class="callout-header d-flex align-content-center" data-bs-toggle="collapse" data-bs-target=".callout-6-contents" aria-controls="callout-6" aria-expanded="false" aria-label="Toggle callout">
-<div class="callout-icon-container">
-<i class="callout-icon"></i>
-</div>
-<div class="callout-title-container flex-fill">
-If you’ve thought about the above questions, have a look here
-</div>
-<div class="callout-btn-toggle d-inline-block border-0 py-1 ps-1 pe-0 float-end"><i class="callout-toggle"></i></div>
-</div>
-<div id="callout-6" class="callout-6-contents callout-collapse collapse">
-<div class="callout-body-container callout-body">
-<p>When constructing an epicurve, we need to decide on the resolution, i.e.&nbsp;the time interval for a single bar. A rule of thumb is to use one third or one fourth of the average incubation period as an interval. For our investigation this means we should use approximately a 6h interval.</p>
-<p>This seems a good choice, as we saw that the daily interval was too coarse to really see the signal we are after. The epicurve and the summary of the incubation period show that there seemed to be a rapid onset of symptoms following exposure. This is in line with our previous suspicion that a virus or a toxin might be the causative agent in the outbreak.</p>
-<p>The unimodal shape with the sharp peak suggests a point source, while the tail on the right-hand side could be explained by secondary cases or background noise. Also, people that only consumed a little contaminated food and therefore only a low infectious dose could have a longer incubation period and could explain the late cases.</p>
-<p>The above results are in line with norovirus as the prime suspect, but the symptoms are not a textbook fit. There are too few people that experienced vomiting! Looking forward to receiving the lab results!</p>
-</div>
-</div>
-</div>
-</section>
-</section>
-<section id="describe-the-outbreak-in-terms-of-person." class="level2">
-<h2 class="anchored" data-anchor-id="describe-the-outbreak-in-terms-of-person.">3.5. Describe the outbreak in terms of person.</h2>
-<p>You could use <code>tabyl()</code>. For more detail on the <code>tabyl()</code> function and the <code>adorn_XYZ</code> helpers, see the <a href="https://cran.r-project.org/web/packages/janitor/vignettes/tabyls.html">janitor documentation</a> or <a href="https://epirhandbook.com/en/descriptive-tables.html#tbl_janitor">The Epidemiologist R Handbook section on tabulation</a>.</p>
-<section id="a-create-a-cross-tabulation-of-case-with-group." class="level3">
-<h3 class="anchored" data-anchor-id="a-create-a-cross-tabulation-of-case-with-group.">a) Create a cross-tabulation of <code>case</code> with <code>group</code>.</h3>
-<div class="cell">
-<details open="">
-<summary>Code</summary>
-<div class="sourceCode cell-code" id="cb7"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a>copdata <span class="sc">%&gt;%</span> </span>
-<span id="cb7-2"><a href="#cb7-2" aria-hidden="true" tabindex="-1"></a>  janitor<span class="sc">::</span><span class="fu">tabyl</span>(case, group) <span class="sc">%&gt;%</span> </span>
-<span id="cb7-3"><a href="#cb7-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">adorn_totals</span>() <span class="sc">%&gt;%</span> </span>
-<span id="cb7-4"><a href="#cb7-4" aria-hidden="true" tabindex="-1"></a>  <span class="fu">adorn_percentages</span>() <span class="sc">%&gt;%</span> </span>
-<span id="cb7-5"><a href="#cb7-5" aria-hidden="true" tabindex="-1"></a>  <span class="fu">adorn_pct_formatting</span>() </span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-</details>
-<div class="cell-output-display">
-
-<div data-pagedtable="false">
-  <script data-pagedtable-source="" type="application/json">
-{"columns":[{"label":[""],"name":["_rn_"],"type":[""],"align":["left"]},{"label":["case"],"name":[1],"type":["chr"],"align":["left"]},{"label":["teacher"],"name":[2],"type":["chr"],"align":["left"]},{"label":["student"],"name":[3],"type":["chr"],"align":["left"]}],"data":[{"1":"FALSE","2":"5.6%","3":"94.4%","_rn_":"1"},{"1":"TRUE","2":"2.8%","3":"97.2%","_rn_":"2"},{"1":"Total","2":"4.0%","3":"96.0%","_rn_":"3"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}}
-  </script>
-</div>
-</div>
-</div>
-</section>
-<section id="b-create-a-cross-tabulation-of-case-with-sex." class="level3">
-<h3 class="anchored" data-anchor-id="b-create-a-cross-tabulation-of-case-with-sex.">b) Create a cross-tabulation of <code>case</code> with <code>sex</code>.</h3>
-<div class="cell">
-<details open="">
-<summary>Code</summary>
-<div class="sourceCode cell-code" id="cb8"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a>copdata <span class="sc">%&gt;%</span> </span>
-<span id="cb8-2"><a href="#cb8-2" aria-hidden="true" tabindex="-1"></a>  janitor<span class="sc">::</span><span class="fu">tabyl</span>(case, sex) <span class="sc">%&gt;%</span> </span>
-<span id="cb8-3"><a href="#cb8-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">adorn_totals</span>() <span class="sc">%&gt;%</span> </span>
-<span id="cb8-4"><a href="#cb8-4" aria-hidden="true" tabindex="-1"></a>  <span class="fu">adorn_percentages</span>() <span class="sc">%&gt;%</span> </span>
-<span id="cb8-5"><a href="#cb8-5" aria-hidden="true" tabindex="-1"></a>  <span class="fu">adorn_pct_formatting</span>() </span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-</details>
-<div class="cell-output-display">
-
-<div data-pagedtable="false">
-  <script data-pagedtable-source="" type="application/json">
-{"columns":[{"label":[""],"name":["_rn_"],"type":[""],"align":["left"]},{"label":["case"],"name":[1],"type":["chr"],"align":["left"]},{"label":["female"],"name":[2],"type":["chr"],"align":["left"]},{"label":["male"],"name":[3],"type":["chr"],"align":["left"]}],"data":[{"1":"FALSE","2":"59.6%","3":"40.4%","_rn_":"1"},{"1":"TRUE","2":"54.2%","3":"45.8%","_rn_":"2"},{"1":"Total","2":"56.5%","3":"43.5%","_rn_":"3"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}}
-  </script>
-</div>
-</div>
-</div>
-<div class="callout callout-style-default callout-warning callout-titled" title="Let's stop and... think!">
-<div class="callout-header d-flex align-content-center" data-bs-toggle="collapse" data-bs-target=".callout-7-contents" aria-controls="callout-7" aria-expanded="false" aria-label="Toggle callout">
-<div class="callout-icon-container">
-<i class="callout-icon"></i>
-</div>
-<div class="callout-title-container flex-fill">
-Let’s stop and… think!
-</div>
-<div class="callout-btn-toggle d-inline-block border-0 py-1 ps-1 pe-0 float-end"><i class="callout-toggle"></i></div>
-</div>
-<div id="callout-7" class="callout-7-contents callout-collapse collapse">
-<div class="callout-body-container callout-body">
-<p>What do you think of these results?</p>
-</div>
-</div>
-</div>
-<div class="callout callout-style-default callout-note callout-titled" title="If you've thought about the above questions, have a look here">
-<div class="callout-header d-flex align-content-center" data-bs-toggle="collapse" data-bs-target=".callout-8-contents" aria-controls="callout-8" aria-expanded="false" aria-label="Toggle callout">
-<div class="callout-icon-container">
-<i class="callout-icon"></i>
-</div>
-<div class="callout-title-container flex-fill">
-If you’ve thought about the above questions, have a look here
-</div>
-<div class="callout-btn-toggle d-inline-block border-0 py-1 ps-1 pe-0 float-end"><i class="callout-toggle"></i></div>
-</div>
-<div id="callout-8" class="callout-8-contents callout-collapse collapse">
-<div class="callout-body-container callout-body">
-<p>The distribution of the cohort regarding sex, group and class also didn’t reveal anything unusual. Students seem a bit more affected by the outbreak than teachers and the attack rate is higher for older students in higher classes. This, however, is a purely descriptive result.</p>
-</div>
-</div>
-</div>
-<div class="callout callout-style-default callout-important callout-titled" title="Do you feel like a challenge?">
-<div class="callout-header d-flex align-content-center" data-bs-toggle="collapse" data-bs-target=".callout-9-contents" aria-controls="callout-9" aria-expanded="false" aria-label="Toggle callout">
-<div class="callout-icon-container">
-<i class="callout-icon"></i>
-</div>
-<div class="callout-title-container flex-fill">
-Do you feel like a challenge?
-</div>
-<div class="callout-btn-toggle d-inline-block border-0 py-1 ps-1 pe-0 float-end"><i class="callout-toggle"></i></div>
-</div>
-<div id="callout-9" class="callout-9-contents callout-collapse collapse">
-<div class="callout-body-container callout-body">
-<p>If you want, create an age-sex pyramid using the <code>apyramid</code> package. To do so, first create an age category variable with the <code>epkit</code> function <code>age_categories()</code>.</p>
-</div>
-</div>
-</div>
-</section>
-<section id="c-extra---age-sex-pyramid-of-cases." class="level3">
-<h3 class="anchored" data-anchor-id="c-extra---age-sex-pyramid-of-cases.">c) Extra - Age-sex pyramid of cases.</h3>
-<p>Hint: Change <code>show_midpoint = FALSE</code> to <code>TRUE</code> to see skewedness in the data patterns more easily.</p>
-<div class="cell">
-<details open="">
-<summary>Code</summary>
-<div class="sourceCode cell-code" id="cb9"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a>copdata <span class="ot">&lt;-</span> copdata <span class="sc">%&gt;%</span> </span>
-<span id="cb9-2"><a href="#cb9-2" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Create age categories:</span></span>
-<span id="cb9-3"><a href="#cb9-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">age_cat =</span> epikit<span class="sc">::</span><span class="fu">age_categories</span>(</span>
-<span id="cb9-4"><a href="#cb9-4" aria-hidden="true" tabindex="-1"></a>    <span class="co"># Name of age column:</span></span>
-<span id="cb9-5"><a href="#cb9-5" aria-hidden="true" tabindex="-1"></a>    <span class="at">x =</span> age, </span>
-<span id="cb9-6"><a href="#cb9-6" aria-hidden="true" tabindex="-1"></a>    <span class="co"># Define the age categories:</span></span>
-<span id="cb9-7"><a href="#cb9-7" aria-hidden="true" tabindex="-1"></a>    <span class="at">breakers =</span> <span class="fu">c</span>(<span class="dv">0</span>, <span class="dv">10</span>, <span class="dv">16</span>, <span class="dv">18</span>, <span class="dv">20</span>, <span class="dv">50</span>, <span class="dv">70</span>)</span>
-<span id="cb9-8"><a href="#cb9-8" aria-hidden="true" tabindex="-1"></a>    )</span>
-<span id="cb9-9"><a href="#cb9-9" aria-hidden="true" tabindex="-1"></a>  )</span>
-<span id="cb9-10"><a href="#cb9-10" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb9-11"><a href="#cb9-11" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb9-12"><a href="#cb9-12" aria-hidden="true" tabindex="-1"></a><span class="co"># Check age categories:</span></span>
-<span id="cb9-13"><a href="#cb9-13" aria-hidden="true" tabindex="-1"></a>janitor<span class="sc">::</span><span class="fu">tabyl</span>(copdata, age_cat)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-</details>
-<div class="cell-output-display">
-
-<div data-pagedtable="false">
-  <script data-pagedtable-source="" type="application/json">
-{"columns":[{"label":["age_cat"],"name":[1],"type":["fct"],"align":["left"]},{"label":["n"],"name":[2],"type":["int"],"align":["right"]},{"label":["percent"],"name":[3],"type":["dbl"],"align":["right"]}],"data":[{"1":"0-9","2":"0","3":"0.00000000"},{"1":"10-15","2":"11","3":"0.02917772"},{"1":"16-17","2":"201","3":"0.53315650"},{"1":"18-19","2":"147","3":"0.38992042"},{"1":"20-49","2":"11","3":"0.02917772"},{"1":"50-69","2":"7","3":"0.01856764"},{"1":"70+","2":"0","3":"0.00000000"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}}
-  </script>
-</div>
-</div>
-<details open="">
-<summary>Code</summary>
-<div class="sourceCode cell-code" id="cb10"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb10-1"><a href="#cb10-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Pipe copdata:</span></span>
-<span id="cb10-2"><a href="#cb10-2" aria-hidden="true" tabindex="-1"></a>agesex <span class="ot">&lt;-</span> copdata <span class="sc">%&gt;%</span> </span>
-<span id="cb10-3"><a href="#cb10-3" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Filter for cases only:</span></span>
-<span id="cb10-4"><a href="#cb10-4" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(case <span class="sc">==</span> <span class="cn">TRUE</span>) <span class="sc">%&gt;%</span> </span>
-<span id="cb10-5"><a href="#cb10-5" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Create age sex pyramid:</span></span>
-<span id="cb10-6"><a href="#cb10-6" aria-hidden="true" tabindex="-1"></a>  apyramid<span class="sc">::</span><span class="fu">age_pyramid</span>(</span>
-<span id="cb10-7"><a href="#cb10-7" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Specify column containing age categories:</span></span>
-<span id="cb10-8"><a href="#cb10-8" aria-hidden="true" tabindex="-1"></a>    <span class="at">age_group =</span> <span class="st">"age_cat"</span>,</span>
-<span id="cb10-9"><a href="#cb10-9" aria-hidden="true" tabindex="-1"></a>    <span class="co"># Specify column containing sex:</span></span>
-<span id="cb10-10"><a href="#cb10-10" aria-hidden="true" tabindex="-1"></a>    <span class="at">split_by =</span> <span class="st">"sex"</span>, </span>
-<span id="cb10-11"><a href="#cb10-11" aria-hidden="true" tabindex="-1"></a>    <span class="co"># Don't show midpoint on the graph:</span></span>
-<span id="cb10-12"><a href="#cb10-12" aria-hidden="true" tabindex="-1"></a>    <span class="at">show_midpoint =</span> <span class="cn">FALSE</span></span>
-<span id="cb10-13"><a href="#cb10-13" aria-hidden="true" tabindex="-1"></a>    )</span>
-<span id="cb10-14"><a href="#cb10-14" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb10-15"><a href="#cb10-15" aria-hidden="true" tabindex="-1"></a><span class="co"># Print plot:</span></span>
-<span id="cb10-16"><a href="#cb10-16" aria-hidden="true" tabindex="-1"></a>agesex</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-</details>
-<div class="cell-output-display">
-<p><img src="SimOb_RInject11_files/figure-html/unnamed-chunk-7-1.png" class="img-fluid" width="672"></p>
-</div>
-</div>
-</section>
-</section>
-<section id="explore-the-distribution-of-all-clinical-signs-symptoms." class="level2">
-<h2 class="anchored" data-anchor-id="explore-the-distribution-of-all-clinical-signs-symptoms.">3.6 Explore the distribution of all clinical signs (symptoms).</h2>
-<section id="a-create-a-summary-table-with-symptoms-stratified-by-case-definition-and-present-an-overall-column-as-well.-you-could-use-tabyl-or-gtsummarytbl_summary.-you-can-find-further-information-about-gtsummary-in-the-epidemiologist-r-handbook-section-on-gtsummary." class="level3">
-<h3 class="anchored" data-anchor-id="a-create-a-summary-table-with-symptoms-stratified-by-case-definition-and-present-an-overall-column-as-well.-you-could-use-tabyl-or-gtsummarytbl_summary.-you-can-find-further-information-about-gtsummary-in-the-epidemiologist-r-handbook-section-on-gtsummary.">a) Create a summary table with symptoms stratified by case definition, and present an overall column as well. You could use <code>tabyl()</code> or <code>gtsummary::tbl_summary()</code>. You can find further information about <code>gtsummary</code> in <a href="https://www.epirhandbook.com/en/descriptive-tables.html#tbl_gt">The Epidemiologist R handbook section on gtsummary</a>.</h3>
-<div class="cell">
-<details open="">
-<summary>Code</summary>
-<div class="sourceCode cell-code" id="cb11"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb11-1"><a href="#cb11-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Create summary table:</span></span>
-<span id="cb11-2"><a href="#cb11-2" aria-hidden="true" tabindex="-1"></a>tabsymptoms <span class="ot">&lt;-</span> copdata <span class="sc">%&gt;%</span> </span>
-<span id="cb11-3"><a href="#cb11-3" aria-hidden="true" tabindex="-1"></a>    <span class="co"># Select person characteristics to summarise:</span></span>
-<span id="cb11-4"><a href="#cb11-4" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(case, diarrhoea, bloody, vomiting,</span>
-<span id="cb11-5"><a href="#cb11-5" aria-hidden="true" tabindex="-1"></a>             abdo, nausea, fever,headache, jointpain) <span class="sc">%&gt;%</span> </span>
-<span id="cb11-6"><a href="#cb11-6" aria-hidden="true" tabindex="-1"></a>  <span class="co"># transform clinical symptoms to factors, so NA can be accounted properly in the table</span></span>
-<span id="cb11-7"><a href="#cb11-7" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">mutate</span>(</span>
-<span id="cb11-8"><a href="#cb11-8" aria-hidden="true" tabindex="-1"></a>    <span class="fu">across</span>(<span class="at">.cols =</span> <span class="fu">c</span>(diarrhoea, bloody, vomiting,</span>
-<span id="cb11-9"><a href="#cb11-9" aria-hidden="true" tabindex="-1"></a>             abdo, nausea, fever,headache, jointpain), </span>
-<span id="cb11-10"><a href="#cb11-10" aria-hidden="true" tabindex="-1"></a>           <span class="at">.fns =</span> <span class="sc">~</span><span class="fu">as.factor</span>(.))) <span class="sc">%&gt;%</span></span>
-<span id="cb11-11"><a href="#cb11-11" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Make NA a explicit level of factor variables</span></span>
-<span id="cb11-12"><a href="#cb11-12" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">mutate</span>(</span>
-<span id="cb11-13"><a href="#cb11-13" aria-hidden="true" tabindex="-1"></a>    <span class="fu">across</span>(<span class="at">.cols =</span> <span class="fu">c</span>(diarrhoea, bloody, vomiting,</span>
-<span id="cb11-14"><a href="#cb11-14" aria-hidden="true" tabindex="-1"></a>           abdo, nausea, fever,headache, jointpain),</span>
-<span id="cb11-15"><a href="#cb11-15" aria-hidden="true" tabindex="-1"></a>           <span class="at">.fns =</span> <span class="sc">~</span>forcats<span class="sc">::</span><span class="fu">fct_na_value_to_level</span>(.))) <span class="sc">%&gt;%</span> </span>
-<span id="cb11-16"><a href="#cb11-16" aria-hidden="true" tabindex="-1"></a>    </span>
-<span id="cb11-17"><a href="#cb11-17" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Create the summary table:</span></span>
-<span id="cb11-18"><a href="#cb11-18" aria-hidden="true" tabindex="-1"></a>  gtsummary<span class="sc">::</span><span class="fu">tbl_summary</span>(</span>
-<span id="cb11-19"><a href="#cb11-19" aria-hidden="true" tabindex="-1"></a>    <span class="co"># Stratify by case:</span></span>
-<span id="cb11-20"><a href="#cb11-20" aria-hidden="true" tabindex="-1"></a>    <span class="at">by =</span> case, </span>
-<span id="cb11-21"><a href="#cb11-21" aria-hidden="true" tabindex="-1"></a>    <span class="co"># Calculate row percentages:</span></span>
-<span id="cb11-22"><a href="#cb11-22" aria-hidden="true" tabindex="-1"></a>    <span class="at">percent =</span> <span class="st">"column"</span>,</span>
-<span id="cb11-23"><a href="#cb11-23" aria-hidden="true" tabindex="-1"></a>    <span class="co"># Create nice labels:</span></span>
-<span id="cb11-24"><a href="#cb11-24" aria-hidden="true" tabindex="-1"></a>    <span class="at">label  =</span> <span class="fu">list</span>(</span>
-<span id="cb11-25"><a href="#cb11-25" aria-hidden="true" tabindex="-1"></a>      diarrhoea   <span class="sc">~</span> <span class="st">"Diarrhoea"</span>,                           </span>
-<span id="cb11-26"><a href="#cb11-26" aria-hidden="true" tabindex="-1"></a>      bloody      <span class="sc">~</span> <span class="st">"Dysentary"</span>,</span>
-<span id="cb11-27"><a href="#cb11-27" aria-hidden="true" tabindex="-1"></a>      vomiting    <span class="sc">~</span> <span class="st">"Vomiting"</span>,</span>
-<span id="cb11-28"><a href="#cb11-28" aria-hidden="true" tabindex="-1"></a>      abdo        <span class="sc">~</span> <span class="st">"Abdominal pain"</span>,</span>
-<span id="cb11-29"><a href="#cb11-29" aria-hidden="true" tabindex="-1"></a>      nausea      <span class="sc">~</span> <span class="st">"Nausea"</span>, </span>
-<span id="cb11-30"><a href="#cb11-30" aria-hidden="true" tabindex="-1"></a>      fever       <span class="sc">~</span> <span class="st">"Fever"</span>, </span>
-<span id="cb11-31"><a href="#cb11-31" aria-hidden="true" tabindex="-1"></a>      headache    <span class="sc">~</span> <span class="st">"Headache"</span>, </span>
-<span id="cb11-32"><a href="#cb11-32" aria-hidden="true" tabindex="-1"></a>      jointpain   <span class="sc">~</span> <span class="st">"Joint pain"</span>)</span>
-<span id="cb11-33"><a href="#cb11-33" aria-hidden="true" tabindex="-1"></a>    </span>
-<span id="cb11-34"><a href="#cb11-34" aria-hidden="true" tabindex="-1"></a>  ) <span class="sc">%&gt;%</span> </span>
-<span id="cb11-35"><a href="#cb11-35" aria-hidden="true" tabindex="-1"></a>  </span>
-<span id="cb11-36"><a href="#cb11-36" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Add totals:</span></span>
-<span id="cb11-37"><a href="#cb11-37" aria-hidden="true" tabindex="-1"></a>  <span class="fu">add_overall</span>() <span class="sc">%&gt;%</span> </span>
-<span id="cb11-38"><a href="#cb11-38" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Make variable names bold and italics:</span></span>
-<span id="cb11-39"><a href="#cb11-39" aria-hidden="true" tabindex="-1"></a>  <span class="fu">bold_labels</span>() <span class="sc">%&gt;%</span> </span>
-<span id="cb11-40"><a href="#cb11-40" aria-hidden="true" tabindex="-1"></a>  <span class="fu">italicize_labels</span>() <span class="sc">%&gt;%</span> </span>
-<span id="cb11-41"><a href="#cb11-41" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Modify header:</span></span>
-<span id="cb11-42"><a href="#cb11-42" aria-hidden="true" tabindex="-1"></a>  <span class="fu">modify_header</span>(</span>
-<span id="cb11-43"><a href="#cb11-43" aria-hidden="true" tabindex="-1"></a>    <span class="at">label =</span> <span class="st">"**Characteristic**"</span>,</span>
-<span id="cb11-44"><a href="#cb11-44" aria-hidden="true" tabindex="-1"></a>    <span class="at">stat_0 =</span> <span class="st">"**Overall**</span><span class="sc">\n</span><span class="st"> **N** = {N}"</span>,</span>
-<span id="cb11-45"><a href="#cb11-45" aria-hidden="true" tabindex="-1"></a>    <span class="at">stat_1 =</span> <span class="st">"**Non-case**</span><span class="sc">\n</span><span class="st"> **N** = {n}"</span>,</span>
-<span id="cb11-46"><a href="#cb11-46" aria-hidden="true" tabindex="-1"></a>    <span class="at">stat_2 =</span> <span class="st">"**Case**</span><span class="sc">\n</span><span class="st"> **N** = {n}"</span>, </span>
-<span id="cb11-47"><a href="#cb11-47" aria-hidden="true" tabindex="-1"></a>    )</span>
-<span id="cb11-48"><a href="#cb11-48" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb11-49"><a href="#cb11-49" aria-hidden="true" tabindex="-1"></a><span class="co"># Print the table:</span></span>
-<span id="cb11-50"><a href="#cb11-50" aria-hidden="true" tabindex="-1"></a>tabsymptoms</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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-  font-size: 100%;
-  vertical-align: 0;
-}
-
-#gvwfubwoxr .gt_indent_1 {
-  text-indent: 5px;
-}
-
-#gvwfubwoxr .gt_indent_2 {
-  text-indent: 10px;
-}
-
-#gvwfubwoxr .gt_indent_3 {
-  text-indent: 15px;
-}
-
-#gvwfubwoxr .gt_indent_4 {
-  text-indent: 20px;
-}
-
-#gvwfubwoxr .gt_indent_5 {
-  text-indent: 25px;
-}
-</style>
-<table class="gt_table" data-quarto-disable-processing="false" data-quarto-bootstrap="false">
-  <thead>
-    
-    <tr class="gt_col_headings">
-      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="<strong>Characteristic</strong>"><strong>Characteristic</strong></th>
-      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="<strong>Overall</strong>
-<strong>N</strong> = 377<span class=&quot;gt_footnote_marks&quot; style=&quot;white-space:nowrap;font-style:italic;font-weight:normal;&quot;><sup>1</sup></span>"><strong>Overall</strong>
-<strong>N</strong> = 377<span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;"><sup>1</sup></span></th>
-      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="<strong>Non-case</strong>
-<strong>N</strong> = 161<span class=&quot;gt_footnote_marks&quot; style=&quot;white-space:nowrap;font-style:italic;font-weight:normal;&quot;><sup>1</sup></span>"><strong>Non-case</strong>
-<strong>N</strong> = 161<span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;"><sup>1</sup></span></th>
-      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="<strong>Case</strong>
-<strong>N</strong> = 216<span class=&quot;gt_footnote_marks&quot; style=&quot;white-space:nowrap;font-style:italic;font-weight:normal;&quot;><sup>1</sup></span>"><strong>Case</strong>
-<strong>N</strong> = 216<span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;"><sup>1</sup></span></th>
-    </tr>
-  </thead>
-  <tbody class="gt_table_body">
-    <tr><td headers="label" class="gt_row gt_left" style="font-weight: bold; font-style: italic;">Diarrhoea</td>
-<td headers="stat_0" class="gt_row gt_center"></td>
-<td headers="stat_1" class="gt_row gt_center"></td>
-<td headers="stat_2" class="gt_row gt_center"></td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;FALSE</td>
-<td headers="stat_0" class="gt_row gt_center">46 (18%)</td>
-<td headers="stat_1" class="gt_row gt_center">40 (100%)</td>
-<td headers="stat_2" class="gt_row gt_center">6 (2.8%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;TRUE</td>
-<td headers="stat_0" class="gt_row gt_center">206 (82%)</td>
-<td headers="stat_1" class="gt_row gt_center">0 (0%)</td>
-<td headers="stat_2" class="gt_row gt_center">206 (97%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left" style="font-weight: bold; font-style: italic;">Dysentary</td>
-<td headers="stat_0" class="gt_row gt_center"></td>
-<td headers="stat_1" class="gt_row gt_center"></td>
-<td headers="stat_2" class="gt_row gt_center"></td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;FALSE</td>
-<td headers="stat_0" class="gt_row gt_center">189 (97%)</td>
-<td headers="stat_1" class="gt_row gt_center">42 (100%)</td>
-<td headers="stat_2" class="gt_row gt_center">147 (97%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;TRUE</td>
-<td headers="stat_0" class="gt_row gt_center">5 (2.6%)</td>
-<td headers="stat_1" class="gt_row gt_center">0 (0%)</td>
-<td headers="stat_2" class="gt_row gt_center">5 (3.3%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left" style="font-weight: bold; font-style: italic;">Vomiting</td>
-<td headers="stat_0" class="gt_row gt_center"></td>
-<td headers="stat_1" class="gt_row gt_center"></td>
-<td headers="stat_2" class="gt_row gt_center"></td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;FALSE</td>
-<td headers="stat_0" class="gt_row gt_center">149 (69%)</td>
-<td headers="stat_1" class="gt_row gt_center">42 (100%)</td>
-<td headers="stat_2" class="gt_row gt_center">107 (62%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;TRUE</td>
-<td headers="stat_0" class="gt_row gt_center">66 (31%)</td>
-<td headers="stat_1" class="gt_row gt_center">0 (0%)</td>
-<td headers="stat_2" class="gt_row gt_center">66 (38%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left" style="font-weight: bold; font-style: italic;">Abdominal pain</td>
-<td headers="stat_0" class="gt_row gt_center"></td>
-<td headers="stat_1" class="gt_row gt_center"></td>
-<td headers="stat_2" class="gt_row gt_center"></td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;FALSE</td>
-<td headers="stat_0" class="gt_row gt_center">35 (14%)</td>
-<td headers="stat_1" class="gt_row gt_center">6 (12%)</td>
-<td headers="stat_2" class="gt_row gt_center">29 (15%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;TRUE</td>
-<td headers="stat_0" class="gt_row gt_center">207 (86%)</td>
-<td headers="stat_1" class="gt_row gt_center">44 (88%)</td>
-<td headers="stat_2" class="gt_row gt_center">163 (85%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left" style="font-weight: bold; font-style: italic;">Nausea</td>
-<td headers="stat_0" class="gt_row gt_center"></td>
-<td headers="stat_1" class="gt_row gt_center"></td>
-<td headers="stat_2" class="gt_row gt_center"></td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;FALSE</td>
-<td headers="stat_0" class="gt_row gt_center">55 (25%)</td>
-<td headers="stat_1" class="gt_row gt_center">12 (26%)</td>
-<td headers="stat_2" class="gt_row gt_center">43 (24%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;TRUE</td>
-<td headers="stat_0" class="gt_row gt_center">169 (75%)</td>
-<td headers="stat_1" class="gt_row gt_center">34 (74%)</td>
-<td headers="stat_2" class="gt_row gt_center">135 (76%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left" style="font-weight: bold; font-style: italic;">Fever</td>
-<td headers="stat_0" class="gt_row gt_center"></td>
-<td headers="stat_1" class="gt_row gt_center"></td>
-<td headers="stat_2" class="gt_row gt_center"></td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;FALSE</td>
-<td headers="stat_0" class="gt_row gt_center">127 (74%)</td>
-<td headers="stat_1" class="gt_row gt_center">32 (80%)</td>
-<td headers="stat_2" class="gt_row gt_center">95 (73%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;TRUE</td>
-<td headers="stat_0" class="gt_row gt_center">44 (26%)</td>
-<td headers="stat_1" class="gt_row gt_center">8 (20%)</td>
-<td headers="stat_2" class="gt_row gt_center">36 (27%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left" style="font-weight: bold; font-style: italic;">Headache</td>
-<td headers="stat_0" class="gt_row gt_center"></td>
-<td headers="stat_1" class="gt_row gt_center"></td>
-<td headers="stat_2" class="gt_row gt_center"></td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;FALSE</td>
-<td headers="stat_0" class="gt_row gt_center">83 (38%)</td>
-<td headers="stat_1" class="gt_row gt_center">11 (25%)</td>
-<td headers="stat_2" class="gt_row gt_center">72 (41%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;TRUE</td>
-<td headers="stat_0" class="gt_row gt_center">137 (62%)</td>
-<td headers="stat_1" class="gt_row gt_center">33 (75%)</td>
-<td headers="stat_2" class="gt_row gt_center">104 (59%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left" style="font-weight: bold; font-style: italic;">Joint pain</td>
-<td headers="stat_0" class="gt_row gt_center"></td>
-<td headers="stat_1" class="gt_row gt_center"></td>
-<td headers="stat_2" class="gt_row gt_center"></td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;FALSE</td>
-<td headers="stat_0" class="gt_row gt_center">159 (85%)</td>
-<td headers="stat_1" class="gt_row gt_center">32 (84%)</td>
-<td headers="stat_2" class="gt_row gt_center">127 (85%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;TRUE</td>
-<td headers="stat_0" class="gt_row gt_center">29 (15%)</td>
-<td headers="stat_1" class="gt_row gt_center">6 (16%)</td>
-<td headers="stat_2" class="gt_row gt_center">23 (15%)</td></tr>
-  </tbody>
-  
-  <tfoot class="gt_footnotes">
-    <tr>
-      <td class="gt_footnote" colspan="4"><span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;"><sup>1</sup></span> n (%)</td>
-    </tr>
-  </tfoot>
-</table>
-</div>
-</div>
-</div>
-<div class="callout callout-style-default callout-warning callout-titled" title="Let's stop and... think!">
-<div class="callout-header d-flex align-content-center" data-bs-toggle="collapse" data-bs-target=".callout-10-contents" aria-controls="callout-10" aria-expanded="false" aria-label="Toggle callout">
-<div class="callout-icon-container">
-<i class="callout-icon"></i>
-</div>
-<div class="callout-title-container flex-fill">
-Let’s stop and… think!
-</div>
-<div class="callout-btn-toggle d-inline-block border-0 py-1 ps-1 pe-0 float-end"><i class="callout-toggle"></i></div>
-</div>
-<div id="callout-10" class="callout-10-contents callout-collapse collapse">
-<div class="callout-body-container callout-body">
-<p>Do you think the symptoms selected for the case definition were the right ones, or would you change anything?</p>
-</div>
-</div>
-</div>
-<div class="callout callout-style-default callout-important callout-titled" title="Do you feel like a challenge?">
-<div class="callout-header d-flex align-content-center" data-bs-toggle="collapse" data-bs-target=".callout-11-contents" aria-controls="callout-11" aria-expanded="false" aria-label="Toggle callout">
-<div class="callout-icon-container">
-<i class="callout-icon"></i>
-</div>
-<div class="callout-title-container flex-fill">
-Do you feel like a challenge?
-</div>
-<div class="callout-btn-toggle d-inline-block border-0 py-1 ps-1 pe-0 float-end"><i class="callout-toggle"></i></div>
-</div>
-<div id="callout-11" class="callout-11-contents callout-collapse collapse">
-<div class="callout-body-container callout-body">
-<p>If you want, present the symptoms in an ordered bar chart. To do so, reshape the data using <code>pivot_longer()</code>, and then <code>group_by()</code>, <code>summarise()</code> and <code>count()</code> to tally up the counts for each symptom, stratified by case definition. Use <code>ggplot2::coord_flip()</code> for a better visualisation.</p>
-</div>
-</div>
-</div>
-</section>
-<section id="b-extra---bar-plot-of-symptoms-stratified-by-case-definition." class="level3">
-<h3 class="anchored" data-anchor-id="b-extra---bar-plot-of-symptoms-stratified-by-case-definition.">b) Extra - Bar plot of symptoms stratified by case definition.</h3>
-<div class="cell">
-<details open="">
-<summary>Code</summary>
-<div class="sourceCode cell-code" id="cb12"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb12-1"><a href="#cb12-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Create list of symptom variables:</span></span>
-<span id="cb12-2"><a href="#cb12-2" aria-hidden="true" tabindex="-1"></a>symptoms <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">"diarrhoea"</span>, </span>
-<span id="cb12-3"><a href="#cb12-3" aria-hidden="true" tabindex="-1"></a>              <span class="st">"bloody"</span>, </span>
-<span id="cb12-4"><a href="#cb12-4" aria-hidden="true" tabindex="-1"></a>              <span class="st">"vomiting"</span>, </span>
-<span id="cb12-5"><a href="#cb12-5" aria-hidden="true" tabindex="-1"></a>              <span class="st">"abdo"</span>, </span>
-<span id="cb12-6"><a href="#cb12-6" aria-hidden="true" tabindex="-1"></a>              <span class="st">"nausea"</span>, </span>
-<span id="cb12-7"><a href="#cb12-7" aria-hidden="true" tabindex="-1"></a>              <span class="st">"fever"</span>, </span>
-<span id="cb12-8"><a href="#cb12-8" aria-hidden="true" tabindex="-1"></a>              <span class="st">"headache"</span>, </span>
-<span id="cb12-9"><a href="#cb12-9" aria-hidden="true" tabindex="-1"></a>              <span class="st">"jointpain"</span>)</span>
-<span id="cb12-10"><a href="#cb12-10" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-11"><a href="#cb12-11" aria-hidden="true" tabindex="-1"></a><span class="co"># Create nice labels for case definition:</span></span>
-<span id="cb12-12"><a href="#cb12-12" aria-hidden="true" tabindex="-1"></a>caselabs <span class="ot">&lt;-</span> ggplot2<span class="sc">::</span><span class="fu">as_labeller</span>(<span class="fu">c</span>(<span class="st">`</span><span class="at">FALSE</span><span class="st">`</span> <span class="ot">=</span> <span class="st">"Non-case"</span>, </span>
-<span id="cb12-13"><a href="#cb12-13" aria-hidden="true" tabindex="-1"></a>                                   <span class="st">`</span><span class="at">TRUE</span><span class="st">`</span> <span class="ot">=</span> <span class="st">"Case"</span>))</span>
-<span id="cb12-14"><a href="#cb12-14" aria-hidden="true" tabindex="-1"></a><span class="co"># Select variables and cases:</span></span>
-<span id="cb12-15"><a href="#cb12-15" aria-hidden="true" tabindex="-1"></a>symptom_bar <span class="ot">&lt;-</span> copdata <span class="sc">%&gt;%</span> </span>
-<span id="cb12-16"><a href="#cb12-16" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Select symptom columns:</span></span>
-<span id="cb12-17"><a href="#cb12-17" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(case, <span class="fu">c</span>(<span class="fu">all_of</span>(symptoms))) <span class="sc">%&gt;%</span></span>
-<span id="cb12-18"><a href="#cb12-18" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Drop NAs:</span></span>
-<span id="cb12-19"><a href="#cb12-19" aria-hidden="true" tabindex="-1"></a>  <span class="fu">drop_na</span>() <span class="sc">%&gt;%</span> </span>
-<span id="cb12-20"><a href="#cb12-20" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Reshape (pivot longer):</span></span>
-<span id="cb12-21"><a href="#cb12-21" aria-hidden="true" tabindex="-1"></a>  <span class="fu">pivot_longer</span>(<span class="sc">!</span>case, </span>
-<span id="cb12-22"><a href="#cb12-22" aria-hidden="true" tabindex="-1"></a>               <span class="at">names_to =</span> <span class="st">"Symptoms"</span>, </span>
-<span id="cb12-23"><a href="#cb12-23" aria-hidden="true" tabindex="-1"></a>               <span class="at">values_drop_na =</span> <span class="cn">TRUE</span>) <span class="sc">%&gt;%</span> </span>
-<span id="cb12-24"><a href="#cb12-24" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Keep only TRUE values:</span></span>
-<span id="cb12-25"><a href="#cb12-25" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(value <span class="sc">==</span> <span class="cn">TRUE</span>) <span class="sc">%&gt;%</span> </span>
-<span id="cb12-26"><a href="#cb12-26" aria-hidden="true" tabindex="-1"></a> </span>
-<span id="cb12-27"><a href="#cb12-27" aria-hidden="true" tabindex="-1"></a>   <span class="co"># Group by symptoms and case:</span></span>
-<span id="cb12-28"><a href="#cb12-28" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(Symptoms, case) <span class="sc">%&gt;%</span> </span>
-<span id="cb12-29"><a href="#cb12-29" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Count for each symptom by case:</span></span>
-<span id="cb12-30"><a href="#cb12-30" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">summarise</span>(<span class="at">count =</span> <span class="fu">n</span>()) <span class="sc">%&gt;%</span> </span>
-<span id="cb12-31"><a href="#cb12-31" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Create plot:</span></span>
-<span id="cb12-32"><a href="#cb12-32" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggplot</span>(</span>
-<span id="cb12-33"><a href="#cb12-33" aria-hidden="true" tabindex="-1"></a>    <span class="at">mapping =</span> <span class="fu">aes</span>(</span>
-<span id="cb12-34"><a href="#cb12-34" aria-hidden="true" tabindex="-1"></a>    <span class="co"># Order symptom bars so most common ones are ontop:</span></span>
-<span id="cb12-35"><a href="#cb12-35" aria-hidden="true" tabindex="-1"></a>    <span class="at">x =</span> <span class="fu">reorder</span>(Symptoms, <span class="fu">desc</span>(count), <span class="at">decreasing =</span> <span class="cn">TRUE</span>), </span>
-<span id="cb12-36"><a href="#cb12-36" aria-hidden="true" tabindex="-1"></a>    <span class="at">y =</span> count)) <span class="sc">+</span></span>
-<span id="cb12-37"><a href="#cb12-37" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Display bars as proportions</span></span>
-<span id="cb12-38"><a href="#cb12-38" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_bar</span>(<span class="at">stat =</span> <span class="st">"identity"</span>) <span class="sc">+</span></span>
-<span id="cb12-39"><a href="#cb12-39" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Update x axis label:</span></span>
-<span id="cb12-40"><a href="#cb12-40" aria-hidden="true" tabindex="-1"></a>  <span class="fu">xlab</span>(<span class="st">"Symptoms"</span>) <span class="sc">+</span></span>
-<span id="cb12-41"><a href="#cb12-41" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Update y axis label:</span></span>
-<span id="cb12-42"><a href="#cb12-42" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ylab</span>(<span class="st">"Proportion of respondents"</span>) <span class="sc">+</span></span>
-<span id="cb12-43"><a href="#cb12-43" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Flip plot on its side so symptom labels are clear:</span></span>
-<span id="cb12-44"><a href="#cb12-44" aria-hidden="true" tabindex="-1"></a>  <span class="fu">coord_flip</span>() <span class="sc">+</span></span>
-<span id="cb12-45"><a href="#cb12-45" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Facet the plot by (labelled) case:</span></span>
-<span id="cb12-46"><a href="#cb12-46" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_wrap</span>(<span class="at">facets =</span> <span class="st">"case"</span>,</span>
-<span id="cb12-47"><a href="#cb12-47" aria-hidden="true" tabindex="-1"></a>             <span class="at">labeller =</span> caselabs,</span>
-<span id="cb12-48"><a href="#cb12-48" aria-hidden="true" tabindex="-1"></a>             <span class="at">ncol =</span> <span class="dv">2</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-</details>
-<div class="cell-output cell-output-stderr">
-<pre><code>`summarise()` has grouped output by 'Symptoms'. You can override using the
-`.groups` argument.</code></pre>
-</div>
-<details open="">
-<summary>Code</summary>
-<div class="sourceCode cell-code" id="cb14"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb14-1"><a href="#cb14-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Print plot:</span></span>
-<span id="cb14-2"><a href="#cb14-2" aria-hidden="true" tabindex="-1"></a>symptom_bar</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-</details>
-<div class="cell-output-display">
-<p><img src="SimOb_RInject11_files/figure-html/symptom_barplot-1.png" class="img-fluid" width="672"></p>
-</div>
-</div>
-</section>
-</section>
-<section id="attack-proportions" class="level2">
-<h2 class="anchored" data-anchor-id="attack-proportions">3.7 Attack proportions</h2>
-<section id="a-calculate-the-overall-attack-proportions-percentage-of-cases-among-the-total-observed-individuals.-you-could-use-tabyl." class="level3">
-<h3 class="anchored" data-anchor-id="a-calculate-the-overall-attack-proportions-percentage-of-cases-among-the-total-observed-individuals.-you-could-use-tabyl.">a) Calculate the overall attack proportions (percentage of cases among the total observed individuals). You could use <code>tabyl()</code>.</h3>
-<div class="cell">
-<details open="">
-<summary>Code</summary>
-<div class="sourceCode cell-code" id="cb15"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb15-1"><a href="#cb15-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Create table of case status:</span></span>
-<span id="cb15-2"><a href="#cb15-2" aria-hidden="true" tabindex="-1"></a>total_ap <span class="ot">&lt;-</span> <span class="fu">tabyl</span>(copdata, case) <span class="sc">%&gt;%</span> </span>
-<span id="cb15-3"><a href="#cb15-3" aria-hidden="true" tabindex="-1"></a> <span class="co"># Add row totals:</span></span>
-<span id="cb15-4"><a href="#cb15-4" aria-hidden="true" tabindex="-1"></a>  <span class="fu">adorn_totals</span>(<span class="at">where =</span> <span class="st">"row"</span>) <span class="sc">%&gt;%</span> </span>
-<span id="cb15-5"><a href="#cb15-5" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Add percentages with 1 digit after the decimal point:</span></span>
-<span id="cb15-6"><a href="#cb15-6" aria-hidden="true" tabindex="-1"></a>  <span class="fu">adorn_pct_formatting</span>(<span class="at">digits =</span> <span class="dv">1</span>) <span class="sc">%&gt;%</span> </span>
-<span id="cb15-7"><a href="#cb15-7" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Filter to rows where case is TRUE:</span></span>
-<span id="cb15-8"><a href="#cb15-8" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(case <span class="sc">==</span> <span class="cn">TRUE</span>) <span class="sc">%&gt;%</span> </span>
-<span id="cb15-9"><a href="#cb15-9" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Select the column percent:</span></span>
-<span id="cb15-10"><a href="#cb15-10" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(percent) <span class="sc">%&gt;%</span> </span>
-<span id="cb15-11"><a href="#cb15-11" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Extract (pull) the value from this cell:</span></span>
-<span id="cb15-12"><a href="#cb15-12" aria-hidden="true" tabindex="-1"></a>  <span class="fu">pull</span>()</span>
-<span id="cb15-13"><a href="#cb15-13" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb15-14"><a href="#cb15-14" aria-hidden="true" tabindex="-1"></a><span class="co"># Print result:</span></span>
-<span id="cb15-15"><a href="#cb15-15" aria-hidden="true" tabindex="-1"></a>total_ap</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-</details>
-<div class="cell-output cell-output-stdout">
-<pre><code>[1] "57.3%"</code></pre>
-</div>
-</div>
-<div class="callout callout-style-default callout-warning callout-titled" title="Let's stop and... think!">
-<div class="callout-header d-flex align-content-center" data-bs-toggle="collapse" data-bs-target=".callout-12-contents" aria-controls="callout-12" aria-expanded="false" aria-label="Toggle callout">
-<div class="callout-icon-container">
-<i class="callout-icon"></i>
-</div>
-<div class="callout-title-container flex-fill">
-Let’s stop and… think!
-</div>
-<div class="callout-btn-toggle d-inline-block border-0 py-1 ps-1 pe-0 float-end"><i class="callout-toggle"></i></div>
-</div>
-<div id="callout-12" class="callout-12-contents callout-collapse collapse">
-<div class="callout-body-container callout-body">
-<p>What can you infer from the overall attack proportion about this outbreak and possible vehicles/exposures?</p>
-</div>
-</div>
-</div>
-<div class="callout callout-style-default callout-note callout-titled" title="If you've thought about the above questions, have a look here">
-<div class="callout-header d-flex align-content-center" data-bs-toggle="collapse" data-bs-target=".callout-13-contents" aria-controls="callout-13" aria-expanded="false" aria-label="Toggle callout">
-<div class="callout-icon-container">
-<i class="callout-icon"></i>
-</div>
-<div class="callout-title-container flex-fill">
-If you’ve thought about the above questions, have a look here
-</div>
-<div class="callout-btn-toggle d-inline-block border-0 py-1 ps-1 pe-0 float-end"><i class="callout-toggle"></i></div>
-</div>
-<div id="callout-13" class="callout-13-contents callout-collapse collapse">
-<div class="callout-body-container callout-body">
-<p>The overall attack proportion is 57.3%. This means that more than half of the people who ate a meal were cases!</p>
-</div>
-</div>
-</div>
-</section>
-<section id="b-calculate-attack-proportions-for-group-class-and-sex-by-case-status.-you-could-use-tabyl-as-you-did-in-3.4-or-gtsummarytbl_summary." class="level3">
-<h3 class="anchored" data-anchor-id="b-calculate-attack-proportions-for-group-class-and-sex-by-case-status.-you-could-use-tabyl-as-you-did-in-3.4-or-gtsummarytbl_summary.">b) Calculate attack proportions for <code>group</code>, <code>class</code> and <code>sex</code> by case status. You could use <code>tabyl()</code> (as you did in 3.4) or <code>gtsummary::tbl_summary</code>.</h3>
-<div class="cell">
-<details open="">
-<summary>Code</summary>
-<div class="sourceCode cell-code" id="cb17"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb17-1"><a href="#cb17-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Table to calculate attack proportions:</span></span>
-<span id="cb17-2"><a href="#cb17-2" aria-hidden="true" tabindex="-1"></a>attack_prop <span class="ot">&lt;-</span> copdata <span class="sc">%&gt;%</span> </span>
-<span id="cb17-3"><a href="#cb17-3" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Select columns:</span></span>
-<span id="cb17-4"><a href="#cb17-4" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span> (case, class, group, sex) <span class="sc">%&gt;%</span> </span>
-<span id="cb17-5"><a href="#cb17-5" aria-hidden="true" tabindex="-1"></a>  </span>
-<span id="cb17-6"><a href="#cb17-6" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Create table:</span></span>
-<span id="cb17-7"><a href="#cb17-7" aria-hidden="true" tabindex="-1"></a>  <span class="fu">tbl_summary</span>(</span>
-<span id="cb17-8"><a href="#cb17-8" aria-hidden="true" tabindex="-1"></a>    <span class="co"># Stratified by case</span></span>
-<span id="cb17-9"><a href="#cb17-9" aria-hidden="true" tabindex="-1"></a>    <span class="at">by =</span> case,</span>
-<span id="cb17-10"><a href="#cb17-10" aria-hidden="true" tabindex="-1"></a>    <span class="co"># with row percentages</span></span>
-<span id="cb17-11"><a href="#cb17-11" aria-hidden="true" tabindex="-1"></a>    <span class="at">percent =</span> <span class="st">"row"</span>) <span class="sc">%&gt;%</span></span>
-<span id="cb17-12"><a href="#cb17-12" aria-hidden="true" tabindex="-1"></a>  </span>
-<span id="cb17-13"><a href="#cb17-13" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Add totals:</span></span>
-<span id="cb17-14"><a href="#cb17-14" aria-hidden="true" tabindex="-1"></a>  <span class="fu">add_overall</span>() <span class="sc">%&gt;%</span></span>
-<span id="cb17-15"><a href="#cb17-15" aria-hidden="true" tabindex="-1"></a>  </span>
-<span id="cb17-16"><a href="#cb17-16" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Make variable names bold and italics:</span></span>
-<span id="cb17-17"><a href="#cb17-17" aria-hidden="true" tabindex="-1"></a>  <span class="fu">bold_labels</span>() <span class="sc">%&gt;%</span> </span>
-<span id="cb17-18"><a href="#cb17-18" aria-hidden="true" tabindex="-1"></a>  <span class="fu">italicize_labels</span>() <span class="sc">%&gt;%</span> </span>
-<span id="cb17-19"><a href="#cb17-19" aria-hidden="true" tabindex="-1"></a>  </span>
-<span id="cb17-20"><a href="#cb17-20" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Modify header:</span></span>
-<span id="cb17-21"><a href="#cb17-21" aria-hidden="true" tabindex="-1"></a>  <span class="fu">modify_header</span>(</span>
-<span id="cb17-22"><a href="#cb17-22" aria-hidden="true" tabindex="-1"></a>    <span class="at">label =</span> <span class="st">"**Characteristic**"</span>,</span>
-<span id="cb17-23"><a href="#cb17-23" aria-hidden="true" tabindex="-1"></a>    <span class="at">stat_0 =</span> <span class="st">"**Overall** **N** = {N}"</span>,</span>
-<span id="cb17-24"><a href="#cb17-24" aria-hidden="true" tabindex="-1"></a>    <span class="at">stat_1 =</span> <span class="st">"**Non-case** **N** = {n}"</span>,</span>
-<span id="cb17-25"><a href="#cb17-25" aria-hidden="true" tabindex="-1"></a>    <span class="at">stat_2 =</span> <span class="st">"**Case** **N** = {n}"</span></span>
-<span id="cb17-26"><a href="#cb17-26" aria-hidden="true" tabindex="-1"></a>  )</span>
-<span id="cb17-27"><a href="#cb17-27" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb17-28"><a href="#cb17-28" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb17-29"><a href="#cb17-29" aria-hidden="true" tabindex="-1"></a><span class="co"># Print table:</span></span>
-<span id="cb17-30"><a href="#cb17-30" aria-hidden="true" tabindex="-1"></a>attack_prop</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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-      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="<strong>Overall</strong> <strong>N</strong> = 377<span class=&quot;gt_footnote_marks&quot; style=&quot;white-space:nowrap;font-style:italic;font-weight:normal;&quot;><sup>1</sup></span>"><strong>Overall</strong> <strong>N</strong> = 377<span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;"><sup>1</sup></span></th>
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-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;1</td>
-<td headers="stat_0" class="gt_row gt_center">131 (100%)</td>
-<td headers="stat_1" class="gt_row gt_center">63 (48%)</td>
-<td headers="stat_2" class="gt_row gt_center">68 (52%)</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;2</td>
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-<td headers="stat_1" class="gt_row gt_center">44 (44%)</td>
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-<td headers="stat_1" class="gt_row gt_center">16</td>
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-<td headers="stat_0" class="gt_row gt_center">15 (100%)</td>
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-<td headers="stat_2" class="gt_row gt_center">210 (58%)</td></tr>
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-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;female</td>
-<td headers="stat_0" class="gt_row gt_center">213 (100%)</td>
-<td headers="stat_1" class="gt_row gt_center">96 (45%)</td>
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-</section>
-</section>
-<section id="draft-the-relevant-paragraphs-and-tables-andor-figures-in-your-outbreak-report-methods-and-results." class="level2">
-<h2 class="anchored" data-anchor-id="draft-the-relevant-paragraphs-and-tables-andor-figures-in-your-outbreak-report-methods-and-results.">3.8 Draft the relevant paragraph(s) and table(s) and/or figure(s) in your outbreak report (Methods and results).</h2>
-<div class="callout callout-style-default callout-tip callout-titled" title="Need a little bit of help?">
-<div class="callout-header d-flex align-content-center" data-bs-toggle="collapse" data-bs-target=".callout-14-contents" aria-controls="callout-14" aria-expanded="false" aria-label="Toggle callout">
-<div class="callout-icon-container">
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-</div>
-<div id="callout-14" class="callout-14-contents callout-collapse collapse">
-<div class="callout-body-container callout-body">
-<p>To save your tabyl table as a .docx, you could convert your tabyl to flextable (<code>as_flex_table()</code>), ensure only one line per row (<code>flextable::autofit()</code>) and save as .docx (<code>flextable::save_as_docx(path = "nameoftable.docx"</code>)</p>
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-<div class="sourceCode" id="cb18" data-shortcodes="false"><pre class="sourceCode markdown code-with-copy"><code class="sourceCode markdown"><span id="cb18-1"><a href="#cb18-1" aria-hidden="true" tabindex="-1"></a><span class="co">---</span></span>
-<span id="cb18-2"><a href="#cb18-2" aria-hidden="true" tabindex="-1"></a><span class="an">title:</span><span class="co"> "Laboratory data and descriptive analysis"</span></span>
-<span id="cb18-3"><a href="#cb18-3" aria-hidden="true" tabindex="-1"></a><span class="an">editor:</span><span class="co"> visual</span></span>
-<span id="cb18-4"><a href="#cb18-4" aria-hidden="true" tabindex="-1"></a><span class="co">---</span></span>
-<span id="cb18-5"><a href="#cb18-5" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb18-6"><a href="#cb18-6" aria-hidden="true" tabindex="-1"></a><span class="fu">## 1. Learning outcomes</span></span>
-<span id="cb18-7"><a href="#cb18-7" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb18-8"><a href="#cb18-8" aria-hidden="true" tabindex="-1"></a>At the end of the session, participants will be able to:</span>
-<span id="cb18-9"><a href="#cb18-9" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb18-10"><a href="#cb18-10" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span>Merge two datasets using a common column.</span>
-<span id="cb18-11"><a href="#cb18-11" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span>Discuss which descriptive statistics would you use to describe the cases</span>
-<span id="cb18-12"><a href="#cb18-12" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span>Describe the cases by person and place (this is an outbreak in a high school, so we won't explore "place" this time!)</span>
-<span id="cb18-13"><a href="#cb18-13" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span>Generate preliminary hypotheses bases on the descriptions.</span>
-<span id="cb18-14"><a href="#cb18-14" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb18-15"><a href="#cb18-15" aria-hidden="true" tabindex="-1"></a><span class="fu">## 2. Story/plot description</span></span>
-<span id="cb18-16"><a href="#cb18-16" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb18-17"><a href="#cb18-17" aria-hidden="true" tabindex="-1"></a>You received data from the laboratory regarding <span class="sc">\[</span>**WE NEED LAB DATA AND DECISIONS ON WHAT TO ADD HERE**<span class="sc">\]</span>. You will have to merge the questionnaire data with the data coming from the laboratory.</span>
-<span id="cb18-18"><a href="#cb18-18" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb18-19"><a href="#cb18-19" aria-hidden="true" tabindex="-1"></a>Now that your dataset is clean and the lab data merged, it is time to describe the people identified as cases so far and generate a hypothesis to inform the next steps in the outbreak investigation. Note that you should try to have a data analysis plan *before* any data collection. Nevertheless, during the urgency of outbreak investigations, questionnaire development may sometimes occur before you had time to draft a proper analysis plan (as it is the case in this simulated outbreak). Drawing "dummy" tables and graphs will help you precise the type of statistics you will carry out later on.</span>
-<span id="cb18-20"><a href="#cb18-20" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb18-21"><a href="#cb18-21" aria-hidden="true" tabindex="-1"></a><span class="fu">## 3. Questions/Assignments</span></span>
-<span id="cb18-22"><a href="#cb18-22" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb18-23"><a href="#cb18-23" aria-hidden="true" tabindex="-1"></a>If you closed your R project, please open it again and load the clean dataset. We recommend you use the <span class="in">`Copenhagen_clean2_2024.rds`</span> provided by us in EVA. This is just to be sure all needed changes in that file for the code to work have been done (as you may have changed the file in a slightly different way than we have).</span>
-<span id="cb18-24"><a href="#cb18-24" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb18-25"><a href="#cb18-25" aria-hidden="true" tabindex="-1"></a><span class="fu">## 3.1 Install packages (if needed) and load libraries</span></span>
-<span id="cb18-26"><a href="#cb18-26" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb18-29"><a href="#cb18-29" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
-<span id="cb18-30"><a href="#cb18-30" aria-hidden="true" tabindex="-1"></a><span class="co"># Load the required libraries into the current R session:</span></span>
-<span id="cb18-31"><a href="#cb18-31" aria-hidden="true" tabindex="-1"></a>pacman<span class="sc">::</span><span class="fu">p_load</span>(rio, </span>
-<span id="cb18-32"><a href="#cb18-32" aria-hidden="true" tabindex="-1"></a>               here, </span>
-<span id="cb18-33"><a href="#cb18-33" aria-hidden="true" tabindex="-1"></a>               tidyverse, </span>
-<span id="cb18-34"><a href="#cb18-34" aria-hidden="true" tabindex="-1"></a>               skimr,</span>
-<span id="cb18-35"><a href="#cb18-35" aria-hidden="true" tabindex="-1"></a>               plyr,</span>
-<span id="cb18-36"><a href="#cb18-36" aria-hidden="true" tabindex="-1"></a>               janitor,</span>
-<span id="cb18-37"><a href="#cb18-37" aria-hidden="true" tabindex="-1"></a>               lubridate,</span>
-<span id="cb18-38"><a href="#cb18-38" aria-hidden="true" tabindex="-1"></a>               gtsummary, </span>
-<span id="cb18-39"><a href="#cb18-39" aria-hidden="true" tabindex="-1"></a>               flextable,</span>
-<span id="cb18-40"><a href="#cb18-40" aria-hidden="true" tabindex="-1"></a>               officer,</span>
-<span id="cb18-41"><a href="#cb18-41" aria-hidden="true" tabindex="-1"></a>               epikit, </span>
-<span id="cb18-42"><a href="#cb18-42" aria-hidden="true" tabindex="-1"></a>               apyramid, </span>
-<span id="cb18-43"><a href="#cb18-43" aria-hidden="true" tabindex="-1"></a>               scales)</span>
-<span id="cb18-44"><a href="#cb18-44" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb18-45"><a href="#cb18-45" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb18-46"><a href="#cb18-46" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb18-47"><a href="#cb18-47" aria-hidden="true" tabindex="-1"></a><span class="fu">## 3.2 Import your data</span></span>
-<span id="cb18-48"><a href="#cb18-48" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb18-49"><a href="#cb18-49" aria-hidden="true" tabindex="-1"></a><span class="in">```{r, Import_data}</span></span>
-<span id="cb18-50"><a href="#cb18-50" aria-hidden="true" tabindex="-1"></a><span class="co"># Import the clean data set:</span></span>
-<span id="cb18-51"><a href="#cb18-51" aria-hidden="true" tabindex="-1"></a>copdata <span class="ot">&lt;-</span> rio<span class="sc">::</span><span class="fu">import</span>(here<span class="sc">::</span><span class="fu">here</span>(<span class="st">"data"</span>, <span class="st">"Copenhagen_clean2_2024.rds"</span>)) </span>
-<span id="cb18-52"><a href="#cb18-52" aria-hidden="true" tabindex="-1"></a>lab <span class="ot">&lt;-</span> rio<span class="sc">::</span><span class="fu">import</span>(here<span class="sc">::</span><span class="fu">here</span>(<span class="st">"data"</span>, <span class="st">"lab_pos.xlsx"</span>)) </span>
-<span id="cb18-53"><a href="#cb18-53" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb18-54"><a href="#cb18-54" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb18-55"><a href="#cb18-55" aria-hidden="true" tabindex="-1"></a><span class="fu">## 3.3 Merge and explore the lab data</span></span>
-<span id="cb18-56"><a href="#cb18-56" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb18-57"><a href="#cb18-57" aria-hidden="true" tabindex="-1"></a><span class="in">```{r, merge_lab_data}</span></span>
-<span id="cb18-58"><a href="#cb18-58" aria-hidden="true" tabindex="-1"></a>copdatalab <span class="ot">&lt;-</span> <span class="fu">left_join</span>(copdata, lab, <span class="at">by =</span> <span class="st">"id"</span>)</span>
-<span id="cb18-59"><a href="#cb18-59" aria-hidden="true" tabindex="-1"></a><span class="co"># Tabulate:</span></span>
-<span id="cb18-60"><a href="#cb18-60" aria-hidden="true" tabindex="-1"></a>janitor<span class="sc">::</span><span class="fu">tabyl</span>(<span class="at">dat =</span> copdatalab, result)</span>
-<span id="cb18-61"><a href="#cb18-61" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb18-62"><a href="#cb18-62" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb18-63"><a href="#cb18-63" aria-hidden="true" tabindex="-1"></a><span class="fu">## 3.4 Describe the outbreak in terms of time.</span></span>
-<span id="cb18-64"><a href="#cb18-64" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb18-65"><a href="#cb18-65" aria-hidden="true" tabindex="-1"></a>You could use <span class="in">`ggplot2`</span>.To learn more about <span class="in">`ggplot2`</span> you can check <span class="co">[</span><span class="ot">The Epidemiologist R Handbook: Chapter on ggplot</span><span class="co">](https://extranet.ecdc.europa.eu/Training/PM/Documents/Module%20Preparations/Intro%20Course/Track%20-%20Simulated%20Outbreak/Injects/The%20Epidemiologist%20R%20Handbook:%20Chapter%20on%20ggplot)</span> and <span class="co">[</span><span class="ot">ggplot2 - Elegant Graphics for Data Analysis</span><span class="co">](https://ggplot2-book.org/index.html)</span>.</span>
-<span id="cb18-66"><a href="#cb18-66" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb18-67"><a href="#cb18-67" aria-hidden="true" tabindex="-1"></a><span class="fu">### a) Create a histogram to visualize the incubation period (`geom_histogram` is a good function to use for this!).</span></span>
-<span id="cb18-68"><a href="#cb18-68" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb18-69"><a href="#cb18-69" aria-hidden="true" tabindex="-1"></a>::: {.callout-warning title="Let's stop and... think!" collapse="true"}</span>
-<span id="cb18-70"><a href="#cb18-70" aria-hidden="true" tabindex="-1"></a>What can you infer from the incubation periods in the histogram?</span>
-<span id="cb18-71"><a href="#cb18-71" aria-hidden="true" tabindex="-1"></a>:::</span>
-<span id="cb18-72"><a href="#cb18-72" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb18-75"><a href="#cb18-75" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
-<span id="cb18-76"><a href="#cb18-76" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: inc_time</span></span>
-<span id="cb18-77"><a href="#cb18-77" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb18-78"><a href="#cb18-78" aria-hidden="true" tabindex="-1"></a><span class="co"># Create a dataset with only cases</span></span>
-<span id="cb18-79"><a href="#cb18-79" aria-hidden="true" tabindex="-1"></a>cases <span class="ot">&lt;-</span> copdata <span class="sc">%&gt;%</span> </span>
-<span id="cb18-80"><a href="#cb18-80" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(case <span class="sc">==</span> <span class="cn">TRUE</span>)</span>
-<span id="cb18-81"><a href="#cb18-81" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb18-82"><a href="#cb18-82" aria-hidden="true" tabindex="-1"></a>incplot <span class="ot">&lt;-</span> cases <span class="sc">%&gt;%</span> </span>
-<span id="cb18-83"><a href="#cb18-83" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Create an empty ggplot frame:</span></span>
-<span id="cb18-84"><a href="#cb18-84" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggplot</span>() <span class="sc">+</span></span>
-<span id="cb18-85"><a href="#cb18-85" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Add a histogram of incubation:</span></span>
-<span id="cb18-86"><a href="#cb18-86" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_histogram</span>(</span>
-<span id="cb18-87"><a href="#cb18-87" aria-hidden="true" tabindex="-1"></a>    <span class="at">mapping =</span> <span class="fu">aes</span>(<span class="at">x =</span> incubation), </span>
-<span id="cb18-88"><a href="#cb18-88" aria-hidden="true" tabindex="-1"></a>    <span class="co"># Set bin widths to 6 hours:</span></span>
-<span id="cb18-89"><a href="#cb18-89" aria-hidden="true" tabindex="-1"></a>    <span class="at">binwidth =</span> <span class="dv">6</span>) <span class="sc">+</span></span>
-<span id="cb18-90"><a href="#cb18-90" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Adapt scale to better fit data</span></span>
-<span id="cb18-91"><a href="#cb18-91" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_continuous</span>(<span class="at">breaks =</span> <span class="fu">seq</span>(<span class="dv">0</span>, <span class="dv">48</span>, <span class="dv">6</span>)) <span class="sc">+</span> </span>
-<span id="cb18-92"><a href="#cb18-92" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Label x and y axes:</span></span>
-<span id="cb18-93"><a href="#cb18-93" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">x =</span> <span class="st">"Incubation period in 6-hour bins"</span>,</span>
-<span id="cb18-94"><a href="#cb18-94" aria-hidden="true" tabindex="-1"></a>       <span class="at">y =</span> <span class="st">"Number of cases"</span>)</span>
-<span id="cb18-95"><a href="#cb18-95" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb18-96"><a href="#cb18-96" aria-hidden="true" tabindex="-1"></a><span class="co"># Print plot:</span></span>
-<span id="cb18-97"><a href="#cb18-97" aria-hidden="true" tabindex="-1"></a>incplot</span>
-<span id="cb18-98"><a href="#cb18-98" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb18-99"><a href="#cb18-99" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb18-100"><a href="#cb18-100" aria-hidden="true" tabindex="-1"></a><span class="fu">### b) Create an epicurve for the date and time of onset (using onset_datetime), limiting the input data to cases.</span></span>
-<span id="cb18-101"><a href="#cb18-101" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb18-102"><a href="#cb18-102" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span>Use <span class="in">`scale_x_datetime()`</span> and chose a value for <span class="in">`date_breaks()`</span> based on your calculated incubation period.</span>
-<span id="cb18-103"><a href="#cb18-103" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span>Label your x and y axes using <span class="in">`labs()`</span>.</span>
-<span id="cb18-104"><a href="#cb18-104" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb18-105"><a href="#cb18-105" aria-hidden="true" tabindex="-1"></a>::: {.callout-tip title="Need a little bit of help?" collapse="true"}</span>
-<span id="cb18-106"><a href="#cb18-106" aria-hidden="true" tabindex="-1"></a>A rule of thumb is to use one third or one fourth of the average incubation period as an interval. For our investigation this means we should use approximately a 6h interval for the <span class="in">`date_breaks`</span> argument.</span>
-<span id="cb18-107"><a href="#cb18-107" aria-hidden="true" tabindex="-1"></a>:::</span>
-<span id="cb18-108"><a href="#cb18-108" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb18-111"><a href="#cb18-111" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
-<span id="cb18-112"><a href="#cb18-112" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: epicurve date-time</span></span>
-<span id="cb18-113"><a href="#cb18-113" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb18-114"><a href="#cb18-114" aria-hidden="true" tabindex="-1"></a><span class="co"># Create a vector with sequences every 6h from the first to the last case</span></span>
-<span id="cb18-115"><a href="#cb18-115" aria-hidden="true" tabindex="-1"></a>breaks_6h <span class="ot">&lt;-</span> <span class="fu">seq</span>(<span class="at">from =</span> <span class="fu">min</span>(cases<span class="sc">$</span>onset_datetime, <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
-<span id="cb18-116"><a href="#cb18-116" aria-hidden="true" tabindex="-1"></a>                 <span class="at">to =</span> <span class="fu">max</span>(cases<span class="sc">$</span>onset_datetime, <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
-<span id="cb18-117"><a href="#cb18-117" aria-hidden="true" tabindex="-1"></a>                 <span class="at">by =</span> <span class="st">"6 hours"</span>)</span>
-<span id="cb18-118"><a href="#cb18-118" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb18-119"><a href="#cb18-119" aria-hidden="true" tabindex="-1"></a><span class="co"># Fetch cases data:</span></span>
-<span id="cb18-120"><a href="#cb18-120" aria-hidden="true" tabindex="-1"></a>epicurve_datetime <span class="ot">&lt;-</span> cases <span class="sc">%&gt;%</span>  </span>
-<span id="cb18-121"><a href="#cb18-121" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Add factor onset_datetime to ggplot aesthetic:</span></span>
-<span id="cb18-122"><a href="#cb18-122" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggplot</span>(</span>
-<span id="cb18-123"><a href="#cb18-123" aria-hidden="true" tabindex="-1"></a>    <span class="at">mapping =</span> <span class="fu">aes</span>(<span class="at">x =</span> onset_datetime)) <span class="sc">+</span> </span>
-<span id="cb18-124"><a href="#cb18-124" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Add geom_histogram:</span></span>
-<span id="cb18-125"><a href="#cb18-125" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_histogram</span>(</span>
-<span id="cb18-126"><a href="#cb18-126" aria-hidden="true" tabindex="-1"></a>    <span class="co"># Apply the vector of requences created above</span></span>
-<span id="cb18-127"><a href="#cb18-127" aria-hidden="true" tabindex="-1"></a>    <span class="at">breaks =</span> breaks_6h) <span class="sc">+</span></span>
-<span id="cb18-128"><a href="#cb18-128" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Adapt scale to data and adjust axis label angle:</span></span>
-<span id="cb18-129"><a href="#cb18-129" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_datetime</span>(</span>
-<span id="cb18-130"><a href="#cb18-130" aria-hidden="true" tabindex="-1"></a>    <span class="at">date_breaks =</span> <span class="st">"6 hours"</span>,</span>
-<span id="cb18-131"><a href="#cb18-131" aria-hidden="true" tabindex="-1"></a>    <span class="at">labels =</span> <span class="fu">label_date_short</span>()) <span class="sc">+</span></span>
-<span id="cb18-132"><a href="#cb18-132" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Update x and y axis labels:</span></span>
-<span id="cb18-133"><a href="#cb18-133" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">x =</span> <span class="st">"Date and time of onset symptoms"</span>, </span>
-<span id="cb18-134"><a href="#cb18-134" aria-hidden="true" tabindex="-1"></a>       <span class="at">y =</span> <span class="st">"Number of cases"</span>) <span class="sc">+</span></span>
-<span id="cb18-135"><a href="#cb18-135" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Remove unnecessary grid lines:</span></span>
-<span id="cb18-136"><a href="#cb18-136" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_bw</span>()</span>
-<span id="cb18-137"><a href="#cb18-137" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb18-138"><a href="#cb18-138" aria-hidden="true" tabindex="-1"></a><span class="co"># Print epicurve:</span></span>
-<span id="cb18-139"><a href="#cb18-139" aria-hidden="true" tabindex="-1"></a>epicurve_datetime</span>
-<span id="cb18-140"><a href="#cb18-140" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb18-141"><a href="#cb18-141" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb18-142"><a href="#cb18-142" aria-hidden="true" tabindex="-1"></a>c)  Building up on your previous epicurve, create another epicurve to compare between sexes and additionally investigate how teachers versus students were distributed.</span>
-<span id="cb18-143"><a href="#cb18-143" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb18-144"><a href="#cb18-144" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span>Use <span class="in">`fill = group`</span></span>
-<span id="cb18-145"><a href="#cb18-145" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span>Use sex as your facets for <span class="in">`facet_wrap()`</span></span>
-<span id="cb18-146"><a href="#cb18-146" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb18-147"><a href="#cb18-147" aria-hidden="true" tabindex="-1"></a>::: {.callout-tip title="Need a little bit of help?" collapse="true"}</span>
-<span id="cb18-148"><a href="#cb18-148" aria-hidden="true" tabindex="-1"></a><span class="in">`fill`</span> adds an additional variable to be displayed in the plot: <span class="in">`group`</span> is going to determine the fill-colour of our bars. <span class="in">`facet_wrap`</span> splits the graph in two: one each for the two levels of <span class="in">`sex`</span>.</span>
-<span id="cb18-149"><a href="#cb18-149" aria-hidden="true" tabindex="-1"></a>:::</span>
-<span id="cb18-150"><a href="#cb18-150" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb18-151"><a href="#cb18-151" aria-hidden="true" tabindex="-1"></a>::: {.callout-important title="Do you feel like a challenge?" collapse="true"}</span>
-<span id="cb18-152"><a href="#cb18-152" aria-hidden="true" tabindex="-1"></a>If you want, try using <span class="in">`str_glue()`</span> to add the total number of cases to the sub-title of the plot. <span class="in">`str_glue()`</span> is a very useful function that allows you to dynamically create a summary statistic from your data within some normal text.</span>
-<span id="cb18-153"><a href="#cb18-153" aria-hidden="true" tabindex="-1"></a>:::</span>
-<span id="cb18-154"><a href="#cb18-154" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb18-157"><a href="#cb18-157" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
-<span id="cb18-158"><a href="#cb18-158" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: epicurve strata</span></span>
-<span id="cb18-159"><a href="#cb18-159" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb18-160"><a href="#cb18-160" aria-hidden="true" tabindex="-1"></a>epicurve_strata <span class="ot">&lt;-</span> cases <span class="sc">%&gt;%</span> </span>
-<span id="cb18-161"><a href="#cb18-161" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Add factor onset_day to ggplot aesthetic:</span></span>
-<span id="cb18-162"><a href="#cb18-162" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggplot</span>(</span>
-<span id="cb18-163"><a href="#cb18-163" aria-hidden="true" tabindex="-1"></a>    <span class="at">mapping =</span> <span class="fu">aes</span>(<span class="at">x =</span> onset_datetime, <span class="at">fill =</span> group)) <span class="sc">+</span> </span>
-<span id="cb18-164"><a href="#cb18-164" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Add nicer fill colours:</span></span>
-<span id="cb18-165"><a href="#cb18-165" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_fill_manual</span>(<span class="at">values =</span> <span class="fu">c</span>(<span class="st">"darkred"</span>, <span class="st">"lightblue"</span>)) <span class="sc">+</span></span>
-<span id="cb18-166"><a href="#cb18-166" aria-hidden="true" tabindex="-1"></a>    <span class="co"># Add geom_histogram:</span></span>
-<span id="cb18-167"><a href="#cb18-167" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_histogram</span>(</span>
-<span id="cb18-168"><a href="#cb18-168" aria-hidden="true" tabindex="-1"></a>    <span class="co"># Apply the vector of requences created above</span></span>
-<span id="cb18-169"><a href="#cb18-169" aria-hidden="true" tabindex="-1"></a>    <span class="at">breaks =</span> breaks_6h) <span class="sc">+</span></span>
-<span id="cb18-170"><a href="#cb18-170" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Adjust x axis scales to a suitable unit:</span></span>
-<span id="cb18-171"><a href="#cb18-171" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_datetime</span>(</span>
-<span id="cb18-172"><a href="#cb18-172" aria-hidden="true" tabindex="-1"></a>    <span class="at">date_breaks =</span> <span class="st">"6 hours"</span>, </span>
-<span id="cb18-173"><a href="#cb18-173" aria-hidden="true" tabindex="-1"></a>    <span class="at">labels =</span> <span class="fu">label_date_short</span>()) <span class="sc">+</span></span>
-<span id="cb18-174"><a href="#cb18-174" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Update x and y axis labels:</span></span>
-<span id="cb18-175"><a href="#cb18-175" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">x =</span> <span class="st">"Date and time of onset"</span>, </span>
-<span id="cb18-176"><a href="#cb18-176" aria-hidden="true" tabindex="-1"></a>       <span class="at">y =</span> <span class="st">"Number of cases"</span>, </span>
-<span id="cb18-177"><a href="#cb18-177" aria-hidden="true" tabindex="-1"></a>       <span class="at">fill =</span> <span class="st">"Group"</span>, </span>
-<span id="cb18-178"><a href="#cb18-178" aria-hidden="true" tabindex="-1"></a>       <span class="at">title =</span> <span class="st">"Epicurve of the outbreak, stratified by sex"</span>,</span>
-<span id="cb18-179"><a href="#cb18-179" aria-hidden="true" tabindex="-1"></a>       <span class="at">subtitle =</span> <span class="fu">str_glue</span>(<span class="st">"Spetses, October 2024, N = {sum(copdata$case)}"</span>)) <span class="sc">+</span></span>
-<span id="cb18-180"><a href="#cb18-180" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Stratify by sex:</span></span>
-<span id="cb18-181"><a href="#cb18-181" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_wrap</span>(<span class="at">facets =</span> <span class="st">"sex"</span>,</span>
-<span id="cb18-182"><a href="#cb18-182" aria-hidden="true" tabindex="-1"></a>             <span class="at">ncol =</span> <span class="dv">2</span>) <span class="sc">+</span></span>
-<span id="cb18-183"><a href="#cb18-183" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Add theme:</span></span>
-<span id="cb18-184"><a href="#cb18-184" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_bw</span>()</span>
-<span id="cb18-185"><a href="#cb18-185" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb18-186"><a href="#cb18-186" aria-hidden="true" tabindex="-1"></a><span class="co"># Print epicurve:</span></span>
-<span id="cb18-187"><a href="#cb18-187" aria-hidden="true" tabindex="-1"></a>epicurve_strata </span>
-<span id="cb18-188"><a href="#cb18-188" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb18-189"><a href="#cb18-189" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb18-190"><a href="#cb18-190" aria-hidden="true" tabindex="-1"></a>::: {.callout-warning title="Let's stop and... think!" collapse="true"}</span>
-<span id="cb18-191"><a href="#cb18-191" aria-hidden="true" tabindex="-1"></a>What does the stratified epicurve tell you? Does the shape of the epicurve support a viral or toxic aetiology? What other information can you obtain from it?</span>
-<span id="cb18-192"><a href="#cb18-192" aria-hidden="true" tabindex="-1"></a>:::</span>
-<span id="cb18-193"><a href="#cb18-193" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb18-194"><a href="#cb18-194" aria-hidden="true" tabindex="-1"></a>::: {.callout-note title="If you've thought about the above questions, have a look here" collapse="true"}</span>
-<span id="cb18-195"><a href="#cb18-195" aria-hidden="true" tabindex="-1"></a>When constructing an epicurve, we need to decide on the resolution, i.e. the time interval for a single bar. A rule of thumb is to use one third or one fourth of the average incubation period as an interval. For our investigation this means we should use approximately a 6h interval.</span>
-<span id="cb18-196"><a href="#cb18-196" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb18-197"><a href="#cb18-197" aria-hidden="true" tabindex="-1"></a>This seems a good choice, as we saw that the daily interval was too coarse to really see the signal we are after. The epicurve and the summary of the incubation period show that there seemed to be a rapid onset of symptoms following exposure. This is in line with our previous suspicion that a virus or a toxin might be the causative agent in the outbreak.</span>
-<span id="cb18-198"><a href="#cb18-198" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb18-199"><a href="#cb18-199" aria-hidden="true" tabindex="-1"></a>The unimodal shape with the sharp peak suggests a point source, while the tail on the right-hand side could be explained by secondary cases or background noise. Also, people that only consumed a little contaminated food and therefore only a low infectious dose could have a longer incubation period and could explain the late cases.</span>
-<span id="cb18-200"><a href="#cb18-200" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb18-201"><a href="#cb18-201" aria-hidden="true" tabindex="-1"></a>The above results are in line with norovirus as the prime suspect, but the symptoms are not a textbook fit. There are too few people that experienced vomiting! Looking forward to receiving the lab results!</span>
-<span id="cb18-202"><a href="#cb18-202" aria-hidden="true" tabindex="-1"></a>:::</span>
-<span id="cb18-203"><a href="#cb18-203" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb18-204"><a href="#cb18-204" aria-hidden="true" tabindex="-1"></a><span class="fu">## 3.5. Describe the outbreak in terms of person.</span></span>
-<span id="cb18-205"><a href="#cb18-205" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb18-206"><a href="#cb18-206" aria-hidden="true" tabindex="-1"></a>You could use <span class="in">`tabyl()`</span>. For more detail on the <span class="in">`tabyl()`</span> function and the <span class="in">`adorn_XYZ`</span> helpers, see the <span class="co">[</span><span class="ot">janitor documentation</span><span class="co">](https://cran.r-project.org/web/packages/janitor/vignettes/tabyls.html)</span> or <span class="co">[</span><span class="ot">The Epidemiologist R Handbook section on tabulation</span><span class="co">](https://epirhandbook.com/en/descriptive-tables.html#tbl_janitor)</span>.</span>
-<span id="cb18-207"><a href="#cb18-207" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb18-208"><a href="#cb18-208" aria-hidden="true" tabindex="-1"></a><span class="fu">### a) Create a cross-tabulation of `case` with `group`.</span></span>
-<span id="cb18-209"><a href="#cb18-209" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb18-212"><a href="#cb18-212" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
-<span id="cb18-213"><a href="#cb18-213" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: Cross-tab cases</span></span>
-<span id="cb18-214"><a href="#cb18-214" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb18-215"><a href="#cb18-215" aria-hidden="true" tabindex="-1"></a>copdata <span class="sc">%&gt;%</span> </span>
-<span id="cb18-216"><a href="#cb18-216" aria-hidden="true" tabindex="-1"></a>  janitor<span class="sc">::</span><span class="fu">tabyl</span>(case, group) <span class="sc">%&gt;%</span> </span>
-<span id="cb18-217"><a href="#cb18-217" aria-hidden="true" tabindex="-1"></a>  <span class="fu">adorn_totals</span>() <span class="sc">%&gt;%</span> </span>
-<span id="cb18-218"><a href="#cb18-218" aria-hidden="true" tabindex="-1"></a>  <span class="fu">adorn_percentages</span>() <span class="sc">%&gt;%</span> </span>
-<span id="cb18-219"><a href="#cb18-219" aria-hidden="true" tabindex="-1"></a>  <span class="fu">adorn_pct_formatting</span>() </span>
-<span id="cb18-220"><a href="#cb18-220" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb18-221"><a href="#cb18-221" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb18-222"><a href="#cb18-222" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb18-223"><a href="#cb18-223" aria-hidden="true" tabindex="-1"></a><span class="fu">### b) Create a cross-tabulation of `case` with `sex`.</span></span>
-<span id="cb18-224"><a href="#cb18-224" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb18-227"><a href="#cb18-227" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
-<span id="cb18-228"><a href="#cb18-228" aria-hidden="true" tabindex="-1"></a>copdata <span class="sc">%&gt;%</span> </span>
-<span id="cb18-229"><a href="#cb18-229" aria-hidden="true" tabindex="-1"></a>  janitor<span class="sc">::</span><span class="fu">tabyl</span>(case, sex) <span class="sc">%&gt;%</span> </span>
-<span id="cb18-230"><a href="#cb18-230" aria-hidden="true" tabindex="-1"></a>  <span class="fu">adorn_totals</span>() <span class="sc">%&gt;%</span> </span>
-<span id="cb18-231"><a href="#cb18-231" aria-hidden="true" tabindex="-1"></a>  <span class="fu">adorn_percentages</span>() <span class="sc">%&gt;%</span> </span>
-<span id="cb18-232"><a href="#cb18-232" aria-hidden="true" tabindex="-1"></a>  <span class="fu">adorn_pct_formatting</span>() </span>
-<span id="cb18-233"><a href="#cb18-233" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb18-234"><a href="#cb18-234" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb18-235"><a href="#cb18-235" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb18-236"><a href="#cb18-236" aria-hidden="true" tabindex="-1"></a>::: {.callout-warning title="Let's stop and... think!" collapse="true"}</span>
-<span id="cb18-237"><a href="#cb18-237" aria-hidden="true" tabindex="-1"></a>What do you think of these results?</span>
-<span id="cb18-238"><a href="#cb18-238" aria-hidden="true" tabindex="-1"></a>:::</span>
-<span id="cb18-239"><a href="#cb18-239" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb18-240"><a href="#cb18-240" aria-hidden="true" tabindex="-1"></a>::: {.callout-note title="If you've thought about the above questions, have a look here" collapse="true"}</span>
-<span id="cb18-241"><a href="#cb18-241" aria-hidden="true" tabindex="-1"></a>The distribution of the cohort regarding sex, group and class also didn't reveal anything unusual. Students seem a bit more affected by the outbreak than teachers and the attack rate is higher for older students in higher classes. This, however, is a purely descriptive result.</span>
-<span id="cb18-242"><a href="#cb18-242" aria-hidden="true" tabindex="-1"></a>:::</span>
-<span id="cb18-243"><a href="#cb18-243" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb18-244"><a href="#cb18-244" aria-hidden="true" tabindex="-1"></a>::: {.callout-important title="Do you feel like a challenge?" collapse="true"}</span>
-<span id="cb18-245"><a href="#cb18-245" aria-hidden="true" tabindex="-1"></a>If you want, create an age-sex pyramid using the <span class="in">`apyramid`</span> package. To do so, first create an age category variable with the <span class="in">`epkit`</span> function <span class="in">`age_categories()`</span>.</span>
-<span id="cb18-246"><a href="#cb18-246" aria-hidden="true" tabindex="-1"></a>:::</span>
-<span id="cb18-247"><a href="#cb18-247" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb18-248"><a href="#cb18-248" aria-hidden="true" tabindex="-1"></a><span class="fu">### c) Extra - Age-sex pyramid of cases.</span></span>
-<span id="cb18-249"><a href="#cb18-249" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb18-250"><a href="#cb18-250" aria-hidden="true" tabindex="-1"></a>Hint: Change <span class="in">`show_midpoint = FALSE`</span> to <span class="in">`TRUE`</span> to see skewedness in the data patterns more easily.</span>
-<span id="cb18-251"><a href="#cb18-251" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb18-254"><a href="#cb18-254" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
-<span id="cb18-255"><a href="#cb18-255" aria-hidden="true" tabindex="-1"></a>copdata <span class="ot">&lt;-</span> copdata <span class="sc">%&gt;%</span> </span>
-<span id="cb18-256"><a href="#cb18-256" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Create age categories:</span></span>
-<span id="cb18-257"><a href="#cb18-257" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">age_cat =</span> epikit<span class="sc">::</span><span class="fu">age_categories</span>(</span>
-<span id="cb18-258"><a href="#cb18-258" aria-hidden="true" tabindex="-1"></a>    <span class="co"># Name of age column:</span></span>
-<span id="cb18-259"><a href="#cb18-259" aria-hidden="true" tabindex="-1"></a>    <span class="at">x =</span> age, </span>
-<span id="cb18-260"><a href="#cb18-260" aria-hidden="true" tabindex="-1"></a>    <span class="co"># Define the age categories:</span></span>
-<span id="cb18-261"><a href="#cb18-261" aria-hidden="true" tabindex="-1"></a>    <span class="at">breakers =</span> <span class="fu">c</span>(<span class="dv">0</span>, <span class="dv">10</span>, <span class="dv">16</span>, <span class="dv">18</span>, <span class="dv">20</span>, <span class="dv">50</span>, <span class="dv">70</span>)</span>
-<span id="cb18-262"><a href="#cb18-262" aria-hidden="true" tabindex="-1"></a>    )</span>
-<span id="cb18-263"><a href="#cb18-263" aria-hidden="true" tabindex="-1"></a>  )</span>
-<span id="cb18-264"><a href="#cb18-264" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb18-265"><a href="#cb18-265" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb18-266"><a href="#cb18-266" aria-hidden="true" tabindex="-1"></a><span class="co"># Check age categories:</span></span>
-<span id="cb18-267"><a href="#cb18-267" aria-hidden="true" tabindex="-1"></a>janitor<span class="sc">::</span><span class="fu">tabyl</span>(copdata, age_cat)</span>
-<span id="cb18-268"><a href="#cb18-268" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb18-269"><a href="#cb18-269" aria-hidden="true" tabindex="-1"></a><span class="co"># Pipe copdata:</span></span>
-<span id="cb18-270"><a href="#cb18-270" aria-hidden="true" tabindex="-1"></a>agesex <span class="ot">&lt;-</span> copdata <span class="sc">%&gt;%</span> </span>
-<span id="cb18-271"><a href="#cb18-271" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Filter for cases only:</span></span>
-<span id="cb18-272"><a href="#cb18-272" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(case <span class="sc">==</span> <span class="cn">TRUE</span>) <span class="sc">%&gt;%</span> </span>
-<span id="cb18-273"><a href="#cb18-273" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Create age sex pyramid:</span></span>
-<span id="cb18-274"><a href="#cb18-274" aria-hidden="true" tabindex="-1"></a>  apyramid<span class="sc">::</span><span class="fu">age_pyramid</span>(</span>
-<span id="cb18-275"><a href="#cb18-275" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Specify column containing age categories:</span></span>
-<span id="cb18-276"><a href="#cb18-276" aria-hidden="true" tabindex="-1"></a>    <span class="at">age_group =</span> <span class="st">"age_cat"</span>,</span>
-<span id="cb18-277"><a href="#cb18-277" aria-hidden="true" tabindex="-1"></a>    <span class="co"># Specify column containing sex:</span></span>
-<span id="cb18-278"><a href="#cb18-278" aria-hidden="true" tabindex="-1"></a>    <span class="at">split_by =</span> <span class="st">"sex"</span>, </span>
-<span id="cb18-279"><a href="#cb18-279" aria-hidden="true" tabindex="-1"></a>    <span class="co"># Don't show midpoint on the graph:</span></span>
-<span id="cb18-280"><a href="#cb18-280" aria-hidden="true" tabindex="-1"></a>    <span class="at">show_midpoint =</span> <span class="cn">FALSE</span></span>
-<span id="cb18-281"><a href="#cb18-281" aria-hidden="true" tabindex="-1"></a>    )</span>
-<span id="cb18-282"><a href="#cb18-282" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb18-283"><a href="#cb18-283" aria-hidden="true" tabindex="-1"></a><span class="co"># Print plot:</span></span>
-<span id="cb18-284"><a href="#cb18-284" aria-hidden="true" tabindex="-1"></a>agesex</span>
-<span id="cb18-285"><a href="#cb18-285" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb18-286"><a href="#cb18-286" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb18-287"><a href="#cb18-287" aria-hidden="true" tabindex="-1"></a><span class="fu">## 3.6 Explore the distribution of all clinical signs (symptoms).</span></span>
-<span id="cb18-288"><a href="#cb18-288" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb18-289"><a href="#cb18-289" aria-hidden="true" tabindex="-1"></a><span class="fu">### a) Create a summary table with symptoms stratified by case definition, and present an overall column as well. You could use `tabyl()` or `gtsummary::tbl_summary()`. You can find further information about `gtsummary` in [The Epidemiologist R handbook section on gtsummary](https://www.epirhandbook.com/en/descriptive-tables.html#tbl_gt).</span></span>
-<span id="cb18-290"><a href="#cb18-290" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb18-293"><a href="#cb18-293" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
-<span id="cb18-294"><a href="#cb18-294" aria-hidden="true" tabindex="-1"></a><span class="co"># Create summary table:</span></span>
-<span id="cb18-295"><a href="#cb18-295" aria-hidden="true" tabindex="-1"></a>tabsymptoms <span class="ot">&lt;-</span> copdata <span class="sc">%&gt;%</span> </span>
-<span id="cb18-296"><a href="#cb18-296" aria-hidden="true" tabindex="-1"></a>    <span class="co"># Select person characteristics to summarise:</span></span>
-<span id="cb18-297"><a href="#cb18-297" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(case, diarrhoea, bloody, vomiting,</span>
-<span id="cb18-298"><a href="#cb18-298" aria-hidden="true" tabindex="-1"></a>             abdo, nausea, fever,headache, jointpain) <span class="sc">%&gt;%</span> </span>
-<span id="cb18-299"><a href="#cb18-299" aria-hidden="true" tabindex="-1"></a>  <span class="co"># transform clinical symptoms to factors, so NA can be accounted properly in the table</span></span>
-<span id="cb18-300"><a href="#cb18-300" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">mutate</span>(</span>
-<span id="cb18-301"><a href="#cb18-301" aria-hidden="true" tabindex="-1"></a>    <span class="fu">across</span>(<span class="at">.cols =</span> <span class="fu">c</span>(diarrhoea, bloody, vomiting,</span>
-<span id="cb18-302"><a href="#cb18-302" aria-hidden="true" tabindex="-1"></a>             abdo, nausea, fever,headache, jointpain), </span>
-<span id="cb18-303"><a href="#cb18-303" aria-hidden="true" tabindex="-1"></a>           <span class="at">.fns =</span> <span class="sc">~</span><span class="fu">as.factor</span>(.))) <span class="sc">%&gt;%</span></span>
-<span id="cb18-304"><a href="#cb18-304" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Make NA a explicit level of factor variables</span></span>
-<span id="cb18-305"><a href="#cb18-305" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">mutate</span>(</span>
-<span id="cb18-306"><a href="#cb18-306" aria-hidden="true" tabindex="-1"></a>    <span class="fu">across</span>(<span class="at">.cols =</span> <span class="fu">c</span>(diarrhoea, bloody, vomiting,</span>
-<span id="cb18-307"><a href="#cb18-307" aria-hidden="true" tabindex="-1"></a>           abdo, nausea, fever,headache, jointpain),</span>
-<span id="cb18-308"><a href="#cb18-308" aria-hidden="true" tabindex="-1"></a>           <span class="at">.fns =</span> <span class="sc">~</span>forcats<span class="sc">::</span><span class="fu">fct_na_value_to_level</span>(.))) <span class="sc">%&gt;%</span> </span>
-<span id="cb18-309"><a href="#cb18-309" aria-hidden="true" tabindex="-1"></a>    </span>
-<span id="cb18-310"><a href="#cb18-310" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Create the summary table:</span></span>
-<span id="cb18-311"><a href="#cb18-311" aria-hidden="true" tabindex="-1"></a>  gtsummary<span class="sc">::</span><span class="fu">tbl_summary</span>(</span>
-<span id="cb18-312"><a href="#cb18-312" aria-hidden="true" tabindex="-1"></a>    <span class="co"># Stratify by case:</span></span>
-<span id="cb18-313"><a href="#cb18-313" aria-hidden="true" tabindex="-1"></a>    <span class="at">by =</span> case, </span>
-<span id="cb18-314"><a href="#cb18-314" aria-hidden="true" tabindex="-1"></a>    <span class="co"># Calculate row percentages:</span></span>
-<span id="cb18-315"><a href="#cb18-315" aria-hidden="true" tabindex="-1"></a>    <span class="at">percent =</span> <span class="st">"column"</span>,</span>
-<span id="cb18-316"><a href="#cb18-316" aria-hidden="true" tabindex="-1"></a>    <span class="co"># Create nice labels:</span></span>
-<span id="cb18-317"><a href="#cb18-317" aria-hidden="true" tabindex="-1"></a>    <span class="at">label  =</span> <span class="fu">list</span>(</span>
-<span id="cb18-318"><a href="#cb18-318" aria-hidden="true" tabindex="-1"></a>      diarrhoea   <span class="sc">~</span> <span class="st">"Diarrhoea"</span>,                           </span>
-<span id="cb18-319"><a href="#cb18-319" aria-hidden="true" tabindex="-1"></a>      bloody      <span class="sc">~</span> <span class="st">"Dysentary"</span>,</span>
-<span id="cb18-320"><a href="#cb18-320" aria-hidden="true" tabindex="-1"></a>      vomiting    <span class="sc">~</span> <span class="st">"Vomiting"</span>,</span>
-<span id="cb18-321"><a href="#cb18-321" aria-hidden="true" tabindex="-1"></a>      abdo        <span class="sc">~</span> <span class="st">"Abdominal pain"</span>,</span>
-<span id="cb18-322"><a href="#cb18-322" aria-hidden="true" tabindex="-1"></a>      nausea      <span class="sc">~</span> <span class="st">"Nausea"</span>, </span>
-<span id="cb18-323"><a href="#cb18-323" aria-hidden="true" tabindex="-1"></a>      fever       <span class="sc">~</span> <span class="st">"Fever"</span>, </span>
-<span id="cb18-324"><a href="#cb18-324" aria-hidden="true" tabindex="-1"></a>      headache    <span class="sc">~</span> <span class="st">"Headache"</span>, </span>
-<span id="cb18-325"><a href="#cb18-325" aria-hidden="true" tabindex="-1"></a>      jointpain   <span class="sc">~</span> <span class="st">"Joint pain"</span>)</span>
-<span id="cb18-326"><a href="#cb18-326" aria-hidden="true" tabindex="-1"></a>    </span>
-<span id="cb18-327"><a href="#cb18-327" aria-hidden="true" tabindex="-1"></a>  ) <span class="sc">%&gt;%</span> </span>
-<span id="cb18-328"><a href="#cb18-328" aria-hidden="true" tabindex="-1"></a>  </span>
-<span id="cb18-329"><a href="#cb18-329" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Add totals:</span></span>
-<span id="cb18-330"><a href="#cb18-330" aria-hidden="true" tabindex="-1"></a>  <span class="fu">add_overall</span>() <span class="sc">%&gt;%</span> </span>
-<span id="cb18-331"><a href="#cb18-331" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Make variable names bold and italics:</span></span>
-<span id="cb18-332"><a href="#cb18-332" aria-hidden="true" tabindex="-1"></a>  <span class="fu">bold_labels</span>() <span class="sc">%&gt;%</span> </span>
-<span id="cb18-333"><a href="#cb18-333" aria-hidden="true" tabindex="-1"></a>  <span class="fu">italicize_labels</span>() <span class="sc">%&gt;%</span> </span>
-<span id="cb18-334"><a href="#cb18-334" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Modify header:</span></span>
-<span id="cb18-335"><a href="#cb18-335" aria-hidden="true" tabindex="-1"></a>  <span class="fu">modify_header</span>(</span>
-<span id="cb18-336"><a href="#cb18-336" aria-hidden="true" tabindex="-1"></a>    <span class="at">label =</span> <span class="st">"**Characteristic**"</span>,</span>
-<span id="cb18-337"><a href="#cb18-337" aria-hidden="true" tabindex="-1"></a>    <span class="at">stat_0 =</span> <span class="st">"**Overall**</span><span class="sc">\n</span><span class="st"> **N** = {N}"</span>,</span>
-<span id="cb18-338"><a href="#cb18-338" aria-hidden="true" tabindex="-1"></a>    <span class="at">stat_1 =</span> <span class="st">"**Non-case**</span><span class="sc">\n</span><span class="st"> **N** = {n}"</span>,</span>
-<span id="cb18-339"><a href="#cb18-339" aria-hidden="true" tabindex="-1"></a>    <span class="at">stat_2 =</span> <span class="st">"**Case**</span><span class="sc">\n</span><span class="st"> **N** = {n}"</span>, </span>
-<span id="cb18-340"><a href="#cb18-340" aria-hidden="true" tabindex="-1"></a>    )</span>
-<span id="cb18-341"><a href="#cb18-341" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb18-342"><a href="#cb18-342" aria-hidden="true" tabindex="-1"></a><span class="co"># Print the table:</span></span>
-<span id="cb18-343"><a href="#cb18-343" aria-hidden="true" tabindex="-1"></a>tabsymptoms</span>
-<span id="cb18-344"><a href="#cb18-344" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb18-345"><a href="#cb18-345" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb18-346"><a href="#cb18-346" aria-hidden="true" tabindex="-1"></a>::: {.callout-warning title="Let's stop and... think!" collapse="true"}</span>
-<span id="cb18-347"><a href="#cb18-347" aria-hidden="true" tabindex="-1"></a>Do you think the symptoms selected for the case definition were the right ones, or would you change anything?</span>
-<span id="cb18-348"><a href="#cb18-348" aria-hidden="true" tabindex="-1"></a>:::</span>
-<span id="cb18-349"><a href="#cb18-349" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb18-350"><a href="#cb18-350" aria-hidden="true" tabindex="-1"></a>::: {.callout-important title="Do you feel like a challenge?" collapse="true"}</span>
-<span id="cb18-351"><a href="#cb18-351" aria-hidden="true" tabindex="-1"></a>If you want, present the symptoms in an ordered bar chart. To do so, reshape the data using <span class="in">`pivot_longer()`</span>, and then <span class="in">`group_by()`</span>, <span class="in">`summarise()`</span> and <span class="in">`count()`</span> to tally up the counts for each symptom, stratified by case definition. Use <span class="in">`ggplot2::coord_flip()`</span> for a better visualisation.</span>
-<span id="cb18-352"><a href="#cb18-352" aria-hidden="true" tabindex="-1"></a>:::</span>
-<span id="cb18-353"><a href="#cb18-353" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb18-354"><a href="#cb18-354" aria-hidden="true" tabindex="-1"></a><span class="fu">### b) Extra - Bar plot of symptoms stratified by case definition.</span></span>
-<span id="cb18-355"><a href="#cb18-355" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb18-356"><a href="#cb18-356" aria-hidden="true" tabindex="-1"></a><span class="in">```{r symptom_barplot}</span></span>
-<span id="cb18-357"><a href="#cb18-357" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb18-358"><a href="#cb18-358" aria-hidden="true" tabindex="-1"></a><span class="co"># Create list of symptom variables:</span></span>
-<span id="cb18-359"><a href="#cb18-359" aria-hidden="true" tabindex="-1"></a>symptoms <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">"diarrhoea"</span>, </span>
-<span id="cb18-360"><a href="#cb18-360" aria-hidden="true" tabindex="-1"></a>              <span class="st">"bloody"</span>, </span>
-<span id="cb18-361"><a href="#cb18-361" aria-hidden="true" tabindex="-1"></a>              <span class="st">"vomiting"</span>, </span>
-<span id="cb18-362"><a href="#cb18-362" aria-hidden="true" tabindex="-1"></a>              <span class="st">"abdo"</span>, </span>
-<span id="cb18-363"><a href="#cb18-363" aria-hidden="true" tabindex="-1"></a>              <span class="st">"nausea"</span>, </span>
-<span id="cb18-364"><a href="#cb18-364" aria-hidden="true" tabindex="-1"></a>              <span class="st">"fever"</span>, </span>
-<span id="cb18-365"><a href="#cb18-365" aria-hidden="true" tabindex="-1"></a>              <span class="st">"headache"</span>, </span>
-<span id="cb18-366"><a href="#cb18-366" aria-hidden="true" tabindex="-1"></a>              <span class="st">"jointpain"</span>)</span>
-<span id="cb18-367"><a href="#cb18-367" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb18-368"><a href="#cb18-368" aria-hidden="true" tabindex="-1"></a><span class="co"># Create nice labels for case definition:</span></span>
-<span id="cb18-369"><a href="#cb18-369" aria-hidden="true" tabindex="-1"></a>caselabs <span class="ot">&lt;-</span> ggplot2<span class="sc">::</span><span class="fu">as_labeller</span>(<span class="fu">c</span>(<span class="st">`</span><span class="at">FALSE</span><span class="st">`</span> <span class="ot">=</span> <span class="st">"Non-case"</span>, </span>
-<span id="cb18-370"><a href="#cb18-370" aria-hidden="true" tabindex="-1"></a>                                   <span class="st">`</span><span class="at">TRUE</span><span class="st">`</span> <span class="ot">=</span> <span class="st">"Case"</span>))</span>
-<span id="cb18-371"><a href="#cb18-371" aria-hidden="true" tabindex="-1"></a><span class="co"># Select variables and cases:</span></span>
-<span id="cb18-372"><a href="#cb18-372" aria-hidden="true" tabindex="-1"></a>symptom_bar <span class="ot">&lt;-</span> copdata <span class="sc">%&gt;%</span> </span>
-<span id="cb18-373"><a href="#cb18-373" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Select symptom columns:</span></span>
-<span id="cb18-374"><a href="#cb18-374" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(case, <span class="fu">c</span>(<span class="fu">all_of</span>(symptoms))) <span class="sc">%&gt;%</span></span>
-<span id="cb18-375"><a href="#cb18-375" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Drop NAs:</span></span>
-<span id="cb18-376"><a href="#cb18-376" aria-hidden="true" tabindex="-1"></a>  <span class="fu">drop_na</span>() <span class="sc">%&gt;%</span> </span>
-<span id="cb18-377"><a href="#cb18-377" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Reshape (pivot longer):</span></span>
-<span id="cb18-378"><a href="#cb18-378" aria-hidden="true" tabindex="-1"></a>  <span class="fu">pivot_longer</span>(<span class="sc">!</span>case, </span>
-<span id="cb18-379"><a href="#cb18-379" aria-hidden="true" tabindex="-1"></a>               <span class="at">names_to =</span> <span class="st">"Symptoms"</span>, </span>
-<span id="cb18-380"><a href="#cb18-380" aria-hidden="true" tabindex="-1"></a>               <span class="at">values_drop_na =</span> <span class="cn">TRUE</span>) <span class="sc">%&gt;%</span> </span>
-<span id="cb18-381"><a href="#cb18-381" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Keep only TRUE values:</span></span>
-<span id="cb18-382"><a href="#cb18-382" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(value <span class="sc">==</span> <span class="cn">TRUE</span>) <span class="sc">%&gt;%</span> </span>
-<span id="cb18-383"><a href="#cb18-383" aria-hidden="true" tabindex="-1"></a> </span>
-<span id="cb18-384"><a href="#cb18-384" aria-hidden="true" tabindex="-1"></a>   <span class="co"># Group by symptoms and case:</span></span>
-<span id="cb18-385"><a href="#cb18-385" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(Symptoms, case) <span class="sc">%&gt;%</span> </span>
-<span id="cb18-386"><a href="#cb18-386" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Count for each symptom by case:</span></span>
-<span id="cb18-387"><a href="#cb18-387" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">summarise</span>(<span class="at">count =</span> <span class="fu">n</span>()) <span class="sc">%&gt;%</span> </span>
-<span id="cb18-388"><a href="#cb18-388" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Create plot:</span></span>
-<span id="cb18-389"><a href="#cb18-389" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggplot</span>(</span>
-<span id="cb18-390"><a href="#cb18-390" aria-hidden="true" tabindex="-1"></a>    <span class="at">mapping =</span> <span class="fu">aes</span>(</span>
-<span id="cb18-391"><a href="#cb18-391" aria-hidden="true" tabindex="-1"></a>    <span class="co"># Order symptom bars so most common ones are ontop:</span></span>
-<span id="cb18-392"><a href="#cb18-392" aria-hidden="true" tabindex="-1"></a>    <span class="at">x =</span> <span class="fu">reorder</span>(Symptoms, <span class="fu">desc</span>(count), <span class="at">decreasing =</span> <span class="cn">TRUE</span>), </span>
-<span id="cb18-393"><a href="#cb18-393" aria-hidden="true" tabindex="-1"></a>    <span class="at">y =</span> count)) <span class="sc">+</span></span>
-<span id="cb18-394"><a href="#cb18-394" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Display bars as proportions</span></span>
-<span id="cb18-395"><a href="#cb18-395" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_bar</span>(<span class="at">stat =</span> <span class="st">"identity"</span>) <span class="sc">+</span></span>
-<span id="cb18-396"><a href="#cb18-396" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Update x axis label:</span></span>
-<span id="cb18-397"><a href="#cb18-397" aria-hidden="true" tabindex="-1"></a>  <span class="fu">xlab</span>(<span class="st">"Symptoms"</span>) <span class="sc">+</span></span>
-<span id="cb18-398"><a href="#cb18-398" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Update y axis label:</span></span>
-<span id="cb18-399"><a href="#cb18-399" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ylab</span>(<span class="st">"Proportion of respondents"</span>) <span class="sc">+</span></span>
-<span id="cb18-400"><a href="#cb18-400" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Flip plot on its side so symptom labels are clear:</span></span>
-<span id="cb18-401"><a href="#cb18-401" aria-hidden="true" tabindex="-1"></a>  <span class="fu">coord_flip</span>() <span class="sc">+</span></span>
-<span id="cb18-402"><a href="#cb18-402" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Facet the plot by (labelled) case:</span></span>
-<span id="cb18-403"><a href="#cb18-403" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_wrap</span>(<span class="at">facets =</span> <span class="st">"case"</span>,</span>
-<span id="cb18-404"><a href="#cb18-404" aria-hidden="true" tabindex="-1"></a>             <span class="at">labeller =</span> caselabs,</span>
-<span id="cb18-405"><a href="#cb18-405" aria-hidden="true" tabindex="-1"></a>             <span class="at">ncol =</span> <span class="dv">2</span>)</span>
-<span id="cb18-406"><a href="#cb18-406" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb18-407"><a href="#cb18-407" aria-hidden="true" tabindex="-1"></a><span class="co"># Print plot:</span></span>
-<span id="cb18-408"><a href="#cb18-408" aria-hidden="true" tabindex="-1"></a>symptom_bar</span>
-<span id="cb18-409"><a href="#cb18-409" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb18-410"><a href="#cb18-410" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb18-411"><a href="#cb18-411" aria-hidden="true" tabindex="-1"></a><span class="fu">## 3.7 Attack proportions</span></span>
-<span id="cb18-412"><a href="#cb18-412" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb18-413"><a href="#cb18-413" aria-hidden="true" tabindex="-1"></a><span class="fu">### a) Calculate the overall attack proportions (percentage of cases among the total observed individuals). You could use `tabyl()`.</span></span>
-<span id="cb18-414"><a href="#cb18-414" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb18-417"><a href="#cb18-417" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
-<span id="cb18-418"><a href="#cb18-418" aria-hidden="true" tabindex="-1"></a><span class="co"># Create table of case status:</span></span>
-<span id="cb18-419"><a href="#cb18-419" aria-hidden="true" tabindex="-1"></a>total_ap <span class="ot">&lt;-</span> <span class="fu">tabyl</span>(copdata, case) <span class="sc">%&gt;%</span> </span>
-<span id="cb18-420"><a href="#cb18-420" aria-hidden="true" tabindex="-1"></a> <span class="co"># Add row totals:</span></span>
-<span id="cb18-421"><a href="#cb18-421" aria-hidden="true" tabindex="-1"></a>  <span class="fu">adorn_totals</span>(<span class="at">where =</span> <span class="st">"row"</span>) <span class="sc">%&gt;%</span> </span>
-<span id="cb18-422"><a href="#cb18-422" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Add percentages with 1 digit after the decimal point:</span></span>
-<span id="cb18-423"><a href="#cb18-423" aria-hidden="true" tabindex="-1"></a>  <span class="fu">adorn_pct_formatting</span>(<span class="at">digits =</span> <span class="dv">1</span>) <span class="sc">%&gt;%</span> </span>
-<span id="cb18-424"><a href="#cb18-424" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Filter to rows where case is TRUE:</span></span>
-<span id="cb18-425"><a href="#cb18-425" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(case <span class="sc">==</span> <span class="cn">TRUE</span>) <span class="sc">%&gt;%</span> </span>
-<span id="cb18-426"><a href="#cb18-426" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Select the column percent:</span></span>
-<span id="cb18-427"><a href="#cb18-427" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(percent) <span class="sc">%&gt;%</span> </span>
-<span id="cb18-428"><a href="#cb18-428" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Extract (pull) the value from this cell:</span></span>
-<span id="cb18-429"><a href="#cb18-429" aria-hidden="true" tabindex="-1"></a>  <span class="fu">pull</span>()</span>
-<span id="cb18-430"><a href="#cb18-430" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb18-431"><a href="#cb18-431" aria-hidden="true" tabindex="-1"></a><span class="co"># Print result:</span></span>
-<span id="cb18-432"><a href="#cb18-432" aria-hidden="true" tabindex="-1"></a>total_ap</span>
-<span id="cb18-433"><a href="#cb18-433" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb18-434"><a href="#cb18-434" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb18-435"><a href="#cb18-435" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb18-436"><a href="#cb18-436" aria-hidden="true" tabindex="-1"></a>::: {.callout-warning title="Let's stop and... think!" collapse="true"}</span>
-<span id="cb18-437"><a href="#cb18-437" aria-hidden="true" tabindex="-1"></a>What can you infer from the overall attack proportion about this outbreak and possible vehicles/exposures?</span>
-<span id="cb18-438"><a href="#cb18-438" aria-hidden="true" tabindex="-1"></a>:::</span>
-<span id="cb18-439"><a href="#cb18-439" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb18-440"><a href="#cb18-440" aria-hidden="true" tabindex="-1"></a>::: {.callout-note title="If you've thought about the above questions, have a look here" collapse="true"}</span>
-<span id="cb18-441"><a href="#cb18-441" aria-hidden="true" tabindex="-1"></a>The overall attack proportion is 57.3%. This means that more than half of the people who ate a meal were cases!</span>
-<span id="cb18-442"><a href="#cb18-442" aria-hidden="true" tabindex="-1"></a>:::</span>
-<span id="cb18-443"><a href="#cb18-443" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb18-444"><a href="#cb18-444" aria-hidden="true" tabindex="-1"></a><span class="fu">### b) Calculate attack proportions for `group`, `class` and `sex` by case status. You could use `tabyl()` (as you did in 3.4) or `gtsummary::tbl_summary`.</span></span>
-<span id="cb18-445"><a href="#cb18-445" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb18-448"><a href="#cb18-448" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
-<span id="cb18-449"><a href="#cb18-449" aria-hidden="true" tabindex="-1"></a><span class="co"># Table to calculate attack proportions:</span></span>
-<span id="cb18-450"><a href="#cb18-450" aria-hidden="true" tabindex="-1"></a>attack_prop <span class="ot">&lt;-</span> copdata <span class="sc">%&gt;%</span> </span>
-<span id="cb18-451"><a href="#cb18-451" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Select columns:</span></span>
-<span id="cb18-452"><a href="#cb18-452" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span> (case, class, group, sex) <span class="sc">%&gt;%</span> </span>
-<span id="cb18-453"><a href="#cb18-453" aria-hidden="true" tabindex="-1"></a>  </span>
-<span id="cb18-454"><a href="#cb18-454" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Create table:</span></span>
-<span id="cb18-455"><a href="#cb18-455" aria-hidden="true" tabindex="-1"></a>  <span class="fu">tbl_summary</span>(</span>
-<span id="cb18-456"><a href="#cb18-456" aria-hidden="true" tabindex="-1"></a>    <span class="co"># Stratified by case</span></span>
-<span id="cb18-457"><a href="#cb18-457" aria-hidden="true" tabindex="-1"></a>    <span class="at">by =</span> case,</span>
-<span id="cb18-458"><a href="#cb18-458" aria-hidden="true" tabindex="-1"></a>    <span class="co"># with row percentages</span></span>
-<span id="cb18-459"><a href="#cb18-459" aria-hidden="true" tabindex="-1"></a>    <span class="at">percent =</span> <span class="st">"row"</span>) <span class="sc">%&gt;%</span></span>
-<span id="cb18-460"><a href="#cb18-460" aria-hidden="true" tabindex="-1"></a>  </span>
-<span id="cb18-461"><a href="#cb18-461" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Add totals:</span></span>
-<span id="cb18-462"><a href="#cb18-462" aria-hidden="true" tabindex="-1"></a>  <span class="fu">add_overall</span>() <span class="sc">%&gt;%</span></span>
-<span id="cb18-463"><a href="#cb18-463" aria-hidden="true" tabindex="-1"></a>  </span>
-<span id="cb18-464"><a href="#cb18-464" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Make variable names bold and italics:</span></span>
-<span id="cb18-465"><a href="#cb18-465" aria-hidden="true" tabindex="-1"></a>  <span class="fu">bold_labels</span>() <span class="sc">%&gt;%</span> </span>
-<span id="cb18-466"><a href="#cb18-466" aria-hidden="true" tabindex="-1"></a>  <span class="fu">italicize_labels</span>() <span class="sc">%&gt;%</span> </span>
-<span id="cb18-467"><a href="#cb18-467" aria-hidden="true" tabindex="-1"></a>  </span>
-<span id="cb18-468"><a href="#cb18-468" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Modify header:</span></span>
-<span id="cb18-469"><a href="#cb18-469" aria-hidden="true" tabindex="-1"></a>  <span class="fu">modify_header</span>(</span>
-<span id="cb18-470"><a href="#cb18-470" aria-hidden="true" tabindex="-1"></a>    <span class="at">label =</span> <span class="st">"**Characteristic**"</span>,</span>
-<span id="cb18-471"><a href="#cb18-471" aria-hidden="true" tabindex="-1"></a>    <span class="at">stat_0 =</span> <span class="st">"**Overall** **N** = {N}"</span>,</span>
-<span id="cb18-472"><a href="#cb18-472" aria-hidden="true" tabindex="-1"></a>    <span class="at">stat_1 =</span> <span class="st">"**Non-case** **N** = {n}"</span>,</span>
-<span id="cb18-473"><a href="#cb18-473" aria-hidden="true" tabindex="-1"></a>    <span class="at">stat_2 =</span> <span class="st">"**Case** **N** = {n}"</span></span>
-<span id="cb18-474"><a href="#cb18-474" aria-hidden="true" tabindex="-1"></a>  )</span>
-<span id="cb18-475"><a href="#cb18-475" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb18-476"><a href="#cb18-476" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb18-477"><a href="#cb18-477" aria-hidden="true" tabindex="-1"></a><span class="co"># Print table:</span></span>
-<span id="cb18-478"><a href="#cb18-478" aria-hidden="true" tabindex="-1"></a>attack_prop</span>
-<span id="cb18-479"><a href="#cb18-479" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb18-480"><a href="#cb18-480" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb18-481"><a href="#cb18-481" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb18-482"><a href="#cb18-482" aria-hidden="true" tabindex="-1"></a><span class="fu">## 3.8 Draft the relevant paragraph(s) and table(s) and/or figure(s) in your outbreak report (Methods and results).</span></span>
-<span id="cb18-483"><a href="#cb18-483" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb18-484"><a href="#cb18-484" aria-hidden="true" tabindex="-1"></a>::: {.callout-tip title="Need a little bit of help?" collapse="true"}</span>
-<span id="cb18-485"><a href="#cb18-485" aria-hidden="true" tabindex="-1"></a>To save your tabyl table as a .docx, you could convert your tabyl to flextable (<span class="in">`as_flex_table()`</span>), ensure only one line per row (<span class="in">`flextable::autofit()`</span>) and save as .docx (<span class="in">`flextable::save_as_docx(path = "nameoftable.docx"`</span>)</span>
-<span id="cb18-486"><a href="#cb18-486" aria-hidden="true" tabindex="-1"></a>:::</span>
-</code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-</div></div></div></div></div>
-</div> <!-- /content -->
-
-
-
-</body></html>
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-  <li><a href="#learning-outcomes" id="toc-learning-outcomes" class="nav-link active" data-scroll-target="#learning-outcomes">1. Learning outcomes</a></li>
-  <li><a href="#storyplot-description" id="toc-storyplot-description" class="nav-link" data-scroll-target="#storyplot-description">2. Story/plot description</a></li>
-  <li><a href="#questionsassignments" id="toc-questionsassignments" class="nav-link" data-scroll-target="#questionsassignments">3. Questions/Assignments</a></li>
-  <li><a href="#install-packages-and-load-libraries" id="toc-install-packages-and-load-libraries" class="nav-link" data-scroll-target="#install-packages-and-load-libraries">3.1. Install packages and load libraries</a></li>
-  <li><a href="#import-your-data" id="toc-import-your-data" class="nav-link" data-scroll-target="#import-your-data">3.2. Import your data</a></li>
-  <li><a href="#hypothesis-tests-for-other-variables" id="toc-hypothesis-tests-for-other-variables" class="nav-link" data-scroll-target="#hypothesis-tests-for-other-variables">3.3. Hypothesis tests for other variables</a></li>
-  <li><a href="#risk-ratios" id="toc-risk-ratios" class="nav-link" data-scroll-target="#risk-ratios">3.4. Risk Ratios</a>
-  <ul class="collapse">
-  <li><a href="#a-calculate-95-ci-risk-ratios-for-food" id="toc-a-calculate-95-ci-risk-ratios-for-food" class="nav-link" data-scroll-target="#a-calculate-95-ci-risk-ratios-for-food">a) Calculate 95% CI Risk Ratios for food</a></li>
-  <li><a href="#b-prepare-the-rr-table-for-publication" id="toc-b-prepare-the-rr-table-for-publication" class="nav-link" data-scroll-target="#b-prepare-the-rr-table-for-publication">b) Prepare the RR table for publication</a></li>
-  </ul></li>
-  <li><a href="#optional-dose-response" id="toc-optional-dose-response" class="nav-link" data-scroll-target="#optional-dose-response">3.5. (Optional) Dose Response</a></li>
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-<div class="quarto-title-block"><div><h1 class="title">Univariable analysis</h1><button type="button" class="btn code-tools-button dropdown-toggle" id="quarto-code-tools-menu" data-bs-toggle="dropdown" aria-expanded="false"><i class="bi"></i> Code</button><ul class="dropdown-menu dropdown-menu-end" aria-labelelledby="quarto-code-tools-menu"><li><a id="quarto-show-all-code" class="dropdown-item" href="javascript:void(0)" role="button">Show All Code</a></li><li><a id="quarto-hide-all-code" class="dropdown-item" href="javascript:void(0)" role="button">Hide All Code</a></li><li><hr class="dropdown-divider"></li><li><a id="quarto-view-source" class="dropdown-item" href="javascript:void(0)" role="button">View Source</a></li></ul></div></div>
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-  
-
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-
-<section id="learning-outcomes" class="level2">
-<h2 class="anchored" data-anchor-id="learning-outcomes">1. Learning outcomes</h2>
-<p>At the end of the session, participants will be able to:</p>
-<ul>
-<li><p>Perform hypothesis tests.</p></li>
-<li><p>Estimate risk ratios (also called relative risk) for categorical data.</p></li>
-<li><p>Interpret the univariable results.</p></li>
-<li><p>(Optional) Investigate dose-response relationships in categorical data.2. Story/plot description</p></li>
-</ul>
-</section>
-<section id="storyplot-description" class="level2">
-<h2 class="anchored" data-anchor-id="storyplot-description">2. Story/plot description</h2>
-<p>You have just estimated risk ratios (RR) of individual food items manually to better understand the concept. RR give you an idea of which food items could be the culprit(s) of the outbreak.</p>
-<p>Now, you will practice performing hypothesis testing and calculating RR in R to investigate the associations of suspicious food items with the disease.</p>
-<p>Note that, although this is not an exhaustive list of tests, the following can be useful for this exercise (relevant for comparisons between two groups):</p>
-<ul>
-<li><p><strong>For continuous variables:</strong></p>
-<ul>
-<li><p><code>shapiro.test()</code> (for checking if normally distributed)</p></li>
-<li><p><code>t.test()</code> (for normal distributions)</p></li>
-<li><p><code>wilcox.test()</code> (for non-parametric testing. Used to determine if two numeric samples are from the same distribution, when their populations are not normally distributed or have unequal variance.)</p></li>
-</ul></li>
-<li><p><strong>For categorical variables:</strong></p>
-<ul>
-<li><p><code>chisq.test()</code> (if all comparisons include at least 5 cases)</p></li>
-<li><p><code>fisher.test()</code> (if there are &lt; 5 cases for any comparison)</p></li>
-</ul></li>
-</ul>
-</section>
-<section id="questionsassignments" class="level2">
-<h2 class="anchored" data-anchor-id="questionsassignments">3. Questions/Assignments</h2>
-</section>
-<section id="install-packages-and-load-libraries" class="level2">
-<h2 class="anchored" data-anchor-id="install-packages-and-load-libraries">3.1. Install packages and load libraries</h2>
-<div class="cell">
-<details open="">
-<summary>Code</summary>
-<div class="sourceCode cell-code" id="cb1"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Load the required libraries into the current R session:</span></span>
-<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a>pacman<span class="sc">::</span><span class="fu">p_load</span>(rio, </span>
-<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a>               here, </span>
-<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a>               tidyverse, </span>
-<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a>               skimr,</span>
-<span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a>               plyr,</span>
-<span id="cb1-7"><a href="#cb1-7" aria-hidden="true" tabindex="-1"></a>               janitor,</span>
-<span id="cb1-8"><a href="#cb1-8" aria-hidden="true" tabindex="-1"></a>               lubridate,</span>
-<span id="cb1-9"><a href="#cb1-9" aria-hidden="true" tabindex="-1"></a>               gtsummary, </span>
-<span id="cb1-10"><a href="#cb1-10" aria-hidden="true" tabindex="-1"></a>               flextable,</span>
-<span id="cb1-11"><a href="#cb1-11" aria-hidden="true" tabindex="-1"></a>               officer,</span>
-<span id="cb1-12"><a href="#cb1-12" aria-hidden="true" tabindex="-1"></a>               epikit, </span>
-<span id="cb1-13"><a href="#cb1-13" aria-hidden="true" tabindex="-1"></a>               apyramid, </span>
-<span id="cb1-14"><a href="#cb1-14" aria-hidden="true" tabindex="-1"></a>               scales,</span>
-<span id="cb1-15"><a href="#cb1-15" aria-hidden="true" tabindex="-1"></a>               EpiStats,</span>
-<span id="cb1-16"><a href="#cb1-16" aria-hidden="true" tabindex="-1"></a>               broom)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-</details>
-</div>
-</section>
-<section id="import-your-data" class="level2">
-<h2 class="anchored" data-anchor-id="import-your-data">3.2. Import your data</h2>
-<div class="cell">
-<details open="">
-<summary>Code</summary>
-<div class="sourceCode cell-code" id="cb2"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Import the raw data set:</span></span>
-<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a>copdata <span class="ot">&lt;-</span> rio<span class="sc">::</span><span class="fu">import</span>(here<span class="sc">::</span><span class="fu">here</span>(<span class="st">"data"</span>, <span class="st">"Copenhagen_clean2_2024.rds"</span>))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-</details>
-</div>
-</section>
-<section id="hypothesis-tests-for-other-variables" class="level2">
-<h2 class="anchored" data-anchor-id="hypothesis-tests-for-other-variables">3.3. Hypothesis tests for other variables</h2>
-<p>Check if the following variables are associated with being a case: age, sex, class and group.</p>
-<section id="a-age" class="level4">
-<h4 class="anchored" data-anchor-id="a-age">a) age</h4>
-<p>With the Shapiro-Wilk test we check if the variables are following the normal distribution. The null hypothesis is that the data follow a normal distribution, therefore, rejecting the null hypothesis means that the data do not follow the normal distribution. A p-value below the cutoff for rejecting the null hypothesis, e.g., a p-value&lt;0.05 means that we reject the null hypothesis that the data follow the normal distribution. For age, the p-value is &lt;0.05, therefore we reject the null hypothesis that the data are normally distributed. As we see in the graph most frequently reported age is &lt;20 years.</p>
-<div class="cell">
-<details open="">
-<summary>Code</summary>
-<div class="sourceCode cell-code" id="cb3"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Check if age overall follows a normal distribution:</span></span>
-<span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a><span class="fu">shapiro.test</span>(copdata<span class="sc">$</span>age)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-</details>
-<div class="cell-output cell-output-stdout">
-<pre><code>
-    Shapiro-Wilk normality test
-
-data:  copdata$age
-W = 0.31302, p-value &lt; 2.2e-16</code></pre>
-</div>
-<details open="">
-<summary>Code</summary>
-<div class="sourceCode cell-code" id="cb5"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Can simply have a look at</span></span>
-<span id="cb5-2"><a href="#cb5-2" aria-hidden="true" tabindex="-1"></a><span class="fu">hist</span>(copdata<span class="sc">$</span>age)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-</details>
-<div class="cell-output-display">
-<p><img src="SimOb_RInject14_files/figure-html/unnamed-chunk-2-1.png" class="img-fluid" width="672"></p>
-</div>
-<details open="">
-<summary>Code</summary>
-<div class="sourceCode cell-code" id="cb6"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Looking only at the students:</span></span>
-<span id="cb6-2"><a href="#cb6-2" aria-hidden="true" tabindex="-1"></a>students <span class="ot">&lt;-</span> copdata <span class="sc">%&gt;%</span> </span>
-<span id="cb6-3"><a href="#cb6-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(group <span class="sc">==</span> <span class="st">"student"</span>)</span>
-<span id="cb6-4"><a href="#cb6-4" aria-hidden="true" tabindex="-1"></a><span class="fu">hist</span>(students<span class="sc">$</span>age)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-</details>
-<div class="cell-output-display">
-<p><img src="SimOb_RInject14_files/figure-html/unnamed-chunk-2-2.png" class="img-fluid" width="672"></p>
-</div>
-</div>
-<p>Age overall (nor within the students’ group) is not normally distributed.</p>
-<p>We compare the age for cases and non-cases using the Wilcoxon test that is used when the data are not normally distributed. The null hypothesis is that there is no difference in the age between the two groups compared. Given that p-value&gt;0.05 we do not reject the null hypothesis.</p>
-<div class="cell">
-<details open="">
-<summary>Code</summary>
-<div class="sourceCode cell-code" id="cb7"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Perform Wilcoxon rank sum test on age and sex:</span></span>
-<span id="cb7-2"><a href="#cb7-2" aria-hidden="true" tabindex="-1"></a><span class="fu">wilcox.test</span>(age <span class="sc">~</span> case, </span>
-<span id="cb7-3"><a href="#cb7-3" aria-hidden="true" tabindex="-1"></a>            <span class="at">data =</span> copdata)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-</details>
-<div class="cell-output cell-output-stdout">
-<pre><code>
-    Wilcoxon rank sum test with continuity correction
-
-data:  age by case
-W = 15934, p-value = 0.1512
-alternative hypothesis: true location shift is not equal to 0</code></pre>
-</div>
-</div>
-</section>
-<section id="b-sex" class="level4">
-<h4 class="anchored" data-anchor-id="b-sex">b) sex</h4>
-<div class="cell">
-<details open="">
-<summary>Code</summary>
-<div class="sourceCode cell-code" id="cb9"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a>copdata <span class="sc">%&gt;%</span> </span>
-<span id="cb9-2"><a href="#cb9-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(sex, case) <span class="sc">%&gt;%</span> </span>
-<span id="cb9-3"><a href="#cb9-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">tbl_summary</span>(<span class="at">by =</span> case) <span class="sc">%&gt;%</span> </span>
-<span id="cb9-4"><a href="#cb9-4" aria-hidden="true" tabindex="-1"></a>  <span class="fu">add_p</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-</details>
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-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;female</td>
-<td headers="stat_1" class="gt_row gt_center">96 (60%)</td>
-<td headers="stat_2" class="gt_row gt_center">117 (54%)</td>
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-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;male</td>
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-<td headers="stat_2" class="gt_row gt_center">99 (46%)</td>
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-  </tbody>
-  
-  <tfoot class="gt_footnotes">
-    <tr>
-      <td class="gt_footnote" colspan="4"><span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;"><sup>1</sup></span> n (%)</td>
-    </tr>
-    <tr>
-      <td class="gt_footnote" colspan="4"><span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;"><sup>2</sup></span> Pearson’s Chi-squared test</td>
-    </tr>
-  </tfoot>
-</table>
-</div>
-</div>
-</div>
-<div class="callout callout-style-default callout-warning callout-titled" title="Let's stop and... think!">
-<div class="callout-header d-flex align-content-center" data-bs-toggle="collapse" data-bs-target=".callout-1-contents" aria-controls="callout-1" aria-expanded="false" aria-label="Toggle callout">
-<div class="callout-icon-container">
-<i class="callout-icon"></i>
-</div>
-<div class="callout-title-container flex-fill">
-Let’s stop and… think!
-</div>
-<div class="callout-btn-toggle d-inline-block border-0 py-1 ps-1 pe-0 float-end"><i class="callout-toggle"></i></div>
-</div>
-<div id="callout-1" class="callout-1-contents callout-collapse collapse">
-<div class="callout-body-container callout-body">
-<p>What do these results tell you - is there an association between sex and being a case?</p>
-<p>Do these results differ from what you expected when looking at the descriptive figures (case proportions stratified by sex, or the age sex pyramid)? If so, why do you think this is?</p>
-</div>
-</div>
-</div>
-</section>
-<section id="c-class" class="level4">
-<h4 class="anchored" data-anchor-id="c-class">c) class</h4>
-<div class="cell">
-<details open="">
-<summary>Code</summary>
-<div class="sourceCode cell-code" id="cb10"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb10-1"><a href="#cb10-1" aria-hidden="true" tabindex="-1"></a>copdata <span class="sc">%&gt;%</span> </span>
-<span id="cb10-2"><a href="#cb10-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(class, case) <span class="sc">%&gt;%</span> </span>
-<span id="cb10-3"><a href="#cb10-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">tbl_summary</span>(<span class="at">by =</span> case) <span class="sc">%&gt;%</span> </span>
-<span id="cb10-4"><a href="#cb10-4" aria-hidden="true" tabindex="-1"></a>  <span class="fu">add_p</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-</details>
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-      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="<strong>FALSE</strong>, N = 161<span class=&quot;gt_footnote_marks&quot; style=&quot;white-space:nowrap;font-style:italic;font-weight:normal;&quot;><sup>1</sup></span>"><strong>FALSE</strong>, N = 161<span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;"><sup>1</sup></span></th>
-      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="<strong>TRUE</strong>, N = 216<span class=&quot;gt_footnote_marks&quot; style=&quot;white-space:nowrap;font-style:italic;font-weight:normal;&quot;><sup>1</sup></span>"><strong>TRUE</strong>, N = 216<span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;"><sup>1</sup></span></th>
-      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="<strong>p-value</strong><span class=&quot;gt_footnote_marks&quot; style=&quot;white-space:nowrap;font-style:italic;font-weight:normal;&quot;><sup>2</sup></span>"><strong>p-value</strong><span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;"><sup>2</sup></span></th>
-    </tr>
-  </thead>
-  <tbody class="gt_table_body">
-    <tr><td headers="label" class="gt_row gt_left">class</td>
-<td headers="stat_1" class="gt_row gt_center"></td>
-<td headers="stat_2" class="gt_row gt_center"></td>
-<td headers="p.value" class="gt_row gt_center">0.090</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;1</td>
-<td headers="stat_1" class="gt_row gt_center">63 (43%)</td>
-<td headers="stat_2" class="gt_row gt_center">68 (34%)</td>
-<td headers="p.value" class="gt_row gt_center"></td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;2</td>
-<td headers="stat_1" class="gt_row gt_center">44 (30%)</td>
-<td headers="stat_2" class="gt_row gt_center">57 (29%)</td>
-<td headers="p.value" class="gt_row gt_center"></td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;3</td>
-<td headers="stat_1" class="gt_row gt_center">38 (26%)</td>
-<td headers="stat_2" class="gt_row gt_center">73 (37%)</td>
-<td headers="p.value" class="gt_row gt_center"></td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;Unknown</td>
-<td headers="stat_1" class="gt_row gt_center">16</td>
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-<td headers="p.value" class="gt_row gt_center"></td></tr>
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-  
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-      <td class="gt_footnote" colspan="4"><span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;"><sup>1</sup></span> n (%)</td>
-    </tr>
-    <tr>
-      <td class="gt_footnote" colspan="4"><span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;"><sup>2</sup></span> Pearson’s Chi-squared test</td>
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-<p>In this case you have a 2x3 contingency table of class against disease status, we can use a chi-square here. P-value of the association between class and age is p-val = 0.09, suggesting that there is an association at the p-val = 0.20 level among at least one of the 3 different classes. Fellows may want to investigate this further, but in the remaining of the study this is not explored further.</p>
-<p>A hypothesis could be that one of the classes sat together and, for whatever reason, they ate more of the contaminated food item at those tables. This could be further studied if we had the spatial distribution of where the people sat at the dinner. Another hypothesis is that one of the classes differ from other classes in some characteristics that made them more susceptible or more exposed to the infected food item.</p>
-</section>
-<section id="d-group" class="level4">
-<h4 class="anchored" data-anchor-id="d-group">d) group</h4>
-<div class="cell">
-<details open="">
-<summary>Code</summary>
-<div class="sourceCode cell-code" id="cb11"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb11-1"><a href="#cb11-1" aria-hidden="true" tabindex="-1"></a>copdata <span class="sc">%&gt;%</span> </span>
-<span id="cb11-2"><a href="#cb11-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(group, case) <span class="sc">%&gt;%</span> </span>
-<span id="cb11-3"><a href="#cb11-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">tbl_summary</span>(<span class="at">by =</span> case) <span class="sc">%&gt;%</span> </span>
-<span id="cb11-4"><a href="#cb11-4" aria-hidden="true" tabindex="-1"></a>  <span class="fu">add_p</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-</details>
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-<p>P-value of the association between group and age is p-val = 0.2, suggesting that there may be an association at the p-val = 0.20 level. Explanations are like above, with variable class.</p>
-</section>
-<section id="lets-do-all-together" class="level4">
-<h4 class="anchored" data-anchor-id="lets-do-all-together">Let’s do all together</h4>
-<div class="cell">
-<details open="">
-<summary>Code</summary>
-<div class="sourceCode cell-code" id="cb12"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb12-1"><a href="#cb12-1" aria-hidden="true" tabindex="-1"></a>copdata <span class="sc">%&gt;%</span> </span>
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-<span id="cb12-3"><a href="#cb12-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">tbl_summary</span>(<span class="at">by =</span> case) <span class="sc">%&gt;%</span> </span>
-<span id="cb12-4"><a href="#cb12-4" aria-hidden="true" tabindex="-1"></a>  <span class="fu">add_p</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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-  <thead>
-    
-    <tr class="gt_col_headings">
-      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="<strong>Characteristic</strong>"><strong>Characteristic</strong></th>
-      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="<strong>FALSE</strong>, N = 161<span class=&quot;gt_footnote_marks&quot; style=&quot;white-space:nowrap;font-style:italic;font-weight:normal;&quot;><sup>1</sup></span>"><strong>FALSE</strong>, N = 161<span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;"><sup>1</sup></span></th>
-      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="<strong>TRUE</strong>, N = 216<span class=&quot;gt_footnote_marks&quot; style=&quot;white-space:nowrap;font-style:italic;font-weight:normal;&quot;><sup>1</sup></span>"><strong>TRUE</strong>, N = 216<span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;"><sup>1</sup></span></th>
-      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="<strong>p-value</strong><span class=&quot;gt_footnote_marks&quot; style=&quot;white-space:nowrap;font-style:italic;font-weight:normal;&quot;><sup>2</sup></span>"><strong>p-value</strong><span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;"><sup>2</sup></span></th>
-    </tr>
-  </thead>
-  <tbody class="gt_table_body">
-    <tr><td headers="label" class="gt_row gt_left">sex</td>
-<td headers="stat_1" class="gt_row gt_center"></td>
-<td headers="stat_2" class="gt_row gt_center"></td>
-<td headers="p.value" class="gt_row gt_center">0.3</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;female</td>
-<td headers="stat_1" class="gt_row gt_center">96 (60%)</td>
-<td headers="stat_2" class="gt_row gt_center">117 (54%)</td>
-<td headers="p.value" class="gt_row gt_center"></td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;male</td>
-<td headers="stat_1" class="gt_row gt_center">65 (40%)</td>
-<td headers="stat_2" class="gt_row gt_center">99 (46%)</td>
-<td headers="p.value" class="gt_row gt_center"></td></tr>
-    <tr><td headers="label" class="gt_row gt_left">class</td>
-<td headers="stat_1" class="gt_row gt_center"></td>
-<td headers="stat_2" class="gt_row gt_center"></td>
-<td headers="p.value" class="gt_row gt_center">0.090</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;1</td>
-<td headers="stat_1" class="gt_row gt_center">63 (43%)</td>
-<td headers="stat_2" class="gt_row gt_center">68 (34%)</td>
-<td headers="p.value" class="gt_row gt_center"></td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;2</td>
-<td headers="stat_1" class="gt_row gt_center">44 (30%)</td>
-<td headers="stat_2" class="gt_row gt_center">57 (29%)</td>
-<td headers="p.value" class="gt_row gt_center"></td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;3</td>
-<td headers="stat_1" class="gt_row gt_center">38 (26%)</td>
-<td headers="stat_2" class="gt_row gt_center">73 (37%)</td>
-<td headers="p.value" class="gt_row gt_center"></td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;Unknown</td>
-<td headers="stat_1" class="gt_row gt_center">16</td>
-<td headers="stat_2" class="gt_row gt_center">18</td>
-<td headers="p.value" class="gt_row gt_center"></td></tr>
-    <tr><td headers="label" class="gt_row gt_left">group</td>
-<td headers="stat_1" class="gt_row gt_center"></td>
-<td headers="stat_2" class="gt_row gt_center"></td>
-<td headers="p.value" class="gt_row gt_center">0.2</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;teacher</td>
-<td headers="stat_1" class="gt_row gt_center">9 (5.6%)</td>
-<td headers="stat_2" class="gt_row gt_center">6 (2.8%)</td>
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-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;student</td>
-<td headers="stat_1" class="gt_row gt_center">152 (94%)</td>
-<td headers="stat_2" class="gt_row gt_center">210 (97%)</td>
-<td headers="p.value" class="gt_row gt_center"></td></tr>
-  </tbody>
-  
-  <tfoot class="gt_footnotes">
-    <tr>
-      <td class="gt_footnote" colspan="4"><span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;"><sup>1</sup></span> n (%)</td>
-    </tr>
-    <tr>
-      <td class="gt_footnote" colspan="4"><span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;"><sup>2</sup></span> Pearson’s Chi-squared test</td>
-    </tr>
-  </tfoot>
-</table>
-</div>
-</div>
-</div>
-</section>
-</section>
-<section id="risk-ratios" class="level2">
-<h2 class="anchored" data-anchor-id="risk-ratios">3.4. Risk Ratios</h2>
-<p>The risk ratios of each food item (including the 2x2 table) are reported below. The output of the <code>CS()</code> command is two tables: one with the 2x2 table and one with the risk difference, the risk ratio and the attributable fraction among exposed as well as the attributable fraction among the population (and the confidence intervals for all the estimates). The Chi-square and the p-value are also reported. In the second part, a table with all the food items is printed including attack rates for exposed and unexposed as well as risk ratios and the 95% confidence intervals (CI ll and CI ul, for the lower and upper interval) and p-values.</p>
-<section id="a-calculate-95-ci-risk-ratios-for-food" class="level3">
-<h3 class="anchored" data-anchor-id="a-calculate-95-ci-risk-ratios-for-food">a) Calculate 95% CI Risk Ratios for food</h3>
-<p>To see if food items (note these are categorical variables) are associated with being a case, calculate risk ratios and 95% confidence intervals for food items. You can calculate this individually for each food item (using <code>CSTable()</code>), or all at once (see hint below).</p>
-<div class="callout callout-style-default callout-tip callout-titled" title="Need a little bit of help?">
-<div class="callout-header d-flex align-content-center" data-bs-toggle="collapse" data-bs-target=".callout-2-contents" aria-controls="callout-2" aria-expanded="false" aria-label="Toggle callout">
-<div class="callout-icon-container">
-<i class="callout-icon"></i>
-</div>
-<div class="callout-title-container flex-fill">
-Need a little bit of help?
-</div>
-<div class="callout-btn-toggle d-inline-block border-0 py-1 ps-1 pe-0 float-end"><i class="callout-toggle"></i></div>
-</div>
-<div id="callout-2" class="callout-2-contents callout-collapse collapse">
-<div class="callout-body-container callout-body">
-<p>Create a <code>food_vars</code> vector containing food variables of interest and use the function <code>CSTable()</code> of the EpiStats package.</p>
-<p><em>Using this hint, you will create a table with the name of exposure variables, the total number of exposed, the number of exposed cases, the attack rate among the exposed, the total number of unexposed, the number of unexposed cases, the attack rate among the unexposed, risk ratios, 95% percent confidence intervals, and p-values. Amazing, isn’t it? Have a look at <code>??CSTable</code> to learn more.</em></p>
-</div>
-</div>
-</div>
-<div class="cell">
-<details open="">
-<summary>Code</summary>
-<div class="sourceCode cell-code" id="cb13"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb13-1"><a href="#cb13-1" aria-hidden="true" tabindex="-1"></a><span class="co"># You could use the EpiStats package for each food item</span></span>
-<span id="cb13-2"><a href="#cb13-2" aria-hidden="true" tabindex="-1"></a><span class="fu">CS</span>(copdata, <span class="st">"case"</span>, <span class="st">"feta"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-</details>
-<div class="cell-output cell-output-stdout">
-<pre><code>$df1
-          Cases Non Cases Total Risk
-Exposed     156       115   271 0.58
-Unexposed    60        42   102 0.59
-Total       216       157   373 0.58
-
-$df2
-                Point estimate 95%CI.ll 95%CI.ul
-Risk difference          -0.01    -0.12     0.10
-Risk ratio                0.98     0.81     1.19
-Prev. frac. ex.           0.02    -0.19     0.19
-Prev. frac. pop           0.02       NA       NA
-chi2(1)                   0.05       NA       NA
-Pr&gt;chi2                  0.826       NA       NA</code></pre>
-</div>
-<details open="">
-<summary>Code</summary>
-<div class="sourceCode cell-code" id="cb15"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb15-1"><a href="#cb15-1" aria-hidden="true" tabindex="-1"></a><span class="fu">CS</span>(copdata, <span class="st">"case"</span>, <span class="st">"sardines"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-</details>
-<div class="cell-output cell-output-stdout">
-<pre><code>$df1
-          Cases Non Cases Total Risk
-Exposed     150       105   255 0.59
-Unexposed    65        52   117 0.56
-Total       215       157   372 0.58
-
-$df2
-                Point estimate 95%CI.ll 95%CI.ul
-Risk difference           0.03    -0.08     0.14
-Risk ratio                1.06     0.87     1.28
-Attr. frac. ex.           0.06    -0.14     0.22
-Attr. frac. pop           0.04       NA       NA
-chi2(1)                   0.35       NA       NA
-Pr&gt;chi2                  0.553       NA       NA</code></pre>
-</div>
-<details open="">
-<summary>Code</summary>
-<div class="sourceCode cell-code" id="cb17"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb17-1"><a href="#cb17-1" aria-hidden="true" tabindex="-1"></a><span class="fu">CS</span>(copdata, <span class="st">"case"</span>, <span class="st">"eggplant"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-</details>
-<div class="cell-output cell-output-stdout">
-<pre><code>$df1
-          Cases Non Cases Total Risk
-Exposed     123        93   216 0.57
-Unexposed    83        60   143 0.58
-Total       206       153   359 0.57
-
-$df2
-                Point estimate 95%CI.ll 95%CI.ul
-Risk difference          -0.01    -0.12     0.09
-Risk ratio                0.98     0.82     1.18
-Prev. frac. ex.           0.02    -0.18     0.18
-Prev. frac. pop           0.01       NA       NA
-chi2(1)                   0.04       NA       NA
-Pr&gt;chi2                  0.837       NA       NA</code></pre>
-</div>
-<details open="">
-<summary>Code</summary>
-<div class="sourceCode cell-code" id="cb19"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb19-1"><a href="#cb19-1" aria-hidden="true" tabindex="-1"></a><span class="fu">CS</span>(copdata, <span class="st">"case"</span>, <span class="st">"moussaka"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-</details>
-<div class="cell-output cell-output-stdout">
-<pre><code>$df1
-          Cases Non Cases Total Risk
-Exposed     201       137   338 0.59
-Unexposed    14        22    36 0.39
-Total       215       159   374 0.57
-
-$df2
-                Point estimate 95%CI.ll 95%CI.ul
-Risk difference           0.21     0.04     0.37
-Risk ratio                1.53     1.01     2.32
-Attr. frac. ex.           0.35     0.01     0.57
-Attr. frac. pop           0.32       NA       NA
-chi2(1)                   5.64       NA       NA
-Pr&gt;chi2                  0.018       NA       NA</code></pre>
-</div>
-</div>
-<div class="cell">
-<details open="">
-<summary>Code</summary>
-<div class="sourceCode cell-code" id="cb21"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb21-1"><a href="#cb21-1" aria-hidden="true" tabindex="-1"></a><span class="co"># You can save time (and probably typos!) by creating a vector for food variables...</span></span>
-<span id="cb21-2"><a href="#cb21-2" aria-hidden="true" tabindex="-1"></a>food_vars <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">"feta"</span>, <span class="st">"sardines"</span>, <span class="st">"eggplant"</span>, <span class="st">"moussaka"</span>, </span>
-<span id="cb21-3"><a href="#cb21-3" aria-hidden="true" tabindex="-1"></a>               <span class="st">"orzo"</span>, <span class="st">"greeksal"</span>, <span class="st">"dessert"</span>, <span class="st">"bread"</span>,  </span>
-<span id="cb21-4"><a href="#cb21-4" aria-hidden="true" tabindex="-1"></a>               <span class="st">"champagne"</span>, <span class="st">"beer"</span>, <span class="st">"redwine"</span>, <span class="st">"whitewine"</span>)</span>
-<span id="cb21-5"><a href="#cb21-5" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb21-6"><a href="#cb21-6" aria-hidden="true" tabindex="-1"></a><span class="co"># ...and using EpiStats::CSTable() to run all variables together!</span></span>
-<span id="cb21-7"><a href="#cb21-7" aria-hidden="true" tabindex="-1"></a><span class="fu">CSTable</span>(copdata, <span class="st">"case"</span>, food_vars)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-</details>
-<div class="cell-output cell-output-stdout">
-<pre><code>$df
-          Tot.Exp Cases.Exp AR.Exp% Tot.Unexp Cases.Unexp AR.Unexp%   RR CI.ll
-orzo          338       202   59.76        36          13     36.11 1.65  1.06
-moussaka      338       201   59.47        36          14     38.89 1.53  1.01
-champagne     316       187   59.18        48          21     43.75 1.35  0.97
-greeksal      211       114   54.03       154          95     61.69 0.88  0.73
-dessert       149        90   60.40       198         106     53.54 1.13  0.94
-beer          281       166   59.07        78          41     52.56 1.12  0.89
-redwine        80        42   52.50       259         150     57.92 0.91  0.72
-sardines      255       150   58.82       117          65     55.56 1.06  0.87
-whitewine     260       150   57.69        98          54     55.10 1.05  0.85
-bread         342       196   57.31        29          16     55.17 1.04  0.74
-feta          271       156   57.56       102          60     58.82 0.98  0.81
-eggplant      216       123   56.94       143          83     58.04 0.98  0.82
-          CI.ul p(Chi2)
-orzo       2.58   0.006
-moussaka   2.32   0.018
-champagne  1.89   0.044
-greeksal   1.04   0.144
-dessert    1.36   0.202
-beer       1.42   0.303
-redwine    1.14   0.393
-sardines   1.28   0.553
-whitewine  1.29   0.659
-bread      1.46   0.823
-feta       1.19   0.826
-eggplant   1.18   0.837</code></pre>
-</div>
-</div>
-</section>
-<section id="b-prepare-the-rr-table-for-publication" class="level3">
-<h3 class="anchored" data-anchor-id="b-prepare-the-rr-table-for-publication">b) Prepare the RR table for publication</h3>
-<div class="callout callout-style-default callout-tip callout-titled" title="Need a little bit of help?">
-<div class="callout-header d-flex align-content-center" data-bs-toggle="collapse" data-bs-target=".callout-3-contents" aria-controls="callout-3" aria-expanded="false" aria-label="Toggle callout">
-<div class="callout-icon-container">
-<i class="callout-icon"></i>
-</div>
-<div class="callout-title-container flex-fill">
-Need a little bit of help?
-</div>
-<div class="callout-btn-toggle d-inline-block border-0 py-1 ps-1 pe-0 float-end"><i class="callout-toggle"></i></div>
-</div>
-<div id="callout-3" class="callout-3-contents callout-collapse collapse">
-<div class="callout-body-container callout-body">
-<p>Use <code>flextable()</code> and <code>set_header_labels()</code>.</p>
-</div>
-</div>
-</div>
-<div class="cell">
-<details open="">
-<summary>Code</summary>
-<div class="sourceCode cell-code" id="cb23"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb23-1"><a href="#cb23-1" aria-hidden="true" tabindex="-1"></a>rr_tbl <span class="ot">&lt;-</span> <span class="fu">CSTable</span>(copdata, <span class="st">"case"</span>, food_vars) <span class="sc">%&gt;%</span> </span>
-<span id="cb23-2"><a href="#cb23-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">as.data.frame</span>() <span class="sc">%&gt;%</span> </span>
-<span id="cb23-3"><a href="#cb23-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">rownames_to_column</span>() <span class="sc">%&gt;%</span> </span>
-<span id="cb23-4"><a href="#cb23-4" aria-hidden="true" tabindex="-1"></a>  <span class="fu">flextable</span>() <span class="sc">%&gt;%</span> </span>
-<span id="cb23-5"><a href="#cb23-5" aria-hidden="true" tabindex="-1"></a>   <span class="fu">set_header_labels</span>(</span>
-<span id="cb23-6"><a href="#cb23-6" aria-hidden="true" tabindex="-1"></a>     <span class="at">values =</span> <span class="fu">c</span>(<span class="st">"Food Item"</span>,</span>
-<span id="cb23-7"><a href="#cb23-7" aria-hidden="true" tabindex="-1"></a>                <span class="st">"Total exposed"</span>,     </span>
-<span id="cb23-8"><a href="#cb23-8" aria-hidden="true" tabindex="-1"></a>               <span class="st">"Cases exposed"</span>, </span>
-<span id="cb23-9"><a href="#cb23-9" aria-hidden="true" tabindex="-1"></a>               <span class="st">"AR among exposed"</span>,    </span>
-<span id="cb23-10"><a href="#cb23-10" aria-hidden="true" tabindex="-1"></a>               <span class="st">"Total unexposed"</span>,</span>
-<span id="cb23-11"><a href="#cb23-11" aria-hidden="true" tabindex="-1"></a>               <span class="st">"Cases unexposed"</span>,</span>
-<span id="cb23-12"><a href="#cb23-12" aria-hidden="true" tabindex="-1"></a>               <span class="st">"AR among unexposed"</span>,</span>
-<span id="cb23-13"><a href="#cb23-13" aria-hidden="true" tabindex="-1"></a>               <span class="st">"RR"</span>,         </span>
-<span id="cb23-14"><a href="#cb23-14" aria-hidden="true" tabindex="-1"></a>               <span class="st">"95% lower CI"</span>,             </span>
-<span id="cb23-15"><a href="#cb23-15" aria-hidden="true" tabindex="-1"></a>               <span class="st">"95% upper CI"</span>,</span>
-<span id="cb23-16"><a href="#cb23-16" aria-hidden="true" tabindex="-1"></a>               <span class="st">"p-value"</span>))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-</details>
-</div>
-<div class="callout callout-style-default callout-warning callout-titled" title="Let's stop and... think!">
-<div class="callout-header d-flex align-content-center" data-bs-toggle="collapse" data-bs-target=".callout-4-contents" aria-controls="callout-4" aria-expanded="false" aria-label="Toggle callout">
-<div class="callout-icon-container">
-<i class="callout-icon"></i>
-</div>
-<div class="callout-title-container flex-fill">
-Let’s stop and… think!
-</div>
-<div class="callout-btn-toggle d-inline-block border-0 py-1 ps-1 pe-0 float-end"><i class="callout-toggle"></i></div>
-</div>
-<div id="callout-4" class="callout-4-contents callout-collapse collapse">
-<div class="callout-body-container callout-body">
-<p>· What can you infer from this table?</p>
-<p>· Considering the relative risks, which food or drink items do you think were most likely to be the vehicle(s) of infection in this outbreak?</p>
-<p>· Do you think there are any confounders or effect modifiers? If so, how would you investigate these further?</p>
-</div>
-</div>
-</div>
-<p>The interesting results here are that the food items that are most suspicious are orzo, moussaka and champagne. Orzo as such is unlikely to be contaminated, but as you can see in the picture (and from the dinner night), it was served with pesto! Maybe it was the pesto? Who-ho-ho!</p>
-<p>Before one jumps into conclusions, consider that this result could be due to confounding! Maybe orzo was "clean" but eaten by all the people who ate the food item that actually was contaminated!(Optional)</p>
-</section>
-</section>
-<section id="optional-dose-response" class="level2">
-<h2 class="anchored" data-anchor-id="optional-dose-response">3.5. (Optional) Dose Response</h2>
-<p>Check for a dose-response relationship between the food items with the highest RR values (the top 3) and being a case.</p>
-<section id="a-orzo" class="level4">
-<h4 class="anchored" data-anchor-id="a-orzo">a) Orzo</h4>
-<div class="cell">
-<details open="">
-<summary>Code</summary>
-<div class="sourceCode cell-code" id="cb24"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb24-1"><a href="#cb24-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Binomial regression for RRs. </span></span>
-<span id="cb24-2"><a href="#cb24-2" aria-hidden="true" tabindex="-1"></a><span class="co"># The outcome needs to be exponentiated so we can interpret it properly!</span></span>
-<span id="cb24-3"><a href="#cb24-3" aria-hidden="true" tabindex="-1"></a>binom_orzoD <span class="ot">&lt;-</span> <span class="fu">glm</span>(case <span class="sc">~</span> orzoD, <span class="at">data =</span> copdata, </span>
-<span id="cb24-4"><a href="#cb24-4" aria-hidden="true" tabindex="-1"></a>             <span class="at">family =</span> <span class="fu">binomial</span>(<span class="at">link =</span> <span class="st">"log"</span>))</span>
-<span id="cb24-5"><a href="#cb24-5" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb24-6"><a href="#cb24-6" aria-hidden="true" tabindex="-1"></a><span class="co"># To get exponentiated:</span></span>
-<span id="cb24-7"><a href="#cb24-7" aria-hidden="true" tabindex="-1"></a>binom_orzoD_exp <span class="ot">&lt;-</span> <span class="fu">glm</span>(case <span class="sc">~</span> orzoD, <span class="at">data =</span> copdata, </span>
-<span id="cb24-8"><a href="#cb24-8" aria-hidden="true" tabindex="-1"></a>                       <span class="at">family =</span> <span class="fu">binomial</span>(<span class="at">link =</span> <span class="st">"log"</span>)) <span class="sc">%&gt;%</span> </span>
-<span id="cb24-9"><a href="#cb24-9" aria-hidden="true" tabindex="-1"></a>  <span class="fu">tidy</span>(<span class="at">exponentiate =</span> <span class="cn">TRUE</span>, </span>
-<span id="cb24-10"><a href="#cb24-10" aria-hidden="true" tabindex="-1"></a>       <span class="at">conf.int =</span> <span class="cn">TRUE</span>)</span>
-<span id="cb24-11"><a href="#cb24-11" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb24-12"><a href="#cb24-12" aria-hidden="true" tabindex="-1"></a>binom_orzoD_exp</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-</details>
-<div class="cell-output-display">
-
-<div data-pagedtable="false">
-  <script data-pagedtable-source="" type="application/json">
-{"columns":[{"label":["term"],"name":[1],"type":["chr"],"align":["left"]},{"label":["estimate"],"name":[2],"type":["dbl"],"align":["right"]},{"label":["std.error"],"name":[3],"type":["dbl"],"align":["right"]},{"label":["statistic"],"name":[4],"type":["dbl"],"align":["right"]},{"label":["p.value"],"name":[5],"type":["dbl"],"align":["right"]},{"label":["conf.low"],"name":[6],"type":["dbl"],"align":["right"]},{"label":["conf.high"],"name":[7],"type":["dbl"],"align":["right"]}],"data":[{"1":"(Intercept)","2":"0.3611111","3":"0.2216676","4":"-4.595030","5":"4.326861e-06","6":"0.2177022","7":"0.5235932"},{"1":"orzoD1","2":"1.5032967","3":"0.2473185","4":"1.648322","5":"9.928664e-02","6":"0.9630031","7":"2.5843736"},{"1":"orzoD2","2":"1.6191677","3":"0.2302560","4":"2.092941","5":"3.635441e-02","6":"1.0901317","7":"2.7190340"},{"1":"orzoD3","2":"1.8570136","3":"0.2343410","4":"2.641320","5":"8.258357e-03","6":"1.2344067","7":"3.1353759"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}}
-  </script>
-</div>
-</div>
-</div>
-<div class="callout callout-style-default callout-warning callout-titled" title="Let's stop and... think!">
-<div class="callout-header d-flex align-content-center" data-bs-toggle="collapse" data-bs-target=".callout-5-contents" aria-controls="callout-5" aria-expanded="false" aria-label="Toggle callout">
-<div class="callout-icon-container">
-<i class="callout-icon"></i>
-</div>
-<div class="callout-title-container flex-fill">
-Let’s stop and… think!
-</div>
-<div class="callout-btn-toggle d-inline-block border-0 py-1 ps-1 pe-0 float-end"><i class="callout-toggle"></i></div>
-</div>
-<div id="callout-5" class="callout-5-contents callout-collapse collapse">
-<div class="callout-body-container callout-body">
-<p>What do these results tell you?</p>
-</div>
-</div>
-</div>
-<p>Results suggest a dose response relationship of having eaten orzo, pointing towards orzoas the potential vehicle. The higher the amount of orzothey ate, the stronger is the association (RR) with getting ill/being a case.</p>
-</section>
-<section id="b-moussaka" class="level4">
-<h4 class="anchored" data-anchor-id="b-moussaka">b) Moussaka</h4>
-<div class="cell">
-<details open="">
-<summary>Code</summary>
-<div class="sourceCode cell-code" id="cb25"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb25-1"><a href="#cb25-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Let's get the results directly exponentiated</span></span>
-<span id="cb25-2"><a href="#cb25-2" aria-hidden="true" tabindex="-1"></a>binom_moussakaD_exp <span class="ot">&lt;-</span> <span class="fu">glm</span>(case <span class="sc">~</span> moussakaD, <span class="at">data =</span> copdata, </span>
-<span id="cb25-3"><a href="#cb25-3" aria-hidden="true" tabindex="-1"></a>                       <span class="at">family =</span> <span class="fu">binomial</span>(<span class="at">link =</span> <span class="st">"log"</span>)) <span class="sc">%&gt;%</span> </span>
-<span id="cb25-4"><a href="#cb25-4" aria-hidden="true" tabindex="-1"></a>  <span class="fu">tidy</span>(<span class="at">exponentiate =</span> <span class="cn">TRUE</span>, </span>
-<span id="cb25-5"><a href="#cb25-5" aria-hidden="true" tabindex="-1"></a>       <span class="at">conf.int =</span> <span class="cn">TRUE</span>)</span>
-<span id="cb25-6"><a href="#cb25-6" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb25-7"><a href="#cb25-7" aria-hidden="true" tabindex="-1"></a>binom_moussakaD_exp</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-</details>
-<div class="cell-output-display">
-
-<div data-pagedtable="false">
-  <script data-pagedtable-source="" type="application/json">
-{"columns":[{"label":["term"],"name":[1],"type":["chr"],"align":["left"]},{"label":["estimate"],"name":[2],"type":["dbl"],"align":["right"]},{"label":["std.error"],"name":[3],"type":["dbl"],"align":["right"]},{"label":["statistic"],"name":[4],"type":["dbl"],"align":["right"]},{"label":["p.value"],"name":[5],"type":["dbl"],"align":["right"]},{"label":["conf.low"],"name":[6],"type":["dbl"],"align":["right"]},{"label":["conf.high"],"name":[7],"type":["dbl"],"align":["right"]}],"data":[{"1":"(Intercept)","2":"0.4411765","3":"0.1930091","4":"-4.239749","5":"2.237698e-05","6":"0.2831200","7":"0.6077136"},{"1":"moussakaD1","2":"1.2952381","3":"0.2190130","4":"1.181183","5":"2.375299e-01","6":"0.8713777","7":"2.0919282"},{"1":"moussakaD2","2":"1.3057971","3":"0.2031051","4":"1.313673","5":"1.889563e-01","6":"0.9210122","7":"2.0641020"},{"1":"moussakaD3","2":"1.4068966","3":"0.2104204","4":"1.622401","5":"1.047176e-01","6":"0.9704903","7":"2.2457999"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}}
-  </script>
-</div>
-</div>
-</div>
-</section>
-<section id="c-champagne" class="level4">
-<h4 class="anchored" data-anchor-id="c-champagne">c) Champagne</h4>
-<div class="cell">
-<details open="">
-<summary>Code</summary>
-<div class="sourceCode cell-code" id="cb26"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb26-1"><a href="#cb26-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Let's get the results directly exponentiated</span></span>
-<span id="cb26-2"><a href="#cb26-2" aria-hidden="true" tabindex="-1"></a>binom_champagneD_exp <span class="ot">&lt;-</span> <span class="fu">glm</span>(case <span class="sc">~</span> champagneD, <span class="at">data =</span> copdata, </span>
-<span id="cb26-3"><a href="#cb26-3" aria-hidden="true" tabindex="-1"></a>                       <span class="at">family =</span> <span class="fu">binomial</span>(<span class="at">link =</span> <span class="st">"log"</span>)) <span class="sc">%&gt;%</span> </span>
-<span id="cb26-4"><a href="#cb26-4" aria-hidden="true" tabindex="-1"></a>  <span class="fu">tidy</span>(<span class="at">exponentiate =</span> <span class="cn">TRUE</span>, </span>
-<span id="cb26-5"><a href="#cb26-5" aria-hidden="true" tabindex="-1"></a>       <span class="at">conf.int =</span> <span class="cn">TRUE</span>)</span>
-<span id="cb26-6"><a href="#cb26-6" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb26-7"><a href="#cb26-7" aria-hidden="true" tabindex="-1"></a>binom_champagneD_exp</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-</details>
-<div class="cell-output-display">
-
-<div data-pagedtable="false">
-  <script data-pagedtable-source="" type="application/json">
-{"columns":[{"label":["term"],"name":[1],"type":["chr"],"align":["left"]},{"label":["estimate"],"name":[2],"type":["dbl"],"align":["right"]},{"label":["std.error"],"name":[3],"type":["dbl"],"align":["right"]},{"label":["statistic"],"name":[4],"type":["dbl"],"align":["right"]},{"label":["p.value"],"name":[5],"type":["dbl"],"align":["right"]},{"label":["conf.low"],"name":[6],"type":["dbl"],"align":["right"]},{"label":["conf.high"],"name":[7],"type":["dbl"],"align":["right"]}],"data":[{"1":"(Intercept)","2":"0.437500","3":"0.1636579","4":"-5.051259","5":"4.389084e-07","6":"0.3031501","7":"0.5783488"},{"1":"champagneD1","2":"1.243697","3":"0.1757581","4":"1.240846","5":"2.146625e-01","6":"0.9108670","7":"1.8273842"},{"1":"champagneD2","2":"1.662338","3":"0.1879006","4":"2.704753","5":"6.835512e-03","6":"1.1689254","7":"2.4799348"},{"1":"champagneD3","2":"1.478992","3":"0.1865618","4":"2.097753","5":"3.592697e-02","6":"1.0484831","7":"2.2050611"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}}
-  </script>
-</div>
-</div>
-</div>
-</section>
-</section>
-<section id="summary" class="level2">
-<h2 class="anchored" data-anchor-id="summary">3.6 Summary</h2>
-<p>As a summary of what you've done above, answer these questions:</p>
-<ul>
-<li><p>Is the respondents' sex associated with being a case?</p></li>
-<li><p>Is the school class associated with being a case?</p></li>
-<li><p>Which foods increase the risk of being a case?</p></li>
-<li><p>(Optional) Is there a dose-response relationship between the food items and being a case?</p></li>
-<li><p>What do you think is the most likely culprit(s) of this outbreak at this point? Are there any risk factors you would like to highlight?</p></li>
-</ul>
-
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-</script><div class="modal fade" id="quarto-embedded-source-code-modal" tabindex="-1" aria-labelledby="quarto-embedded-source-code-modal-label" aria-hidden="true"><div class="modal-dialog modal-dialog-scrollable"><div class="modal-content"><div class="modal-header"><h5 class="modal-title" id="quarto-embedded-source-code-modal-label">Source Code</h5><button class="btn-close" data-bs-dismiss="modal"></button></div><div class="modal-body"><div class="">
-<div class="sourceCode" id="cb27" data-shortcodes="false"><pre class="sourceCode markdown code-with-copy"><code class="sourceCode markdown"><span id="cb27-1"><a href="#cb27-1" aria-hidden="true" tabindex="-1"></a><span class="co">---</span></span>
-<span id="cb27-2"><a href="#cb27-2" aria-hidden="true" tabindex="-1"></a><span class="an">title:</span><span class="co"> "Univariable analysis"</span></span>
-<span id="cb27-3"><a href="#cb27-3" aria-hidden="true" tabindex="-1"></a><span class="an">editor:</span><span class="co"> visual</span></span>
-<span id="cb27-4"><a href="#cb27-4" aria-hidden="true" tabindex="-1"></a><span class="co">---</span></span>
-<span id="cb27-5"><a href="#cb27-5" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb27-6"><a href="#cb27-6" aria-hidden="true" tabindex="-1"></a><span class="fu">## 1. Learning outcomes</span></span>
-<span id="cb27-7"><a href="#cb27-7" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb27-8"><a href="#cb27-8" aria-hidden="true" tabindex="-1"></a>At the end of the session, participants will be able to:</span>
-<span id="cb27-9"><a href="#cb27-9" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb27-10"><a href="#cb27-10" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span>Perform hypothesis tests.</span>
-<span id="cb27-11"><a href="#cb27-11" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb27-12"><a href="#cb27-12" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span>Estimate risk ratios (also called relative risk) for categorical data.</span>
-<span id="cb27-13"><a href="#cb27-13" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb27-14"><a href="#cb27-14" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span>Interpret the univariable results.</span>
-<span id="cb27-15"><a href="#cb27-15" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb27-16"><a href="#cb27-16" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span>(Optional) Investigate dose-response relationships in categorical data.2. Story/plot description</span>
-<span id="cb27-17"><a href="#cb27-17" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb27-18"><a href="#cb27-18" aria-hidden="true" tabindex="-1"></a><span class="fu">## 2. Story/plot description</span></span>
-<span id="cb27-19"><a href="#cb27-19" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb27-20"><a href="#cb27-20" aria-hidden="true" tabindex="-1"></a>You have just estimated risk ratios (RR) of individual food items manually to better understand the concept. RR give you an idea of which food items could be the culprit(s) of the outbreak.</span>
-<span id="cb27-21"><a href="#cb27-21" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb27-22"><a href="#cb27-22" aria-hidden="true" tabindex="-1"></a>Now, you will practice performing hypothesis testing and calculating RR in R to investigate the associations of suspicious food items with the disease.</span>
-<span id="cb27-23"><a href="#cb27-23" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb27-24"><a href="#cb27-24" aria-hidden="true" tabindex="-1"></a>Note that, although this is not an exhaustive list of tests, the following can be useful for this exercise (relevant for comparisons between two groups):</span>
-<span id="cb27-25"><a href="#cb27-25" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb27-26"><a href="#cb27-26" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span>**For continuous variables:**</span>
-<span id="cb27-27"><a href="#cb27-27" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb27-28"><a href="#cb27-28" aria-hidden="true" tabindex="-1"></a><span class="ss">    -   </span><span class="in">`shapiro.test()`</span> (for checking if normally distributed)</span>
-<span id="cb27-29"><a href="#cb27-29" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb27-30"><a href="#cb27-30" aria-hidden="true" tabindex="-1"></a><span class="ss">    -   </span><span class="in">`t.test()`</span> (for normal distributions)</span>
-<span id="cb27-31"><a href="#cb27-31" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb27-32"><a href="#cb27-32" aria-hidden="true" tabindex="-1"></a><span class="ss">    -   </span><span class="in">`wilcox.test()`</span> (for non-parametric testing. Used to determine if two numeric samples are from the same distribution, when their populations are not normally distributed or have unequal variance.)</span>
-<span id="cb27-33"><a href="#cb27-33" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb27-34"><a href="#cb27-34" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span>**For categorical variables:**</span>
-<span id="cb27-35"><a href="#cb27-35" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb27-36"><a href="#cb27-36" aria-hidden="true" tabindex="-1"></a><span class="ss">    -   </span><span class="in">`chisq.test()`</span> (if all comparisons include at least 5 cases)</span>
-<span id="cb27-37"><a href="#cb27-37" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb27-38"><a href="#cb27-38" aria-hidden="true" tabindex="-1"></a><span class="ss">    -   </span><span class="in">`fisher.test()`</span> (if there are <span class="sc">\&lt;</span> 5 cases for any comparison)</span>
-<span id="cb27-39"><a href="#cb27-39" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb27-40"><a href="#cb27-40" aria-hidden="true" tabindex="-1"></a><span class="fu">## 3. Questions/Assignments</span></span>
-<span id="cb27-41"><a href="#cb27-41" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb27-42"><a href="#cb27-42" aria-hidden="true" tabindex="-1"></a><span class="fu">## 3.1. Install packages and load libraries</span></span>
-<span id="cb27-43"><a href="#cb27-43" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb27-46"><a href="#cb27-46" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
-<span id="cb27-47"><a href="#cb27-47" aria-hidden="true" tabindex="-1"></a><span class="co"># Load the required libraries into the current R session:</span></span>
-<span id="cb27-48"><a href="#cb27-48" aria-hidden="true" tabindex="-1"></a>pacman<span class="sc">::</span><span class="fu">p_load</span>(rio, </span>
-<span id="cb27-49"><a href="#cb27-49" aria-hidden="true" tabindex="-1"></a>               here, </span>
-<span id="cb27-50"><a href="#cb27-50" aria-hidden="true" tabindex="-1"></a>               tidyverse, </span>
-<span id="cb27-51"><a href="#cb27-51" aria-hidden="true" tabindex="-1"></a>               skimr,</span>
-<span id="cb27-52"><a href="#cb27-52" aria-hidden="true" tabindex="-1"></a>               plyr,</span>
-<span id="cb27-53"><a href="#cb27-53" aria-hidden="true" tabindex="-1"></a>               janitor,</span>
-<span id="cb27-54"><a href="#cb27-54" aria-hidden="true" tabindex="-1"></a>               lubridate,</span>
-<span id="cb27-55"><a href="#cb27-55" aria-hidden="true" tabindex="-1"></a>               gtsummary, </span>
-<span id="cb27-56"><a href="#cb27-56" aria-hidden="true" tabindex="-1"></a>               flextable,</span>
-<span id="cb27-57"><a href="#cb27-57" aria-hidden="true" tabindex="-1"></a>               officer,</span>
-<span id="cb27-58"><a href="#cb27-58" aria-hidden="true" tabindex="-1"></a>               epikit, </span>
-<span id="cb27-59"><a href="#cb27-59" aria-hidden="true" tabindex="-1"></a>               apyramid, </span>
-<span id="cb27-60"><a href="#cb27-60" aria-hidden="true" tabindex="-1"></a>               scales,</span>
-<span id="cb27-61"><a href="#cb27-61" aria-hidden="true" tabindex="-1"></a>               EpiStats,</span>
-<span id="cb27-62"><a href="#cb27-62" aria-hidden="true" tabindex="-1"></a>               broom)</span>
-<span id="cb27-63"><a href="#cb27-63" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb27-64"><a href="#cb27-64" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb27-65"><a href="#cb27-65" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb27-66"><a href="#cb27-66" aria-hidden="true" tabindex="-1"></a><span class="fu">## 3.2. Import your data</span></span>
-<span id="cb27-67"><a href="#cb27-67" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb27-68"><a href="#cb27-68" aria-hidden="true" tabindex="-1"></a><span class="in">```{r, Import_data}</span></span>
-<span id="cb27-69"><a href="#cb27-69" aria-hidden="true" tabindex="-1"></a><span class="co"># Import the raw data set:</span></span>
-<span id="cb27-70"><a href="#cb27-70" aria-hidden="true" tabindex="-1"></a>copdata <span class="ot">&lt;-</span> rio<span class="sc">::</span><span class="fu">import</span>(here<span class="sc">::</span><span class="fu">here</span>(<span class="st">"data"</span>, <span class="st">"Copenhagen_clean2_2024.rds"</span>))</span>
-<span id="cb27-71"><a href="#cb27-71" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb27-72"><a href="#cb27-72" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb27-73"><a href="#cb27-73" aria-hidden="true" tabindex="-1"></a><span class="fu">## 3.3. Hypothesis tests for other variables</span></span>
-<span id="cb27-74"><a href="#cb27-74" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb27-75"><a href="#cb27-75" aria-hidden="true" tabindex="-1"></a>Check if the following variables are associated with being a case: age, sex, class and group.</span>
-<span id="cb27-76"><a href="#cb27-76" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb27-77"><a href="#cb27-77" aria-hidden="true" tabindex="-1"></a><span class="fu">#### a) age</span></span>
-<span id="cb27-78"><a href="#cb27-78" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb27-79"><a href="#cb27-79" aria-hidden="true" tabindex="-1"></a>With the Shapiro-Wilk test we check if the variables are following the normal distribution. The null hypothesis is that the data follow a normal distribution, therefore, rejecting the null hypothesis means that the data do not follow the normal distribution. A p-value below the cutoff for rejecting the null hypothesis, e.g., a p-value<span class="sc">\&lt;</span>0.05 means that we reject the null hypothesis that the data follow the normal distribution. For age, the p-value is <span class="sc">\&lt;</span>0.05, therefore we reject the null hypothesis that the data are normally distributed. As we see in the graph most frequently reported age is <span class="sc">\&lt;</span>20 years.</span>
-<span id="cb27-80"><a href="#cb27-80" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb27-83"><a href="#cb27-83" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
-<span id="cb27-84"><a href="#cb27-84" aria-hidden="true" tabindex="-1"></a><span class="co"># Check if age overall follows a normal distribution:</span></span>
-<span id="cb27-85"><a href="#cb27-85" aria-hidden="true" tabindex="-1"></a><span class="fu">shapiro.test</span>(copdata<span class="sc">$</span>age)</span>
-<span id="cb27-86"><a href="#cb27-86" aria-hidden="true" tabindex="-1"></a><span class="co"># Can simply have a look at</span></span>
-<span id="cb27-87"><a href="#cb27-87" aria-hidden="true" tabindex="-1"></a><span class="fu">hist</span>(copdata<span class="sc">$</span>age)</span>
-<span id="cb27-88"><a href="#cb27-88" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb27-89"><a href="#cb27-89" aria-hidden="true" tabindex="-1"></a><span class="co"># Looking only at the students:</span></span>
-<span id="cb27-90"><a href="#cb27-90" aria-hidden="true" tabindex="-1"></a>students <span class="ot">&lt;-</span> copdata <span class="sc">%&gt;%</span> </span>
-<span id="cb27-91"><a href="#cb27-91" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(group <span class="sc">==</span> <span class="st">"student"</span>)</span>
-<span id="cb27-92"><a href="#cb27-92" aria-hidden="true" tabindex="-1"></a><span class="fu">hist</span>(students<span class="sc">$</span>age)</span>
-<span id="cb27-93"><a href="#cb27-93" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb27-94"><a href="#cb27-94" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb27-95"><a href="#cb27-95" aria-hidden="true" tabindex="-1"></a>Age overall (nor within the students' group) is not normally distributed.</span>
-<span id="cb27-96"><a href="#cb27-96" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb27-97"><a href="#cb27-97" aria-hidden="true" tabindex="-1"></a>We compare the age for cases and non-cases using the Wilcoxon test that is used when the data are not normally distributed. The null hypothesis is that there is no difference in the age between the two groups compared. Given that p-value<span class="sc">\&gt;</span>0.05 we do not reject the null hypothesis.</span>
-<span id="cb27-98"><a href="#cb27-98" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb27-101"><a href="#cb27-101" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
-<span id="cb27-102"><a href="#cb27-102" aria-hidden="true" tabindex="-1"></a><span class="co"># Perform Wilcoxon rank sum test on age and sex:</span></span>
-<span id="cb27-103"><a href="#cb27-103" aria-hidden="true" tabindex="-1"></a><span class="fu">wilcox.test</span>(age <span class="sc">~</span> case, </span>
-<span id="cb27-104"><a href="#cb27-104" aria-hidden="true" tabindex="-1"></a>            <span class="at">data =</span> copdata)</span>
-<span id="cb27-105"><a href="#cb27-105" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb27-106"><a href="#cb27-106" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb27-107"><a href="#cb27-107" aria-hidden="true" tabindex="-1"></a><span class="fu">#### b) sex</span></span>
-<span id="cb27-108"><a href="#cb27-108" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb27-111"><a href="#cb27-111" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
-<span id="cb27-112"><a href="#cb27-112" aria-hidden="true" tabindex="-1"></a>copdata <span class="sc">%&gt;%</span> </span>
-<span id="cb27-113"><a href="#cb27-113" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(sex, case) <span class="sc">%&gt;%</span> </span>
-<span id="cb27-114"><a href="#cb27-114" aria-hidden="true" tabindex="-1"></a>  <span class="fu">tbl_summary</span>(<span class="at">by =</span> case) <span class="sc">%&gt;%</span> </span>
-<span id="cb27-115"><a href="#cb27-115" aria-hidden="true" tabindex="-1"></a>  <span class="fu">add_p</span>()</span>
-<span id="cb27-116"><a href="#cb27-116" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb27-117"><a href="#cb27-117" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb27-118"><a href="#cb27-118" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb27-119"><a href="#cb27-119" aria-hidden="true" tabindex="-1"></a>::: {.callout-warning title="Let's stop and... think!" collapse="true"}</span>
-<span id="cb27-120"><a href="#cb27-120" aria-hidden="true" tabindex="-1"></a>What do these results tell you - is there an association between sex and being a case?</span>
-<span id="cb27-121"><a href="#cb27-121" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb27-122"><a href="#cb27-122" aria-hidden="true" tabindex="-1"></a>Do these results differ from what you expected when looking at the descriptive figures (case proportions stratified by sex, or the age sex pyramid)? If so, why do you think this is?</span>
-<span id="cb27-123"><a href="#cb27-123" aria-hidden="true" tabindex="-1"></a>:::</span>
-<span id="cb27-124"><a href="#cb27-124" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb27-125"><a href="#cb27-125" aria-hidden="true" tabindex="-1"></a><span class="fu">#### c) class</span></span>
-<span id="cb27-126"><a href="#cb27-126" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb27-129"><a href="#cb27-129" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
-<span id="cb27-130"><a href="#cb27-130" aria-hidden="true" tabindex="-1"></a>copdata <span class="sc">%&gt;%</span> </span>
-<span id="cb27-131"><a href="#cb27-131" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(class, case) <span class="sc">%&gt;%</span> </span>
-<span id="cb27-132"><a href="#cb27-132" aria-hidden="true" tabindex="-1"></a>  <span class="fu">tbl_summary</span>(<span class="at">by =</span> case) <span class="sc">%&gt;%</span> </span>
-<span id="cb27-133"><a href="#cb27-133" aria-hidden="true" tabindex="-1"></a>  <span class="fu">add_p</span>()</span>
-<span id="cb27-134"><a href="#cb27-134" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb27-135"><a href="#cb27-135" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb27-136"><a href="#cb27-136" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb27-137"><a href="#cb27-137" aria-hidden="true" tabindex="-1"></a>In this case you have a 2x3 contingency table of class against disease status, we can use a chi-square here. P-value of the association between class and age is p-val = 0.09, suggesting that there is an association at the p-val = 0.20 level among at least one of the 3 different classes. Fellows may want to investigate this further, but in the remaining of the study this is not explored further.</span>
-<span id="cb27-138"><a href="#cb27-138" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb27-139"><a href="#cb27-139" aria-hidden="true" tabindex="-1"></a>A hypothesis could be that one of the classes sat together and, for whatever reason, they ate more of the contaminated food item at those tables. This could be further studied if we had the spatial distribution of where the people sat at the dinner. Another hypothesis is that one of the classes differ from other classes in some characteristics that made them more susceptible or more exposed to the infected food item.</span>
-<span id="cb27-140"><a href="#cb27-140" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb27-141"><a href="#cb27-141" aria-hidden="true" tabindex="-1"></a><span class="fu">#### d) group</span></span>
-<span id="cb27-142"><a href="#cb27-142" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb27-145"><a href="#cb27-145" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
-<span id="cb27-146"><a href="#cb27-146" aria-hidden="true" tabindex="-1"></a>copdata <span class="sc">%&gt;%</span> </span>
-<span id="cb27-147"><a href="#cb27-147" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(group, case) <span class="sc">%&gt;%</span> </span>
-<span id="cb27-148"><a href="#cb27-148" aria-hidden="true" tabindex="-1"></a>  <span class="fu">tbl_summary</span>(<span class="at">by =</span> case) <span class="sc">%&gt;%</span> </span>
-<span id="cb27-149"><a href="#cb27-149" aria-hidden="true" tabindex="-1"></a>  <span class="fu">add_p</span>()</span>
-<span id="cb27-150"><a href="#cb27-150" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb27-151"><a href="#cb27-151" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb27-152"><a href="#cb27-152" aria-hidden="true" tabindex="-1"></a>P-value of the association between group and age is p-val = 0.2, suggesting that there may be an association at the p-val = 0.20 level. Explanations are like above, with variable class.</span>
-<span id="cb27-153"><a href="#cb27-153" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb27-154"><a href="#cb27-154" aria-hidden="true" tabindex="-1"></a><span class="fu">#### Let's do all together</span></span>
-<span id="cb27-155"><a href="#cb27-155" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb27-158"><a href="#cb27-158" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
-<span id="cb27-159"><a href="#cb27-159" aria-hidden="true" tabindex="-1"></a>copdata <span class="sc">%&gt;%</span> </span>
-<span id="cb27-160"><a href="#cb27-160" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(sex, class, group, case) <span class="sc">%&gt;%</span> </span>
-<span id="cb27-161"><a href="#cb27-161" aria-hidden="true" tabindex="-1"></a>  <span class="fu">tbl_summary</span>(<span class="at">by =</span> case) <span class="sc">%&gt;%</span> </span>
-<span id="cb27-162"><a href="#cb27-162" aria-hidden="true" tabindex="-1"></a>  <span class="fu">add_p</span>()</span>
-<span id="cb27-163"><a href="#cb27-163" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb27-164"><a href="#cb27-164" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb27-165"><a href="#cb27-165" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb27-166"><a href="#cb27-166" aria-hidden="true" tabindex="-1"></a><span class="fu">## 3.4. Risk Ratios</span></span>
-<span id="cb27-167"><a href="#cb27-167" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb27-168"><a href="#cb27-168" aria-hidden="true" tabindex="-1"></a>The risk ratios of each food item (including the 2x2 table) are reported below. The output of the <span class="in">`CS()`</span> command is two tables: one with the 2x2 table and one with the risk difference, the risk ratio and the attributable fraction among exposed as well as the attributable fraction among the population (and the confidence intervals for all the estimates). The Chi-square and the p-value are also reported. In the second part, a table with all the food items is printed including attack rates for exposed and unexposed as well as risk ratios and the 95% confidence intervals (CI ll and CI ul, for the lower and upper interval) and p-values.</span>
-<span id="cb27-169"><a href="#cb27-169" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb27-170"><a href="#cb27-170" aria-hidden="true" tabindex="-1"></a><span class="fu">### a) Calculate 95% CI Risk Ratios for food</span></span>
-<span id="cb27-171"><a href="#cb27-171" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb27-172"><a href="#cb27-172" aria-hidden="true" tabindex="-1"></a>To see if food items (note these are categorical variables) are associated with being a case, calculate risk ratios and 95% confidence intervals for food items. You can calculate this individually for each food item (using <span class="in">`CSTable()`</span>), or all at once (see hint below).</span>
-<span id="cb27-173"><a href="#cb27-173" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb27-174"><a href="#cb27-174" aria-hidden="true" tabindex="-1"></a>::: {.callout-tip title="Need a little bit of help?" collapse="true"}</span>
-<span id="cb27-175"><a href="#cb27-175" aria-hidden="true" tabindex="-1"></a>Create a <span class="in">`food_vars`</span> vector containing food variables of interest and use the function <span class="in">`CSTable()`</span> of the EpiStats package.</span>
-<span id="cb27-176"><a href="#cb27-176" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb27-177"><a href="#cb27-177" aria-hidden="true" tabindex="-1"></a>*Using this hint, you will create a table with the name of exposure variables, the total number of exposed, the number of exposed cases, the attack rate among the exposed, the total number of unexposed, the number of unexposed cases, the attack rate among the unexposed, risk ratios, 95% percent confidence intervals, and p-values. Amazing, isn't it? Have a look at `??CSTable` to learn more.*</span>
-<span id="cb27-178"><a href="#cb27-178" aria-hidden="true" tabindex="-1"></a>:::</span>
-<span id="cb27-179"><a href="#cb27-179" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb27-182"><a href="#cb27-182" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
-<span id="cb27-183"><a href="#cb27-183" aria-hidden="true" tabindex="-1"></a><span class="co"># You could use the EpiStats package for each food item</span></span>
-<span id="cb27-184"><a href="#cb27-184" aria-hidden="true" tabindex="-1"></a><span class="fu">CS</span>(copdata, <span class="st">"case"</span>, <span class="st">"feta"</span>)</span>
-<span id="cb27-185"><a href="#cb27-185" aria-hidden="true" tabindex="-1"></a><span class="fu">CS</span>(copdata, <span class="st">"case"</span>, <span class="st">"sardines"</span>)</span>
-<span id="cb27-186"><a href="#cb27-186" aria-hidden="true" tabindex="-1"></a><span class="fu">CS</span>(copdata, <span class="st">"case"</span>, <span class="st">"eggplant"</span>)</span>
-<span id="cb27-187"><a href="#cb27-187" aria-hidden="true" tabindex="-1"></a><span class="fu">CS</span>(copdata, <span class="st">"case"</span>, <span class="st">"moussaka"</span>)</span>
-<span id="cb27-188"><a href="#cb27-188" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb27-189"><a href="#cb27-189" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb27-192"><a href="#cb27-192" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
-<span id="cb27-193"><a href="#cb27-193" aria-hidden="true" tabindex="-1"></a><span class="co"># You can save time (and probably typos!) by creating a vector for food variables...</span></span>
-<span id="cb27-194"><a href="#cb27-194" aria-hidden="true" tabindex="-1"></a>food_vars <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">"feta"</span>, <span class="st">"sardines"</span>, <span class="st">"eggplant"</span>, <span class="st">"moussaka"</span>, </span>
-<span id="cb27-195"><a href="#cb27-195" aria-hidden="true" tabindex="-1"></a>               <span class="st">"orzo"</span>, <span class="st">"greeksal"</span>, <span class="st">"dessert"</span>, <span class="st">"bread"</span>,  </span>
-<span id="cb27-196"><a href="#cb27-196" aria-hidden="true" tabindex="-1"></a>               <span class="st">"champagne"</span>, <span class="st">"beer"</span>, <span class="st">"redwine"</span>, <span class="st">"whitewine"</span>)</span>
-<span id="cb27-197"><a href="#cb27-197" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb27-198"><a href="#cb27-198" aria-hidden="true" tabindex="-1"></a><span class="co"># ...and using EpiStats::CSTable() to run all variables together!</span></span>
-<span id="cb27-199"><a href="#cb27-199" aria-hidden="true" tabindex="-1"></a><span class="fu">CSTable</span>(copdata, <span class="st">"case"</span>, food_vars)</span>
-<span id="cb27-200"><a href="#cb27-200" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb27-201"><a href="#cb27-201" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb27-202"><a href="#cb27-202" aria-hidden="true" tabindex="-1"></a><span class="fu">### b) Prepare the RR table for publication</span></span>
-<span id="cb27-203"><a href="#cb27-203" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb27-204"><a href="#cb27-204" aria-hidden="true" tabindex="-1"></a>::: {.callout-tip title="Need a little bit of help?" collapse="true"}</span>
-<span id="cb27-205"><a href="#cb27-205" aria-hidden="true" tabindex="-1"></a>Use <span class="in">`flextable()`</span> and <span class="in">`set_header_labels()`</span>.</span>
-<span id="cb27-206"><a href="#cb27-206" aria-hidden="true" tabindex="-1"></a>:::</span>
-<span id="cb27-207"><a href="#cb27-207" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb27-210"><a href="#cb27-210" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
-<span id="cb27-211"><a href="#cb27-211" aria-hidden="true" tabindex="-1"></a>rr_tbl <span class="ot">&lt;-</span> <span class="fu">CSTable</span>(copdata, <span class="st">"case"</span>, food_vars) <span class="sc">%&gt;%</span> </span>
-<span id="cb27-212"><a href="#cb27-212" aria-hidden="true" tabindex="-1"></a>  <span class="fu">as.data.frame</span>() <span class="sc">%&gt;%</span> </span>
-<span id="cb27-213"><a href="#cb27-213" aria-hidden="true" tabindex="-1"></a>  <span class="fu">rownames_to_column</span>() <span class="sc">%&gt;%</span> </span>
-<span id="cb27-214"><a href="#cb27-214" aria-hidden="true" tabindex="-1"></a>  <span class="fu">flextable</span>() <span class="sc">%&gt;%</span> </span>
-<span id="cb27-215"><a href="#cb27-215" aria-hidden="true" tabindex="-1"></a>   <span class="fu">set_header_labels</span>(</span>
-<span id="cb27-216"><a href="#cb27-216" aria-hidden="true" tabindex="-1"></a>     <span class="at">values =</span> <span class="fu">c</span>(<span class="st">"Food Item"</span>,</span>
-<span id="cb27-217"><a href="#cb27-217" aria-hidden="true" tabindex="-1"></a>                <span class="st">"Total exposed"</span>,     </span>
-<span id="cb27-218"><a href="#cb27-218" aria-hidden="true" tabindex="-1"></a>               <span class="st">"Cases exposed"</span>, </span>
-<span id="cb27-219"><a href="#cb27-219" aria-hidden="true" tabindex="-1"></a>               <span class="st">"AR among exposed"</span>,    </span>
-<span id="cb27-220"><a href="#cb27-220" aria-hidden="true" tabindex="-1"></a>               <span class="st">"Total unexposed"</span>,</span>
-<span id="cb27-221"><a href="#cb27-221" aria-hidden="true" tabindex="-1"></a>               <span class="st">"Cases unexposed"</span>,</span>
-<span id="cb27-222"><a href="#cb27-222" aria-hidden="true" tabindex="-1"></a>               <span class="st">"AR among unexposed"</span>,</span>
-<span id="cb27-223"><a href="#cb27-223" aria-hidden="true" tabindex="-1"></a>               <span class="st">"RR"</span>,         </span>
-<span id="cb27-224"><a href="#cb27-224" aria-hidden="true" tabindex="-1"></a>               <span class="st">"95% lower CI"</span>,             </span>
-<span id="cb27-225"><a href="#cb27-225" aria-hidden="true" tabindex="-1"></a>               <span class="st">"95% upper CI"</span>,</span>
-<span id="cb27-226"><a href="#cb27-226" aria-hidden="true" tabindex="-1"></a>               <span class="st">"p-value"</span>))</span>
-<span id="cb27-227"><a href="#cb27-227" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb27-228"><a href="#cb27-228" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb27-229"><a href="#cb27-229" aria-hidden="true" tabindex="-1"></a>::: {.callout-warning title="Let's stop and... think!" collapse="true"}</span>
-<span id="cb27-230"><a href="#cb27-230" aria-hidden="true" tabindex="-1"></a>· What can you infer from this table?</span>
-<span id="cb27-231"><a href="#cb27-231" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb27-232"><a href="#cb27-232" aria-hidden="true" tabindex="-1"></a>· Considering the relative risks, which food or drink items do you think were most likely to be the vehicle(s) of infection in this outbreak?</span>
-<span id="cb27-233"><a href="#cb27-233" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb27-234"><a href="#cb27-234" aria-hidden="true" tabindex="-1"></a>· Do you think there are any confounders or effect modifiers? If so, how would you investigate these further?</span>
-<span id="cb27-235"><a href="#cb27-235" aria-hidden="true" tabindex="-1"></a>:::</span>
-<span id="cb27-236"><a href="#cb27-236" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb27-237"><a href="#cb27-237" aria-hidden="true" tabindex="-1"></a>The interesting results here are that the food items that are most suspicious are orzo, moussaka and champagne. Orzo as such is unlikely to be contaminated, but as you can see in the picture (and from the dinner night), it was served with pesto! Maybe it was the pesto? Who-ho-ho!</span>
-<span id="cb27-238"><a href="#cb27-238" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb27-239"><a href="#cb27-239" aria-hidden="true" tabindex="-1"></a>Before one jumps into conclusions, consider that this result could be due to confounding! Maybe orzo was \"clean\" but eaten by all the people who ate the food item that actually was contaminated!(Optional)</span>
-<span id="cb27-240"><a href="#cb27-240" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb27-241"><a href="#cb27-241" aria-hidden="true" tabindex="-1"></a><span class="fu">## 3.5. (Optional) Dose Response</span></span>
-<span id="cb27-242"><a href="#cb27-242" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb27-243"><a href="#cb27-243" aria-hidden="true" tabindex="-1"></a>Check for a dose-response relationship between the food items with the highest RR values (the top 3) and being a case.</span>
-<span id="cb27-244"><a href="#cb27-244" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb27-245"><a href="#cb27-245" aria-hidden="true" tabindex="-1"></a><span class="fu">#### a) Orzo</span></span>
-<span id="cb27-246"><a href="#cb27-246" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb27-249"><a href="#cb27-249" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
-<span id="cb27-250"><a href="#cb27-250" aria-hidden="true" tabindex="-1"></a><span class="co"># Binomial regression for RRs. </span></span>
-<span id="cb27-251"><a href="#cb27-251" aria-hidden="true" tabindex="-1"></a><span class="co"># The outcome needs to be exponentiated so we can interpret it properly!</span></span>
-<span id="cb27-252"><a href="#cb27-252" aria-hidden="true" tabindex="-1"></a>binom_orzoD <span class="ot">&lt;-</span> <span class="fu">glm</span>(case <span class="sc">~</span> orzoD, <span class="at">data =</span> copdata, </span>
-<span id="cb27-253"><a href="#cb27-253" aria-hidden="true" tabindex="-1"></a>             <span class="at">family =</span> <span class="fu">binomial</span>(<span class="at">link =</span> <span class="st">"log"</span>))</span>
-<span id="cb27-254"><a href="#cb27-254" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb27-255"><a href="#cb27-255" aria-hidden="true" tabindex="-1"></a><span class="co"># To get exponentiated:</span></span>
-<span id="cb27-256"><a href="#cb27-256" aria-hidden="true" tabindex="-1"></a>binom_orzoD_exp <span class="ot">&lt;-</span> <span class="fu">glm</span>(case <span class="sc">~</span> orzoD, <span class="at">data =</span> copdata, </span>
-<span id="cb27-257"><a href="#cb27-257" aria-hidden="true" tabindex="-1"></a>                       <span class="at">family =</span> <span class="fu">binomial</span>(<span class="at">link =</span> <span class="st">"log"</span>)) <span class="sc">%&gt;%</span> </span>
-<span id="cb27-258"><a href="#cb27-258" aria-hidden="true" tabindex="-1"></a>  <span class="fu">tidy</span>(<span class="at">exponentiate =</span> <span class="cn">TRUE</span>, </span>
-<span id="cb27-259"><a href="#cb27-259" aria-hidden="true" tabindex="-1"></a>       <span class="at">conf.int =</span> <span class="cn">TRUE</span>)</span>
-<span id="cb27-260"><a href="#cb27-260" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb27-261"><a href="#cb27-261" aria-hidden="true" tabindex="-1"></a>binom_orzoD_exp</span>
-<span id="cb27-262"><a href="#cb27-262" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb27-263"><a href="#cb27-263" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb27-264"><a href="#cb27-264" aria-hidden="true" tabindex="-1"></a>::: {.callout-warning title="Let's stop and... think!" collapse="true"}</span>
-<span id="cb27-265"><a href="#cb27-265" aria-hidden="true" tabindex="-1"></a>What do these results tell you?</span>
-<span id="cb27-266"><a href="#cb27-266" aria-hidden="true" tabindex="-1"></a>:::</span>
-<span id="cb27-267"><a href="#cb27-267" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb27-268"><a href="#cb27-268" aria-hidden="true" tabindex="-1"></a>Results suggest a dose response relationship of having eaten orzo, pointing towards orzoas the potential vehicle. The higher the amount of orzothey ate, the stronger is the association (RR) with getting ill/being a case.</span>
-<span id="cb27-269"><a href="#cb27-269" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb27-270"><a href="#cb27-270" aria-hidden="true" tabindex="-1"></a><span class="fu">#### b) Moussaka</span></span>
-<span id="cb27-271"><a href="#cb27-271" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb27-274"><a href="#cb27-274" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
-<span id="cb27-275"><a href="#cb27-275" aria-hidden="true" tabindex="-1"></a><span class="co"># Let's get the results directly exponentiated</span></span>
-<span id="cb27-276"><a href="#cb27-276" aria-hidden="true" tabindex="-1"></a>binom_moussakaD_exp <span class="ot">&lt;-</span> <span class="fu">glm</span>(case <span class="sc">~</span> moussakaD, <span class="at">data =</span> copdata, </span>
-<span id="cb27-277"><a href="#cb27-277" aria-hidden="true" tabindex="-1"></a>                       <span class="at">family =</span> <span class="fu">binomial</span>(<span class="at">link =</span> <span class="st">"log"</span>)) <span class="sc">%&gt;%</span> </span>
-<span id="cb27-278"><a href="#cb27-278" aria-hidden="true" tabindex="-1"></a>  <span class="fu">tidy</span>(<span class="at">exponentiate =</span> <span class="cn">TRUE</span>, </span>
-<span id="cb27-279"><a href="#cb27-279" aria-hidden="true" tabindex="-1"></a>       <span class="at">conf.int =</span> <span class="cn">TRUE</span>)</span>
-<span id="cb27-280"><a href="#cb27-280" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb27-281"><a href="#cb27-281" aria-hidden="true" tabindex="-1"></a>binom_moussakaD_exp</span>
-<span id="cb27-282"><a href="#cb27-282" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb27-283"><a href="#cb27-283" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb27-284"><a href="#cb27-284" aria-hidden="true" tabindex="-1"></a><span class="fu">#### c) Champagne</span></span>
-<span id="cb27-285"><a href="#cb27-285" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb27-288"><a href="#cb27-288" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
-<span id="cb27-289"><a href="#cb27-289" aria-hidden="true" tabindex="-1"></a><span class="co"># Let's get the results directly exponentiated</span></span>
-<span id="cb27-290"><a href="#cb27-290" aria-hidden="true" tabindex="-1"></a>binom_champagneD_exp <span class="ot">&lt;-</span> <span class="fu">glm</span>(case <span class="sc">~</span> champagneD, <span class="at">data =</span> copdata, </span>
-<span id="cb27-291"><a href="#cb27-291" aria-hidden="true" tabindex="-1"></a>                       <span class="at">family =</span> <span class="fu">binomial</span>(<span class="at">link =</span> <span class="st">"log"</span>)) <span class="sc">%&gt;%</span> </span>
-<span id="cb27-292"><a href="#cb27-292" aria-hidden="true" tabindex="-1"></a>  <span class="fu">tidy</span>(<span class="at">exponentiate =</span> <span class="cn">TRUE</span>, </span>
-<span id="cb27-293"><a href="#cb27-293" aria-hidden="true" tabindex="-1"></a>       <span class="at">conf.int =</span> <span class="cn">TRUE</span>)</span>
-<span id="cb27-294"><a href="#cb27-294" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb27-295"><a href="#cb27-295" aria-hidden="true" tabindex="-1"></a>binom_champagneD_exp</span>
-<span id="cb27-296"><a href="#cb27-296" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb27-297"><a href="#cb27-297" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb27-298"><a href="#cb27-298" aria-hidden="true" tabindex="-1"></a><span class="fu">## 3.6 Summary</span></span>
-<span id="cb27-299"><a href="#cb27-299" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb27-300"><a href="#cb27-300" aria-hidden="true" tabindex="-1"></a>As a summary of what you\'ve done above, answer these questions:</span>
-<span id="cb27-301"><a href="#cb27-301" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb27-302"><a href="#cb27-302" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span>Is the respondents\' sex associated with being a case?</span>
-<span id="cb27-303"><a href="#cb27-303" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb27-304"><a href="#cb27-304" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span>Is the school class associated with being a case?</span>
-<span id="cb27-305"><a href="#cb27-305" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb27-306"><a href="#cb27-306" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span>Which foods increase the risk of being a case?</span>
-<span id="cb27-307"><a href="#cb27-307" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb27-308"><a href="#cb27-308" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span>(Optional) Is there a dose-response relationship between the food items and being a case?</span>
-<span id="cb27-309"><a href="#cb27-309" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb27-310"><a href="#cb27-310" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span>What do you think is the most likely culprit(s) of this outbreak at this point? Are there any risk factors you would like to highlight?</span>
-</code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-</div></div></div></div></div>
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-  <li><a href="#install-packages-and-load-libraries" id="toc-install-packages-and-load-libraries" class="nav-link" data-scroll-target="#install-packages-and-load-libraries">3.2. Install packages and load libraries</a></li>
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-  <ul class="collapse">
-  <li><a href="#a-moussaka-as-exposure-of-interest-stratified-by-having-eaten-orzo" id="toc-a-moussaka-as-exposure-of-interest-stratified-by-having-eaten-orzo" class="nav-link" data-scroll-target="#a-moussaka-as-exposure-of-interest-stratified-by-having-eaten-orzo">a) Moussaka as exposure of interest, stratified by having eaten orzo</a></li>
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-<section id="learning-outcomes" class="level2">
-<h2 class="anchored" data-anchor-id="learning-outcomes">1. Learning outcomes</h2>
-<p>At the end of the session, participants will be able to:</p>
-<ul>
-<li><p>Consider the effect of confounding and effect modification on the association between exposure and disease,</p></li>
-<li><p>Perform stratified analysis using the Mantel-Haenszel approach</p></li>
-</ul>
-<p><em>The use of stratified analysis, is the first step to identify confounding factors and effect modifiers one by one (after, of course, thinking which variables could potentially be confounders or effect modifiers). As the final step, you will be using Regression Models to account for confounding and check for effect modification. We will see these with you in the Multivariable Module (MVA), next year.</em></p>
-</section>
-<section id="storyplot-description" class="level2">
-<h2 class="anchored" data-anchor-id="storyplot-description">2. Story/plot description</h2>
-<p>From the univariable analysis, it seems that eating orzpo and eating moussaka as well as drinking champagne are associated with the highest risk of becoming ill.&nbsp;There are, however, many other food items that are associated with an increased risk (even if not statistically significant).</p>
-<p>You should next think about potential confounders and about effect modification. Think about which variables you might want to check for effect modification or confounding. One common strategy is to base this decision on the results obtained in the univariable analysis and a p-value threshold of 0.20-0.25. Also, food items that are known risk factors for gastroenteritis could also be included regardless of their univariable p-value.</p>
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-<section id="questionsassignments" class="level2">
-<h2 class="anchored" data-anchor-id="questionsassignments">3. Questions/Assignments</h2>
-</section>
-<section id="confounders-and-effect-modification" class="level2">
-<h2 class="anchored" data-anchor-id="confounders-and-effect-modification">3.1. Confounders and effect modification</h2>
-<p>Discuss how to identify potential confounders and effect modification. Draw dummy tables before coding to have clear what you want to achieve.</p>
-</section>
-<section id="install-packages-and-load-libraries" class="level2">
-<h2 class="anchored" data-anchor-id="install-packages-and-load-libraries">3.2. Install packages and load libraries</h2>
-<div class="cell">
-<details open="">
-<summary>Code</summary>
-<div class="sourceCode cell-code" id="cb1"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Load the required libraries into the current R session:</span></span>
-<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a>pacman<span class="sc">::</span><span class="fu">p_load</span>(rio, </span>
-<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a>               here, </span>
-<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a>               tidyverse, </span>
-<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a>               skimr,</span>
-<span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a>               plyr,</span>
-<span id="cb1-7"><a href="#cb1-7" aria-hidden="true" tabindex="-1"></a>               janitor,</span>
-<span id="cb1-8"><a href="#cb1-8" aria-hidden="true" tabindex="-1"></a>               lubridate,</span>
-<span id="cb1-9"><a href="#cb1-9" aria-hidden="true" tabindex="-1"></a>               gtsummary, </span>
-<span id="cb1-10"><a href="#cb1-10" aria-hidden="true" tabindex="-1"></a>               flextable,</span>
-<span id="cb1-11"><a href="#cb1-11" aria-hidden="true" tabindex="-1"></a>               officer,</span>
-<span id="cb1-12"><a href="#cb1-12" aria-hidden="true" tabindex="-1"></a>               epikit, </span>
-<span id="cb1-13"><a href="#cb1-13" aria-hidden="true" tabindex="-1"></a>               apyramid, </span>
-<span id="cb1-14"><a href="#cb1-14" aria-hidden="true" tabindex="-1"></a>               scales,</span>
-<span id="cb1-15"><a href="#cb1-15" aria-hidden="true" tabindex="-1"></a>               EpiStats)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-</details>
-</div>
-</section>
-<section id="import-your-data" class="level2">
-<h2 class="anchored" data-anchor-id="import-your-data">3.3. Import your data</h2>
-<div class="cell">
-<details open="">
-<summary>Code</summary>
-<div class="sourceCode cell-code" id="cb2"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Import the raw data set: </span></span>
-<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a>copdata <span class="ot">&lt;-</span> rio<span class="sc">::</span><span class="fu">import</span>(here<span class="sc">::</span><span class="fu">here</span>(<span class="st">"data"</span>, <span class="st">"Copenhagen_clean2_2024.rds"</span>))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-</details>
-</div>
-</section>
-<section id="consider-and-assess-for-confounding-andor-effect-modification" class="level2">
-<h2 class="anchored" data-anchor-id="consider-and-assess-for-confounding-andor-effect-modification">3.4. Consider and assess for confounding and/or effect modification</h2>
-<p>Have a look at the relative risk for being a case having eaten a specific food item (for example, moussake), when stratified by another variable (for example, orzo). You may consider stratifying by orzo, as it has the highest RR in the univariable analysis.</p>
-<p>There are many variables in this dataset and it might not make sense to stratify each variable by each other variable on our search for effect modifiers and confounders.</p>
-<p>However, we also don’t want to be too restrictive as a variable which actually (i.e.&nbsp;causally) is associated with the outcome might not show a significant association at the significant level we decided (say 5%) in the univariable analysis due to confounding. Therefore, we could test all variables statistically significant at the 15%, 20%, or 25% level (specific percentage to be decided by your group). In our solutions here, we are looking at moussaka and champagne, stratified by orzo, but you may decide to look at other food items as well.</p>
-<div class="callout callout-style-default callout-tip callout-titled" title="Need a little bit of help?">
-<div class="callout-header d-flex align-content-center" data-bs-toggle="collapse" data-bs-target=".callout-1-contents" aria-controls="callout-1" aria-expanded="false" aria-label="Toggle callout">
-<div class="callout-icon-container">
-<i class="callout-icon"></i>
-</div>
-<div class="callout-title-container flex-fill">
-Need a little bit of help?
-</div>
-<div class="callout-btn-toggle d-inline-block border-0 py-1 ps-1 pe-0 float-end"><i class="callout-toggle"></i></div>
-</div>
-<div id="callout-1" class="callout-1-contents callout-collapse collapse">
-<div class="callout-body-container callout-body">
-<p>Use the function <code>csinter()</code> of the EpiStats package.</p>
-</div>
-</div>
-</div>
-<section id="a-moussaka-as-exposure-of-interest-stratified-by-having-eaten-orzo" class="level3">
-<h3 class="anchored" data-anchor-id="a-moussaka-as-exposure-of-interest-stratified-by-having-eaten-orzo">a) Moussaka as exposure of interest, stratified by having eaten orzo</h3>
-<p>If we stratify the <strong>effect of moussaka by orzo</strong> we ask the question: does eating orzo modify or confound the association between eating moussaka and being a case?</p>
-<div class="cell">
-<details open="">
-<summary>Code</summary>
-<div class="sourceCode cell-code" id="cb3"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a>stratall <span class="ot">&lt;-</span> copdata <span class="sc">%&gt;%</span> </span>
-<span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Mutate across to convert cases to numeric:</span></span>
-<span id="cb3-3"><a href="#cb3-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="fu">across</span>(<span class="at">.cols =</span> case, </span>
-<span id="cb3-4"><a href="#cb3-4" aria-hidden="true" tabindex="-1"></a>                <span class="at">.fns =</span> <span class="sc">~</span> <span class="fu">as.numeric</span>(.)))</span>
-<span id="cb3-5"><a href="#cb3-5" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb3-6"><a href="#cb3-6" aria-hidden="true" tabindex="-1"></a><span class="co"># Pass data to the csinter function:</span></span>
-<span id="cb3-7"><a href="#cb3-7" aria-hidden="true" tabindex="-1"></a>orzostrata <span class="ot">&lt;-</span> <span class="fu">csinter</span>(<span class="at">x =</span> stratall, </span>
-<span id="cb3-8"><a href="#cb3-8" aria-hidden="true" tabindex="-1"></a>                       <span class="at">cases =</span> <span class="st">"case"</span>, </span>
-<span id="cb3-9"><a href="#cb3-9" aria-hidden="true" tabindex="-1"></a>                       <span class="at">exposure =</span> <span class="st">"moussaka"</span>, </span>
-<span id="cb3-10"><a href="#cb3-10" aria-hidden="true" tabindex="-1"></a>                       <span class="at">by =</span> <span class="st">"orzo"</span>)</span>
-<span id="cb3-11"><a href="#cb3-11" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb3-12"><a href="#cb3-12" aria-hidden="true" tabindex="-1"></a>orzostrata</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-</details>
-<div class="cell-output cell-output-stdout">
-<pre><code>$df1
-  CSInter case - moussaka by(orzo) Total Cases Risk %          P.est. Stats
-1                         orzo = 1   338  &lt;NA&gt;     NA Risk difference  0.10
-2                          Exposed   330   198  60.00      Risk Ratio  1.20
-3                        Unexposed     8     4  50.00 Attrib.risk.exp  0.17
-4                                     NA  &lt;NA&gt;     NA Attrib.risk.pop  0.16
-5                         orzo = 0    36  &lt;NA&gt;     NA Risk difference  0.02
-6                          Exposed     8     3  37.50      Risk Ratio  1.05
-7                        Unexposed    28    10  35.71 Attrib.risk.exp  0.05
-8                                     NA  &lt;NA&gt;     NA Attrib.risk.pop  0.01
-9              Missing / Missing %     3  0.8%     NA            &lt;NA&gt;    NA
-  95%CI.ll 95%CI.ul
-1    -0.25     0.45
-2     0.60     2.41
-3    -0.68     0.59
-4       NA       NA
-5    -0.36     0.40
-6     0.38     2.92
-7    -1.65     0.66
-8       NA       NA
-9       NA       NA
-
-$df2
-                    Point Estimate Chi2 p.value  Stats 95%CI.ll 95%CI.ul
-1        Woolf test of homogeneity 0.04   0.833     NA       NA       NA
-2            Crude RR for moussaka   NA      NA   1.53     1.01     2.32
-3 MH RR moussaka adjusted for orzo   NA      NA   1.15     0.64     2.04
-4   Adjusted/crude relative change   NA      NA -25.08       NA       NA</code></pre>
-</div>
-</div>
-<p>Let’s check if orzo is associated with moussaka (if we are thinking moussaka may be a confounder, we need to see if there is an association between the potential confounder (moussaka) and the exposure (orzo)):</p>
-<div class="cell">
-<details open="">
-<summary>Code</summary>
-<div class="sourceCode cell-code" id="cb5"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Perform Wilcoxon rank sum test on orzo and moussaka:</span></span>
-<span id="cb5-2"><a href="#cb5-2" aria-hidden="true" tabindex="-1"></a><span class="fu">wilcox.test</span>(orzo <span class="sc">~</span> moussaka, </span>
-<span id="cb5-3"><a href="#cb5-3" aria-hidden="true" tabindex="-1"></a>            <span class="at">data =</span> copdata)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-</details>
-<div class="cell-output cell-output-stdout">
-<pre><code>
-    Wilcoxon rank sum test with continuity correction
-
-data:  orzo by moussaka
-W = 1496, p-value &lt; 2.2e-16
-alternative hypothesis: true location shift is not equal to 0</code></pre>
-</div>
-</div>
-</section>
-<section id="b-champagne-as-exposure-of-interest-stratified-by-having-eaten-orzo" class="level3">
-<h3 class="anchored" data-anchor-id="b-champagne-as-exposure-of-interest-stratified-by-having-eaten-orzo">b) Champagne as exposure of interest, stratified by having eaten orzo</h3>
-<div class="cell">
-<details open="">
-<summary>Code</summary>
-<div class="sourceCode cell-code" id="cb7"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Pass data to the csinter function:</span></span>
-<span id="cb7-2"><a href="#cb7-2" aria-hidden="true" tabindex="-1"></a>champstrata <span class="ot">&lt;-</span> <span class="fu">csinter</span>(<span class="at">x =</span> stratall, </span>
-<span id="cb7-3"><a href="#cb7-3" aria-hidden="true" tabindex="-1"></a>                       <span class="at">cases =</span> <span class="st">"case"</span>, </span>
-<span id="cb7-4"><a href="#cb7-4" aria-hidden="true" tabindex="-1"></a>                       <span class="at">exposure =</span> <span class="st">"champagne"</span>, </span>
-<span id="cb7-5"><a href="#cb7-5" aria-hidden="true" tabindex="-1"></a>                       <span class="at">by =</span> <span class="st">"orzo"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-</details>
-</div>
-<div class="callout callout-style-default callout-warning callout-titled" title="Let's stop and... think!">
-<div class="callout-header d-flex align-content-center" data-bs-toggle="collapse" data-bs-target=".callout-2-contents" aria-controls="callout-2" aria-expanded="false" aria-label="Toggle callout">
-<div class="callout-icon-container">
-<i class="callout-icon"></i>
-</div>
-<div class="callout-title-container flex-fill">
-Let’s stop and… think!
-</div>
-<div class="callout-btn-toggle d-inline-block border-0 py-1 ps-1 pe-0 float-end"><i class="callout-toggle"></i></div>
-</div>
-<div id="callout-2" class="callout-2-contents callout-collapse collapse">
-<div class="callout-body-container callout-body">
-<p>Are there any indications to make you think there may be effect modification and/or confounding?</p>
-</div>
-</div>
-</div>
-<p>We could stratify by orzo (the strongest risk factor in the univariable analysis), to examine if orzo confounds the association between eating moussaka and being a case. Before stratification, we will need to check if orzo meets the conditions of being a confounder. For a variable to be a confounder it needs to be associated both with the outcome (being a case) and with the exposure (and not be in the causal pathway between exposure and outcome). We know from univariable analysis that orzo is associated with being a case. If we run a Wilcoxon rank sum test, we will see that orzo is also associated with moussaka (indeed, you can see that most people either had both moussaka and orzo or neither of these food items, so they are associated with each other).</p>
-<p>Above, we uses <code>ccinter</code> to stratify and save the object as "orzostrata".</p>
-<p>orzostrata: Within the stratum of the people who ate orzo, moussaka has no significant effect (RR = 1.20, CI: 0.60 - 2.41). The same holds within the stratum of people who didn't eat orzo (RR = 1.05, CI = 0.38, 2.92). The adjusted MH-RR also suggests that moussaka has no effect (RRadj = 1.15, CI: 0.80 - 2.85). &nbsp;To identify confounding, we want to look at the % change between the crude and the adjusted RR. This is given by the csinter output "Adjusted/crude relative change".&nbsp; The difference between the crude and the MH-RR in this case is &gt;20% suggesting that orzo confounds the association between moussaka and the disease.</p>
-<p>This result suggest that moussaka is not a risk factor of the disease and that the crude observed effect was due to the confounding effect of orzo.</p>
-<p>If you stratify by moussaka, you see that moussaka does not confound the association between orzo and the disease. The same applies if you stratify the exposure to orzo by other variables. The above, the higher RR for orzo and the dose response relationship we found earlier for orzo (remember this was optional) provide additional evidence that there was something going on with the orzo with pesto dish!</p>
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-</script><div class="modal fade" id="quarto-embedded-source-code-modal" tabindex="-1" aria-labelledby="quarto-embedded-source-code-modal-label" aria-hidden="true"><div class="modal-dialog modal-dialog-scrollable"><div class="modal-content"><div class="modal-header"><h5 class="modal-title" id="quarto-embedded-source-code-modal-label">Source Code</h5><button class="btn-close" data-bs-dismiss="modal"></button></div><div class="modal-body"><div class="">
-<div class="sourceCode" id="cb8" data-shortcodes="false"><pre class="sourceCode markdown code-with-copy"><code class="sourceCode markdown"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a><span class="co">---</span></span>
-<span id="cb8-2"><a href="#cb8-2" aria-hidden="true" tabindex="-1"></a><span class="an">title:</span><span class="co"> "Stratified analysis"</span></span>
-<span id="cb8-3"><a href="#cb8-3" aria-hidden="true" tabindex="-1"></a><span class="an">editor:</span><span class="co"> visual</span></span>
-<span id="cb8-4"><a href="#cb8-4" aria-hidden="true" tabindex="-1"></a><span class="co">---</span></span>
-<span id="cb8-5"><a href="#cb8-5" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb8-6"><a href="#cb8-6" aria-hidden="true" tabindex="-1"></a><span class="fu">## 1. Learning outcomes</span></span>
-<span id="cb8-7"><a href="#cb8-7" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb8-8"><a href="#cb8-8" aria-hidden="true" tabindex="-1"></a>At the end of the session, participants will be able to:</span>
-<span id="cb8-9"><a href="#cb8-9" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb8-10"><a href="#cb8-10" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span>Consider the effect of confounding and effect modification on the association between exposure and disease,</span>
-<span id="cb8-11"><a href="#cb8-11" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb8-12"><a href="#cb8-12" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span>Perform stratified analysis using the Mantel-Haenszel approach</span>
-<span id="cb8-13"><a href="#cb8-13" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb8-14"><a href="#cb8-14" aria-hidden="true" tabindex="-1"></a>*The use of stratified analysis, is the first step to identify confounding factors and effect modifiers one by one (after, of course, thinking which variables could potentially be confounders or effect modifiers). As the final step, you will be using Regression Models to account for confounding and check for effect modification. We will see these with you in the Multivariable Module (MVA), next year.*</span>
-<span id="cb8-15"><a href="#cb8-15" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb8-16"><a href="#cb8-16" aria-hidden="true" tabindex="-1"></a><span class="fu">## 2. Story/plot description</span></span>
-<span id="cb8-17"><a href="#cb8-17" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb8-18"><a href="#cb8-18" aria-hidden="true" tabindex="-1"></a>From the univariable analysis, it seems that eating orzpo and eating moussaka as well as drinking champagne are associated with the highest risk of becoming ill. There are, however, many other food items that are associated with an increased risk (even if not statistically significant).</span>
-<span id="cb8-19"><a href="#cb8-19" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb8-20"><a href="#cb8-20" aria-hidden="true" tabindex="-1"></a>You should next think about potential confounders and about effect modification. Think about which variables you might want to check for effect modification or confounding. One common strategy is to base this decision on the results obtained in the univariable analysis and a p-value threshold of 0.20-0.25. Also, food items that are known risk factors for gastroenteritis could also be included regardless of their univariable p-value.</span>
-<span id="cb8-21"><a href="#cb8-21" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb8-22"><a href="#cb8-22" aria-hidden="true" tabindex="-1"></a><span class="fu">## 3. Questions/Assignments</span></span>
-<span id="cb8-23"><a href="#cb8-23" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb8-24"><a href="#cb8-24" aria-hidden="true" tabindex="-1"></a><span class="fu">## 3.1. Confounders and effect modification</span></span>
-<span id="cb8-25"><a href="#cb8-25" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb8-26"><a href="#cb8-26" aria-hidden="true" tabindex="-1"></a>Discuss how to identify potential confounders and effect modification. Draw dummy tables before coding to have clear what you want to achieve.</span>
-<span id="cb8-27"><a href="#cb8-27" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb8-28"><a href="#cb8-28" aria-hidden="true" tabindex="-1"></a><span class="fu">## 3.2. Install packages and load libraries</span></span>
-<span id="cb8-29"><a href="#cb8-29" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb8-32"><a href="#cb8-32" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
-<span id="cb8-33"><a href="#cb8-33" aria-hidden="true" tabindex="-1"></a><span class="co"># Load the required libraries into the current R session:</span></span>
-<span id="cb8-34"><a href="#cb8-34" aria-hidden="true" tabindex="-1"></a>pacman<span class="sc">::</span><span class="fu">p_load</span>(rio, </span>
-<span id="cb8-35"><a href="#cb8-35" aria-hidden="true" tabindex="-1"></a>               here, </span>
-<span id="cb8-36"><a href="#cb8-36" aria-hidden="true" tabindex="-1"></a>               tidyverse, </span>
-<span id="cb8-37"><a href="#cb8-37" aria-hidden="true" tabindex="-1"></a>               skimr,</span>
-<span id="cb8-38"><a href="#cb8-38" aria-hidden="true" tabindex="-1"></a>               plyr,</span>
-<span id="cb8-39"><a href="#cb8-39" aria-hidden="true" tabindex="-1"></a>               janitor,</span>
-<span id="cb8-40"><a href="#cb8-40" aria-hidden="true" tabindex="-1"></a>               lubridate,</span>
-<span id="cb8-41"><a href="#cb8-41" aria-hidden="true" tabindex="-1"></a>               gtsummary, </span>
-<span id="cb8-42"><a href="#cb8-42" aria-hidden="true" tabindex="-1"></a>               flextable,</span>
-<span id="cb8-43"><a href="#cb8-43" aria-hidden="true" tabindex="-1"></a>               officer,</span>
-<span id="cb8-44"><a href="#cb8-44" aria-hidden="true" tabindex="-1"></a>               epikit, </span>
-<span id="cb8-45"><a href="#cb8-45" aria-hidden="true" tabindex="-1"></a>               apyramid, </span>
-<span id="cb8-46"><a href="#cb8-46" aria-hidden="true" tabindex="-1"></a>               scales,</span>
-<span id="cb8-47"><a href="#cb8-47" aria-hidden="true" tabindex="-1"></a>               EpiStats)</span>
-<span id="cb8-48"><a href="#cb8-48" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb8-49"><a href="#cb8-49" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb8-50"><a href="#cb8-50" aria-hidden="true" tabindex="-1"></a><span class="fu">## 3.3. Import your data</span></span>
-<span id="cb8-51"><a href="#cb8-51" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb8-52"><a href="#cb8-52" aria-hidden="true" tabindex="-1"></a><span class="in">```{r, Import_data}</span></span>
-<span id="cb8-53"><a href="#cb8-53" aria-hidden="true" tabindex="-1"></a><span class="co"># Import the raw data set: </span></span>
-<span id="cb8-54"><a href="#cb8-54" aria-hidden="true" tabindex="-1"></a>copdata <span class="ot">&lt;-</span> rio<span class="sc">::</span><span class="fu">import</span>(here<span class="sc">::</span><span class="fu">here</span>(<span class="st">"data"</span>, <span class="st">"Copenhagen_clean2_2024.rds"</span>))</span>
-<span id="cb8-55"><a href="#cb8-55" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb8-56"><a href="#cb8-56" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb8-57"><a href="#cb8-57" aria-hidden="true" tabindex="-1"></a><span class="fu">## 3.4. Consider and assess for confounding and/or effect modification</span></span>
-<span id="cb8-58"><a href="#cb8-58" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb8-59"><a href="#cb8-59" aria-hidden="true" tabindex="-1"></a>Have a look at the relative risk for being a case having eaten a specific food item (for example, moussake), when stratified by another variable (for example, orzo). You may consider stratifying by orzo, as it has the highest</span>
-<span id="cb8-60"><a href="#cb8-60" aria-hidden="true" tabindex="-1"></a>RR in the univariable analysis.</span>
-<span id="cb8-61"><a href="#cb8-61" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb8-62"><a href="#cb8-62" aria-hidden="true" tabindex="-1"></a>There are many variables in this dataset and it might not make sense to stratify each variable by each other variable on our search for effect modifiers and confounders.</span>
-<span id="cb8-63"><a href="#cb8-63" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb8-64"><a href="#cb8-64" aria-hidden="true" tabindex="-1"></a>However, we also don't want to be too restrictive as a variable which actually (i.e. causally) is associated with the outcome might not show a significant association at the significant level we decided (say 5%) in the univariable analysis due to confounding. Therefore, we could test all variables statistically significant at the 15%, 20%, or 25% level (specific percentage to be decided by your group). In our solutions here, we are looking at moussaka and champagne, stratified by orzo, but you may decide to look at other food items as well.</span>
-<span id="cb8-65"><a href="#cb8-65" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb8-66"><a href="#cb8-66" aria-hidden="true" tabindex="-1"></a>::: {.callout-tip title="Need a little bit of help?" collapse="true"}</span>
-<span id="cb8-67"><a href="#cb8-67" aria-hidden="true" tabindex="-1"></a>Use the function <span class="in">`csinter()`</span> of the EpiStats package.</span>
-<span id="cb8-68"><a href="#cb8-68" aria-hidden="true" tabindex="-1"></a>:::</span>
-<span id="cb8-69"><a href="#cb8-69" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb8-70"><a href="#cb8-70" aria-hidden="true" tabindex="-1"></a><span class="fu">### a) Moussaka as exposure of interest, stratified by having eaten orzo</span></span>
-<span id="cb8-71"><a href="#cb8-71" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb8-72"><a href="#cb8-72" aria-hidden="true" tabindex="-1"></a>If we stratify the **effect of moussaka by orzo** we ask the question: does eating orzo modify or confound the association between eating moussaka and being a case?</span>
-<span id="cb8-73"><a href="#cb8-73" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb8-76"><a href="#cb8-76" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
-<span id="cb8-77"><a href="#cb8-77" aria-hidden="true" tabindex="-1"></a>stratall <span class="ot">&lt;-</span> copdata <span class="sc">%&gt;%</span> </span>
-<span id="cb8-78"><a href="#cb8-78" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Mutate across to convert cases to numeric:</span></span>
-<span id="cb8-79"><a href="#cb8-79" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="fu">across</span>(<span class="at">.cols =</span> case, </span>
-<span id="cb8-80"><a href="#cb8-80" aria-hidden="true" tabindex="-1"></a>                <span class="at">.fns =</span> <span class="sc">~</span> <span class="fu">as.numeric</span>(.)))</span>
-<span id="cb8-81"><a href="#cb8-81" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb8-82"><a href="#cb8-82" aria-hidden="true" tabindex="-1"></a><span class="co"># Pass data to the csinter function:</span></span>
-<span id="cb8-83"><a href="#cb8-83" aria-hidden="true" tabindex="-1"></a>orzostrata <span class="ot">&lt;-</span> <span class="fu">csinter</span>(<span class="at">x =</span> stratall, </span>
-<span id="cb8-84"><a href="#cb8-84" aria-hidden="true" tabindex="-1"></a>                       <span class="at">cases =</span> <span class="st">"case"</span>, </span>
-<span id="cb8-85"><a href="#cb8-85" aria-hidden="true" tabindex="-1"></a>                       <span class="at">exposure =</span> <span class="st">"moussaka"</span>, </span>
-<span id="cb8-86"><a href="#cb8-86" aria-hidden="true" tabindex="-1"></a>                       <span class="at">by =</span> <span class="st">"orzo"</span>)</span>
-<span id="cb8-87"><a href="#cb8-87" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb8-88"><a href="#cb8-88" aria-hidden="true" tabindex="-1"></a>orzostrata</span>
-<span id="cb8-89"><a href="#cb8-89" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb8-90"><a href="#cb8-90" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb8-91"><a href="#cb8-91" aria-hidden="true" tabindex="-1"></a>Let's check if orzo is associated with moussaka (if we are thinking moussaka may be a confounder, we need to see if there is an association between the potential confounder (moussaka) and the exposure (orzo)):</span>
-<span id="cb8-92"><a href="#cb8-92" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb8-95"><a href="#cb8-95" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
-<span id="cb8-96"><a href="#cb8-96" aria-hidden="true" tabindex="-1"></a><span class="co"># Perform Wilcoxon rank sum test on orzo and moussaka:</span></span>
-<span id="cb8-97"><a href="#cb8-97" aria-hidden="true" tabindex="-1"></a><span class="fu">wilcox.test</span>(orzo <span class="sc">~</span> moussaka, </span>
-<span id="cb8-98"><a href="#cb8-98" aria-hidden="true" tabindex="-1"></a>            <span class="at">data =</span> copdata)</span>
-<span id="cb8-99"><a href="#cb8-99" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb8-100"><a href="#cb8-100" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb8-101"><a href="#cb8-101" aria-hidden="true" tabindex="-1"></a><span class="fu">### b) Champagne as exposure of interest, stratified by having eaten orzo</span></span>
-<span id="cb8-102"><a href="#cb8-102" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb8-105"><a href="#cb8-105" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
-<span id="cb8-106"><a href="#cb8-106" aria-hidden="true" tabindex="-1"></a><span class="co"># Pass data to the csinter function:</span></span>
-<span id="cb8-107"><a href="#cb8-107" aria-hidden="true" tabindex="-1"></a>champstrata <span class="ot">&lt;-</span> <span class="fu">csinter</span>(<span class="at">x =</span> stratall, </span>
-<span id="cb8-108"><a href="#cb8-108" aria-hidden="true" tabindex="-1"></a>                       <span class="at">cases =</span> <span class="st">"case"</span>, </span>
-<span id="cb8-109"><a href="#cb8-109" aria-hidden="true" tabindex="-1"></a>                       <span class="at">exposure =</span> <span class="st">"champagne"</span>, </span>
-<span id="cb8-110"><a href="#cb8-110" aria-hidden="true" tabindex="-1"></a>                       <span class="at">by =</span> <span class="st">"orzo"</span>)</span>
-<span id="cb8-111"><a href="#cb8-111" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb8-112"><a href="#cb8-112" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb8-113"><a href="#cb8-113" aria-hidden="true" tabindex="-1"></a>::: {.callout-warning title="Let's stop and... think!" collapse="true"}</span>
-<span id="cb8-114"><a href="#cb8-114" aria-hidden="true" tabindex="-1"></a>Are there any indications to make you think there may be effect modification and/or confounding?</span>
-<span id="cb8-115"><a href="#cb8-115" aria-hidden="true" tabindex="-1"></a>:::</span>
-<span id="cb8-116"><a href="#cb8-116" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb8-117"><a href="#cb8-117" aria-hidden="true" tabindex="-1"></a>We could stratify by orzo (the strongest risk factor in the univariable analysis), to examine if orzo confounds the association between eating moussaka and being a case. Before stratification, we will need to check if orzo meets the conditions of being a confounder. For a variable to be a confounder it needs to be associated both with the outcome (being a case) and with the exposure (and not be in the causal pathway between exposure and outcome). We know from univariable analysis that orzo is associated with being a case. If we run a Wilcoxon rank sum test, we will see that orzo is also associated with moussaka (indeed, you can see that most people either had both moussaka and orzo or neither of these food items, so they are associated with each other).</span>
-<span id="cb8-118"><a href="#cb8-118" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb8-119"><a href="#cb8-119" aria-hidden="true" tabindex="-1"></a>Above, we uses <span class="in">`ccinter`</span> to stratify and save the object as \"orzostrata\".</span>
-<span id="cb8-120"><a href="#cb8-120" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb8-121"><a href="#cb8-121" aria-hidden="true" tabindex="-1"></a>orzostrata: Within the stratum of the people who ate orzo, moussaka has no significant effect (RR = 1.20, CI: 0.60 - 2.41). The same holds within the stratum of people who didn\'t eat orzo (RR = 1.05, CI = 0.38, 2.92). The adjusted MH-RR also suggests that moussaka has no effect (RRadj = 1.15, CI: 0.80 - 2.85). &nbsp;To identify confounding, we want to look at the % change between the crude and the adjusted RR. This is given by the csinter output \"Adjusted/crude relative change\".&nbsp; The difference between the crude and the MH-RR in this case is <span class="sc">\&gt;</span>20% suggesting that orzo confounds the association between moussaka and the disease.</span>
-<span id="cb8-122"><a href="#cb8-122" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb8-123"><a href="#cb8-123" aria-hidden="true" tabindex="-1"></a>This result suggest that moussaka is not a risk factor of the disease and that the crude observed effect was due to the confounding effect of orzo.</span>
-<span id="cb8-124"><a href="#cb8-124" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb8-125"><a href="#cb8-125" aria-hidden="true" tabindex="-1"></a>If you stratify by moussaka, you see that moussaka does not confound the association between orzo and the disease. The same applies if you stratify the exposure to orzo by other variables. The above, the higher RR for orzo and the dose response relationship we found earlier for orzo (remember this was optional) provide additional evidence that there was something going on with the orzo with pesto dish!</span>
-</code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-</div></div></div></div></div>
-</div> <!-- /content -->
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diff --git a/docs/Stratified.html b/docs/Stratified.html
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-<title>Spetses Introductory Course - Stratified analysis</title>
+<title>Stratified analysis – Spetses Introductory Course</title>
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+pre > code.sourceCode > span { display: inline-block; text-indent: -5em; padding-left: 5em; }
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@@ -81,7 +81,13 @@
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- <span class="menu-text">Laboratory data and descriptive analysis</span></a>
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@@ -184,8 +192,8 @@ <h2 id="toc-title">On this page</h2>
   <li><a href="#import-your-data" id="toc-import-your-data" class="nav-link" data-scroll-target="#import-your-data">3.3. Import your data</a></li>
   <li><a href="#consider-and-assess-for-confounding-andor-effect-modification" id="toc-consider-and-assess-for-confounding-andor-effect-modification" class="nav-link" data-scroll-target="#consider-and-assess-for-confounding-andor-effect-modification">3.4. Consider and assess for confounding and/or effect modification</a>
   <ul class="collapse">
-  <li><a href="#a-moussaka-as-exposure-of-interest-stratified-by-having-eaten-orzo" id="toc-a-moussaka-as-exposure-of-interest-stratified-by-having-eaten-orzo" class="nav-link" data-scroll-target="#a-moussaka-as-exposure-of-interest-stratified-by-having-eaten-orzo">a) Moussaka as exposure of interest, stratified by having eaten orzo</a></li>
-  <li><a href="#b-champagne-as-exposure-of-interest-stratified-by-having-eaten-orzo" id="toc-b-champagne-as-exposure-of-interest-stratified-by-having-eaten-orzo" class="nav-link" data-scroll-target="#b-champagne-as-exposure-of-interest-stratified-by-having-eaten-orzo">b) Champagne as exposure of interest, stratified by having eaten orzo</a></li>
+  <li><a href="#a-veal-as-exposure-of-interest-stratified-by-having-eaten-pasta" id="toc-a-veal-as-exposure-of-interest-stratified-by-having-eaten-pasta" class="nav-link" data-scroll-target="#a-veal-as-exposure-of-interest-stratified-by-having-eaten-pasta">a) Veal as exposure of interest, stratified by having eaten pasta</a></li>
+  <li><a href="#b-champagne-as-exposure-of-interest-stratified-by-having-eaten-pasta" id="toc-b-champagne-as-exposure-of-interest-stratified-by-having-eaten-pasta" class="nav-link" data-scroll-target="#b-champagne-as-exposure-of-interest-stratified-by-having-eaten-pasta">b) Champagne as exposure of interest, stratified by having eaten pasta</a></li>
   </ul></li>
   </ul>
 </nav>
@@ -208,8 +216,10 @@ <h2 id="toc-title">On this page</h2>
   </div>
   
 
+
 </header>
 
+
 <section id="learning-outcomes" class="level2">
 <h2 class="anchored" data-anchor-id="learning-outcomes">1. Learning outcomes</h2>
 <p>At the end of the session, participants will be able to:</p>
@@ -221,7 +231,7 @@ <h2 class="anchored" data-anchor-id="learning-outcomes">1. Learning outcomes</h2
 </section>
 <section id="storyplot-description" class="level2">
 <h2 class="anchored" data-anchor-id="storyplot-description">2. Story/plot description</h2>
-<p>From the univariable analysis, it seems that eating orzpo and eating moussaka as well as drinking champagne are associated with the highest risk of becoming ill.&nbsp;There are, however, many other food items that are associated with an increased risk (even if not statistically significant).</p>
+<p>From the univariable analysis, it seems that eating pasta and eating veal as well as drinking champagne are associated with the highest risk of becoming ill.&nbsp;There are, however, many other food items that are associated with an increased risk (even if not statistically significant).</p>
 <p>You should next think about potential confounders and about effect modification. Think about which variables you might want to check for effect modification or confounding. One common strategy is to base this decision on the results obtained in the univariable analysis and a p-value threshold of 0.20-0.25. Also, food items that are known risk factors for gastroenteritis could also be included regardless of their univariable p-value.</p>
 </section>
 <section id="questionsassignments" class="level2">
@@ -234,8 +244,8 @@ <h2 class="anchored" data-anchor-id="confounders-and-effect-modification">3.1. C
 <section id="install-packages-and-load-libraries" class="level2">
 <h2 class="anchored" data-anchor-id="install-packages-and-load-libraries">3.2. Install packages and load libraries</h2>
 <div class="cell">
-<details open="">
-<summary>Code</summary>
+<details open="" class="code-fold">
+<summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb1"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Load the required libraries into the current R session:</span></span>
 <span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a>pacman<span class="sc">::</span><span class="fu">p_load</span>(rio, </span>
 <span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a>               here, </span>
@@ -257,18 +267,18 @@ <h2 class="anchored" data-anchor-id="install-packages-and-load-libraries">3.2. I
 <section id="import-your-data" class="level2">
 <h2 class="anchored" data-anchor-id="import-your-data">3.3. Import your data</h2>
 <div class="cell">
-<details open="">
-<summary>Code</summary>
+<details open="" class="code-fold">
+<summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb2"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Import the raw data set: </span></span>
-<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a>copdata <span class="ot">&lt;-</span> rio<span class="sc">::</span><span class="fu">import</span>(here<span class="sc">::</span><span class="fu">here</span>(<span class="st">"data"</span>, <span class="st">"Spetses_clean2_2024.rds"</span>))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a>copdata <span class="ot">&lt;-</span> rio<span class="sc">::</span><span class="fu">import</span>(here<span class="sc">::</span><span class="fu">here</span>(<span class="st">"data"</span>, <span class="st">"Spetses_clean2_2024.rds"</span>), <span class="at">trust =</span> T)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
 </div>
 </section>
 <section id="consider-and-assess-for-confounding-andor-effect-modification" class="level2">
 <h2 class="anchored" data-anchor-id="consider-and-assess-for-confounding-andor-effect-modification">3.4. Consider and assess for confounding and/or effect modification</h2>
-<p>Have a look at the relative risk for being a case having eaten a specific food item (for example, moussake), when stratified by another variable (for example, orzo). You may consider stratifying by orzo, as it has the highest RR in the univariable analysis.</p>
+<p>Have a look at the relative risk for being a case having eaten a specific food item (for example, veal), when stratified by another variable (for example, pasta). You may consider stratifying by pasta, as it has the highest RR in the univariable analysis.</p>
 <p>There are many variables in this dataset and it might not make sense to stratify each variable by each other variable on our search for effect modifiers and confounders.</p>
-<p>However, we also don’t want to be too restrictive as a variable which actually (i.e.&nbsp;causally) is associated with the outcome might not show a significant association at the significant level we decided (say 5%) in the univariable analysis due to confounding. Therefore, we could test all variables statistically significant at the 15%, 20%, or 25% level (specific percentage to be decided by your group). In our solutions here, we are looking at moussaka and champagne, stratified by orzo, but you may decide to look at other food items as well.</p>
+<p>However, we also don’t want to be too restrictive as a variable which actually (i.e.&nbsp;causally) is associated with the outcome might not show a significant association at the significant level we decided (say 5%) in the univariable analysis due to confounding. Therefore, we could test all variables statistically significant at the 15%, 20%, or 25% level (specific percentage to be decided by your group). In our solutions here, we are looking at veal and champagne, stratified by pasta, but you may decide to look at other food items as well.</p>
 <div class="callout callout-style-default callout-tip callout-titled" title="Need a little bit of help?">
 <div class="callout-header d-flex align-content-center" data-bs-toggle="collapse" data-bs-target=".callout-1-contents" aria-controls="callout-1" aria-expanded="false" aria-label="Toggle callout">
 <div class="callout-icon-container">
@@ -285,37 +295,37 @@ <h2 class="anchored" data-anchor-id="consider-and-assess-for-confounding-andor-e
 </div>
 </div>
 </div>
-<section id="a-moussaka-as-exposure-of-interest-stratified-by-having-eaten-orzo" class="level3">
-<h3 class="anchored" data-anchor-id="a-moussaka-as-exposure-of-interest-stratified-by-having-eaten-orzo">a) Moussaka as exposure of interest, stratified by having eaten orzo</h3>
-<p>If we stratify the <strong>effect of moussaka by orzo</strong> we ask the question: does eating orzo modify or confound the association between eating moussaka and being a case?</p>
+<section id="a-veal-as-exposure-of-interest-stratified-by-having-eaten-pasta" class="level3">
+<h3 class="anchored" data-anchor-id="a-veal-as-exposure-of-interest-stratified-by-having-eaten-pasta">a) Veal as exposure of interest, stratified by having eaten pasta</h3>
+<p>If we stratify the <strong>effect of veal by pasta</strong> we ask the question: does eating pasta modify or confound the association between eating veal and being a case?</p>
 <div class="cell">
-<details open="">
-<summary>Code</summary>
+<details open="" class="code-fold">
+<summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb3"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a>stratall <span class="ot">&lt;-</span> copdata <span class="sc">%&gt;%</span> </span>
 <span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a>  <span class="co"># Mutate across to convert cases to numeric:</span></span>
 <span id="cb3-3"><a href="#cb3-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="fu">across</span>(<span class="at">.cols =</span> case, </span>
 <span id="cb3-4"><a href="#cb3-4" aria-hidden="true" tabindex="-1"></a>                <span class="at">.fns =</span> <span class="sc">~</span> <span class="fu">as.numeric</span>(.)))</span>
 <span id="cb3-5"><a href="#cb3-5" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb3-6"><a href="#cb3-6" aria-hidden="true" tabindex="-1"></a><span class="co"># Pass data to the csinter function:</span></span>
-<span id="cb3-7"><a href="#cb3-7" aria-hidden="true" tabindex="-1"></a>orzostrata <span class="ot">&lt;-</span> <span class="fu">csinter</span>(<span class="at">x =</span> stratall, </span>
+<span id="cb3-7"><a href="#cb3-7" aria-hidden="true" tabindex="-1"></a>pastastrata <span class="ot">&lt;-</span> <span class="fu">csinter</span>(<span class="at">x =</span> stratall, </span>
 <span id="cb3-8"><a href="#cb3-8" aria-hidden="true" tabindex="-1"></a>                       <span class="at">cases =</span> <span class="st">"case"</span>, </span>
-<span id="cb3-9"><a href="#cb3-9" aria-hidden="true" tabindex="-1"></a>                       <span class="at">exposure =</span> <span class="st">"moussaka"</span>, </span>
-<span id="cb3-10"><a href="#cb3-10" aria-hidden="true" tabindex="-1"></a>                       <span class="at">by =</span> <span class="st">"orzo"</span>)</span>
+<span id="cb3-9"><a href="#cb3-9" aria-hidden="true" tabindex="-1"></a>                       <span class="at">exposure =</span> <span class="st">"veal"</span>, </span>
+<span id="cb3-10"><a href="#cb3-10" aria-hidden="true" tabindex="-1"></a>                       <span class="at">by =</span> <span class="st">"pasta"</span>)</span>
 <span id="cb3-11"><a href="#cb3-11" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb3-12"><a href="#cb3-12" aria-hidden="true" tabindex="-1"></a>orzostrata</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<span id="cb3-12"><a href="#cb3-12" aria-hidden="true" tabindex="-1"></a>pastastrata</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
 <div class="cell-output cell-output-stdout">
 <pre><code>$df1
-  CSInter case - moussaka by(orzo) Total Cases Risk %          P.est. Stats
-1                         orzo = 1   338  &lt;NA&gt;     NA Risk difference  0.10
-2                          Exposed   330   198  60.00      Risk Ratio  1.20
-3                        Unexposed     8     4  50.00 Attrib.risk.exp  0.17
-4                                     NA  &lt;NA&gt;     NA Attrib.risk.pop  0.16
-5                         orzo = 0    36  &lt;NA&gt;     NA Risk difference  0.02
-6                          Exposed     8     3  37.50      Risk Ratio  1.05
-7                        Unexposed    28    10  35.71 Attrib.risk.exp  0.05
-8                                     NA  &lt;NA&gt;     NA Attrib.risk.pop  0.01
-9              Missing / Missing %     3  0.8%     NA            &lt;NA&gt;    NA
+  CSInter case - veal by(pasta) Total Cases Risk %          P.est. Stats
+1                     pasta = 1   338  &lt;NA&gt;     NA Risk difference  0.10
+2                       Exposed   330   198  60.00      Risk Ratio  1.20
+3                     Unexposed     8     4  50.00 Attrib.risk.exp  0.17
+4                                  NA  &lt;NA&gt;     NA Attrib.risk.pop  0.16
+5                     pasta = 0    36  &lt;NA&gt;     NA Risk difference  0.02
+6                       Exposed     8     3  37.50      Risk Ratio  1.05
+7                     Unexposed    28    10  35.71 Attrib.risk.exp  0.05
+8                                  NA  &lt;NA&gt;     NA Attrib.risk.pop  0.01
+9           Missing / Missing %     3  0.8%     NA            &lt;NA&gt;    NA
   95%CI.ll 95%CI.ul
 1    -0.25     0.45
 2     0.60     2.41
@@ -328,41 +338,41 @@ <h3 class="anchored" data-anchor-id="a-moussaka-as-exposure-of-interest-stratifi
 9       NA       NA
 
 $df2
-                    Point Estimate Chi2 p.value  Stats 95%CI.ll 95%CI.ul
-1        Woolf test of homogeneity 0.04   0.833     NA       NA       NA
-2            Crude RR for moussaka   NA      NA   1.53     1.01     2.32
-3 MH RR moussaka adjusted for orzo   NA      NA   1.15     0.64     2.04
-4   Adjusted/crude relative change   NA      NA -25.08       NA       NA</code></pre>
+                  Point Estimate Chi2 p.value  Stats 95%CI.ll 95%CI.ul
+1      Woolf test of homogeneity 0.04   0.833     NA       NA       NA
+2              Crude RR for veal   NA      NA   1.53     1.01     2.32
+3  MH RR veal adjusted for pasta   NA      NA   1.15     0.64     2.04
+4 Adjusted/crude relative change   NA      NA -25.08       NA       NA</code></pre>
 </div>
 </div>
-<p>Let’s check if orzo is associated with moussaka (if we are thinking moussaka may be a confounder, we need to see if there is an association between the potential confounder (moussaka) and the exposure (orzo)):</p>
+<p>Let’s check if pasta is associated with veal (if we are thinking pasta may be a confounder, we need to see if there is an association between the potential confounder (pasta) and the exposure (veal)):</p>
 <div class="cell">
-<details open="">
-<summary>Code</summary>
-<div class="sourceCode cell-code" id="cb5"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Perform Wilcoxon rank sum test on orzo and moussaka:</span></span>
-<span id="cb5-2"><a href="#cb5-2" aria-hidden="true" tabindex="-1"></a><span class="fu">wilcox.test</span>(orzo <span class="sc">~</span> moussaka, </span>
+<details open="" class="code-fold">
+<summary>Show the code</summary>
+<div class="sourceCode cell-code" id="cb5"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Perform Wilcoxon rank sum test on veal and pasta:</span></span>
+<span id="cb5-2"><a href="#cb5-2" aria-hidden="true" tabindex="-1"></a><span class="fu">wilcox.test</span>(veal <span class="sc">~</span> pasta, </span>
 <span id="cb5-3"><a href="#cb5-3" aria-hidden="true" tabindex="-1"></a>            <span class="at">data =</span> copdata)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
 <div class="cell-output cell-output-stdout">
 <pre><code>
     Wilcoxon rank sum test with continuity correction
 
-data:  orzo by moussaka
+data:  veal by pasta
 W = 1496, p-value &lt; 2.2e-16
 alternative hypothesis: true location shift is not equal to 0</code></pre>
 </div>
 </div>
 </section>
-<section id="b-champagne-as-exposure-of-interest-stratified-by-having-eaten-orzo" class="level3">
-<h3 class="anchored" data-anchor-id="b-champagne-as-exposure-of-interest-stratified-by-having-eaten-orzo">b) Champagne as exposure of interest, stratified by having eaten orzo</h3>
+<section id="b-champagne-as-exposure-of-interest-stratified-by-having-eaten-pasta" class="level3">
+<h3 class="anchored" data-anchor-id="b-champagne-as-exposure-of-interest-stratified-by-having-eaten-pasta">b) Champagne as exposure of interest, stratified by having eaten pasta</h3>
 <div class="cell">
-<details open="">
-<summary>Code</summary>
+<details open="" class="code-fold">
+<summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb7"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Pass data to the csinter function:</span></span>
 <span id="cb7-2"><a href="#cb7-2" aria-hidden="true" tabindex="-1"></a>champstrata <span class="ot">&lt;-</span> <span class="fu">csinter</span>(<span class="at">x =</span> stratall, </span>
 <span id="cb7-3"><a href="#cb7-3" aria-hidden="true" tabindex="-1"></a>                       <span class="at">cases =</span> <span class="st">"case"</span>, </span>
 <span id="cb7-4"><a href="#cb7-4" aria-hidden="true" tabindex="-1"></a>                       <span class="at">exposure =</span> <span class="st">"champagne"</span>, </span>
-<span id="cb7-5"><a href="#cb7-5" aria-hidden="true" tabindex="-1"></a>                       <span class="at">by =</span> <span class="st">"orzo"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<span id="cb7-5"><a href="#cb7-5" aria-hidden="true" tabindex="-1"></a>                       <span class="at">by =</span> <span class="st">"pasta"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
 </div>
 <div class="callout callout-style-default callout-warning callout-titled" title="Let's stop and... think!">
@@ -381,11 +391,11 @@ <h3 class="anchored" data-anchor-id="b-champagne-as-exposure-of-interest-stratif
 </div>
 </div>
 </div>
-<p>We could stratify by orzo (the strongest risk factor in the univariable analysis), to examine if orzo confounds the association between eating moussaka and being a case. Before stratification, we will need to check if orzo meets the conditions of being a confounder. For a variable to be a confounder it needs to be associated both with the outcome (being a case) and with the exposure (and not be in the causal pathway between exposure and outcome). We know from univariable analysis that orzo is associated with being a case. If we run a Wilcoxon rank sum test, we will see that orzo is also associated with moussaka (indeed, you can see that most people either had both moussaka and orzo or neither of these food items, so they are associated with each other).</p>
-<p>Above, we uses <code>ccinter</code> to stratify and save the object as “orzostrata”.</p>
-<p>orzostrata: Within the stratum of the people who ate orzo, moussaka has no significant effect (RR = 1.20, CI: 0.60 - 2.41). The same holds within the stratum of people who didn’t eat orzo (RR = 1.05, CI = 0.38, 2.92). The adjusted MH-RR also suggests that moussaka has no effect (RRadj = 1.15, CI: 0.80 - 2.85). &nbsp;To identify confounding, we want to look at the % change between the crude and the adjusted RR. This is given by the csinter output “Adjusted/crude relative change”.&nbsp; The difference between the crude and the MH-RR in this case is &gt;20% suggesting that orzo confounds the association between moussaka and the disease.</p>
-<p>This result suggest that moussaka is not a risk factor of the disease and that the crude observed effect was due to the confounding effect of orzo.</p>
-<p>If you stratify by moussaka, you see that moussaka does not confound the association between orzo and the disease. The same applies if you stratify the exposure to orzo by other variables. The above, the higher RR for orzo and the dose response relationship we found earlier for orzo (remember this was optional) provide additional evidence that there was something going on with the orzo with pesto dish!</p>
+<p>We could stratify by pasta (the strongest risk factor in the univariable analysis), to examine if pasta confounds the association between eating veal and being a case. Before stratification, we will need to check if pasta meets the conditions of being a confounder. For a variable to be a confounder it needs to be associated both with the outcome (being a case) and with the exposure (and not be in the causal pathway between exposure and outcome). We know from univariable analysis that pasta is associated with being a case. If we run a Wilcoxon rank sum test, we will see that pasta is also associated with veal (indeed, you can see that most people either had both pasta and veal or neither of these food items, so they are associated with each other).</p>
+<p>Above, we uses <code>ccinter</code> to stratify and save the object as “pastastrata”.</p>
+<p>pastastrata: Within the stratum of the people who ate pasta, veal has no significant effect (RR = 1.20, CI: 0.60 - 2.41). The same holds within the stratum of people who didn’t eat pasta (RR = 1.05, CI = 0.38, 2.92). The adjusted MH-RR also suggests that veal has no effect (RRadj = 1.15, CI: 0.80 - 2.85). &nbsp;To identify confounding, we want to look at the % change between the crude and the adjusted RR. This is given by the csinter output “Adjusted/crude relative change”.&nbsp; The difference between the crude and the MH-RR in this case is &gt;20% suggesting that pasta confounds the association between veal and the disease.</p>
+<p>This result suggest that veal is not a risk factor of the disease and that the crude observed effect was due to the confounding effect of pasta.</p>
+<p>If you stratify by veal, you see that veal does not confound the association between pasta and the disease. The same applies if you stratify the exposure to pasta by other variables. The above, the higher RR for pasta and the dose response relationship we found earlier for pasta (remember this was optional) provide additional evidence that there was something going on with the pasta with pesto dish!</p>
 
 
 <!-- -->
@@ -437,6 +447,33 @@ <h3 class="anchored" data-anchor-id="b-champagne-as-exposure-of-interest-stratif
       }
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+    const alternateTheme = document.querySelector('#giscus-alt-theme')?.value ?? 'dark';
+    let newTheme = '';
+    if(darkModeDefault) {
+      newTheme = isAlternate ? baseTheme : alternateTheme;
+    } else {
+      newTheme = isAlternate ? alternateTheme : baseTheme;
+    }
+    const changeGiscusTheme = () => {
+      // From: https://github.com/giscus/giscus/issues/336
+      const sendMessage = (message) => {
+        const iframe = document.querySelector('iframe.giscus-frame');
+        if (!iframe) return;
+        iframe.contentWindow.postMessage({ giscus: message }, 'https://giscus.app');
+      }
+      sendMessage({
+        setConfig: {
+          theme: newTheme
+        }
+      });
+    }
+    const isGiscussLoaded = window.document.querySelector('iframe.giscus-frame') !== null;
+    if (isGiscussLoaded) {
+      changeGiscusTheme();
+    }
+  }
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@@ -503,13 +540,15 @@ <h3 class="anchored" data-anchor-id="b-champagne-as-exposure-of-interest-stratif
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+  const darkModeDefault = false;
+  let localAlternateSentinel = darkModeDefault ? 'alternate' : 'default';
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     // Read the current dark / light value 
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     toggleColorMode(toAlternate);
     setStyleSentinel(toAlternate);
+    toggleGiscusIfUsed(toAlternate, darkModeDefault);
   };
   // Ensure there is a toggle, if there isn't float one in the top right
   if (window.document.querySelector('.quarto-color-scheme-toggle') === null) {
@@ -544,18 +583,7 @@ <h3 class="anchored" data-anchor-id="b-champagne-as-exposure-of-interest-stratif
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-          childEl.remove();
-        }
-      }
-      return codeEl.innerText;
-    }
-  });
-  clipboard.on('success', function(e) {
+  const onCopySuccess = function(e) {
     // button target
     const button = e.trigger;
     // don't keep focus
@@ -587,7 +615,29 @@ <h3 class="anchored" data-anchor-id="b-champagne-as-exposure-of-interest-stratif
     }, 1000);
     // clear code selection
     e.clearSelection();
+  }
+  const getTextToCopy = function(trigger) {
+      const codeEl = trigger.previousElementSibling.cloneNode(true);
+      for (const childEl of codeEl.children) {
+        if (isCodeAnnotation(childEl)) {
+          childEl.remove();
+        }
+      }
+      return codeEl.innerText;
+  }
+  const clipboard = new window.ClipboardJS('.code-copy-button:not([data-in-quarto-modal])', {
+    text: getTextToCopy
   });
+  clipboard.on('success', onCopySuccess);
+  if (window.document.getElementById('quarto-embedded-source-code-modal')) {
+    // For code content inside modals, clipBoardJS needs to be initialized with a container option
+    // TODO: Check when it could be a function (https://github.com/zenorocha/clipboard.js/issues/860)
+    const clipboardModal = new window.ClipboardJS('.code-copy-button[data-in-quarto-modal]', {
+      text: getTextToCopy,
+      container: window.document.getElementById('quarto-embedded-source-code-modal')
+    });
+    clipboardModal.on('success', onCopySuccess);
+  }
   const viewSource = window.document.getElementById('quarto-view-source') ||
                      window.document.getElementById('quarto-code-tools-source');
   if (viewSource) {
@@ -639,10 +689,27 @@ <h3 class="anchored" data-anchor-id="b-champagne-as-exposure-of-interest-stratif
   if (showAllCode) {
     showAllCode.addEventListener("click", toggleCodeHandler(true));
   }
-  function tippyHover(el, contentFn) {
+    var localhostRegex = new RegExp(/^(?:http|https):\/\/localhost\:?[0-9]*\//);
+    var mailtoRegex = new RegExp(/^mailto:/);
+      var filterRegex = new RegExp('/' + window.location.host + '/');
+    var isInternal = (href) => {
+        return filterRegex.test(href) || localhostRegex.test(href) || mailtoRegex.test(href);
+    }
+    // Inspect non-navigation links and adorn them if external
+ 	var links = window.document.querySelectorAll('a[href]:not(.nav-link):not(.navbar-brand):not(.toc-action):not(.sidebar-link):not(.sidebar-item-toggle):not(.pagination-link):not(.no-external):not([aria-hidden]):not(.dropdown-item):not(.quarto-navigation-tool):not(.about-link)');
+    for (var i=0; i<links.length; i++) {
+      const link = links[i];
+      if (!isInternal(link.href)) {
+        // undo the damage that might have been done by quarto-nav.js in the case of
+        // links that we want to consider external
+        if (link.dataset.originalHref !== undefined) {
+          link.href = link.dataset.originalHref;
+        }
+      }
+    }
+  function tippyHover(el, contentFn, onTriggerFn, onUntriggerFn) {
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       allowHTML: true,
-      content: contentFn,
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       arrow: false,
@@ -652,8 +719,17 @@ <h3 class="anchored" data-anchor-id="b-champagne-as-exposure-of-interest-stratif
       interactive: true,
       interactiveBorder: 10,
       theme: 'quarto',
-      placement: 'bottom-start'
+      placement: 'bottom-start',
     };
+    if (contentFn) {
+      config.content = contentFn;
+    }
+    if (onTriggerFn) {
+      config.onTrigger = onTriggerFn;
+    }
+    if (onUntriggerFn) {
+      config.onUntrigger = onUntriggerFn;
+    }
     window.tippy(el, config); 
   }
   const noterefs = window.document.querySelectorAll('a[role="doc-noteref"]');
@@ -665,7 +741,130 @@ <h3 class="anchored" data-anchor-id="b-champagne-as-exposure-of-interest-stratif
       try { href = new URL(href).hash; } catch {}
       const id = href.replace(/^#\/?/, "");
       const note = window.document.getElementById(id);
-      return note.innerHTML;
+      if (note) {
+        return note.innerHTML;
+      } else {
+        return "";
+      }
+    });
+  }
+  const xrefs = window.document.querySelectorAll('a.quarto-xref');
+  const processXRef = (id, note) => {
+    // Strip column container classes
+    const stripColumnClz = (el) => {
+      el.classList.remove("page-full", "page-columns");
+      if (el.children) {
+        for (const child of el.children) {
+          stripColumnClz(child);
+        }
+      }
+    }
+    stripColumnClz(note)
+    if (id === null || id.startsWith('sec-')) {
+      // Special case sections, only their first couple elements
+      const container = document.createElement("div");
+      if (note.children && note.children.length > 2) {
+        container.appendChild(note.children[0].cloneNode(true));
+        for (let i = 1; i < note.children.length; i++) {
+          const child = note.children[i];
+          if (child.tagName === "P" && child.innerText === "") {
+            continue;
+          } else {
+            container.appendChild(child.cloneNode(true));
+            break;
+          }
+        }
+        if (window.Quarto?.typesetMath) {
+          window.Quarto.typesetMath(container);
+        }
+        return container.innerHTML
+      } else {
+        if (window.Quarto?.typesetMath) {
+          window.Quarto.typesetMath(note);
+        }
+        return note.innerHTML;
+      }
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+      // Remove any anchor links if they are present
+      const anchorLink = note.querySelector('a.anchorjs-link');
+      if (anchorLink) {
+        anchorLink.remove();
+      }
+      if (window.Quarto?.typesetMath) {
+        window.Quarto.typesetMath(note);
+      }
+      // TODO in 1.5, we should make sure this works without a callout special case
+      if (note.classList.contains("callout")) {
+        return note.outerHTML;
+      } else {
+        return note.innerHTML;
+      }
+    }
+  }
+  for (var i=0; i<xrefs.length; i++) {
+    const xref = xrefs[i];
+    tippyHover(xref, undefined, function(instance) {
+      instance.disable();
+      let url = xref.getAttribute('href');
+      let hash = undefined; 
+      if (url.startsWith('#')) {
+        hash = url;
+      } else {
+        try { hash = new URL(url).hash; } catch {}
+      }
+      if (hash) {
+        const id = hash.replace(/^#\/?/, "");
+        const note = window.document.getElementById(id);
+        if (note !== null) {
+          try {
+            const html = processXRef(id, note.cloneNode(true));
+            instance.setContent(html);
+          } finally {
+            instance.enable();
+            instance.show();
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+          // See if we can fetch this
+          fetch(url.split('#')[0])
+          .then(res => res.text())
+          .then(html => {
+            const parser = new DOMParser();
+            const htmlDoc = parser.parseFromString(html, "text/html");
+            const note = htmlDoc.getElementById(id);
+            if (note !== null) {
+              const html = processXRef(id, note);
+              instance.setContent(html);
+            } 
+          }).finally(() => {
+            instance.enable();
+            instance.show();
+          });
+        }
+      } else {
+        // See if we can fetch a full url (with no hash to target)
+        // This is a special case and we should probably do some content thinning / targeting
+        fetch(url)
+        .then(res => res.text())
+        .then(html => {
+          const parser = new DOMParser();
+          const htmlDoc = parser.parseFromString(html, "text/html");
+          const note = htmlDoc.querySelector('main.content');
+          if (note !== null) {
+            // This should only happen for chapter cross references
+            // (since there is no id in the URL)
+            // remove the first header
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+              note.children[0].remove();
+            }
+            const html = processXRef(null, note);
+            instance.setContent(html);
+          } 
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@@ -709,6 +908,7 @@ <h3 class="anchored" data-anchor-id="b-champagne-as-exposure-of-interest-stratif
             }
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             div.style.height = height + 4 + "px";
+            div.style.left = 0;
             let gutterDiv = window.document.getElementById("code-annotation-line-highlight-gutter");
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               gutterDiv = window.document.createElement("div");
@@ -734,6 +934,32 @@ <h3 class="anchored" data-anchor-id="b-champagne-as-exposure-of-interest-stratif
         });
         selectedAnnoteEl = undefined;
       };
+        // Handle positioning of the toggle
+    window.addEventListener(
+      "resize",
+      throttle(() => {
+        elRect = undefined;
+        if (selectedAnnoteEl) {
+          selectCodeLines(selectedAnnoteEl);
+        }
+      }, 10)
+    );
+    function throttle(fn, ms) {
+    let throttle = false;
+    let timer;
+      return (...args) => {
+        if(!throttle) { // first call gets through
+            fn.apply(this, args);
+            throttle = true;
+        } else { // all the others get throttled
+            if(timer) clearTimeout(timer); // cancel #2
+            timer = setTimeout(() => {
+              fn.apply(this, args);
+              timer = throttle = false;
+            }, ms);
+        }
+      };
+    }
       // Attach click handler to the DT
       const annoteDls = window.document.querySelectorAll('dt[data-target-cell]');
       for (const annoteDlNode of annoteDls) {
@@ -810,7 +1036,7 @@ <h3 class="anchored" data-anchor-id="b-champagne-as-exposure-of-interest-stratif
 <span id="cb8-15"><a href="#cb8-15" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb8-16"><a href="#cb8-16" aria-hidden="true" tabindex="-1"></a><span class="fu">## 2. Story/plot description</span></span>
 <span id="cb8-17"><a href="#cb8-17" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb8-18"><a href="#cb8-18" aria-hidden="true" tabindex="-1"></a>From the univariable analysis, it seems that eating orzpo and eating moussaka as well as drinking champagne are associated with the highest risk of becoming ill. There are, however, many other food items that are associated with an increased risk (even if not statistically significant).</span>
+<span id="cb8-18"><a href="#cb8-18" aria-hidden="true" tabindex="-1"></a>From the univariable analysis, it seems that eating pasta and eating veal as well as drinking champagne are associated with the highest risk of becoming ill. There are, however, many other food items that are associated with an increased risk (even if not statistically significant).</span>
 <span id="cb8-19"><a href="#cb8-19" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb8-20"><a href="#cb8-20" aria-hidden="true" tabindex="-1"></a>You should next think about potential confounders and about effect modification. Think about which variables you might want to check for effect modification or confounding. One common strategy is to base this decision on the results obtained in the univariable analysis and a p-value threshold of 0.20-0.25. Also, food items that are known risk factors for gastroenteritis could also be included regardless of their univariable p-value.</span>
 <span id="cb8-21"><a href="#cb8-21" aria-hidden="true" tabindex="-1"></a></span>
@@ -843,25 +1069,25 @@ <h3 class="anchored" data-anchor-id="b-champagne-as-exposure-of-interest-stratif
 <span id="cb8-50"><a href="#cb8-50" aria-hidden="true" tabindex="-1"></a><span class="fu">## 3.3. Import your data</span></span>
 <span id="cb8-51"><a href="#cb8-51" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb8-52"><a href="#cb8-52" aria-hidden="true" tabindex="-1"></a><span class="in">```{r, Import_data}</span></span>
-<span id="cb8-53"><a href="#cb8-53" aria-hidden="true" tabindex="-1"></a><span class="co"># Import the raw data set: </span></span>
-<span id="cb8-54"><a href="#cb8-54" aria-hidden="true" tabindex="-1"></a>copdata <span class="ot">&lt;-</span> rio<span class="sc">::</span><span class="fu">import</span>(here<span class="sc">::</span><span class="fu">here</span>(<span class="st">"data"</span>, <span class="st">"Spetses_clean2_2024.rds"</span>))</span>
+<span id="cb8-53"><a href="#cb8-53" aria-hidden="true" tabindex="-1"></a><span class="in"># Import the raw data set: </span></span>
+<span id="cb8-54"><a href="#cb8-54" aria-hidden="true" tabindex="-1"></a><span class="in">copdata &lt;- rio::import(here::here("data", "Spetses_clean2_2024.rds"), trust = T)</span></span>
 <span id="cb8-55"><a href="#cb8-55" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
 <span id="cb8-56"><a href="#cb8-56" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb8-57"><a href="#cb8-57" aria-hidden="true" tabindex="-1"></a><span class="fu">## 3.4. Consider and assess for confounding and/or effect modification</span></span>
 <span id="cb8-58"><a href="#cb8-58" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb8-59"><a href="#cb8-59" aria-hidden="true" tabindex="-1"></a>Have a look at the relative risk for being a case having eaten a specific food item (for example, moussake), when stratified by another variable (for example, orzo). You may consider stratifying by orzo, as it has the highest RR in the univariable analysis.</span>
+<span id="cb8-59"><a href="#cb8-59" aria-hidden="true" tabindex="-1"></a>Have a look at the relative risk for being a case having eaten a specific food item (for example, veal), when stratified by another variable (for example, pasta). You may consider stratifying by pasta, as it has the highest RR in the univariable analysis.</span>
 <span id="cb8-60"><a href="#cb8-60" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb8-61"><a href="#cb8-61" aria-hidden="true" tabindex="-1"></a>There are many variables in this dataset and it might not make sense to stratify each variable by each other variable on our search for effect modifiers and confounders.</span>
 <span id="cb8-62"><a href="#cb8-62" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb8-63"><a href="#cb8-63" aria-hidden="true" tabindex="-1"></a>However, we also don't want to be too restrictive as a variable which actually (i.e. causally) is associated with the outcome might not show a significant association at the significant level we decided (say 5%) in the univariable analysis due to confounding. Therefore, we could test all variables statistically significant at the 15%, 20%, or 25% level (specific percentage to be decided by your group). In our solutions here, we are looking at moussaka and champagne, stratified by orzo, but you may decide to look at other food items as well.</span>
+<span id="cb8-63"><a href="#cb8-63" aria-hidden="true" tabindex="-1"></a>However, we also don't want to be too restrictive as a variable which actually (i.e. causally) is associated with the outcome might not show a significant association at the significant level we decided (say 5%) in the univariable analysis due to confounding. Therefore, we could test all variables statistically significant at the 15%, 20%, or 25% level (specific percentage to be decided by your group). In our solutions here, we are looking at veal and champagne, stratified by pasta, but you may decide to look at other food items as well.</span>
 <span id="cb8-64"><a href="#cb8-64" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb8-65"><a href="#cb8-65" aria-hidden="true" tabindex="-1"></a>::: {.callout-tip title="Need a little bit of help?" collapse="true"}</span>
 <span id="cb8-66"><a href="#cb8-66" aria-hidden="true" tabindex="-1"></a>Use the function <span class="in">`csinter()`</span> of the EpiStats package.</span>
 <span id="cb8-67"><a href="#cb8-67" aria-hidden="true" tabindex="-1"></a>:::</span>
 <span id="cb8-68"><a href="#cb8-68" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb8-69"><a href="#cb8-69" aria-hidden="true" tabindex="-1"></a><span class="fu">### a) Moussaka as exposure of interest, stratified by having eaten orzo</span></span>
+<span id="cb8-69"><a href="#cb8-69" aria-hidden="true" tabindex="-1"></a><span class="fu">### a) Veal as exposure of interest, stratified by having eaten pasta</span></span>
 <span id="cb8-70"><a href="#cb8-70" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb8-71"><a href="#cb8-71" aria-hidden="true" tabindex="-1"></a>If we stratify the **effect of moussaka by orzo** we ask the question: does eating orzo modify or confound the association between eating moussaka and being a case?</span>
+<span id="cb8-71"><a href="#cb8-71" aria-hidden="true" tabindex="-1"></a>If we stratify the **effect of veal by pasta** we ask the question: does eating pasta modify or confound the association between eating veal and being a case?</span>
 <span id="cb8-72"><a href="#cb8-72" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb8-75"><a href="#cb8-75" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
 <span id="cb8-76"><a href="#cb8-76" aria-hidden="true" tabindex="-1"></a>stratall <span class="ot">&lt;-</span> copdata <span class="sc">%&gt;%</span> </span>
@@ -870,49 +1096,50 @@ <h3 class="anchored" data-anchor-id="b-champagne-as-exposure-of-interest-stratif
 <span id="cb8-79"><a href="#cb8-79" aria-hidden="true" tabindex="-1"></a>                <span class="at">.fns =</span> <span class="sc">~</span> <span class="fu">as.numeric</span>(.)))</span>
 <span id="cb8-80"><a href="#cb8-80" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb8-81"><a href="#cb8-81" aria-hidden="true" tabindex="-1"></a><span class="co"># Pass data to the csinter function:</span></span>
-<span id="cb8-82"><a href="#cb8-82" aria-hidden="true" tabindex="-1"></a>orzostrata <span class="ot">&lt;-</span> <span class="fu">csinter</span>(<span class="at">x =</span> stratall, </span>
+<span id="cb8-82"><a href="#cb8-82" aria-hidden="true" tabindex="-1"></a>pastastrata <span class="ot">&lt;-</span> <span class="fu">csinter</span>(<span class="at">x =</span> stratall, </span>
 <span id="cb8-83"><a href="#cb8-83" aria-hidden="true" tabindex="-1"></a>                       <span class="at">cases =</span> <span class="st">"case"</span>, </span>
-<span id="cb8-84"><a href="#cb8-84" aria-hidden="true" tabindex="-1"></a>                       <span class="at">exposure =</span> <span class="st">"moussaka"</span>, </span>
-<span id="cb8-85"><a href="#cb8-85" aria-hidden="true" tabindex="-1"></a>                       <span class="at">by =</span> <span class="st">"orzo"</span>)</span>
+<span id="cb8-84"><a href="#cb8-84" aria-hidden="true" tabindex="-1"></a>                       <span class="at">exposure =</span> <span class="st">"veal"</span>, </span>
+<span id="cb8-85"><a href="#cb8-85" aria-hidden="true" tabindex="-1"></a>                       <span class="at">by =</span> <span class="st">"pasta"</span>)</span>
 <span id="cb8-86"><a href="#cb8-86" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb8-87"><a href="#cb8-87" aria-hidden="true" tabindex="-1"></a>orzostrata</span>
+<span id="cb8-87"><a href="#cb8-87" aria-hidden="true" tabindex="-1"></a>pastastrata</span>
 <span id="cb8-88"><a href="#cb8-88" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
 <span id="cb8-89"><a href="#cb8-89" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb8-90"><a href="#cb8-90" aria-hidden="true" tabindex="-1"></a>Let's check if orzo is associated with moussaka (if we are thinking moussaka may be a confounder, we need to see if there is an association between the potential confounder (moussaka) and the exposure (orzo)):</span>
+<span id="cb8-90"><a href="#cb8-90" aria-hidden="true" tabindex="-1"></a>Let's check if pasta is associated with veal (if we are thinking pasta may be a confounder, we need to see if there is an association between the potential confounder (pasta) and the exposure (veal)):</span>
 <span id="cb8-91"><a href="#cb8-91" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb8-94"><a href="#cb8-94" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
-<span id="cb8-95"><a href="#cb8-95" aria-hidden="true" tabindex="-1"></a><span class="co"># Perform Wilcoxon rank sum test on orzo and moussaka:</span></span>
-<span id="cb8-96"><a href="#cb8-96" aria-hidden="true" tabindex="-1"></a><span class="fu">wilcox.test</span>(orzo <span class="sc">~</span> moussaka, </span>
+<span id="cb8-95"><a href="#cb8-95" aria-hidden="true" tabindex="-1"></a><span class="co"># Perform Wilcoxon rank sum test on veal and pasta:</span></span>
+<span id="cb8-96"><a href="#cb8-96" aria-hidden="true" tabindex="-1"></a><span class="fu">wilcox.test</span>(veal <span class="sc">~</span> pasta, </span>
 <span id="cb8-97"><a href="#cb8-97" aria-hidden="true" tabindex="-1"></a>            <span class="at">data =</span> copdata)</span>
 <span id="cb8-98"><a href="#cb8-98" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
 <span id="cb8-99"><a href="#cb8-99" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb8-100"><a href="#cb8-100" aria-hidden="true" tabindex="-1"></a><span class="fu">### b) Champagne as exposure of interest, stratified by having eaten orzo</span></span>
+<span id="cb8-100"><a href="#cb8-100" aria-hidden="true" tabindex="-1"></a><span class="fu">### b) Champagne as exposure of interest, stratified by having eaten pasta</span></span>
 <span id="cb8-101"><a href="#cb8-101" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb8-104"><a href="#cb8-104" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
 <span id="cb8-105"><a href="#cb8-105" aria-hidden="true" tabindex="-1"></a><span class="co"># Pass data to the csinter function:</span></span>
 <span id="cb8-106"><a href="#cb8-106" aria-hidden="true" tabindex="-1"></a>champstrata <span class="ot">&lt;-</span> <span class="fu">csinter</span>(<span class="at">x =</span> stratall, </span>
 <span id="cb8-107"><a href="#cb8-107" aria-hidden="true" tabindex="-1"></a>                       <span class="at">cases =</span> <span class="st">"case"</span>, </span>
 <span id="cb8-108"><a href="#cb8-108" aria-hidden="true" tabindex="-1"></a>                       <span class="at">exposure =</span> <span class="st">"champagne"</span>, </span>
-<span id="cb8-109"><a href="#cb8-109" aria-hidden="true" tabindex="-1"></a>                       <span class="at">by =</span> <span class="st">"orzo"</span>)</span>
+<span id="cb8-109"><a href="#cb8-109" aria-hidden="true" tabindex="-1"></a>                       <span class="at">by =</span> <span class="st">"pasta"</span>)</span>
 <span id="cb8-110"><a href="#cb8-110" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
 <span id="cb8-111"><a href="#cb8-111" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb8-112"><a href="#cb8-112" aria-hidden="true" tabindex="-1"></a>::: {.callout-warning title="Let's stop and... think!" collapse="true"}</span>
 <span id="cb8-113"><a href="#cb8-113" aria-hidden="true" tabindex="-1"></a>Are there any indications to make you think there may be effect modification and/or confounding?</span>
 <span id="cb8-114"><a href="#cb8-114" aria-hidden="true" tabindex="-1"></a>:::</span>
 <span id="cb8-115"><a href="#cb8-115" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb8-116"><a href="#cb8-116" aria-hidden="true" tabindex="-1"></a>We could stratify by orzo (the strongest risk factor in the univariable analysis), to examine if orzo confounds the association between eating moussaka and being a case. Before stratification, we will need to check if orzo meets the conditions of being a confounder. For a variable to be a confounder it needs to be associated both with the outcome (being a case) and with the exposure (and not be in the causal pathway between exposure and outcome). We know from univariable analysis that orzo is associated with being a case. If we run a Wilcoxon rank sum test, we will see that orzo is also associated with moussaka (indeed, you can see that most people either had both moussaka and orzo or neither of these food items, so they are associated with each other).</span>
+<span id="cb8-116"><a href="#cb8-116" aria-hidden="true" tabindex="-1"></a>We could stratify by pasta (the strongest risk factor in the univariable analysis), to examine if pasta confounds the association between eating veal and being a case. Before stratification, we will need to check if pasta meets the conditions of being a confounder. For a variable to be a confounder it needs to be associated both with the outcome (being a case) and with the exposure (and not be in the causal pathway between exposure and outcome). We know from univariable analysis that pasta is associated with being a case. If we run a Wilcoxon rank sum test, we will see that pasta is also associated with veal (indeed, you can see that most people either had both pasta and veal or neither of these food items, so they are associated with each other).</span>
 <span id="cb8-117"><a href="#cb8-117" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb8-118"><a href="#cb8-118" aria-hidden="true" tabindex="-1"></a>Above, we uses <span class="in">`ccinter`</span> to stratify and save the object as "orzostrata".</span>
+<span id="cb8-118"><a href="#cb8-118" aria-hidden="true" tabindex="-1"></a>Above, we uses <span class="in">`ccinter`</span> to stratify and save the object as "pastastrata".</span>
 <span id="cb8-119"><a href="#cb8-119" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb8-120"><a href="#cb8-120" aria-hidden="true" tabindex="-1"></a>orzostrata: Within the stratum of the people who ate orzo, moussaka has no significant effect (RR = 1.20, CI: 0.60 - 2.41). The same holds within the stratum of people who didn't eat orzo (RR = 1.05, CI = 0.38, 2.92). The adjusted MH-RR also suggests that moussaka has no effect (RRadj = 1.15, CI: 0.80 - 2.85). &nbsp;To identify confounding, we want to look at the % change between the crude and the adjusted RR. This is given by the csinter output "Adjusted/crude relative change".&nbsp; The difference between the crude and the MH-RR in this case is <span class="sc">\&gt;</span>20% suggesting that orzo confounds the association between moussaka and the disease.</span>
+<span id="cb8-120"><a href="#cb8-120" aria-hidden="true" tabindex="-1"></a>pastastrata: Within the stratum of the people who ate pasta, veal has no significant effect (RR = 1.20, CI: 0.60 - 2.41). The same holds within the stratum of people who didn't eat pasta (RR = 1.05, CI = 0.38, 2.92). The adjusted MH-RR also suggests that veal has no effect (RRadj = 1.15, CI: 0.80 - 2.85). &nbsp;To identify confounding, we want to look at the % change between the crude and the adjusted RR. This is given by the csinter output "Adjusted/crude relative change".&nbsp; The difference between the crude and the MH-RR in this case is <span class="sc">\&gt;</span>20% suggesting that pasta confounds the association between veal and the disease.</span>
 <span id="cb8-121"><a href="#cb8-121" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb8-122"><a href="#cb8-122" aria-hidden="true" tabindex="-1"></a>This result suggest that moussaka is not a risk factor of the disease and that the crude observed effect was due to the confounding effect of orzo.</span>
+<span id="cb8-122"><a href="#cb8-122" aria-hidden="true" tabindex="-1"></a>This result suggest that veal is not a risk factor of the disease and that the crude observed effect was due to the confounding effect of pasta.</span>
 <span id="cb8-123"><a href="#cb8-123" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb8-124"><a href="#cb8-124" aria-hidden="true" tabindex="-1"></a>If you stratify by moussaka, you see that moussaka does not confound the association between orzo and the disease. The same applies if you stratify the exposure to orzo by other variables. The above, the higher RR for orzo and the dose response relationship we found earlier for orzo (remember this was optional) provide additional evidence that there was something going on with the orzo with pesto dish!</span>
-</code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<span id="cb8-124"><a href="#cb8-124" aria-hidden="true" tabindex="-1"></a>If you stratify by veal, you see that veal does not confound the association between pasta and the disease. The same applies if you stratify the exposure to pasta by other variables. The above, the higher RR for pasta and the dose response relationship we found earlier for pasta (remember this was optional) provide additional evidence that there was something going on with the pasta with pesto dish!</span>
+</code><button title="Copy to Clipboard" class="code-copy-button" data-in-quarto-modal=""><i class="bi"></i></button></pre></div>
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diff --git a/docs/Univariate.html b/docs/Univariate.html
index 7e3e985..9204be5 100644
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-<title>Spetses Introductory Course - Univariable analysis</title>
+<title>Univariable analysis – Spetses Introductory Course</title>
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-pre > code.sourceCode > span { display: inline-block; line-height: 1.25; }
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 pre > code.sourceCode > span:empty { height: 1.2em; }
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@@ -33,7 +33,7 @@
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-pre > code.sourceCode > span { text-indent: -5em; padding-left: 5em; }
+pre > code.sourceCode > span { display: inline-block; text-indent: -5em; padding-left: 5em; }
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@@ -81,7 +81,13 @@
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+
 <section id="learning-outcomes" class="level2">
 <h2 class="anchored" data-anchor-id="learning-outcomes">1. Learning outcomes</h2>
 <p>At the end of the session, participants will be able to:</p>
@@ -247,8 +257,8 @@ <h2 class="anchored" data-anchor-id="questionsassignments">3. Questions/Assignme
 <section id="install-packages-and-load-libraries" class="level2">
 <h2 class="anchored" data-anchor-id="install-packages-and-load-libraries">3.1. Install packages and load libraries</h2>
 <div class="cell">
-<details open="">
-<summary>Code</summary>
+<details open="" class="code-fold">
+<summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb1"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Load the required libraries into the current R session:</span></span>
 <span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a>pacman<span class="sc">::</span><span class="fu">p_load</span>(rio, </span>
 <span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a>               here, </span>
@@ -271,10 +281,10 @@ <h2 class="anchored" data-anchor-id="install-packages-and-load-libraries">3.1. I
 <section id="import-your-data" class="level2">
 <h2 class="anchored" data-anchor-id="import-your-data">3.2. Import your data</h2>
 <div class="cell">
-<details open="">
-<summary>Code</summary>
+<details open="" class="code-fold">
+<summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb2"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Import the raw data set:</span></span>
-<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a>copdata <span class="ot">&lt;-</span> rio<span class="sc">::</span><span class="fu">import</span>(here<span class="sc">::</span><span class="fu">here</span>(<span class="st">"data"</span>, <span class="st">"Spetses_clean2_2024.rds"</span>))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a>copdata <span class="ot">&lt;-</span> rio<span class="sc">::</span><span class="fu">import</span>(here<span class="sc">::</span><span class="fu">here</span>(<span class="st">"data"</span>, <span class="st">"Spetses_clean2_2024.rds"</span>), <span class="at">trust =</span> T)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
 </div>
 </section>
@@ -285,8 +295,8 @@ <h2 class="anchored" data-anchor-id="hypothesis-tests-for-other-variables">3.3.
 <h4 class="anchored" data-anchor-id="a-age">a) age</h4>
 <p>With the Shapiro-Wilk test we check if the variables are following the normal distribution. The null hypothesis is that the data follow a normal distribution, therefore, rejecting the null hypothesis means that the data do not follow the normal distribution. A p-value below the cutoff for rejecting the null hypothesis, e.g., a p-value&lt;0.05 means that we reject the null hypothesis that the data follow the normal distribution. For age, the p-value is &lt;0.05, therefore we reject the null hypothesis that the data are normally distributed. As we see in the graph most frequently reported age is &lt;20 years.</p>
 <div class="cell">
-<details open="">
-<summary>Code</summary>
+<details open="" class="code-fold">
+<summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb3"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Check if age overall follows a normal distribution:</span></span>
 <span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a><span class="fu">shapiro.test</span>(copdata<span class="sc">$</span>age)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
@@ -297,30 +307,38 @@ <h4 class="anchored" data-anchor-id="a-age">a) age</h4>
 data:  copdata$age
 W = 0.31302, p-value &lt; 2.2e-16</code></pre>
 </div>
-<details open="">
-<summary>Code</summary>
+<details open="" class="code-fold">
+<summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb5"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Can simply have a look at</span></span>
 <span id="cb5-2"><a href="#cb5-2" aria-hidden="true" tabindex="-1"></a><span class="fu">hist</span>(copdata<span class="sc">$</span>age)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
 <div class="cell-output-display">
-<p><img src="Univariate_files/figure-html/unnamed-chunk-2-1.png" class="img-fluid" width="672"></p>
+<div>
+<figure class="figure">
+<p><img src="Univariate_files/figure-html/unnamed-chunk-2-1.png" class="img-fluid figure-img" width="672"></p>
+</figure>
+</div>
 </div>
-<details open="">
-<summary>Code</summary>
+<details open="" class="code-fold">
+<summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb6"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Looking only at the students:</span></span>
 <span id="cb6-2"><a href="#cb6-2" aria-hidden="true" tabindex="-1"></a>students <span class="ot">&lt;-</span> copdata <span class="sc">%&gt;%</span> </span>
 <span id="cb6-3"><a href="#cb6-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(group <span class="sc">==</span> <span class="st">"student"</span>)</span>
 <span id="cb6-4"><a href="#cb6-4" aria-hidden="true" tabindex="-1"></a><span class="fu">hist</span>(students<span class="sc">$</span>age)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
 <div class="cell-output-display">
-<p><img src="Univariate_files/figure-html/unnamed-chunk-2-2.png" class="img-fluid" width="672"></p>
+<div>
+<figure class="figure">
+<p><img src="Univariate_files/figure-html/unnamed-chunk-2-2.png" class="img-fluid figure-img" width="672"></p>
+</figure>
+</div>
 </div>
 </div>
 <p>Age overall (nor within the students’ group) is not normally distributed.</p>
 <p>We compare the age for cases and non-cases using the Wilcoxon test that is used when the data are not normally distributed. The null hypothesis is that there is no difference in the age between the two groups compared. Given that p-value&gt;0.05 we do not reject the null hypothesis.</p>
 <div class="cell">
-<details open="">
-<summary>Code</summary>
+<details open="" class="code-fold">
+<summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb7"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Perform Wilcoxon rank sum test on age and sex:</span></span>
 <span id="cb7-2"><a href="#cb7-2" aria-hidden="true" tabindex="-1"></a><span class="fu">wilcox.test</span>(age <span class="sc">~</span> case, </span>
 <span id="cb7-3"><a href="#cb7-3" aria-hidden="true" tabindex="-1"></a>            <span class="at">data =</span> copdata)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
@@ -338,32 +356,31 @@ <h4 class="anchored" data-anchor-id="a-age">a) age</h4>
 <section id="b-sex" class="level4">
 <h4 class="anchored" data-anchor-id="b-sex">b) sex</h4>
 <div class="cell">
-<details open="">
-<summary>Code</summary>
+<details open="" class="code-fold">
+<summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb9"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a>copdata <span class="sc">%&gt;%</span> </span>
 <span id="cb9-2"><a href="#cb9-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(sex, case) <span class="sc">%&gt;%</span> </span>
 <span id="cb9-3"><a href="#cb9-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">tbl_summary</span>(<span class="at">by =</span> case) <span class="sc">%&gt;%</span> </span>
 <span id="cb9-4"><a href="#cb9-4" aria-hidden="true" tabindex="-1"></a>  <span class="fu">add_p</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
 <div class="cell-output-display">
-
-<div id="ejslxsewkv" style="padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;">
-<style>#ejslxsewkv table {
+<div id="lajpkaqnqf" style="padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;">
+<style>#lajpkaqnqf table {
   font-family: system-ui, 'Segoe UI', Roboto, Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol', 'Noto Color Emoji';
   -webkit-font-smoothing: antialiased;
   -moz-osx-font-smoothing: grayscale;
 }
 
-#ejslxsewkv thead, #ejslxsewkv tbody, #ejslxsewkv tfoot, #ejslxsewkv tr, #ejslxsewkv td, #ejslxsewkv th {
+#lajpkaqnqf thead, #lajpkaqnqf tbody, #lajpkaqnqf tfoot, #lajpkaqnqf tr, #lajpkaqnqf td, #lajpkaqnqf th {
   border-style: none;
 }
 
-#ejslxsewkv p {
+#lajpkaqnqf p {
   margin: 0;
   padding: 0;
 }
 
-#ejslxsewkv .gt_table {
+#lajpkaqnqf .gt_table {
   display: table;
   border-collapse: collapse;
   line-height: normal;
@@ -389,12 +406,12 @@ <h4 class="anchored" data-anchor-id="b-sex">b) sex</h4>
   border-left-color: #D3D3D3;
 }
 
-#ejslxsewkv .gt_caption {
+#lajpkaqnqf .gt_caption {
   padding-top: 4px;
   padding-bottom: 4px;
 }
 
-#ejslxsewkv .gt_title {
+#lajpkaqnqf .gt_title {
   color: #333333;
   font-size: 125%;
   font-weight: initial;
@@ -406,7 +423,7 @@ <h4 class="anchored" data-anchor-id="b-sex">b) sex</h4>
   border-bottom-width: 0;
 }
 
-#ejslxsewkv .gt_subtitle {
+#lajpkaqnqf .gt_subtitle {
   color: #333333;
   font-size: 85%;
   font-weight: initial;
@@ -418,7 +435,7 @@ <h4 class="anchored" data-anchor-id="b-sex">b) sex</h4>
   border-top-width: 0;
 }
 
-#ejslxsewkv .gt_heading {
+#lajpkaqnqf .gt_heading {
   background-color: #FFFFFF;
   text-align: center;
   border-bottom-color: #FFFFFF;
@@ -430,13 +447,13 @@ <h4 class="anchored" data-anchor-id="b-sex">b) sex</h4>
   border-right-color: #D3D3D3;
 }
 
-#ejslxsewkv .gt_bottom_border {
+#lajpkaqnqf .gt_bottom_border {
   border-bottom-style: solid;
   border-bottom-width: 2px;
   border-bottom-color: #D3D3D3;
 }
 
-#ejslxsewkv .gt_col_headings {
+#lajpkaqnqf .gt_col_headings {
   border-top-style: solid;
   border-top-width: 2px;
   border-top-color: #D3D3D3;
@@ -451,7 +468,7 @@ <h4 class="anchored" data-anchor-id="b-sex">b) sex</h4>
   border-right-color: #D3D3D3;
 }
 
-#ejslxsewkv .gt_col_heading {
+#lajpkaqnqf .gt_col_heading {
   color: #333333;
   background-color: #FFFFFF;
   font-size: 100%;
@@ -471,7 +488,7 @@ <h4 class="anchored" data-anchor-id="b-sex">b) sex</h4>
   overflow-x: hidden;
 }
 
-#ejslxsewkv .gt_column_spanner_outer {
+#lajpkaqnqf .gt_column_spanner_outer {
   color: #333333;
   background-color: #FFFFFF;
   font-size: 100%;
@@ -483,15 +500,15 @@ <h4 class="anchored" data-anchor-id="b-sex">b) sex</h4>
   padding-right: 4px;
 }
 
-#ejslxsewkv .gt_column_spanner_outer:first-child {
+#lajpkaqnqf .gt_column_spanner_outer:first-child {
   padding-left: 0;
 }
 
-#ejslxsewkv .gt_column_spanner_outer:last-child {
+#lajpkaqnqf .gt_column_spanner_outer:last-child {
   padding-right: 0;
 }
 
-#ejslxsewkv .gt_column_spanner {
+#lajpkaqnqf .gt_column_spanner {
   border-bottom-style: solid;
   border-bottom-width: 2px;
   border-bottom-color: #D3D3D3;
@@ -503,11 +520,11 @@ <h4 class="anchored" data-anchor-id="b-sex">b) sex</h4>
   width: 100%;
 }
 
-#ejslxsewkv .gt_spanner_row {
+#lajpkaqnqf .gt_spanner_row {
   border-bottom-style: hidden;
 }
 
-#ejslxsewkv .gt_group_heading {
+#lajpkaqnqf .gt_group_heading {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -533,7 +550,7 @@ <h4 class="anchored" data-anchor-id="b-sex">b) sex</h4>
   text-align: left;
 }
 
-#ejslxsewkv .gt_empty_group_heading {
+#lajpkaqnqf .gt_empty_group_heading {
   padding: 0.5px;
   color: #333333;
   background-color: #FFFFFF;
@@ -548,15 +565,15 @@ <h4 class="anchored" data-anchor-id="b-sex">b) sex</h4>
   vertical-align: middle;
 }
 
-#ejslxsewkv .gt_from_md > :first-child {
+#lajpkaqnqf .gt_from_md > :first-child {
   margin-top: 0;
 }
 
-#ejslxsewkv .gt_from_md > :last-child {
+#lajpkaqnqf .gt_from_md > :last-child {
   margin-bottom: 0;
 }
 
-#ejslxsewkv .gt_row {
+#lajpkaqnqf .gt_row {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -575,7 +592,7 @@ <h4 class="anchored" data-anchor-id="b-sex">b) sex</h4>
   overflow-x: hidden;
 }
 
-#ejslxsewkv .gt_stub {
+#lajpkaqnqf .gt_stub {
   color: #333333;
   background-color: #FFFFFF;
   font-size: 100%;
@@ -588,7 +605,7 @@ <h4 class="anchored" data-anchor-id="b-sex">b) sex</h4>
   padding-right: 5px;
 }
 
-#ejslxsewkv .gt_stub_row_group {
+#lajpkaqnqf .gt_stub_row_group {
   color: #333333;
   background-color: #FFFFFF;
   font-size: 100%;
@@ -602,15 +619,15 @@ <h4 class="anchored" data-anchor-id="b-sex">b) sex</h4>
   vertical-align: top;
 }
 
-#ejslxsewkv .gt_row_group_first td {
+#lajpkaqnqf .gt_row_group_first td {
   border-top-width: 2px;
 }
 
-#ejslxsewkv .gt_row_group_first th {
+#lajpkaqnqf .gt_row_group_first th {
   border-top-width: 2px;
 }
 
-#ejslxsewkv .gt_summary_row {
+#lajpkaqnqf .gt_summary_row {
   color: #333333;
   background-color: #FFFFFF;
   text-transform: inherit;
@@ -620,16 +637,16 @@ <h4 class="anchored" data-anchor-id="b-sex">b) sex</h4>
   padding-right: 5px;
 }
 
-#ejslxsewkv .gt_first_summary_row {
+#lajpkaqnqf .gt_first_summary_row {
   border-top-style: solid;
   border-top-color: #D3D3D3;
 }
 
-#ejslxsewkv .gt_first_summary_row.thick {
+#lajpkaqnqf .gt_first_summary_row.thick {
   border-top-width: 2px;
 }
 
-#ejslxsewkv .gt_last_summary_row {
+#lajpkaqnqf .gt_last_summary_row {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -639,7 +656,7 @@ <h4 class="anchored" data-anchor-id="b-sex">b) sex</h4>
   border-bottom-color: #D3D3D3;
 }
 
-#ejslxsewkv .gt_grand_summary_row {
+#lajpkaqnqf .gt_grand_summary_row {
   color: #333333;
   background-color: #FFFFFF;
   text-transform: inherit;
@@ -649,7 +666,7 @@ <h4 class="anchored" data-anchor-id="b-sex">b) sex</h4>
   padding-right: 5px;
 }
 
-#ejslxsewkv .gt_first_grand_summary_row {
+#lajpkaqnqf .gt_first_grand_summary_row {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -659,7 +676,7 @@ <h4 class="anchored" data-anchor-id="b-sex">b) sex</h4>
   border-top-color: #D3D3D3;
 }
 
-#ejslxsewkv .gt_last_grand_summary_row_top {
+#lajpkaqnqf .gt_last_grand_summary_row_top {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -669,11 +686,11 @@ <h4 class="anchored" data-anchor-id="b-sex">b) sex</h4>
   border-bottom-color: #D3D3D3;
 }
 
-#ejslxsewkv .gt_striped {
+#lajpkaqnqf .gt_striped {
   background-color: rgba(128, 128, 128, 0.05);
 }
 
-#ejslxsewkv .gt_table_body {
+#lajpkaqnqf .gt_table_body {
   border-top-style: solid;
   border-top-width: 2px;
   border-top-color: #D3D3D3;
@@ -682,7 +699,7 @@ <h4 class="anchored" data-anchor-id="b-sex">b) sex</h4>
   border-bottom-color: #D3D3D3;
 }
 
-#ejslxsewkv .gt_footnotes {
+#lajpkaqnqf .gt_footnotes {
   color: #333333;
   background-color: #FFFFFF;
   border-bottom-style: none;
@@ -696,7 +713,7 @@ <h4 class="anchored" data-anchor-id="b-sex">b) sex</h4>
   border-right-color: #D3D3D3;
 }
 
-#ejslxsewkv .gt_footnote {
+#lajpkaqnqf .gt_footnote {
   margin: 0px;
   font-size: 90%;
   padding-top: 4px;
@@ -705,7 +722,7 @@ <h4 class="anchored" data-anchor-id="b-sex">b) sex</h4>
   padding-right: 5px;
 }
 
-#ejslxsewkv .gt_sourcenotes {
+#lajpkaqnqf .gt_sourcenotes {
   color: #333333;
   background-color: #FFFFFF;
   border-bottom-style: none;
@@ -719,7 +736,7 @@ <h4 class="anchored" data-anchor-id="b-sex">b) sex</h4>
   border-right-color: #D3D3D3;
 }
 
-#ejslxsewkv .gt_sourcenote {
+#lajpkaqnqf .gt_sourcenote {
   font-size: 90%;
   padding-top: 4px;
   padding-bottom: 4px;
@@ -727,100 +744,127 @@ <h4 class="anchored" data-anchor-id="b-sex">b) sex</h4>
   padding-right: 5px;
 }
 
-#ejslxsewkv .gt_left {
+#lajpkaqnqf .gt_left {
   text-align: left;
 }
 
-#ejslxsewkv .gt_center {
+#lajpkaqnqf .gt_center {
   text-align: center;
 }
 
-#ejslxsewkv .gt_right {
+#lajpkaqnqf .gt_right {
   text-align: right;
   font-variant-numeric: tabular-nums;
 }
 
-#ejslxsewkv .gt_font_normal {
+#lajpkaqnqf .gt_font_normal {
   font-weight: normal;
 }
 
-#ejslxsewkv .gt_font_bold {
+#lajpkaqnqf .gt_font_bold {
   font-weight: bold;
 }
 
-#ejslxsewkv .gt_font_italic {
+#lajpkaqnqf .gt_font_italic {
   font-style: italic;
 }
 
-#ejslxsewkv .gt_super {
+#lajpkaqnqf .gt_super {
   font-size: 65%;
 }
 
-#ejslxsewkv .gt_footnote_marks {
+#lajpkaqnqf .gt_footnote_marks {
   font-size: 75%;
   vertical-align: 0.4em;
   position: initial;
 }
 
-#ejslxsewkv .gt_asterisk {
+#lajpkaqnqf .gt_asterisk {
   font-size: 100%;
   vertical-align: 0;
 }
 
-#ejslxsewkv .gt_indent_1 {
+#lajpkaqnqf .gt_indent_1 {
   text-indent: 5px;
 }
 
-#ejslxsewkv .gt_indent_2 {
+#lajpkaqnqf .gt_indent_2 {
   text-indent: 10px;
 }
 
-#ejslxsewkv .gt_indent_3 {
+#lajpkaqnqf .gt_indent_3 {
   text-indent: 15px;
 }
 
-#ejslxsewkv .gt_indent_4 {
+#lajpkaqnqf .gt_indent_4 {
   text-indent: 20px;
 }
 
-#ejslxsewkv .gt_indent_5 {
+#lajpkaqnqf .gt_indent_5 {
   text-indent: 25px;
 }
+
+#lajpkaqnqf .katex-display {
+  display: inline-flex !important;
+  margin-bottom: 0.75em !important;
+}
+
+#lajpkaqnqf div.Reactable > div.rt-table > div.rt-thead > div.rt-tr.rt-tr-group-header > div.rt-th-group:after {
+  height: 0px !important;
+}
 </style>
-<table class="gt_table" data-quarto-disable-processing="false" data-quarto-bootstrap="false">
-  <thead>
-    
-    <tr class="gt_col_headings">
-      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="<strong>Characteristic</strong>"><strong>Characteristic</strong></th>
-      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="<strong>FALSE</strong>, N = 161<span class=&quot;gt_footnote_marks&quot; style=&quot;white-space:nowrap;font-style:italic;font-weight:normal;&quot;><sup>1</sup></span>"><strong>FALSE</strong>, N = 161<span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;"><sup>1</sup></span></th>
-      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="<strong>TRUE</strong>, N = 216<span class=&quot;gt_footnote_marks&quot; style=&quot;white-space:nowrap;font-style:italic;font-weight:normal;&quot;><sup>1</sup></span>"><strong>TRUE</strong>, N = 216<span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;"><sup>1</sup></span></th>
-      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="<strong>p-value</strong><span class=&quot;gt_footnote_marks&quot; style=&quot;white-space:nowrap;font-style:italic;font-weight:normal;&quot;><sup>2</sup></span>"><strong>p-value</strong><span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;"><sup>2</sup></span></th>
-    </tr>
-  </thead>
-  <tbody class="gt_table_body">
-    <tr><td headers="label" class="gt_row gt_left">sex</td>
-<td headers="stat_1" class="gt_row gt_center"></td>
-<td headers="stat_2" class="gt_row gt_center"></td>
-<td headers="p.value" class="gt_row gt_center">0.3</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;female</td>
-<td headers="stat_1" class="gt_row gt_center">96 (60%)</td>
-<td headers="stat_2" class="gt_row gt_center">117 (54%)</td>
-<td headers="p.value" class="gt_row gt_center"></td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;male</td>
-<td headers="stat_1" class="gt_row gt_center">65 (40%)</td>
-<td headers="stat_2" class="gt_row gt_center">99 (46%)</td>
-<td headers="p.value" class="gt_row gt_center"></td></tr>
-  </tbody>
-  
-  <tfoot class="gt_footnotes">
-    <tr>
-      <td class="gt_footnote" colspan="4"><span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;"><sup>1</sup></span> n (%)</td>
-    </tr>
-    <tr>
-      <td class="gt_footnote" colspan="4"><span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;"><sup>2</sup></span> Pearson’s Chi-squared test</td>
-    </tr>
-  </tfoot>
+
+<table class="gt_table caption-top table table-sm table-striped small" data-quarto-postprocess="true" data-quarto-disable-processing="false" data-quarto-bootstrap="false">
+<colgroup>
+<col style="width: 25%">
+<col style="width: 25%">
+<col style="width: 25%">
+<col style="width: 25%">
+</colgroup>
+<thead>
+<tr class="header gt_col_headings">
+<th id="label" class="gt_col_heading gt_columns_bottom_border gt_left" data-quarto-table-cell-role="th" scope="col"><strong>Characteristic</strong></th>
+<th id="stat_1" class="gt_col_heading gt_columns_bottom_border gt_center" data-quarto-table-cell-role="th" scope="col"><strong>0</strong><br>
+N = 161<span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;line-height:0;"><sup>1</sup></span></th>
+<th id="stat_2" class="gt_col_heading gt_columns_bottom_border gt_center" data-quarto-table-cell-role="th" scope="col"><strong>1</strong><br>
+N = 216<span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;line-height:0;"><sup>1</sup></span></th>
+<th id="p.value" class="gt_col_heading gt_columns_bottom_border gt_center" data-quarto-table-cell-role="th" scope="col"><strong>p-value</strong><span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;line-height:0;"><sup>2</sup></span></th>
+</tr>
+</thead>
+<tbody class="gt_table_body">
+<tr class="odd">
+<td class="gt_row gt_left" headers="label">sex</td>
+<td class="gt_row gt_center" headers="stat_1"><br>
+</td>
+<td class="gt_row gt_center" headers="stat_2"><br>
+</td>
+<td class="gt_row gt_center" headers="p.value">0.3</td>
+</tr>
+<tr class="even">
+<td class="gt_row gt_left" headers="label">&nbsp;&nbsp;&nbsp;&nbsp;female</td>
+<td class="gt_row gt_center" headers="stat_1">96 (60%)</td>
+<td class="gt_row gt_center" headers="stat_2">117 (54%)</td>
+<td class="gt_row gt_center" headers="p.value"><br>
+</td>
+</tr>
+<tr class="odd">
+<td class="gt_row gt_left" headers="label">&nbsp;&nbsp;&nbsp;&nbsp;male</td>
+<td class="gt_row gt_center" headers="stat_1">65 (40%)</td>
+<td class="gt_row gt_center" headers="stat_2">99 (46%)</td>
+<td class="gt_row gt_center" headers="p.value"><br>
+</td>
+</tr>
+</tbody><tfoot class="gt_footnotes">
+<tr class="even">
+<td colspan="4" class="gt_footnote"><span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;line-height:0;"><sup>1</sup></span> n (%)</td>
+</tr>
+<tr class="odd">
+<td colspan="4" class="gt_footnote"><span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;line-height:0;"><sup>2</sup></span> Pearson’s Chi-squared test</td>
+</tr>
+</tfoot>
+
 </table>
+
 </div>
 </div>
 </div>
@@ -845,32 +889,31 @@ <h4 class="anchored" data-anchor-id="b-sex">b) sex</h4>
 <section id="c-class" class="level4">
 <h4 class="anchored" data-anchor-id="c-class">c) class</h4>
 <div class="cell">
-<details open="">
-<summary>Code</summary>
+<details open="" class="code-fold">
+<summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb10"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb10-1"><a href="#cb10-1" aria-hidden="true" tabindex="-1"></a>copdata <span class="sc">%&gt;%</span> </span>
 <span id="cb10-2"><a href="#cb10-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(class, case) <span class="sc">%&gt;%</span> </span>
 <span id="cb10-3"><a href="#cb10-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">tbl_summary</span>(<span class="at">by =</span> case) <span class="sc">%&gt;%</span> </span>
 <span id="cb10-4"><a href="#cb10-4" aria-hidden="true" tabindex="-1"></a>  <span class="fu">add_p</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
 <div class="cell-output-display">
-
-<div id="ohmysgnkhm" style="padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;">
-<style>#ohmysgnkhm table {
+<div id="djtelkbgfr" style="padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;">
+<style>#djtelkbgfr table {
   font-family: system-ui, 'Segoe UI', Roboto, Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol', 'Noto Color Emoji';
   -webkit-font-smoothing: antialiased;
   -moz-osx-font-smoothing: grayscale;
 }
 
-#ohmysgnkhm thead, #ohmysgnkhm tbody, #ohmysgnkhm tfoot, #ohmysgnkhm tr, #ohmysgnkhm td, #ohmysgnkhm th {
+#djtelkbgfr thead, #djtelkbgfr tbody, #djtelkbgfr tfoot, #djtelkbgfr tr, #djtelkbgfr td, #djtelkbgfr th {
   border-style: none;
 }
 
-#ohmysgnkhm p {
+#djtelkbgfr p {
   margin: 0;
   padding: 0;
 }
 
-#ohmysgnkhm .gt_table {
+#djtelkbgfr .gt_table {
   display: table;
   border-collapse: collapse;
   line-height: normal;
@@ -896,12 +939,12 @@ <h4 class="anchored" data-anchor-id="c-class">c) class</h4>
   border-left-color: #D3D3D3;
 }
 
-#ohmysgnkhm .gt_caption {
+#djtelkbgfr .gt_caption {
   padding-top: 4px;
   padding-bottom: 4px;
 }
 
-#ohmysgnkhm .gt_title {
+#djtelkbgfr .gt_title {
   color: #333333;
   font-size: 125%;
   font-weight: initial;
@@ -913,7 +956,7 @@ <h4 class="anchored" data-anchor-id="c-class">c) class</h4>
   border-bottom-width: 0;
 }
 
-#ohmysgnkhm .gt_subtitle {
+#djtelkbgfr .gt_subtitle {
   color: #333333;
   font-size: 85%;
   font-weight: initial;
@@ -925,7 +968,7 @@ <h4 class="anchored" data-anchor-id="c-class">c) class</h4>
   border-top-width: 0;
 }
 
-#ohmysgnkhm .gt_heading {
+#djtelkbgfr .gt_heading {
   background-color: #FFFFFF;
   text-align: center;
   border-bottom-color: #FFFFFF;
@@ -937,13 +980,13 @@ <h4 class="anchored" data-anchor-id="c-class">c) class</h4>
   border-right-color: #D3D3D3;
 }
 
-#ohmysgnkhm .gt_bottom_border {
+#djtelkbgfr .gt_bottom_border {
   border-bottom-style: solid;
   border-bottom-width: 2px;
   border-bottom-color: #D3D3D3;
 }
 
-#ohmysgnkhm .gt_col_headings {
+#djtelkbgfr .gt_col_headings {
   border-top-style: solid;
   border-top-width: 2px;
   border-top-color: #D3D3D3;
@@ -958,7 +1001,7 @@ <h4 class="anchored" data-anchor-id="c-class">c) class</h4>
   border-right-color: #D3D3D3;
 }
 
-#ohmysgnkhm .gt_col_heading {
+#djtelkbgfr .gt_col_heading {
   color: #333333;
   background-color: #FFFFFF;
   font-size: 100%;
@@ -978,7 +1021,7 @@ <h4 class="anchored" data-anchor-id="c-class">c) class</h4>
   overflow-x: hidden;
 }
 
-#ohmysgnkhm .gt_column_spanner_outer {
+#djtelkbgfr .gt_column_spanner_outer {
   color: #333333;
   background-color: #FFFFFF;
   font-size: 100%;
@@ -990,15 +1033,15 @@ <h4 class="anchored" data-anchor-id="c-class">c) class</h4>
   padding-right: 4px;
 }
 
-#ohmysgnkhm .gt_column_spanner_outer:first-child {
+#djtelkbgfr .gt_column_spanner_outer:first-child {
   padding-left: 0;
 }
 
-#ohmysgnkhm .gt_column_spanner_outer:last-child {
+#djtelkbgfr .gt_column_spanner_outer:last-child {
   padding-right: 0;
 }
 
-#ohmysgnkhm .gt_column_spanner {
+#djtelkbgfr .gt_column_spanner {
   border-bottom-style: solid;
   border-bottom-width: 2px;
   border-bottom-color: #D3D3D3;
@@ -1010,11 +1053,11 @@ <h4 class="anchored" data-anchor-id="c-class">c) class</h4>
   width: 100%;
 }
 
-#ohmysgnkhm .gt_spanner_row {
+#djtelkbgfr .gt_spanner_row {
   border-bottom-style: hidden;
 }
 
-#ohmysgnkhm .gt_group_heading {
+#djtelkbgfr .gt_group_heading {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -1040,7 +1083,7 @@ <h4 class="anchored" data-anchor-id="c-class">c) class</h4>
   text-align: left;
 }
 
-#ohmysgnkhm .gt_empty_group_heading {
+#djtelkbgfr .gt_empty_group_heading {
   padding: 0.5px;
   color: #333333;
   background-color: #FFFFFF;
@@ -1055,15 +1098,15 @@ <h4 class="anchored" data-anchor-id="c-class">c) class</h4>
   vertical-align: middle;
 }
 
-#ohmysgnkhm .gt_from_md > :first-child {
+#djtelkbgfr .gt_from_md > :first-child {
   margin-top: 0;
 }
 
-#ohmysgnkhm .gt_from_md > :last-child {
+#djtelkbgfr .gt_from_md > :last-child {
   margin-bottom: 0;
 }
 
-#ohmysgnkhm .gt_row {
+#djtelkbgfr .gt_row {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -1082,7 +1125,7 @@ <h4 class="anchored" data-anchor-id="c-class">c) class</h4>
   overflow-x: hidden;
 }
 
-#ohmysgnkhm .gt_stub {
+#djtelkbgfr .gt_stub {
   color: #333333;
   background-color: #FFFFFF;
   font-size: 100%;
@@ -1095,7 +1138,7 @@ <h4 class="anchored" data-anchor-id="c-class">c) class</h4>
   padding-right: 5px;
 }
 
-#ohmysgnkhm .gt_stub_row_group {
+#djtelkbgfr .gt_stub_row_group {
   color: #333333;
   background-color: #FFFFFF;
   font-size: 100%;
@@ -1109,15 +1152,15 @@ <h4 class="anchored" data-anchor-id="c-class">c) class</h4>
   vertical-align: top;
 }
 
-#ohmysgnkhm .gt_row_group_first td {
+#djtelkbgfr .gt_row_group_first td {
   border-top-width: 2px;
 }
 
-#ohmysgnkhm .gt_row_group_first th {
+#djtelkbgfr .gt_row_group_first th {
   border-top-width: 2px;
 }
 
-#ohmysgnkhm .gt_summary_row {
+#djtelkbgfr .gt_summary_row {
   color: #333333;
   background-color: #FFFFFF;
   text-transform: inherit;
@@ -1127,16 +1170,16 @@ <h4 class="anchored" data-anchor-id="c-class">c) class</h4>
   padding-right: 5px;
 }
 
-#ohmysgnkhm .gt_first_summary_row {
+#djtelkbgfr .gt_first_summary_row {
   border-top-style: solid;
   border-top-color: #D3D3D3;
 }
 
-#ohmysgnkhm .gt_first_summary_row.thick {
+#djtelkbgfr .gt_first_summary_row.thick {
   border-top-width: 2px;
 }
 
-#ohmysgnkhm .gt_last_summary_row {
+#djtelkbgfr .gt_last_summary_row {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -1146,7 +1189,7 @@ <h4 class="anchored" data-anchor-id="c-class">c) class</h4>
   border-bottom-color: #D3D3D3;
 }
 
-#ohmysgnkhm .gt_grand_summary_row {
+#djtelkbgfr .gt_grand_summary_row {
   color: #333333;
   background-color: #FFFFFF;
   text-transform: inherit;
@@ -1156,7 +1199,7 @@ <h4 class="anchored" data-anchor-id="c-class">c) class</h4>
   padding-right: 5px;
 }
 
-#ohmysgnkhm .gt_first_grand_summary_row {
+#djtelkbgfr .gt_first_grand_summary_row {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -1166,7 +1209,7 @@ <h4 class="anchored" data-anchor-id="c-class">c) class</h4>
   border-top-color: #D3D3D3;
 }
 
-#ohmysgnkhm .gt_last_grand_summary_row_top {
+#djtelkbgfr .gt_last_grand_summary_row_top {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -1176,11 +1219,11 @@ <h4 class="anchored" data-anchor-id="c-class">c) class</h4>
   border-bottom-color: #D3D3D3;
 }
 
-#ohmysgnkhm .gt_striped {
+#djtelkbgfr .gt_striped {
   background-color: rgba(128, 128, 128, 0.05);
 }
 
-#ohmysgnkhm .gt_table_body {
+#djtelkbgfr .gt_table_body {
   border-top-style: solid;
   border-top-width: 2px;
   border-top-color: #D3D3D3;
@@ -1189,7 +1232,7 @@ <h4 class="anchored" data-anchor-id="c-class">c) class</h4>
   border-bottom-color: #D3D3D3;
 }
 
-#ohmysgnkhm .gt_footnotes {
+#djtelkbgfr .gt_footnotes {
   color: #333333;
   background-color: #FFFFFF;
   border-bottom-style: none;
@@ -1203,7 +1246,7 @@ <h4 class="anchored" data-anchor-id="c-class">c) class</h4>
   border-right-color: #D3D3D3;
 }
 
-#ohmysgnkhm .gt_footnote {
+#djtelkbgfr .gt_footnote {
   margin: 0px;
   font-size: 90%;
   padding-top: 4px;
@@ -1212,7 +1255,7 @@ <h4 class="anchored" data-anchor-id="c-class">c) class</h4>
   padding-right: 5px;
 }
 
-#ohmysgnkhm .gt_sourcenotes {
+#djtelkbgfr .gt_sourcenotes {
   color: #333333;
   background-color: #FFFFFF;
   border-bottom-style: none;
@@ -1226,7 +1269,7 @@ <h4 class="anchored" data-anchor-id="c-class">c) class</h4>
   border-right-color: #D3D3D3;
 }
 
-#ohmysgnkhm .gt_sourcenote {
+#djtelkbgfr .gt_sourcenote {
   font-size: 90%;
   padding-top: 4px;
   padding-bottom: 4px;
@@ -1234,108 +1277,141 @@ <h4 class="anchored" data-anchor-id="c-class">c) class</h4>
   padding-right: 5px;
 }
 
-#ohmysgnkhm .gt_left {
+#djtelkbgfr .gt_left {
   text-align: left;
 }
 
-#ohmysgnkhm .gt_center {
+#djtelkbgfr .gt_center {
   text-align: center;
 }
 
-#ohmysgnkhm .gt_right {
+#djtelkbgfr .gt_right {
   text-align: right;
   font-variant-numeric: tabular-nums;
 }
 
-#ohmysgnkhm .gt_font_normal {
+#djtelkbgfr .gt_font_normal {
   font-weight: normal;
 }
 
-#ohmysgnkhm .gt_font_bold {
+#djtelkbgfr .gt_font_bold {
   font-weight: bold;
 }
 
-#ohmysgnkhm .gt_font_italic {
+#djtelkbgfr .gt_font_italic {
   font-style: italic;
 }
 
-#ohmysgnkhm .gt_super {
+#djtelkbgfr .gt_super {
   font-size: 65%;
 }
 
-#ohmysgnkhm .gt_footnote_marks {
+#djtelkbgfr .gt_footnote_marks {
   font-size: 75%;
   vertical-align: 0.4em;
   position: initial;
 }
 
-#ohmysgnkhm .gt_asterisk {
+#djtelkbgfr .gt_asterisk {
   font-size: 100%;
   vertical-align: 0;
 }
 
-#ohmysgnkhm .gt_indent_1 {
+#djtelkbgfr .gt_indent_1 {
   text-indent: 5px;
 }
 
-#ohmysgnkhm .gt_indent_2 {
+#djtelkbgfr .gt_indent_2 {
   text-indent: 10px;
 }
 
-#ohmysgnkhm .gt_indent_3 {
+#djtelkbgfr .gt_indent_3 {
   text-indent: 15px;
 }
 
-#ohmysgnkhm .gt_indent_4 {
+#djtelkbgfr .gt_indent_4 {
   text-indent: 20px;
 }
 
-#ohmysgnkhm .gt_indent_5 {
+#djtelkbgfr .gt_indent_5 {
   text-indent: 25px;
 }
+
+#djtelkbgfr .katex-display {
+  display: inline-flex !important;
+  margin-bottom: 0.75em !important;
+}
+
+#djtelkbgfr div.Reactable > div.rt-table > div.rt-thead > div.rt-tr.rt-tr-group-header > div.rt-th-group:after {
+  height: 0px !important;
+}
 </style>
-<table class="gt_table" data-quarto-disable-processing="false" data-quarto-bootstrap="false">
-  <thead>
-    
-    <tr class="gt_col_headings">
-      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="<strong>Characteristic</strong>"><strong>Characteristic</strong></th>
-      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="<strong>FALSE</strong>, N = 161<span class=&quot;gt_footnote_marks&quot; style=&quot;white-space:nowrap;font-style:italic;font-weight:normal;&quot;><sup>1</sup></span>"><strong>FALSE</strong>, N = 161<span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;"><sup>1</sup></span></th>
-      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="<strong>TRUE</strong>, N = 216<span class=&quot;gt_footnote_marks&quot; style=&quot;white-space:nowrap;font-style:italic;font-weight:normal;&quot;><sup>1</sup></span>"><strong>TRUE</strong>, N = 216<span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;"><sup>1</sup></span></th>
-      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="<strong>p-value</strong><span class=&quot;gt_footnote_marks&quot; style=&quot;white-space:nowrap;font-style:italic;font-weight:normal;&quot;><sup>2</sup></span>"><strong>p-value</strong><span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;"><sup>2</sup></span></th>
-    </tr>
-  </thead>
-  <tbody class="gt_table_body">
-    <tr><td headers="label" class="gt_row gt_left">class</td>
-<td headers="stat_1" class="gt_row gt_center"></td>
-<td headers="stat_2" class="gt_row gt_center"></td>
-<td headers="p.value" class="gt_row gt_center">0.090</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;1</td>
-<td headers="stat_1" class="gt_row gt_center">63 (43%)</td>
-<td headers="stat_2" class="gt_row gt_center">68 (34%)</td>
-<td headers="p.value" class="gt_row gt_center"></td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;2</td>
-<td headers="stat_1" class="gt_row gt_center">44 (30%)</td>
-<td headers="stat_2" class="gt_row gt_center">57 (29%)</td>
-<td headers="p.value" class="gt_row gt_center"></td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;3</td>
-<td headers="stat_1" class="gt_row gt_center">38 (26%)</td>
-<td headers="stat_2" class="gt_row gt_center">73 (37%)</td>
-<td headers="p.value" class="gt_row gt_center"></td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;Unknown</td>
-<td headers="stat_1" class="gt_row gt_center">16</td>
-<td headers="stat_2" class="gt_row gt_center">18</td>
-<td headers="p.value" class="gt_row gt_center"></td></tr>
-  </tbody>
-  
-  <tfoot class="gt_footnotes">
-    <tr>
-      <td class="gt_footnote" colspan="4"><span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;"><sup>1</sup></span> n (%)</td>
-    </tr>
-    <tr>
-      <td class="gt_footnote" colspan="4"><span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;"><sup>2</sup></span> Pearson’s Chi-squared test</td>
-    </tr>
-  </tfoot>
+
+<table class="gt_table caption-top table table-sm table-striped small" data-quarto-postprocess="true" data-quarto-disable-processing="false" data-quarto-bootstrap="false">
+<colgroup>
+<col style="width: 25%">
+<col style="width: 25%">
+<col style="width: 25%">
+<col style="width: 25%">
+</colgroup>
+<thead>
+<tr class="header gt_col_headings">
+<th id="label" class="gt_col_heading gt_columns_bottom_border gt_left" data-quarto-table-cell-role="th" scope="col"><strong>Characteristic</strong></th>
+<th id="stat_1" class="gt_col_heading gt_columns_bottom_border gt_center" data-quarto-table-cell-role="th" scope="col"><strong>0</strong><br>
+N = 161<span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;line-height:0;"><sup>1</sup></span></th>
+<th id="stat_2" class="gt_col_heading gt_columns_bottom_border gt_center" data-quarto-table-cell-role="th" scope="col"><strong>1</strong><br>
+N = 216<span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;line-height:0;"><sup>1</sup></span></th>
+<th id="p.value" class="gt_col_heading gt_columns_bottom_border gt_center" data-quarto-table-cell-role="th" scope="col"><strong>p-value</strong><span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;line-height:0;"><sup>2</sup></span></th>
+</tr>
+</thead>
+<tbody class="gt_table_body">
+<tr class="odd">
+<td class="gt_row gt_left" headers="label">class</td>
+<td class="gt_row gt_center" headers="stat_1"><br>
+</td>
+<td class="gt_row gt_center" headers="stat_2"><br>
+</td>
+<td class="gt_row gt_center" headers="p.value">0.090</td>
+</tr>
+<tr class="even">
+<td class="gt_row gt_left" headers="label">&nbsp;&nbsp;&nbsp;&nbsp;1</td>
+<td class="gt_row gt_center" headers="stat_1">63 (43%)</td>
+<td class="gt_row gt_center" headers="stat_2">68 (34%)</td>
+<td class="gt_row gt_center" headers="p.value"><br>
+</td>
+</tr>
+<tr class="odd">
+<td class="gt_row gt_left" headers="label">&nbsp;&nbsp;&nbsp;&nbsp;2</td>
+<td class="gt_row gt_center" headers="stat_1">44 (30%)</td>
+<td class="gt_row gt_center" headers="stat_2">57 (29%)</td>
+<td class="gt_row gt_center" headers="p.value"><br>
+</td>
+</tr>
+<tr class="even">
+<td class="gt_row gt_left" headers="label">&nbsp;&nbsp;&nbsp;&nbsp;3</td>
+<td class="gt_row gt_center" headers="stat_1">38 (26%)</td>
+<td class="gt_row gt_center" headers="stat_2">73 (37%)</td>
+<td class="gt_row gt_center" headers="p.value"><br>
+</td>
+</tr>
+<tr class="odd">
+<td class="gt_row gt_left" headers="label">&nbsp;&nbsp;&nbsp;&nbsp;Unknown</td>
+<td class="gt_row gt_center" headers="stat_1">16</td>
+<td class="gt_row gt_center" headers="stat_2">18</td>
+<td class="gt_row gt_center" headers="p.value"><br>
+</td>
+</tr>
+</tbody><tfoot class="gt_footnotes">
+<tr class="even">
+<td colspan="4" class="gt_footnote"><span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;line-height:0;"><sup>1</sup></span> n (%)</td>
+</tr>
+<tr class="odd">
+<td colspan="4" class="gt_footnote"><span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;line-height:0;"><sup>2</sup></span> Pearson’s Chi-squared test</td>
+</tr>
+</tfoot>
+
 </table>
+
 </div>
 </div>
 </div>
@@ -1345,32 +1421,31 @@ <h4 class="anchored" data-anchor-id="c-class">c) class</h4>
 <section id="d-group" class="level4">
 <h4 class="anchored" data-anchor-id="d-group">d) group</h4>
 <div class="cell">
-<details open="">
-<summary>Code</summary>
+<details open="" class="code-fold">
+<summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb11"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb11-1"><a href="#cb11-1" aria-hidden="true" tabindex="-1"></a>copdata <span class="sc">%&gt;%</span> </span>
 <span id="cb11-2"><a href="#cb11-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(group, case) <span class="sc">%&gt;%</span> </span>
 <span id="cb11-3"><a href="#cb11-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">tbl_summary</span>(<span class="at">by =</span> case) <span class="sc">%&gt;%</span> </span>
 <span id="cb11-4"><a href="#cb11-4" aria-hidden="true" tabindex="-1"></a>  <span class="fu">add_p</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
 <div class="cell-output-display">
-
-<div id="atlbekfxdc" style="padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;">
-<style>#atlbekfxdc table {
+<div id="wygfakeuin" style="padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;">
+<style>#wygfakeuin table {
   font-family: system-ui, 'Segoe UI', Roboto, Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol', 'Noto Color Emoji';
   -webkit-font-smoothing: antialiased;
   -moz-osx-font-smoothing: grayscale;
 }
 
-#atlbekfxdc thead, #atlbekfxdc tbody, #atlbekfxdc tfoot, #atlbekfxdc tr, #atlbekfxdc td, #atlbekfxdc th {
+#wygfakeuin thead, #wygfakeuin tbody, #wygfakeuin tfoot, #wygfakeuin tr, #wygfakeuin td, #wygfakeuin th {
   border-style: none;
 }
 
-#atlbekfxdc p {
+#wygfakeuin p {
   margin: 0;
   padding: 0;
 }
 
-#atlbekfxdc .gt_table {
+#wygfakeuin .gt_table {
   display: table;
   border-collapse: collapse;
   line-height: normal;
@@ -1396,12 +1471,12 @@ <h4 class="anchored" data-anchor-id="d-group">d) group</h4>
   border-left-color: #D3D3D3;
 }
 
-#atlbekfxdc .gt_caption {
+#wygfakeuin .gt_caption {
   padding-top: 4px;
   padding-bottom: 4px;
 }
 
-#atlbekfxdc .gt_title {
+#wygfakeuin .gt_title {
   color: #333333;
   font-size: 125%;
   font-weight: initial;
@@ -1413,7 +1488,7 @@ <h4 class="anchored" data-anchor-id="d-group">d) group</h4>
   border-bottom-width: 0;
 }
 
-#atlbekfxdc .gt_subtitle {
+#wygfakeuin .gt_subtitle {
   color: #333333;
   font-size: 85%;
   font-weight: initial;
@@ -1425,7 +1500,7 @@ <h4 class="anchored" data-anchor-id="d-group">d) group</h4>
   border-top-width: 0;
 }
 
-#atlbekfxdc .gt_heading {
+#wygfakeuin .gt_heading {
   background-color: #FFFFFF;
   text-align: center;
   border-bottom-color: #FFFFFF;
@@ -1437,13 +1512,13 @@ <h4 class="anchored" data-anchor-id="d-group">d) group</h4>
   border-right-color: #D3D3D3;
 }
 
-#atlbekfxdc .gt_bottom_border {
+#wygfakeuin .gt_bottom_border {
   border-bottom-style: solid;
   border-bottom-width: 2px;
   border-bottom-color: #D3D3D3;
 }
 
-#atlbekfxdc .gt_col_headings {
+#wygfakeuin .gt_col_headings {
   border-top-style: solid;
   border-top-width: 2px;
   border-top-color: #D3D3D3;
@@ -1458,7 +1533,7 @@ <h4 class="anchored" data-anchor-id="d-group">d) group</h4>
   border-right-color: #D3D3D3;
 }
 
-#atlbekfxdc .gt_col_heading {
+#wygfakeuin .gt_col_heading {
   color: #333333;
   background-color: #FFFFFF;
   font-size: 100%;
@@ -1478,7 +1553,7 @@ <h4 class="anchored" data-anchor-id="d-group">d) group</h4>
   overflow-x: hidden;
 }
 
-#atlbekfxdc .gt_column_spanner_outer {
+#wygfakeuin .gt_column_spanner_outer {
   color: #333333;
   background-color: #FFFFFF;
   font-size: 100%;
@@ -1490,15 +1565,15 @@ <h4 class="anchored" data-anchor-id="d-group">d) group</h4>
   padding-right: 4px;
 }
 
-#atlbekfxdc .gt_column_spanner_outer:first-child {
+#wygfakeuin .gt_column_spanner_outer:first-child {
   padding-left: 0;
 }
 
-#atlbekfxdc .gt_column_spanner_outer:last-child {
+#wygfakeuin .gt_column_spanner_outer:last-child {
   padding-right: 0;
 }
 
-#atlbekfxdc .gt_column_spanner {
+#wygfakeuin .gt_column_spanner {
   border-bottom-style: solid;
   border-bottom-width: 2px;
   border-bottom-color: #D3D3D3;
@@ -1510,11 +1585,11 @@ <h4 class="anchored" data-anchor-id="d-group">d) group</h4>
   width: 100%;
 }
 
-#atlbekfxdc .gt_spanner_row {
+#wygfakeuin .gt_spanner_row {
   border-bottom-style: hidden;
 }
 
-#atlbekfxdc .gt_group_heading {
+#wygfakeuin .gt_group_heading {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -1540,7 +1615,7 @@ <h4 class="anchored" data-anchor-id="d-group">d) group</h4>
   text-align: left;
 }
 
-#atlbekfxdc .gt_empty_group_heading {
+#wygfakeuin .gt_empty_group_heading {
   padding: 0.5px;
   color: #333333;
   background-color: #FFFFFF;
@@ -1555,15 +1630,15 @@ <h4 class="anchored" data-anchor-id="d-group">d) group</h4>
   vertical-align: middle;
 }
 
-#atlbekfxdc .gt_from_md > :first-child {
+#wygfakeuin .gt_from_md > :first-child {
   margin-top: 0;
 }
 
-#atlbekfxdc .gt_from_md > :last-child {
+#wygfakeuin .gt_from_md > :last-child {
   margin-bottom: 0;
 }
 
-#atlbekfxdc .gt_row {
+#wygfakeuin .gt_row {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -1582,7 +1657,7 @@ <h4 class="anchored" data-anchor-id="d-group">d) group</h4>
   overflow-x: hidden;
 }
 
-#atlbekfxdc .gt_stub {
+#wygfakeuin .gt_stub {
   color: #333333;
   background-color: #FFFFFF;
   font-size: 100%;
@@ -1595,7 +1670,7 @@ <h4 class="anchored" data-anchor-id="d-group">d) group</h4>
   padding-right: 5px;
 }
 
-#atlbekfxdc .gt_stub_row_group {
+#wygfakeuin .gt_stub_row_group {
   color: #333333;
   background-color: #FFFFFF;
   font-size: 100%;
@@ -1609,15 +1684,15 @@ <h4 class="anchored" data-anchor-id="d-group">d) group</h4>
   vertical-align: top;
 }
 
-#atlbekfxdc .gt_row_group_first td {
+#wygfakeuin .gt_row_group_first td {
   border-top-width: 2px;
 }
 
-#atlbekfxdc .gt_row_group_first th {
+#wygfakeuin .gt_row_group_first th {
   border-top-width: 2px;
 }
 
-#atlbekfxdc .gt_summary_row {
+#wygfakeuin .gt_summary_row {
   color: #333333;
   background-color: #FFFFFF;
   text-transform: inherit;
@@ -1627,16 +1702,16 @@ <h4 class="anchored" data-anchor-id="d-group">d) group</h4>
   padding-right: 5px;
 }
 
-#atlbekfxdc .gt_first_summary_row {
+#wygfakeuin .gt_first_summary_row {
   border-top-style: solid;
   border-top-color: #D3D3D3;
 }
 
-#atlbekfxdc .gt_first_summary_row.thick {
+#wygfakeuin .gt_first_summary_row.thick {
   border-top-width: 2px;
 }
 
-#atlbekfxdc .gt_last_summary_row {
+#wygfakeuin .gt_last_summary_row {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -1646,7 +1721,7 @@ <h4 class="anchored" data-anchor-id="d-group">d) group</h4>
   border-bottom-color: #D3D3D3;
 }
 
-#atlbekfxdc .gt_grand_summary_row {
+#wygfakeuin .gt_grand_summary_row {
   color: #333333;
   background-color: #FFFFFF;
   text-transform: inherit;
@@ -1656,7 +1731,7 @@ <h4 class="anchored" data-anchor-id="d-group">d) group</h4>
   padding-right: 5px;
 }
 
-#atlbekfxdc .gt_first_grand_summary_row {
+#wygfakeuin .gt_first_grand_summary_row {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -1666,7 +1741,7 @@ <h4 class="anchored" data-anchor-id="d-group">d) group</h4>
   border-top-color: #D3D3D3;
 }
 
-#atlbekfxdc .gt_last_grand_summary_row_top {
+#wygfakeuin .gt_last_grand_summary_row_top {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -1676,11 +1751,11 @@ <h4 class="anchored" data-anchor-id="d-group">d) group</h4>
   border-bottom-color: #D3D3D3;
 }
 
-#atlbekfxdc .gt_striped {
+#wygfakeuin .gt_striped {
   background-color: rgba(128, 128, 128, 0.05);
 }
 
-#atlbekfxdc .gt_table_body {
+#wygfakeuin .gt_table_body {
   border-top-style: solid;
   border-top-width: 2px;
   border-top-color: #D3D3D3;
@@ -1689,7 +1764,7 @@ <h4 class="anchored" data-anchor-id="d-group">d) group</h4>
   border-bottom-color: #D3D3D3;
 }
 
-#atlbekfxdc .gt_footnotes {
+#wygfakeuin .gt_footnotes {
   color: #333333;
   background-color: #FFFFFF;
   border-bottom-style: none;
@@ -1703,7 +1778,7 @@ <h4 class="anchored" data-anchor-id="d-group">d) group</h4>
   border-right-color: #D3D3D3;
 }
 
-#atlbekfxdc .gt_footnote {
+#wygfakeuin .gt_footnote {
   margin: 0px;
   font-size: 90%;
   padding-top: 4px;
@@ -1712,7 +1787,7 @@ <h4 class="anchored" data-anchor-id="d-group">d) group</h4>
   padding-right: 5px;
 }
 
-#atlbekfxdc .gt_sourcenotes {
+#wygfakeuin .gt_sourcenotes {
   color: #333333;
   background-color: #FFFFFF;
   border-bottom-style: none;
@@ -1726,7 +1801,7 @@ <h4 class="anchored" data-anchor-id="d-group">d) group</h4>
   border-right-color: #D3D3D3;
 }
 
-#atlbekfxdc .gt_sourcenote {
+#wygfakeuin .gt_sourcenote {
   font-size: 90%;
   padding-top: 4px;
   padding-bottom: 4px;
@@ -1734,100 +1809,127 @@ <h4 class="anchored" data-anchor-id="d-group">d) group</h4>
   padding-right: 5px;
 }
 
-#atlbekfxdc .gt_left {
+#wygfakeuin .gt_left {
   text-align: left;
 }
 
-#atlbekfxdc .gt_center {
+#wygfakeuin .gt_center {
   text-align: center;
 }
 
-#atlbekfxdc .gt_right {
+#wygfakeuin .gt_right {
   text-align: right;
   font-variant-numeric: tabular-nums;
 }
 
-#atlbekfxdc .gt_font_normal {
+#wygfakeuin .gt_font_normal {
   font-weight: normal;
 }
 
-#atlbekfxdc .gt_font_bold {
+#wygfakeuin .gt_font_bold {
   font-weight: bold;
 }
 
-#atlbekfxdc .gt_font_italic {
+#wygfakeuin .gt_font_italic {
   font-style: italic;
 }
 
-#atlbekfxdc .gt_super {
+#wygfakeuin .gt_super {
   font-size: 65%;
 }
 
-#atlbekfxdc .gt_footnote_marks {
+#wygfakeuin .gt_footnote_marks {
   font-size: 75%;
   vertical-align: 0.4em;
   position: initial;
 }
 
-#atlbekfxdc .gt_asterisk {
+#wygfakeuin .gt_asterisk {
   font-size: 100%;
   vertical-align: 0;
 }
 
-#atlbekfxdc .gt_indent_1 {
+#wygfakeuin .gt_indent_1 {
   text-indent: 5px;
 }
 
-#atlbekfxdc .gt_indent_2 {
+#wygfakeuin .gt_indent_2 {
   text-indent: 10px;
 }
 
-#atlbekfxdc .gt_indent_3 {
+#wygfakeuin .gt_indent_3 {
   text-indent: 15px;
 }
 
-#atlbekfxdc .gt_indent_4 {
+#wygfakeuin .gt_indent_4 {
   text-indent: 20px;
 }
 
-#atlbekfxdc .gt_indent_5 {
+#wygfakeuin .gt_indent_5 {
   text-indent: 25px;
 }
+
+#wygfakeuin .katex-display {
+  display: inline-flex !important;
+  margin-bottom: 0.75em !important;
+}
+
+#wygfakeuin div.Reactable > div.rt-table > div.rt-thead > div.rt-tr.rt-tr-group-header > div.rt-th-group:after {
+  height: 0px !important;
+}
 </style>
-<table class="gt_table" data-quarto-disable-processing="false" data-quarto-bootstrap="false">
-  <thead>
-    
-    <tr class="gt_col_headings">
-      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="<strong>Characteristic</strong>"><strong>Characteristic</strong></th>
-      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="<strong>FALSE</strong>, N = 161<span class=&quot;gt_footnote_marks&quot; style=&quot;white-space:nowrap;font-style:italic;font-weight:normal;&quot;><sup>1</sup></span>"><strong>FALSE</strong>, N = 161<span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;"><sup>1</sup></span></th>
-      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="<strong>TRUE</strong>, N = 216<span class=&quot;gt_footnote_marks&quot; style=&quot;white-space:nowrap;font-style:italic;font-weight:normal;&quot;><sup>1</sup></span>"><strong>TRUE</strong>, N = 216<span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;"><sup>1</sup></span></th>
-      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="<strong>p-value</strong><span class=&quot;gt_footnote_marks&quot; style=&quot;white-space:nowrap;font-style:italic;font-weight:normal;&quot;><sup>2</sup></span>"><strong>p-value</strong><span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;"><sup>2</sup></span></th>
-    </tr>
-  </thead>
-  <tbody class="gt_table_body">
-    <tr><td headers="label" class="gt_row gt_left">group</td>
-<td headers="stat_1" class="gt_row gt_center"></td>
-<td headers="stat_2" class="gt_row gt_center"></td>
-<td headers="p.value" class="gt_row gt_center">0.2</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;teacher</td>
-<td headers="stat_1" class="gt_row gt_center">9 (5.6%)</td>
-<td headers="stat_2" class="gt_row gt_center">6 (2.8%)</td>
-<td headers="p.value" class="gt_row gt_center"></td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;student</td>
-<td headers="stat_1" class="gt_row gt_center">152 (94%)</td>
-<td headers="stat_2" class="gt_row gt_center">210 (97%)</td>
-<td headers="p.value" class="gt_row gt_center"></td></tr>
-  </tbody>
-  
-  <tfoot class="gt_footnotes">
-    <tr>
-      <td class="gt_footnote" colspan="4"><span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;"><sup>1</sup></span> n (%)</td>
-    </tr>
-    <tr>
-      <td class="gt_footnote" colspan="4"><span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;"><sup>2</sup></span> Pearson’s Chi-squared test</td>
-    </tr>
-  </tfoot>
+
+<table class="gt_table caption-top table table-sm table-striped small" data-quarto-postprocess="true" data-quarto-disable-processing="false" data-quarto-bootstrap="false">
+<colgroup>
+<col style="width: 25%">
+<col style="width: 25%">
+<col style="width: 25%">
+<col style="width: 25%">
+</colgroup>
+<thead>
+<tr class="header gt_col_headings">
+<th id="label" class="gt_col_heading gt_columns_bottom_border gt_left" data-quarto-table-cell-role="th" scope="col"><strong>Characteristic</strong></th>
+<th id="stat_1" class="gt_col_heading gt_columns_bottom_border gt_center" data-quarto-table-cell-role="th" scope="col"><strong>0</strong><br>
+N = 161<span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;line-height:0;"><sup>1</sup></span></th>
+<th id="stat_2" class="gt_col_heading gt_columns_bottom_border gt_center" data-quarto-table-cell-role="th" scope="col"><strong>1</strong><br>
+N = 216<span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;line-height:0;"><sup>1</sup></span></th>
+<th id="p.value" class="gt_col_heading gt_columns_bottom_border gt_center" data-quarto-table-cell-role="th" scope="col"><strong>p-value</strong><span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;line-height:0;"><sup>2</sup></span></th>
+</tr>
+</thead>
+<tbody class="gt_table_body">
+<tr class="odd">
+<td class="gt_row gt_left" headers="label">group</td>
+<td class="gt_row gt_center" headers="stat_1"><br>
+</td>
+<td class="gt_row gt_center" headers="stat_2"><br>
+</td>
+<td class="gt_row gt_center" headers="p.value">0.2</td>
+</tr>
+<tr class="even">
+<td class="gt_row gt_left" headers="label">&nbsp;&nbsp;&nbsp;&nbsp;teacher</td>
+<td class="gt_row gt_center" headers="stat_1">9 (5.6%)</td>
+<td class="gt_row gt_center" headers="stat_2">6 (2.8%)</td>
+<td class="gt_row gt_center" headers="p.value"><br>
+</td>
+</tr>
+<tr class="odd">
+<td class="gt_row gt_left" headers="label">&nbsp;&nbsp;&nbsp;&nbsp;student</td>
+<td class="gt_row gt_center" headers="stat_1">152 (94%)</td>
+<td class="gt_row gt_center" headers="stat_2">210 (97%)</td>
+<td class="gt_row gt_center" headers="p.value"><br>
+</td>
+</tr>
+</tbody><tfoot class="gt_footnotes">
+<tr class="even">
+<td colspan="4" class="gt_footnote"><span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;line-height:0;"><sup>1</sup></span> n (%)</td>
+</tr>
+<tr class="odd">
+<td colspan="4" class="gt_footnote"><span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;line-height:0;"><sup>2</sup></span> Pearson’s Chi-squared test</td>
+</tr>
+</tfoot>
+
 </table>
+
 </div>
 </div>
 </div>
@@ -1836,32 +1938,31 @@ <h4 class="anchored" data-anchor-id="d-group">d) group</h4>
 <section id="lets-do-all-together" class="level4">
 <h4 class="anchored" data-anchor-id="lets-do-all-together">Let’s do all together</h4>
 <div class="cell">
-<details open="">
-<summary>Code</summary>
+<details open="" class="code-fold">
+<summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb12"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb12-1"><a href="#cb12-1" aria-hidden="true" tabindex="-1"></a>copdata <span class="sc">%&gt;%</span> </span>
 <span id="cb12-2"><a href="#cb12-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(sex, class, group, case) <span class="sc">%&gt;%</span> </span>
 <span id="cb12-3"><a href="#cb12-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">tbl_summary</span>(<span class="at">by =</span> case) <span class="sc">%&gt;%</span> </span>
 <span id="cb12-4"><a href="#cb12-4" aria-hidden="true" tabindex="-1"></a>  <span class="fu">add_p</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
 <div class="cell-output-display">
-
-<div id="dxysyxwvuc" style="padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;">
-<style>#dxysyxwvuc table {
+<div id="prrbsxdxip" style="padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;">
+<style>#prrbsxdxip table {
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-#dxysyxwvuc thead, #dxysyxwvuc tbody, #dxysyxwvuc tfoot, #dxysyxwvuc tr, #dxysyxwvuc td, #dxysyxwvuc th {
+#prrbsxdxip thead, #prrbsxdxip tbody, #prrbsxdxip tfoot, #prrbsxdxip tr, #prrbsxdxip td, #prrbsxdxip th {
   border-style: none;
 }
 
-#dxysyxwvuc p {
+#prrbsxdxip p {
   margin: 0;
   padding: 0;
 }
 
-#dxysyxwvuc .gt_table {
+#prrbsxdxip .gt_table {
   display: table;
   border-collapse: collapse;
   line-height: normal;
@@ -1887,12 +1988,12 @@ <h4 class="anchored" data-anchor-id="lets-do-all-together">Let’s do all togeth
   border-left-color: #D3D3D3;
 }
 
-#dxysyxwvuc .gt_caption {
+#prrbsxdxip .gt_caption {
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 }
 
-#dxysyxwvuc .gt_title {
+#prrbsxdxip .gt_title {
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@@ -1904,7 +2005,7 @@ <h4 class="anchored" data-anchor-id="lets-do-all-together">Let’s do all togeth
   border-bottom-width: 0;
 }
 
-#dxysyxwvuc .gt_subtitle {
+#prrbsxdxip .gt_subtitle {
   color: #333333;
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@@ -1916,7 +2017,7 @@ <h4 class="anchored" data-anchor-id="lets-do-all-together">Let’s do all togeth
   border-top-width: 0;
 }
 
-#dxysyxwvuc .gt_heading {
+#prrbsxdxip .gt_heading {
   background-color: #FFFFFF;
   text-align: center;
   border-bottom-color: #FFFFFF;
@@ -1928,13 +2029,13 @@ <h4 class="anchored" data-anchor-id="lets-do-all-together">Let’s do all togeth
   border-right-color: #D3D3D3;
 }
 
-#dxysyxwvuc .gt_bottom_border {
+#prrbsxdxip .gt_bottom_border {
   border-bottom-style: solid;
   border-bottom-width: 2px;
   border-bottom-color: #D3D3D3;
 }
 
-#dxysyxwvuc .gt_col_headings {
+#prrbsxdxip .gt_col_headings {
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   border-top-width: 2px;
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@@ -1949,7 +2050,7 @@ <h4 class="anchored" data-anchor-id="lets-do-all-together">Let’s do all togeth
   border-right-color: #D3D3D3;
 }
 
-#dxysyxwvuc .gt_col_heading {
+#prrbsxdxip .gt_col_heading {
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@@ -1969,7 +2070,7 @@ <h4 class="anchored" data-anchor-id="lets-do-all-together">Let’s do all togeth
   overflow-x: hidden;
 }
 
-#dxysyxwvuc .gt_column_spanner_outer {
+#prrbsxdxip .gt_column_spanner_outer {
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@@ -1981,15 +2082,15 @@ <h4 class="anchored" data-anchor-id="lets-do-all-together">Let’s do all togeth
   padding-right: 4px;
 }
 
-#dxysyxwvuc .gt_column_spanner_outer:first-child {
+#prrbsxdxip .gt_column_spanner_outer:first-child {
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-#dxysyxwvuc .gt_column_spanner_outer:last-child {
+#prrbsxdxip .gt_column_spanner_outer:last-child {
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+#prrbsxdxip .gt_column_spanner {
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@@ -2001,11 +2102,11 @@ <h4 class="anchored" data-anchor-id="lets-do-all-together">Let’s do all togeth
   width: 100%;
 }
 
-#dxysyxwvuc .gt_spanner_row {
+#prrbsxdxip .gt_spanner_row {
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+#prrbsxdxip .gt_group_heading {
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@@ -2031,7 +2132,7 @@ <h4 class="anchored" data-anchor-id="lets-do-all-together">Let’s do all togeth
   text-align: left;
 }
 
-#dxysyxwvuc .gt_empty_group_heading {
+#prrbsxdxip .gt_empty_group_heading {
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@@ -2046,15 +2147,15 @@ <h4 class="anchored" data-anchor-id="lets-do-all-together">Let’s do all togeth
   vertical-align: middle;
 }
 
-#dxysyxwvuc .gt_from_md > :first-child {
+#prrbsxdxip .gt_from_md > :first-child {
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-#dxysyxwvuc .gt_from_md > :last-child {
+#prrbsxdxip .gt_from_md > :last-child {
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+#prrbsxdxip .gt_row {
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@@ -2073,7 +2174,7 @@ <h4 class="anchored" data-anchor-id="lets-do-all-together">Let’s do all togeth
   overflow-x: hidden;
 }
 
-#dxysyxwvuc .gt_stub {
+#prrbsxdxip .gt_stub {
   color: #333333;
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@@ -2086,7 +2187,7 @@ <h4 class="anchored" data-anchor-id="lets-do-all-together">Let’s do all togeth
   padding-right: 5px;
 }
 
-#dxysyxwvuc .gt_stub_row_group {
+#prrbsxdxip .gt_stub_row_group {
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@@ -2100,15 +2201,15 @@ <h4 class="anchored" data-anchor-id="lets-do-all-together">Let’s do all togeth
   vertical-align: top;
 }
 
-#dxysyxwvuc .gt_row_group_first td {
+#prrbsxdxip .gt_row_group_first td {
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-#dxysyxwvuc .gt_row_group_first th {
+#prrbsxdxip .gt_row_group_first th {
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-#dxysyxwvuc .gt_summary_row {
+#prrbsxdxip .gt_summary_row {
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@@ -2118,16 +2219,16 @@ <h4 class="anchored" data-anchor-id="lets-do-all-together">Let’s do all togeth
   padding-right: 5px;
 }
 
-#dxysyxwvuc .gt_first_summary_row {
+#prrbsxdxip .gt_first_summary_row {
   border-top-style: solid;
   border-top-color: #D3D3D3;
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-#dxysyxwvuc .gt_first_summary_row.thick {
+#prrbsxdxip .gt_first_summary_row.thick {
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-#dxysyxwvuc .gt_last_summary_row {
+#prrbsxdxip .gt_last_summary_row {
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@@ -2137,7 +2238,7 @@ <h4 class="anchored" data-anchor-id="lets-do-all-together">Let’s do all togeth
   border-bottom-color: #D3D3D3;
 }
 
-#dxysyxwvuc .gt_grand_summary_row {
+#prrbsxdxip .gt_grand_summary_row {
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   background-color: #FFFFFF;
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@@ -2147,7 +2248,7 @@ <h4 class="anchored" data-anchor-id="lets-do-all-together">Let’s do all togeth
   padding-right: 5px;
 }
 
-#dxysyxwvuc .gt_first_grand_summary_row {
+#prrbsxdxip .gt_first_grand_summary_row {
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@@ -2157,7 +2258,7 @@ <h4 class="anchored" data-anchor-id="lets-do-all-together">Let’s do all togeth
   border-top-color: #D3D3D3;
 }
 
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+#prrbsxdxip .gt_last_grand_summary_row_top {
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@@ -2167,11 +2268,11 @@ <h4 class="anchored" data-anchor-id="lets-do-all-together">Let’s do all togeth
   border-bottom-color: #D3D3D3;
 }
 
-#dxysyxwvuc .gt_striped {
+#prrbsxdxip .gt_striped {
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-#dxysyxwvuc .gt_table_body {
+#prrbsxdxip .gt_table_body {
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@@ -2180,7 +2281,7 @@ <h4 class="anchored" data-anchor-id="lets-do-all-together">Let’s do all togeth
   border-bottom-color: #D3D3D3;
 }
 
-#dxysyxwvuc .gt_footnotes {
+#prrbsxdxip .gt_footnotes {
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@@ -2194,7 +2295,7 @@ <h4 class="anchored" data-anchor-id="lets-do-all-together">Let’s do all togeth
   border-right-color: #D3D3D3;
 }
 
-#dxysyxwvuc .gt_footnote {
+#prrbsxdxip .gt_footnote {
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@@ -2203,7 +2304,7 @@ <h4 class="anchored" data-anchor-id="lets-do-all-together">Let’s do all togeth
   padding-right: 5px;
 }
 
-#dxysyxwvuc .gt_sourcenotes {
+#prrbsxdxip .gt_sourcenotes {
   color: #333333;
   background-color: #FFFFFF;
   border-bottom-style: none;
@@ -2217,7 +2318,7 @@ <h4 class="anchored" data-anchor-id="lets-do-all-together">Let’s do all togeth
   border-right-color: #D3D3D3;
 }
 
-#dxysyxwvuc .gt_sourcenote {
+#prrbsxdxip .gt_sourcenote {
   font-size: 90%;
   padding-top: 4px;
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@@ -2225,132 +2326,185 @@ <h4 class="anchored" data-anchor-id="lets-do-all-together">Let’s do all togeth
   padding-right: 5px;
 }
 
-#dxysyxwvuc .gt_left {
+#prrbsxdxip .gt_left {
   text-align: left;
 }
 
-#dxysyxwvuc .gt_center {
+#prrbsxdxip .gt_center {
   text-align: center;
 }
 
-#dxysyxwvuc .gt_right {
+#prrbsxdxip .gt_right {
   text-align: right;
   font-variant-numeric: tabular-nums;
 }
 
-#dxysyxwvuc .gt_font_normal {
+#prrbsxdxip .gt_font_normal {
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 }
 
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+#prrbsxdxip .gt_font_bold {
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+#prrbsxdxip .gt_super {
   font-size: 65%;
 }
 
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+#prrbsxdxip .gt_footnote_marks {
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+#prrbsxdxip .gt_asterisk {
   font-size: 100%;
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+#prrbsxdxip .gt_indent_1 {
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-#dxysyxwvuc .gt_indent_2 {
+#prrbsxdxip .gt_indent_2 {
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-#dxysyxwvuc .gt_indent_3 {
+#prrbsxdxip .gt_indent_3 {
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+#prrbsxdxip .gt_indent_4 {
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 }
 
-#dxysyxwvuc .gt_indent_5 {
+#prrbsxdxip .gt_indent_5 {
   text-indent: 25px;
 }
+
+#prrbsxdxip .katex-display {
+  display: inline-flex !important;
+  margin-bottom: 0.75em !important;
+}
+
+#prrbsxdxip div.Reactable > div.rt-table > div.rt-thead > div.rt-tr.rt-tr-group-header > div.rt-th-group:after {
+  height: 0px !important;
+}
 </style>
-<table class="gt_table" data-quarto-disable-processing="false" data-quarto-bootstrap="false">
-  <thead>
-    
-    <tr class="gt_col_headings">
-      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="<strong>Characteristic</strong>"><strong>Characteristic</strong></th>
-      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="<strong>FALSE</strong>, N = 161<span class=&quot;gt_footnote_marks&quot; style=&quot;white-space:nowrap;font-style:italic;font-weight:normal;&quot;><sup>1</sup></span>"><strong>FALSE</strong>, N = 161<span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;"><sup>1</sup></span></th>
-      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="<strong>TRUE</strong>, N = 216<span class=&quot;gt_footnote_marks&quot; style=&quot;white-space:nowrap;font-style:italic;font-weight:normal;&quot;><sup>1</sup></span>"><strong>TRUE</strong>, N = 216<span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;"><sup>1</sup></span></th>
-      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="<strong>p-value</strong><span class=&quot;gt_footnote_marks&quot; style=&quot;white-space:nowrap;font-style:italic;font-weight:normal;&quot;><sup>2</sup></span>"><strong>p-value</strong><span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;"><sup>2</sup></span></th>
-    </tr>
-  </thead>
-  <tbody class="gt_table_body">
-    <tr><td headers="label" class="gt_row gt_left">sex</td>
-<td headers="stat_1" class="gt_row gt_center"></td>
-<td headers="stat_2" class="gt_row gt_center"></td>
-<td headers="p.value" class="gt_row gt_center">0.3</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;female</td>
-<td headers="stat_1" class="gt_row gt_center">96 (60%)</td>
-<td headers="stat_2" class="gt_row gt_center">117 (54%)</td>
-<td headers="p.value" class="gt_row gt_center"></td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;male</td>
-<td headers="stat_1" class="gt_row gt_center">65 (40%)</td>
-<td headers="stat_2" class="gt_row gt_center">99 (46%)</td>
-<td headers="p.value" class="gt_row gt_center"></td></tr>
-    <tr><td headers="label" class="gt_row gt_left">class</td>
-<td headers="stat_1" class="gt_row gt_center"></td>
-<td headers="stat_2" class="gt_row gt_center"></td>
-<td headers="p.value" class="gt_row gt_center">0.090</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;1</td>
-<td headers="stat_1" class="gt_row gt_center">63 (43%)</td>
-<td headers="stat_2" class="gt_row gt_center">68 (34%)</td>
-<td headers="p.value" class="gt_row gt_center"></td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;2</td>
-<td headers="stat_1" class="gt_row gt_center">44 (30%)</td>
-<td headers="stat_2" class="gt_row gt_center">57 (29%)</td>
-<td headers="p.value" class="gt_row gt_center"></td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;3</td>
-<td headers="stat_1" class="gt_row gt_center">38 (26%)</td>
-<td headers="stat_2" class="gt_row gt_center">73 (37%)</td>
-<td headers="p.value" class="gt_row gt_center"></td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;Unknown</td>
-<td headers="stat_1" class="gt_row gt_center">16</td>
-<td headers="stat_2" class="gt_row gt_center">18</td>
-<td headers="p.value" class="gt_row gt_center"></td></tr>
-    <tr><td headers="label" class="gt_row gt_left">group</td>
-<td headers="stat_1" class="gt_row gt_center"></td>
-<td headers="stat_2" class="gt_row gt_center"></td>
-<td headers="p.value" class="gt_row gt_center">0.2</td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;teacher</td>
-<td headers="stat_1" class="gt_row gt_center">9 (5.6%)</td>
-<td headers="stat_2" class="gt_row gt_center">6 (2.8%)</td>
-<td headers="p.value" class="gt_row gt_center"></td></tr>
-    <tr><td headers="label" class="gt_row gt_left">&nbsp;&nbsp;&nbsp;&nbsp;student</td>
-<td headers="stat_1" class="gt_row gt_center">152 (94%)</td>
-<td headers="stat_2" class="gt_row gt_center">210 (97%)</td>
-<td headers="p.value" class="gt_row gt_center"></td></tr>
-  </tbody>
-  
-  <tfoot class="gt_footnotes">
-    <tr>
-      <td class="gt_footnote" colspan="4"><span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;"><sup>1</sup></span> n (%)</td>
-    </tr>
-    <tr>
-      <td class="gt_footnote" colspan="4"><span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;"><sup>2</sup></span> Pearson’s Chi-squared test</td>
-    </tr>
-  </tfoot>
+
+<table class="gt_table caption-top table table-sm table-striped small" data-quarto-postprocess="true" data-quarto-disable-processing="false" data-quarto-bootstrap="false">
+<colgroup>
+<col style="width: 25%">
+<col style="width: 25%">
+<col style="width: 25%">
+<col style="width: 25%">
+</colgroup>
+<thead>
+<tr class="header gt_col_headings">
+<th id="label" class="gt_col_heading gt_columns_bottom_border gt_left" data-quarto-table-cell-role="th" scope="col"><strong>Characteristic</strong></th>
+<th id="stat_1" class="gt_col_heading gt_columns_bottom_border gt_center" data-quarto-table-cell-role="th" scope="col"><strong>0</strong><br>
+N = 161<span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;line-height:0;"><sup>1</sup></span></th>
+<th id="stat_2" class="gt_col_heading gt_columns_bottom_border gt_center" data-quarto-table-cell-role="th" scope="col"><strong>1</strong><br>
+N = 216<span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;line-height:0;"><sup>1</sup></span></th>
+<th id="p.value" class="gt_col_heading gt_columns_bottom_border gt_center" data-quarto-table-cell-role="th" scope="col"><strong>p-value</strong><span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;line-height:0;"><sup>2</sup></span></th>
+</tr>
+</thead>
+<tbody class="gt_table_body">
+<tr class="odd">
+<td class="gt_row gt_left" headers="label">sex</td>
+<td class="gt_row gt_center" headers="stat_1"><br>
+</td>
+<td class="gt_row gt_center" headers="stat_2"><br>
+</td>
+<td class="gt_row gt_center" headers="p.value">0.3</td>
+</tr>
+<tr class="even">
+<td class="gt_row gt_left" headers="label">&nbsp;&nbsp;&nbsp;&nbsp;female</td>
+<td class="gt_row gt_center" headers="stat_1">96 (60%)</td>
+<td class="gt_row gt_center" headers="stat_2">117 (54%)</td>
+<td class="gt_row gt_center" headers="p.value"><br>
+</td>
+</tr>
+<tr class="odd">
+<td class="gt_row gt_left" headers="label">&nbsp;&nbsp;&nbsp;&nbsp;male</td>
+<td class="gt_row gt_center" headers="stat_1">65 (40%)</td>
+<td class="gt_row gt_center" headers="stat_2">99 (46%)</td>
+<td class="gt_row gt_center" headers="p.value"><br>
+</td>
+</tr>
+<tr class="even">
+<td class="gt_row gt_left" headers="label">class</td>
+<td class="gt_row gt_center" headers="stat_1"><br>
+</td>
+<td class="gt_row gt_center" headers="stat_2"><br>
+</td>
+<td class="gt_row gt_center" headers="p.value">0.090</td>
+</tr>
+<tr class="odd">
+<td class="gt_row gt_left" headers="label">&nbsp;&nbsp;&nbsp;&nbsp;1</td>
+<td class="gt_row gt_center" headers="stat_1">63 (43%)</td>
+<td class="gt_row gt_center" headers="stat_2">68 (34%)</td>
+<td class="gt_row gt_center" headers="p.value"><br>
+</td>
+</tr>
+<tr class="even">
+<td class="gt_row gt_left" headers="label">&nbsp;&nbsp;&nbsp;&nbsp;2</td>
+<td class="gt_row gt_center" headers="stat_1">44 (30%)</td>
+<td class="gt_row gt_center" headers="stat_2">57 (29%)</td>
+<td class="gt_row gt_center" headers="p.value"><br>
+</td>
+</tr>
+<tr class="odd">
+<td class="gt_row gt_left" headers="label">&nbsp;&nbsp;&nbsp;&nbsp;3</td>
+<td class="gt_row gt_center" headers="stat_1">38 (26%)</td>
+<td class="gt_row gt_center" headers="stat_2">73 (37%)</td>
+<td class="gt_row gt_center" headers="p.value"><br>
+</td>
+</tr>
+<tr class="even">
+<td class="gt_row gt_left" headers="label">&nbsp;&nbsp;&nbsp;&nbsp;Unknown</td>
+<td class="gt_row gt_center" headers="stat_1">16</td>
+<td class="gt_row gt_center" headers="stat_2">18</td>
+<td class="gt_row gt_center" headers="p.value"><br>
+</td>
+</tr>
+<tr class="odd">
+<td class="gt_row gt_left" headers="label">group</td>
+<td class="gt_row gt_center" headers="stat_1"><br>
+</td>
+<td class="gt_row gt_center" headers="stat_2"><br>
+</td>
+<td class="gt_row gt_center" headers="p.value">0.2</td>
+</tr>
+<tr class="even">
+<td class="gt_row gt_left" headers="label">&nbsp;&nbsp;&nbsp;&nbsp;teacher</td>
+<td class="gt_row gt_center" headers="stat_1">9 (5.6%)</td>
+<td class="gt_row gt_center" headers="stat_2">6 (2.8%)</td>
+<td class="gt_row gt_center" headers="p.value"><br>
+</td>
+</tr>
+<tr class="odd">
+<td class="gt_row gt_left" headers="label">&nbsp;&nbsp;&nbsp;&nbsp;student</td>
+<td class="gt_row gt_center" headers="stat_1">152 (94%)</td>
+<td class="gt_row gt_center" headers="stat_2">210 (97%)</td>
+<td class="gt_row gt_center" headers="p.value"><br>
+</td>
+</tr>
+</tbody><tfoot class="gt_footnotes">
+<tr class="even">
+<td colspan="4" class="gt_footnote"><span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;line-height:0;"><sup>1</sup></span> n (%)</td>
+</tr>
+<tr class="odd">
+<td colspan="4" class="gt_footnote"><span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;line-height:0;"><sup>2</sup></span> Pearson’s Chi-squared test</td>
+</tr>
+</tfoot>
+
 </table>
+
 </div>
 </div>
 </div>
@@ -2380,8 +2534,8 @@ <h3 class="anchored" data-anchor-id="a-calculate-95-ci-risk-ratios-for-food">a)
 </div>
 </div>
 <div class="cell">
-<details open="">
-<summary>Code</summary>
+<details open="" class="code-fold">
+<summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb13"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb13-1"><a href="#cb13-1" aria-hidden="true" tabindex="-1"></a><span class="co"># You could use the EpiStats package for each food item</span></span>
 <span id="cb13-2"><a href="#cb13-2" aria-hidden="true" tabindex="-1"></a><span class="fu">CS</span>(copdata, <span class="st">"case"</span>, <span class="st">"feta"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
@@ -2401,8 +2555,8 @@ <h3 class="anchored" data-anchor-id="a-calculate-95-ci-risk-ratios-for-food">a)
 chi2(1)                   0.05       NA       NA
 Pr&gt;chi2                  0.826       NA       NA</code></pre>
 </div>
-<details open="">
-<summary>Code</summary>
+<details open="" class="code-fold">
+<summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb15"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb15-1"><a href="#cb15-1" aria-hidden="true" tabindex="-1"></a><span class="fu">CS</span>(copdata, <span class="st">"case"</span>, <span class="st">"sardines"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
 <div class="cell-output cell-output-stdout">
@@ -2421,8 +2575,8 @@ <h3 class="anchored" data-anchor-id="a-calculate-95-ci-risk-ratios-for-food">a)
 chi2(1)                   0.35       NA       NA
 Pr&gt;chi2                  0.553       NA       NA</code></pre>
 </div>
-<details open="">
-<summary>Code</summary>
+<details open="" class="code-fold">
+<summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb17"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb17-1"><a href="#cb17-1" aria-hidden="true" tabindex="-1"></a><span class="fu">CS</span>(copdata, <span class="st">"case"</span>, <span class="st">"eggplant"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
 <div class="cell-output cell-output-stdout">
@@ -2441,33 +2595,33 @@ <h3 class="anchored" data-anchor-id="a-calculate-95-ci-risk-ratios-for-food">a)
 chi2(1)                   0.04       NA       NA
 Pr&gt;chi2                  0.837       NA       NA</code></pre>
 </div>
-<details open="">
-<summary>Code</summary>
-<div class="sourceCode cell-code" id="cb19"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb19-1"><a href="#cb19-1" aria-hidden="true" tabindex="-1"></a><span class="fu">CS</span>(copdata, <span class="st">"case"</span>, <span class="st">"moussaka"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<details open="" class="code-fold">
+<summary>Show the code</summary>
+<div class="sourceCode cell-code" id="cb19"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb19-1"><a href="#cb19-1" aria-hidden="true" tabindex="-1"></a><span class="fu">CS</span>(copdata, <span class="st">"case"</span>, <span class="st">"pasta"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
 <div class="cell-output cell-output-stdout">
 <pre><code>$df1
           Cases Non Cases Total Risk
-Exposed     201       137   338 0.59
-Unexposed    14        22    36 0.39
+Exposed     202       136   338 0.60
+Unexposed    13        23    36 0.36
 Total       215       159   374 0.57
 
 $df2
                 Point estimate 95%CI.ll 95%CI.ul
-Risk difference           0.21     0.04     0.37
-Risk ratio                1.53     1.01     2.32
-Attr. frac. ex.           0.35     0.01     0.57
-Attr. frac. pop           0.32       NA       NA
-chi2(1)                   5.64       NA       NA
-Pr&gt;chi2                  0.018       NA       NA</code></pre>
+Risk difference           0.24     0.07     0.40
+Risk ratio                1.65     1.06     2.58
+Attr. frac. ex.           0.40     0.06     0.61
+Attr. frac. pop           0.37       NA       NA
+chi2(1)                   7.45       NA       NA
+Pr&gt;chi2                  0.006       NA       NA</code></pre>
 </div>
 </div>
 <div class="cell">
-<details open="">
-<summary>Code</summary>
+<details open="" class="code-fold">
+<summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb21"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb21-1"><a href="#cb21-1" aria-hidden="true" tabindex="-1"></a><span class="co"># You can save time (and probably typos!) by creating a vector for food variables...</span></span>
-<span id="cb21-2"><a href="#cb21-2" aria-hidden="true" tabindex="-1"></a>food_vars <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">"feta"</span>, <span class="st">"sardines"</span>, <span class="st">"eggplant"</span>, <span class="st">"moussaka"</span>, </span>
-<span id="cb21-3"><a href="#cb21-3" aria-hidden="true" tabindex="-1"></a>               <span class="st">"orzo"</span>, <span class="st">"greeksal"</span>, <span class="st">"dessert"</span>, <span class="st">"bread"</span>,  </span>
+<span id="cb21-2"><a href="#cb21-2" aria-hidden="true" tabindex="-1"></a>food_vars <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">"feta"</span>, <span class="st">"sardines"</span>, <span class="st">"eggplant"</span>, <span class="st">"pasta"</span>, </span>
+<span id="cb21-3"><a href="#cb21-3" aria-hidden="true" tabindex="-1"></a>               <span class="st">"veal"</span>, <span class="st">"tomsal"</span>, <span class="st">"dessert"</span>, <span class="st">"bread"</span>,  </span>
 <span id="cb21-4"><a href="#cb21-4" aria-hidden="true" tabindex="-1"></a>               <span class="st">"champagne"</span>, <span class="st">"beer"</span>, <span class="st">"redwine"</span>, <span class="st">"whitewine"</span>)</span>
 <span id="cb21-5"><a href="#cb21-5" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb21-6"><a href="#cb21-6" aria-hidden="true" tabindex="-1"></a><span class="co"># ...and using EpiStats::CSTable() to run all variables together!</span></span>
@@ -2476,10 +2630,10 @@ <h3 class="anchored" data-anchor-id="a-calculate-95-ci-risk-ratios-for-food">a)
 <div class="cell-output cell-output-stdout">
 <pre><code>$df
           Tot.Exp Cases.Exp AR.Exp% Tot.Unexp Cases.Unexp AR.Unexp%   RR CI.ll
-orzo          338       202   59.76        36          13     36.11 1.65  1.06
-moussaka      338       201   59.47        36          14     38.89 1.53  1.01
+pasta         338       202   59.76        36          13     36.11 1.65  1.06
+veal          338       201   59.47        36          14     38.89 1.53  1.01
 champagne     316       187   59.18        48          21     43.75 1.35  0.97
-greeksal      211       114   54.03       154          95     61.69 0.88  0.73
+tomsal        211       114   54.03       154          95     61.69 0.88  0.73
 dessert       149        90   60.40       198         106     53.54 1.13  0.94
 beer          281       166   59.07        78          41     52.56 1.12  0.89
 redwine        80        42   52.50       259         150     57.92 0.91  0.72
@@ -2489,10 +2643,10 @@ <h3 class="anchored" data-anchor-id="a-calculate-95-ci-risk-ratios-for-food">a)
 feta          271       156   57.56       102          60     58.82 0.98  0.81
 eggplant      216       123   56.94       143          83     58.04 0.98  0.82
           CI.ul p(Chi2)
-orzo       2.58   0.006
-moussaka   2.32   0.018
+pasta      2.58   0.006
+veal       2.32   0.018
 champagne  1.89   0.044
-greeksal   1.04   0.144
+tomsal     1.04   0.144
 dessert    1.36   0.202
 beer       1.42   0.303
 redwine    1.14   0.393
@@ -2523,8 +2677,8 @@ <h3 class="anchored" data-anchor-id="b-prepare-the-rr-table-for-publication">b)
 </div>
 </div>
 <div class="cell">
-<details open="">
-<summary>Code</summary>
+<details open="" class="code-fold">
+<summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb23"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb23-1"><a href="#cb23-1" aria-hidden="true" tabindex="-1"></a>rr_tbl <span class="ot">&lt;-</span> <span class="fu">CSTable</span>(copdata, <span class="st">"case"</span>, food_vars) <span class="sc">%&gt;%</span> </span>
 <span id="cb23-2"><a href="#cb23-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">as.data.frame</span>() <span class="sc">%&gt;%</span> </span>
 <span id="cb23-3"><a href="#cb23-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">rownames_to_column</span>() <span class="sc">%&gt;%</span> </span>
@@ -2561,32 +2715,32 @@ <h3 class="anchored" data-anchor-id="b-prepare-the-rr-table-for-publication">b)
 </div>
 </div>
 </div>
-<p>The interesting results here are that the food items that are most suspicious are orzo, moussaka and champagne. Orzo as such is unlikely to be contaminated, but as you can see in the picture (and from the dinner night), it was served with pesto! Maybe it was the pesto? Who-ho-ho!</p>
-<p>Before one jumps into conclusions, consider that this result could be due to confounding! Maybe orzo was “clean” but eaten by all the people who ate the food item that actually was contaminated!(Optional)</p>
+<p>The interesting results here are that the food items that are most suspicious are pasta,veal and champagne. Pasta as such is unlikely to be contaminated, but as you can see in the picture (and from the dinner night), it was served with pesto! Maybe it was the pesto? Who-ho-ho!</p>
+<p>Before one jumps into conclusions, consider that this result could be due to confounding! Maybe pasta was “clean” but eaten by all the people who ate the food item that actually was contaminated!(Optional)</p>
 </section>
 </section>
 <section id="optional-dose-response" class="level2">
 <h2 class="anchored" data-anchor-id="optional-dose-response">3.5. (Optional) Dose Response</h2>
 <p>Check for a dose-response relationship between the food items with the highest RR values (the top 3) and being a case.</p>
-<section id="a-orzo" class="level4">
-<h4 class="anchored" data-anchor-id="a-orzo">a) Orzo</h4>
+<section id="a-pasta" class="level4">
+<h4 class="anchored" data-anchor-id="a-pasta">a) Pasta</h4>
 <p>Using <code>epitools::riskratio</code> function:</p>
 <div class="cell">
-<details open="">
-<summary>Code</summary>
-<div class="sourceCode cell-code" id="cb24"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb24-1"><a href="#cb24-1" aria-hidden="true" tabindex="-1"></a>epitools<span class="sc">::</span><span class="fu">riskratio</span>(copdata<span class="sc">$</span>orzoD,</span>
-<span id="cb24-2"><a href="#cb24-2" aria-hidden="true" tabindex="-1"></a>                    copdata<span class="sc">$</span>case,</span>
+<details open="" class="code-fold">
+<summary>Show the code</summary>
+<div class="sourceCode cell-code" id="cb24"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb24-1"><a href="#cb24-1" aria-hidden="true" tabindex="-1"></a>epitools<span class="sc">::</span><span class="fu">riskratio</span>(copdata<span class="sc">$</span>pastaD,                     </span>
+<span id="cb24-2"><a href="#cb24-2" aria-hidden="true" tabindex="-1"></a>                    copdata<span class="sc">$</span>case,                     </span>
 <span id="cb24-3"><a href="#cb24-3" aria-hidden="true" tabindex="-1"></a>                    <span class="at">conf.level =</span> <span class="fl">0.95</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
 <div class="cell-output cell-output-stdout">
 <pre><code>$data
          Outcome
-Predictor FALSE TRUE Total
-    0        23   13    36
-    1        32   38    70
-    2        76  107   183
-    3        28   57    85
-    Total   159  215   374
+Predictor   0   1 Total
+    0      23  13    36
+    1      32  38    70
+    2      76 107   183
+    3      28  57    85
+    Total 159 215   374
 
 $measure
          risk ratio with 95% C.I.
@@ -2613,26 +2767,20 @@ <h4 class="anchored" data-anchor-id="a-orzo">a) Orzo</h4>
 </div>
 <p>Using a binomial regression:</p>
 <div class="cell">
-<details open="">
-<summary>Code</summary>
-<div class="sourceCode cell-code" id="cb26"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb26-1"><a href="#cb26-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Binomial regression for RRs. </span></span>
-<span id="cb26-2"><a href="#cb26-2" aria-hidden="true" tabindex="-1"></a><span class="co"># The outcome needs to be exponentiated so we can interpret it properly!</span></span>
-<span id="cb26-3"><a href="#cb26-3" aria-hidden="true" tabindex="-1"></a>binom_orzoD <span class="ot">&lt;-</span> <span class="fu">glm</span>(case <span class="sc">~</span> orzoD, <span class="at">data =</span> copdata, </span>
-<span id="cb26-4"><a href="#cb26-4" aria-hidden="true" tabindex="-1"></a>             <span class="at">family =</span> <span class="fu">binomial</span>(<span class="at">link =</span> <span class="st">"log"</span>))</span>
-<span id="cb26-5"><a href="#cb26-5" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb26-6"><a href="#cb26-6" aria-hidden="true" tabindex="-1"></a><span class="co"># To get exponentiated:</span></span>
-<span id="cb26-7"><a href="#cb26-7" aria-hidden="true" tabindex="-1"></a>binom_orzoD_exp <span class="ot">&lt;-</span> <span class="fu">glm</span>(case <span class="sc">~</span> orzoD, <span class="at">data =</span> copdata, </span>
-<span id="cb26-8"><a href="#cb26-8" aria-hidden="true" tabindex="-1"></a>                       <span class="at">family =</span> <span class="fu">binomial</span>(<span class="at">link =</span> <span class="st">"log"</span>)) <span class="sc">%&gt;%</span> </span>
-<span id="cb26-9"><a href="#cb26-9" aria-hidden="true" tabindex="-1"></a>  <span class="fu">tidy</span>(<span class="at">exponentiate =</span> <span class="cn">TRUE</span>, </span>
-<span id="cb26-10"><a href="#cb26-10" aria-hidden="true" tabindex="-1"></a>       <span class="at">conf.int =</span> <span class="cn">TRUE</span>)</span>
-<span id="cb26-11"><a href="#cb26-11" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb26-12"><a href="#cb26-12" aria-hidden="true" tabindex="-1"></a>binom_orzoD_exp</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<details open="" class="code-fold">
+<summary>Show the code</summary>
+<div class="sourceCode cell-code" id="cb26"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb26-1"><a href="#cb26-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Let's get the results directly exponentiated</span></span>
+<span id="cb26-2"><a href="#cb26-2" aria-hidden="true" tabindex="-1"></a>binom_pastaD_exp <span class="ot">&lt;-</span> <span class="fu">glm</span>(case <span class="sc">~</span> pastaD, <span class="at">data =</span> copdata, </span>
+<span id="cb26-3"><a href="#cb26-3" aria-hidden="true" tabindex="-1"></a>                       <span class="at">family =</span> <span class="fu">binomial</span>(<span class="at">link =</span> <span class="st">"log"</span>)) <span class="sc">%&gt;%</span> </span>
+<span id="cb26-4"><a href="#cb26-4" aria-hidden="true" tabindex="-1"></a>  <span class="fu">tidy</span>(<span class="at">exponentiate =</span> <span class="cn">TRUE</span>, </span>
+<span id="cb26-5"><a href="#cb26-5" aria-hidden="true" tabindex="-1"></a>       <span class="at">conf.int =</span> <span class="cn">TRUE</span>)</span>
+<span id="cb26-6"><a href="#cb26-6" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb26-7"><a href="#cb26-7" aria-hidden="true" tabindex="-1"></a>binom_pastaD_exp</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
 <div class="cell-output-display">
-
 <div data-pagedtable="false">
   <script data-pagedtable-source="" type="application/json">
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 </div>
@@ -2653,27 +2801,27 @@ <h4 class="anchored" data-anchor-id="a-orzo">a) Orzo</h4>
 </div>
 </div>
 </div>
-<p>Results suggest a dose response relationship of having eaten orzo, pointing towards orzoas the potential vehicle. The higher the amount of orzothey ate, the stronger is the association (RR) with getting ill/being a case.</p>
+<p>Results suggest a dose response relationship of having eaten pasta, pointing towards pasta as the potential vehicle. The higher the amount of pasta they ate, the stronger is the association (RR) with getting ill/being a case.</p>
 </section>
-<section id="b-moussaka" class="level4">
-<h4 class="anchored" data-anchor-id="b-moussaka">b) Moussaka</h4>
+<section id="b-veal" class="level4">
+<h4 class="anchored" data-anchor-id="b-veal">b) Veal</h4>
 <p>Using <code>epitools::riskratio</code> function:</p>
 <div class="cell">
-<details open="">
-<summary>Code</summary>
-<div class="sourceCode cell-code" id="cb27"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb27-1"><a href="#cb27-1" aria-hidden="true" tabindex="-1"></a>epitools<span class="sc">::</span><span class="fu">riskratio</span>(copdata<span class="sc">$</span>moussakaD,                     </span>
-<span id="cb27-2"><a href="#cb27-2" aria-hidden="true" tabindex="-1"></a>                    copdata<span class="sc">$</span>case,                     </span>
+<details open="" class="code-fold">
+<summary>Show the code</summary>
+<div class="sourceCode cell-code" id="cb27"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb27-1"><a href="#cb27-1" aria-hidden="true" tabindex="-1"></a>epitools<span class="sc">::</span><span class="fu">riskratio</span>(copdata<span class="sc">$</span>vealD,</span>
+<span id="cb27-2"><a href="#cb27-2" aria-hidden="true" tabindex="-1"></a>                    copdata<span class="sc">$</span>case,</span>
 <span id="cb27-3"><a href="#cb27-3" aria-hidden="true" tabindex="-1"></a>                    <span class="at">conf.level =</span> <span class="fl">0.95</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
 <div class="cell-output cell-output-stdout">
 <pre><code>$data
          Outcome
-Predictor FALSE TRUE Total
-    0        19   15    34
-    1        30   40    70
-    2        78  106   184
-    3        33   54    87
-    Total   160  215   375
+Predictor   0   1 Total
+    0      19  15    34
+    1      30  40    70
+    2      78 106   184
+    3      33  54    87
+    Total 160 215   375
 
 $measure
          risk ratio with 95% C.I.
@@ -2700,21 +2848,25 @@ <h4 class="anchored" data-anchor-id="b-moussaka">b) Moussaka</h4>
 </div>
 <p>Using a binomial regression:</p>
 <div class="cell">
-<details open="">
-<summary>Code</summary>
-<div class="sourceCode cell-code" id="cb29"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb29-1"><a href="#cb29-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Let's get the results directly exponentiated</span></span>
-<span id="cb29-2"><a href="#cb29-2" aria-hidden="true" tabindex="-1"></a>binom_moussakaD_exp <span class="ot">&lt;-</span> <span class="fu">glm</span>(case <span class="sc">~</span> moussakaD, <span class="at">data =</span> copdata, </span>
-<span id="cb29-3"><a href="#cb29-3" aria-hidden="true" tabindex="-1"></a>                       <span class="at">family =</span> <span class="fu">binomial</span>(<span class="at">link =</span> <span class="st">"log"</span>)) <span class="sc">%&gt;%</span> </span>
-<span id="cb29-4"><a href="#cb29-4" aria-hidden="true" tabindex="-1"></a>  <span class="fu">tidy</span>(<span class="at">exponentiate =</span> <span class="cn">TRUE</span>, </span>
-<span id="cb29-5"><a href="#cb29-5" aria-hidden="true" tabindex="-1"></a>       <span class="at">conf.int =</span> <span class="cn">TRUE</span>)</span>
-<span id="cb29-6"><a href="#cb29-6" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb29-7"><a href="#cb29-7" aria-hidden="true" tabindex="-1"></a>binom_moussakaD_exp</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<details open="" class="code-fold">
+<summary>Show the code</summary>
+<div class="sourceCode cell-code" id="cb29"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb29-1"><a href="#cb29-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Binomial regression for RRs. </span></span>
+<span id="cb29-2"><a href="#cb29-2" aria-hidden="true" tabindex="-1"></a><span class="co"># The outcome needs to be exponentiated so we can interpret it properly!</span></span>
+<span id="cb29-3"><a href="#cb29-3" aria-hidden="true" tabindex="-1"></a>binom_vealD <span class="ot">&lt;-</span> <span class="fu">glm</span>(case <span class="sc">~</span> vealD, <span class="at">data =</span> copdata, </span>
+<span id="cb29-4"><a href="#cb29-4" aria-hidden="true" tabindex="-1"></a>             <span class="at">family =</span> <span class="fu">binomial</span>(<span class="at">link =</span> <span class="st">"log"</span>))</span>
+<span id="cb29-5"><a href="#cb29-5" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb29-6"><a href="#cb29-6" aria-hidden="true" tabindex="-1"></a><span class="co"># To get exponentiated:</span></span>
+<span id="cb29-7"><a href="#cb29-7" aria-hidden="true" tabindex="-1"></a>binom_vealD_exp <span class="ot">&lt;-</span> <span class="fu">glm</span>(case <span class="sc">~</span> vealD, <span class="at">data =</span> copdata, </span>
+<span id="cb29-8"><a href="#cb29-8" aria-hidden="true" tabindex="-1"></a>                       <span class="at">family =</span> <span class="fu">binomial</span>(<span class="at">link =</span> <span class="st">"log"</span>)) <span class="sc">%&gt;%</span> </span>
+<span id="cb29-9"><a href="#cb29-9" aria-hidden="true" tabindex="-1"></a>  <span class="fu">tidy</span>(<span class="at">exponentiate =</span> <span class="cn">TRUE</span>, </span>
+<span id="cb29-10"><a href="#cb29-10" aria-hidden="true" tabindex="-1"></a>       <span class="at">conf.int =</span> <span class="cn">TRUE</span>)</span>
+<span id="cb29-11"><a href="#cb29-11" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb29-12"><a href="#cb29-12" aria-hidden="true" tabindex="-1"></a>binom_vealD_exp</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
 <div class="cell-output-display">
-
 <div data-pagedtable="false">
   <script data-pagedtable-source="" type="application/json">
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@@ -2724,8 +2876,8 @@ <h4 class="anchored" data-anchor-id="b-moussaka">b) Moussaka</h4>
 <h4 class="anchored" data-anchor-id="c-champagne">c) Champagne</h4>
 <p>Using <code>epitools::riskratio</code> function:</p>
 <div class="cell">
-<details open="">
-<summary>Code</summary>
+<details open="" class="code-fold">
+<summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb30"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb30-1"><a href="#cb30-1" aria-hidden="true" tabindex="-1"></a>epitools<span class="sc">::</span><span class="fu">riskratio</span>(copdata<span class="sc">$</span>champagneD,                     </span>
 <span id="cb30-2"><a href="#cb30-2" aria-hidden="true" tabindex="-1"></a>                    copdata<span class="sc">$</span>case,                     </span>
 <span id="cb30-3"><a href="#cb30-3" aria-hidden="true" tabindex="-1"></a>                    <span class="at">conf.level =</span> <span class="fl">0.95</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
@@ -2733,12 +2885,12 @@ <h4 class="anchored" data-anchor-id="c-champagne">c) Champagne</h4>
 <div class="cell-output cell-output-stdout">
 <pre><code>$data
          Outcome
-Predictor FALSE TRUE Total
-    0        27   21    48
-    1        93  111   204
-    2        12   32    44
-    3        24   44    68
-    Total   156  208   364
+Predictor   0   1 Total
+    0      27  21    48
+    1      93 111   204
+    2      12  32    44
+    3      24  44    68
+    Total 156 208   364
 
 $measure
          risk ratio with 95% C.I.
@@ -2765,8 +2917,8 @@ <h4 class="anchored" data-anchor-id="c-champagne">c) Champagne</h4>
 </div>
 <p>Using a binomial regression:</p>
 <div class="cell">
-<details open="">
-<summary>Code</summary>
+<details open="" class="code-fold">
+<summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb32"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb32-1"><a href="#cb32-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Let's get the results directly exponentiated</span></span>
 <span id="cb32-2"><a href="#cb32-2" aria-hidden="true" tabindex="-1"></a>binom_champagneD_exp <span class="ot">&lt;-</span> <span class="fu">glm</span>(case <span class="sc">~</span> champagneD, <span class="at">data =</span> copdata, </span>
 <span id="cb32-3"><a href="#cb32-3" aria-hidden="true" tabindex="-1"></a>                       <span class="at">family =</span> <span class="fu">binomial</span>(<span class="at">link =</span> <span class="st">"log"</span>)) <span class="sc">%&gt;%</span> </span>
@@ -2776,7 +2928,6 @@ <h4 class="anchored" data-anchor-id="c-champagne">c) Champagne</h4>
 <span id="cb32-7"><a href="#cb32-7" aria-hidden="true" tabindex="-1"></a>binom_champagneD_exp</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
 <div class="cell-output-display">
-
 <div data-pagedtable="false">
   <script data-pagedtable-source="" type="application/json">
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@@ -2846,6 +2997,33 @@ <h2 class="anchored" data-anchor-id="summary">3.6 Summary</h2>
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+      const sendMessage = (message) => {
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+        if (!iframe) return;
+        iframe.contentWindow.postMessage({ giscus: message }, 'https://giscus.app');
+      }
+      sendMessage({
+        setConfig: {
+          theme: newTheme
+        }
+      });
+    }
+    const isGiscussLoaded = window.document.querySelector('iframe.giscus-frame') !== null;
+    if (isGiscussLoaded) {
+      changeGiscusTheme();
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+  }
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@@ -2912,13 +3090,15 @@ <h2 class="anchored" data-anchor-id="summary">3.6 Summary</h2>
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-  let localAlternateSentinel = 'default';
+  const darkModeDefault = false;
+  let localAlternateSentinel = darkModeDefault ? 'alternate' : 'default';
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   window.quartoToggleColorScheme = () => {
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     setStyleSentinel(toAlternate);
+    toggleGiscusIfUsed(toAlternate, darkModeDefault);
   };
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@@ -2953,18 +3133,7 @@ <h2 class="anchored" data-anchor-id="summary">3.6 Summary</h2>
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-  clipboard.on('success', function(e) {
+  const onCopySuccess = function(e) {
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     // don't keep focus
@@ -2996,7 +3165,29 @@ <h2 class="anchored" data-anchor-id="summary">3.6 Summary</h2>
     }, 1000);
     // clear code selection
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+  }
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+    // TODO: Check when it could be a function (https://github.com/zenorocha/clipboard.js/issues/860)
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@@ -3048,10 +3239,27 @@ <h2 class="anchored" data-anchor-id="summary">3.6 Summary</h2>
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@@ -3061,8 +3269,17 @@ <h2 class="anchored" data-anchor-id="summary">3.6 Summary</h2>
       interactive: true,
       interactiveBorder: 10,
       theme: 'quarto',
-      placement: 'bottom-start'
+      placement: 'bottom-start',
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       const id = href.replace(/^#\/?/, "");
       const note = window.document.getElementById(id);
-      return note.innerHTML;
+      if (note) {
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+      if (el.children) {
+        for (const child of el.children) {
+          stripColumnClz(child);
+        }
+      }
+    }
+    stripColumnClz(note)
+    if (id === null || id.startsWith('sec-')) {
+      // Special case sections, only their first couple elements
+      const container = document.createElement("div");
+      if (note.children && note.children.length > 2) {
+        container.appendChild(note.children[0].cloneNode(true));
+        for (let i = 1; i < note.children.length; i++) {
+          const child = note.children[i];
+          if (child.tagName === "P" && child.innerText === "") {
+            continue;
+          } else {
+            container.appendChild(child.cloneNode(true));
+            break;
+          }
+        }
+        if (window.Quarto?.typesetMath) {
+          window.Quarto.typesetMath(container);
+        }
+        return container.innerHTML
+      } else {
+        if (window.Quarto?.typesetMath) {
+          window.Quarto.typesetMath(note);
+        }
+        return note.innerHTML;
+      }
+    } else {
+      // Remove any anchor links if they are present
+      const anchorLink = note.querySelector('a.anchorjs-link');
+      if (anchorLink) {
+        anchorLink.remove();
+      }
+      if (window.Quarto?.typesetMath) {
+        window.Quarto.typesetMath(note);
+      }
+      // TODO in 1.5, we should make sure this works without a callout special case
+      if (note.classList.contains("callout")) {
+        return note.outerHTML;
+      } else {
+        return note.innerHTML;
+      }
+    }
+  }
+  for (var i=0; i<xrefs.length; i++) {
+    const xref = xrefs[i];
+    tippyHover(xref, undefined, function(instance) {
+      instance.disable();
+      let url = xref.getAttribute('href');
+      let hash = undefined; 
+      if (url.startsWith('#')) {
+        hash = url;
+      } else {
+        try { hash = new URL(url).hash; } catch {}
+      }
+      if (hash) {
+        const id = hash.replace(/^#\/?/, "");
+        const note = window.document.getElementById(id);
+        if (note !== null) {
+          try {
+            const html = processXRef(id, note.cloneNode(true));
+            instance.setContent(html);
+          } finally {
+            instance.enable();
+            instance.show();
+          }
+        } else {
+          // See if we can fetch this
+          fetch(url.split('#')[0])
+          .then(res => res.text())
+          .then(html => {
+            const parser = new DOMParser();
+            const htmlDoc = parser.parseFromString(html, "text/html");
+            const note = htmlDoc.getElementById(id);
+            if (note !== null) {
+              const html = processXRef(id, note);
+              instance.setContent(html);
+            } 
+          }).finally(() => {
+            instance.enable();
+            instance.show();
+          });
+        }
+      } else {
+        // See if we can fetch a full url (with no hash to target)
+        // This is a special case and we should probably do some content thinning / targeting
+        fetch(url)
+        .then(res => res.text())
+        .then(html => {
+          const parser = new DOMParser();
+          const htmlDoc = parser.parseFromString(html, "text/html");
+          const note = htmlDoc.querySelector('main.content');
+          if (note !== null) {
+            // This should only happen for chapter cross references
+            // (since there is no id in the URL)
+            // remove the first header
+            if (note.children.length > 0 && note.children[0].tagName === "HEADER") {
+              note.children[0].remove();
+            }
+            const html = processXRef(null, note);
+            instance.setContent(html);
+          } 
+        }).finally(() => {
+          instance.enable();
+          instance.show();
+        });
+      }
+    }, function(instance) {
     });
   }
       let selectedAnnoteEl;
@@ -3118,6 +3458,7 @@ <h2 class="anchored" data-anchor-id="summary">3.6 Summary</h2>
             }
             div.style.top = top - 2 + "px";
             div.style.height = height + 4 + "px";
+            div.style.left = 0;
             let gutterDiv = window.document.getElementById("code-annotation-line-highlight-gutter");
             if (gutterDiv === null) {
               gutterDiv = window.document.createElement("div");
@@ -3143,6 +3484,32 @@ <h2 class="anchored" data-anchor-id="summary">3.6 Summary</h2>
         });
         selectedAnnoteEl = undefined;
       };
+        // Handle positioning of the toggle
+    window.addEventListener(
+      "resize",
+      throttle(() => {
+        elRect = undefined;
+        if (selectedAnnoteEl) {
+          selectCodeLines(selectedAnnoteEl);
+        }
+      }, 10)
+    );
+    function throttle(fn, ms) {
+    let throttle = false;
+    let timer;
+      return (...args) => {
+        if(!throttle) { // first call gets through
+            fn.apply(this, args);
+            throttle = true;
+        } else { // all the others get throttled
+            if(timer) clearTimeout(timer); // cancel #2
+            timer = setTimeout(() => {
+              fn.apply(this, args);
+              timer = throttle = false;
+            }, ms);
+        }
+      };
+    }
       // Attach click handler to the DT
       const annoteDls = window.document.querySelectorAll('dt[data-target-cell]');
       for (const annoteDlNode of annoteDls) {
@@ -3268,8 +3635,8 @@ <h2 class="anchored" data-anchor-id="summary">3.6 Summary</h2>
 <span id="cb33-66"><a href="#cb33-66" aria-hidden="true" tabindex="-1"></a><span class="fu">## 3.2. Import your data</span></span>
 <span id="cb33-67"><a href="#cb33-67" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb33-68"><a href="#cb33-68" aria-hidden="true" tabindex="-1"></a><span class="in">```{r, Import_data}</span></span>
-<span id="cb33-69"><a href="#cb33-69" aria-hidden="true" tabindex="-1"></a><span class="co"># Import the raw data set:</span></span>
-<span id="cb33-70"><a href="#cb33-70" aria-hidden="true" tabindex="-1"></a>copdata <span class="ot">&lt;-</span> rio<span class="sc">::</span><span class="fu">import</span>(here<span class="sc">::</span><span class="fu">here</span>(<span class="st">"data"</span>, <span class="st">"Spetses_clean2_2024.rds"</span>))</span>
+<span id="cb33-69"><a href="#cb33-69" aria-hidden="true" tabindex="-1"></a><span class="in"># Import the raw data set:</span></span>
+<span id="cb33-70"><a href="#cb33-70" aria-hidden="true" tabindex="-1"></a><span class="in">copdata &lt;- rio::import(here::here("data", "Spetses_clean2_2024.rds"), trust = T)</span></span>
 <span id="cb33-71"><a href="#cb33-71" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
 <span id="cb33-72"><a href="#cb33-72" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb33-73"><a href="#cb33-73" aria-hidden="true" tabindex="-1"></a><span class="fu">## 3.3. Hypothesis tests for other variables</span></span>
@@ -3372,13 +3739,13 @@ <h2 class="anchored" data-anchor-id="summary">3.6 Summary</h2>
 <span id="cb33-184"><a href="#cb33-184" aria-hidden="true" tabindex="-1"></a><span class="fu">CS</span>(copdata, <span class="st">"case"</span>, <span class="st">"feta"</span>)</span>
 <span id="cb33-185"><a href="#cb33-185" aria-hidden="true" tabindex="-1"></a><span class="fu">CS</span>(copdata, <span class="st">"case"</span>, <span class="st">"sardines"</span>)</span>
 <span id="cb33-186"><a href="#cb33-186" aria-hidden="true" tabindex="-1"></a><span class="fu">CS</span>(copdata, <span class="st">"case"</span>, <span class="st">"eggplant"</span>)</span>
-<span id="cb33-187"><a href="#cb33-187" aria-hidden="true" tabindex="-1"></a><span class="fu">CS</span>(copdata, <span class="st">"case"</span>, <span class="st">"moussaka"</span>)</span>
+<span id="cb33-187"><a href="#cb33-187" aria-hidden="true" tabindex="-1"></a><span class="fu">CS</span>(copdata, <span class="st">"case"</span>, <span class="st">"pasta"</span>)</span>
 <span id="cb33-188"><a href="#cb33-188" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
 <span id="cb33-189"><a href="#cb33-189" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb33-192"><a href="#cb33-192" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
 <span id="cb33-193"><a href="#cb33-193" aria-hidden="true" tabindex="-1"></a><span class="co"># You can save time (and probably typos!) by creating a vector for food variables...</span></span>
-<span id="cb33-194"><a href="#cb33-194" aria-hidden="true" tabindex="-1"></a>food_vars <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">"feta"</span>, <span class="st">"sardines"</span>, <span class="st">"eggplant"</span>, <span class="st">"moussaka"</span>, </span>
-<span id="cb33-195"><a href="#cb33-195" aria-hidden="true" tabindex="-1"></a>               <span class="st">"orzo"</span>, <span class="st">"greeksal"</span>, <span class="st">"dessert"</span>, <span class="st">"bread"</span>,  </span>
+<span id="cb33-194"><a href="#cb33-194" aria-hidden="true" tabindex="-1"></a>food_vars <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">"feta"</span>, <span class="st">"sardines"</span>, <span class="st">"eggplant"</span>, <span class="st">"pasta"</span>, </span>
+<span id="cb33-195"><a href="#cb33-195" aria-hidden="true" tabindex="-1"></a>               <span class="st">"veal"</span>, <span class="st">"tomsal"</span>, <span class="st">"dessert"</span>, <span class="st">"bread"</span>,  </span>
 <span id="cb33-196"><a href="#cb33-196" aria-hidden="true" tabindex="-1"></a>               <span class="st">"champagne"</span>, <span class="st">"beer"</span>, <span class="st">"redwine"</span>, <span class="st">"whitewine"</span>)</span>
 <span id="cb33-197"><a href="#cb33-197" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb33-198"><a href="#cb33-198" aria-hidden="true" tabindex="-1"></a><span class="co"># ...and using EpiStats::CSTable() to run all variables together!</span></span>
@@ -3418,67 +3785,67 @@ <h2 class="anchored" data-anchor-id="summary">3.6 Summary</h2>
 <span id="cb33-234"><a href="#cb33-234" aria-hidden="true" tabindex="-1"></a>· Do you think there are any confounders or effect modifiers? If so, how would you investigate these further?</span>
 <span id="cb33-235"><a href="#cb33-235" aria-hidden="true" tabindex="-1"></a>:::</span>
 <span id="cb33-236"><a href="#cb33-236" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb33-237"><a href="#cb33-237" aria-hidden="true" tabindex="-1"></a>The interesting results here are that the food items that are most suspicious are orzo, moussaka and champagne. Orzo as such is unlikely to be contaminated, but as you can see in the picture (and from the dinner night), it was served with pesto! Maybe it was the pesto? Who-ho-ho!</span>
+<span id="cb33-237"><a href="#cb33-237" aria-hidden="true" tabindex="-1"></a>The interesting results here are that the food items that are most suspicious are pasta,veal and champagne. Pasta as such is unlikely to be contaminated, but as you can see in the picture (and from the dinner night), it was served with pesto! Maybe it was the pesto? Who-ho-ho!</span>
 <span id="cb33-238"><a href="#cb33-238" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb33-239"><a href="#cb33-239" aria-hidden="true" tabindex="-1"></a>Before one jumps into conclusions, consider that this result could be due to confounding! Maybe orzo was "clean" but eaten by all the people who ate the food item that actually was contaminated!(Optional)</span>
+<span id="cb33-239"><a href="#cb33-239" aria-hidden="true" tabindex="-1"></a>Before one jumps into conclusions, consider that this result could be due to confounding! Maybe pasta was "clean" but eaten by all the people who ate the food item that actually was contaminated!(Optional)</span>
 <span id="cb33-240"><a href="#cb33-240" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb33-241"><a href="#cb33-241" aria-hidden="true" tabindex="-1"></a><span class="fu">## 3.5. (Optional) Dose Response</span></span>
 <span id="cb33-242"><a href="#cb33-242" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb33-243"><a href="#cb33-243" aria-hidden="true" tabindex="-1"></a>Check for a dose-response relationship between the food items with the highest RR values (the top 3) and being a case.</span>
 <span id="cb33-244"><a href="#cb33-244" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb33-245"><a href="#cb33-245" aria-hidden="true" tabindex="-1"></a><span class="fu">#### a) Orzo</span></span>
+<span id="cb33-245"><a href="#cb33-245" aria-hidden="true" tabindex="-1"></a><span class="fu">#### a) Pasta</span></span>
 <span id="cb33-246"><a href="#cb33-246" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb33-247"><a href="#cb33-247" aria-hidden="true" tabindex="-1"></a>Using <span class="in">`epitools::riskratio`</span> function:</span>
 <span id="cb33-248"><a href="#cb33-248" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb33-251"><a href="#cb33-251" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
-<span id="cb33-252"><a href="#cb33-252" aria-hidden="true" tabindex="-1"></a>epitools<span class="sc">::</span><span class="fu">riskratio</span>(copdata<span class="sc">$</span>orzoD,</span>
-<span id="cb33-253"><a href="#cb33-253" aria-hidden="true" tabindex="-1"></a>                    copdata<span class="sc">$</span>case,</span>
+<span id="cb33-252"><a href="#cb33-252" aria-hidden="true" tabindex="-1"></a>epitools<span class="sc">::</span><span class="fu">riskratio</span>(copdata<span class="sc">$</span>pastaD,                     </span>
+<span id="cb33-253"><a href="#cb33-253" aria-hidden="true" tabindex="-1"></a>                    copdata<span class="sc">$</span>case,                     </span>
 <span id="cb33-254"><a href="#cb33-254" aria-hidden="true" tabindex="-1"></a>                    <span class="at">conf.level =</span> <span class="fl">0.95</span>)</span>
 <span id="cb33-255"><a href="#cb33-255" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
 <span id="cb33-256"><a href="#cb33-256" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb33-257"><a href="#cb33-257" aria-hidden="true" tabindex="-1"></a>Using a binomial regression:</span>
 <span id="cb33-258"><a href="#cb33-258" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb33-261"><a href="#cb33-261" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
-<span id="cb33-262"><a href="#cb33-262" aria-hidden="true" tabindex="-1"></a><span class="co"># Binomial regression for RRs. </span></span>
-<span id="cb33-263"><a href="#cb33-263" aria-hidden="true" tabindex="-1"></a><span class="co"># The outcome needs to be exponentiated so we can interpret it properly!</span></span>
-<span id="cb33-264"><a href="#cb33-264" aria-hidden="true" tabindex="-1"></a>binom_orzoD <span class="ot">&lt;-</span> <span class="fu">glm</span>(case <span class="sc">~</span> orzoD, <span class="at">data =</span> copdata, </span>
-<span id="cb33-265"><a href="#cb33-265" aria-hidden="true" tabindex="-1"></a>             <span class="at">family =</span> <span class="fu">binomial</span>(<span class="at">link =</span> <span class="st">"log"</span>))</span>
-<span id="cb33-266"><a href="#cb33-266" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb33-267"><a href="#cb33-267" aria-hidden="true" tabindex="-1"></a><span class="co"># To get exponentiated:</span></span>
-<span id="cb33-268"><a href="#cb33-268" aria-hidden="true" tabindex="-1"></a>binom_orzoD_exp <span class="ot">&lt;-</span> <span class="fu">glm</span>(case <span class="sc">~</span> orzoD, <span class="at">data =</span> copdata, </span>
-<span id="cb33-269"><a href="#cb33-269" aria-hidden="true" tabindex="-1"></a>                       <span class="at">family =</span> <span class="fu">binomial</span>(<span class="at">link =</span> <span class="st">"log"</span>)) <span class="sc">%&gt;%</span> </span>
-<span id="cb33-270"><a href="#cb33-270" aria-hidden="true" tabindex="-1"></a>  <span class="fu">tidy</span>(<span class="at">exponentiate =</span> <span class="cn">TRUE</span>, </span>
-<span id="cb33-271"><a href="#cb33-271" aria-hidden="true" tabindex="-1"></a>       <span class="at">conf.int =</span> <span class="cn">TRUE</span>)</span>
-<span id="cb33-272"><a href="#cb33-272" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb33-273"><a href="#cb33-273" aria-hidden="true" tabindex="-1"></a>binom_orzoD_exp</span>
-<span id="cb33-274"><a href="#cb33-274" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb33-275"><a href="#cb33-275" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb33-276"><a href="#cb33-276" aria-hidden="true" tabindex="-1"></a>::: {.callout-warning title="Let's stop and... think!" collapse="true"}</span>
-<span id="cb33-277"><a href="#cb33-277" aria-hidden="true" tabindex="-1"></a>What do these results tell you?</span>
-<span id="cb33-278"><a href="#cb33-278" aria-hidden="true" tabindex="-1"></a>:::</span>
-<span id="cb33-279"><a href="#cb33-279" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb33-280"><a href="#cb33-280" aria-hidden="true" tabindex="-1"></a>Results suggest a dose response relationship of having eaten orzo, pointing towards orzoas the potential vehicle. The higher the amount of orzothey ate, the stronger is the association (RR) with getting ill/being a case.</span>
-<span id="cb33-281"><a href="#cb33-281" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb33-282"><a href="#cb33-282" aria-hidden="true" tabindex="-1"></a><span class="fu">#### b) Moussaka</span></span>
-<span id="cb33-283"><a href="#cb33-283" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb33-284"><a href="#cb33-284" aria-hidden="true" tabindex="-1"></a>Using <span class="in">`epitools::riskratio`</span> function:</span>
-<span id="cb33-285"><a href="#cb33-285" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb33-288"><a href="#cb33-288" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
-<span id="cb33-289"><a href="#cb33-289" aria-hidden="true" tabindex="-1"></a>epitools<span class="sc">::</span><span class="fu">riskratio</span>(copdata<span class="sc">$</span>moussakaD,                     </span>
-<span id="cb33-290"><a href="#cb33-290" aria-hidden="true" tabindex="-1"></a>                    copdata<span class="sc">$</span>case,                     </span>
-<span id="cb33-291"><a href="#cb33-291" aria-hidden="true" tabindex="-1"></a>                    <span class="at">conf.level =</span> <span class="fl">0.95</span>)</span>
-<span id="cb33-292"><a href="#cb33-292" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb33-293"><a href="#cb33-293" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb33-294"><a href="#cb33-294" aria-hidden="true" tabindex="-1"></a>Using a binomial regression:</span>
-<span id="cb33-295"><a href="#cb33-295" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb33-298"><a href="#cb33-298" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
-<span id="cb33-299"><a href="#cb33-299" aria-hidden="true" tabindex="-1"></a><span class="co"># Let's get the results directly exponentiated</span></span>
-<span id="cb33-300"><a href="#cb33-300" aria-hidden="true" tabindex="-1"></a>binom_moussakaD_exp <span class="ot">&lt;-</span> <span class="fu">glm</span>(case <span class="sc">~</span> moussakaD, <span class="at">data =</span> copdata, </span>
+<span id="cb33-262"><a href="#cb33-262" aria-hidden="true" tabindex="-1"></a><span class="co"># Let's get the results directly exponentiated</span></span>
+<span id="cb33-263"><a href="#cb33-263" aria-hidden="true" tabindex="-1"></a>binom_pastaD_exp <span class="ot">&lt;-</span> <span class="fu">glm</span>(case <span class="sc">~</span> pastaD, <span class="at">data =</span> copdata, </span>
+<span id="cb33-264"><a href="#cb33-264" aria-hidden="true" tabindex="-1"></a>                       <span class="at">family =</span> <span class="fu">binomial</span>(<span class="at">link =</span> <span class="st">"log"</span>)) <span class="sc">%&gt;%</span> </span>
+<span id="cb33-265"><a href="#cb33-265" aria-hidden="true" tabindex="-1"></a>  <span class="fu">tidy</span>(<span class="at">exponentiate =</span> <span class="cn">TRUE</span>, </span>
+<span id="cb33-266"><a href="#cb33-266" aria-hidden="true" tabindex="-1"></a>       <span class="at">conf.int =</span> <span class="cn">TRUE</span>)</span>
+<span id="cb33-267"><a href="#cb33-267" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb33-268"><a href="#cb33-268" aria-hidden="true" tabindex="-1"></a>binom_pastaD_exp</span>
+<span id="cb33-269"><a href="#cb33-269" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb33-270"><a href="#cb33-270" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb33-271"><a href="#cb33-271" aria-hidden="true" tabindex="-1"></a>::: {.callout-warning title="Let's stop and... think!" collapse="true"}</span>
+<span id="cb33-272"><a href="#cb33-272" aria-hidden="true" tabindex="-1"></a>What do these results tell you?</span>
+<span id="cb33-273"><a href="#cb33-273" aria-hidden="true" tabindex="-1"></a>:::</span>
+<span id="cb33-274"><a href="#cb33-274" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb33-275"><a href="#cb33-275" aria-hidden="true" tabindex="-1"></a>Results suggest a dose response relationship of having eaten pasta, pointing towards pasta as the potential vehicle. The higher the amount of pasta they ate, the stronger is the association (RR) with getting ill/being a case.</span>
+<span id="cb33-276"><a href="#cb33-276" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb33-277"><a href="#cb33-277" aria-hidden="true" tabindex="-1"></a><span class="fu">#### b) Veal</span></span>
+<span id="cb33-278"><a href="#cb33-278" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb33-279"><a href="#cb33-279" aria-hidden="true" tabindex="-1"></a>Using <span class="in">`epitools::riskratio`</span> function:</span>
+<span id="cb33-280"><a href="#cb33-280" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb33-283"><a href="#cb33-283" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb33-284"><a href="#cb33-284" aria-hidden="true" tabindex="-1"></a>epitools<span class="sc">::</span><span class="fu">riskratio</span>(copdata<span class="sc">$</span>vealD,</span>
+<span id="cb33-285"><a href="#cb33-285" aria-hidden="true" tabindex="-1"></a>                    copdata<span class="sc">$</span>case,</span>
+<span id="cb33-286"><a href="#cb33-286" aria-hidden="true" tabindex="-1"></a>                    <span class="at">conf.level =</span> <span class="fl">0.95</span>)</span>
+<span id="cb33-287"><a href="#cb33-287" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb33-288"><a href="#cb33-288" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb33-289"><a href="#cb33-289" aria-hidden="true" tabindex="-1"></a>Using a binomial regression:</span>
+<span id="cb33-290"><a href="#cb33-290" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb33-293"><a href="#cb33-293" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
+<span id="cb33-294"><a href="#cb33-294" aria-hidden="true" tabindex="-1"></a><span class="co"># Binomial regression for RRs. </span></span>
+<span id="cb33-295"><a href="#cb33-295" aria-hidden="true" tabindex="-1"></a><span class="co"># The outcome needs to be exponentiated so we can interpret it properly!</span></span>
+<span id="cb33-296"><a href="#cb33-296" aria-hidden="true" tabindex="-1"></a>binom_vealD <span class="ot">&lt;-</span> <span class="fu">glm</span>(case <span class="sc">~</span> vealD, <span class="at">data =</span> copdata, </span>
+<span id="cb33-297"><a href="#cb33-297" aria-hidden="true" tabindex="-1"></a>             <span class="at">family =</span> <span class="fu">binomial</span>(<span class="at">link =</span> <span class="st">"log"</span>))</span>
+<span id="cb33-298"><a href="#cb33-298" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb33-299"><a href="#cb33-299" aria-hidden="true" tabindex="-1"></a><span class="co"># To get exponentiated:</span></span>
+<span id="cb33-300"><a href="#cb33-300" aria-hidden="true" tabindex="-1"></a>binom_vealD_exp <span class="ot">&lt;-</span> <span class="fu">glm</span>(case <span class="sc">~</span> vealD, <span class="at">data =</span> copdata, </span>
 <span id="cb33-301"><a href="#cb33-301" aria-hidden="true" tabindex="-1"></a>                       <span class="at">family =</span> <span class="fu">binomial</span>(<span class="at">link =</span> <span class="st">"log"</span>)) <span class="sc">%&gt;%</span> </span>
 <span id="cb33-302"><a href="#cb33-302" aria-hidden="true" tabindex="-1"></a>  <span class="fu">tidy</span>(<span class="at">exponentiate =</span> <span class="cn">TRUE</span>, </span>
 <span id="cb33-303"><a href="#cb33-303" aria-hidden="true" tabindex="-1"></a>       <span class="at">conf.int =</span> <span class="cn">TRUE</span>)</span>
 <span id="cb33-304"><a href="#cb33-304" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb33-305"><a href="#cb33-305" aria-hidden="true" tabindex="-1"></a>binom_moussakaD_exp</span>
+<span id="cb33-305"><a href="#cb33-305" aria-hidden="true" tabindex="-1"></a>binom_vealD_exp</span>
 <span id="cb33-306"><a href="#cb33-306" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
 <span id="cb33-307"><a href="#cb33-307" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb33-308"><a href="#cb33-308" aria-hidden="true" tabindex="-1"></a><span class="fu">#### c) Champagne</span></span>
@@ -3516,10 +3883,11 @@ <h2 class="anchored" data-anchor-id="summary">3.6 Summary</h2>
 <span id="cb33-344"><a href="#cb33-344" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span>(Optional) Is there a dose-response relationship between the food items and being a case?</span>
 <span id="cb33-345"><a href="#cb33-345" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb33-346"><a href="#cb33-346" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span>What do you think is the most likely culprit(s) of this outbreak at this point? Are there any risk factors you would like to highlight?</span>
-</code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</code><button title="Copy to Clipboard" class="code-copy-button" data-in-quarto-modal=""><i class="bi"></i></button></pre></div>
 </div></div></div></div></div>
 </div> <!-- /content -->
 
 
 
+
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\ No newline at end of file
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index eb4aac4..7ab7a55 100644
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+<meta name="generator" content="quarto-1.5.57">
 
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-pre > code.sourceCode > span { display: inline-block; line-height: 1.25; }
+pre > code.sourceCode > span { line-height: 1.25; }
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-pre > code.sourceCode > span { text-indent: -5em; padding-left: 5em; }
+pre > code.sourceCode > span { display: inline-block; text-indent: -5em; padding-left: 5em; }
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@@ -81,7 +81,13 @@
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-    <nav class="navbar navbar-expand-lg navbar-dark ">
+    <nav class="navbar navbar-expand-lg " data-bs-theme="dark">
       <div class="navbar-container container-fluid">
-      <div class="navbar-brand-container">
+      <div class="navbar-brand-container mx-auto">
     <a class="navbar-brand" href="./index.html">
     <span class="navbar-title">Spetses Introductory Course</span>
     </a>
   </div>
             <div id="quarto-search" class="" title="Search"></div>
-          <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbarCollapse" aria-controls="navbarCollapse" aria-expanded="false" aria-label="Toggle navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
+          <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbarCollapse" aria-controls="navbarCollapse" role="menu" aria-expanded="false" aria-label="Toggle navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
   <span class="navbar-toggler-icon"></span>
 </button>
           <div class="collapse navbar-collapse" id="navbarCollapse">
             <ul class="navbar-nav navbar-nav-scroll me-auto">
   <li class="nav-item">
-    <a class="nav-link active" href="./index.html" rel="" target="" aria-current="page">
- <span class="menu-text">What is this?</span></a>
+    <a class="nav-link active" href="./index.html" aria-current="page"> 
+<span class="menu-text">What is this?</span></a>
   </li>  
   <li class="nav-item">
-    <a class="nav-link" href="./Data_import_cleaning.html" rel="" target="">
- <span class="menu-text">Data import and cleaning</span></a>
+    <a class="nav-link" href="./Data_import_cleaning.html"> 
+<span class="menu-text">Data import and cleaning</span></a>
   </li>  
   <li class="nav-item">
-    <a class="nav-link" href="./Case_definition.html" rel="" target="">
- <span class="menu-text">Case Definition</span></a>
+    <a class="nav-link" href="./Case_definition.html"> 
+<span class="menu-text">Case Definition</span></a>
   </li>  
   <li class="nav-item">
-    <a class="nav-link" href="./Lab_and_Descriptive.html" rel="" target="">
- <span class="menu-text">Laboratory data and descriptive analysis</span></a>
+    <a class="nav-link" href="./Lab_and_Descriptive.html"> 
+<span class="menu-text">Laboratory data and descriptive analysis</span></a>
   </li>  
   <li class="nav-item">
-    <a class="nav-link" href="./Univariate.html" rel="" target="">
- <span class="menu-text">Univariable analysis</span></a>
+    <a class="nav-link" href="./Univariate.html"> 
+<span class="menu-text">Univariable analysis</span></a>
   </li>  
   <li class="nav-item">
-    <a class="nav-link" href="./Stratified.html" rel="" target="">
- <span class="menu-text">Stratified analysis</span></a>
+    <a class="nav-link" href="./Stratified.html"> 
+<span class="menu-text">Stratified analysis</span></a>
   </li>  
 </ul>
-            <div class="quarto-navbar-tools ms-auto">
+          </div> <!-- /navcollapse -->
+            <div class="quarto-navbar-tools">
     <div class="dropdown">
-      <a href="" title="" id="quarto-navigation-tool-dropdown-0" class="quarto-navigation-tool dropdown-toggle px-1" data-bs-toggle="dropdown" aria-expanded="false" aria-label=""><i class="bi bi-github"></i></a>
-      <ul class="dropdown-menu dropdown-menu-end" aria-labelledby="quarto-navigation-tool-dropdown-0">
+      <a href="" title="" id="quarto-navigation-tool-dropdown-0" class="quarto-navigation-tool dropdown-toggle px-1" data-bs-toggle="dropdown" aria-expanded="false" role="link" aria-label=""><i class="bi bi-github"></i></a>
+      <ul class="dropdown-menu" aria-labelledby="quarto-navigation-tool-dropdown-0">
           <li>
-            <a class="dropdown-item quarto-navbar-tools ms-auto-item" href="https://github.com/EPIET/IntroCourse_inCop/tree/main">
+            <a class="dropdown-item quarto-navbar-tools-item" href="https://github.com/EPIET/IntroCourse_inCop/tree/main">
             Source Code
             </a>
           </li>
           <li>
-            <a class="dropdown-item quarto-navbar-tools ms-auto-item" href="https://github.com/EPIET/IntroCourse_inCop/issues">
+            <a class="dropdown-item quarto-navbar-tools-item" href="https://github.com/EPIET/IntroCourse_inCop/issues">
             Report a code issue
             </a>
           </li>
@@ -160,7 +168,6 @@
     </div>
   <a href="" class="quarto-color-scheme-toggle quarto-navigation-tool  px-1" onclick="window.quartoToggleColorScheme(); return false;" title="Toggle dark mode"><i class="bi"></i></a>
 </div>
-          </div> <!-- /navcollapse -->
       </div> <!-- /container-fluid -->
     </nav>
 </header>
@@ -196,8 +203,10 @@ <h2 id="toc-title">On this page</h2>
   </div>
   
 
+
 </header>
 
+
 <section id="background" class="level3">
 <h3 class="anchored" data-anchor-id="background">Background:</h3>
 <p>This website contains the R teaching material that can be used (it is not compulsory) during the <strong>Simulated Outbreak (SimOb) section of the Introductory Course module</strong> of the EPIET (European Programme of Intervention Epidemiology Training), EUPHEM (European Public Health Microbiology) and MediPIET (Mediterranean and Black Sea Programme for Intervention Epidemiology Training) fellowships.</p>
@@ -262,6 +271,33 @@ <h1></h1>
       }
     }
   }
+  const toggleGiscusIfUsed = (isAlternate, darkModeDefault) => {
+    const baseTheme = document.querySelector('#giscus-base-theme')?.value ?? 'light';
+    const alternateTheme = document.querySelector('#giscus-alt-theme')?.value ?? 'dark';
+    let newTheme = '';
+    if(darkModeDefault) {
+      newTheme = isAlternate ? baseTheme : alternateTheme;
+    } else {
+      newTheme = isAlternate ? alternateTheme : baseTheme;
+    }
+    const changeGiscusTheme = () => {
+      // From: https://github.com/giscus/giscus/issues/336
+      const sendMessage = (message) => {
+        const iframe = document.querySelector('iframe.giscus-frame');
+        if (!iframe) return;
+        iframe.contentWindow.postMessage({ giscus: message }, 'https://giscus.app');
+      }
+      sendMessage({
+        setConfig: {
+          theme: newTheme
+        }
+      });
+    }
+    const isGiscussLoaded = window.document.querySelector('iframe.giscus-frame') !== null;
+    if (isGiscussLoaded) {
+      changeGiscusTheme();
+    }
+  }
   const toggleColorMode = (alternate) => {
     // Switch the stylesheets
     const alternateStylesheets = window.document.querySelectorAll('link.quarto-color-scheme.quarto-color-alternate');
@@ -328,13 +364,15 @@ <h1></h1>
       return localAlternateSentinel;
     }
   }
-  let localAlternateSentinel = 'default';
+  const darkModeDefault = false;
+  let localAlternateSentinel = darkModeDefault ? 'alternate' : 'default';
   // Dark / light mode switch
   window.quartoToggleColorScheme = () => {
     // Read the current dark / light value 
     let toAlternate = !hasAlternateSentinel();
     toggleColorMode(toAlternate);
     setStyleSentinel(toAlternate);
+    toggleGiscusIfUsed(toAlternate, darkModeDefault);
   };
   // Ensure there is a toggle, if there isn't float one in the top right
   if (window.document.querySelector('.quarto-color-scheme-toggle') === null) {
@@ -369,18 +407,7 @@ <h1></h1>
     }
     return false;
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-  const clipboard = new window.ClipboardJS('.code-copy-button', {
-    text: function(trigger) {
-      const codeEl = trigger.previousElementSibling.cloneNode(true);
-      for (const childEl of codeEl.children) {
-        if (isCodeAnnotation(childEl)) {
-          childEl.remove();
-        }
-      }
-      return codeEl.innerText;
-    }
-  });
-  clipboard.on('success', function(e) {
+  const onCopySuccess = function(e) {
     // button target
     const button = e.trigger;
     // don't keep focus
@@ -412,7 +439,29 @@ <h1></h1>
     }, 1000);
     // clear code selection
     e.clearSelection();
+  }
+  const getTextToCopy = function(trigger) {
+      const codeEl = trigger.previousElementSibling.cloneNode(true);
+      for (const childEl of codeEl.children) {
+        if (isCodeAnnotation(childEl)) {
+          childEl.remove();
+        }
+      }
+      return codeEl.innerText;
+  }
+  const clipboard = new window.ClipboardJS('.code-copy-button:not([data-in-quarto-modal])', {
+    text: getTextToCopy
   });
+  clipboard.on('success', onCopySuccess);
+  if (window.document.getElementById('quarto-embedded-source-code-modal')) {
+    // For code content inside modals, clipBoardJS needs to be initialized with a container option
+    // TODO: Check when it could be a function (https://github.com/zenorocha/clipboard.js/issues/860)
+    const clipboardModal = new window.ClipboardJS('.code-copy-button[data-in-quarto-modal]', {
+      text: getTextToCopy,
+      container: window.document.getElementById('quarto-embedded-source-code-modal')
+    });
+    clipboardModal.on('success', onCopySuccess);
+  }
   const viewSource = window.document.getElementById('quarto-view-source') ||
                      window.document.getElementById('quarto-code-tools-source');
   if (viewSource) {
@@ -464,10 +513,27 @@ <h1></h1>
   if (showAllCode) {
     showAllCode.addEventListener("click", toggleCodeHandler(true));
   }
-  function tippyHover(el, contentFn) {
+    var localhostRegex = new RegExp(/^(?:http|https):\/\/localhost\:?[0-9]*\//);
+    var mailtoRegex = new RegExp(/^mailto:/);
+      var filterRegex = new RegExp('/' + window.location.host + '/');
+    var isInternal = (href) => {
+        return filterRegex.test(href) || localhostRegex.test(href) || mailtoRegex.test(href);
+    }
+    // Inspect non-navigation links and adorn them if external
+ 	var links = window.document.querySelectorAll('a[href]:not(.nav-link):not(.navbar-brand):not(.toc-action):not(.sidebar-link):not(.sidebar-item-toggle):not(.pagination-link):not(.no-external):not([aria-hidden]):not(.dropdown-item):not(.quarto-navigation-tool):not(.about-link)');
+    for (var i=0; i<links.length; i++) {
+      const link = links[i];
+      if (!isInternal(link.href)) {
+        // undo the damage that might have been done by quarto-nav.js in the case of
+        // links that we want to consider external
+        if (link.dataset.originalHref !== undefined) {
+          link.href = link.dataset.originalHref;
+        }
+      }
+    }
+  function tippyHover(el, contentFn, onTriggerFn, onUntriggerFn) {
     const config = {
       allowHTML: true,
-      content: contentFn,
       maxWidth: 500,
       delay: 100,
       arrow: false,
@@ -477,8 +543,17 @@ <h1></h1>
       interactive: true,
       interactiveBorder: 10,
       theme: 'quarto',
-      placement: 'bottom-start'
+      placement: 'bottom-start',
     };
+    if (contentFn) {
+      config.content = contentFn;
+    }
+    if (onTriggerFn) {
+      config.onTrigger = onTriggerFn;
+    }
+    if (onUntriggerFn) {
+      config.onUntrigger = onUntriggerFn;
+    }
     window.tippy(el, config); 
   }
   const noterefs = window.document.querySelectorAll('a[role="doc-noteref"]');
@@ -490,7 +565,130 @@ <h1></h1>
       try { href = new URL(href).hash; } catch {}
       const id = href.replace(/^#\/?/, "");
       const note = window.document.getElementById(id);
-      return note.innerHTML;
+      if (note) {
+        return note.innerHTML;
+      } else {
+        return "";
+      }
+    });
+  }
+  const xrefs = window.document.querySelectorAll('a.quarto-xref');
+  const processXRef = (id, note) => {
+    // Strip column container classes
+    const stripColumnClz = (el) => {
+      el.classList.remove("page-full", "page-columns");
+      if (el.children) {
+        for (const child of el.children) {
+          stripColumnClz(child);
+        }
+      }
+    }
+    stripColumnClz(note)
+    if (id === null || id.startsWith('sec-')) {
+      // Special case sections, only their first couple elements
+      const container = document.createElement("div");
+      if (note.children && note.children.length > 2) {
+        container.appendChild(note.children[0].cloneNode(true));
+        for (let i = 1; i < note.children.length; i++) {
+          const child = note.children[i];
+          if (child.tagName === "P" && child.innerText === "") {
+            continue;
+          } else {
+            container.appendChild(child.cloneNode(true));
+            break;
+          }
+        }
+        if (window.Quarto?.typesetMath) {
+          window.Quarto.typesetMath(container);
+        }
+        return container.innerHTML
+      } else {
+        if (window.Quarto?.typesetMath) {
+          window.Quarto.typesetMath(note);
+        }
+        return note.innerHTML;
+      }
+    } else {
+      // Remove any anchor links if they are present
+      const anchorLink = note.querySelector('a.anchorjs-link');
+      if (anchorLink) {
+        anchorLink.remove();
+      }
+      if (window.Quarto?.typesetMath) {
+        window.Quarto.typesetMath(note);
+      }
+      // TODO in 1.5, we should make sure this works without a callout special case
+      if (note.classList.contains("callout")) {
+        return note.outerHTML;
+      } else {
+        return note.innerHTML;
+      }
+    }
+  }
+  for (var i=0; i<xrefs.length; i++) {
+    const xref = xrefs[i];
+    tippyHover(xref, undefined, function(instance) {
+      instance.disable();
+      let url = xref.getAttribute('href');
+      let hash = undefined; 
+      if (url.startsWith('#')) {
+        hash = url;
+      } else {
+        try { hash = new URL(url).hash; } catch {}
+      }
+      if (hash) {
+        const id = hash.replace(/^#\/?/, "");
+        const note = window.document.getElementById(id);
+        if (note !== null) {
+          try {
+            const html = processXRef(id, note.cloneNode(true));
+            instance.setContent(html);
+          } finally {
+            instance.enable();
+            instance.show();
+          }
+        } else {
+          // See if we can fetch this
+          fetch(url.split('#')[0])
+          .then(res => res.text())
+          .then(html => {
+            const parser = new DOMParser();
+            const htmlDoc = parser.parseFromString(html, "text/html");
+            const note = htmlDoc.getElementById(id);
+            if (note !== null) {
+              const html = processXRef(id, note);
+              instance.setContent(html);
+            } 
+          }).finally(() => {
+            instance.enable();
+            instance.show();
+          });
+        }
+      } else {
+        // See if we can fetch a full url (with no hash to target)
+        // This is a special case and we should probably do some content thinning / targeting
+        fetch(url)
+        .then(res => res.text())
+        .then(html => {
+          const parser = new DOMParser();
+          const htmlDoc = parser.parseFromString(html, "text/html");
+          const note = htmlDoc.querySelector('main.content');
+          if (note !== null) {
+            // This should only happen for chapter cross references
+            // (since there is no id in the URL)
+            // remove the first header
+            if (note.children.length > 0 && note.children[0].tagName === "HEADER") {
+              note.children[0].remove();
+            }
+            const html = processXRef(null, note);
+            instance.setContent(html);
+          } 
+        }).finally(() => {
+          instance.enable();
+          instance.show();
+        });
+      }
+    }, function(instance) {
     });
   }
       let selectedAnnoteEl;
@@ -534,6 +732,7 @@ <h1></h1>
             }
             div.style.top = top - 2 + "px";
             div.style.height = height + 4 + "px";
+            div.style.left = 0;
             let gutterDiv = window.document.getElementById("code-annotation-line-highlight-gutter");
             if (gutterDiv === null) {
               gutterDiv = window.document.createElement("div");
@@ -559,6 +758,32 @@ <h1></h1>
         });
         selectedAnnoteEl = undefined;
       };
+        // Handle positioning of the toggle
+    window.addEventListener(
+      "resize",
+      throttle(() => {
+        elRect = undefined;
+        if (selectedAnnoteEl) {
+          selectCodeLines(selectedAnnoteEl);
+        }
+      }, 10)
+    );
+    function throttle(fn, ms) {
+    let throttle = false;
+    let timer;
+      return (...args) => {
+        if(!throttle) { // first call gets through
+            fn.apply(this, args);
+            throttle = true;
+        } else { // all the others get throttled
+            if(timer) clearTimeout(timer); // cancel #2
+            timer = setTimeout(() => {
+              fn.apply(this, args);
+              timer = throttle = false;
+            }, ms);
+        }
+      };
+    }
       // Attach click handler to the DT
       const annoteDls = window.document.querySelectorAll('dt[data-target-cell]');
       for (const annoteDlNode of annoteDls) {
@@ -634,10 +859,11 @@ <h1></h1>
 <span id="cb1-14"><a href="#cb1-14" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span><span class="in">`Univariate.qmd`</span></span>
 <span id="cb1-15"><a href="#cb1-15" aria-hidden="true" tabindex="-1"></a><span class="ss">-   </span><span class="in">`Stratified.qmd`</span></span>
 <span id="cb1-16"><a href="#cb1-16" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1-17"><a href="#cb1-17" aria-hidden="true" tabindex="-1"></a><span class="fu"># </span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<span id="cb1-17"><a href="#cb1-17" aria-hidden="true" tabindex="-1"></a><span class="fu"># </span></span></code><button title="Copy to Clipboard" class="code-copy-button" data-in-quarto-modal=""><i class="bi"></i></button></pre></div>
 </div></div></div></div></div>
 </div> <!-- /content -->
 
 
 
+
 </body></html>
\ No newline at end of file
diff --git a/docs/search.json b/docs/search.json
index 92a5c83..14f5b76 100644
--- a/docs/search.json
+++ b/docs/search.json
@@ -1,423 +1,73 @@
 [
   {
-    "objectID": "index.html",
-    "href": "index.html",
-    "title": "Spetses Introductory Course",
-    "section": "",
-    "text": "Background:\nThis website contains the R teaching material that can be used (it is not compulsory) during the Simulated Outbreak (SimOb) section of the Introductory Course module of the EPIET (European Programme of Intervention Epidemiology Training), EUPHEM (European Public Health Microbiology) and MediPIET (Mediterranean and Black Sea Programme for Intervention Epidemiology Training) fellowships.\nThe SimOb is an 4-day exercise where fellows are provided a series of injects through which they will follow a 10-steps framework to investigate a specific outbreak. Some of the injects are dedicated to R-exercises that can be proved quite handy during an outbreak investigation. Those injects are maintained in this repository and are stored as different .qmd files, under the root folder:\n\nData_import_cleaning.qmd\nCase_definition.qmd\nLab_and_Descriptive.qmd\nUnivariate.qmd\nStratified.qmd"
-  },
-  {
-    "objectID": "about.html",
-    "href": "about.html",
-    "title": "About",
-    "section": "",
-    "text": "About this site\n\n1 + 1\n\n[1] 2"
-  },
-  {
-    "objectID": "SimOb_RInject09.html",
-    "href": "SimOb_RInject09.html",
-    "title": "Inject 09: Data import and cleaning",
-    "section": "",
-    "text": "At the end of the session, participants will be able to:\n\nImport datasets in R\nApply basic data management techniques to explore and familiarise themselves with the dataset\nApply strategies for data cleaning"
-  },
-  {
-    "objectID": "SimOb_RInject09.html#install-packages-and-load-libraries",
-    "href": "SimOb_RInject09.html#install-packages-and-load-libraries",
-    "title": "Inject 09: Data import and cleaning",
-    "section": "3.1. Install packages and load libraries",
-    "text": "3.1. Install packages and load libraries\n\nLoad the R packages you will be using. Update this list of packages as you dig into the code: You may realise you need packages you didn’t think about at the beginning. Remember that it is a good practice to load tydiverse at the end of your package list, to avoid masking functions.\n\n\n\nCode\n# Check if the 'pacman' package is installed, if not install it:\nif (!requireNamespace(\"pacman\", quietly = TRUE)) install.packages(\"pacman\")\n\n# Load the required libraries into the current R session:\npacman::p_load(rio, \n               here, \n               tidyverse, \n               skimr,\n               plyr,\n               janitor,\n               lubridate,\n               gtsummary, \n               flextable,\n               officer,\n               epikit, \n               apyramid, \n               scales)"
-  },
-  {
-    "objectID": "SimOb_RInject09.html#import-your-data",
-    "href": "SimOb_RInject09.html#import-your-data",
-    "title": "Inject 09: Data import and cleaning",
-    "section": "3.2. Import your data",
-    "text": "3.2. Import your data\n\nYou can find the spetses_school.csv file in EVA. Feel free to explore the codebook as well.\n\n\n\nCode\n# Import the raw data set:\ncopdata &lt;- rio::import(here::here(\"data\", \"spetses_school.csv\"))"
-  },
-  {
-    "objectID": "SimOb_RInject09.html#explore-and-clean-your-data",
-    "href": "SimOb_RInject09.html#explore-and-clean-your-data",
-    "title": "Inject 09: Data import and cleaning",
-    "section": "3.3. Explore and clean your data",
-    "text": "3.3. Explore and clean your data\n\nHave a look at the structure of your data. Here you have some questions that may help you get started:\n\nHow many observations and variables does the dataset contain?\nWhat types of variables do you have and what types of values are recorded?\nDo any of the values of the other variables look implausible? Which ones and why? What will you do about it?\n\nGo ahead and clean your data as you see fit.\n\n\n\n\n\n\n\nNeed a little bit of help?\n\n\n\n\n\nYou could use head(), dim(), str(), or skim() to have a quick look at the data and get an idea of which variables you want to explore further. You can also do some tables\n\n\n\n\n\nCode\nhead(copdata)\n\n\n\n\n  \n\n\n\nCode\ndim(copdata)\n\n\n[1] 397  40\n\n\nCode\nstr(copdata)\n\n\n'data.frame':   397 obs. of  40 variables:\n $ id        : chr  \"sp-2-001\" \"sp-3-003\" \"sp-1-005\" \"sp-2-006\" ...\n $ sex       : chr  \"male\" \"female\" \"female\" \"male\" ...\n $ age       : int  18 18 17 17 18 180 16 15 43 16 ...\n $ group     : int  1 1 1 1 1 1 0 1 0 1 ...\n $ class     : int  2 3 1 2 3 2 NA 1 NA 1 ...\n $ diarrhoea : int  1 NA NA NA 1 1 NA 0 1 NA ...\n $ bloody    : int  0 NA NA NA 0 0 NA 0 NA NA ...\n $ vomiting  : int  0 NA NA NA 0 0 NA 0 NA NA ...\n $ abdo      : int  1 NA 1 NA 1 1 NA 0 NA NA ...\n $ nausea    : int  0 NA 1 NA 1 0 NA 1 1 NA ...\n $ fever     : int  NA NA NA NA 0 0 NA 0 NA NA ...\n $ headache  : int  0 NA 1 NA 1 0 NA 1 1 NA ...\n $ jointpain : int  0 NA NA NA 0 0 NA 0 1 NA ...\n $ starthour : int  9 NA NA NA 15 15 NA NA 3 NA ...\n $ meal      : int  1 1 1 0 1 1 1 1 1 1 ...\n $ feta      : int  1 0 NA 0 1 1 1 1 1 1 ...\n $ fetaD     : int  2 0 NA 0 2 2 2 1 2 2 ...\n $ sardines  : int  1 0 NA 0 1 1 1 0 1 1 ...\n $ sardinesD : int  2 0 NA 0 2 2 2 0 2 2 ...\n $ eggplant  : int  0 0 NA 0 1 1 1 0 NA 1 ...\n $ eggplantD : int  0 0 NA 0 2 2 2 0 NA 2 ...\n $ moussaka  : int  1 1 1 1 1 1 1 1 1 1 ...\n $ moussakaD : int  2 1 0 0 2 2 1 3 2 2 ...\n $ orzo      : int  1 1 1 0 1 1 1 1 1 1 ...\n $ orzoD     : int  3 3 1 0 2 2 2 3 2 3 ...\n $ greeksal  : int  1 1 NA 0 1 1 1 1 1 1 ...\n $ greeksalD : int  1 3 NA 0 2 2 2 2 2 2 ...\n $ bread     : int  1 1 1 0 1 1 1 1 1 1 ...\n $ breadD    : int  2 3 1 0 2 2 2 2 2 2 ...\n $ dessert   : int  1 1 NA 0 1 1 0 1 NA NA ...\n $ dessertD  : int  2 3 NA 0 2 2 0 1 NA NA ...\n $ champagne : int  1 1 0 1 1 1 1 1 1 1 ...\n $ champagneD: int  1 1 0 3 1 1 2 1 1 1 ...\n $ beer      : int  1 0 0 1 1 1 NA 1 NA 1 ...\n $ beerD     : int  3 0 0 3 2 3 NA 1 NA 2 ...\n $ redwine   : int  0 1 0 1 0 0 1 0 1 0 ...\n $ redwineD  : int  0 3 0 3 0 0 2 0 2 0 ...\n $ whitewine : int  0 0 0 1 1 1 1 1 NA 0 ...\n $ whitewineD: int  0 0 0 3 3 2 1 3 NA 0 ...\n $ dayonset  : chr  \"6oct2024\" \"\" \"\" \"\" ...\n\n\nCode\nskimr::skim(copdata)\n\n\n\nData summary\n\n\nName\ncopdata\n\n\nNumber of rows\n397\n\n\nNumber of columns\n40\n\n\n_______________________\n\n\n\nColumn type frequency:\n\n\n\ncharacter\n3\n\n\nnumeric\n37\n\n\n________________________\n\n\n\nGroup variables\nNone\n\n\n\nVariable type: character\n\n\n\n\n\n\n\n\n\n\n\n\n\nskim_variable\nn_missing\ncomplete_rate\nmin\nmax\nempty\nn_unique\nwhitespace\n\n\n\n\nid\n0\n1\n8\n8\n0\n397\n0\n\n\nsex\n0\n1\n4\n6\n0\n2\n0\n\n\ndayonset\n0\n1\n0\n8\n175\n4\n0\n\n\n\nVariable type: numeric\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nskim_variable\nn_missing\ncomplete_rate\nmean\nsd\np0\np25\np50\np75\np100\nhist\n\n\n\n\nage\n0\n1.00\n18.57\n9.92\n8\n16\n17\n18\n180\n▇▁▁▁▁\n\n\ngroup\n0\n1.00\n0.96\n0.20\n0\n1\n1\n1\n1\n▁▁▁▁▇\n\n\nclass\n36\n0.91\n1.94\n0.84\n1\n1\n2\n3\n3\n▇▁▆▁▇\n\n\ndiarrhoea\n141\n0.64\n0.82\n0.39\n0\n1\n1\n1\n1\n▂▁▁▁▇\n\n\nbloody\n200\n0.50\n0.03\n0.17\n0\n0\n0\n0\n1\n▇▁▁▁▁\n\n\nvomiting\n179\n0.55\n0.30\n0.46\n0\n0\n0\n1\n1\n▇▁▁▁▃\n\n\nabdo\n152\n0.62\n0.85\n0.35\n0\n1\n1\n1\n1\n▂▁▁▁▇\n\n\nnausea\n170\n0.57\n0.76\n0.43\n0\n1\n1\n1\n1\n▂▁▁▁▇\n\n\nfever\n223\n0.44\n0.26\n0.44\n0\n0\n0\n1\n1\n▇▁▁▁▃\n\n\nheadache\n174\n0.56\n0.63\n0.48\n0\n0\n1\n1\n1\n▅▁▁▁▇\n\n\njointpain\n207\n0.48\n0.16\n0.37\n0\n0\n0\n0\n1\n▇▁▁▁▂\n\n\nstarthour\n177\n0.55\n12.49\n4.92\n3\n9\n9\n15\n21\n▁▇▁▆▃\n\n\nmeal\n9\n0.98\n0.97\n0.17\n0\n1\n1\n1\n1\n▁▁▁▁▇\n\n\nfeta\n16\n0.96\n0.71\n0.45\n0\n0\n1\n1\n1\n▃▁▁▁▇\n\n\nfetaD\n16\n0.96\n1.32\n1.00\n0\n0\n2\n2\n3\n▆▅▁▇▂\n\n\nsardines\n17\n0.96\n0.67\n0.47\n0\n0\n1\n1\n1\n▃▁▁▁▇\n\n\nsardinesD\n17\n0.96\n1.34\n1.04\n0\n0\n2\n2\n3\n▆▂▁▇▂\n\n\neggplant\n30\n0.92\n0.59\n0.49\n0\n0\n1\n1\n1\n▆▁▁▁▇\n\n\neggplantD\n30\n0.92\n1.14\n1.05\n0\n0\n1\n2\n3\n▇▂▁▇▂\n\n\nmoussaka\n15\n0.96\n0.89\n0.31\n0\n1\n1\n1\n1\n▁▁▁▁▇\n\n\nmoussakaD\n14\n0.96\n1.83\n0.91\n0\n1\n2\n2\n3\n▂▃▁▇▃\n\n\norzo\n15\n0.96\n0.88\n0.32\n0\n1\n1\n1\n1\n▁▁▁▁▇\n\n\norzoD\n15\n0.96\n1.81\n0.91\n0\n1\n2\n2\n3\n▂▃▁▇▃\n\n\ngreeksal\n24\n0.94\n0.57\n0.50\n0\n0\n1\n1\n1\n▆▁▁▁▇\n\n\ngreeksalD\n24\n0.94\n1.08\n1.06\n0\n0\n1\n2\n3\n▇▂▁▆▂\n\n\nbread\n18\n0.95\n0.91\n0.29\n0\n1\n1\n1\n1\n▁▁▁▁▇\n\n\nbreadD\n18\n0.95\n1.75\n0.71\n0\n2\n2\n2\n3\n▁▂▁▇▁\n\n\ndessert\n42\n0.89\n0.42\n0.49\n0\n0\n0\n1\n1\n▇▁▁▁▆\n\n\ndessertD\n42\n0.89\n0.83\n1.06\n0\n0\n0\n2\n3\n▇▁▁▃▁\n\n\nchampagne\n25\n0.94\n0.87\n0.34\n0\n1\n1\n1\n1\n▁▁▁▁▇\n\n\nchampagneD\n25\n0.94\n1.37\n0.93\n0\n1\n1\n2\n3\n▂▇▁▂▃\n\n\nbeer\n30\n0.92\n0.78\n0.42\n0\n1\n1\n1\n1\n▂▁▁▁▇\n\n\nbeerD\n35\n0.91\n1.94\n1.23\n0\n1\n3\n3\n3\n▃▂▁▂▇\n\n\nredwine\n50\n0.87\n0.23\n0.42\n0\n0\n0\n0\n1\n▇▁▁▁▂\n\n\nredwineD\n52\n0.87\n0.45\n0.92\n0\n0\n0\n0\n3\n▇▁▁▁▁\n\n\nwhitewine\n31\n0.92\n0.73\n0.45\n0\n0\n1\n1\n1\n▃▁▁▁▇\n\n\nwhitewineD\n36\n0.91\n1.58\n1.21\n0\n0\n2\n3\n3\n▆▅▁▅▇\n\n\n\n\n\nCode\nnames(copdata)\n\n\n [1] \"id\"         \"sex\"        \"age\"        \"group\"      \"class\"     \n [6] \"diarrhoea\"  \"bloody\"     \"vomiting\"   \"abdo\"       \"nausea\"    \n[11] \"fever\"      \"headache\"   \"jointpain\"  \"starthour\"  \"meal\"      \n[16] \"feta\"       \"fetaD\"      \"sardines\"   \"sardinesD\"  \"eggplant\"  \n[21] \"eggplantD\"  \"moussaka\"   \"moussakaD\"  \"orzo\"       \"orzoD\"     \n[26] \"greeksal\"   \"greeksalD\"  \"bread\"      \"breadD\"     \"dessert\"   \n[31] \"dessertD\"   \"champagne\"  \"champagneD\" \"beer\"       \"beerD\"     \n[36] \"redwine\"    \"redwineD\"   \"whitewine\"  \"whitewineD\" \"dayonset\"  \n\n\nLet’s explore and manage some variables in detail.\n\nAge\nThrough visual exploration of the age histogram we see that there is at least one very high value, likely implausible. You can then create a cross-tabulation of variables age and group to have a better idea of how your data looks like.\n\n\nCode\n# Have a look at the histogram  \nhist(copdata$age)   \n\n\n\n\n\nCode\n# Create cross-tab with the group variable:  \njanitor::tabyl(dat = copdata, \n               var1 = age, \n               var2 = group)\n\n\n\n\n  \n\n\n\nNote that group is coded as 0 and 1, and these may be difficult to interpret when they mean something other than “no” and “yes”, respectively. From the codebook, you know that teachers are represented by 0, and students by 1. Let’s change this to make our lives easier:\n\n\nCode\n# Convert group to a factor and label 0 as teacher, 1 as student:\ncopdata &lt;- copdata %&gt;% \n  mutate(group = factor(group, \n                        labels = c(\"teacher\", \"student\")))\n\n\nNow, have a look at your cross-tab again:\n\n\nCode\njanitor::tabyl(dat = copdata, \n               var1 = age, \n               var2 = group) \n\n\n\n\n  \n\n\n\nWith this table, we can more easily identify ages that are likely to be typographic errors. Specifically:\n\nThere is one teacher aged 16 (likely digit reversal - should be 61)\nThere is one student aged 8 (likely missing a digit - should be 18)\nThere is one student aged 180 (likely has an extra digit - should be 18)\n\nAssuming you have contacted the school to make sure your suspicions about the actual ages are correct, we can now correct them, using case_when(). We create logical conditions to identify the incorrect ages, combining the values for age with the group they belong to:\n\n\nCode\n# Update incorrect ages to the correct values with case_when:  \ncopdata &lt;- copdata %&gt;%       \n  mutate(age =                         \n           case_when(                            \n             # Where respondent is 16 and a teacher, change age to 61:\n             age == 16 & group == \"teacher\" ~ 61,              \n             # where respondent is 8 or 180 and a student, change age to 18:\n             age == 8 & group == \"student\" ~ 18,              \n             age == 180 & group == \"student\" ~ 18,              \n             # Keep remaining values as is:              \n             .default = as.numeric(age)   \n             # if .default is not working, try:\n             # TRUE ~ age\n             )                    \n         ) \n\n\n\n\n\n\n\n\nCreate summary table for the dose response columns (those finishing with a capital “D”) to have a look at their distribution.\n\n\n\n\n\n\n\nNeed a little bit of help?\n\n\n\n\n\nUse gtsummary::tbl_summary().\n\n\n\n\n\nCode\n#| label: check_dose_cols\n#| tbl-cap: no caption\n\n# Create summary table for dose response columns: \ndrtable &lt;- copdata %&gt;%       \n  # Select all the columns with column names that end in upper case 'D':   \n  select(ends_with(\"D\", ignore.case = FALSE)) %&gt;%       \n  # Create the summary table, excluding missing values:   \n  gtsummary::tbl_summary(missing = \"no\") \n\n  # Print the summary table: \ndrtable\n\n\n\n\n\n\n  \n    \n    \n      Characteristic\n      N = 3971\n    \n  \n  \n    fetaD\n\n        0\n110 (29%)\n        1\n78 (20%)\n        2\n155 (41%)\n        3\n38 (10.0%)\n    sardinesD\n\n        0\n125 (33%)\n        1\n35 (9.2%)\n        2\n184 (48%)\n        3\n36 (9.5%)\n    eggplantD\n\n        0\n151 (41%)\n        1\n41 (11%)\n        2\n147 (40%)\n        3\n28 (7.6%)\n    moussakaD\n\n        0\n42 (11%)\n        1\n70 (18%)\n        2\n184 (48%)\n        3\n87 (23%)\n    orzoD\n\n        0\n44 (12%)\n        1\n70 (18%)\n        2\n183 (48%)\n        3\n85 (22%)\n    greeksalD\n\n        0\n162 (43%)\n        1\n52 (14%)\n        2\n126 (34%)\n        3\n33 (8.8%)\n    breadD\n\n        0\n35 (9.2%)\n        1\n51 (13%)\n        2\n267 (70%)\n        3\n26 (6.9%)\n    dessertD\n\n        0\n206 (58%)\n        1\n33 (9.3%)\n        2\n87 (25%)\n        3\n29 (8.2%)\n    champagneD\n\n        0\n49 (13%)\n        1\n208 (56%)\n        2\n45 (12%)\n        3\n70 (19%)\n    beerD\n\n        0\n81 (22%)\n        1\n41 (11%)\n        2\n57 (16%)\n        3\n183 (51%)\n    redwineD\n\n        0\n266 (77%)\n        1\n31 (9.0%)\n        2\n21 (6.1%)\n        3\n27 (7.8%)\n    whitewineD\n\n        0\n100 (28%)\n        1\n73 (20%)\n        2\n67 (19%)\n        3\n121 (34%)\n  \n  \n  \n    \n      1 n (%)"
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-    "section": "3.4. Modify variables format",
-    "text": "3.4. Modify variables format\nThere are some “column types” you would like to modify because either R needs some specific types of data when working with certain variables, or because it will be easier for you to visualise and interpret the data. For example, the variable “group” is encoded as 0 and 1, when you create a table later on, you don’t want to see 0’s and 1’s because that doesn’t mean anything. It is better to change this variable to a factor, with explicit labels, so they actually mean something when they come out in your table.\n\n\n\n\n\nYou can use mutate to modify the type of the following variables:\n\nTable 1: Variable types to modify\n\n\n\n\n\n\n\n\nVariable name\nOriginal\nDesired\nHint\n\n\n\n\nsex\ncharacter\nfactor\nmutate(), as.factor()\n\n\nclass\ninteger\nfactor\nmutate(), as.factor()\n\n\nAll the clinical symptom variables\ninteger\nlogical\nmutate(across()), as.logical()\n\n\nAll the food variables representing the amount of specific foods eaten (those finishing with a capital “D”)\ninteger\nfactor\nmutate(across()), as.factor()\n\n\ndayonset\ncharacter\ndate\nlubridate::dmy()\n\n\nstarthour and dayonset together\ninteger (starthour)\ndate (dayonset)\nPOSIXct, POSIXt\nlubridate::ymd_h() could have inside stringr::str_glue() with dayonset and starthour\n\n\n\n\nSex, group and class\nLet’s start transforming one-by-one the first two variables in the table: sex, and class.\n\n\nCode\ncopdata &lt;- copdata %&gt;% \n  dplyr::mutate(\n    sex = as.factor(sex),\n    class = as.factor(class))\n\n\n\n\nSymptoms and Food variables\nFor these variables, we are going to show you a couple of different ways to carry out the same variable type transformation in a set of variables, so you don’t need to do one variable at a time. We are showing you these ways so you see alternative ways to do the same thing.\n\nFor the variables that are clinical symptoms, we will list them one by one and show you the use of mutate(across( )).\n\n\n\nCode\ncopdata &lt;- copdata %&gt;% \n  dplyr::mutate(\n    # clinical symptoms\n    across(.cols = c(diarrhoea, bloody, vomiting,\n             abdo, nausea, fever,headache, jointpain), \n           .fns = ~ as.logical(.)\n           )\n    )\n\n\n\nFor the variables that are food doses, we will show you how to first create a vector of names, following by using mutate(across(all_of( ))) on this vector.\n\n\n\nCode\n# Create a vector with all the food variables representing the amount of specific foods items eaten (those finishing with a capital \"D\")\n# One way of doing it:\nfood_dose &lt;- copdata %&gt;% \n    dplyr::select(\n      ends_with(\"D\", ignore.case = FALSE)) %&gt;% \n    names()\n\n# Another way of doing it:\n# food_dose &lt;- c(\"fetaD\", \"sardinesD\", \"eggplantD\", \"moussakaD\", \n#                 \"orzoD\", \"greeksalD\", \"dessertD\", \"breadD\", \n#                 \"champagneD\", \"beerD\", \"redwineD\", \"whitewineD\")\n\n\ncopdata &lt;- copdata %&gt;% \n  dplyr::mutate(\n    # food dose variables\n    across(.cols = all_of(food_dose), \n           .fns = ~as.factor(.))) \n\n\n\n\n\n\n\n\nNote\n\n\n\n\n\nThe tilde (~) above is used to apply the transformation as.logical(.) to each selected column, which in our case is either all columns included in food_items and food_dose.\n\n\n\n\n\nDate and time variables\n\n\n\n\n\n\nLet’s stop and… think!\n\n\n\n\n\nWhy may you want to transform the variable starthour to date-time?\n\n\n\n\n\n\n\n\nYou can use lubridate::dmy() to mutate the dayonset variable into a date variable. Note that we are using the function dmy() because dates are formatted as day, then month (abbreviated character string), then year (i.e. “12nov2006”).\n\n\nCode\n# Have a look at how the data is stored\nhead(copdata$dayonset)\n\n\n[1] \"6oct2024\" \"\"         \"\"         \"\"         \"6oct2024\" \"7oct2024\"\n\n\nCode\nclass(copdata$dayonset)\n\n\n[1] \"character\"\n\n\nCode\n# Update copdata:\ncopdata &lt;- copdata %&gt;% \n  # Change column to date class:\n  dplyr::mutate(\n    dayonset = lubridate::dmy(dayonset))\n\n# Check class of updated column:\nclass(copdata$dayonset)\n\n\n[1] \"Date\"\n\n\nCode\n# Have a look at your data now:\nhead(copdata$dayonset)\n\n\n[1] \"2024-10-06\" NA           NA           NA           \"2024-10-06\"\n[6] \"2024-10-07\"\n\n\nHaving a variable that defines “time” in an outbreak investigation can be very useful when creating a case definition. An hour of the day, without a date associated with it doesn’t help you much, thus, you should merge together day and time of onset of symptoms into a single variable. Moreover, you will be using this combined variable later on to estimate an incubation period and create your epicurve. We can combine these two variables by using the lubridate::ymd_h() function.\nBefore we proceed, it would be wise to check if any respondents have a value for dayonset but not starthour, or vice versa. The lubridate date-time conversion functions do not have an explicit argument for dealing with missing values, but the truncated = … argument can help prevent spurious date-times being derived from a date-time combination where one value is missing.\nWe can check if we have any missing values by cross-tabulating starthour with dayonset:\n\n\nCode\n# Cross-tabulate dayonset with starthour:\njanitor::tabyl(dat = copdata, \n               var1 = starthour, \n               var2 = dayonset)\n\n\n\n\n  \n\n\n\nThis shows us that there are two respondents who had an onset date, but are missing onset time (starthour). Since starthour is represented by 1 - 2 digits, we can specify that we want lubridate to also parse date-time combinations that are truncated by up to two digits:\n\n\nCode\ncopdata &lt;- copdata %&gt;% \n  # Combine dayonset and starthour in a new date time variable:\n  mutate(onset_datetime = \n           lubridate::ymd_h(\n             str_glue(\"{dayonset}, {starthour}\"), \n                                           # Deal with missing starthour:\n                                           truncated = 2))\n\n\nWarning: There was 1 warning in `mutate()`.\nℹ In argument: `onset_datetime = lubridate::ymd_h(str_glue(\"{dayonset},\n  {starthour}\"), truncated = 2)`.\nCaused by warning:\n!  175 failed to parse.\n\n\nNote that we needed to use str_glue() to concatenate dayonset and starthour together before we could convert the variable to a date-time object. This is because the ymd_h() function expects a single character string, containing both the date and the time, as input.\nThe argument truncated = 2 will result in dates with missing starthour still being converted to date-time, with the missing time being set to 00:00 (midnight). Whether you want to deal with missing starthour in this way or prefer to code these date-times as NA will depend on how you want them to be represented in your analysis.\nNow we can check that everything in the new combined date-time variable has parsed correctly:\n\n\nCode\nhead(copdata$dayonset)\n\n\n[1] \"2024-10-06\" NA           NA           NA           \"2024-10-06\"\n[6] \"2024-10-07\"\n\n\nCode\nhead(copdata$starthour)\n\n\n[1]  9 NA NA NA 15 15\n\n\nCode\nhead(copdata$onset_datetime)\n\n\n[1] \"2024-10-06 09:00:00 UTC\" NA                       \n[3] NA                        NA                       \n[5] \"2024-10-06 15:00:00 UTC\" \"2024-10-07 15:00:00 UTC\""
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-    "text": "3.5. Export clean data\nSave the cleaned data set before proceeding with using your case definition to identify cases in your dataset. Use the .rds format, as it preserves column classes. This ensures you will have only minimal cleaning to do after importing the data into R at the next inject.\nSave your clean data as a new file “Copenhagen_clean1_YOURINITIALS.rds”, under the data folder of your Rproject.\n\n\n\n\n\n\nNeed a little bit of help?\n\n\n\n\n\nUse rio::export().\n\n\n\n\n\nCode\nrio::export(x = copdata, \n            file = here::here(\"data\", \"Copenhagen_clean1_2024.rds\"))"
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-    "title": "Stratified analysis",
-    "section": "",
-    "text": "At the end of the session, participants will be able to:\n\nConsider the effect of confounding and effect modification on the association between exposure and disease,\nPerform stratified analysis using the Mantel-Haenszel approach\n\nThe use of stratified analysis, is the first step to identify confounding factors and effect modifiers one by one (after, of course, thinking which variables could potentially be confounders or effect modifiers). As the final step, you will be using Regression Models to account for confounding and check for effect modification. We will see these with you in the Multivariable Module (MVA), next year."
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-    "title": "Stratified analysis",
-    "section": "3.2. Install packages and load libraries",
-    "text": "3.2. Install packages and load libraries\n\n\nCode\n# Load the required libraries into the current R session:\npacman::p_load(rio, \n               here, \n               tidyverse, \n               skimr,\n               plyr,\n               janitor,\n               lubridate,\n               gtsummary, \n               flextable,\n               officer,\n               epikit, \n               apyramid, \n               scales,\n               EpiStats)"
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-    "title": "Stratified analysis",
-    "section": "3.3. Import your data",
-    "text": "3.3. Import your data\n\n\nCode\n# Import the raw data set: \ncopdata &lt;- rio::import(here::here(\"data\", \"Copenhagen_clean2_2024.rds\"))"
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-    "title": "Inject 14: Stratified analysis",
-    "section": "3. Risk Ratio",
-    "text": "3. Risk Ratio\n\na) Veal as exposure of interest, stratified by having eaten pasta\n\n\nCode\nstratall &lt;- copdata %&gt;% \n  # Mutate across to convert cases to numeric:\n  mutate(across(.cols = case, \n                .fns = ~ as.numeric(.)))\n\n# Pass data to the csinter function:\npastastrata &lt;- csinter(x = stratall, \n                       cases = \"case\", \n                       exposure = \"veal\", \n                       by = \"pasta\")\n\npastastrata\n\n\n$df1\n  CSInter case - veal by(pasta) Total Cases Risk %          P.est. Stats\n1                     pasta = 1   338  &lt;NA&gt;     NA Risk difference  0.10\n2                       Exposed   330   198  60.00      Risk Ratio  1.20\n3                     Unexposed     8     4  50.00 Attrib.risk.exp  0.17\n4                                  NA  &lt;NA&gt;     NA Attrib.risk.pop  0.16\n5                     pasta = 0    36  &lt;NA&gt;     NA Risk difference  0.02\n6                       Exposed     8     3  37.50      Risk Ratio  1.05\n7                     Unexposed    28    10  35.71 Attrib.risk.exp  0.05\n8                                  NA  &lt;NA&gt;     NA Attrib.risk.pop  0.01\n9           Missing / Missing %     3  0.8%     NA            &lt;NA&gt;    NA\n  95%CI-ll 95%CI-ul\n1    -0.25     0.45\n2     0.60     2.41\n3    -0.68     0.59\n4       NA       NA\n5    -0.36     0.40\n6     0.38     2.92\n7    -1.65     0.66\n8       NA       NA\n9       NA       NA\n\n$df2\n                  Point Estimate Chi2 p.value  Stats 95%CI-ll 95%CI-ul\n1      Woolf test of homogeneity 0.04   0.833     NA       NA       NA\n2              Crude RR for veal   NA      NA   1.53     1.01     2.32\n3  MH RR veal adjusted for pasta   NA      NA   1.15     0.64     2.04\n4 Adjusted/crude relative change   NA      NA -25.08       NA       NA\n\n\nLet’s check if pasta is associated with veal (if we are thinking veal may be a confounder, we need to see if there is an association between the potential confounder (veal) and the exposure (pasta)):\n\n\nCode\n# Perform Wilcoxon rank sum test on pasta and veal:\nwilcox.test(pasta ~ veal, \n            data = copdata)\n\n\n\n    Wilcoxon rank sum test with continuity correction\n\ndata:  pasta by veal\nW = 1496, p-value &lt; 2.2e-16\nalternative hypothesis: true location shift is not equal to 0\n\n\n\n\nc) Champagne as exposure of interest, stratified by having eaten pasta\n\n\nCode\n# Pass data to the csinter function:\nchampstrata &lt;- csinter(x = stratall, \n                       cases = \"case\", \n                       exposure = \"champagne\", \n                       by = \"pasta\")"
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-    "text": "At the end of the session, participants will be able to:\n\nMerge two datasets using a common column.\nDiscuss which descriptive statistics would you use to describe the cases\nDescribe the cases by person and place (this is an outbreak in a high school, so we won’t explore “place” this time!)\nGenerate preliminary hypotheses bases on the descriptions."
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-    "text": "Code\n# Load the required libraries into the current R session:\npacman::p_load(rio, \n               here, \n               tidyverse, \n               skimr,\n               plyr,\n               janitor,\n               lubridate,\n               gtsummary, \n               flextable,\n               officer,\n               epikit, \n               apyramid, \n               scales)"
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-    "section": "3.2 Import your data",
-    "text": "3.2 Import your data\n\n\nCode\n# Import the clean data set:\ncopdata &lt;- rio::import(here::here(\"data\", \"Copenhagen_clean2_2024.rds\")) \nlab &lt;- rio::import(here::here(\"data\", \"lab_pos.xlsx\"))"
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-    "text": "3. Time\n\na) Incubation period histogram\n\n\nCode\n#| label: inc_time\n\n# Create a dataset with only cases\ncases &lt;- copdata %&gt;% \n  filter(case == TRUE)\n\nincplot &lt;- cases %&gt;% \n  # Create an empty ggplot frame:\n  ggplot() +\n  # Add a histogram of incubation:\n  geom_histogram(\n    mapping = aes(x = incubation), \n    # Set bin widths to 6 hours:\n    binwidth = 6) +\n  # Adapt scale to better fit data\n  scale_x_continuous(breaks = seq(0, 48, 6)) + \n  # Label x and y axes:\n  labs(x = \"Incubation period in 6-hour bins\",\n       y = \"Number of cases\")\n\n# Print plot:\nincplot\n\n\n\n\n\n\n\nb) Epicurve for date and time of onset\n\n\nCode\n# Create a vector with sequences every 6h from the first to the last case\nbreaks_6h &lt;- seq(from = min(cases$onset_datetime, na.rm = TRUE),\n                 to = max(cases$onset_datetime, na.rm = TRUE),\n                 by = \"6 hours\")\n\n# Fetch cases data:\nepicurve_datetime &lt;- cases %&gt;%  \n  # Add factor onset_datetime to ggplot aesthetic:\n  ggplot(\n    mapping = aes(x = onset_datetime)) + \n  # Add geom_histogram:\n  geom_histogram(\n    # Apply the vector of requences created above\n    breaks = breaks_6h) +\n  # Adapt scale to data and adjust axis label angle:\n  scale_x_datetime(\n    date_breaks = \"6 hours\",\n    labels = label_date_short()) +\n  # Update x and y axis labels:\n  labs(x = \"Date and time of onset symptoms\", \n       y = \"Number of cases\") +\n  # Remove unnecessary grid lines:\n  theme_bw()\n\n# Print epicurve:\nepicurve_datetime\n\n\n\n\n\n\n\nCode\nepicurve_strata &lt;- cases %&gt;% \n  # Add factor onset_day to ggplot aesthetic:\n  ggplot(\n    mapping = aes(x = onset_datetime, fill = group)) + \n  # Add nicer fill colours:\n  scale_fill_manual(values = c(\"darkred\", \"lightblue\")) +\n    # Add geom_histogram:\n  geom_histogram(\n    # Apply the vector of requences created above\n    breaks = breaks_6h) +\n  # Adjust x axis scales to a suitable unit:\n  scale_x_datetime(\n    date_breaks = \"6 hours\", \n    labels = label_date_short()) +\n  # Update x and y axis labels:\n  labs(x = \"Date and time of onset\", \n       y = \"Number of cases\", \n       fill = \"Group\", \n       title = \"Epicurve of the outbreak, stratified by sex\",\n       subtitle = str_glue(\"Copenhagen, November 2006, N = {sum(copdata$case)}\")) +\n  # Stratify by sex:\n  facet_wrap(facets = \"sex\",\n             ncol = 2) +\n  # Add theme:\n  theme_bw()\n\n# Print epicurve:\nepicurve_strata"
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-    "section": "4. Person",
-    "text": "4. Person\n\na) Cross-tabulation of cases with group\n\n\nCode\ncopdata %&gt;% \n  janitor::tabyl(case, group) %&gt;% \n  adorn_totals() %&gt;% \n  adorn_percentages() %&gt;% \n  adorn_pct_formatting() \n\n\n\n\n  \n\n\n\n\n\nb) Cross-tabulation of cases with sex\n\n\nCode\ncopdata %&gt;% \n  janitor::tabyl(case, sex) %&gt;% \n  adorn_totals() %&gt;% \n  adorn_percentages() %&gt;% \n  adorn_pct_formatting() \n\n\n\n\n  \n\n\n\n\n\nc) Extra - Age-sex pyramid of cases\n\n\nCode\ncopdata &lt;- copdata %&gt;% \n  # Create age categories:\n  mutate(age_cat = epikit::age_categories(\n    # Name of age column:\n    x = age, \n    # Define the age categories:\n    breakers = c(0, 10, 16, 18, 20, 50, 70)\n    )\n  )\n\n\n# Check age categories:\njanitor::tabyl(copdata, age_cat)\n\n\n\n\n  \n\n\n\nCode\n# Pipe copdata:\nagesex &lt;- copdata %&gt;% \n  # Filter for cases only:\n  filter(case == TRUE) %&gt;% \n  # Create age sex pyramid:\n  apyramid::age_pyramid(\n  # Specify column containing age categories:\n    age_group = \"age_cat\",\n    # Specify column containing sex:\n    split_by = \"sex\", \n    # Don't show midpoint on the graph:\n    show_midpoint = FALSE\n    )\n\n# Print plot:\nagesex\n\n\n\n\n\n(Hint: change show_midpoint = FALSE to TRUE to see skewedness in the data patterns more easily)."
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-    "section": "5. Symptoms",
-    "text": "5. Symptoms\n\nSummary table of symptoms, stratified by case definition\n\n\n\nCode\n# Create summary table:\ntabsymptoms &lt;- copdata %&gt;% \n    # Select person characteristics to summarise:\n  select(case, diarrhoea, bloody, vomiting,\n             abdo, nausea, fever,headache, jointpain) %&gt;% \n  # transform clinical symptoms to factors, so NA can be accounted properly in the table\n  dplyr::mutate(\n    across(.cols = c(diarrhoea, bloody, vomiting,\n             abdo, nausea, fever,headache, jointpain), \n           .fns = ~as.factor(.))) %&gt;%\n  # Make NA a explicit level of factor variables\n  dplyr::mutate(\n    across(.cols = c(diarrhoea, bloody, vomiting,\n           abdo, nausea, fever,headache, jointpain),\n           .fns = ~forcats::fct_na_value_to_level(.))) %&gt;% \n    \n  # Create the summary table:\n  gtsummary::tbl_summary(\n    # Stratify by case:\n    by = case, \n    # Calculate row percentages:\n    percent = \"column\",\n    # Create nice labels:\n    label  = list(\n      diarrhoea   ~ \"Diarrhoea\",                           \n      bloody      ~ \"Dysentary\",\n      vomiting    ~ \"Vomiting\",\n      abdo        ~ \"Abdominal pain\",\n      nausea      ~ \"Nausea\", \n      fever       ~ \"Fever\", \n      headache    ~ \"Headache\", \n      jointpain   ~ \"Joint pain\")\n    \n  ) %&gt;% \n  \n  # Add totals:\n  add_overall() %&gt;% \n  # Make variable names bold and italics:\n  bold_labels() %&gt;% \n  italicize_labels() %&gt;% \n  # Modify header:\n  modify_header(\n    label = \"**Characteristic**\",\n    stat_0 = \"**Overall**\\n **N** = {N}\",\n    stat_1 = \"**Non-case**\\n **N** = {n}\",\n    stat_2 = \"**Case**\\n **N** = {n}\", \n    )\n\n# Print the table:\ntabsymptoms\n\n\n\n\n\n\n  \n    \n    \n      Characteristic\n      Overall\nN = 3771\n      Non-case\nN = 1611\n      Case\nN = 2161\n    \n  \n  \n    Diarrhoea\n\n\n\n        FALSE\n46 (18%)\n40 (100%)\n6 (2.8%)\n        TRUE\n206 (82%)\n0 (0%)\n206 (97%)\n    Dysentary\n\n\n\n        FALSE\n189 (97%)\n42 (100%)\n147 (97%)\n        TRUE\n5 (2.6%)\n0 (0%)\n5 (3.3%)\n    Vomiting\n\n\n\n        FALSE\n149 (69%)\n42 (100%)\n107 (62%)\n        TRUE\n66 (31%)\n0 (0%)\n66 (38%)\n    Abdominal pain\n\n\n\n        FALSE\n35 (14%)\n6 (12%)\n29 (15%)\n        TRUE\n207 (86%)\n44 (88%)\n163 (85%)\n    Nausea\n\n\n\n        FALSE\n55 (25%)\n12 (26%)\n43 (24%)\n        TRUE\n169 (75%)\n34 (74%)\n135 (76%)\n    Fever\n\n\n\n        FALSE\n127 (74%)\n32 (80%)\n95 (73%)\n        TRUE\n44 (26%)\n8 (20%)\n36 (27%)\n    Headache\n\n\n\n        FALSE\n83 (38%)\n11 (25%)\n72 (41%)\n        TRUE\n137 (62%)\n33 (75%)\n104 (59%)\n    Joint pain\n\n\n\n        FALSE\n159 (85%)\n32 (84%)\n127 (85%)\n        TRUE\n29 (15%)\n6 (16%)\n23 (15%)\n  \n  \n  \n    \n      1 n (%)\n    \n  \n\n\n\n\n\nBar plot of symptoms stratified by case definition\n\n\n\nCode\n# Create list of symptom variables:\nsymptoms &lt;- c(\"diarrhoea\", \n              \"bloody\", \n              \"vomiting\", \n              \"abdo\", \n              \"nausea\", \n              \"fever\", \n              \"headache\", \n              \"jointpain\")\n\n# Create nice labels for case definition:\ncaselabs &lt;- ggplot2::as_labeller(c(`FALSE` = \"Non-case\", \n                                   `TRUE` = \"Case\"))\n# Select variables and cases:\nsymptom_bar &lt;- copdata %&gt;% \n  # Select symptom columns:\n  select(case, c(all_of(symptoms))) %&gt;%\n  # Drop NAs:\n  drop_na() %&gt;% \n  # Reshape (pivot longer):\n  pivot_longer(!case, \n               names_to = \"Symptoms\", \n               values_drop_na = TRUE) %&gt;% \n  # Keep only TRUE values:\n  filter(value == TRUE) %&gt;% \n \n   # Group by symptoms and case:\n  group_by(Symptoms, case) %&gt;% \n  # Count for each symptom by case:\n  dplyr::summarise(count = n()) %&gt;% \n  # Create plot:\n  ggplot(\n    mapping = aes(\n    # Order symptom bars so most common ones are ontop:\n    x = reorder(Symptoms, desc(count), decreasing = TRUE), \n    y = count)) +\n  # Display bars as proportions\n  geom_bar(stat = \"identity\") +\n  # Update x axis label:\n  xlab(\"Symptoms\") +\n  # Update y axis label:\n  ylab(\"Proportion of respondents\") +\n  # Flip plot on its side so symptom labels are clear:\n  coord_flip() +\n  # Facet the plot by (labelled) case:\n  facet_wrap(facets = \"case\",\n             labeller = caselabs,\n             ncol = 2)\n\n\n`summarise()` has grouped output by 'Symptoms'. You can override using the\n`.groups` argument.\n\n\nCode\n# Print plot:\nsymptom_bar"
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-    "section": "3.7 Attack proportions",
-    "text": "3.7 Attack proportions\n\na) Calculate the overall attack proportions (percentage of cases among the total observed individuals). You could use tabyl().\n\n\nCode\n# Create table of case status:\ntotal_ap &lt;- tabyl(copdata, case) %&gt;% \n # Add row totals:\n  adorn_totals(where = \"row\") %&gt;% \n  # Add percentages with 1 digit after the decimal point:\n  adorn_pct_formatting(digits = 1) %&gt;% \n  # Filter to rows where case is TRUE:\n  filter(case == TRUE) %&gt;% \n  # Select the column percent:\n  select(percent) %&gt;% \n  # Extract (pull) the value from this cell:\n  pull()\n\n# Print result:\ntotal_ap\n\n\n[1] \"57.3%\"\n\n\n\n\n\n\n\n\nLet’s stop and… think!\n\n\n\n\n\nWhat can you infer from the overall attack proportion about this outbreak and possible vehicles/exposures?\n\n\n\n\n\n\n\n\n\nIf you’ve thought about the above questions, have a look here\n\n\n\n\n\nThe overall attack proportion is 57.3%. This means that more than half of the people who ate a meal were cases!\n\n\n\n\n\nb) Calculate attack proportions for group, class and sex by case status. You could use tabyl() (as you did in 3.4) or gtsummary::tbl_summary.\n\n\nCode\n# Table to calculate attack proportions:\nattack_prop &lt;- copdata %&gt;% \n  # Select columns:\n  select (case, class, group, sex) %&gt;% \n  \n  # Create table:\n  tbl_summary(\n    # Stratified by case\n    by = case,\n    # with row percentages\n    percent = \"row\") %&gt;%\n  \n  # Add totals:\n  add_overall() %&gt;%\n  \n  # Make variable names bold and italics:\n  bold_labels() %&gt;% \n  italicize_labels() %&gt;% \n  \n  # Modify header:\n  modify_header(\n    label = \"**Characteristic**\",\n    stat_0 = \"**Overall** **N** = {N}\",\n    stat_1 = \"**Non-case** **N** = {n}\",\n    stat_2 = \"**Case** **N** = {n}\"\n  )\n\n\n# Print table:\nattack_prop\n\n\n\n\n\n\n  \n    \n    \n      Characteristic\n      Overall N = 3771\n      Non-case N = 1611\n      Case N = 2161\n    \n  \n  \n    class\n\n\n\n        1\n131 (100%)\n63 (48%)\n68 (52%)\n        2\n101 (100%)\n44 (44%)\n57 (56%)\n        3\n111 (100%)\n38 (34%)\n73 (66%)\n        Unknown\n34\n16\n18\n    group\n\n\n\n        teacher\n15 (100%)\n9 (60%)\n6 (40%)\n        student\n362 (100%)\n152 (42%)\n210 (58%)\n    sex\n\n\n\n        female\n213 (100%)\n96 (45%)\n117 (55%)\n        male\n164 (100%)\n65 (40%)\n99 (60%)\n  \n  \n  \n    \n      1 n (%)"
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-    "text": "At the end of the session, participants will be able to:\n\nApply case definition criteria to a dataset using logical arguments in R"
+    "text": "At the end of the session, participants will be able to:\n\nPerform hypothesis tests.\nEstimate risk ratios (also called relative risk) for categorical data.\nInterpret the univariable results.\n(Optional) Investigate dose-response relationships in categorical data."
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-    "text": "# Load the required libraries into the current R session:\npacman::p_load(rio, \n               here, \n               tidyverse, \n               skimr,\n               plyr,\n               janitor,\n               lubridate,\n               gtsummary, \n               flextable,\n               officer,\n               epikit, \n               apyramid, \n               scales)"
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-    "section": "3.2 Import your data",
-    "text": "3.2 Import your data\n\n\nCode\n# Import the clean data set:\ncopdata &lt;- rio::import(here::here(\"data\", \"Copenhagen_clean1_2024.rds\"))"
+    "text": "At the end of the session, participants will be able to:\n\nPerform hypothesis tests.\nEstimate risk ratios (also called relative risk) for categorical data.\nInterpret the univariable results.\n(Optional) Investigate dose-response relationships in categorical data."
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-    "title": "Case Definition - Inj10",
-    "section": "3. Identify cases",
-    "text": "3. Identify cases\n\na) Ate a meal at the school dinner\nKeep in your dataset only those who ate a meal.\n\ncopdata &lt;- copdata %&gt;% \n  filter(meal == TRUE)\n\n\n\nb) Fell ill after the start of the meal\nDefine “fell ill” as any person having had diarrhoea with OR without blood, OR vomiting. Note that we the concept of having eaten a meal is already included as per one of the steps above:\n\ncopdata &lt;- copdata %&gt;% \n  mutate(gastrosymptoms = case_when(\n    # Those had diarrhoea...\n    diarrhoea == TRUE |\n      #or bloody diarrhoea...\n    bloody == TRUE |\n      # or vomiting, are marked as TRUE (fell ill after the meal)\n    vomiting == TRUE ~ TRUE,\n    # The rest are FALSE. This includes those who ate a meal but had no symptoms (did not fell ill after the meal)\n    .default = FALSE)\n    )\n\n\n\nc) Fell ill within the time period of interest\nCreate a new meal_datetime variable as per 11 Nov 2006, at 18:00h:\n\n# Start with copdata:\ncopdata &lt;- copdata %&gt;% \n  # Create new column for meal date and time:\n  mutate(meal_datetime = lubridate::ymd_hm(\"2006-11-11 18:00\"))\n\nCalculate incubation time and its median:\n\ncopdata &lt;- copdata %&gt;% \n  mutate(incubation = onset_datetime - meal_datetime,\n         incubation = as.numeric (incubation))\n\n\nmedian(as.numeric(copdata$incubation), na.rm = TRUE)\n\n[1] 15\n\n\nWe see that the median incubation time is 15 hours. This is useful information, as incubation periods tend to be relatively pathogen-specific. We can now refine the case definition and limit the maximum incubation period from the meal time to 48 hours (2 days) after the meal, as the data points to a fast-acting bacterial toxin or a virus.\n\ncopdata &lt;- copdata %&gt;% \n  mutate(case = case_when(\n    # Those who had symptoms &lt;48h from the meal are cases (TRUE)\n    gastrosymptoms == TRUE & \n      onset_datetime &gt;= meal_datetime &\n      onset_datetime &lt;= (meal_datetime + days(2)) ~ TRUE,\n    # Those who had symptoms &gt;48h from the meal are non-cases (FALSE)\n    gastrosymptoms == TRUE & \n      onset_datetime &gt; (meal_datetime + days(2)) ~ FALSE,\n    # The rest are considered non-cases. Including, those who had no symptoms at all, who have missing data on the onset_datetime variable, or who had symptoms before eating the meal \n    .default = FALSE)\n  )\n\nNote that we may be incurring in misclassification bias with the code above. The last section indicates that if a person had clinical symptoms before eating the meal, they are considered as non-cases. However, it could be that a person had symptoms before the meal, and yet, still got infected by the pathogen when eating their meal (bad luck, we know…).\nMoreover, if you remember from inject 9, there were a couple of people with an dayonset, but no starthour. The code we used (lubridate::ymd_h with argument truncated = 2) results in dates with missing starthour being converted to date-time, with the missing time being set to 00:00 (midnight). This means that these two people don’t fulfill the case definition criteria because we marked their symptoms started early in the morning of Nov 11 (at 00:00), before the meal time (18:00), and thus, they did not “fell ill within the time period of interest”.\nThe two situations above are a reminder that you need to be both careful and aware of the implications of your data analysis decisions.\n\n# Tabulate cases:\njanitor::tabyl(dat = copdata, case)\n\n  case   n   percent\n FALSE 161 0.4270557\n  TRUE 216 0.5729443\n\n\nLet’s have a look at how many people ate a meal, had symptoms, and were considered as cases after applying our case definition:\n\ncopdata %&gt;% \n  summarise(atemeal = sum(meal == TRUE),\n            hadsympt = sum(gastrosymptoms == TRUE),\n            nb_cases = sum(case == TRUE)\n            )\n\n  atemeal hadsympt nb_cases\n1     377      216      216"
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+    "section": "2. Story/plot description",
+    "text": "2. Story/plot description\nYou have just estimated risk ratios (RR) of individual food items manually to better understand the concept. RR give you an idea of which food items could be the culprit(s) of the outbreak.\nNow, you will practice performing hypothesis testing and calculating RR in R to investigate the associations of suspicious food items with the disease.\nNote that, although this is not an exhaustive list of tests, the following can be useful for this exercise (relevant for comparisons between two groups):\n\nFor continuous variables:\n\nshapiro.test() (for checking if normally distributed)\nt.test() (for normal distributions)\nwilcox.test() (for non-parametric testing. Used to determine if two numeric samples are from the same distribution, when their populations are not normally distributed or have unequal variance.)\n\nFor categorical variables:\n\nchisq.test() (if all comparisons include at least 5 cases)\nfisher.test() (if there are &lt; 5 cases for any comparison)"
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     "title": "Univariable analysis",
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-    "text": "At the end of the session, participants will be able to:\n\nPerform hypothesis tests.\nEstimate risk ratios (also called relative risk) for categorical data.\nInterpret the univariable results.\n(Optional) Investigate dose-response relationships in categorical data.2. Story/plot description"
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     "title": "Univariable analysis",
     "section": "3.1. Install packages and load libraries",
-    "text": "3.1. Install packages and load libraries\n\n\nCode\n# Load the required libraries into the current R session:\npacman::p_load(rio, \n               here, \n               tidyverse, \n               skimr,\n               plyr,\n               janitor,\n               lubridate,\n               gtsummary, \n               flextable,\n               officer,\n               epikit, \n               apyramid, \n               scales,\n               EpiStats,\n               broom)"
+    "text": "3.1. Install packages and load libraries\n\n\nShow the code\n# Load the required libraries into the current R session:\npacman::p_load(rio, \n               here, \n               tidyverse, \n               skimr,\n               plyr,\n               janitor,\n               lubridate,\n               gtsummary, \n               flextable,\n               officer,\n               epikit, \n               apyramid, \n               scales,\n               EpiStats,\n               broom)"
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     "section": "3.2. Import your data",
-    "text": "3.2. Import your data\n\n\nCode\n# Import the raw data set:\ncopdata &lt;- rio::import(here::here(\"data\", \"Copenhagen_clean2_2024.rds\"))"
+    "text": "3.2. Import your data\n\n\nShow the code\n# Import the raw data set:\ncopdata &lt;- rio::import(here::here(\"data\", \"Spetses_clean2_2024.rds\"), trust = T)"
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     "title": "Univariable analysis",
     "section": "3.3. Hypothesis tests for other variables",
-    "text": "3.3. Hypothesis tests for other variables\nCheck if the following variables are associated with being a case: age, sex, class and group.\n\na) age\nWith the Shapiro-Wilk test we check if the variables are following the normal distribution. The null hypothesis is that the data follow a normal distribution, therefore, rejecting the null hypothesis means that the data do not follow the normal distribution. A p-value below the cutoff for rejecting the null hypothesis, e.g., a p-value&lt;0.05 means that we reject the null hypothesis that the data follow the normal distribution. For age, the p-value is &lt;0.05, therefore we reject the null hypothesis that the data are normally distributed. As we see in the graph most frequently reported age is &lt;20 years.\n\n\nCode\n# Check if age overall follows a normal distribution:\nshapiro.test(copdata$age)\n\n\n\n    Shapiro-Wilk normality test\n\ndata:  copdata$age\nW = 0.31302, p-value &lt; 2.2e-16\n\n\nCode\n# Can simply have a look at\nhist(copdata$age)\n\n\n\n\n\nCode\n# Looking only at the students:\nstudents &lt;- copdata %&gt;% \n  filter(group == \"student\")\nhist(students$age)\n\n\n\n\n\nAge overall (nor within the students’ group) is not normally distributed.\nWe compare the age for cases and non-cases using the Wilcoxon test that is used when the data are not normally distributed. The null hypothesis is that there is no difference in the age between the two groups compared. Given that p-value&gt;0.05 we do not reject the null hypothesis.\n\n\nCode\n# Perform Wilcoxon rank sum test on age and sex:\nwilcox.test(age ~ case, \n            data = copdata)\n\n\n\n    Wilcoxon rank sum test with continuity correction\n\ndata:  age by case\nW = 15934, p-value = 0.1512\nalternative hypothesis: true location shift is not equal to 0\n\n\n\n\nb) sex\n\n\nCode\ncopdata %&gt;% \n  select(sex, case) %&gt;% \n  tbl_summary(by = case) %&gt;% \n  add_p()\n\n\n\n\n\n\n  \n    \n    \n      Characteristic\n      FALSE, N = 1611\n      TRUE, N = 2161\n      p-value2\n    \n  \n  \n    sex\n\n\n0.3\n        female\n96 (60%)\n117 (54%)\n\n        male\n65 (40%)\n99 (46%)\n\n  \n  \n  \n    \n      1 n (%)\n    \n    \n      2 Pearson’s Chi-squared test\n    \n  \n\n\n\n\n\n\n\n\n\n\nLet’s stop and… think!\n\n\n\n\n\nWhat do these results tell you - is there an association between sex and being a case?\nDo these results differ from what you expected when looking at the descriptive figures (case proportions stratified by sex, or the age sex pyramid)? If so, why do you think this is?\n\n\n\n\n\nc) class\n\n\nCode\ncopdata %&gt;% \n  select(class, case) %&gt;% \n  tbl_summary(by = case) %&gt;% \n  add_p()\n\n\n\n\n\n\n  \n    \n    \n      Characteristic\n      FALSE, N = 1611\n      TRUE, N = 2161\n      p-value2\n    \n  \n  \n    class\n\n\n0.090\n        1\n63 (43%)\n68 (34%)\n\n        2\n44 (30%)\n57 (29%)\n\n        3\n38 (26%)\n73 (37%)\n\n        Unknown\n16\n18\n\n  \n  \n  \n    \n      1 n (%)\n    \n    \n      2 Pearson’s Chi-squared test\n    \n  \n\n\n\n\nIn this case you have a 2x3 contingency table of class against disease status, we can use a chi-square here. P-value of the association between class and age is p-val = 0.09, suggesting that there is an association at the p-val = 0.20 level among at least one of the 3 different classes. Fellows may want to investigate this further, but in the remaining of the study this is not explored further.\nA hypothesis could be that one of the classes sat together and, for whatever reason, they ate more of the contaminated food item at those tables. This could be further studied if we had the spatial distribution of where the people sat at the dinner. Another hypothesis is that one of the classes differ from other classes in some characteristics that made them more susceptible or more exposed to the infected food item.\n\n\nd) group\n\n\nCode\ncopdata %&gt;% \n  select(group, case) %&gt;% \n  tbl_summary(by = case) %&gt;% \n  add_p()\n\n\n\n\n\n\n  \n    \n    \n      Characteristic\n      FALSE, N = 1611\n      TRUE, N = 2161\n      p-value2\n    \n  \n  \n    group\n\n\n0.2\n        teacher\n9 (5.6%)\n6 (2.8%)\n\n        student\n152 (94%)\n210 (97%)\n\n  \n  \n  \n    \n      1 n (%)\n    \n    \n      2 Pearson’s Chi-squared test\n    \n  \n\n\n\n\nP-value of the association between group and age is p-val = 0.2, suggesting that there may be an association at the p-val = 0.20 level. Explanations are like above, with variable class.\n\n\nLet’s do all together\n\n\nCode\ncopdata %&gt;% \n  select(sex, class, group, case) %&gt;% \n  tbl_summary(by = case) %&gt;% \n  add_p()\n\n\n\n\n\n\n  \n    \n    \n      Characteristic\n      FALSE, N = 1611\n      TRUE, N = 2161\n      p-value2\n    \n  \n  \n    sex\n\n\n0.3\n        female\n96 (60%)\n117 (54%)\n\n        male\n65 (40%)\n99 (46%)\n\n    class\n\n\n0.090\n        1\n63 (43%)\n68 (34%)\n\n        2\n44 (30%)\n57 (29%)\n\n        3\n38 (26%)\n73 (37%)\n\n        Unknown\n16\n18\n\n    group\n\n\n0.2\n        teacher\n9 (5.6%)\n6 (2.8%)\n\n        student\n152 (94%)\n210 (97%)\n\n  \n  \n  \n    \n      1 n (%)\n    \n    \n      2 Pearson’s Chi-squared test"
+    "text": "3.3. Hypothesis tests for other variables\nCheck if the following variables are associated with being a case: age, sex, class and group.\n\na) age\nWith the Shapiro-Wilk test we check if the variables are following the normal distribution. The null hypothesis is that the data follow a normal distribution, therefore, rejecting the null hypothesis means that the data do not follow the normal distribution. A p-value below the cutoff for rejecting the null hypothesis, e.g., a p-value&lt;0.05 means that we reject the null hypothesis that the data follow the normal distribution. For age, the p-value is &lt;0.05, therefore we reject the null hypothesis that the data are normally distributed. As we see in the graph most frequently reported age is &lt;20 years.\n\n\nShow the code\n# Check if age overall follows a normal distribution:\nshapiro.test(copdata$age)\n\n\n\n    Shapiro-Wilk normality test\n\ndata:  copdata$age\nW = 0.31302, p-value &lt; 2.2e-16\n\n\nShow the code\n# Can simply have a look at\nhist(copdata$age)\n\n\n\n\n\n\n\n\n\nShow the code\n# Looking only at the students:\nstudents &lt;- copdata %&gt;% \n  filter(group == \"student\")\nhist(students$age)\n\n\n\n\n\n\n\n\n\nAge overall (nor within the students’ group) is not normally distributed.\nWe compare the age for cases and non-cases using the Wilcoxon test that is used when the data are not normally distributed. The null hypothesis is that there is no difference in the age between the two groups compared. Given that p-value&gt;0.05 we do not reject the null hypothesis.\n\n\nShow the code\n# Perform Wilcoxon rank sum test on age and sex:\nwilcox.test(age ~ case, \n            data = copdata)\n\n\n\n    Wilcoxon rank sum test with continuity correction\n\ndata:  age by case\nW = 15934, p-value = 0.1512\nalternative hypothesis: true location shift is not equal to 0\n\n\n\n\nb) sex\n\n\nShow the code\ncopdata %&gt;% \n  select(sex, case) %&gt;% \n  tbl_summary(by = case) %&gt;% \n  add_p()\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nCharacteristic\n0\nN = 1611\n1\nN = 2161\np-value2\n\n\n\n\nsex\n\n\n\n\n0.3\n\n\n    female\n96 (60%)\n117 (54%)\n\n\n\n\n    male\n65 (40%)\n99 (46%)\n\n\n\n\n\n1 n (%)\n\n\n2 Pearson’s Chi-squared test\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLet’s stop and… think!\n\n\n\n\n\nWhat do these results tell you - is there an association between sex and being a case?\nDo these results differ from what you expected when looking at the descriptive figures (case proportions stratified by sex, or the age sex pyramid)? If so, why do you think this is?\n\n\n\n\n\nc) class\n\n\nShow the code\ncopdata %&gt;% \n  select(class, case) %&gt;% \n  tbl_summary(by = case) %&gt;% \n  add_p()\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nCharacteristic\n0\nN = 1611\n1\nN = 2161\np-value2\n\n\n\n\nclass\n\n\n\n\n0.090\n\n\n    1\n63 (43%)\n68 (34%)\n\n\n\n\n    2\n44 (30%)\n57 (29%)\n\n\n\n\n    3\n38 (26%)\n73 (37%)\n\n\n\n\n    Unknown\n16\n18\n\n\n\n\n\n1 n (%)\n\n\n2 Pearson’s Chi-squared test\n\n\n\n\n\n\n\n\nIn this case you have a 2x3 contingency table of class against disease status, we can use a chi-square here. P-value of the association between class and age is p-val = 0.09, suggesting that there is an association at the p-val = 0.20 level among at least one of the 3 different classes. Fellows may want to investigate this further, but in the remaining of the study this is not explored further.\nA hypothesis could be that one of the classes sat together and, for whatever reason, they ate more of the contaminated food item at those tables. This could be further studied if we had the spatial distribution of where the people sat at the dinner. Another hypothesis is that one of the classes differ from other classes in some characteristics that made them more susceptible or more exposed to the infected food item.\n\n\nd) group\n\n\nShow the code\ncopdata %&gt;% \n  select(group, case) %&gt;% \n  tbl_summary(by = case) %&gt;% \n  add_p()\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nCharacteristic\n0\nN = 1611\n1\nN = 2161\np-value2\n\n\n\n\ngroup\n\n\n\n\n0.2\n\n\n    teacher\n9 (5.6%)\n6 (2.8%)\n\n\n\n\n    student\n152 (94%)\n210 (97%)\n\n\n\n\n\n1 n (%)\n\n\n2 Pearson’s Chi-squared test\n\n\n\n\n\n\n\n\nP-value of the association between group and age is p-val = 0.2, suggesting that there may be an association at the p-val = 0.20 level. Explanations are like above, with variable class.\n\n\nLet’s do all together\n\n\nShow the code\ncopdata %&gt;% \n  select(sex, class, group, case) %&gt;% \n  tbl_summary(by = case) %&gt;% \n  add_p()\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nCharacteristic\n0\nN = 1611\n1\nN = 2161\np-value2\n\n\n\n\nsex\n\n\n\n\n0.3\n\n\n    female\n96 (60%)\n117 (54%)\n\n\n\n\n    male\n65 (40%)\n99 (46%)\n\n\n\n\nclass\n\n\n\n\n0.090\n\n\n    1\n63 (43%)\n68 (34%)\n\n\n\n\n    2\n44 (30%)\n57 (29%)\n\n\n\n\n    3\n38 (26%)\n73 (37%)\n\n\n\n\n    Unknown\n16\n18\n\n\n\n\ngroup\n\n\n\n\n0.2\n\n\n    teacher\n9 (5.6%)\n6 (2.8%)\n\n\n\n\n    student\n152 (94%)\n210 (97%)\n\n\n\n\n\n1 n (%)\n\n\n2 Pearson’s Chi-squared test"
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-    "text": "3.4. Risk Ratios\nThe risk ratios of each food item (including the 2x2 table) are reported below. The output of the CS() command is two tables: one with the 2x2 table and one with the risk difference, the risk ratio and the attributable fraction among exposed as well as the attributable fraction among the population (and the confidence intervals for all the estimates). The Chi-square and the p-value are also reported. In the second part, a table with all the food items is printed including attack rates for exposed and unexposed as well as risk ratios and the 95% confidence intervals (CI ll and CI ul, for the lower and upper interval) and p-values.\n\na) Calculate 95% CI Risk Ratios for food\nTo see if food items (note these are categorical variables) are associated with being a case, calculate risk ratios and 95% confidence intervals for food items. You can calculate this individually for each food item (using CSTable()), or all at once (see hint below).\n\n\n\n\n\n\nNeed a little bit of help?\n\n\n\n\n\nCreate a food_vars vector containing food variables of interest and use the function CSTable() of the EpiStats package.\nUsing this hint, you will create a table with the name of exposure variables, the total number of exposed, the number of exposed cases, the attack rate among the exposed, the total number of unexposed, the number of unexposed cases, the attack rate among the unexposed, risk ratios, 95% percent confidence intervals, and p-values. Amazing, isn’t it? Have a look at ??CSTable to learn more.\n\n\n\n\n\nCode\n# You could use the EpiStats package for each food item\nCS(copdata, \"case\", \"feta\")\n\n\n$df1\n          Cases Non Cases Total Risk\nExposed     156       115   271 0.58\nUnexposed    60        42   102 0.59\nTotal       216       157   373 0.58\n\n$df2\n                Point estimate 95%CI.ll 95%CI.ul\nRisk difference          -0.01    -0.12     0.10\nRisk ratio                0.98     0.81     1.19\nPrev. frac. ex.           0.02    -0.19     0.19\nPrev. frac. pop           0.02       NA       NA\nchi2(1)                   0.05       NA       NA\nPr&gt;chi2                  0.826       NA       NA\n\n\nCode\nCS(copdata, \"case\", \"sardines\")\n\n\n$df1\n          Cases Non Cases Total Risk\nExposed     150       105   255 0.59\nUnexposed    65        52   117 0.56\nTotal       215       157   372 0.58\n\n$df2\n                Point estimate 95%CI.ll 95%CI.ul\nRisk difference           0.03    -0.08     0.14\nRisk ratio                1.06     0.87     1.28\nAttr. frac. ex.           0.06    -0.14     0.22\nAttr. frac. pop           0.04       NA       NA\nchi2(1)                   0.35       NA       NA\nPr&gt;chi2                  0.553       NA       NA\n\n\nCode\nCS(copdata, \"case\", \"eggplant\")\n\n\n$df1\n          Cases Non Cases Total Risk\nExposed     123        93   216 0.57\nUnexposed    83        60   143 0.58\nTotal       206       153   359 0.57\n\n$df2\n                Point estimate 95%CI.ll 95%CI.ul\nRisk difference          -0.01    -0.12     0.09\nRisk ratio                0.98     0.82     1.18\nPrev. frac. ex.           0.02    -0.18     0.18\nPrev. frac. pop           0.01       NA       NA\nchi2(1)                   0.04       NA       NA\nPr&gt;chi2                  0.837       NA       NA\n\n\nCode\nCS(copdata, \"case\", \"moussaka\")\n\n\n$df1\n          Cases Non Cases Total Risk\nExposed     201       137   338 0.59\nUnexposed    14        22    36 0.39\nTotal       215       159   374 0.57\n\n$df2\n                Point estimate 95%CI.ll 95%CI.ul\nRisk difference           0.21     0.04     0.37\nRisk ratio                1.53     1.01     2.32\nAttr. frac. ex.           0.35     0.01     0.57\nAttr. frac. pop           0.32       NA       NA\nchi2(1)                   5.64       NA       NA\nPr&gt;chi2                  0.018       NA       NA\n\n\n\n\nCode\n# You can save time (and probably typos!) by creating a vector for food variables...\nfood_vars &lt;- c(\"feta\", \"sardines\", \"eggplant\", \"moussaka\", \n               \"orzo\", \"greeksal\", \"dessert\", \"bread\",  \n               \"champagne\", \"beer\", \"redwine\", \"whitewine\")\n\n# ...and using EpiStats::CSTable() to run all variables together!\nCSTable(copdata, \"case\", food_vars)\n\n\n$df\n          Tot.Exp Cases.Exp AR.Exp% Tot.Unexp Cases.Unexp AR.Unexp%   RR CI.ll\norzo          338       202   59.76        36          13     36.11 1.65  1.06\nmoussaka      338       201   59.47        36          14     38.89 1.53  1.01\nchampagne     316       187   59.18        48          21     43.75 1.35  0.97\ngreeksal      211       114   54.03       154          95     61.69 0.88  0.73\ndessert       149        90   60.40       198         106     53.54 1.13  0.94\nbeer          281       166   59.07        78          41     52.56 1.12  0.89\nredwine        80        42   52.50       259         150     57.92 0.91  0.72\nsardines      255       150   58.82       117          65     55.56 1.06  0.87\nwhitewine     260       150   57.69        98          54     55.10 1.05  0.85\nbread         342       196   57.31        29          16     55.17 1.04  0.74\nfeta          271       156   57.56       102          60     58.82 0.98  0.81\neggplant      216       123   56.94       143          83     58.04 0.98  0.82\n          CI.ul p(Chi2)\norzo       2.58   0.006\nmoussaka   2.32   0.018\nchampagne  1.89   0.044\ngreeksal   1.04   0.144\ndessert    1.36   0.202\nbeer       1.42   0.303\nredwine    1.14   0.393\nsardines   1.28   0.553\nwhitewine  1.29   0.659\nbread      1.46   0.823\nfeta       1.19   0.826\neggplant   1.18   0.837\n\n\n\n\nb) Prepare the RR table for publication\n\n\n\n\n\n\nNeed a little bit of help?\n\n\n\n\n\nUse flextable() and set_header_labels().\n\n\n\n\n\nCode\nrr_tbl &lt;- CSTable(copdata, \"case\", food_vars) %&gt;% \n  as.data.frame() %&gt;% \n  rownames_to_column() %&gt;% \n  flextable() %&gt;% \n   set_header_labels(\n     values = c(\"Food Item\",\n                \"Total exposed\",     \n               \"Cases exposed\", \n               \"AR among exposed\",    \n               \"Total unexposed\",\n               \"Cases unexposed\",\n               \"AR among unexposed\",\n               \"RR\",         \n               \"95% lower CI\",             \n               \"95% upper CI\",\n               \"p-value\"))\n\n\n\n\n\n\n\n\nLet’s stop and… think!\n\n\n\n\n\n· What can you infer from this table?\n· Considering the relative risks, which food or drink items do you think were most likely to be the vehicle(s) of infection in this outbreak?\n· Do you think there are any confounders or effect modifiers? If so, how would you investigate these further?\n\n\n\nThe interesting results here are that the food items that are most suspicious are orzo, moussaka and champagne. Orzo as such is unlikely to be contaminated, but as you can see in the picture (and from the dinner night), it was served with pesto! Maybe it was the pesto? Who-ho-ho!\nBefore one jumps into conclusions, consider that this result could be due to confounding! Maybe orzo was \"clean\" but eaten by all the people who ate the food item that actually was contaminated!(Optional)"
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-    "section": "5. Dose Response",
-    "text": "5. Dose Response\n\na) Orzo\n\n\nCode\n# Binomial regression for RRs. \n# The outcome needs to be exponentiated so we can interpret it properly!\nbinom_orzoD &lt;- glm(case ~ orzoD, data = copdata, \n             family = binomial(link = \"log\"))\n\n# To get exponentiated:\nbinom_orzoD_exp &lt;- glm(case ~ orzoD, data = copdata, \n                       family = binomial(link = \"log\")) %&gt;% \n  tidy(exponentiate = TRUE, \n       conf.int = TRUE)\n\nbinom_orzoD_exp\n\n\n\n\n  \n\n\n\n\n\nb) Moussaka\n\n\nCode\n# Let's get the results directly exponentiated\nbinom_moussakaD_exp &lt;- glm(case ~ moussakaD, data = copdata, \n                       family = binomial(link = \"log\")) %&gt;% \n  tidy(exponentiate = TRUE, \n       conf.int = TRUE)\n\nbinom_moussakaD_exp\n\n\n\n\n  \n\n\n\n\n\nc) Champagne\n\n\nCode\n# Let's get the results directly exponentiated\nbinom_champagneD_exp &lt;- glm(case ~ champagneD, data = copdata, \n                       family = binomial(link = \"log\")) %&gt;% \n  tidy(exponentiate = TRUE, \n       conf.int = TRUE)\n\nbinom_champagneD_exp"
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-    "text": "At the end of the session, participants will be able to:\n\nApply case definition criteria to a dataset using logical arguments in R"
+    "text": "3.4. Risk Ratios\nThe risk ratios of each food item (including the 2x2 table) are reported below. The output of the CS() command is two tables: one with the 2x2 table and one with the risk difference, the risk ratio and the attributable fraction among exposed as well as the attributable fraction among the population (and the confidence intervals for all the estimates). The Chi-square and the p-value are also reported. In the second part, a table with all the food items is printed including attack rates for exposed and unexposed as well as risk ratios and the 95% confidence intervals (CI ll and CI ul, for the lower and upper interval) and p-values.\n\na) Calculate 95% CI Risk Ratios for food\nTo see if food items (note these are categorical variables) are associated with being a case, calculate risk ratios and 95% confidence intervals for food items. You can calculate this individually for each food item (using CSTable()), or all at once (see hint below).\n\n\n\n\n\n\nNeed a little bit of help?\n\n\n\n\n\nCreate a food_vars vector containing food variables of interest and use the function CSTable() of the EpiStats package.\nUsing this hint, you will create a table with the name of exposure variables, the total number of exposed, the number of exposed cases, the attack rate among the exposed, the total number of unexposed, the number of unexposed cases, the attack rate among the unexposed, risk ratios, 95% percent confidence intervals, and p-values. Amazing, isn’t it? Have a look at ??CSTable to learn more.\n\n\n\n\n\nShow the code\n# You could use the EpiStats package for each food item\nCS(copdata, \"case\", \"feta\")\n\n\n$df1\n          Cases Non Cases Total Risk\nExposed     156       115   271 0.58\nUnexposed    60        42   102 0.59\nTotal       216       157   373 0.58\n\n$df2\n                Point estimate 95%CI.ll 95%CI.ul\nRisk difference          -0.01    -0.12     0.10\nRisk ratio                0.98     0.81     1.19\nPrev. frac. ex.           0.02    -0.19     0.19\nPrev. frac. pop           0.02       NA       NA\nchi2(1)                   0.05       NA       NA\nPr&gt;chi2                  0.826       NA       NA\n\n\nShow the code\nCS(copdata, \"case\", \"sardines\")\n\n\n$df1\n          Cases Non Cases Total Risk\nExposed     150       105   255 0.59\nUnexposed    65        52   117 0.56\nTotal       215       157   372 0.58\n\n$df2\n                Point estimate 95%CI.ll 95%CI.ul\nRisk difference           0.03    -0.08     0.14\nRisk ratio                1.06     0.87     1.28\nAttr. frac. ex.           0.06    -0.14     0.22\nAttr. frac. pop           0.04       NA       NA\nchi2(1)                   0.35       NA       NA\nPr&gt;chi2                  0.553       NA       NA\n\n\nShow the code\nCS(copdata, \"case\", \"eggplant\")\n\n\n$df1\n          Cases Non Cases Total Risk\nExposed     123        93   216 0.57\nUnexposed    83        60   143 0.58\nTotal       206       153   359 0.57\n\n$df2\n                Point estimate 95%CI.ll 95%CI.ul\nRisk difference          -0.01    -0.12     0.09\nRisk ratio                0.98     0.82     1.18\nPrev. frac. ex.           0.02    -0.18     0.18\nPrev. frac. pop           0.01       NA       NA\nchi2(1)                   0.04       NA       NA\nPr&gt;chi2                  0.837       NA       NA\n\n\nShow the code\nCS(copdata, \"case\", \"pasta\")\n\n\n$df1\n          Cases Non Cases Total Risk\nExposed     202       136   338 0.60\nUnexposed    13        23    36 0.36\nTotal       215       159   374 0.57\n\n$df2\n                Point estimate 95%CI.ll 95%CI.ul\nRisk difference           0.24     0.07     0.40\nRisk ratio                1.65     1.06     2.58\nAttr. frac. ex.           0.40     0.06     0.61\nAttr. frac. pop           0.37       NA       NA\nchi2(1)                   7.45       NA       NA\nPr&gt;chi2                  0.006       NA       NA\n\n\n\n\nShow the code\n# You can save time (and probably typos!) by creating a vector for food variables...\nfood_vars &lt;- c(\"feta\", \"sardines\", \"eggplant\", \"pasta\", \n               \"veal\", \"tomsal\", \"dessert\", \"bread\",  \n               \"champagne\", \"beer\", \"redwine\", \"whitewine\")\n\n# ...and using EpiStats::CSTable() to run all variables together!\nCSTable(copdata, \"case\", food_vars)\n\n\n$df\n          Tot.Exp Cases.Exp AR.Exp% Tot.Unexp Cases.Unexp AR.Unexp%   RR CI.ll\npasta         338       202   59.76        36          13     36.11 1.65  1.06\nveal          338       201   59.47        36          14     38.89 1.53  1.01\nchampagne     316       187   59.18        48          21     43.75 1.35  0.97\ntomsal        211       114   54.03       154          95     61.69 0.88  0.73\ndessert       149        90   60.40       198         106     53.54 1.13  0.94\nbeer          281       166   59.07        78          41     52.56 1.12  0.89\nredwine        80        42   52.50       259         150     57.92 0.91  0.72\nsardines      255       150   58.82       117          65     55.56 1.06  0.87\nwhitewine     260       150   57.69        98          54     55.10 1.05  0.85\nbread         342       196   57.31        29          16     55.17 1.04  0.74\nfeta          271       156   57.56       102          60     58.82 0.98  0.81\neggplant      216       123   56.94       143          83     58.04 0.98  0.82\n          CI.ul p(Chi2)\npasta      2.58   0.006\nveal       2.32   0.018\nchampagne  1.89   0.044\ntomsal     1.04   0.144\ndessert    1.36   0.202\nbeer       1.42   0.303\nredwine    1.14   0.393\nsardines   1.28   0.553\nwhitewine  1.29   0.659\nbread      1.46   0.823\nfeta       1.19   0.826\neggplant   1.18   0.837\n\n\n\n\nb) Prepare the RR table for publication\n\n\n\n\n\n\nNeed a little bit of help?\n\n\n\n\n\nUse flextable() and set_header_labels().\n\n\n\n\n\nShow the code\nrr_tbl &lt;- CSTable(copdata, \"case\", food_vars) %&gt;% \n  as.data.frame() %&gt;% \n  rownames_to_column() %&gt;% \n  flextable() %&gt;% \n   set_header_labels(\n     values = c(\"Food Item\",\n                \"Total exposed\",     \n               \"Cases exposed\", \n               \"AR among exposed\",    \n               \"Total unexposed\",\n               \"Cases unexposed\",\n               \"AR among unexposed\",\n               \"RR\",         \n               \"95% lower CI\",             \n               \"95% upper CI\",\n               \"p-value\"))\n\n\n\n\n\n\n\n\nLet’s stop and… think!\n\n\n\n\n\n· What can you infer from this table?\n· Considering the relative risks, which food or drink items do you think were most likely to be the vehicle(s) of infection in this outbreak?\n· Do you think there are any confounders or effect modifiers? If so, how would you investigate these further?\n\n\n\nThe interesting results here are that the food items that are most suspicious are pasta,veal and champagne. Pasta as such is unlikely to be contaminated, but as you can see in the picture (and from the dinner night), it was served with pesto! Maybe it was the pesto? Who-ho-ho!\nBefore one jumps into conclusions, consider that this result could be due to confounding! Maybe pasta was “clean” but eaten by all the people who ate the food item that actually was contaminated!(Optional)"
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-    "text": "2. Story/plot description\nYou will now create a new column in the data set to hold the case definition you decided in a previous step during your investigation. You can call this column case and set it to TRUE if the individual meets the case definition criteria and FALSE if not. You will use this column later on for any calculations needed (descriptive statistics, two-by-two tables to compute measures of association, etc.) to figure out the culprit of this outbreak."
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-    "text": "3. Questions/Assignments\nIf you closed your R project, please open it again and load the clean dataset. We strongly recommend you use the Copenhagen_clean1_2024.rds provided by us in EVA. This is just to be sure all needed changes in that file for the code to work have been done (as you may have changed the file in a slightly different way than we have).\nTo be sure we all start from the same case definition, let’s agree that a case was defined as a person who:\n\nattended the school dinner on 11 November 2006 (i.e. is on the linelist)\nate a meal at the school dinner (i.e. was exposed)\nfell ill after the start of the meal\nfell ill within the time period of interest after the school dinner\nsuffered from diarrhoea with or without blood, or vomiting\n\nNon cases (not-ill) were defined as people who:\n\nattended the school dinner on 11 November 2006 (i.e. are on the linelist)\nate a meal at the school dinner (i.e. were exposed)\ndid not fall ill within the time period of interest\ndid not develop diarrhoea (with or without blood) or vomiting"
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-    "section": "3.1 Install packages (if needed) and load libraries",
-    "text": "3.1 Install packages (if needed) and load libraries\n\n\nCode\n# Load the required libraries into the current R session:\npacman::p_load(rio, \n               here, \n               tidyverse, \n               skimr,\n               plyr,\n               janitor,\n               lubridate,\n               gtsummary, \n               flextable,\n               officer,\n               epikit, \n               apyramid, \n               scales)"
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-    "section": "3.3 Identify the variables you need to apply the case definition criteria.",
-    "text": "3.3 Identify the variables you need to apply the case definition criteria.\n\n\n\n\n\n\nNeed a little bit of help?\n\n\n\n\n\nThe variables we need from the dataset to apply the above case definition are: meal, onset_datetime, diarrhoea, bloody and vomiting."
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-    "section": "3.4 Create a new case column to hold the binary case definition variable. Let’s think about how to do this little by little:",
-    "text": "3.4 Create a new case column to hold the binary case definition variable. Let’s think about how to do this little by little:\n\na) Ate a meal at the school dinner\nYou decide to exclude any people from the cohort who didn’t eat at the dinner, because we specifically hypothesised a food item to be the vehicle of infection in this outbreak. Thus, filter your dataset to those who ate a meal: Keep in your dataset only those who ate a meal.\n\n\n\n\n\n\nNeed a little bit of help?\n\n\n\n\n\nfilter by those with meal == TRUE.\n\n\n\n\n\n\n\n\n\nLet’s stop and… think!\n\n\n\n\n\nWhat are some of the implications this decision may lead to? (excluding any people from the cohort who didn’t eat at the dinner)\n\n\n\n\n\nCode\ncopdata &lt;- copdata %&gt;% \n  filter(meal == TRUE)\n\n\n\n\n\n\n\n\nOnce you’ve thought about the above, have a look here\n\n\n\n\n\nSeven of the respondents actually said they did not eat the meal, but when it came to the questions about which food items they ate, they provided answers! This issue could have been minimised by:\n- At the survey state, one could adjust the design of an electronic questionnaire to prevent key questions from being skipped. This can come with both pros and cons. Allow fellows to discuss if time allows.\n- Explore your data further, realise this is the case, and recode the meal variable for these individuals as TRUE. =&gt; This would be the way to go, but is not what we did in our example because we tried to keep it simple, and also because it is good to show that you may not always clean the data perfectly, and that has consequences: You can highlight the importance or really explore your data in depth.\nBy making the above decision, we may be missing cases and non-cases people, and thus, modifying the final estimate of our measure of association. =&gt; It is very important to know your data, explore it deeply and try to clean it as well as possible. Every step one makes when cleaning the data may have a consequence, and we should be aware of it when making the data cleaning decisions and when interpreting the results.\n\n\n\n\n\nb) Fell ill after the start of the meal\nWe define “fell ill” as any person having had diarrhoea with OR without blood, OR vomiting. To capture this information easily, you will create a new gastrosymptoms variable. This variable will indicate that the person had one OR (“or” is R is achieved by using |) more of the clinical symptoms in your definition.\nNote that we the concept of having eaten a meal is already included as per one of the steps above.\n\n\n\n\n\n\nNeed a little bit of help?\n\n\n\n\n\nCreate a new gastrosymptoms column in your line list with mutate() and case_when().\n\n\n\n\n\n\n\n\n\nLet’s stop and… think!\n\n\n\n\n\nWhat are some of the implications this decision may lead to? (defining “fell ill” as any person who reported having diarrhoea with OR without blood, OR vomiting)\n\n\n\n\n\n\n\n\n\nIf you’ve thought about the above, have a look here\n\n\n\n\n\nHaving one clinical symptom enough to be considered a potential case at this point may be considered too unspecific (low specificity). For example, a person who ate at the dinner party and developed diarrhoea for other reasons other than food poisoning (say, they recently started on antibiotics known for unbalancing the intestinal flora and causing diarrhoea) could be misclassified as a potential case. (Note we talk about potential case, and not case; that is because here we are not talking about cases per-se yet, but this decision has implications for when applying the case definition below).\nMoreover, those who did not report clinical symptoms will be defined as non-cases. Thus, we are assuming that these individuals did not develop symptoms because they didn’t report them. The missing values could be due to, for example, them skipping the questions in the questionnaire. Some individuals may be reluctant to report symptoms, due to shame, fear of repercussion, or others. It is important to think ahead, before the interview, about ways to minimise these situations. For example, through questionnaire design, you may impede skipping questions; you could promote trust by using the right interviewers (in some cases this will be someone from the community, in others someone form specific NGOs, someone of a specific race or gender, etc); choose to carry out online questionnaires vs in person (or vice versa, depending on the situation), etc.\n\n\n\n\n\nCode\ncopdata &lt;- copdata %&gt;% \n  mutate(gastrosymptoms = case_when(\n    # Those had diarrhoea...\n    diarrhoea == TRUE |\n      #or bloody diarrhoea...\n    bloody == TRUE |\n      # or vomiting, are marked as TRUE (fell ill after the meal)\n    vomiting == TRUE ~ TRUE,\n    # The rest are FALSE. This includes those who ate a meal but had no symptoms (did not fell ill after the meal)\n    .default = FALSE)\n    )\n\n\n\n\nc) Fell ill within the time period of interest\n\nHmmm… what is the time period of interest? You could start calculating the incubation period, which can be defined by calculating the time between exposure (the meal) and onset of symptoms, and then looking at the distribution of these time differences. In this outbreak, incubation periods are easy to calculate, because everyone was exposed at (roughly) the same time and on the same day (eating the meal at the school dinner party).\nDinner was served at 18:00 roughly for everyone. Create a new meal_datetime variable with this information for all people in your dataset (as per 05 Oct 2024, at 18:00h).\n\n\n\nCode\n# Start with copdata:\ncopdata &lt;- copdata %&gt;% \n  # Create new column for meal date and time:\n  mutate(meal_datetime = lubridate::ymd_hm(\"2024-10-05 18:00\"))\n\n\n\nYou can calculate the incubation period for each participant by subtracting onset_datetime - meal_datetime.\nThen, take the median() of that column to calculate a median incubation period, this will help you start having some hypothesis about the type of pathogen we are dealing with until the lab results come back.\n\n\n\n\n\n\n\nNeed a little bit of help?\n\n\n\n\n\nBe aware of the type of variable incubation is (check class()), as well as of missing valuesNA.\n\n\n\n\n\n\n\n\n\nNote\n\n\n\n\n\nThe incubation period of a disease is the time from an exposure resulting in infection until the onset of disease. Because infection usually occurs immediately after exposure, the incubation period is generally the duration of the period from the onset of infection to the onset of disease – Rothman, Greenland, Lash (2017): Modern Epidemiology, 3rd edition\n\n\n\n\n\nCode\ncopdata &lt;- copdata %&gt;% \n  mutate(incubation = onset_datetime - meal_datetime,\n         incubation = as.numeric (incubation))\n\n\nmedian(as.numeric(copdata$incubation), na.rm = TRUE)\n\n\n[1] 15\n\n\n\n\n\n\n\n\nNow we are messing up with your brain…\n\n\n\n\n\nIf you pay attention, there were two individuals who don’t have a time recorded, only a date, the day of the dinner. These people probably got sick the same night they had the dinner, but the exact time was not recoded. They way we’ve managed the data will insert “00:00” in a missing value of a time. This means that the 2 people that got sick the day of the dinner have been recorded sick before the dinner (in the early morning of the day of the dinner). The two people have a negative incubation period! This should not happen. If you feel the force is with you, you can modify this and fix the error (if you do this, your results will be a bit different than your colleagues, but similar). We will continue with this “erroneous” data, for easiness. But note that in real life, as soon as you realise there is a coding error like this, you would have to go back to your cleaning code and fix the error! This may happen a couple of times during your outbreak investigation, and is totally normal. Cleaning data is not an easy job!\n\n\n\nWe see that the median incubation time is 15 hours. This is useful information, as incubation periods tend to be relatively pathogen-specific.\nBased on this, say you define and limit the maximum incubation period to 48 hours after the meal, as the data points to a fast-acting bacterial toxin or a virus. That is, dinner participants should have developed (onset_datetime) at least one symptom (diarrhoea, bloody or vomiting) 48h after meal_datetime to become a case.\n\n\n\n\n\n\nLet’s stop and… think!\n\n\n\n\n\nWhat are some of the implications this decision may lead to? (implications of this case definition)\n\n\n\n\n\n\n\n\n\nIf you’ve thought about the above, have a look here\n\n\n\n\n\nAll those not developing at least one symptom (diarrhoea with OR without blood, OR vomiting) 48h after the dinner are considered non-cases. This could (depending on how you decide to analyse your data) include those who had no symptoms at all, those who have missing data on the onset_datetime variable, and/or those who had symptoms before eating the meal. This is a reminder that you need to be both careful and aware of the implications of your data analysis decisions. If a person had clinical symptoms before eating the meal, they are considered as not-cases. However, it could be that a person had symptoms before the meal, and yet, still got infected by the pathogen when eating their meal (bad luck, we know…). According to this definition, we would be missing that case.\n\n\n\n\n\nd) Finally, with this information you can create a new case column to hold the binary (TRUE/FALSE) case definition variable.\n\n\nCode\ncopdata &lt;- copdata %&gt;% \n  mutate(case = case_when(\n    # Those who had symptoms &lt;48h from the meal are cases (TRUE)\n    gastrosymptoms == TRUE & \n      onset_datetime &gt;= meal_datetime &\n      onset_datetime &lt;= (meal_datetime + days(2)) ~ TRUE,\n    # Those who had symptoms &gt;48h from the meal are non-cases (FALSE)\n    gastrosymptoms == TRUE & \n      onset_datetime &gt; (meal_datetime + days(2)) ~ FALSE,\n    # The rest are considered non-cases. Including, those who had no symptoms at all, who have missing data on the onset_datetime variable, or who had symptoms before eating the meal \n    .default = FALSE)\n  )\n\n\nNote that we may be incurring in misclassification bias with the code above. The last section indicates that if a person had clinical symptoms before eating the meal, they are considered as non-cases. However, it could be that a person had symptoms before the meal, and yet, still got infected by the pathogen when eating their meal (bad luck, we know…).\nMoreover, if you remember, there were a couple of people with an dayonset, but no starthour. The code we used (lubridate::ymd_h with argument truncated = 2) results in dates with missing starthour being converted to date-time, with the missing time being set to 00:00 (midnight). This means that these two people don’t fulfill the case definition criteria because we marked their symptoms started early in the morning of Nov 11 (at 00:00), before the meal time (18:00), and thus, they did not “fell ill within the time period of interest”.\nThe two situations above are a reminder that you need to be both careful and aware of the implications of your data analysis decisions.\n\n\n\n\n\n\nLet’s stop and… think!\n\n\n\n\n\nWhat do you think are the risks of mis-classifying cases as non-cases in your analysis?\n\n\n\n\n\n\n\n\n\nIf you’ve thought about the above, have a look here\n\n\n\n\n\nWe will have bias either towards or away from the null, depending on the proportions of subjects misclassified.\n\n\n\n\n\nCode\n# Tabulate cases:\njanitor::tabyl(dat = copdata, case)\n\n\n\n\n  \n\n\n\nLet’s have a look at how many people ate a meal, had symptoms, and were considered as cases after applying our case definition:\n\n\nCode\ncopdata %&gt;% \n  summarise(atemeal = sum(meal == TRUE),\n            hadsympt = sum(gastrosymptoms == TRUE),\n            nb_cases = sum(case == TRUE)\n            )"
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-    "text": "At the end of the session, participants will be able to:\n\nMerge two datasets using a common column.\nDiscuss which descriptive statistics would you use to describe the cases\nDescribe the cases by person and place (this is an outbreak in a high school, so we won’t explore “place” this time!)\nGenerate preliminary hypotheses bases on the descriptions."
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-    "text": "2. Story/plot description\nYou received data from the laboratory regarding [WE NEED LAB DATA AND DECISIONS ON WHAT TO ADD HERE]. You will have to merge the questionnaire data with the data coming from the laboratory.\nNow that your dataset is clean and the lab data merged, it is time to describe the people identified as cases so far and generate a hypothesis to inform the next steps in the outbreak investigation. Note that you should try to have a data analysis plan before any data collection. Nevertheless, during the urgency of outbreak investigations, questionnaire development may sometimes occur before you had time to draft a proper analysis plan (as it is the case in this simulated outbreak). Drawing “dummy” tables and graphs will help you precise the type of statistics you will carry out later on."
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-    "text": "3. Questions/Assignments\nIf you closed your R project, please open it again and load the clean dataset. We recommend you use the Copenhagen_clean2_2024.rds provided by us in EVA. This is just to be sure all needed changes in that file for the code to work have been done (as you may have changed the file in a slightly different way than we have)."
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-    "section": "3.1 Install packages (if needed) and load libraries",
-    "text": "3.1 Install packages (if needed) and load libraries\n\n\nCode\n# Load the required libraries into the current R session:\npacman::p_load(rio, \n               here, \n               tidyverse, \n               skimr,\n               plyr,\n               janitor,\n               lubridate,\n               gtsummary, \n               flextable,\n               officer,\n               epikit, \n               apyramid, \n               scales)"
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-    "section": "3.4 Describe the outbreak in terms of time.",
-    "text": "3.4 Describe the outbreak in terms of time.\nYou could use ggplot2.To learn more about ggplot2 you can check The Epidemiologist R Handbook: Chapter on ggplot and ggplot2 - Elegant Graphics for Data Analysis.\n\na) Create a histogram to visualize the incubation period (geom_histogram is a good function to use for this!).\n\n\n\n\n\n\nLet’s stop and… think!\n\n\n\n\n\nWhat can you infer from the incubation periods in the histogram?\n\n\n\n\n\nCode\n#| label: inc_time\n\n# Create a dataset with only cases\ncases &lt;- copdata %&gt;% \n  filter(case == TRUE)\n\nincplot &lt;- cases %&gt;% \n  # Create an empty ggplot frame:\n  ggplot() +\n  # Add a histogram of incubation:\n  geom_histogram(\n    mapping = aes(x = incubation), \n    # Set bin widths to 6 hours:\n    binwidth = 6) +\n  # Adapt scale to better fit data\n  scale_x_continuous(breaks = seq(0, 48, 6)) + \n  # Label x and y axes:\n  labs(x = \"Incubation period in 6-hour bins\",\n       y = \"Number of cases\")\n\n# Print plot:\nincplot\n\n\n\n\n\n\n\nb) Create an epicurve for the date and time of onset (using onset_datetime), limiting the input data to cases.\n\nUse scale_x_datetime() and chose a value for date_breaks() based on your calculated incubation period.\nLabel your x and y axes using labs().\n\n\n\n\n\n\n\nNeed a little bit of help?\n\n\n\n\n\nA rule of thumb is to use one third or one fourth of the average incubation period as an interval. For our investigation this means we should use approximately a 6h interval for the date_breaks argument.\n\n\n\n\n\nCode\n# Create a vector with sequences every 6h from the first to the last case\nbreaks_6h &lt;- seq(from = min(cases$onset_datetime, na.rm = TRUE),\n                 to = max(cases$onset_datetime, na.rm = TRUE),\n                 by = \"6 hours\")\n\n# Fetch cases data:\nepicurve_datetime &lt;- cases %&gt;%  \n  # Add factor onset_datetime to ggplot aesthetic:\n  ggplot(\n    mapping = aes(x = onset_datetime)) + \n  # Add geom_histogram:\n  geom_histogram(\n    # Apply the vector of requences created above\n    breaks = breaks_6h) +\n  # Adapt scale to data and adjust axis label angle:\n  scale_x_datetime(\n    date_breaks = \"6 hours\",\n    labels = label_date_short()) +\n  # Update x and y axis labels:\n  labs(x = \"Date and time of onset symptoms\", \n       y = \"Number of cases\") +\n  # Remove unnecessary grid lines:\n  theme_bw()\n\n# Print epicurve:\nepicurve_datetime\n\n\n\n\n\n\nBuilding up on your previous epicurve, create another epicurve to compare between sexes and additionally investigate how teachers versus students were distributed.\n\n\nUse fill = group\nUse sex as your facets for facet_wrap()\n\n\n\n\n\n\n\nNeed a little bit of help?\n\n\n\n\n\nfill adds an additional variable to be displayed in the plot: group is going to determine the fill-colour of our bars. facet_wrap splits the graph in two: one each for the two levels of sex.\n\n\n\n\n\n\n\n\n\nDo you feel like a challenge?\n\n\n\n\n\nIf you want, try using str_glue() to add the total number of cases to the sub-title of the plot. str_glue() is a very useful function that allows you to dynamically create a summary statistic from your data within some normal text.\n\n\n\n\n\nCode\nepicurve_strata &lt;- cases %&gt;% \n  # Add factor onset_day to ggplot aesthetic:\n  ggplot(\n    mapping = aes(x = onset_datetime, fill = group)) + \n  # Add nicer fill colours:\n  scale_fill_manual(values = c(\"darkred\", \"lightblue\")) +\n    # Add geom_histogram:\n  geom_histogram(\n    # Apply the vector of requences created above\n    breaks = breaks_6h) +\n  # Adjust x axis scales to a suitable unit:\n  scale_x_datetime(\n    date_breaks = \"6 hours\", \n    labels = label_date_short()) +\n  # Update x and y axis labels:\n  labs(x = \"Date and time of onset\", \n       y = \"Number of cases\", \n       fill = \"Group\", \n       title = \"Epicurve of the outbreak, stratified by sex\",\n       subtitle = str_glue(\"Spetses, October 2024, N = {sum(copdata$case)}\")) +\n  # Stratify by sex:\n  facet_wrap(facets = \"sex\",\n             ncol = 2) +\n  # Add theme:\n  theme_bw()\n\n# Print epicurve:\nepicurve_strata \n\n\n\n\n\n\n\n\n\n\n\nLet’s stop and… think!\n\n\n\n\n\nWhat does the stratified epicurve tell you? Does the shape of the epicurve support a viral or toxic aetiology? What other information can you obtain from it?\n\n\n\n\n\n\n\n\n\nIf you’ve thought about the above questions, have a look here\n\n\n\n\n\nWhen constructing an epicurve, we need to decide on the resolution, i.e. the time interval for a single bar. A rule of thumb is to use one third or one fourth of the average incubation period as an interval. For our investigation this means we should use approximately a 6h interval.\nThis seems a good choice, as we saw that the daily interval was too coarse to really see the signal we are after. The epicurve and the summary of the incubation period show that there seemed to be a rapid onset of symptoms following exposure. This is in line with our previous suspicion that a virus or a toxin might be the causative agent in the outbreak.\nThe unimodal shape with the sharp peak suggests a point source, while the tail on the right-hand side could be explained by secondary cases or background noise. Also, people that only consumed a little contaminated food and therefore only a low infectious dose could have a longer incubation period and could explain the late cases.\nThe above results are in line with norovirus as the prime suspect, but the symptoms are not a textbook fit. There are too few people that experienced vomiting! Looking forward to receiving the lab results!"
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-    "text": "3.5. Describe the outbreak in terms of person.\nYou could use tabyl(). For more detail on the tabyl() function and the adorn_XYZ helpers, see the janitor documentation or The Epidemiologist R Handbook section on tabulation.\n\na) Create a cross-tabulation of case with group.\n\n\nCode\ncopdata %&gt;% \n  janitor::tabyl(case, group) %&gt;% \n  adorn_totals() %&gt;% \n  adorn_percentages() %&gt;% \n  adorn_pct_formatting() \n\n\n\n\n  \n\n\n\n\n\nb) Create a cross-tabulation of case with sex.\n\n\nCode\ncopdata %&gt;% \n  janitor::tabyl(case, sex) %&gt;% \n  adorn_totals() %&gt;% \n  adorn_percentages() %&gt;% \n  adorn_pct_formatting() \n\n\n\n\n  \n\n\n\n\n\n\n\n\n\nLet’s stop and… think!\n\n\n\n\n\nWhat do you think of these results?\n\n\n\n\n\n\n\n\n\nIf you’ve thought about the above questions, have a look here\n\n\n\n\n\nThe distribution of the cohort regarding sex, group and class also didn’t reveal anything unusual. Students seem a bit more affected by the outbreak than teachers and the attack rate is higher for older students in higher classes. This, however, is a purely descriptive result.\n\n\n\n\n\n\n\n\n\nDo you feel like a challenge?\n\n\n\n\n\nIf you want, create an age-sex pyramid using the apyramid package. To do so, first create an age category variable with the epkit function age_categories().\n\n\n\n\n\nc) Extra - Age-sex pyramid of cases.\nHint: Change show_midpoint = FALSE to TRUE to see skewedness in the data patterns more easily.\n\n\nCode\ncopdata &lt;- copdata %&gt;% \n  # Create age categories:\n  mutate(age_cat = epikit::age_categories(\n    # Name of age column:\n    x = age, \n    # Define the age categories:\n    breakers = c(0, 10, 16, 18, 20, 50, 70)\n    )\n  )\n\n\n# Check age categories:\njanitor::tabyl(copdata, age_cat)\n\n\n\n\n  \n\n\n\nCode\n# Pipe copdata:\nagesex &lt;- copdata %&gt;% \n  # Filter for cases only:\n  filter(case == TRUE) %&gt;% \n  # Create age sex pyramid:\n  apyramid::age_pyramid(\n  # Specify column containing age categories:\n    age_group = \"age_cat\",\n    # Specify column containing sex:\n    split_by = \"sex\", \n    # Don't show midpoint on the graph:\n    show_midpoint = FALSE\n    )\n\n# Print plot:\nagesex"
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-    "section": "3.6 Explore the distribution of all clinical signs (symptoms).",
-    "text": "3.6 Explore the distribution of all clinical signs (symptoms).\n\na) Create a summary table with symptoms stratified by case definition, and present an overall column as well. You could use tabyl() or gtsummary::tbl_summary(). You can find further information about gtsummary in The Epidemiologist R handbook section on gtsummary.\n\n\nCode\n# Create summary table:\ntabsymptoms &lt;- copdata %&gt;% \n    # Select person characteristics to summarise:\n  select(case, diarrhoea, bloody, vomiting,\n             abdo, nausea, fever,headache, jointpain) %&gt;% \n  # transform clinical symptoms to factors, so NA can be accounted properly in the table\n  dplyr::mutate(\n    across(.cols = c(diarrhoea, bloody, vomiting,\n             abdo, nausea, fever,headache, jointpain), \n           .fns = ~as.factor(.))) %&gt;%\n  # Make NA a explicit level of factor variables\n  dplyr::mutate(\n    across(.cols = c(diarrhoea, bloody, vomiting,\n           abdo, nausea, fever,headache, jointpain),\n           .fns = ~forcats::fct_na_value_to_level(.))) %&gt;% \n    \n  # Create the summary table:\n  gtsummary::tbl_summary(\n    # Stratify by case:\n    by = case, \n    # Calculate row percentages:\n    percent = \"column\",\n    # Create nice labels:\n    label  = list(\n      diarrhoea   ~ \"Diarrhoea\",                           \n      bloody      ~ \"Dysentary\",\n      vomiting    ~ \"Vomiting\",\n      abdo        ~ \"Abdominal pain\",\n      nausea      ~ \"Nausea\", \n      fever       ~ \"Fever\", \n      headache    ~ \"Headache\", \n      jointpain   ~ \"Joint pain\")\n    \n  ) %&gt;% \n  \n  # Add totals:\n  add_overall() %&gt;% \n  # Make variable names bold and italics:\n  bold_labels() %&gt;% \n  italicize_labels() %&gt;% \n  # Modify header:\n  modify_header(\n    label = \"**Characteristic**\",\n    stat_0 = \"**Overall**\\n **N** = {N}\",\n    stat_1 = \"**Non-case**\\n **N** = {n}\",\n    stat_2 = \"**Case**\\n **N** = {n}\", \n    )\n\n# Print the table:\ntabsymptoms\n\n\n\n\n\n\n  \n    \n    \n      Characteristic\n      Overall\nN = 3771\n      Non-case\nN = 1611\n      Case\nN = 2161\n    \n  \n  \n    Diarrhoea\n\n\n\n        FALSE\n46 (18%)\n40 (100%)\n6 (2.8%)\n        TRUE\n206 (82%)\n0 (0%)\n206 (97%)\n    Dysentary\n\n\n\n        FALSE\n189 (97%)\n42 (100%)\n147 (97%)\n        TRUE\n5 (2.6%)\n0 (0%)\n5 (3.3%)\n    Vomiting\n\n\n\n        FALSE\n149 (69%)\n42 (100%)\n107 (62%)\n        TRUE\n66 (31%)\n0 (0%)\n66 (38%)\n    Abdominal pain\n\n\n\n        FALSE\n35 (14%)\n6 (12%)\n29 (15%)\n        TRUE\n207 (86%)\n44 (88%)\n163 (85%)\n    Nausea\n\n\n\n        FALSE\n55 (25%)\n12 (26%)\n43 (24%)\n        TRUE\n169 (75%)\n34 (74%)\n135 (76%)\n    Fever\n\n\n\n        FALSE\n127 (74%)\n32 (80%)\n95 (73%)\n        TRUE\n44 (26%)\n8 (20%)\n36 (27%)\n    Headache\n\n\n\n        FALSE\n83 (38%)\n11 (25%)\n72 (41%)\n        TRUE\n137 (62%)\n33 (75%)\n104 (59%)\n    Joint pain\n\n\n\n        FALSE\n159 (85%)\n32 (84%)\n127 (85%)\n        TRUE\n29 (15%)\n6 (16%)\n23 (15%)\n  \n  \n  \n    \n      1 n (%)\n    \n  \n\n\n\n\n\n\n\n\n\n\nLet’s stop and… think!\n\n\n\n\n\nDo you think the symptoms selected for the case definition were the right ones, or would you change anything?\n\n\n\n\n\n\n\n\n\nDo you feel like a challenge?\n\n\n\n\n\nIf you want, present the symptoms in an ordered bar chart. To do so, reshape the data using pivot_longer(), and then group_by(), summarise() and count() to tally up the counts for each symptom, stratified by case definition. Use ggplot2::coord_flip() for a better visualisation.\n\n\n\n\n\nb) Extra - Bar plot of symptoms stratified by case definition.\n\n\nCode\n# Create list of symptom variables:\nsymptoms &lt;- c(\"diarrhoea\", \n              \"bloody\", \n              \"vomiting\", \n              \"abdo\", \n              \"nausea\", \n              \"fever\", \n              \"headache\", \n              \"jointpain\")\n\n# Create nice labels for case definition:\ncaselabs &lt;- ggplot2::as_labeller(c(`FALSE` = \"Non-case\", \n                                   `TRUE` = \"Case\"))\n# Select variables and cases:\nsymptom_bar &lt;- copdata %&gt;% \n  # Select symptom columns:\n  select(case, c(all_of(symptoms))) %&gt;%\n  # Drop NAs:\n  drop_na() %&gt;% \n  # Reshape (pivot longer):\n  pivot_longer(!case, \n               names_to = \"Symptoms\", \n               values_drop_na = TRUE) %&gt;% \n  # Keep only TRUE values:\n  filter(value == TRUE) %&gt;% \n \n   # Group by symptoms and case:\n  group_by(Symptoms, case) %&gt;% \n  # Count for each symptom by case:\n  dplyr::summarise(count = n()) %&gt;% \n  # Create plot:\n  ggplot(\n    mapping = aes(\n    # Order symptom bars so most common ones are ontop:\n    x = reorder(Symptoms, desc(count), decreasing = TRUE), \n    y = count)) +\n  # Display bars as proportions\n  geom_bar(stat = \"identity\") +\n  # Update x axis label:\n  xlab(\"Symptoms\") +\n  # Update y axis label:\n  ylab(\"Proportion of respondents\") +\n  # Flip plot on its side so symptom labels are clear:\n  coord_flip() +\n  # Facet the plot by (labelled) case:\n  facet_wrap(facets = \"case\",\n             labeller = caselabs,\n             ncol = 2)\n\n\n`summarise()` has grouped output by 'Symptoms'. You can override using the\n`.groups` argument.\n\n\nCode\n# Print plot:\nsymptom_bar"
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-    "text": "3.8 Draft the relevant paragraph(s) and table(s) and/or figure(s) in your outbreak report (Methods and results).\n\n\n\n\n\n\nNeed a little bit of help?\n\n\n\n\n\nTo save your tabyl table as a .docx, you could convert your tabyl to flextable (as_flex_table()), ensure only one line per row (flextable::autofit()) and save as .docx (flextable::save_as_docx(path = \"nameoftable.docx\")"
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-    "text": "3.3 Merge and explore the lab data\n\n\nCode\ncopdatalab &lt;- left_join(copdata, lab, by = \"id\")\n# Tabulate:\njanitor::tabyl(dat = copdatalab, result)"
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-    "text": "2. Story/plot description\nYou have just estimated risk ratios (RR) of individual food items manually to better understand the concept. RR give you an idea of which food items could be the culprit(s) of the outbreak.\nNow, you will practice performing hypothesis testing and calculating RR in R to investigate the associations of suspicious food items with the disease.\nNote that, although this is not an exhaustive list of tests, the following can be useful for this exercise (relevant for comparisons between two groups):\n\nFor continuous variables:\n\nshapiro.test() (for checking if normally distributed)\nt.test() (for normal distributions)\nwilcox.test() (for non-parametric testing. Used to determine if two numeric samples are from the same distribution, when their populations are not normally distributed or have unequal variance.)\n\nFor categorical variables:\n\nchisq.test() (if all comparisons include at least 5 cases)\nfisher.test() (if there are &lt; 5 cases for any comparison)"
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-    "text": "3.5. (Optional) Dose Response\nCheck for a dose-response relationship between the food items with the highest RR values (the top 3) and being a case.\n\na) Orzo\n\n\nCode\n# Binomial regression for RRs. \n# The outcome needs to be exponentiated so we can interpret it properly!\nbinom_orzoD &lt;- glm(case ~ orzoD, data = copdata, \n             family = binomial(link = \"log\"))\n\n# To get exponentiated:\nbinom_orzoD_exp &lt;- glm(case ~ orzoD, data = copdata, \n                       family = binomial(link = \"log\")) %&gt;% \n  tidy(exponentiate = TRUE, \n       conf.int = TRUE)\n\nbinom_orzoD_exp\n\n\n\n\n  \n\n\n\n\n\n\n\n\n\nLet’s stop and… think!\n\n\n\n\n\nWhat do these results tell you?\n\n\n\nResults suggest a dose response relationship of having eaten orzo, pointing towards orzoas the potential vehicle. The higher the amount of orzothey ate, the stronger is the association (RR) with getting ill/being a case.\n\n\nb) Moussaka\n\n\nCode\n# Let's get the results directly exponentiated\nbinom_moussakaD_exp &lt;- glm(case ~ moussakaD, data = copdata, \n                       family = binomial(link = \"log\")) %&gt;% \n  tidy(exponentiate = TRUE, \n       conf.int = TRUE)\n\nbinom_moussakaD_exp\n\n\n\n\n  \n\n\n\n\n\nc) Champagne\n\n\nCode\n# Let's get the results directly exponentiated\nbinom_champagneD_exp &lt;- glm(case ~ champagneD, data = copdata, \n                       family = binomial(link = \"log\")) %&gt;% \n  tidy(exponentiate = TRUE, \n       conf.int = TRUE)\n\nbinom_champagneD_exp"
+    "text": "3.5. (Optional) Dose Response\nCheck for a dose-response relationship between the food items with the highest RR values (the top 3) and being a case.\n\na) Pasta\nUsing epitools::riskratio function:\n\n\nShow the code\nepitools::riskratio(copdata$pastaD,                     \n                    copdata$case,                     \n                    conf.level = 0.95)\n\n\n$data\n         Outcome\nPredictor   0   1 Total\n    0      23  13    36\n    1      32  38    70\n    2      76 107   183\n    3      28  57    85\n    Total 159 215   374\n\n$measure\n         risk ratio with 95% C.I.\nPredictor estimate     lower    upper\n        0 1.000000        NA       NA\n        1 1.503297 0.9257878 2.441057\n        2 1.619168 1.0310538 2.542742\n        3 1.857014 1.1730804 2.939696\n\n$p.value\n         two-sided\nPredictor  midp.exact fisher.exact chi.square\n        0          NA           NA         NA\n        1 0.081364161  0.100893180 0.07613202\n        2 0.015337953  0.017069765 0.01373981\n        3 0.002010616  0.002370887 0.00162311\n\n$correction\n[1] FALSE\n\nattr(,\"method\")\n[1] \"Unconditional MLE & normal approximation (Wald) CI\"\n\n\nUsing a binomial regression:\n\n\nShow the code\n# Let's get the results directly exponentiated\nbinom_pastaD_exp &lt;- glm(case ~ pastaD, data = copdata, \n                       family = binomial(link = \"log\")) %&gt;% \n  tidy(exponentiate = TRUE, \n       conf.int = TRUE)\n\nbinom_pastaD_exp\n\n\n\n  \n\n\n\n\n\n\n\n\n\nLet’s stop and… think!\n\n\n\n\n\nWhat do these results tell you?\n\n\n\nResults suggest a dose response relationship of having eaten pasta, pointing towards pasta as the potential vehicle. The higher the amount of pasta they ate, the stronger is the association (RR) with getting ill/being a case.\n\n\nb) Veal\nUsing epitools::riskratio function:\n\n\nShow the code\nepitools::riskratio(copdata$vealD,\n                    copdata$case,\n                    conf.level = 0.95)\n\n\n$data\n         Outcome\nPredictor   0   1 Total\n    0      19  15    34\n    1      30  40    70\n    2      78 106   184\n    3      33  54    87\n    Total 160 215   375\n\n$measure\n         risk ratio with 95% C.I.\nPredictor estimate     lower    upper\n        0 1.000000        NA       NA\n        1 1.295238 0.8431805 1.989659\n        2 1.305797 0.8769752 1.944304\n        3 1.406897 0.9314233 2.125090\n\n$p.value\n         two-sided\nPredictor midp.exact fisher.exact chi.square\n        0         NA           NA         NA\n        1 0.22186384    0.2951163 0.21193013\n        2 0.15357032    0.1884220 0.14587265\n        3 0.07956697    0.1017336 0.07298506\n\n$correction\n[1] FALSE\n\nattr(,\"method\")\n[1] \"Unconditional MLE & normal approximation (Wald) CI\"\n\n\nUsing a binomial regression:\n\n\nShow the code\n# Binomial regression for RRs. \n# The outcome needs to be exponentiated so we can interpret it properly!\nbinom_vealD &lt;- glm(case ~ vealD, data = copdata, \n             family = binomial(link = \"log\"))\n\n# To get exponentiated:\nbinom_vealD_exp &lt;- glm(case ~ vealD, data = copdata, \n                       family = binomial(link = \"log\")) %&gt;% \n  tidy(exponentiate = TRUE, \n       conf.int = TRUE)\n\nbinom_vealD_exp\n\n\n\n  \n\n\n\n\n\nc) Champagne\nUsing epitools::riskratio function:\n\n\nShow the code\nepitools::riskratio(copdata$champagneD,                     \n                    copdata$case,                     \n                    conf.level = 0.95)\n\n\n$data\n         Outcome\nPredictor   0   1 Total\n    0      27  21    48\n    1      93 111   204\n    2      12  32    44\n    3      24  44    68\n    Total 156 208   364\n\n$measure\n         risk ratio with 95% C.I.\nPredictor estimate     lower    upper\n        0 1.000000        NA       NA\n        1 1.243697 0.8812613 1.755193\n        2 1.662338 1.1502014 2.402507\n        3 1.478992 1.0260296 2.131923\n\n$p.value\n         two-sided\nPredictor  midp.exact fisher.exact  chi.square\n        0          NA           NA          NA\n        1 0.188922591  0.201367850 0.183280359\n        2 0.005584928  0.006247816 0.004961941\n        3 0.027627736  0.036334120 0.025117641\n\n$correction\n[1] FALSE\n\nattr(,\"method\")\n[1] \"Unconditional MLE & normal approximation (Wald) CI\"\n\n\nUsing a binomial regression:\n\n\nShow the code\n# Let's get the results directly exponentiated\nbinom_champagneD_exp &lt;- glm(case ~ champagneD, data = copdata, \n                       family = binomial(link = \"log\")) %&gt;% \n  tidy(exponentiate = TRUE, \n       conf.int = TRUE)\n\nbinom_champagneD_exp"
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-    "text": "3.6 Summary\nAs a summary of what you've done above, answer these questions:\n\nIs the respondents' sex associated with being a case?\nIs the school class associated with being a case?\nWhich foods increase the risk of being a case?\n(Optional) Is there a dose-response relationship between the food items and being a case?\nWhat do you think is the most likely culprit(s) of this outbreak at this point? Are there any risk factors you would like to highlight?"
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-    "text": "At the end of the session, participants will be able to:\n\nConsider the effect of confounding and effect modification on the association between exposure and disease,\nPerform stratified analysis using the Mantel-Haenszel approach\n\nThe use of stratified analysis, is the first step to identify confounding factors and effect modifiers one by one (after, of course, thinking which variables could potentially be confounders or effect modifiers). As the final step, you will be using Regression Models to account for confounding and check for effect modification. We will see these with you in the Multivariable Module (MVA), next year."
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-    "text": "2. Story/plot description\nFrom the univariable analysis, it seems that eating orzpo and eating moussaka as well as drinking champagne are associated with the highest risk of becoming ill. There are, however, many other food items that are associated with an increased risk (even if not statistically significant).\nYou should next think about potential confounders and about effect modification. Think about which variables you might want to check for effect modification or confounding. One common strategy is to base this decision on the results obtained in the univariable analysis and a p-value threshold of 0.20-0.25. Also, food items that are known risk factors for gastroenteritis could also be included regardless of their univariable p-value."
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-    "text": "3.1. Confounders and effect modification\nDiscuss how to identify potential confounders and effect modification. Draw dummy tables before coding to have clear what you want to achieve."
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-    "section": "3.4. Consider and assess for confounding and/or effect modification",
-    "text": "3.4. Consider and assess for confounding and/or effect modification\nHave a look at the relative risk for being a case having eaten a specific food item (for example, moussake), when stratified by another variable (for example, orzo). You may consider stratifying by orzo, as it has the highest RR in the univariable analysis.\nThere are many variables in this dataset and it might not make sense to stratify each variable by each other variable on our search for effect modifiers and confounders.\nHowever, we also don’t want to be too restrictive as a variable which actually (i.e. causally) is associated with the outcome might not show a significant association at the significant level we decided (say 5%) in the univariable analysis due to confounding. Therefore, we could test all variables statistically significant at the 15%, 20%, or 25% level (specific percentage to be decided by your group). In our solutions here, we are looking at moussaka and champagne, stratified by orzo, but you may decide to look at other food items as well.\n\n\n\n\n\n\nNeed a little bit of help?\n\n\n\n\n\nUse the function csinter() of the EpiStats package.\n\n\n\n\na) Moussaka as exposure of interest, stratified by having eaten orzo\nIf we stratify the effect of moussaka by orzo we ask the question: does eating orzo modify or confound the association between eating moussaka and being a case?\n\n\nCode\nstratall &lt;- copdata %&gt;% \n  # Mutate across to convert cases to numeric:\n  mutate(across(.cols = case, \n                .fns = ~ as.numeric(.)))\n\n# Pass data to the csinter function:\norzostrata &lt;- csinter(x = stratall, \n                       cases = \"case\", \n                       exposure = \"moussaka\", \n                       by = \"orzo\")\n\norzostrata\n\n\n$df1\n  CSInter case - moussaka by(orzo) Total Cases Risk %          P.est. Stats\n1                         orzo = 1   338  &lt;NA&gt;     NA Risk difference  0.10\n2                          Exposed   330   198  60.00      Risk Ratio  1.20\n3                        Unexposed     8     4  50.00 Attrib.risk.exp  0.17\n4                                     NA  &lt;NA&gt;     NA Attrib.risk.pop  0.16\n5                         orzo = 0    36  &lt;NA&gt;     NA Risk difference  0.02\n6                          Exposed     8     3  37.50      Risk Ratio  1.05\n7                        Unexposed    28    10  35.71 Attrib.risk.exp  0.05\n8                                     NA  &lt;NA&gt;     NA Attrib.risk.pop  0.01\n9              Missing / Missing %     3  0.8%     NA            &lt;NA&gt;    NA\n  95%CI.ll 95%CI.ul\n1    -0.25     0.45\n2     0.60     2.41\n3    -0.68     0.59\n4       NA       NA\n5    -0.36     0.40\n6     0.38     2.92\n7    -1.65     0.66\n8       NA       NA\n9       NA       NA\n\n$df2\n                    Point Estimate Chi2 p.value  Stats 95%CI.ll 95%CI.ul\n1        Woolf test of homogeneity 0.04   0.833     NA       NA       NA\n2            Crude RR for moussaka   NA      NA   1.53     1.01     2.32\n3 MH RR moussaka adjusted for orzo   NA      NA   1.15     0.64     2.04\n4   Adjusted/crude relative change   NA      NA -25.08       NA       NA\n\n\nLet’s check if orzo is associated with moussaka (if we are thinking moussaka may be a confounder, we need to see if there is an association between the potential confounder (moussaka) and the exposure (orzo)):\n\n\nCode\n# Perform Wilcoxon rank sum test on orzo and moussaka:\nwilcox.test(orzo ~ moussaka, \n            data = copdata)\n\n\n\n    Wilcoxon rank sum test with continuity correction\n\ndata:  orzo by moussaka\nW = 1496, p-value &lt; 2.2e-16\nalternative hypothesis: true location shift is not equal to 0\n\n\n\n\nb) Champagne as exposure of interest, stratified by having eaten orzo\n\n\nCode\n# Pass data to the csinter function:\nchampstrata &lt;- csinter(x = stratall, \n                       cases = \"case\", \n                       exposure = \"champagne\", \n                       by = \"orzo\")\n\n\n\n\n\n\n\n\nLet’s stop and… think!\n\n\n\n\n\nAre there any indications to make you think there may be effect modification and/or confounding?\n\n\n\nWe could stratify by orzo (the strongest risk factor in the univariable analysis), to examine if orzo confounds the association between eating moussaka and being a case. Before stratification, we will need to check if orzo meets the conditions of being a confounder. For a variable to be a confounder it needs to be associated both with the outcome (being a case) and with the exposure (and not be in the causal pathway between exposure and outcome). We know from univariable analysis that orzo is associated with being a case. If we run a Wilcoxon rank sum test, we will see that orzo is also associated with moussaka (indeed, you can see that most people either had both moussaka and orzo or neither of these food items, so they are associated with each other).\nAbove, we uses ccinter to stratify and save the object as \"orzostrata\".\norzostrata: Within the stratum of the people who ate orzo, moussaka has no significant effect (RR = 1.20, CI: 0.60 - 2.41). The same holds within the stratum of people who didn't eat orzo (RR = 1.05, CI = 0.38, 2.92). The adjusted MH-RR also suggests that moussaka has no effect (RRadj = 1.15, CI: 0.80 - 2.85).  To identify confounding, we want to look at the % change between the crude and the adjusted RR. This is given by the csinter output \"Adjusted/crude relative change\".  The difference between the crude and the MH-RR in this case is &gt;20% suggesting that orzo confounds the association between moussaka and the disease.\nThis result suggest that moussaka is not a risk factor of the disease and that the crude observed effect was due to the confounding effect of orzo.\nIf you stratify by moussaka, you see that moussaka does not confound the association between orzo and the disease. The same applies if you stratify the exposure to orzo by other variables. The above, the higher RR for orzo and the dose response relationship we found earlier for orzo (remember this was optional) provide additional evidence that there was something going on with the orzo with pesto dish!"
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-    "text": "At the end of the session, participants will be able to:\n\nImport datasets in R\nApply basic data management techniques to explore and familiarise themselves with the dataset\nApply strategies for data cleaning"
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-    "text": "3.1. Install packages and load libraries\n\nLoad the R packages you will be using. Update this list of packages as you dig into the code: You may realise you need packages you didn’t think about at the beginning. Remember that it is a good practice to load tydiverse at the end of your package list, to avoid masking functions.\n\n\n\nCode\n# Check if the 'pacman' package is installed, if not install it:\nif (!requireNamespace(\"pacman\", quietly = TRUE)) install.packages(\"pacman\")\n\n# Load the required libraries into the current R session:\npacman::p_load(rio, \n               here, \n               tidyverse, \n               skimr,\n               plyr,\n               janitor,\n               lubridate,\n               gtsummary, \n               flextable,\n               officer,\n               epikit, \n               apyramid, \n               scales)"
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-    "text": "3.2. Import your data\n\nYou can find the spetses_school.csv file in EVA. Feel free to explore the codebook as well.\n\n\n\nCode\n# Import the raw data set:\ncopdata &lt;- rio::import(here::here(\"data\", \"spetses_school.csv\"))"
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-    "text": "3.3. Explore and clean your data\n\nHave a look at the structure of your data. Here you have some questions that may help you get started:\n\nHow many observations and variables does the dataset contain?\nWhat types of variables do you have and what types of values are recorded?\nDo any of the values of the other variables look implausible? Which ones and why? What will you do about it?\n\nGo ahead and clean your data as you see fit.\n\n\n\n\n\n\n\nNeed a little bit of help?\n\n\n\n\n\nYou could use head(), dim(), str(), or skim() to have a quick look at the data and get an idea of which variables you want to explore further. You can also do some tables\n\n\n\n\n\nCode\nhead(copdata)\n\n\n\n\n  \n\n\n\nCode\ndim(copdata)\n\n\n[1] 397  40\n\n\nCode\nstr(copdata)\n\n\n'data.frame':   397 obs. of  40 variables:\n $ id        : chr  \"sp-2-001\" \"sp-3-003\" \"sp-1-005\" \"sp-2-006\" ...\n $ sex       : chr  \"male\" \"female\" \"female\" \"male\" ...\n $ age       : int  18 18 17 17 18 180 16 15 43 16 ...\n $ group     : int  1 1 1 1 1 1 0 1 0 1 ...\n $ class     : int  2 3 1 2 3 2 NA 1 NA 1 ...\n $ diarrhoea : int  1 NA NA NA 1 1 NA 0 1 NA ...\n $ bloody    : int  0 NA NA NA 0 0 NA 0 NA NA ...\n $ vomiting  : int  0 NA NA NA 0 0 NA 0 NA NA ...\n $ abdo      : int  1 NA 1 NA 1 1 NA 0 NA NA ...\n $ nausea    : int  0 NA 1 NA 1 0 NA 1 1 NA ...\n $ fever     : int  NA NA NA NA 0 0 NA 0 NA NA ...\n $ headache  : int  0 NA 1 NA 1 0 NA 1 1 NA ...\n $ jointpain : int  0 NA NA NA 0 0 NA 0 1 NA ...\n $ starthour : int  9 NA NA NA 15 15 NA NA 3 NA ...\n $ meal      : int  1 1 1 0 1 1 1 1 1 1 ...\n $ feta      : int  1 0 NA 0 1 1 1 1 1 1 ...\n $ fetaD     : int  2 0 NA 0 2 2 2 1 2 2 ...\n $ sardines  : int  1 0 NA 0 1 1 1 0 1 1 ...\n $ sardinesD : int  2 0 NA 0 2 2 2 0 2 2 ...\n $ eggplant  : int  0 0 NA 0 1 1 1 0 NA 1 ...\n $ eggplantD : int  0 0 NA 0 2 2 2 0 NA 2 ...\n $ moussaka  : int  1 1 1 1 1 1 1 1 1 1 ...\n $ moussakaD : int  2 1 0 0 2 2 1 3 2 2 ...\n $ orzo      : int  1 1 1 0 1 1 1 1 1 1 ...\n $ orzoD     : int  3 3 1 0 2 2 2 3 2 3 ...\n $ greeksal  : int  1 1 NA 0 1 1 1 1 1 1 ...\n $ greeksalD : int  1 3 NA 0 2 2 2 2 2 2 ...\n $ bread     : int  1 1 1 0 1 1 1 1 1 1 ...\n $ breadD    : int  2 3 1 0 2 2 2 2 2 2 ...\n $ dessert   : int  1 1 NA 0 1 1 0 1 NA NA ...\n $ dessertD  : int  2 3 NA 0 2 2 0 1 NA NA ...\n $ champagne : int  1 1 0 1 1 1 1 1 1 1 ...\n $ champagneD: int  1 1 0 3 1 1 2 1 1 1 ...\n $ beer      : int  1 0 0 1 1 1 NA 1 NA 1 ...\n $ beerD     : int  3 0 0 3 2 3 NA 1 NA 2 ...\n $ redwine   : int  0 1 0 1 0 0 1 0 1 0 ...\n $ redwineD  : int  0 3 0 3 0 0 2 0 2 0 ...\n $ whitewine : int  0 0 0 1 1 1 1 1 NA 0 ...\n $ whitewineD: int  0 0 0 3 3 2 1 3 NA 0 ...\n $ dayonset  : chr  \"6oct2024\" \"\" \"\" \"\" ...\n\n\nCode\nskimr::skim(copdata)\n\n\n\nData summary\n\n\nName\ncopdata\n\n\nNumber of rows\n397\n\n\nNumber of columns\n40\n\n\n_______________________\n\n\n\nColumn type frequency:\n\n\n\ncharacter\n3\n\n\nnumeric\n37\n\n\n________________________\n\n\n\nGroup variables\nNone\n\n\n\nVariable type: character\n\n\n\n\n\n\n\n\n\n\n\n\n\nskim_variable\nn_missing\ncomplete_rate\nmin\nmax\nempty\nn_unique\nwhitespace\n\n\n\n\nid\n0\n1\n8\n8\n0\n397\n0\n\n\nsex\n0\n1\n4\n6\n0\n2\n0\n\n\ndayonset\n0\n1\n0\n8\n175\n4\n0\n\n\n\nVariable type: numeric\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nskim_variable\nn_missing\ncomplete_rate\nmean\nsd\np0\np25\np50\np75\np100\nhist\n\n\n\n\nage\n0\n1.00\n18.57\n9.92\n8\n16\n17\n18\n180\n▇▁▁▁▁\n\n\ngroup\n0\n1.00\n0.96\n0.20\n0\n1\n1\n1\n1\n▁▁▁▁▇\n\n\nclass\n36\n0.91\n1.94\n0.84\n1\n1\n2\n3\n3\n▇▁▆▁▇\n\n\ndiarrhoea\n141\n0.64\n0.82\n0.39\n0\n1\n1\n1\n1\n▂▁▁▁▇\n\n\nbloody\n200\n0.50\n0.03\n0.17\n0\n0\n0\n0\n1\n▇▁▁▁▁\n\n\nvomiting\n179\n0.55\n0.30\n0.46\n0\n0\n0\n1\n1\n▇▁▁▁▃\n\n\nabdo\n152\n0.62\n0.85\n0.35\n0\n1\n1\n1\n1\n▂▁▁▁▇\n\n\nnausea\n170\n0.57\n0.76\n0.43\n0\n1\n1\n1\n1\n▂▁▁▁▇\n\n\nfever\n223\n0.44\n0.26\n0.44\n0\n0\n0\n1\n1\n▇▁▁▁▃\n\n\nheadache\n174\n0.56\n0.63\n0.48\n0\n0\n1\n1\n1\n▅▁▁▁▇\n\n\njointpain\n207\n0.48\n0.16\n0.37\n0\n0\n0\n0\n1\n▇▁▁▁▂\n\n\nstarthour\n177\n0.55\n12.49\n4.92\n3\n9\n9\n15\n21\n▁▇▁▆▃\n\n\nmeal\n9\n0.98\n0.97\n0.17\n0\n1\n1\n1\n1\n▁▁▁▁▇\n\n\nfeta\n16\n0.96\n0.71\n0.45\n0\n0\n1\n1\n1\n▃▁▁▁▇\n\n\nfetaD\n16\n0.96\n1.32\n1.00\n0\n0\n2\n2\n3\n▆▅▁▇▂\n\n\nsardines\n17\n0.96\n0.67\n0.47\n0\n0\n1\n1\n1\n▃▁▁▁▇\n\n\nsardinesD\n17\n0.96\n1.34\n1.04\n0\n0\n2\n2\n3\n▆▂▁▇▂\n\n\neggplant\n30\n0.92\n0.59\n0.49\n0\n0\n1\n1\n1\n▆▁▁▁▇\n\n\neggplantD\n30\n0.92\n1.14\n1.05\n0\n0\n1\n2\n3\n▇▂▁▇▂\n\n\nmoussaka\n15\n0.96\n0.89\n0.31\n0\n1\n1\n1\n1\n▁▁▁▁▇\n\n\nmoussakaD\n14\n0.96\n1.83\n0.91\n0\n1\n2\n2\n3\n▂▃▁▇▃\n\n\norzo\n15\n0.96\n0.88\n0.32\n0\n1\n1\n1\n1\n▁▁▁▁▇\n\n\norzoD\n15\n0.96\n1.81\n0.91\n0\n1\n2\n2\n3\n▂▃▁▇▃\n\n\ngreeksal\n24\n0.94\n0.57\n0.50\n0\n0\n1\n1\n1\n▆▁▁▁▇\n\n\ngreeksalD\n24\n0.94\n1.08\n1.06\n0\n0\n1\n2\n3\n▇▂▁▆▂\n\n\nbread\n18\n0.95\n0.91\n0.29\n0\n1\n1\n1\n1\n▁▁▁▁▇\n\n\nbreadD\n18\n0.95\n1.75\n0.71\n0\n2\n2\n2\n3\n▁▂▁▇▁\n\n\ndessert\n42\n0.89\n0.42\n0.49\n0\n0\n0\n1\n1\n▇▁▁▁▆\n\n\ndessertD\n42\n0.89\n0.83\n1.06\n0\n0\n0\n2\n3\n▇▁▁▃▁\n\n\nchampagne\n25\n0.94\n0.87\n0.34\n0\n1\n1\n1\n1\n▁▁▁▁▇\n\n\nchampagneD\n25\n0.94\n1.37\n0.93\n0\n1\n1\n2\n3\n▂▇▁▂▃\n\n\nbeer\n30\n0.92\n0.78\n0.42\n0\n1\n1\n1\n1\n▂▁▁▁▇\n\n\nbeerD\n35\n0.91\n1.94\n1.23\n0\n1\n3\n3\n3\n▃▂▁▂▇\n\n\nredwine\n50\n0.87\n0.23\n0.42\n0\n0\n0\n0\n1\n▇▁▁▁▂\n\n\nredwineD\n52\n0.87\n0.45\n0.92\n0\n0\n0\n0\n3\n▇▁▁▁▁\n\n\nwhitewine\n31\n0.92\n0.73\n0.45\n0\n0\n1\n1\n1\n▃▁▁▁▇\n\n\nwhitewineD\n36\n0.91\n1.58\n1.21\n0\n0\n2\n3\n3\n▆▅▁▅▇\n\n\n\n\n\nCode\nnames(copdata)\n\n\n [1] \"id\"         \"sex\"        \"age\"        \"group\"      \"class\"     \n [6] \"diarrhoea\"  \"bloody\"     \"vomiting\"   \"abdo\"       \"nausea\"    \n[11] \"fever\"      \"headache\"   \"jointpain\"  \"starthour\"  \"meal\"      \n[16] \"feta\"       \"fetaD\"      \"sardines\"   \"sardinesD\"  \"eggplant\"  \n[21] \"eggplantD\"  \"moussaka\"   \"moussakaD\"  \"orzo\"       \"orzoD\"     \n[26] \"greeksal\"   \"greeksalD\"  \"bread\"      \"breadD\"     \"dessert\"   \n[31] \"dessertD\"   \"champagne\"  \"champagneD\" \"beer\"       \"beerD\"     \n[36] \"redwine\"    \"redwineD\"   \"whitewine\"  \"whitewineD\" \"dayonset\"  \n\n\nLet’s explore and manage some variables in detail.\n\nAge\nThrough visual exploration of the age histogram we see that there is at least one very high value, likely implausible. You can then create a cross-tabulation of variables age and group to have a better idea of how your data looks like.\n\n\nCode\n# Have a look at the histogram  \nhist(copdata$age)   \n\n\n\n\n\nCode\n# Create cross-tab with the group variable:  \njanitor::tabyl(dat = copdata, \n               var1 = age, \n               var2 = group)\n\n\n\n\n  \n\n\n\nNote that group is coded as 0 and 1, and these may be difficult to interpret when they mean something other than “no” and “yes”, respectively. From the codebook, you know that teachers are represented by 0, and students by 1. Let’s change this to make our lives easier:\n\n\nCode\n# Convert group to a factor and label 0 as teacher, 1 as student:\ncopdata &lt;- copdata %&gt;% \n  mutate(group = factor(group, \n                        labels = c(\"teacher\", \"student\")))\n\n\nNow, have a look at your cross-tab again:\n\n\nCode\njanitor::tabyl(dat = copdata, \n               var1 = age, \n               var2 = group) \n\n\n\n\n  \n\n\n\nWith this table, we can more easily identify ages that are likely to be typographic errors. Specifically:\n\nThere is one teacher aged 16 (likely digit reversal - should be 61)\nThere is one student aged 8 (likely missing a digit - should be 18)\nThere is one student aged 180 (likely has an extra digit - should be 18)\n\nAssuming you have contacted the school to make sure your suspicions about the actual ages are correct, we can now correct them, using case_when(). We create logical conditions to identify the incorrect ages, combining the values for age with the group they belong to:\n\n\nCode\n# Update incorrect ages to the correct values with case_when:  \ncopdata &lt;- copdata %&gt;%       \n  mutate(age =                         \n           case_when(                            \n             # Where respondent is 16 and a teacher, change age to 61:\n             age == 16 & group == \"teacher\" ~ 61,              \n             # where respondent is 8 or 180 and a student, change age to 18:\n             age == 8 & group == \"student\" ~ 18,              \n             age == 180 & group == \"student\" ~ 18,              \n             # Keep remaining values as is:              \n             .default = as.numeric(age)   \n             # if .default is not working, try:\n             # TRUE ~ age\n             )                    \n         ) \n\n\n\n\n\n\n\n\nCreate summary table for the dose response columns (those finishing with a capital “D”) to have a look at their distribution.\n\n\n\n\n\n\n\nNeed a little bit of help?\n\n\n\n\n\nUse gtsummary::tbl_summary().\n\n\n\n\n\nCode\n#| label: check_dose_cols\n#| tbl-cap: no caption\n\n# Create summary table for dose response columns: \ndrtable &lt;- copdata %&gt;%       \n  # Select all the columns with column names that end in upper case 'D':   \n  select(ends_with(\"D\", ignore.case = FALSE)) %&gt;%       \n  # Create the summary table, excluding missing values:   \n  gtsummary::tbl_summary(missing = \"no\") \n\n  # Print the summary table: \ndrtable\n\n\n\n\n\n\n  \n    \n    \n      Characteristic\n      N = 3971\n    \n  \n  \n    fetaD\n\n        0\n110 (29%)\n        1\n78 (20%)\n        2\n155 (41%)\n        3\n38 (10.0%)\n    sardinesD\n\n        0\n125 (33%)\n        1\n35 (9.2%)\n        2\n184 (48%)\n        3\n36 (9.5%)\n    eggplantD\n\n        0\n151 (41%)\n        1\n41 (11%)\n        2\n147 (40%)\n        3\n28 (7.6%)\n    moussakaD\n\n        0\n42 (11%)\n        1\n70 (18%)\n        2\n184 (48%)\n        3\n87 (23%)\n    orzoD\n\n        0\n44 (12%)\n        1\n70 (18%)\n        2\n183 (48%)\n        3\n85 (22%)\n    greeksalD\n\n        0\n162 (43%)\n        1\n52 (14%)\n        2\n126 (34%)\n        3\n33 (8.8%)\n    breadD\n\n        0\n35 (9.2%)\n        1\n51 (13%)\n        2\n267 (70%)\n        3\n26 (6.9%)\n    dessertD\n\n        0\n206 (58%)\n        1\n33 (9.3%)\n        2\n87 (25%)\n        3\n29 (8.2%)\n    champagneD\n\n        0\n49 (13%)\n        1\n208 (56%)\n        2\n45 (12%)\n        3\n70 (19%)\n    beerD\n\n        0\n81 (22%)\n        1\n41 (11%)\n        2\n57 (16%)\n        3\n183 (51%)\n    redwineD\n\n        0\n266 (77%)\n        1\n31 (9.0%)\n        2\n21 (6.1%)\n        3\n27 (7.8%)\n    whitewineD\n\n        0\n100 (28%)\n        1\n73 (20%)\n        2\n67 (19%)\n        3\n121 (34%)\n  \n  \n  \n    \n      1 n (%)"
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-    "section": "3.4. Modify variables format",
-    "text": "3.4. Modify variables format\nThere are some “column types” you would like to modify because either R needs some specific types of data when working with certain variables, or because it will be easier for you to visualise and interpret the data. For example, the variable “group” is encoded as 0 and 1, when you create a table later on, you don’t want to see 0’s and 1’s because that doesn’t mean anything. It is better to change this variable to a factor, with explicit labels, so they actually mean something when they come out in your table.\n\n\n\n\n\nYou can use mutate to modify the type of the following variables:\n\nTable 1: Variable types to modify\n\n\n\n\n\n\n\n\nVariable name\nOriginal\nDesired\nHint\n\n\n\n\nsex\ncharacter\nfactor\nmutate(), as.factor()\n\n\nclass\ninteger\nfactor\nmutate(), as.factor()\n\n\nAll the clinical symptom variables\ninteger\nlogical\nmutate(across()), as.logical()\n\n\nAll the food variables representing the amount of specific foods eaten (those finishing with a capital “D”)\ninteger\nfactor\nmutate(across()), as.factor()\n\n\ndayonset\ncharacter\ndate\nlubridate::dmy()\n\n\nstarthour and dayonset together\ninteger (starthour)\ndate (dayonset)\nPOSIXct, POSIXt\nlubridate::ymd_h() could have inside stringr::str_glue() with dayonset and starthour\n\n\n\n\nSex, group and class\nLet’s start transforming one-by-one the first two variables in the table: sex, and class.\n\n\nCode\ncopdata &lt;- copdata %&gt;% \n  dplyr::mutate(\n    sex = as.factor(sex),\n    class = as.factor(class))\n\n\n\n\nSymptoms and Food variables\nFor these variables, we are going to show you a couple of different ways to carry out the same variable type transformation in a set of variables, so you don’t need to do one variable at a time. We are showing you these ways so you see alternative ways to do the same thing.\n\nFor the variables that are clinical symptoms, we will list them one by one and show you the use of mutate(across( )).\n\n\n\nCode\ncopdata &lt;- copdata %&gt;% \n  dplyr::mutate(\n    # clinical symptoms\n    across(.cols = c(diarrhoea, bloody, vomiting,\n             abdo, nausea, fever,headache, jointpain), \n           .fns = ~ as.logical(.)\n           )\n    )\n\n\n\nFor the variables that are food doses, we will show you how to first create a vector of names, following by using mutate(across(all_of( ))) on this vector.\n\n\n\nCode\n# Create a vector with all the food variables representing the amount of specific foods items eaten (those finishing with a capital \"D\")\n# One way of doing it:\nfood_dose &lt;- copdata %&gt;% \n    dplyr::select(\n      ends_with(\"D\", ignore.case = FALSE)) %&gt;% \n    names()\n\n# Another way of doing it:\n# food_dose &lt;- c(\"fetaD\", \"sardinesD\", \"eggplantD\", \"moussakaD\", \n#                 \"orzoD\", \"greeksalD\", \"dessertD\", \"breadD\", \n#                 \"champagneD\", \"beerD\", \"redwineD\", \"whitewineD\")\n\n\ncopdata &lt;- copdata %&gt;% \n  dplyr::mutate(\n    # food dose variables\n    across(.cols = all_of(food_dose), \n           .fns = ~as.factor(.))) \n\n\n\n\n\n\n\n\nNote\n\n\n\n\n\nThe tilde (~) above is used to apply the transformation as.logical(.) to each selected column, which in our case is either all columns included in food_items and food_dose.\n\n\n\n\n\nDate and time variables\n\n\n\n\n\n\nLet’s stop and… think!\n\n\n\n\n\nWhy may you want to transform the variable starthour to date-time?\n\n\n\n\n\n\n\n\nYou can use lubridate::dmy() to mutate the dayonset variable into a date variable. Note that we are using the function dmy() because dates are formatted as day, then month (abbreviated character string), then year (i.e. “12nov2006”).\n\n\nCode\n# Have a look at how the data is stored\nhead(copdata$dayonset)\n\n\n[1] \"6oct2024\" \"\"         \"\"         \"\"         \"6oct2024\" \"7oct2024\"\n\n\nCode\nclass(copdata$dayonset)\n\n\n[1] \"character\"\n\n\nCode\n# Update copdata:\ncopdata &lt;- copdata %&gt;% \n  # Change column to date class:\n  dplyr::mutate(\n    dayonset = lubridate::dmy(dayonset))\n\n# Check class of updated column:\nclass(copdata$dayonset)\n\n\n[1] \"Date\"\n\n\nCode\n# Have a look at your data now:\nhead(copdata$dayonset)\n\n\n[1] \"2024-10-06\" NA           NA           NA           \"2024-10-06\"\n[6] \"2024-10-07\"\n\n\nHaving a variable that defines “time” in an outbreak investigation can be very useful when creating a case definition. An hour of the day, without a date associated with it doesn’t help you much, thus, you should merge together day and time of onset of symptoms into a single variable. Moreover, you will be using this combined variable later on to estimate an incubation period and create your epicurve. We can combine these two variables by using the lubridate::ymd_h() function.\nBefore we proceed, it would be wise to check if any respondents have a value for dayonset but not starthour, or vice versa. The lubridate date-time conversion functions do not have an explicit argument for dealing with missing values, but the truncated = … argument can help prevent spurious date-times being derived from a date-time combination where one value is missing.\nWe can check if we have any missing values by cross-tabulating starthour with dayonset:\n\n\nCode\n# Cross-tabulate dayonset with starthour:\njanitor::tabyl(dat = copdata, \n               var1 = starthour, \n               var2 = dayonset)\n\n\n\n\n  \n\n\n\nThis shows us that there are two respondents who had an onset date, but are missing onset time (starthour). Since starthour is represented by 1 - 2 digits, we can specify that we want lubridate to also parse date-time combinations that are truncated by up to two digits:\n\n\nCode\ncopdata &lt;- copdata %&gt;% \n  # Combine dayonset and starthour in a new date time variable:\n  mutate(onset_datetime = \n           lubridate::ymd_h(\n             str_glue(\"{dayonset}, {starthour}\"), \n                                           # Deal with missing starthour:\n                                           truncated = 2))\n\n\nWarning: There was 1 warning in `mutate()`.\nℹ In argument: `onset_datetime = lubridate::ymd_h(str_glue(\"{dayonset},\n  {starthour}\"), truncated = 2)`.\nCaused by warning:\n!  175 failed to parse.\n\n\nNote that we needed to use str_glue() to concatenate dayonset and starthour together before we could convert the variable to a date-time object. This is because the ymd_h() function expects a single character string, containing both the date and the time, as input.\nThe argument truncated = 2 will result in dates with missing starthour still being converted to date-time, with the missing time being set to 00:00 (midnight). Whether you want to deal with missing starthour in this way or prefer to code these date-times as NA will depend on how you want them to be represented in your analysis.\nNow we can check that everything in the new combined date-time variable has parsed correctly:\n\n\nCode\nhead(copdata$dayonset)\n\n\n[1] \"2024-10-06\" NA           NA           NA           \"2024-10-06\"\n[6] \"2024-10-07\"\n\n\nCode\nhead(copdata$starthour)\n\n\n[1]  9 NA NA NA 15 15\n\n\nCode\nhead(copdata$onset_datetime)\n\n\n[1] \"2024-10-06 09:00:00 UTC\" NA                       \n[3] NA                        NA                       \n[5] \"2024-10-06 15:00:00 UTC\" \"2024-10-07 15:00:00 UTC\""
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-    "text": "3.5. Export clean data\nSave the cleaned data set before proceeding with using your case definition to identify cases in your dataset. Use the .rds format, as it preserves column classes. This ensures you will have only minimal cleaning to do after importing the data into R at the next inject.\nSave your clean data as a new file “Spetses_clean1_YOURINITIALS.rds”, under the data folder of your Rproject.\n\n\n\n\n\n\nNeed a little bit of help?\n\n\n\n\n\nUse rio::export().\n\n\n\n\n\nCode\nrio::export(x = copdata, \n            file = here::here(\"data\", \"Spetses_clean1_2024.rds\"))"
+    "text": "3.6 Summary\nAs a summary of what you’ve done above, answer these questions:\n\nIs the respondents’ sex associated with being a case?\nIs the school class associated with being a case?\nWhich foods increase the risk of being a case?\n(Optional) Is there a dose-response relationship between the food items and being a case?\nWhat do you think is the most likely culprit(s) of this outbreak at this point? Are there any risk factors you would like to highlight?"
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     "section": "3.1 Install packages (if needed) and load libraries",
-    "text": "3.1 Install packages (if needed) and load libraries\n\n\nCode\n# Load the required libraries into the current R session:\npacman::p_load(rio, \n               here, \n               tidyverse, \n               skimr,\n               plyr,\n               janitor,\n               lubridate,\n               gtsummary, \n               flextable,\n               officer,\n               epikit, \n               apyramid, \n               scales)"
+    "text": "3.1 Install packages (if needed) and load libraries\n\n\nShow the code\n# Load the required libraries into the current R session:\npacman::p_load(rio, \n               here, \n               tidyverse, \n               skimr,\n               plyr,\n               janitor,\n               lubridate,\n               gtsummary, \n               flextable,\n               officer,\n               epikit, \n               apyramid, \n               scales)"
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     "title": "Laboratory data and descriptive analysis",
     "section": "3.2 Import your data",
-    "text": "3.2 Import your data\n\n\nCode\n# Import the clean data set:\ncopdata &lt;- rio::import(here::here(\"data\", \"Spetses_clean2_2024.rds\")) \nlab &lt;- rio::import(here::here(\"data\", \"Lab data.xlsx\"),\n                   skip = 1) # skip the first row."
+    "text": "3.2 Import your data\n\n\nShow the code\n# Import the clean data set:\ncopdata &lt;- rio::import(here::here(\"data\", \"Spetses_clean2_2024.rds\"), trust = T) \nlab &lt;- rio::import(here::here(\"data\", \"Lab data.xlsx\"),\n                   skip = 1) # skip the first row."
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     "title": "Laboratory data and descriptive analysis",
-    "section": "3.3 Merge and explore the lab data",
-    "text": "3.3 Merge and explore the lab data\n\n\nCode\ncopdatalab &lt;- left_join(copdata, lab, by = \"id\")\n# Tabulate:\njanitor::tabyl(dat = copdatalab, result)"
+    "section": "3.3 Merge and explore the lab data. Explore it as you wish!",
+    "text": "3.3 Merge and explore the lab data. Explore it as you wish!\n\n\nShow the code\ncopdatalab &lt;- left_join(copdata, lab, \n                        by = \"id\")\n# Tabulate:\njanitor::tabyl(dat = copdatalab, \"RT-PCR_ecoli_etec\")"
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     "title": "Laboratory data and descriptive analysis",
     "section": "3.4 Describe the outbreak in terms of time.",
-    "text": "3.4 Describe the outbreak in terms of time.\nYou could use ggplot2.To learn more about ggplot2 you can check The Epidemiologist R Handbook: Chapter on ggplot and ggplot2 - Elegant Graphics for Data Analysis.\n\na) Create a histogram to visualize the incubation period (geom_histogram is a good function to use for this!).\n\n\n\n\n\n\nLet’s stop and… think!\n\n\n\n\n\nWhat can you infer from the incubation periods in the histogram?\n\n\n\n\n\nCode\n#| label: inc_time\n\n# Create a dataset with only cases\ncases &lt;- copdata %&gt;% \n  filter(case == TRUE)\n\nincplot &lt;- cases %&gt;% \n  # Create an empty ggplot frame:\n  ggplot() +\n  # Add a histogram of incubation:\n  geom_histogram(\n    mapping = aes(x = incubation), \n    # Set bin widths to 6 hours:\n    binwidth = 6) +\n  # Adapt scale to better fit data\n  scale_x_continuous(breaks = seq(0, 48, 6)) + \n  # Label x and y axes:\n  labs(x = \"Incubation period in 6-hour bins\",\n       y = \"Number of cases\")\n\n# Print plot:\nincplot\n\n\n\n\n\n\n\nb) Create an epicurve for the date and time of onset (using onset_datetime), limiting the input data to cases.\n\nUse scale_x_datetime() and chose a value for date_breaks() based on your calculated incubation period.\nLabel your x and y axes using labs().\n\n\n\n\n\n\n\nNeed a little bit of help?\n\n\n\n\n\nA rule of thumb is to use one third or one fourth of the average incubation period as an interval. For our investigation this means we should use approximately a 6h interval for the date_breaks argument.\n\n\n\n\n\nCode\n# Create a vector with sequences every 6h from the first to the last case\nbreaks_6h &lt;- seq(from = min(cases$onset_datetime, na.rm = TRUE),\n                 to = max(cases$onset_datetime, na.rm = TRUE),\n                 by = \"6 hours\")\n\n# Fetch cases data:\nepicurve_datetime &lt;- cases %&gt;%  \n  # Add factor onset_datetime to ggplot aesthetic:\n  ggplot(\n    mapping = aes(x = onset_datetime)) + \n  # Add geom_histogram:\n  geom_histogram(\n    # Apply the vector of requences created above\n    breaks = breaks_6h) +\n  # Adapt scale to data and adjust axis label angle:\n  scale_x_datetime(\n    date_breaks = \"6 hours\",\n    labels = label_date_short()) +\n  # Update x and y axis labels:\n  labs(x = \"Date and time of onset symptoms\", \n       y = \"Number of cases\") +\n  # Remove unnecessary grid lines:\n  theme_bw()\n\n# Print epicurve:\nepicurve_datetime\n\n\n\n\n\n\nBuilding up on your previous epicurve, create another epicurve to compare between sexes and additionally investigate how teachers versus students were distributed.\n\n\nUse fill = group\nUse sex as your facets for facet_wrap()\n\n\n\n\n\n\n\nNeed a little bit of help?\n\n\n\n\n\nfill adds an additional variable to be displayed in the plot: group is going to determine the fill-colour of our bars. facet_wrap splits the graph in two: one each for the two levels of sex.\n\n\n\n\n\n\n\n\n\nDo you feel like a challenge?\n\n\n\n\n\nIf you want, try using str_glue() to add the total number of cases to the sub-title of the plot. str_glue() is a very useful function that allows you to dynamically create a summary statistic from your data within some normal text.\n\n\n\n\n\nCode\nepicurve_strata &lt;- cases %&gt;% \n  # Add factor onset_day to ggplot aesthetic:\n  ggplot(\n    mapping = aes(x = onset_datetime, fill = group)) + \n  # Add nicer fill colours:\n  scale_fill_manual(values = c(\"darkred\", \"lightblue\")) +\n    # Add geom_histogram:\n  geom_histogram(\n    # Apply the vector of requences created above\n    breaks = breaks_6h) +\n  # Adjust x axis scales to a suitable unit:\n  scale_x_datetime(\n    date_breaks = \"6 hours\", \n    labels = label_date_short()) +\n  # Update x and y axis labels:\n  labs(x = \"Date and time of onset\", \n       y = \"Number of cases\", \n       fill = \"Group\", \n       title = \"Epicurve of the outbreak, stratified by sex\",\n       subtitle = str_glue(\"Spetses, October 2024, N = {sum(copdata$case)}\")) +\n  # Stratify by sex:\n  facet_wrap(facets = \"sex\",\n             ncol = 2) +\n  # Add theme:\n  theme_bw()\n\n# Print epicurve:\nepicurve_strata \n\n\n\n\n\n\n\n\n\n\n\nLet’s stop and… think!\n\n\n\n\n\nWhat does the stratified epicurve tell you? Does the shape of the epicurve support a viral or toxic aetiology? What other information can you obtain from it?\n\n\n\n\n\n\n\n\n\nIf you’ve thought about the above questions, have a look here\n\n\n\n\n\nWhen constructing an epicurve, we need to decide on the resolution, i.e. the time interval for a single bar. A rule of thumb is to use one third or one fourth of the average incubation period as an interval. For our investigation this means we should use approximately a 6h interval.\nThis seems a good choice, as we saw that the daily interval was too coarse to really see the signal we are after. The epicurve and the summary of the incubation period show that there seemed to be a rapid onset of symptoms following exposure. This is in line with our previous suspicion that a virus or a toxin might be the causative agent in the outbreak.\nThe unimodal shape with the sharp peak suggests a point source, while the tail on the right-hand side could be explained by secondary cases or background noise. Also, people that only consumed a little contaminated food and therefore only a low infectious dose could have a longer incubation period and could explain the late cases.\nThe above results are in line with norovirus as the prime suspect, but the symptoms are not a textbook fit. There are too few people that experienced vomiting! Looking forward to receiving the lab results!"
+    "text": "3.4 Describe the outbreak in terms of time.\nYou could use ggplot2.To learn more about ggplot2 you can check The Epidemiologist R Handbook: Chapter on ggplot and ggplot2 - Elegant Graphics for Data Analysis.\n\na) Create a histogram to visualize the incubation period (geom_histogram is a good function to use for this!).\n\n\n\n\n\n\nLet’s stop and… think!\n\n\n\n\n\nWhat can you infer from the incubation periods in the histogram?\n\n\n\n\n\nShow the code\n#| label: inc_time\n\n# Create a dataset with only cases\ncases &lt;- copdata %&gt;% \n  filter(case == 1)\n\nincplot &lt;- cases %&gt;% \n  # Create an empty ggplot frame:\n  ggplot() +\n  # Add a histogram of incubation:\n  geom_histogram(\n    mapping = aes(x = incubation), \n    # Set bin widths to 6 hours:\n    binwidth = 6) +\n  # Adapt scale to better fit data\n  scale_x_continuous(breaks = seq(0, 48, 6)) + \n  # Label x and y axes:\n  labs(x = \"Incubation period in 6-hour bins\",\n       y = \"Number of cases\")\n\n# Print plot:\nincplot\n\n\n\n\n\n\n\n\n\n\n\nb) Create an epicurve for the date and time of onset (using onset_datetime), limiting the input data to cases.\n\nUse scale_x_datetime() and chose a value for date_breaks() based on your calculated incubation period.\nLabel your x and y axes using labs().\n\n\n\n\n\n\n\nNeed a little bit of help?\n\n\n\n\n\nA rule of thumb is to use one third or one fourth of the average incubation period as an interval. For our investigation this means we should use approximately a 6h interval for the date_breaks argument.\n\n\n\n\n\nShow the code\n# Create a vector with sequences every 6h from the first to the last case\nbreaks_6h &lt;- seq(from = min(cases$onset_datetime, na.rm = TRUE),\n                 to = max(cases$onset_datetime, na.rm = TRUE),\n                 by = \"6 hours\")\n\n# Fetch cases data:\nepicurve_datetime &lt;- cases %&gt;%  \n  # Add factor onset_datetime to ggplot aesthetic:\n  ggplot(\n    mapping = aes(x = onset_datetime)) + \n  # Add geom_histogram:\n  geom_histogram(\n    # Apply the vector of requences created above\n    breaks = breaks_6h) +\n  # Adapt scale to data and adjust axis label angle:\n  scale_x_datetime(\n    date_breaks = \"6 hours\",\n    labels = label_date_short()) +\n  # Update x and y axis labels:\n  labs(x = \"Date and time of onset symptoms\", \n       y = \"Number of cases\") +\n  # Remove unnecessary grid lines:\n  theme_bw()\n\n# Print epicurve:\nepicurve_datetime\n\n\n\n\n\n\n\n\n\n\nBuilding up on your previous epicurve, create another epicurve to compare between sexes and additionally investigate how teachers versus students were distributed.\n\n\nUse fill = group\nUse sex as your facets for facet_wrap()\n\n\n\n\n\n\n\nNeed a little bit of help?\n\n\n\n\n\nfill adds an additional variable to be displayed in the plot: group is going to determine the fill-colour of our bars. facet_wrap splits the graph in two: one each for the two levels of sex.\n\n\n\n\n\n\n\n\n\nDo you feel like a challenge?\n\n\n\n\n\nIf you want, try using str_glue() to add the total number of cases to the sub-title of the plot. str_glue() is a very useful function that allows you to dynamically create a summary statistic from your data within some normal text.\n\n\n\n\n\nShow the code\nepicurve_strata &lt;- cases %&gt;% \n  # Add factor onset_day to ggplot aesthetic:\n  ggplot(\n    mapping = aes(x = onset_datetime, fill = group)) + \n  # Add nicer fill colours:\n  scale_fill_manual(values = c(\"darkred\", \"lightblue\")) +\n    # Add geom_histogram:\n  geom_histogram(\n    # Apply the vector of requences created above\n    breaks = breaks_6h) +\n  # Adjust x axis scales to a suitable unit:\n  scale_x_datetime(\n    date_breaks = \"6 hours\", \n    labels = label_date_short()) +\n  # Update x and y axis labels:\n  labs(x = \"Date and time of onset\", \n       y = \"Number of cases\", \n       fill = \"Group\", \n       title = \"Epicurve of the outbreak, stratified by sex\",\n       subtitle = str_glue(\"Spetses, October 2024, N = {sum(copdata$case)}\")) +\n  # Stratify by sex:\n  facet_wrap(facets = \"sex\",\n             ncol = 2) +\n  # Add theme:\n  theme_bw()\n\n# Print epicurve:\nepicurve_strata \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLet’s stop and… think!\n\n\n\n\n\nWhat does the stratified epicurve tell you? Does the shape of the epicurve support a viral or toxic aetiology? What other information can you obtain from it?\n\n\n\n\n\n\n\n\n\nIf you’ve thought about the above questions, have a look here\n\n\n\n\n\nWhen constructing an epicurve, we need to decide on the resolution, i.e. the time interval for a single bar. A rule of thumb is to use one third or one fourth of the average incubation period as an interval. For our investigation this means we should use approximately a 6h interval.\nThis seems a good choice, as we saw that the daily interval was too coarse to really see the signal we are after. The epicurve and the summary of the incubation period show that there seemed to be a rapid onset of symptoms following exposure. This is in line with our previous suspicion that a virus or a toxin might be the causative agent in the outbreak.\nThe unimodal shape with the sharp peak suggests a point source, while the tail on the right-hand side could be explained by secondary cases or background noise. Also, people that only consumed a little contaminated food and therefore only a low infectious dose could have a longer incubation period and could explain the late cases.\nThe above results are in line with norovirus as the prime suspect, but the symptoms are not a textbook fit. There are too few people that experienced vomiting! Looking forward to receiving the lab results!"
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     "section": "3.5. Describe the outbreak in terms of person.",
-    "text": "3.5. Describe the outbreak in terms of person.\nYou could use tabyl(). For more detail on the tabyl() function and the adorn_XYZ helpers, see the janitor documentation or The Epidemiologist R Handbook section on tabulation.\n\na) Create a cross-tabulation of case with group.\n\n\nCode\ncopdata %&gt;% \n  janitor::tabyl(case, group) %&gt;% \n  adorn_totals() %&gt;% \n  adorn_percentages() %&gt;% \n  adorn_pct_formatting() \n\n\n\n\n  \n\n\n\n\n\nb) Create a cross-tabulation of case with sex.\n\n\nCode\ncopdata %&gt;% \n  janitor::tabyl(case, sex) %&gt;% \n  adorn_totals() %&gt;% \n  adorn_percentages() %&gt;% \n  adorn_pct_formatting() \n\n\n\n\n  \n\n\n\n\n\n\n\n\n\nLet’s stop and… think!\n\n\n\n\n\nWhat do you think of these results?\n\n\n\n\n\n\n\n\n\nIf you’ve thought about the above questions, have a look here\n\n\n\n\n\nThe distribution of the cohort regarding sex, group and class also didn’t reveal anything unusual. Students seem a bit more affected by the outbreak than teachers and the attack rate is higher for older students in higher classes. This, however, is a purely descriptive result.\n\n\n\n\n\n\n\n\n\nDo you feel like a challenge?\n\n\n\n\n\nIf you want, create an age-sex pyramid using the apyramid package. To do so, first create an age category variable with the epkit function age_categories().\n\n\n\n\n\nc) Extra - Age-sex pyramid of cases.\nHint: Change show_midpoint = FALSE to TRUE to see skewedness in the data patterns more easily.\n\n\nCode\ncopdata &lt;- copdata %&gt;% \n  # Create age categories:\n  mutate(age_cat = epikit::age_categories(\n    # Name of age column:\n    x = age, \n    # Define the age categories:\n    breakers = c(0, 10, 16, 18, 20, 50, 70)\n    )\n  )\n\n\n# Check age categories:\njanitor::tabyl(copdata, age_cat)\n\n\n\n\n  \n\n\n\nCode\n# Pipe copdata:\nagesex &lt;- copdata %&gt;% \n  # Filter for cases only:\n  filter(case == TRUE) %&gt;% \n  # Create age sex pyramid:\n  apyramid::age_pyramid(\n  # Specify column containing age categories:\n    age_group = \"age_cat\",\n    # Specify column containing sex:\n    split_by = \"sex\", \n    # Don't show midpoint on the graph:\n    show_midpoint = FALSE\n    )\n\n# Print plot:\nagesex"
+    "text": "3.5. Describe the outbreak in terms of person.\nYou could use tabyl(). For more detail on the tabyl() function and the adorn_XYZ helpers, see the janitor documentation or The Epidemiologist R Handbook section on tabulation.\n\na) Create a cross-tabulation of case with group.\n\n\nShow the code\ncopdata %&gt;% \n  janitor::tabyl(case, group) %&gt;% \n  adorn_totals() %&gt;% \n  adorn_percentages() %&gt;% \n  adorn_pct_formatting() \n\n\n\n  \n\n\n\n\n\nb) Create a cross-tabulation of case with sex.\n\n\nShow the code\ncopdata %&gt;% \n  janitor::tabyl(case, sex) %&gt;% \n  adorn_totals() %&gt;% \n  adorn_percentages() %&gt;% \n  adorn_pct_formatting() \n\n\n\n  \n\n\n\n\n\n\n\n\n\nLet’s stop and… think!\n\n\n\n\n\nWhat do you think of these results?\n\n\n\n\n\n\n\n\n\nIf you’ve thought about the above questions, have a look here\n\n\n\n\n\nThe distribution of the cohort regarding sex, group and class also didn’t reveal anything unusual. Students seem a bit more affected by the outbreak than teachers and the attack rate is higher for older students in higher classes. This, however, is a purely descriptive result.\n\n\n\n\n\n\n\n\n\nDo you feel like a challenge?\n\n\n\n\n\nIf you want, create an age-sex pyramid using the apyramid package. To do so, first create an age category variable with the epkit function age_categories().\n\n\n\n\n\nc) Extra - Age-sex pyramid of cases.\nHint: Change show_midpoint = FALSE to TRUE to see skewedness in the data patterns more easily.\n\n\nShow the code\ncopdata &lt;- copdata %&gt;% \n  # Create age categories:\n  mutate(age_cat = epikit::age_categories(\n    # Name of age column:\n    x = age, \n    # Define the age categories:\n    breakers = c(0, 10, 16, 18, 20, 50, 70)\n    )\n  )\n\n\n# Check age categories:\njanitor::tabyl(copdata, age_cat)\n\n\n\n  \n\n\n\nShow the code\n# Pipe copdata:\nagesex &lt;- copdata %&gt;% \n  # Filter for cases only:\n  filter(case == TRUE) %&gt;% \n  # Create age sex pyramid:\n  apyramid::age_pyramid(\n  # Specify column containing age categories:\n    age_group = \"age_cat\",\n    # Specify column containing sex:\n    split_by = \"sex\", \n    # Don't show midpoint on the graph:\n    show_midpoint = FALSE\n    )\n\n# Print plot:\nagesex"
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     "section": "3.6 Explore the distribution of all clinical signs (symptoms).",
-    "text": "3.6 Explore the distribution of all clinical signs (symptoms).\n\na) Create a summary table with symptoms stratified by case definition, and present an overall column as well. You could use tabyl() or gtsummary::tbl_summary(). You can find further information about gtsummary in The Epidemiologist R handbook section on gtsummary.\n\n\nCode\n# Create summary table:\ntabsymptoms &lt;- copdata %&gt;% \n    # Select person characteristics to summarise:\n  select(case, diarrhoea, bloody, vomiting,\n             abdo, nausea, fever,headache, jointpain) %&gt;% \n  # transform clinical symptoms to factors, so NA can be accounted properly in the table\n  dplyr::mutate(\n    across(.cols = c(diarrhoea, bloody, vomiting,\n             abdo, nausea, fever,headache, jointpain), \n           .fns = ~as.factor(.))) %&gt;%\n  # The enxt paragraph does not work with Rversion 4.4.0\n  # Make NA a explicit level of factor variables\n  # dplyr::mutate(\n  #   across(.cols = c(diarrhoea, bloody, vomiting,\n  #          abdo, nausea, fever,headache, jointpain),\n  #          .fns = ~forcats::fct_na_value_to_level(.))) %&gt;% \n     \n  # Create the summary table:\n  gtsummary::tbl_summary(\n    # Stratify by case:\n    by = case, \n    # take care of missing values\n    missing = \"no\", \n    # Calculate row percentages:\n    percent = \"column\",\n    # Create nice labels:\n    label  = list(\n      diarrhoea   ~ \"Diarrhoea\",                           \n      bloody      ~ \"Dysentary\",\n      vomiting    ~ \"Vomiting\",\n      abdo        ~ \"Abdominal pain\",\n      nausea      ~ \"Nausea\", \n      fever       ~ \"Fever\", \n      headache    ~ \"Headache\", \n      jointpain   ~ \"Joint pain\")\n    \n  ) %&gt;% \n  \n  # Add totals:\n  add_overall() %&gt;% \n  # Make variable names bold and italics:\n  bold_labels() %&gt;% \n  italicize_labels() %&gt;% \n  # Modify header:\n  modify_header(\n    label = \"**Characteristic**\",\n    stat_0 = \"**Overall**\\n **N** = {N}\",\n    stat_1 = \"**Non-case**\\n **N** = {n}\",\n    stat_2 = \"**Case**\\n **N** = {n}\", \n    )\n\n# Print the table:\ntabsymptoms\n\n\n\n\n\n\n  \n    \n    \n      Characteristic\n      Overall\nN = 3771\n      Non-case\nN = 1611\n      Case\nN = 2161\n    \n  \n  \n    Diarrhoea\n\n\n\n        FALSE\n46 (18%)\n40 (100%)\n6 (2.8%)\n        TRUE\n206 (82%)\n0 (0%)\n206 (97%)\n    Dysentary\n\n\n\n        FALSE\n189 (97%)\n42 (100%)\n147 (97%)\n        TRUE\n5 (2.6%)\n0 (0%)\n5 (3.3%)\n    Vomiting\n\n\n\n        FALSE\n149 (69%)\n42 (100%)\n107 (62%)\n        TRUE\n66 (31%)\n0 (0%)\n66 (38%)\n    Abdominal pain\n\n\n\n        FALSE\n35 (14%)\n6 (12%)\n29 (15%)\n        TRUE\n207 (86%)\n44 (88%)\n163 (85%)\n    Nausea\n\n\n\n        FALSE\n55 (25%)\n12 (26%)\n43 (24%)\n        TRUE\n169 (75%)\n34 (74%)\n135 (76%)\n    Fever\n\n\n\n        FALSE\n127 (74%)\n32 (80%)\n95 (73%)\n        TRUE\n44 (26%)\n8 (20%)\n36 (27%)\n    Headache\n\n\n\n        FALSE\n83 (38%)\n11 (25%)\n72 (41%)\n        TRUE\n137 (62%)\n33 (75%)\n104 (59%)\n    Joint pain\n\n\n\n        FALSE\n159 (85%)\n32 (84%)\n127 (85%)\n        TRUE\n29 (15%)\n6 (16%)\n23 (15%)\n  \n  \n  \n    \n      1 n (%)\n    \n  \n\n\n\n\n\n\n\n\n\n\nLet’s stop and… think!\n\n\n\n\n\nDo you think the symptoms selected for the case definition were the right ones, or would you change anything?\n\n\n\n\n\n\n\n\n\nDo you feel like a challenge?\n\n\n\n\n\nIf you want, present the symptoms in an ordered bar chart. To do so, reshape the data using pivot_longer(), and then group_by(), summarise() and count() to tally up the counts for each symptom, stratified by case definition. Use ggplot2::coord_flip() for a better visualisation.\n\n\n\n\n\nb) Extra - Bar plot of symptoms stratified by case definition.\n\n\nCode\n# Create list of symptom variables:\nsymptoms &lt;- c(\"diarrhoea\", \n              \"bloody\", \n              \"vomiting\", \n              \"abdo\", \n              \"nausea\", \n              \"fever\", \n              \"headache\", \n              \"jointpain\")\n\n# Create nice labels for case definition:\ncaselabs &lt;- ggplot2::as_labeller(c(`FALSE` = \"Non-case\", \n                                   `TRUE` = \"Case\"))\n# Select variables and cases:\nsymptom_bar &lt;- copdata %&gt;% \n  # Select symptom columns:\n  select(case, c(all_of(symptoms))) %&gt;%\n  # Drop NAs:\n  drop_na() %&gt;% \n  # Reshape (pivot longer):\n  pivot_longer(!case, \n               names_to = \"Symptoms\", \n               values_drop_na = TRUE) %&gt;% \n  # Keep only TRUE values:\n  filter(value == TRUE) %&gt;% \n \n   # Group by symptoms and case:\n  group_by(Symptoms, case) %&gt;% \n  # Count for each symptom by case:\n  dplyr::summarise(count = n()) %&gt;% \n  # Create plot:\n  ggplot(\n    mapping = aes(\n    # Order symptom bars so most common ones are ontop:\n    x = reorder(Symptoms, desc(count), decreasing = TRUE), \n    y = count)) +\n  # Display bars as proportions\n  geom_bar(stat = \"identity\") +\n  # Update x axis label:\n  xlab(\"Symptoms\") +\n  # Update y axis label:\n  ylab(\"Proportion of respondents\") +\n  # Flip plot on its side so symptom labels are clear:\n  coord_flip() +\n  # Facet the plot by (labelled) case:\n  facet_wrap(facets = \"case\",\n             labeller = caselabs,\n             ncol = 2)\n\n\n`summarise()` has grouped output by 'Symptoms'. You can override using the\n`.groups` argument.\n\n\nCode\n# Print plot:\nsymptom_bar"
+    "text": "3.6 Explore the distribution of all clinical signs (symptoms).\n\na) Create a summary table with symptoms stratified by case definition, and present an overall column as well. You could use tabyl() or gtsummary::tbl_summary(). You can find further information about gtsummary in The Epidemiologist R handbook section on gtsummary.\n\n\nShow the code\n# Create summary table:\ntabsymptoms &lt;- copdata %&gt;% \n    # Select person characteristics to summarise:\n  select(case, diarrhoea, bloody, vomiting,\n             abdo, nausea, fever,headache, jointpain) %&gt;% \n  # transform clinical symptoms to factors, so NA can be accounted properly in the table\n  dplyr::mutate(\n    across(.cols = c(diarrhoea, bloody, vomiting,\n             abdo, nausea, fever,headache, jointpain), \n           .fns = ~as.factor(.))) %&gt;%\n  # The enxt paragraph does not work with Rversion 4.4.0\n  # Make NA a explicit level of factor variables\n  # dplyr::mutate(\n  #   across(.cols = c(diarrhoea, bloody, vomiting,\n  #          abdo, nausea, fever,headache, jointpain),\n  #          .fns = ~forcats::fct_na_value_to_level(.))) %&gt;% \n     \n  # Create the summary table:\n  gtsummary::tbl_summary(\n    # Stratify by case:\n    by = case, \n    # take care of missing values\n    missing = \"no\", \n    # Calculate row percentages:\n    percent = \"column\",\n    # Create nice labels:\n    label  = list(\n      diarrhoea   ~ \"Diarrhoea\",                           \n      bloody      ~ \"Dysentary\",\n      vomiting    ~ \"Vomiting\",\n      abdo        ~ \"Abdominal pain\",\n      nausea      ~ \"Nausea\", \n      fever       ~ \"Fever\", \n      headache    ~ \"Headache\", \n      jointpain   ~ \"Joint pain\")\n    \n  ) %&gt;% \n  \n  # Add totals:\n  add_overall() %&gt;% \n  # Make variable names bold and italics:\n  bold_labels() %&gt;% \n  italicize_labels() %&gt;% \n  # Modify header:\n  modify_header(\n    label = \"**Characteristic**\",\n    stat_0 = \"**Overall**\\n **N** = {N}\",\n    stat_1 = \"**Non-case**\\n **N** = {n}\",\n    stat_2 = \"**Case**\\n **N** = {n}\", \n    )\n\n# Print the table:\ntabsymptoms\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nCharacteristic\nOverall N = 3771\nNon-case N = 1611\nCase N = 2161\n\n\n\n\nDiarrhoea\n\n\n\n\n\n\n\n\n    0\n46 (18%)\n40 (100%)\n6 (2.8%)\n\n\n    1\n206 (82%)\n0 (0%)\n206 (97%)\n\n\nDysentary\n\n\n\n\n\n\n\n\n    0\n189 (97%)\n42 (100%)\n147 (97%)\n\n\n    1\n5 (2.6%)\n0 (0%)\n5 (3.3%)\n\n\nVomiting\n\n\n\n\n\n\n\n\n    0\n149 (69%)\n42 (100%)\n107 (62%)\n\n\n    1\n66 (31%)\n0 (0%)\n66 (38%)\n\n\nAbdominal pain\n\n\n\n\n\n\n\n\n    0\n35 (14%)\n6 (12%)\n29 (15%)\n\n\n    1\n207 (86%)\n44 (88%)\n163 (85%)\n\n\nNausea\n\n\n\n\n\n\n\n\n    0\n55 (25%)\n12 (26%)\n43 (24%)\n\n\n    1\n169 (75%)\n34 (74%)\n135 (76%)\n\n\nFever\n\n\n\n\n\n\n\n\n    0\n127 (74%)\n32 (80%)\n95 (73%)\n\n\n    1\n44 (26%)\n8 (20%)\n36 (27%)\n\n\nHeadache\n\n\n\n\n\n\n\n\n    0\n83 (38%)\n11 (25%)\n72 (41%)\n\n\n    1\n137 (62%)\n33 (75%)\n104 (59%)\n\n\nJoint pain\n\n\n\n\n\n\n\n\n    0\n159 (85%)\n32 (84%)\n127 (85%)\n\n\n    1\n29 (15%)\n6 (16%)\n23 (15%)\n\n\n\n1 n (%)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLet’s stop and… think!\n\n\n\n\n\nDo you think the symptoms selected for the case definition were the right ones, or would you change anything?\n\n\n\n\n\n\n\n\n\nDo you feel like a challenge?\n\n\n\n\n\nIf you want, present the symptoms in an ordered bar chart. To do so, reshape the data using pivot_longer(), and then group_by(), summarise() and count() to tally up the counts for each symptom, stratified by case definition. Use ggplot2::coord_flip() for a better visualisation.\n\n\n\n\n\nb) Extra - Bar plot of symptoms stratified by case definition.\n\n\nShow the code\n# Create list of symptom variables:\nsymptoms &lt;- c(\"diarrhoea\", \n              \"bloody\", \n              \"vomiting\", \n              \"abdo\", \n              \"nausea\", \n              \"fever\", \n              \"headache\", \n              \"jointpain\")\n\n# Create nice labels for case definition:\ncaselabs &lt;- ggplot2::as_labeller(c(\"0\" = \"Non-case\", \n                                   \"1\" = \"Case\"))\n# Select variables and cases:\nsymptom_bar &lt;- copdata %&gt;% \n  # Select symptom columns:\n  select(case, c(all_of(symptoms))) %&gt;%\n  # Drop NAs:\n  drop_na() %&gt;% \n  # Reshape (pivot longer):\n  pivot_longer(!case, \n               names_to = \"Symptoms\", \n               values_drop_na = TRUE) %&gt;% \n  # Keep only TRUE values:\n  filter(value == \"1\") %&gt;% \n \n   # Group by symptoms and case:\n  group_by(Symptoms, case) %&gt;% \n  # Count for each symptom by case:\n  dplyr::summarise(count = n()) %&gt;% \n  # Create plot:\n  ggplot(\n    mapping = aes(\n    # Order symptom bars so most common ones are ontop:\n    x = reorder(Symptoms, desc(count), decreasing = TRUE), \n    y = count)) +\n  # Display bars as proportions\n  geom_bar(stat = \"identity\") +\n  # Update x axis label:\n  xlab(\"Symptoms\") +\n  # Update y axis label:\n  ylab(\"Proportion of respondents\") +\n  # Flip plot on its side so symptom labels are clear:\n  coord_flip() +\n  # Facet the plot by (labelled) case:\n  facet_wrap(facets = \"case\",\n             labeller = caselabs,\n             ncol = 2)\n\n\n`summarise()` has grouped output by 'Symptoms'. You can override using the\n`.groups` argument.\n\n\nShow the code\n# Print plot:\nsymptom_bar"
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     "section": "3.7 Attack proportions",
-    "text": "3.7 Attack proportions\n\na) Calculate the overall attack proportions (percentage of cases among the total observed individuals). You could use tabyl().\n\n\nCode\n# Create table of case status:\ntotal_ap &lt;- tabyl(copdata, case) %&gt;% \n # Add row totals:\n  adorn_totals(where = \"row\") %&gt;% \n  # Add percentages with 1 digit after the decimal point:\n  adorn_pct_formatting(digits = 1) %&gt;% \n  # Filter to rows where case is TRUE:\n  filter(case == TRUE) %&gt;% \n  # Select the column percent:\n  select(percent) %&gt;% \n  # Extract (pull) the value from this cell:\n  pull()\n\n# Print result:\ntotal_ap\n\n\n[1] \"57.3%\"\n\n\n\n\n\n\n\n\nLet’s stop and… think!\n\n\n\n\n\nWhat can you infer from the overall attack proportion about this outbreak and possible vehicles/exposures?\n\n\n\n\n\n\n\n\n\nIf you’ve thought about the above questions, have a look here\n\n\n\n\n\nThe overall attack proportion is 57.3%. This means that more than half of the people who ate a meal were cases!\n\n\n\n\n\nb) Calculate attack proportions for group, class and sex by case status. You could use tabyl() (as you did in 3.4) or gtsummary::tbl_summary.\n\n\nCode\n# Table to calculate attack proportions:\nattack_prop &lt;- copdata %&gt;% \n  # Select columns:\n  select (case, class, group, sex) %&gt;% \n  \n  # Create table:\n  tbl_summary(\n    # Stratified by case\n    by = case,\n    # with row percentages\n    percent = \"row\") %&gt;%\n  \n  # Add totals:\n  add_overall() %&gt;%\n  \n  # Make variable names bold and italics:\n  bold_labels() %&gt;% \n  italicize_labels() %&gt;% \n  \n  # Modify header:\n  modify_header(\n    label = \"**Characteristic**\",\n    stat_0 = \"**Overall** **N** = {N}\",\n    stat_1 = \"**Non-case** **N** = {n}\",\n    stat_2 = \"**Case** **N** = {n}\"\n  )\n\n\n# Print table:\nattack_prop\n\n\n\n\n\n\n  \n    \n    \n      Characteristic\n      Overall N = 3771\n      Non-case N = 1611\n      Case N = 2161\n    \n  \n  \n    class\n\n\n\n        1\n131 (100%)\n63 (48%)\n68 (52%)\n        2\n101 (100%)\n44 (44%)\n57 (56%)\n        3\n111 (100%)\n38 (34%)\n73 (66%)\n        Unknown\n34\n16\n18\n    group\n\n\n\n        teacher\n15 (100%)\n9 (60%)\n6 (40%)\n        student\n362 (100%)\n152 (42%)\n210 (58%)\n    sex\n\n\n\n        female\n213 (100%)\n96 (45%)\n117 (55%)\n        male\n164 (100%)\n65 (40%)\n99 (60%)\n  \n  \n  \n    \n      1 n (%)"
+    "text": "3.7 Attack proportions\n\na) Calculate the overall attack proportions (percentage of cases among the total observed individuals). You could use tabyl().\n\n\nShow the code\n# Create table of case status:\ntotal_ap &lt;- tabyl(copdata, case) %&gt;% \n # Add row totals:\n  adorn_totals(where = \"row\") %&gt;% \n  # Add percentages with 1 digit after the decimal point:\n  adorn_pct_formatting(digits = 1) %&gt;% \n  # Filter to rows where case is TRUE:\n  filter(case == \"1\") %&gt;% \n  # Select the column percent:\n  select(percent) %&gt;% \n  # Extract (pull) the value from this cell:\n  pull()\n\n# Print result:\ntotal_ap\n\n\n[1] \"57.3%\"\n\n\n\n\n\n\n\n\nLet’s stop and… think!\n\n\n\n\n\nWhat can you infer from the overall attack proportion about this outbreak and possible vehicles/exposures?\n\n\n\n\n\n\n\n\n\nIf you’ve thought about the above questions, have a look here\n\n\n\n\n\nThe overall attack proportion is 57.3%. This means that more than half of the people who ate a meal were cases!\n\n\n\n\n\nb) Calculate attack proportions for group, class and sex by case status. You could use tabyl() (as you did in 3.4) or gtsummary::tbl_summary.\n\n\nShow the code\n# Table to calculate attack proportions:\nattack_prop &lt;- copdata %&gt;% \n  # Select columns:\n  select (case, class, group, sex) %&gt;% \n  \n  # Create table:\n  tbl_summary(\n    # Stratified by case\n    by = case,\n    # with row percentages\n    percent = \"row\") %&gt;%\n  \n  # Add totals:\n  add_overall() %&gt;%\n  \n  # Make variable names bold and italics:\n  bold_labels() %&gt;% \n  italicize_labels() %&gt;% \n  \n  # Modify header:\n  modify_header(\n    label = \"**Characteristic**\",\n    stat_0 = \"**Overall** **N** = {N}\",\n    stat_1 = \"**Non-case** **N** = {n}\",\n    stat_2 = \"**Case** **N** = {n}\"\n  )\n\n\n# Print table:\nattack_prop\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nCharacteristic\nOverall N = 3771\nNon-case N = 1611\nCase N = 2161\n\n\n\n\nclass\n\n\n\n\n\n\n\n\n    1\n131 (100%)\n63 (48%)\n68 (52%)\n\n\n    2\n101 (100%)\n44 (44%)\n57 (56%)\n\n\n    3\n111 (100%)\n38 (34%)\n73 (66%)\n\n\n    Unknown\n34\n16\n18\n\n\ngroup\n\n\n\n\n\n\n\n\n    teacher\n15 (100%)\n9 (60%)\n6 (40%)\n\n\n    student\n362 (100%)\n152 (42%)\n210 (58%)\n\n\nsex\n\n\n\n\n\n\n\n\n    female\n213 (100%)\n96 (45%)\n117 (55%)\n\n\n    male\n164 (100%)\n65 (40%)\n99 (60%)\n\n\n\n1 n (%)"
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@@ -504,74 +154,109 @@
     "text": "3.8 Draft the relevant paragraph(s) and table(s) and/or figure(s) in your outbreak report (Methods and results).\n\n\n\n\n\n\nNeed a little bit of help?\n\n\n\n\n\nTo save your tabyl table as a .docx, you could convert your tabyl to flextable (as_flex_table()), ensure only one line per row (flextable::autofit()) and save as .docx (flextable::save_as_docx(path = \"nameoftable.docx\")"
   },
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-    "objectID": "Univariate.html",
-    "href": "Univariate.html",
-    "title": "Univariable analysis",
+    "objectID": "Data_import_cleaning.html",
+    "href": "Data_import_cleaning.html",
+    "title": "Data import and cleaning",
     "section": "",
-    "text": "At the end of the session, participants will be able to:\n\nPerform hypothesis tests.\nEstimate risk ratios (also called relative risk) for categorical data.\nInterpret the univariable results.\n(Optional) Investigate dose-response relationships in categorical data."
+    "text": "At the end of the session, participants will be able to:\n\nImport datasets in R\nApply basic data management techniques to explore and familiarise themselves with the dataset\nApply strategies for data cleaning"
   },
   {
-    "objectID": "Univariate.html#learning-outcomes",
-    "href": "Univariate.html#learning-outcomes",
-    "title": "Univariable analysis",
+    "objectID": "Data_import_cleaning.html#install-packages-and-load-libraries",
+    "href": "Data_import_cleaning.html#install-packages-and-load-libraries",
+    "title": "Data import and cleaning",
+    "section": "3.1. Install packages and load libraries",
+    "text": "3.1. Install packages and load libraries\n\nLoad the R packages you will be using. Update this list of packages as you dig into the code: You may realise you need packages you didn’t think about at the beginning. Remember that it is a good practice to load tydiverse at the end of your package list, to avoid masking functions.\n\n\n\nShow the code\n# Check if the 'pacman' package is installed, if not install it:\nif (!requireNamespace(\"pacman\", quietly = TRUE)) install.packages(\"pacman\")\n\n# Load the required libraries into the current R session:\npacman::p_load(rio, \n               here, \n               skimr,\n               plyr,\n               janitor,\n               lubridate,\n               gtsummary, \n               flextable,\n               officer,\n               epikit, \n               apyramid, \n               scales,\n               tidyverse \n)"
+  },
+  {
+    "objectID": "Data_import_cleaning.html#import-your-data",
+    "href": "Data_import_cleaning.html#import-your-data",
+    "title": "Data import and cleaning",
+    "section": "3.2. Import your data",
+    "text": "3.2. Import your data\n\nYou can find the spetses_school.csv file in EVA. Feel free to explore the codebook as well.\n\n\n\nShow the code\n# Import the raw data set:\ncopdata &lt;- import(here::here(\"data\", \"spetses_school.csv\"))"
+  },
+  {
+    "objectID": "Data_import_cleaning.html#explore-and-clean-your-data",
+    "href": "Data_import_cleaning.html#explore-and-clean-your-data",
+    "title": "Data import and cleaning",
+    "section": "3.3. Explore and clean your data",
+    "text": "3.3. Explore and clean your data\n\nHave a look at the structure of your data. Here you have some questions that may help you get started:\n\nHow many observations and variables does the dataset contain?\nWhat types of variables do you have and what types of values are recorded?\nDo any of the values of the other variables look implausible? Which ones and why? What will you do about it?\n\nGo ahead and clean your data as you see fit.\n\n\n\n\n\n\n\nNeed a little bit of help?\n\n\n\n\n\nYou could use head(), dim(), str(), or skim() to have a quick look at the data and get an idea of which variables you want to explore further. You can also do some tables\n\n\n\n\n\nShow the code\nhead(copdata)\n\n\n\n  \n\n\n\nShow the code\ndim(copdata)\n\n\n[1] 397  41\n\n\nShow the code\nstr(copdata)\n\n\n'data.frame':   397 obs. of  41 variables:\n $ id        : chr  \"sp-2-001\" \"sp-3-003\" \"sp-1-005\" \"sp-2-006\" ...\n $ sex       : chr  \"male\" \"female\" \"female\" \"male\" ...\n $ age       : int  18 18 17 17 18 180 16 15 43 16 ...\n $ group     : int  1 1 1 1 1 1 0 1 0 1 ...\n $ class     : int  2 3 1 2 3 2 NA 1 NA 1 ...\n $ ill       : int  1 0 1 0 1 1 0 1 1 0 ...\n $ diarrhoea : int  1 NA NA NA 1 1 NA 0 1 NA ...\n $ bloody    : int  0 NA NA NA 0 0 NA 0 NA NA ...\n $ vomiting  : int  0 NA NA NA 0 0 NA 0 NA NA ...\n $ abdo      : int  1 NA 1 NA 1 1 NA 0 NA NA ...\n $ nausea    : int  0 NA 1 NA 1 0 NA 1 1 NA ...\n $ fever     : int  NA NA NA NA 0 0 NA 0 NA NA ...\n $ headache  : int  0 NA 1 NA 1 0 NA 1 1 NA ...\n $ jointpain : int  0 NA NA NA 0 0 NA 0 1 NA ...\n $ starthour : int  9 NA NA NA 15 15 NA NA 3 NA ...\n $ meal      : int  1 1 1 0 1 1 1 1 1 1 ...\n $ feta      : int  1 0 NA 0 1 1 1 1 1 1 ...\n $ fetaD     : int  2 0 NA 0 2 2 2 1 2 2 ...\n $ sardines  : int  1 0 NA 0 1 1 1 0 1 1 ...\n $ sardinesD : int  2 0 NA 0 2 2 2 0 2 2 ...\n $ eggplant  : int  0 0 NA 0 1 1 1 0 NA 1 ...\n $ eggplantD : int  0 0 NA 0 2 2 2 0 NA 2 ...\n $ veal      : int  1 1 1 1 1 1 1 1 1 1 ...\n $ vealD     : int  2 1 0 0 2 2 1 3 2 2 ...\n $ pasta     : int  1 1 1 0 1 1 1 1 1 1 ...\n $ pastaD    : int  3 3 1 0 2 2 2 3 2 3 ...\n $ tomsal    : int  1 1 NA 0 1 1 1 1 1 1 ...\n $ tomsalD   : int  1 3 NA 0 2 2 2 2 2 2 ...\n $ bread     : int  1 1 1 0 1 1 1 1 1 1 ...\n $ breadD    : int  2 3 1 0 2 2 2 2 2 2 ...\n $ dessert   : int  1 1 NA 0 1 1 0 1 NA NA ...\n $ dessertD  : int  2 3 NA 0 2 2 0 1 NA NA ...\n $ champagne : int  1 1 0 1 1 1 1 1 1 1 ...\n $ champagneD: int  1 1 0 3 1 1 2 1 1 1 ...\n $ beer      : int  1 0 0 1 1 1 NA 1 NA 1 ...\n $ beerD     : int  3 0 0 3 2 3 NA 1 NA 2 ...\n $ redwine   : int  0 1 0 1 0 0 1 0 1 0 ...\n $ redwineD  : int  0 3 0 3 0 0 2 0 2 0 ...\n $ whitewine : int  0 0 0 1 1 1 1 1 NA 0 ...\n $ whitewineD: int  0 0 0 3 3 2 1 3 NA 0 ...\n $ dayonset  : chr  \"6oct2024\" \"\" \"\" \"\" ...\n\n\nShow the code\nskim(copdata)\n\n\n\nData summary\n\n\nName\ncopdata\n\n\nNumber of rows\n397\n\n\nNumber of columns\n41\n\n\n_______________________\n\n\n\nColumn type frequency:\n\n\n\ncharacter\n3\n\n\nnumeric\n38\n\n\n________________________\n\n\n\nGroup variables\nNone\n\n\n\nVariable type: character\n\n\n\n\n\n\n\n\n\n\n\n\n\nskim_variable\nn_missing\ncomplete_rate\nmin\nmax\nempty\nn_unique\nwhitespace\n\n\n\n\nid\n0\n1\n8\n8\n0\n397\n0\n\n\nsex\n0\n1\n4\n6\n0\n2\n0\n\n\ndayonset\n0\n1\n0\n8\n175\n4\n0\n\n\n\nVariable type: numeric\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nskim_variable\nn_missing\ncomplete_rate\nmean\nsd\np0\np25\np50\np75\np100\nhist\n\n\n\n\nage\n0\n1.00\n18.57\n9.92\n8\n16\n17\n18\n180\n▇▁▁▁▁\n\n\ngroup\n0\n1.00\n0.96\n0.20\n0\n1\n1\n1\n1\n▁▁▁▁▇\n\n\nclass\n36\n0.91\n1.94\n0.84\n1\n1\n2\n3\n3\n▇▁▆▁▇\n\n\nill\n0\n1.00\n0.69\n0.47\n0\n0\n1\n1\n1\n▃▁▁▁▇\n\n\ndiarrhoea\n141\n0.64\n0.82\n0.39\n0\n1\n1\n1\n1\n▂▁▁▁▇\n\n\nbloody\n200\n0.50\n0.03\n0.17\n0\n0\n0\n0\n1\n▇▁▁▁▁\n\n\nvomiting\n179\n0.55\n0.30\n0.46\n0\n0\n0\n1\n1\n▇▁▁▁▃\n\n\nabdo\n152\n0.62\n0.85\n0.35\n0\n1\n1\n1\n1\n▂▁▁▁▇\n\n\nnausea\n170\n0.57\n0.76\n0.43\n0\n1\n1\n1\n1\n▂▁▁▁▇\n\n\nfever\n223\n0.44\n0.26\n0.44\n0\n0\n0\n1\n1\n▇▁▁▁▃\n\n\nheadache\n174\n0.56\n0.63\n0.48\n0\n0\n1\n1\n1\n▅▁▁▁▇\n\n\njointpain\n207\n0.48\n0.16\n0.37\n0\n0\n0\n0\n1\n▇▁▁▁▂\n\n\nstarthour\n177\n0.55\n12.49\n4.92\n3\n9\n9\n15\n21\n▁▇▁▆▃\n\n\nmeal\n9\n0.98\n0.97\n0.17\n0\n1\n1\n1\n1\n▁▁▁▁▇\n\n\nfeta\n16\n0.96\n0.71\n0.45\n0\n0\n1\n1\n1\n▃▁▁▁▇\n\n\nfetaD\n16\n0.96\n1.32\n1.00\n0\n0\n2\n2\n3\n▆▅▁▇▂\n\n\nsardines\n17\n0.96\n0.67\n0.47\n0\n0\n1\n1\n1\n▃▁▁▁▇\n\n\nsardinesD\n17\n0.96\n1.34\n1.04\n0\n0\n2\n2\n3\n▆▂▁▇▂\n\n\neggplant\n30\n0.92\n0.59\n0.49\n0\n0\n1\n1\n1\n▆▁▁▁▇\n\n\neggplantD\n30\n0.92\n1.14\n1.05\n0\n0\n1\n2\n3\n▇▂▁▇▂\n\n\nveal\n15\n0.96\n0.89\n0.31\n0\n1\n1\n1\n1\n▁▁▁▁▇\n\n\nvealD\n14\n0.96\n1.83\n0.91\n0\n1\n2\n2\n3\n▂▃▁▇▃\n\n\npasta\n15\n0.96\n0.88\n0.32\n0\n1\n1\n1\n1\n▁▁▁▁▇\n\n\npastaD\n15\n0.96\n1.81\n0.91\n0\n1\n2\n2\n3\n▂▃▁▇▃\n\n\ntomsal\n24\n0.94\n0.57\n0.50\n0\n0\n1\n1\n1\n▆▁▁▁▇\n\n\ntomsalD\n24\n0.94\n1.08\n1.06\n0\n0\n1\n2\n3\n▇▂▁▆▂\n\n\nbread\n18\n0.95\n0.91\n0.29\n0\n1\n1\n1\n1\n▁▁▁▁▇\n\n\nbreadD\n18\n0.95\n1.75\n0.71\n0\n2\n2\n2\n3\n▁▂▁▇▁\n\n\ndessert\n42\n0.89\n0.42\n0.49\n0\n0\n0\n1\n1\n▇▁▁▁▆\n\n\ndessertD\n42\n0.89\n0.83\n1.06\n0\n0\n0\n2\n3\n▇▁▁▃▁\n\n\nchampagne\n25\n0.94\n0.87\n0.34\n0\n1\n1\n1\n1\n▁▁▁▁▇\n\n\nchampagneD\n25\n0.94\n1.37\n0.93\n0\n1\n1\n2\n3\n▂▇▁▂▃\n\n\nbeer\n30\n0.92\n0.78\n0.42\n0\n1\n1\n1\n1\n▂▁▁▁▇\n\n\nbeerD\n35\n0.91\n1.94\n1.23\n0\n1\n3\n3\n3\n▃▂▁▂▇\n\n\nredwine\n50\n0.87\n0.23\n0.42\n0\n0\n0\n0\n1\n▇▁▁▁▂\n\n\nredwineD\n52\n0.87\n0.45\n0.92\n0\n0\n0\n0\n3\n▇▁▁▁▁\n\n\nwhitewine\n31\n0.92\n0.73\n0.45\n0\n0\n1\n1\n1\n▃▁▁▁▇\n\n\nwhitewineD\n36\n0.91\n1.58\n1.21\n0\n0\n2\n3\n3\n▆▅▁▅▇\n\n\n\n\n\nShow the code\nnames(copdata)\n\n\n [1] \"id\"         \"sex\"        \"age\"        \"group\"      \"class\"     \n [6] \"ill\"        \"diarrhoea\"  \"bloody\"     \"vomiting\"   \"abdo\"      \n[11] \"nausea\"     \"fever\"      \"headache\"   \"jointpain\"  \"starthour\" \n[16] \"meal\"       \"feta\"       \"fetaD\"      \"sardines\"   \"sardinesD\" \n[21] \"eggplant\"   \"eggplantD\"  \"veal\"       \"vealD\"      \"pasta\"     \n[26] \"pastaD\"     \"tomsal\"     \"tomsalD\"    \"bread\"      \"breadD\"    \n[31] \"dessert\"    \"dessertD\"   \"champagne\"  \"champagneD\" \"beer\"      \n[36] \"beerD\"      \"redwine\"    \"redwineD\"   \"whitewine\"  \"whitewineD\"\n[41] \"dayonset\"  \n\n\nLet’s explore and manage some variables in detail.\n\nAge\nThrough visual exploration of the age histogram we see that there is at least one very high value, likely implausible. You can then create a cross-tabulation of variables age and group to have a better idea of how your data looks like.\n\n\nShow the code\n# Have a look at the histogram  \nhist(copdata$age)   \n\n\n\n\n\n\n\n\n\nShow the code\n# Create cross-tab with the group variable:  \ntabyl(dat = copdata, \n               var1 = age, \n               var2 = group)\n\n\n\n  \n\n\n\nNote that group is coded as 0 and 1, and these may be difficult to interpret when they mean something other than “no” and “yes”, respectively. From the codebook, you know that teachers are represented by 0, and students by 1. Let’s change this to make our lives easier:\n\n\nShow the code\n# Convert group to a factor and label 0 as teacher, 1 as student:\ncopdata &lt;- copdata %&gt;% \n  mutate(group = factor(group, \n                        labels = c(\"teacher\", \"student\")))\n\n\nNow, have a look at your cross-tab again:\n\n\nShow the code\ntabyl(dat = copdata, \n               var1 = age, \n               var2 = group) \n\n\n\n  \n\n\n\nWith this table, we can more easily identify ages that are likely to be typographic errors. Specifically:\n\nThere is one teacher aged 16 (likely digit reversal - should be 61)\nThere is one student aged 8 (likely missing a digit - should be 18)\nThere is one student aged 180 (likely has an extra digit - should be 18)\n\nAssuming you have contacted the school to make sure your suspicions about the actual ages are correct, we can now correct them, using case_when(). We create logical conditions to identify the incorrect ages, combining the values for age with the group they belong to:\n\n\nShow the code\n# Update incorrect ages to the correct values with case_when:  \ncopdata &lt;- copdata %&gt;%       \n  mutate(age =                         \n           case_when(                            \n             # Where respondent is 16 and a teacher, change age to 61:\n             age == 16 & group == \"teacher\" ~ 61,              \n             # where respondent is 8 or 180 and a student, change age to 18:\n             age == 8 & group == \"student\" ~ 18,              \n             age == 180 & group == \"student\" ~ 18,              \n             # Keep remaining values as is:              \n             .default = as.numeric(age)   \n             # if .default is not working, try:\n             # TRUE ~ age\n             )                    \n         ) \n\n\n\n\n\n\n\n\nCreate summary table for the dose response columns (those finishing with a capital “D”) to have a look at their distribution.\n\n\n\n\n\n\n\nNeed a little bit of help?\n\n\n\n\n\nUse gtsummary::tbl_summary().\n\n\n\n\n\nShow the code\n#| label: check_dose_cols\n#| tbl-cap: no caption\n\n# Create summary table for dose response columns: \ndrtable &lt;- copdata %&gt;%       \n  # Select all the columns with column names that end in upper case 'D':   \n  select(ends_with(\"D\", ignore.case = FALSE)) %&gt;%       \n  # Create the summary table, excluding missing values:   \n  tbl_summary(missing = \"no\") \n\n  # Print the summary table: \ndrtable\n\n\n\n\n\n\n\n\n\n\n\n\nCharacteristic\nN = 3971\n\n\n\n\nfetaD\n\n\n\n\n    0\n110 (29%)\n\n\n    1\n78 (20%)\n\n\n    2\n155 (41%)\n\n\n    3\n38 (10.0%)\n\n\nsardinesD\n\n\n\n\n    0\n125 (33%)\n\n\n    1\n35 (9.2%)\n\n\n    2\n184 (48%)\n\n\n    3\n36 (9.5%)\n\n\neggplantD\n\n\n\n\n    0\n151 (41%)\n\n\n    1\n41 (11%)\n\n\n    2\n147 (40%)\n\n\n    3\n28 (7.6%)\n\n\nvealD\n\n\n\n\n    0\n42 (11%)\n\n\n    1\n70 (18%)\n\n\n    2\n184 (48%)\n\n\n    3\n87 (23%)\n\n\npastaD\n\n\n\n\n    0\n44 (12%)\n\n\n    1\n70 (18%)\n\n\n    2\n183 (48%)\n\n\n    3\n85 (22%)\n\n\ntomsalD\n\n\n\n\n    0\n162 (43%)\n\n\n    1\n52 (14%)\n\n\n    2\n126 (34%)\n\n\n    3\n33 (8.8%)\n\n\nbreadD\n\n\n\n\n    0\n35 (9.2%)\n\n\n    1\n51 (13%)\n\n\n    2\n267 (70%)\n\n\n    3\n26 (6.9%)\n\n\ndessertD\n\n\n\n\n    0\n206 (58%)\n\n\n    1\n33 (9.3%)\n\n\n    2\n87 (25%)\n\n\n    3\n29 (8.2%)\n\n\nchampagneD\n\n\n\n\n    0\n49 (13%)\n\n\n    1\n208 (56%)\n\n\n    2\n45 (12%)\n\n\n    3\n70 (19%)\n\n\nbeerD\n\n\n\n\n    0\n81 (22%)\n\n\n    1\n41 (11%)\n\n\n    2\n57 (16%)\n\n\n    3\n183 (51%)\n\n\nredwineD\n\n\n\n\n    0\n266 (77%)\n\n\n    1\n31 (9.0%)\n\n\n    2\n21 (6.1%)\n\n\n    3\n27 (7.8%)\n\n\nwhitewineD\n\n\n\n\n    0\n100 (28%)\n\n\n    1\n73 (20%)\n\n\n    2\n67 (19%)\n\n\n    3\n121 (34%)\n\n\n\n1 n (%)"
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+    "text": "3.4. Modify variables format\nThere are some “column types” you would like to modify because either R needs some specific types of data when working with certain variables, or because it will be easier for you to visualise and interpret the data. For example, the variable “group” is encoded as 0 and 1, when you create a table later on, you don’t want to see 0’s and 1’s because that doesn’t mean anything. It is better to change this variable to a factor, with explicit labels, so they actually mean something when they come out in your table.\n\n\n\n\n\nYou can use mutate to modify the type of the following variables:\n\nTable 1: Variable types to modify\n\n\n\n\n\n\n\n\nVariable name\nOriginal\nDesired\nHint\n\n\n\n\nsex\ncharacter\nfactor\nmutate(), as.factor()\n\n\nclass\ninteger\nfactor\nmutate(), as.factor()\n\n\nill\ninteger\nfactor\nmutate(), as.factor()\n\n\nAll the clinical symptom variables\ninteger\nfactor\nmutate(across()), as.factor()\n\n\nAll the food variables representing the amount of specific foods eaten (those finishing with a capital “D”)\ninteger\nfactor\nmutate(across()), as.factor()\n\n\ndayonset\ncharacter\ndate\nlubridate::dmy()\n\n\nstarthour and dayonset together\ninteger (starthour)\ndate (dayonset)\nPOSIXct, POSIXt\nlubridate::ymd_h() could have inside stringr::str_glue() with dayonset and starthour\n\n\n\n\nSex, class and ill\nLet’s start transforming one-by-one the first three variables in the table: sex, class and ill.\n\n\nShow the code\ncopdata &lt;- copdata %&gt;% \n  mutate(\n    sex = as.factor(sex),\n    class = as.factor(class),\n    ill = as.factor(ill))\n\n\n\n\nSymptoms and Food variables\nFor these variables, we are going to show you a couple of different ways to carry out the same variable type transformation in a set of variables, so you don’t need to do one variable at a time. We are showing you these ways so you see alternative ways to do the same thing.\n\nFor the variables that are clinical symptoms, we will list them one by one and show you the use of mutate(across( )).\n\n\n\nShow the code\ncopdata &lt;- copdata %&gt;% \n  mutate(\n    # clinical symptoms\n    across(.cols = c(diarrhoea, bloody, vomiting,\n             abdo, nausea, fever,headache, jointpain), \n           .fns = ~ as.factor(.)\n           )\n    )\n\n\n\nFor the variables that are food doses, we will show you how to first create a vector of names, following by using mutate(across(all_of( ))) on this vector.\n\n\n\nShow the code\n# Create a vector with all the food variables representing the amount of specific foods items eaten (those finishing with a capital \"D\")\n# One way of doing it:\nfood_dose &lt;- copdata %&gt;% \n    select(\n      ends_with(\"D\", ignore.case = FALSE)) %&gt;% \n    names()\n\n# Another way of doing it:\n# food_dose &lt;- c(\"fetaD\", \"sardinesD\", \"eggplantD\", \"pastaD\", \n#                 \"vealD\", \"greeksalD\", \"dessertD\", \"breadD\", \n#                 \"champagneD\", \"beerD\", \"redwineD\", \"whitewineD\")\n\n\ncopdata &lt;- copdata %&gt;% \n  mutate(\n    # food dose variables\n    across(.cols = all_of(food_dose), \n           .fns = ~as.factor(.))) \n\n\n\n\n\n\n\n\nNote\n\n\n\n\n\nThe tilde (~) above is used to apply the transformation as.factor(.) to each selected column, which in our case is either all columns included in food_items and food_dose.\n\n\n\n\n\nDate and time variables\n\n\n\n\n\n\nLet’s stop and… think!\n\n\n\n\n\nWhy may you want to transform the variable starthour to date-time?\n\n\n\n\n\n\n\n\nYou can use lubridate::dmy() to mutate the dayonset variable into a date variable. Note that we are using the function dmy() because dates are formatted as day, then month (abbreviated character string), then year (i.e. “12nov2006”).\n\n\nShow the code\n# Have a look at how the data is stored\nhead(copdata$dayonset)\n\n\n[1] \"6oct2024\" \"\"         \"\"         \"\"         \"6oct2024\" \"7oct2024\"\n\n\nShow the code\nclass(copdata$dayonset)\n\n\n[1] \"character\"\n\n\nShow the code\n# Update copdata:\ncopdata &lt;- copdata %&gt;% \n  # Change column to date class:\n  mutate(\n    dayonset = lubridate::dmy(dayonset))\n\n# Check class of updated column:\nclass(copdata$dayonset)\n\n\n[1] \"Date\"\n\n\nShow the code\n# Have a look at your data now:\nhead(copdata$dayonset)\n\n\n[1] \"2024-10-06\" NA           NA           NA           \"2024-10-06\"\n[6] \"2024-10-07\"\n\n\nHaving a variable that defines “time” in an outbreak investigation can be very useful when creating a case definition. An hour of the day, without a date associated with it doesn’t help you much, thus, you should merge together day and time of onset of symptoms into a single variable. Moreover, you will be using this combined variable later on to estimate an incubation period and create your epicurve. We can combine these two variables by using the lubridate::ymd_h() function.\nBefore we proceed, it would be wise to check if any respondents have a value for dayonset but not starthour, or vice versa. The lubridate date-time conversion functions do not have an explicit argument for dealing with missing values, but the truncated = … argument can help prevent spurious date-times being derived from a date-time combination where one value is missing.\nWe can check if we have any missing values by cross-tabulating starthour with dayonset:\n\n\nShow the code\n# Cross-tabulate dayonset with starthour:\ntabyl(dat = copdata, \n               var1 = starthour, \n               var2 = dayonset)\n\n\n\n  \n\n\n\nThis shows us that there are two respondents who had an onset date, but are missing onset time (starthour). Since starthour is represented by 1 - 2 digits, we can specify that we want lubridate to also parse date-time combinations that are truncated by up to two digits:\n\n\nShow the code\ncopdata &lt;- copdata %&gt;% \n  # Combine dayonset and starthour in a new date time variable:\n  mutate(onset_datetime = \n           ymd_h(\n             str_glue(\"{dayonset}, {starthour}\"), \n                                           # Deal with missing starthour:\n                                           truncated = 2))\n\n\nWarning: There was 1 warning in `mutate()`.\nℹ In argument: `onset_datetime = ymd_h(str_glue(\"{dayonset}, {starthour}\"),\n  truncated = 2)`.\nCaused by warning:\n!  175 failed to parse.\n\n\nNote that we needed to use str_glue() to concatenate dayonset and starthour together before we could convert the variable to a date-time object. This is because the ymd_h() function expects a single character string, containing both the date and the time, as input.\nThe argument truncated = 2 will result in dates with missing starthour still being converted to date-time, with the missing time being set to 00:00 (midnight). Whether you want to deal with missing starthour in this way or prefer to code these date-times as NA will depend on how you want them to be represented in your analysis.\nNow we can check that everything in the new combined date-time variable has parsed correctly:\n\n\nShow the code\nhead(copdata$dayonset)\n\n\n[1] \"2024-10-06\" NA           NA           NA           \"2024-10-06\"\n[6] \"2024-10-07\"\n\n\nShow the code\nhead(copdata$starthour)\n\n\n[1]  9 NA NA NA 15 15\n\n\nShow the code\nhead(copdata$onset_datetime)\n\n\n[1] \"2024-10-06 09:00:00 UTC\" NA                       \n[3] NA                        NA                       \n[5] \"2024-10-06 15:00:00 UTC\" \"2024-10-07 15:00:00 UTC\""
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+    "text": "3.5. Export clean data\nSave the cleaned data set before proceeding with using your case definition to identify cases in your dataset. Use the .rds format, as it preserves column classes. This ensures you will have only minimal cleaning to do after importing the data into R at the next inject.\nSave your clean data as a new file “Spetses_clean1_YOURINITIALS.rds”, under the data folder of your Rproject.\n\n\n\n\n\n\nNeed a little bit of help?\n\n\n\n\n\nUse rio::export().\n\n\n\n\n\nShow the code\nexport(x = copdata, \n            file = here::here(\"data\", \"Spetses_clean1_2024.rds\"))"
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-    "text": "At the end of the session, participants will be able to:\n\nPerform hypothesis tests.\nEstimate risk ratios (also called relative risk) for categorical data.\nInterpret the univariable results.\n(Optional) Investigate dose-response relationships in categorical data."
+    "text": "At the end of the session, participants will be able to:\n\nApply case definition criteria to a dataset using logical arguments in R"
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-    "text": "2. Story/plot description\nYou have just estimated risk ratios (RR) of individual food items manually to better understand the concept. RR give you an idea of which food items could be the culprit(s) of the outbreak.\nNow, you will practice performing hypothesis testing and calculating RR in R to investigate the associations of suspicious food items with the disease.\nNote that, although this is not an exhaustive list of tests, the following can be useful for this exercise (relevant for comparisons between two groups):\n\nFor continuous variables:\n\nshapiro.test() (for checking if normally distributed)\nt.test() (for normal distributions)\nwilcox.test() (for non-parametric testing. Used to determine if two numeric samples are from the same distribution, when their populations are not normally distributed or have unequal variance.)\n\nFor categorical variables:\n\nchisq.test() (if all comparisons include at least 5 cases)\nfisher.test() (if there are &lt; 5 cases for any comparison)"
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+    "text": "At the end of the session, participants will be able to:\n\nApply case definition criteria to a dataset using logical arguments in R"
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+    "text": "2. Story/plot description\nYou will now create a new column in the data set to hold the case definition you decided in a previous step during your investigation. You can call this column case and set it to 1 if the individual meets the case definition criteria and 0 if not. You will use this column later on for any calculations needed (descriptive statistics, two-by-two tables to compute measures of association, etc.) to figure out the culprit of this outbreak."
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-    "text": "3.1. Install packages and load libraries\n\n\nCode\n# Load the required libraries into the current R session:\npacman::p_load(rio, \n               here, \n               tidyverse, \n               skimr,\n               plyr,\n               janitor,\n               lubridate,\n               gtsummary, \n               flextable,\n               officer,\n               epikit, \n               apyramid, \n               scales,\n               EpiStats,\n               broom)"
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+    "text": "3. Questions/Assignments\nIf you closed your R project, please open it again and load the clean dataset. We strongly recommend you use the Spetses_clean1_2024.rds. This is just to be sure all needed changes in that file for the code to work have been done (as you may have changed the file in a slightly different way than we have).\nTo be sure we all start from the same case definition, let’s agree that a case was defined as a person who:\n\nattended the school dinner on 11 November 2006 (i.e. is on the linelist)\nate a meal at the school dinner (i.e. was exposed)\nfell ill after the start of the meal\nfell ill within the time period of interest after the school dinner\nsuffered from diarrhoea with or without blood, or vomiting\n\nNon cases (not-ill) were defined as people who:\n\nattended the school dinner on 11 November 2006 (i.e. are on the linelist)\nate a meal at the school dinner (i.e. were exposed)\ndid not fall ill within the time period of interest\ndid not develop diarrhoea (with or without blood) or vomiting"
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-    "text": "3.2. Import your data\n\n\nCode\n# Import the raw data set:\ncopdata &lt;- rio::import(here::here(\"data\", \"Spetses_clean2_2024.rds\"))"
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+    "section": "3.1 Install packages (if needed) and load libraries",
+    "text": "3.1 Install packages (if needed) and load libraries\n\n\nShow the code\n# Load the required libraries into the current R session:\npacman::p_load(rio, \n               here, \n               tidyverse, \n               skimr,\n               plyr,\n               janitor,\n               lubridate,\n               gtsummary, \n               flextable,\n               officer,\n               epikit, \n               apyramid, \n               scales)"
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-    "text": "3.3. Hypothesis tests for other variables\nCheck if the following variables are associated with being a case: age, sex, class and group.\n\na) age\nWith the Shapiro-Wilk test we check if the variables are following the normal distribution. The null hypothesis is that the data follow a normal distribution, therefore, rejecting the null hypothesis means that the data do not follow the normal distribution. A p-value below the cutoff for rejecting the null hypothesis, e.g., a p-value&lt;0.05 means that we reject the null hypothesis that the data follow the normal distribution. For age, the p-value is &lt;0.05, therefore we reject the null hypothesis that the data are normally distributed. As we see in the graph most frequently reported age is &lt;20 years.\n\n\nCode\n# Check if age overall follows a normal distribution:\nshapiro.test(copdata$age)\n\n\n\n    Shapiro-Wilk normality test\n\ndata:  copdata$age\nW = 0.31302, p-value &lt; 2.2e-16\n\n\nCode\n# Can simply have a look at\nhist(copdata$age)\n\n\n\n\n\nCode\n# Looking only at the students:\nstudents &lt;- copdata %&gt;% \n  filter(group == \"student\")\nhist(students$age)\n\n\n\n\n\nAge overall (nor within the students’ group) is not normally distributed.\nWe compare the age for cases and non-cases using the Wilcoxon test that is used when the data are not normally distributed. The null hypothesis is that there is no difference in the age between the two groups compared. Given that p-value&gt;0.05 we do not reject the null hypothesis.\n\n\nCode\n# Perform Wilcoxon rank sum test on age and sex:\nwilcox.test(age ~ case, \n            data = copdata)\n\n\n\n    Wilcoxon rank sum test with continuity correction\n\ndata:  age by case\nW = 15934, p-value = 0.1512\nalternative hypothesis: true location shift is not equal to 0\n\n\n\n\nb) sex\n\n\nCode\ncopdata %&gt;% \n  select(sex, case) %&gt;% \n  tbl_summary(by = case) %&gt;% \n  add_p()\n\n\n\n\n\n\n  \n    \n    \n      Characteristic\n      FALSE, N = 1611\n      TRUE, N = 2161\n      p-value2\n    \n  \n  \n    sex\n\n\n0.3\n        female\n96 (60%)\n117 (54%)\n\n        male\n65 (40%)\n99 (46%)\n\n  \n  \n  \n    \n      1 n (%)\n    \n    \n      2 Pearson’s Chi-squared test\n    \n  \n\n\n\n\n\n\n\n\n\n\nLet’s stop and… think!\n\n\n\n\n\nWhat do these results tell you - is there an association between sex and being a case?\nDo these results differ from what you expected when looking at the descriptive figures (case proportions stratified by sex, or the age sex pyramid)? If so, why do you think this is?\n\n\n\n\n\nc) class\n\n\nCode\ncopdata %&gt;% \n  select(class, case) %&gt;% \n  tbl_summary(by = case) %&gt;% \n  add_p()\n\n\n\n\n\n\n  \n    \n    \n      Characteristic\n      FALSE, N = 1611\n      TRUE, N = 2161\n      p-value2\n    \n  \n  \n    class\n\n\n0.090\n        1\n63 (43%)\n68 (34%)\n\n        2\n44 (30%)\n57 (29%)\n\n        3\n38 (26%)\n73 (37%)\n\n        Unknown\n16\n18\n\n  \n  \n  \n    \n      1 n (%)\n    \n    \n      2 Pearson’s Chi-squared test\n    \n  \n\n\n\n\nIn this case you have a 2x3 contingency table of class against disease status, we can use a chi-square here. P-value of the association between class and age is p-val = 0.09, suggesting that there is an association at the p-val = 0.20 level among at least one of the 3 different classes. Fellows may want to investigate this further, but in the remaining of the study this is not explored further.\nA hypothesis could be that one of the classes sat together and, for whatever reason, they ate more of the contaminated food item at those tables. This could be further studied if we had the spatial distribution of where the people sat at the dinner. Another hypothesis is that one of the classes differ from other classes in some characteristics that made them more susceptible or more exposed to the infected food item.\n\n\nd) group\n\n\nCode\ncopdata %&gt;% \n  select(group, case) %&gt;% \n  tbl_summary(by = case) %&gt;% \n  add_p()\n\n\n\n\n\n\n  \n    \n    \n      Characteristic\n      FALSE, N = 1611\n      TRUE, N = 2161\n      p-value2\n    \n  \n  \n    group\n\n\n0.2\n        teacher\n9 (5.6%)\n6 (2.8%)\n\n        student\n152 (94%)\n210 (97%)\n\n  \n  \n  \n    \n      1 n (%)\n    \n    \n      2 Pearson’s Chi-squared test\n    \n  \n\n\n\n\nP-value of the association between group and age is p-val = 0.2, suggesting that there may be an association at the p-val = 0.20 level. Explanations are like above, with variable class.\n\n\nLet’s do all together\n\n\nCode\ncopdata %&gt;% \n  select(sex, class, group, case) %&gt;% \n  tbl_summary(by = case) %&gt;% \n  add_p()\n\n\n\n\n\n\n  \n    \n    \n      Characteristic\n      FALSE, N = 1611\n      TRUE, N = 2161\n      p-value2\n    \n  \n  \n    sex\n\n\n0.3\n        female\n96 (60%)\n117 (54%)\n\n        male\n65 (40%)\n99 (46%)\n\n    class\n\n\n0.090\n        1\n63 (43%)\n68 (34%)\n\n        2\n44 (30%)\n57 (29%)\n\n        3\n38 (26%)\n73 (37%)\n\n        Unknown\n16\n18\n\n    group\n\n\n0.2\n        teacher\n9 (5.6%)\n6 (2.8%)\n\n        student\n152 (94%)\n210 (97%)\n\n  \n  \n  \n    \n      1 n (%)\n    \n    \n      2 Pearson’s Chi-squared test"
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+    "text": "3.2 Import your data\n\n\nShow the code\n# Import the clean data set:\ncopdata &lt;- rio::import(here::here(\"data\", \"Spetses_clean1_2024.rds\"), trust = TRUE)"
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-    "text": "3.4. Risk Ratios\nThe risk ratios of each food item (including the 2x2 table) are reported below. The output of the CS() command is two tables: one with the 2x2 table and one with the risk difference, the risk ratio and the attributable fraction among exposed as well as the attributable fraction among the population (and the confidence intervals for all the estimates). The Chi-square and the p-value are also reported. In the second part, a table with all the food items is printed including attack rates for exposed and unexposed as well as risk ratios and the 95% confidence intervals (CI ll and CI ul, for the lower and upper interval) and p-values.\n\na) Calculate 95% CI Risk Ratios for food\nTo see if food items (note these are categorical variables) are associated with being a case, calculate risk ratios and 95% confidence intervals for food items. You can calculate this individually for each food item (using CSTable()), or all at once (see hint below).\n\n\n\n\n\n\nNeed a little bit of help?\n\n\n\n\n\nCreate a food_vars vector containing food variables of interest and use the function CSTable() of the EpiStats package.\nUsing this hint, you will create a table with the name of exposure variables, the total number of exposed, the number of exposed cases, the attack rate among the exposed, the total number of unexposed, the number of unexposed cases, the attack rate among the unexposed, risk ratios, 95% percent confidence intervals, and p-values. Amazing, isn’t it? Have a look at ??CSTable to learn more.\n\n\n\n\n\nCode\n# You could use the EpiStats package for each food item\nCS(copdata, \"case\", \"feta\")\n\n\n$df1\n          Cases Non Cases Total Risk\nExposed     156       115   271 0.58\nUnexposed    60        42   102 0.59\nTotal       216       157   373 0.58\n\n$df2\n                Point estimate 95%CI.ll 95%CI.ul\nRisk difference          -0.01    -0.12     0.10\nRisk ratio                0.98     0.81     1.19\nPrev. frac. ex.           0.02    -0.19     0.19\nPrev. frac. pop           0.02       NA       NA\nchi2(1)                   0.05       NA       NA\nPr&gt;chi2                  0.826       NA       NA\n\n\nCode\nCS(copdata, \"case\", \"sardines\")\n\n\n$df1\n          Cases Non Cases Total Risk\nExposed     150       105   255 0.59\nUnexposed    65        52   117 0.56\nTotal       215       157   372 0.58\n\n$df2\n                Point estimate 95%CI.ll 95%CI.ul\nRisk difference           0.03    -0.08     0.14\nRisk ratio                1.06     0.87     1.28\nAttr. frac. ex.           0.06    -0.14     0.22\nAttr. frac. pop           0.04       NA       NA\nchi2(1)                   0.35       NA       NA\nPr&gt;chi2                  0.553       NA       NA\n\n\nCode\nCS(copdata, \"case\", \"eggplant\")\n\n\n$df1\n          Cases Non Cases Total Risk\nExposed     123        93   216 0.57\nUnexposed    83        60   143 0.58\nTotal       206       153   359 0.57\n\n$df2\n                Point estimate 95%CI.ll 95%CI.ul\nRisk difference          -0.01    -0.12     0.09\nRisk ratio                0.98     0.82     1.18\nPrev. frac. ex.           0.02    -0.18     0.18\nPrev. frac. pop           0.01       NA       NA\nchi2(1)                   0.04       NA       NA\nPr&gt;chi2                  0.837       NA       NA\n\n\nCode\nCS(copdata, \"case\", \"moussaka\")\n\n\n$df1\n          Cases Non Cases Total Risk\nExposed     201       137   338 0.59\nUnexposed    14        22    36 0.39\nTotal       215       159   374 0.57\n\n$df2\n                Point estimate 95%CI.ll 95%CI.ul\nRisk difference           0.21     0.04     0.37\nRisk ratio                1.53     1.01     2.32\nAttr. frac. ex.           0.35     0.01     0.57\nAttr. frac. pop           0.32       NA       NA\nchi2(1)                   5.64       NA       NA\nPr&gt;chi2                  0.018       NA       NA\n\n\n\n\nCode\n# You can save time (and probably typos!) by creating a vector for food variables...\nfood_vars &lt;- c(\"feta\", \"sardines\", \"eggplant\", \"moussaka\", \n               \"orzo\", \"greeksal\", \"dessert\", \"bread\",  \n               \"champagne\", \"beer\", \"redwine\", \"whitewine\")\n\n# ...and using EpiStats::CSTable() to run all variables together!\nCSTable(copdata, \"case\", food_vars)\n\n\n$df\n          Tot.Exp Cases.Exp AR.Exp% Tot.Unexp Cases.Unexp AR.Unexp%   RR CI.ll\norzo          338       202   59.76        36          13     36.11 1.65  1.06\nmoussaka      338       201   59.47        36          14     38.89 1.53  1.01\nchampagne     316       187   59.18        48          21     43.75 1.35  0.97\ngreeksal      211       114   54.03       154          95     61.69 0.88  0.73\ndessert       149        90   60.40       198         106     53.54 1.13  0.94\nbeer          281       166   59.07        78          41     52.56 1.12  0.89\nredwine        80        42   52.50       259         150     57.92 0.91  0.72\nsardines      255       150   58.82       117          65     55.56 1.06  0.87\nwhitewine     260       150   57.69        98          54     55.10 1.05  0.85\nbread         342       196   57.31        29          16     55.17 1.04  0.74\nfeta          271       156   57.56       102          60     58.82 0.98  0.81\neggplant      216       123   56.94       143          83     58.04 0.98  0.82\n          CI.ul p(Chi2)\norzo       2.58   0.006\nmoussaka   2.32   0.018\nchampagne  1.89   0.044\ngreeksal   1.04   0.144\ndessert    1.36   0.202\nbeer       1.42   0.303\nredwine    1.14   0.393\nsardines   1.28   0.553\nwhitewine  1.29   0.659\nbread      1.46   0.823\nfeta       1.19   0.826\neggplant   1.18   0.837\n\n\n\n\nb) Prepare the RR table for publication\n\n\n\n\n\n\nNeed a little bit of help?\n\n\n\n\n\nUse flextable() and set_header_labels().\n\n\n\n\n\nCode\nrr_tbl &lt;- CSTable(copdata, \"case\", food_vars) %&gt;% \n  as.data.frame() %&gt;% \n  rownames_to_column() %&gt;% \n  flextable() %&gt;% \n   set_header_labels(\n     values = c(\"Food Item\",\n                \"Total exposed\",     \n               \"Cases exposed\", \n               \"AR among exposed\",    \n               \"Total unexposed\",\n               \"Cases unexposed\",\n               \"AR among unexposed\",\n               \"RR\",         \n               \"95% lower CI\",             \n               \"95% upper CI\",\n               \"p-value\"))\n\n\n\n\n\n\n\n\nLet’s stop and… think!\n\n\n\n\n\n· What can you infer from this table?\n· Considering the relative risks, which food or drink items do you think were most likely to be the vehicle(s) of infection in this outbreak?\n· Do you think there are any confounders or effect modifiers? If so, how would you investigate these further?\n\n\n\nThe interesting results here are that the food items that are most suspicious are orzo, moussaka and champagne. Orzo as such is unlikely to be contaminated, but as you can see in the picture (and from the dinner night), it was served with pesto! Maybe it was the pesto? Who-ho-ho!\nBefore one jumps into conclusions, consider that this result could be due to confounding! Maybe orzo was “clean” but eaten by all the people who ate the food item that actually was contaminated!(Optional)"
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+    "text": "3.3 Identify the variables you need to apply the case definition criteria.\n\n\n\n\n\n\nNeed a little bit of help?\n\n\n\n\n\nThe variables we need from the dataset to apply the above case definition are: meal, ill, onset_datetime, diarrhoea, bloody and vomiting."
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-    "text": "3.5. (Optional) Dose Response\nCheck for a dose-response relationship between the food items with the highest RR values (the top 3) and being a case.\n\na) Orzo\nUsing epitools::riskratio function:\n\n\nCode\nepitools::riskratio(copdata$orzoD,\n                    copdata$case,\n                    conf.level = 0.95)\n\n\n$data\n         Outcome\nPredictor FALSE TRUE Total\n    0        23   13    36\n    1        32   38    70\n    2        76  107   183\n    3        28   57    85\n    Total   159  215   374\n\n$measure\n         risk ratio with 95% C.I.\nPredictor estimate     lower    upper\n        0 1.000000        NA       NA\n        1 1.503297 0.9257878 2.441057\n        2 1.619168 1.0310538 2.542742\n        3 1.857014 1.1730804 2.939696\n\n$p.value\n         two-sided\nPredictor  midp.exact fisher.exact chi.square\n        0          NA           NA         NA\n        1 0.081364161  0.100893180 0.07613202\n        2 0.015337953  0.017069765 0.01373981\n        3 0.002010616  0.002370887 0.00162311\n\n$correction\n[1] FALSE\n\nattr(,\"method\")\n[1] \"Unconditional MLE & normal approximation (Wald) CI\"\n\n\nUsing a binomial regression:\n\n\nCode\n# Binomial regression for RRs. \n# The outcome needs to be exponentiated so we can interpret it properly!\nbinom_orzoD &lt;- glm(case ~ orzoD, data = copdata, \n             family = binomial(link = \"log\"))\n\n# To get exponentiated:\nbinom_orzoD_exp &lt;- glm(case ~ orzoD, data = copdata, \n                       family = binomial(link = \"log\")) %&gt;% \n  tidy(exponentiate = TRUE, \n       conf.int = TRUE)\n\nbinom_orzoD_exp\n\n\n\n\n  \n\n\n\n\n\n\n\n\n\nLet’s stop and… think!\n\n\n\n\n\nWhat do these results tell you?\n\n\n\nResults suggest a dose response relationship of having eaten orzo, pointing towards orzoas the potential vehicle. The higher the amount of orzothey ate, the stronger is the association (RR) with getting ill/being a case.\n\n\nb) Moussaka\nUsing epitools::riskratio function:\n\n\nCode\nepitools::riskratio(copdata$moussakaD,                     \n                    copdata$case,                     \n                    conf.level = 0.95)\n\n\n$data\n         Outcome\nPredictor FALSE TRUE Total\n    0        19   15    34\n    1        30   40    70\n    2        78  106   184\n    3        33   54    87\n    Total   160  215   375\n\n$measure\n         risk ratio with 95% C.I.\nPredictor estimate     lower    upper\n        0 1.000000        NA       NA\n        1 1.295238 0.8431805 1.989659\n        2 1.305797 0.8769752 1.944304\n        3 1.406897 0.9314233 2.125090\n\n$p.value\n         two-sided\nPredictor midp.exact fisher.exact chi.square\n        0         NA           NA         NA\n        1 0.22186384    0.2951163 0.21193013\n        2 0.15357032    0.1884220 0.14587265\n        3 0.07956697    0.1017336 0.07298506\n\n$correction\n[1] FALSE\n\nattr(,\"method\")\n[1] \"Unconditional MLE & normal approximation (Wald) CI\"\n\n\nUsing a binomial regression:\n\n\nCode\n# Let's get the results directly exponentiated\nbinom_moussakaD_exp &lt;- glm(case ~ moussakaD, data = copdata, \n                       family = binomial(link = \"log\")) %&gt;% \n  tidy(exponentiate = TRUE, \n       conf.int = TRUE)\n\nbinom_moussakaD_exp\n\n\n\n\n  \n\n\n\n\n\nc) Champagne\nUsing epitools::riskratio function:\n\n\nCode\nepitools::riskratio(copdata$champagneD,                     \n                    copdata$case,                     \n                    conf.level = 0.95)\n\n\n$data\n         Outcome\nPredictor FALSE TRUE Total\n    0        27   21    48\n    1        93  111   204\n    2        12   32    44\n    3        24   44    68\n    Total   156  208   364\n\n$measure\n         risk ratio with 95% C.I.\nPredictor estimate     lower    upper\n        0 1.000000        NA       NA\n        1 1.243697 0.8812613 1.755193\n        2 1.662338 1.1502014 2.402507\n        3 1.478992 1.0260296 2.131923\n\n$p.value\n         two-sided\nPredictor  midp.exact fisher.exact  chi.square\n        0          NA           NA          NA\n        1 0.188922591  0.201367850 0.183280359\n        2 0.005584928  0.006247816 0.004961941\n        3 0.027627736  0.036334120 0.025117641\n\n$correction\n[1] FALSE\n\nattr(,\"method\")\n[1] \"Unconditional MLE & normal approximation (Wald) CI\"\n\n\nUsing a binomial regression:\n\n\nCode\n# Let's get the results directly exponentiated\nbinom_champagneD_exp &lt;- glm(case ~ champagneD, data = copdata, \n                       family = binomial(link = \"log\")) %&gt;% \n  tidy(exponentiate = TRUE, \n       conf.int = TRUE)\n\nbinom_champagneD_exp"
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+    "section": "3.4 Create a new case column to hold the binary case definition variable. Let’s think about how to do this little by little:",
+    "text": "3.4 Create a new case column to hold the binary case definition variable. Let’s think about how to do this little by little:\n\na) Ate a meal at the school dinner\nYou decide to exclude any people from the cohort who didn’t eat at the dinner, because we specifically hypothesised a food item to be the vehicle of infection in this outbreak. Thus, filter your dataset to those who ate a meal: Keep in your dataset only those who ate a meal.\n\n\n\n\n\n\nNeed a little bit of help?\n\n\n\n\n\nfilter by those with meal == 1.\n\n\n\n\n\n\n\n\n\nLet’s stop and… think!\n\n\n\n\n\nWhat are some of the implications this decision may lead to? (excluding any people from the cohort who didn’t eat at the dinner)\n\n\n\n\n\nShow the code\ncopdata &lt;- copdata %&gt;% \n  filter(meal == 1)\n\n\n\n\n\n\n\n\nOnce you’ve thought about the above, have a look here\n\n\n\n\n\nSeven of the respondents actually said they did not eat the meal, but when it came to the questions about which food items they ate, they provided answers! This issue could have been minimised by:\n- At the survey state, one could adjust the design of an electronic questionnaire to prevent key questions from being skipped. This can come with both pros and cons. Allow fellows to discuss if time allows.\n- Explore your data further, realise this is the case, and recode the meal variable for these individuals as 1. =&gt; This would be the way to go, but is not what we did in our example because we tried to keep it simple, and also because it is good to show that you may not always clean the data perfectly, and that has consequences: You can highlight the importance or really explore your data in depth.\nBy making the above decision, we may be missing cases and non-cases people, and thus, modifying the final estimate of our measure of association. =&gt; It is very important to know your data, explore it deeply and try to clean it as well as possible. Every step one makes when cleaning the data may have a consequence, and we should be aware of it when making the data cleaning decisions and when interpreting the results.\n\n\n\n\n\nb) Fell ill after the start of the meal\nThe questionnaire included a question on whether the person fell ill after the school dinner party (column ill). However, the person may have experience symptoms that are very unspecific to this outbreak (e.g., headache, joint pain…). We may want to only include those who develop gastrointestinal symptoms. That is any person having had diarrhoea with OR without blood, OR vomiting. To capture this information easily, you will create a new gastrosymptoms variable. This variable will indicate that the person had one OR (“or” is R is achieved by using |) more of the clinical symptoms in your definition.\nNote that we the concept of having eaten a meal is already included as per one of the steps above.\n\n\n\n\n\n\nNeed a little bit of help?\n\n\n\n\n\nCreate a new gastrosymptoms column in your line list with mutate() and case_when().\n\n\n\n\n\n\n\n\n\nLet’s stop and… think!\n\n\n\n\n\nWhat are some of the implications this decision may lead to? (defining “fell ill” as any person who reported having diarrhoea with OR without blood, OR vomiting)\n\n\n\n\n\n\n\n\n\nIf you’ve thought about the above, have a look here\n\n\n\n\n\nHaving one gastrointestinal symptom is enough to be considered a potential case at this point, which may still be considered too unspecific (low specificity). For example, a person who ate at the dinner party and developed diarrhoea for other reasons other than food poisoning (say, they recently started on antibiotics known for unbalancing the intestinal flora and causing diarrhoea) could be misclassified as a potential case. (Note we talk about potential case, and not case; that is because here we are not talking about cases per-se yet, but this decision has implications for when applying the case definition below).\nMoreover, those who did not report clinical symptoms will be defined as non-cases. Thus, we are assuming that these individuals did not develop symptoms because they didn’t report them. The missing values could be due to, for example, them skipping the questions in the questionnaire. Some individuals may be reluctant to report symptoms, due to shame, fear of repercussion, or others. It is important to think ahead, before the interview, about ways to minimise these situations. For example, through questionnaire design, you may impede skipping questions; you could promote trust by using the right interviewers (in some cases this will be someone from the community, in others someone form specific NGOs, someone of a specific race or gender, etc); choose to carry out online questionnaires vs in person (or vice versa, depending on the situation), etc.\n\n\n\n\n\nShow the code\ncopdata &lt;- copdata %&gt;% \n  mutate(gastrosymptoms = case_when(\n    # Those had diarrhoea...\n    diarrhoea == \"1\" |\n      #or bloody diarrhoea...\n    bloody == \"1\" |\n      # or vomiting, are marked as 1 (fell ill after the meal)\n    vomiting == \"1\" ~ 1,\n    # The rest are 0 This includes those who ate a meal but had no symptoms (did not fell ill after the meal)\n    .default = 0)\n    )\n\n\n\n\nc) Fell ill within the time period of interest\n\nHmmm… what is the time period of interest? You could start calculating the incubation period, which can be defined by calculating the time between exposure (the meal) and onset of symptoms, and then looking at the distribution of these time differences. In this outbreak, incubation periods are easy to calculate, because everyone was exposed at (roughly) the same time and on the same day (eating the meal at the school dinner party).\nDinner was served at 18:00 roughly for everyone. Create a new meal_datetime variable with this information for all people in your dataset (as per 05 Oct 2024, at 18:00h).\n\n\n\nShow the code\n# Start with copdata:\ncopdata &lt;- copdata %&gt;% \n  # Create new column for meal date and time:\n  mutate(meal_datetime = lubridate::ymd_hm(\"2024-10-05 18:00\"))\n\n\n\nYou can calculate the incubation period for each participant by subtracting onset_datetime - meal_datetime.\nThen, take the median() of that column to calculate a median incubation period, this will help you start having some hypothesis about the type of pathogen we are dealing with until the lab results come back.\n\n\n\n\n\n\n\nNeed a little bit of help?\n\n\n\n\n\nBe aware of the type of variable incubation is (check class()), as well as of missing valuesNA.\n\n\n\n\n\n\n\n\n\nNote\n\n\n\n\n\nThe incubation period of a disease is the time from an exposure resulting in infection until the onset of disease. Because infection usually occurs immediately after exposure, the incubation period is generally the duration of the period from the onset of infection to the onset of disease – Rothman, Greenland, Lash (2017): Modern Epidemiology, 3rd edition\n\n\n\n\n\nShow the code\ncopdata &lt;- copdata %&gt;% \n  mutate(incubation = onset_datetime - meal_datetime,\n         incubation = as.numeric (incubation))\n\n\nmedian(as.numeric(copdata$incubation), na.rm = TRUE)\n\n\n[1] 15\n\n\n\n\n\n\n\n\nNow we are messing up with your brain…\n\n\n\n\n\nIf you pay attention, there were two individuals who don’t have a time recorded, only a date, the day of the dinner. These people probably got sick the same night they had the dinner, but the exact time was not recoded. They way we’ve managed the data will insert “00:00” in a missing value of a time. This means that the 2 people that got sick the day of the dinner have been recorded sick before the dinner (in the early morning of the day of the dinner). The two people have a negative incubation period! This should not happen. If you feel the force is with you, you can modify this and fix the error (if you do this, your results will be a bit different than your colleagues, but similar). We will continue with this “erroneous” data, for easiness. But note that in real life, as soon as you realise there is a coding error like this, you would have to go back to your cleaning code and fix the error! This may happen a couple of times during your outbreak investigation, and is totally normal. Cleaning data is not an easy job!\n\n\n\nWe see that the median incubation time is 15 hours. This is useful information, as incubation periods tend to be relatively pathogen-specific.\nBased on this, say you define and limit the maximum incubation period to 48 hours after the meal, as the data points to a fast-acting bacterial toxin or a virus. That is, dinner participants should have developed (onset_datetime) at least one symptom (diarrhoea, bloody or vomiting) 48h after meal_datetime to become a case.\n\n\n\n\n\n\nLet’s stop and… think!\n\n\n\n\n\nWhat are some of the implications this decision may lead to? (implications of this case definition)\n\n\n\n\n\n\n\n\n\nIf you’ve thought about the above, have a look here\n\n\n\n\n\nAll those not developing at least one symptom (diarrhoea with OR without blood, OR vomiting) 48h after the dinner are considered non-cases. This could (depending on how you decide to analyse your data) include those who had no symptoms at all, those who have missing data on the onset_datetime variable, and/or those who had symptoms before eating the meal. This is a reminder that you need to be both careful and aware of the implications of your data analysis decisions. If a person had clinical symptoms before eating the meal, they are considered as not-cases. However, it could be that a person had symptoms before the meal, and yet, still got infected by the pathogen when eating their meal (bad luck, we know…). According to this definition, we would be missing that case.\n\n\n\n\n\nd) Finally, with this information you can create a new case column to hold the binary (1/0) case definition variable.\n\n\nShow the code\ncopdata &lt;- copdata %&gt;% \n  mutate(case = case_when(\n    # Those who had symptoms &lt;48h from the meal are cases (1)\n    gastrosymptoms == 1 & \n      onset_datetime &gt;= meal_datetime &\n      onset_datetime &lt;= (meal_datetime + days(2)) ~ 1,\n    # Those who had symptoms &gt;48h from the meal are non-cases (FALSE)\n    gastrosymptoms == 1 & \n      onset_datetime &gt; (meal_datetime + days(2)) ~ 0,\n    # The rest are considered non-cases. Including, those who had no symptoms at all, who have missing data on the onset_datetime variable, or who had symptoms before eating the meal \n    .default = 0)\n  )\n\n\nNote that we may be incurring in misclassification bias with the code above. The last section indicates that if a person had clinical symptoms before eating the meal, they are considered as non-cases. However, it could be that a person had symptoms before the meal, and yet, still got infected by the pathogen when eating their meal (bad luck, we know…).\nMoreover, if you remember, there were a couple of people with an dayonset, but no starthour. The code we used (lubridate::ymd_h with argument truncated = 2) results in dates with missing starthour being converted to date-time, with the missing time being set to 00:00 (midnight). This means that these two people don’t fulfill the case definition criteria because we marked their symptoms started early in the morning of Nov 11 (at 00:00), before the meal time (18:00), and thus, they did not “fell ill within the time period of interest”.\nThe two situations above are a reminder that you need to be both careful and aware of the implications of your data analysis decisions.\n\n\n\n\n\n\nLet’s stop and… think!\n\n\n\n\n\nWhat do you think are the risks of mis-classifying cases as non-cases in your analysis?\n\n\n\n\n\n\n\n\n\nIf you’ve thought about the above, have a look here\n\n\n\n\n\nWe will have bias either towards or away from the null, depending on the proportions of subjects misclassified.\n\n\n\n\n\nShow the code\n# Tabulate cases:\njanitor::tabyl(dat = copdata, case)\n\n\n\n  \n\n\n\nLet’s have a look at how many people ate a meal, had symptoms, and were considered as cases after applying our case definition:\n\n\nShow the code\ncopdata %&gt;% \n  summarise(atemeal = sum(meal == 1),\n            hadsympt = sum(gastrosymptoms == 1),\n            nb_cases = sum(case == 1)\n            )"
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-    "text": "3.6 Summary\nAs a summary of what you’ve done above, answer these questions:\n\nIs the respondents’ sex associated with being a case?\nIs the school class associated with being a case?\nWhich foods increase the risk of being a case?\n(Optional) Is there a dose-response relationship between the food items and being a case?\nWhat do you think is the most likely culprit(s) of this outbreak at this point? Are there any risk factors you would like to highlight?"
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+    "text": "Background:\nThis website contains the R teaching material that can be used (it is not compulsory) during the Simulated Outbreak (SimOb) section of the Introductory Course module of the EPIET (European Programme of Intervention Epidemiology Training), EUPHEM (European Public Health Microbiology) and MediPIET (Mediterranean and Black Sea Programme for Intervention Epidemiology Training) fellowships.\nThe SimOb is an 4-day exercise where fellows are provided a series of injects through which they will follow a 10-steps framework to investigate a specific outbreak. Some of the injects are dedicated to R-exercises that can be proved quite handy during an outbreak investigation. Those injects are maintained in this repository and are stored as different .qmd files, under the root folder:\n\nData_import_cleaning.qmd\nCase_definition.qmd\nLab_and_Descriptive.qmd\nUnivariate.qmd\nStratified.qmd"
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-    "text": "2. Story/plot description\nFrom the univariable analysis, it seems that eating orzpo and eating moussaka as well as drinking champagne are associated with the highest risk of becoming ill. There are, however, many other food items that are associated with an increased risk (even if not statistically significant).\nYou should next think about potential confounders and about effect modification. Think about which variables you might want to check for effect modification or confounding. One common strategy is to base this decision on the results obtained in the univariable analysis and a p-value threshold of 0.20-0.25. Also, food items that are known risk factors for gastroenteritis could also be included regardless of their univariable p-value."
+    "text": "2. Story/plot description\nFrom the univariable analysis, it seems that eating pasta and eating veal as well as drinking champagne are associated with the highest risk of becoming ill. There are, however, many other food items that are associated with an increased risk (even if not statistically significant).\nYou should next think about potential confounders and about effect modification. Think about which variables you might want to check for effect modification or confounding. One common strategy is to base this decision on the results obtained in the univariable analysis and a p-value threshold of 0.20-0.25. Also, food items that are known risk factors for gastroenteritis could also be included regardless of their univariable p-value."
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-    "text": "3.2. Install packages and load libraries\n\n\nCode\n# Load the required libraries into the current R session:\npacman::p_load(rio, \n               here, \n               tidyverse, \n               skimr,\n               plyr,\n               janitor,\n               lubridate,\n               gtsummary, \n               flextable,\n               officer,\n               epikit, \n               apyramid, \n               scales,\n               EpiStats)"
+    "text": "3.2. Install packages and load libraries\n\n\nShow the code\n# Load the required libraries into the current R session:\npacman::p_load(rio, \n               here, \n               tidyverse, \n               skimr,\n               plyr,\n               janitor,\n               lubridate,\n               gtsummary, \n               flextable,\n               officer,\n               epikit, \n               apyramid, \n               scales,\n               EpiStats)"
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-    "text": "3.3. Import your data\n\n\nCode\n# Import the raw data set: \ncopdata &lt;- rio::import(here::here(\"data\", \"Spetses_clean2_2024.rds\"))"
+    "text": "3.3. Import your data\n\n\nShow the code\n# Import the raw data set: \ncopdata &lt;- rio::import(here::here(\"data\", \"Spetses_clean2_2024.rds\"), trust = T)"
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     "section": "3.4. Consider and assess for confounding and/or effect modification",
-    "text": "3.4. Consider and assess for confounding and/or effect modification\nHave a look at the relative risk for being a case having eaten a specific food item (for example, moussake), when stratified by another variable (for example, orzo). You may consider stratifying by orzo, as it has the highest RR in the univariable analysis.\nThere are many variables in this dataset and it might not make sense to stratify each variable by each other variable on our search for effect modifiers and confounders.\nHowever, we also don’t want to be too restrictive as a variable which actually (i.e. causally) is associated with the outcome might not show a significant association at the significant level we decided (say 5%) in the univariable analysis due to confounding. Therefore, we could test all variables statistically significant at the 15%, 20%, or 25% level (specific percentage to be decided by your group). In our solutions here, we are looking at moussaka and champagne, stratified by orzo, but you may decide to look at other food items as well.\n\n\n\n\n\n\nNeed a little bit of help?\n\n\n\n\n\nUse the function csinter() of the EpiStats package.\n\n\n\n\na) Moussaka as exposure of interest, stratified by having eaten orzo\nIf we stratify the effect of moussaka by orzo we ask the question: does eating orzo modify or confound the association between eating moussaka and being a case?\n\n\nCode\nstratall &lt;- copdata %&gt;% \n  # Mutate across to convert cases to numeric:\n  mutate(across(.cols = case, \n                .fns = ~ as.numeric(.)))\n\n# Pass data to the csinter function:\norzostrata &lt;- csinter(x = stratall, \n                       cases = \"case\", \n                       exposure = \"moussaka\", \n                       by = \"orzo\")\n\norzostrata\n\n\n$df1\n  CSInter case - moussaka by(orzo) Total Cases Risk %          P.est. Stats\n1                         orzo = 1   338  &lt;NA&gt;     NA Risk difference  0.10\n2                          Exposed   330   198  60.00      Risk Ratio  1.20\n3                        Unexposed     8     4  50.00 Attrib.risk.exp  0.17\n4                                     NA  &lt;NA&gt;     NA Attrib.risk.pop  0.16\n5                         orzo = 0    36  &lt;NA&gt;     NA Risk difference  0.02\n6                          Exposed     8     3  37.50      Risk Ratio  1.05\n7                        Unexposed    28    10  35.71 Attrib.risk.exp  0.05\n8                                     NA  &lt;NA&gt;     NA Attrib.risk.pop  0.01\n9              Missing / Missing %     3  0.8%     NA            &lt;NA&gt;    NA\n  95%CI.ll 95%CI.ul\n1    -0.25     0.45\n2     0.60     2.41\n3    -0.68     0.59\n4       NA       NA\n5    -0.36     0.40\n6     0.38     2.92\n7    -1.65     0.66\n8       NA       NA\n9       NA       NA\n\n$df2\n                    Point Estimate Chi2 p.value  Stats 95%CI.ll 95%CI.ul\n1        Woolf test of homogeneity 0.04   0.833     NA       NA       NA\n2            Crude RR for moussaka   NA      NA   1.53     1.01     2.32\n3 MH RR moussaka adjusted for orzo   NA      NA   1.15     0.64     2.04\n4   Adjusted/crude relative change   NA      NA -25.08       NA       NA\n\n\nLet’s check if orzo is associated with moussaka (if we are thinking moussaka may be a confounder, we need to see if there is an association between the potential confounder (moussaka) and the exposure (orzo)):\n\n\nCode\n# Perform Wilcoxon rank sum test on orzo and moussaka:\nwilcox.test(orzo ~ moussaka, \n            data = copdata)\n\n\n\n    Wilcoxon rank sum test with continuity correction\n\ndata:  orzo by moussaka\nW = 1496, p-value &lt; 2.2e-16\nalternative hypothesis: true location shift is not equal to 0\n\n\n\n\nb) Champagne as exposure of interest, stratified by having eaten orzo\n\n\nCode\n# Pass data to the csinter function:\nchampstrata &lt;- csinter(x = stratall, \n                       cases = \"case\", \n                       exposure = \"champagne\", \n                       by = \"orzo\")\n\n\n\n\n\n\n\n\nLet’s stop and… think!\n\n\n\n\n\nAre there any indications to make you think there may be effect modification and/or confounding?\n\n\n\nWe could stratify by orzo (the strongest risk factor in the univariable analysis), to examine if orzo confounds the association between eating moussaka and being a case. Before stratification, we will need to check if orzo meets the conditions of being a confounder. For a variable to be a confounder it needs to be associated both with the outcome (being a case) and with the exposure (and not be in the causal pathway between exposure and outcome). We know from univariable analysis that orzo is associated with being a case. If we run a Wilcoxon rank sum test, we will see that orzo is also associated with moussaka (indeed, you can see that most people either had both moussaka and orzo or neither of these food items, so they are associated with each other).\nAbove, we uses ccinter to stratify and save the object as “orzostrata”.\norzostrata: Within the stratum of the people who ate orzo, moussaka has no significant effect (RR = 1.20, CI: 0.60 - 2.41). The same holds within the stratum of people who didn’t eat orzo (RR = 1.05, CI = 0.38, 2.92). The adjusted MH-RR also suggests that moussaka has no effect (RRadj = 1.15, CI: 0.80 - 2.85).  To identify confounding, we want to look at the % change between the crude and the adjusted RR. This is given by the csinter output “Adjusted/crude relative change”.  The difference between the crude and the MH-RR in this case is &gt;20% suggesting that orzo confounds the association between moussaka and the disease.\nThis result suggest that moussaka is not a risk factor of the disease and that the crude observed effect was due to the confounding effect of orzo.\nIf you stratify by moussaka, you see that moussaka does not confound the association between orzo and the disease. The same applies if you stratify the exposure to orzo by other variables. The above, the higher RR for orzo and the dose response relationship we found earlier for orzo (remember this was optional) provide additional evidence that there was something going on with the orzo with pesto dish!"
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-    "text": "At the end of the session, participants will be able to:\n\nApply case definition criteria to a dataset using logical arguments in R"
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-    "text": "2. Story/plot description\nYou will now create a new column in the data set to hold the case definition you decided in a previous step during your investigation. You can call this column case and set it to TRUE if the individual meets the case definition criteria and FALSE if not. You will use this column later on for any calculations needed (descriptive statistics, two-by-two tables to compute measures of association, etc.) to figure out the culprit of this outbreak."
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-    "text": "3. Questions/Assignments\nIf you closed your R project, please open it again and load the clean dataset. We strongly recommend you use the Spetses_clean1_2024.rds. This is just to be sure all needed changes in that file for the code to work have been done (as you may have changed the file in a slightly different way than we have).\nTo be sure we all start from the same case definition, let’s agree that a case was defined as a person who:\n\nattended the school dinner on 11 November 2006 (i.e. is on the linelist)\nate a meal at the school dinner (i.e. was exposed)\nfell ill after the start of the meal\nfell ill within the time period of interest after the school dinner\nsuffered from diarrhoea with or without blood, or vomiting\n\nNon cases (not-ill) were defined as people who:\n\nattended the school dinner on 11 November 2006 (i.e. are on the linelist)\nate a meal at the school dinner (i.e. were exposed)\ndid not fall ill within the time period of interest\ndid not develop diarrhoea (with or without blood) or vomiting"
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-    "text": "3.1 Install packages (if needed) and load libraries\n\n\nCode\n# Load the required libraries into the current R session:\npacman::p_load(rio, \n               here, \n               tidyverse, \n               skimr,\n               plyr,\n               janitor,\n               lubridate,\n               gtsummary, \n               flextable,\n               officer,\n               epikit, \n               apyramid, \n               scales)"
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-    "text": "3.2 Import your data\n\n\nCode\n# Import the clean data set:\ncopdata &lt;- rio::import(here::here(\"data\", \"Spetses_clean1_2024.rds\"))"
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-    "text": "3.3 Identify the variables you need to apply the case definition criteria.\n\n\n\n\n\n\nNeed a little bit of help?\n\n\n\n\n\nThe variables we need from the dataset to apply the above case definition are: meal, onset_datetime, diarrhoea, bloody and vomiting."
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-    "section": "3.4 Create a new case column to hold the binary case definition variable. Let’s think about how to do this little by little:",
-    "text": "3.4 Create a new case column to hold the binary case definition variable. Let’s think about how to do this little by little:\n\na) Ate a meal at the school dinner\nYou decide to exclude any people from the cohort who didn’t eat at the dinner, because we specifically hypothesised a food item to be the vehicle of infection in this outbreak. Thus, filter your dataset to those who ate a meal: Keep in your dataset only those who ate a meal.\n\n\n\n\n\n\nNeed a little bit of help?\n\n\n\n\n\nfilter by those with meal == TRUE.\n\n\n\n\n\n\n\n\n\nLet’s stop and… think!\n\n\n\n\n\nWhat are some of the implications this decision may lead to? (excluding any people from the cohort who didn’t eat at the dinner)\n\n\n\n\n\nCode\ncopdata &lt;- copdata %&gt;% \n  filter(meal == TRUE)\n\n\n\n\n\n\n\n\nOnce you’ve thought about the above, have a look here\n\n\n\n\n\nSeven of the respondents actually said they did not eat the meal, but when it came to the questions about which food items they ate, they provided answers! This issue could have been minimised by:\n- At the survey state, one could adjust the design of an electronic questionnaire to prevent key questions from being skipped. This can come with both pros and cons. Allow fellows to discuss if time allows.\n- Explore your data further, realise this is the case, and recode the meal variable for these individuals as TRUE. =&gt; This would be the way to go, but is not what we did in our example because we tried to keep it simple, and also because it is good to show that you may not always clean the data perfectly, and that has consequences: You can highlight the importance or really explore your data in depth.\nBy making the above decision, we may be missing cases and non-cases people, and thus, modifying the final estimate of our measure of association. =&gt; It is very important to know your data, explore it deeply and try to clean it as well as possible. Every step one makes when cleaning the data may have a consequence, and we should be aware of it when making the data cleaning decisions and when interpreting the results.\n\n\n\n\n\nb) Fell ill after the start of the meal\nWe define “fell ill” as any person having had diarrhoea with OR without blood, OR vomiting. To capture this information easily, you will create a new gastrosymptoms variable. This variable will indicate that the person had one OR (“or” is R is achieved by using |) more of the clinical symptoms in your definition.\nNote that we the concept of having eaten a meal is already included as per one of the steps above.\n\n\n\n\n\n\nNeed a little bit of help?\n\n\n\n\n\nCreate a new gastrosymptoms column in your line list with mutate() and case_when().\n\n\n\n\n\n\n\n\n\nLet’s stop and… think!\n\n\n\n\n\nWhat are some of the implications this decision may lead to? (defining “fell ill” as any person who reported having diarrhoea with OR without blood, OR vomiting)\n\n\n\n\n\n\n\n\n\nIf you’ve thought about the above, have a look here\n\n\n\n\n\nHaving one clinical symptom enough to be considered a potential case at this point may be considered too unspecific (low specificity). For example, a person who ate at the dinner party and developed diarrhoea for other reasons other than food poisoning (say, they recently started on antibiotics known for unbalancing the intestinal flora and causing diarrhoea) could be misclassified as a potential case. (Note we talk about potential case, and not case; that is because here we are not talking about cases per-se yet, but this decision has implications for when applying the case definition below).\nMoreover, those who did not report clinical symptoms will be defined as non-cases. Thus, we are assuming that these individuals did not develop symptoms because they didn’t report them. The missing values could be due to, for example, them skipping the questions in the questionnaire. Some individuals may be reluctant to report symptoms, due to shame, fear of repercussion, or others. It is important to think ahead, before the interview, about ways to minimise these situations. For example, through questionnaire design, you may impede skipping questions; you could promote trust by using the right interviewers (in some cases this will be someone from the community, in others someone form specific NGOs, someone of a specific race or gender, etc); choose to carry out online questionnaires vs in person (or vice versa, depending on the situation), etc.\n\n\n\n\n\nCode\ncopdata &lt;- copdata %&gt;% \n  mutate(gastrosymptoms = case_when(\n    # Those had diarrhoea...\n    diarrhoea == TRUE |\n      #or bloody diarrhoea...\n    bloody == TRUE |\n      # or vomiting, are marked as TRUE (fell ill after the meal)\n    vomiting == TRUE ~ TRUE,\n    # The rest are FALSE. This includes those who ate a meal but had no symptoms (did not fell ill after the meal)\n    .default = FALSE)\n    )\n\n\n\n\nc) Fell ill within the time period of interest\n\nHmmm… what is the time period of interest? You could start calculating the incubation period, which can be defined by calculating the time between exposure (the meal) and onset of symptoms, and then looking at the distribution of these time differences. In this outbreak, incubation periods are easy to calculate, because everyone was exposed at (roughly) the same time and on the same day (eating the meal at the school dinner party).\nDinner was served at 18:00 roughly for everyone. Create a new meal_datetime variable with this information for all people in your dataset (as per 05 Oct 2024, at 18:00h).\n\n\n\nCode\n# Start with copdata:\ncopdata &lt;- copdata %&gt;% \n  # Create new column for meal date and time:\n  mutate(meal_datetime = lubridate::ymd_hm(\"2024-10-05 18:00\"))\n\n\n\nYou can calculate the incubation period for each participant by subtracting onset_datetime - meal_datetime.\nThen, take the median() of that column to calculate a median incubation period, this will help you start having some hypothesis about the type of pathogen we are dealing with until the lab results come back.\n\n\n\n\n\n\n\nNeed a little bit of help?\n\n\n\n\n\nBe aware of the type of variable incubation is (check class()), as well as of missing valuesNA.\n\n\n\n\n\n\n\n\n\nNote\n\n\n\n\n\nThe incubation period of a disease is the time from an exposure resulting in infection until the onset of disease. Because infection usually occurs immediately after exposure, the incubation period is generally the duration of the period from the onset of infection to the onset of disease – Rothman, Greenland, Lash (2017): Modern Epidemiology, 3rd edition\n\n\n\n\n\nCode\ncopdata &lt;- copdata %&gt;% \n  mutate(incubation = onset_datetime - meal_datetime,\n         incubation = as.numeric (incubation))\n\n\nmedian(as.numeric(copdata$incubation), na.rm = TRUE)\n\n\n[1] 15\n\n\n\n\n\n\n\n\nNow we are messing up with your brain…\n\n\n\n\n\nIf you pay attention, there were two individuals who don’t have a time recorded, only a date, the day of the dinner. These people probably got sick the same night they had the dinner, but the exact time was not recoded. They way we’ve managed the data will insert “00:00” in a missing value of a time. This means that the 2 people that got sick the day of the dinner have been recorded sick before the dinner (in the early morning of the day of the dinner). The two people have a negative incubation period! This should not happen. If you feel the force is with you, you can modify this and fix the error (if you do this, your results will be a bit different than your colleagues, but similar). We will continue with this “erroneous” data, for easiness. But note that in real life, as soon as you realise there is a coding error like this, you would have to go back to your cleaning code and fix the error! This may happen a couple of times during your outbreak investigation, and is totally normal. Cleaning data is not an easy job!\n\n\n\nWe see that the median incubation time is 15 hours. This is useful information, as incubation periods tend to be relatively pathogen-specific.\nBased on this, say you define and limit the maximum incubation period to 48 hours after the meal, as the data points to a fast-acting bacterial toxin or a virus. That is, dinner participants should have developed (onset_datetime) at least one symptom (diarrhoea, bloody or vomiting) 48h after meal_datetime to become a case.\n\n\n\n\n\n\nLet’s stop and… think!\n\n\n\n\n\nWhat are some of the implications this decision may lead to? (implications of this case definition)\n\n\n\n\n\n\n\n\n\nIf you’ve thought about the above, have a look here\n\n\n\n\n\nAll those not developing at least one symptom (diarrhoea with OR without blood, OR vomiting) 48h after the dinner are considered non-cases. This could (depending on how you decide to analyse your data) include those who had no symptoms at all, those who have missing data on the onset_datetime variable, and/or those who had symptoms before eating the meal. This is a reminder that you need to be both careful and aware of the implications of your data analysis decisions. If a person had clinical symptoms before eating the meal, they are considered as not-cases. However, it could be that a person had symptoms before the meal, and yet, still got infected by the pathogen when eating their meal (bad luck, we know…). According to this definition, we would be missing that case.\n\n\n\n\n\nd) Finally, with this information you can create a new case column to hold the binary (TRUE/FALSE) case definition variable.\n\n\nCode\ncopdata &lt;- copdata %&gt;% \n  mutate(case = case_when(\n    # Those who had symptoms &lt;48h from the meal are cases (TRUE)\n    gastrosymptoms == TRUE & \n      onset_datetime &gt;= meal_datetime &\n      onset_datetime &lt;= (meal_datetime + days(2)) ~ TRUE,\n    # Those who had symptoms &gt;48h from the meal are non-cases (FALSE)\n    gastrosymptoms == TRUE & \n      onset_datetime &gt; (meal_datetime + days(2)) ~ FALSE,\n    # The rest are considered non-cases. Including, those who had no symptoms at all, who have missing data on the onset_datetime variable, or who had symptoms before eating the meal \n    .default = FALSE)\n  )\n\n\nNote that we may be incurring in misclassification bias with the code above. The last section indicates that if a person had clinical symptoms before eating the meal, they are considered as non-cases. However, it could be that a person had symptoms before the meal, and yet, still got infected by the pathogen when eating their meal (bad luck, we know…).\nMoreover, if you remember, there were a couple of people with an dayonset, but no starthour. The code we used (lubridate::ymd_h with argument truncated = 2) results in dates with missing starthour being converted to date-time, with the missing time being set to 00:00 (midnight). This means that these two people don’t fulfill the case definition criteria because we marked their symptoms started early in the morning of Nov 11 (at 00:00), before the meal time (18:00), and thus, they did not “fell ill within the time period of interest”.\nThe two situations above are a reminder that you need to be both careful and aware of the implications of your data analysis decisions.\n\n\n\n\n\n\nLet’s stop and… think!\n\n\n\n\n\nWhat do you think are the risks of mis-classifying cases as non-cases in your analysis?\n\n\n\n\n\n\n\n\n\nIf you’ve thought about the above, have a look here\n\n\n\n\n\nWe will have bias either towards or away from the null, depending on the proportions of subjects misclassified.\n\n\n\n\n\nCode\n# Tabulate cases:\njanitor::tabyl(dat = copdata, case)\n\n\n\n\n  \n\n\n\nLet’s have a look at how many people ate a meal, had symptoms, and were considered as cases after applying our case definition:\n\n\nCode\ncopdata %&gt;% \n  summarise(atemeal = sum(meal == TRUE),\n            hadsympt = sum(gastrosymptoms == TRUE),\n            nb_cases = sum(case == TRUE)\n            )"
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-    "href": "Lab_and_Descriptive.html#merge-and-explore-the-lab-data.-explore-it-as-you-wish",
-    "title": "Laboratory data and descriptive analysis",
-    "section": "3.3 Merge and explore the lab data. Explore it as you wish!",
-    "text": "3.3 Merge and explore the lab data. Explore it as you wish!\n\n\nCode\ncopdatalab &lt;- left_join(copdata, lab, \n                        by = \"id\")\n# Tabulate:\njanitor::tabyl(dat = copdatalab, \"RT-PCR_ecoli_etec\")"
+    "text": "3.4. Consider and assess for confounding and/or effect modification\nHave a look at the relative risk for being a case having eaten a specific food item (for example, veal), when stratified by another variable (for example, pasta). You may consider stratifying by pasta, as it has the highest RR in the univariable analysis.\nThere are many variables in this dataset and it might not make sense to stratify each variable by each other variable on our search for effect modifiers and confounders.\nHowever, we also don’t want to be too restrictive as a variable which actually (i.e. causally) is associated with the outcome might not show a significant association at the significant level we decided (say 5%) in the univariable analysis due to confounding. Therefore, we could test all variables statistically significant at the 15%, 20%, or 25% level (specific percentage to be decided by your group). In our solutions here, we are looking at veal and champagne, stratified by pasta, but you may decide to look at other food items as well.\n\n\n\n\n\n\nNeed a little bit of help?\n\n\n\n\n\nUse the function csinter() of the EpiStats package.\n\n\n\n\na) Veal as exposure of interest, stratified by having eaten pasta\nIf we stratify the effect of veal by pasta we ask the question: does eating pasta modify or confound the association between eating veal and being a case?\n\n\nShow the code\nstratall &lt;- copdata %&gt;% \n  # Mutate across to convert cases to numeric:\n  mutate(across(.cols = case, \n                .fns = ~ as.numeric(.)))\n\n# Pass data to the csinter function:\npastastrata &lt;- csinter(x = stratall, \n                       cases = \"case\", \n                       exposure = \"veal\", \n                       by = \"pasta\")\n\npastastrata\n\n\n$df1\n  CSInter case - veal by(pasta) Total Cases Risk %          P.est. Stats\n1                     pasta = 1   338  &lt;NA&gt;     NA Risk difference  0.10\n2                       Exposed   330   198  60.00      Risk Ratio  1.20\n3                     Unexposed     8     4  50.00 Attrib.risk.exp  0.17\n4                                  NA  &lt;NA&gt;     NA Attrib.risk.pop  0.16\n5                     pasta = 0    36  &lt;NA&gt;     NA Risk difference  0.02\n6                       Exposed     8     3  37.50      Risk Ratio  1.05\n7                     Unexposed    28    10  35.71 Attrib.risk.exp  0.05\n8                                  NA  &lt;NA&gt;     NA Attrib.risk.pop  0.01\n9           Missing / Missing %     3  0.8%     NA            &lt;NA&gt;    NA\n  95%CI.ll 95%CI.ul\n1    -0.25     0.45\n2     0.60     2.41\n3    -0.68     0.59\n4       NA       NA\n5    -0.36     0.40\n6     0.38     2.92\n7    -1.65     0.66\n8       NA       NA\n9       NA       NA\n\n$df2\n                  Point Estimate Chi2 p.value  Stats 95%CI.ll 95%CI.ul\n1      Woolf test of homogeneity 0.04   0.833     NA       NA       NA\n2              Crude RR for veal   NA      NA   1.53     1.01     2.32\n3  MH RR veal adjusted for pasta   NA      NA   1.15     0.64     2.04\n4 Adjusted/crude relative change   NA      NA -25.08       NA       NA\n\n\nLet’s check if pasta is associated with veal (if we are thinking pasta may be a confounder, we need to see if there is an association between the potential confounder (pasta) and the exposure (veal)):\n\n\nShow the code\n# Perform Wilcoxon rank sum test on veal and pasta:\nwilcox.test(veal ~ pasta, \n            data = copdata)\n\n\n\n    Wilcoxon rank sum test with continuity correction\n\ndata:  veal by pasta\nW = 1496, p-value &lt; 2.2e-16\nalternative hypothesis: true location shift is not equal to 0\n\n\n\n\nb) Champagne as exposure of interest, stratified by having eaten pasta\n\n\nShow the code\n# Pass data to the csinter function:\nchampstrata &lt;- csinter(x = stratall, \n                       cases = \"case\", \n                       exposure = \"champagne\", \n                       by = \"pasta\")\n\n\n\n\n\n\n\n\nLet’s stop and… think!\n\n\n\n\n\nAre there any indications to make you think there may be effect modification and/or confounding?\n\n\n\nWe could stratify by pasta (the strongest risk factor in the univariable analysis), to examine if pasta confounds the association between eating veal and being a case. Before stratification, we will need to check if pasta meets the conditions of being a confounder. For a variable to be a confounder it needs to be associated both with the outcome (being a case) and with the exposure (and not be in the causal pathway between exposure and outcome). We know from univariable analysis that pasta is associated with being a case. If we run a Wilcoxon rank sum test, we will see that pasta is also associated with veal (indeed, you can see that most people either had both pasta and veal or neither of these food items, so they are associated with each other).\nAbove, we uses ccinter to stratify and save the object as “pastastrata”.\npastastrata: Within the stratum of the people who ate pasta, veal has no significant effect (RR = 1.20, CI: 0.60 - 2.41). The same holds within the stratum of people who didn’t eat pasta (RR = 1.05, CI = 0.38, 2.92). The adjusted MH-RR also suggests that veal has no effect (RRadj = 1.15, CI: 0.80 - 2.85).  To identify confounding, we want to look at the % change between the crude and the adjusted RR. This is given by the csinter output “Adjusted/crude relative change”.  The difference between the crude and the MH-RR in this case is &gt;20% suggesting that pasta confounds the association between veal and the disease.\nThis result suggest that veal is not a risk factor of the disease and that the crude observed effect was due to the confounding effect of pasta.\nIf you stratify by veal, you see that veal does not confound the association between pasta and the disease. The same applies if you stratify the exposure to pasta by other variables. The above, the higher RR for pasta and the dose response relationship we found earlier for pasta (remember this was optional) provide additional evidence that there was something going on with the pasta with pesto dish!"
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var(--bs-dropdown-header-padding-x);margin-bottom:0;font-size:0.875rem;color:var(--bs-dropdown-header-color);white-space:nowrap}.dropdown-item-text{display:block;padding:var(--bs-dropdown-item-padding-y) var(--bs-dropdown-item-padding-x);color:var(--bs-dropdown-link-color)}.dropdown-menu-dark{--bs-dropdown-color: #dee2e6;--bs-dropdown-bg: #343a40;--bs-dropdown-border-color: #434343;--bs-dropdown-box-shadow: ;--bs-dropdown-link-color: #dee2e6;--bs-dropdown-link-hover-color: #fff;--bs-dropdown-divider-bg: #434343;--bs-dropdown-link-hover-bg: rgba(255, 255, 255, 0.15);--bs-dropdown-link-active-color: #fff;--bs-dropdown-link-active-bg: #375a7f;--bs-dropdown-link-disabled-color: #adb5bd;--bs-dropdown-header-color: #adb5bd}.btn-group,.btn-group-vertical{position:relative;display:inline-flex;vertical-align:middle}.btn-group>.btn,.btn-group-vertical>.btn{position:relative;flex:1 1 auto;-webkit-flex:1 1 auto}.btn-group>.btn-check:checked+.btn,.btn-group>.btn-check:focus+.btn,.btn-group>.btn:hover,.btn-group>.btn:focus,.btn-group>.btn:active,.btn-group>.btn.active,.btn-group-vertical>.btn-check:checked+.btn,.btn-group-vertical>.btn-check:focus+.btn,.btn-group-vertical>.btn:hover,.btn-group-vertical>.btn:focus,.btn-group-vertical>.btn:active,.btn-group-vertical>.btn.active{z-index:1}.btn-toolbar{display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;justify-content:flex-start;-webkit-justify-content:flex-start}.btn-toolbar .input-group{width:auto}.btn-group{border-radius:.25rem}.btn-group>:not(.btn-check:first-child)+.btn,.btn-group>.btn-group:not(:first-child){margin-left:calc(1px*-1)}.btn-group>.btn:not(:last-child):not(.dropdown-toggle),.btn-group>.btn.dropdown-toggle-split:first-child,.btn-group>.btn-group:not(:last-child)>.btn{border-top-right-radius:0;border-bottom-right-radius:0}.btn-group>.btn:nth-child(n+3),.btn-group>:not(.btn-check)+.btn,.btn-group>.btn-group:not(:first-child)>.btn{border-top-left-radius:0;border-bottom-left-radius:0}.dropdown-toggle-split{padding-right:.5625rem;padding-left:.5625rem}.dropdown-toggle-split::after,.dropup .dropdown-toggle-split::after,.dropend .dropdown-toggle-split::after{margin-left:0}.dropstart .dropdown-toggle-split::before{margin-right:0}.btn-sm+.dropdown-toggle-split,.btn-group-sm>.btn+.dropdown-toggle-split{padding-right:.375rem;padding-left:.375rem}.btn-lg+.dropdown-toggle-split,.btn-group-lg>.btn+.dropdown-toggle-split{padding-right:.75rem;padding-left:.75rem}.btn-group-vertical{flex-direction:column;-webkit-flex-direction:column;align-items:flex-start;-webkit-align-items:flex-start;justify-content:center;-webkit-justify-content:center}.btn-group-vertical>.btn,.btn-group-vertical>.btn-group{width:100%}.btn-group-vertical>.btn:not(:first-child),.btn-group-vertical>.btn-group:not(:first-child){margin-top:calc(1px*-1)}.btn-group-vertical>.btn:not(:last-child):not(.dropdown-toggle),.btn-group-vertical>.btn-group:not(:last-child)>.btn{border-bottom-right-radius:0;border-bottom-left-radius:0}.btn-group-vertical>.btn~.btn,.btn-group-vertical>.btn-group:not(:first-child)>.btn{border-top-left-radius:0;border-top-right-radius:0}.nav{--bs-nav-link-padding-x: 2rem;--bs-nav-link-padding-y: 0.5rem;--bs-nav-link-font-weight: ;--bs-nav-link-color: #00bc8c;--bs-nav-link-hover-color: #009670;--bs-nav-link-disabled-color: #6f6f6f;display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding-left:0;margin-bottom:0;list-style:none}.nav-link{display:block;padding:var(--bs-nav-link-padding-y) var(--bs-nav-link-padding-x);font-size:var(--bs-nav-link-font-size);font-weight:var(--bs-nav-link-font-weight);color:var(--bs-nav-link-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background:none;border:0;transition:color .15s ease-in-out,background-color .15s ease-in-out,border-color .15s ease-in-out}@media(prefers-reduced-motion: reduce){.nav-link{transition:none}}.nav-link:hover,.nav-link:focus{color:var(--bs-nav-link-hover-color)}.nav-link:focus-visible{outline:0;box-shadow:0 0 0 .25rem 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.offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-xxl .offcanvas .offcanvas-header{display:none}.navbar-expand-xxl .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}.navbar-expand{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand .navbar-nav .dropdown-menu{position:absolute}.navbar-expand .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand .navbar-nav-scroll{overflow:visible}.navbar-expand .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand .navbar-toggler{display:none}.navbar-expand .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand .offcanvas .offcanvas-header{display:none}.navbar-expand .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}.navbar-dark,.navbar[data-bs-theme=dark]{--bs-navbar-color: #dee2e6;--bs-navbar-hover-color: rgba(255, 255, 255, 0.8);--bs-navbar-disabled-color: rgba(222, 226, 230, 0.75);--bs-navbar-active-color: #fff;--bs-navbar-brand-color: #dee2e6;--bs-navbar-brand-hover-color: #fff;--bs-navbar-toggler-border-color: rgba(222, 226, 230, 0);--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='%23dee2e6' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e")}[data-bs-theme=dark] .navbar-toggler-icon{--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='%23dee2e6' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e")}.card{--bs-card-spacer-y: 1rem;--bs-card-spacer-x: 1rem;--bs-card-title-spacer-y: 0.5rem;--bs-card-title-color: ;--bs-card-subtitle-color: ;--bs-card-border-width: 1px;--bs-card-border-color: rgba(0, 0, 0, 0.175);--bs-card-border-radius: 0.25rem;--bs-card-box-shadow: ;--bs-card-inner-border-radius: calc(0.25rem - 1px);--bs-card-cap-padding-y: 0.5rem;--bs-card-cap-padding-x: 1rem;--bs-card-cap-bg: rgba(52, 58, 64, 0.25);--bs-card-cap-color: ;--bs-card-height: ;--bs-card-color: ;--bs-card-bg: #2d2d2d;--bs-card-img-overlay-padding: 1rem;--bs-card-group-margin: 0.75rem;position:relative;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;min-width:0;height:var(--bs-card-height);color:var(--bs-body-color);word-wrap:break-word;background-color:var(--bs-card-bg);background-clip:border-box;border:var(--bs-card-border-width) solid var(--bs-card-border-color);border-radius:var(--bs-card-border-radius)}.card>hr{margin-right:0;margin-left:0}.card>.list-group{border-top:inherit;border-bottom:inherit}.card>.list-group:first-child{border-top-width:0;border-top-left-radius:var(--bs-card-inner-border-radius);border-top-right-radius:var(--bs-card-inner-border-radius)}.card>.list-group:last-child{border-bottom-width:0;border-bottom-right-radius:var(--bs-card-inner-border-radius);border-bottom-left-radius:var(--bs-card-inner-border-radius)}.card>.card-header+.list-group,.card>.list-group+.card-footer{border-top:0}.card-body{flex:1 1 auto;-webkit-flex:1 1 auto;padding:var(--bs-card-spacer-y) var(--bs-card-spacer-x);color:var(--bs-card-color)}.card-title{margin-bottom:var(--bs-card-title-spacer-y);color:var(--bs-card-title-color)}.card-subtitle{margin-top:calc(-0.5*var(--bs-card-title-spacer-y));margin-bottom:0;color:var(--bs-card-subtitle-color)}.card-text:last-child{margin-bottom:0}.card-link+.card-link{margin-left:var(--bs-card-spacer-x)}.card-header{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);margin-bottom:0;color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-bottom:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-header:first-child{border-radius:var(--bs-card-inner-border-radius) var(--bs-card-inner-border-radius) 0 0}.card-footer{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-top:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-footer:last-child{border-radius:0 0 var(--bs-card-inner-border-radius) var(--bs-card-inner-border-radius)}.card-header-tabs{margin-right:calc(-0.5*var(--bs-card-cap-padding-x));margin-bottom:calc(-1*var(--bs-card-cap-padding-y));margin-left:calc(-0.5*var(--bs-card-cap-padding-x));border-bottom:0}.card-header-tabs .nav-link.active{background-color:var(--bs-card-bg);border-bottom-color:var(--bs-card-bg)}.card-header-pills{margin-right:calc(-0.5*var(--bs-card-cap-padding-x));margin-left:calc(-0.5*var(--bs-card-cap-padding-x))}.card-img-overlay{position:absolute;top:0;right:0;bottom:0;left:0;padding:var(--bs-card-img-overlay-padding);border-radius:var(--bs-card-inner-border-radius)}.card-img,.card-img-top,.card-img-bottom{width:100%}.card-img,.card-img-top{border-top-left-radius:var(--bs-card-inner-border-radius);border-top-right-radius:var(--bs-card-inner-border-radius)}.card-img,.card-img-bottom{border-bottom-right-radius:var(--bs-card-inner-border-radius);border-bottom-left-radius:var(--bs-card-inner-border-radius)}.card-group>.card{margin-bottom:var(--bs-card-group-margin)}@media(min-width: 576px){.card-group{display:flex;display:-webkit-flex;flex-flow:row wrap;-webkit-flex-flow:row wrap}.card-group>.card{flex:1 0 0%;-webkit-flex:1 0 0%;margin-bottom:0}.card-group>.card+.card{margin-left:0;border-left:0}.card-group>.card:not(:last-child){border-top-right-radius:0;border-bottom-right-radius:0}.card-group>.card:not(:last-child) .card-img-top,.card-group>.card:not(:last-child) .card-header{border-top-right-radius:0}.card-group>.card:not(:last-child) .card-img-bottom,.card-group>.card:not(:last-child) .card-footer{border-bottom-right-radius:0}.card-group>.card:not(:first-child){border-top-left-radius:0;border-bottom-left-radius:0}.card-group>.card:not(:first-child) .card-img-top,.card-group>.card:not(:first-child) .card-header{border-top-left-radius:0}.card-group>.card:not(:first-child) .card-img-bottom,.card-group>.card:not(:first-child) .card-footer{border-bottom-left-radius:0}}.accordion{--bs-accordion-color: #fff;--bs-accordion-bg: #222;--bs-accordion-transition: color 0.15s ease-in-out, background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out, border-radius 0.15s ease;--bs-accordion-border-color: #dee2e6;--bs-accordion-border-width: 1px;--bs-accordion-border-radius: 0.25rem;--bs-accordion-inner-border-radius: calc(0.25rem - 1px);--bs-accordion-btn-padding-x: 1.25rem;--bs-accordion-btn-padding-y: 1rem;--bs-accordion-btn-color: #fff;--bs-accordion-btn-bg: #222;--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23fff'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-icon-width: 1.25rem;--bs-accordion-btn-icon-transform: rotate(-180deg);--bs-accordion-btn-icon-transition: transform 0.2s ease-in-out;--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23162433'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-focus-border-color: #9badbf;--bs-accordion-btn-focus-box-shadow: 0 0 0 0.25rem rgba(55, 90, 127, 0.25);--bs-accordion-body-padding-x: 1.25rem;--bs-accordion-body-padding-y: 1rem;--bs-accordion-active-color: #162433;--bs-accordion-active-bg: #d7dee5}.accordion-button{position:relative;display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;width:100%;padding:var(--bs-accordion-btn-padding-y) var(--bs-accordion-btn-padding-x);font-size:1rem;color:var(--bs-accordion-btn-color);text-align:left;background-color:var(--bs-accordion-btn-bg);border:0;border-radius:0;overflow-anchor:none;transition:var(--bs-accordion-transition)}@media(prefers-reduced-motion: reduce){.accordion-button{transition:none}}.accordion-button:not(.collapsed){color:var(--bs-accordion-active-color);background-color:var(--bs-accordion-active-bg);box-shadow:inset 0 calc(-1*var(--bs-accordion-border-width)) 0 var(--bs-accordion-border-color)}.accordion-button:not(.collapsed)::after{background-image:var(--bs-accordion-btn-active-icon);transform:var(--bs-accordion-btn-icon-transform)}.accordion-button::after{flex-shrink:0;-webkit-flex-shrink:0;width:var(--bs-accordion-btn-icon-width);height:var(--bs-accordion-btn-icon-width);margin-left:auto;content:"";background-image:var(--bs-accordion-btn-icon);background-repeat:no-repeat;background-size:var(--bs-accordion-btn-icon-width);transition:var(--bs-accordion-btn-icon-transition)}@media(prefers-reduced-motion: reduce){.accordion-button::after{transition:none}}.accordion-button:hover{z-index:2}.accordion-button:focus{z-index:3;border-color:var(--bs-accordion-btn-focus-border-color);outline:0;box-shadow:var(--bs-accordion-btn-focus-box-shadow)}.accordion-header{margin-bottom:0}.accordion-item{color:var(--bs-accordion-color);background-color:var(--bs-accordion-bg);border:var(--bs-accordion-border-width) solid var(--bs-accordion-border-color)}.accordion-item:first-of-type{border-top-left-radius:var(--bs-accordion-border-radius);border-top-right-radius:var(--bs-accordion-border-radius)}.accordion-item:first-of-type .accordion-button{border-top-left-radius:var(--bs-accordion-inner-border-radius);border-top-right-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:not(:first-of-type){border-top:0}.accordion-item:last-of-type{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-item:last-of-type .accordion-button.collapsed{border-bottom-right-radius:var(--bs-accordion-inner-border-radius);border-bottom-left-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:last-of-type .accordion-collapse{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush .accordion-collapse{border-width:0}.accordion-flush .accordion-item{border-right:0;border-left:0;border-radius:0}.accordion-flush .accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child{border-bottom:0}.accordion-flush .accordion-item .accordion-button,.accordion-flush .accordion-item .accordion-button.collapsed{border-radius:0}[data-bs-theme=dark] .accordion-button::after{--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23879cb2'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23879cb2'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e")}.breadcrumb{--bs-breadcrumb-padding-x: 0.75rem;--bs-breadcrumb-padding-y: 0.375rem;--bs-breadcrumb-margin-bottom: 1rem;--bs-breadcrumb-bg: #434343;--bs-breadcrumb-border-radius: 0.25rem;--bs-breadcrumb-divider-color: rgba(255, 255, 255, 0.75);--bs-breadcrumb-item-padding-x: 0.5rem;--bs-breadcrumb-item-active-color: rgba(255, 255, 255, 0.75);display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding:var(--bs-breadcrumb-padding-y) var(--bs-breadcrumb-padding-x);margin-bottom:var(--bs-breadcrumb-margin-bottom);font-size:var(--bs-breadcrumb-font-size);list-style:none;background-color:var(--bs-breadcrumb-bg);border-radius:var(--bs-breadcrumb-border-radius)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item::before{float:left;padding-right:var(--bs-breadcrumb-item-padding-x);color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider, ">") /* rtl: var(--bs-breadcrumb-divider, ">") */}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x: 0.75rem;--bs-pagination-padding-y: 0.375rem;--bs-pagination-font-size:1rem;--bs-pagination-color: #fff;--bs-pagination-bg: #00bc8c;--bs-pagination-border-width: 0;--bs-pagination-border-color: transparent;--bs-pagination-border-radius: 0.25rem;--bs-pagination-hover-color: #fff;--bs-pagination-hover-bg: #00efb2;--bs-pagination-hover-border-color: transparent;--bs-pagination-focus-color: #009670;--bs-pagination-focus-bg: #ebebeb;--bs-pagination-focus-box-shadow: 0 0 0 0.25rem rgba(55, 90, 127, 0.25);--bs-pagination-active-color: #fff;--bs-pagination-active-bg: #00efb2;--bs-pagination-active-border-color: transparent;--bs-pagination-disabled-color: #fff;--bs-pagination-disabled-bg: #007053;--bs-pagination-disabled-border-color: transparent;display:flex;display:-webkit-flex;padding-left:0;list-style:none}.page-link{position:relative;display:block;padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);font-size:var(--bs-pagination-font-size);color:var(--bs-pagination-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);transition:color .15s ease-in-out,background-color .15s ease-in-out,border-color .15s ease-in-out,box-shadow .15s ease-in-out}@media(prefers-reduced-motion: reduce){.page-link{transition:none}}.page-link:hover{z-index:2;color:var(--bs-pagination-hover-color);background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color)}.page-link:focus{z-index:3;color:var(--bs-pagination-focus-color);background-color:var(--bs-pagination-focus-bg);outline:0;box-shadow:var(--bs-pagination-focus-box-shadow)}.page-link.active,.active>.page-link{z-index:3;color:var(--bs-pagination-active-color);background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color)}.page-link.disabled,.disabled>.page-link{color:var(--bs-pagination-disabled-color);pointer-events:none;background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color)}.page-item:not(:first-child) .page-link{margin-left:calc(0*-1)}.page-item:first-child .page-link{border-top-left-radius:var(--bs-pagination-border-radius);border-bottom-left-radius:var(--bs-pagination-border-radius)}.page-item:last-child .page-link{border-top-right-radius:var(--bs-pagination-border-radius);border-bottom-right-radius:var(--bs-pagination-border-radius)}.pagination-lg{--bs-pagination-padding-x: 1.5rem;--bs-pagination-padding-y: 0.75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius: 0.5rem}.pagination-sm{--bs-pagination-padding-x: 0.5rem;--bs-pagination-padding-y: 0.25rem;--bs-pagination-font-size:0.875rem;--bs-pagination-border-radius: 0.2em}.badge{--bs-badge-padding-x: 0.65em;--bs-badge-padding-y: 0.35em;--bs-badge-font-size:0.75em;--bs-badge-font-weight: 700;--bs-badge-color: #fff;--bs-badge-border-radius: 0.25rem;display:inline-block;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;color:var(--bs-badge-color);text-align:center;white-space:nowrap;vertical-align:baseline;border-radius:var(--bs-badge-border-radius)}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg: transparent;--bs-alert-padding-x: 1rem;--bs-alert-padding-y: 1rem;--bs-alert-margin-bottom: 1rem;--bs-alert-color: inherit;--bs-alert-border-color: transparent;--bs-alert-border: 1px solid var(--bs-alert-border-color);--bs-alert-border-radius: 0.25rem;--bs-alert-link-color: inherit;position:relative;padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border);border-radius:var(--bs-alert-border-radius)}.alert-heading{color:inherit}.alert-link{font-weight:700;color:var(--bs-alert-link-color)}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:1.25rem 1rem}.alert-default{--bs-alert-color: var(--bs-default-text-emphasis);--bs-alert-bg: var(--bs-default-bg-subtle);--bs-alert-border-color: var(--bs-default-border-subtle);--bs-alert-link-color: var(--bs-default-text-emphasis)}.alert-primary{--bs-alert-color: var(--bs-primary-text-emphasis);--bs-alert-bg: var(--bs-primary-bg-subtle);--bs-alert-border-color: var(--bs-primary-border-subtle);--bs-alert-link-color: var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color: var(--bs-secondary-text-emphasis);--bs-alert-bg: var(--bs-secondary-bg-subtle);--bs-alert-border-color: var(--bs-secondary-border-subtle);--bs-alert-link-color: var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color: var(--bs-success-text-emphasis);--bs-alert-bg: var(--bs-success-bg-subtle);--bs-alert-border-color: var(--bs-success-border-subtle);--bs-alert-link-color: var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color: var(--bs-info-text-emphasis);--bs-alert-bg: var(--bs-info-bg-subtle);--bs-alert-border-color: var(--bs-info-border-subtle);--bs-alert-link-color: var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color: var(--bs-warning-text-emphasis);--bs-alert-bg: var(--bs-warning-bg-subtle);--bs-alert-border-color: var(--bs-warning-border-subtle);--bs-alert-link-color: var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color: var(--bs-danger-text-emphasis);--bs-alert-bg: var(--bs-danger-bg-subtle);--bs-alert-border-color: var(--bs-danger-border-subtle);--bs-alert-link-color: var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color: var(--bs-light-text-emphasis);--bs-alert-bg: var(--bs-light-bg-subtle);--bs-alert-border-color: var(--bs-light-border-subtle);--bs-alert-link-color: var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color: var(--bs-dark-text-emphasis);--bs-alert-bg: var(--bs-dark-bg-subtle);--bs-alert-border-color: var(--bs-dark-border-subtle);--bs-alert-link-color: var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:1rem}}.progress,.progress-stacked{--bs-progress-height: 1rem;--bs-progress-font-size:0.75rem;--bs-progress-bg: #434343;--bs-progress-border-radius: 0.25rem;--bs-progress-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.075);--bs-progress-bar-color: #fff;--bs-progress-bar-bg: #375a7f;--bs-progress-bar-transition: width 0.6s ease;display:flex;display:-webkit-flex;height:var(--bs-progress-height);overflow:hidden;font-size:var(--bs-progress-font-size);background-color:var(--bs-progress-bg);border-radius:var(--bs-progress-border-radius)}.progress-bar{display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;justify-content:center;-webkit-justify-content:center;overflow:hidden;color:var(--bs-progress-bar-color);text-align:center;white-space:nowrap;background-color:var(--bs-progress-bar-bg);transition:var(--bs-progress-bar-transition)}@media(prefers-reduced-motion: reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:1s linear infinite progress-bar-stripes}@media(prefers-reduced-motion: reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color: #fff;--bs-list-group-bg: #2d2d2d;--bs-list-group-border-color: #434343;--bs-list-group-border-width: 1px;--bs-list-group-border-radius: 0.25rem;--bs-list-group-item-padding-x: 1rem;--bs-list-group-item-padding-y: 0.5rem;--bs-list-group-action-color: rgba(255, 255, 255, 0.75);--bs-list-group-action-hover-color: #fff;--bs-list-group-action-hover-bg: #434343;--bs-list-group-action-active-color: #fff;--bs-list-group-action-active-bg: #222;--bs-list-group-disabled-color: rgba(255, 255, 255, 0.75);--bs-list-group-disabled-bg: #2d2d2d;--bs-list-group-active-color: #fff;--bs-list-group-active-bg: #375a7f;--bs-list-group-active-border-color: 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var(--bs-list-group-border-color)}.list-group-item:first-child{border-top-left-radius:inherit;border-top-right-radius:inherit}.list-group-item:last-child{border-bottom-right-radius:inherit;border-bottom-left-radius:inherit}.list-group-item.disabled,.list-group-item:disabled{color:var(--bs-list-group-disabled-color);pointer-events:none;background-color:var(--bs-list-group-disabled-bg)}.list-group-item.active{z-index:2;color:var(--bs-list-group-active-color);background-color:var(--bs-list-group-active-bg);border-color:var(--bs-list-group-active-border-color)}.list-group-item+.list-group-item{border-top-width:0}.list-group-item+.list-group-item.active{margin-top:calc(-1*var(--bs-list-group-border-width));border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal>.list-group-item.active{margin-top:0}.list-group-horizontal>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}@media(min-width: 576px){.list-group-horizontal-sm{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-sm>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-sm>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 768px){.list-group-horizontal-md{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-md>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-md>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-md>.list-group-item.active{margin-top:0}.list-group-horizontal-md>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-md>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 992px){.list-group-horizontal-lg{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-lg>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-lg>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1200px){.list-group-horizontal-xl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1400px){.list-group-horizontal-xxl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xxl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xxl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xxl>.list-group-item.active{margin-top:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}.list-group-flush{border-radius:0}.list-group-flush>.list-group-item{border-width:0 0 var(--bs-list-group-border-width)}.list-group-flush>.list-group-item:last-child{border-bottom-width:0}.list-group-item-default{--bs-list-group-color: var(--bs-default-text-emphasis);--bs-list-group-bg: var(--bs-default-bg-subtle);--bs-list-group-border-color: var(--bs-default-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-default-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-default-border-subtle);--bs-list-group-active-color: var(--bs-default-bg-subtle);--bs-list-group-active-bg: var(--bs-default-text-emphasis);--bs-list-group-active-border-color: var(--bs-default-text-emphasis)}.list-group-item-primary{--bs-list-group-color: var(--bs-primary-text-emphasis);--bs-list-group-bg: var(--bs-primary-bg-subtle);--bs-list-group-border-color: var(--bs-primary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-primary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-primary-border-subtle);--bs-list-group-active-color: var(--bs-primary-bg-subtle);--bs-list-group-active-bg: var(--bs-primary-text-emphasis);--bs-list-group-active-border-color: var(--bs-primary-text-emphasis)}.list-group-item-secondary{--bs-list-group-color: var(--bs-secondary-text-emphasis);--bs-list-group-bg: var(--bs-secondary-bg-subtle);--bs-list-group-border-color: var(--bs-secondary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-secondary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-secondary-border-subtle);--bs-list-group-active-color: var(--bs-secondary-bg-subtle);--bs-list-group-active-bg: var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color: var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color: var(--bs-success-text-emphasis);--bs-list-group-bg: var(--bs-success-bg-subtle);--bs-list-group-border-color: var(--bs-success-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-success-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-success-border-subtle);--bs-list-group-active-color: var(--bs-success-bg-subtle);--bs-list-group-active-bg: var(--bs-success-text-emphasis);--bs-list-group-active-border-color: var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color: var(--bs-info-text-emphasis);--bs-list-group-bg: var(--bs-info-bg-subtle);--bs-list-group-border-color: var(--bs-info-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-info-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-info-border-subtle);--bs-list-group-active-color: var(--bs-info-bg-subtle);--bs-list-group-active-bg: var(--bs-info-text-emphasis);--bs-list-group-active-border-color: var(--bs-info-text-emphasis)}.list-group-item-warning{--bs-list-group-color: var(--bs-warning-text-emphasis);--bs-list-group-bg: var(--bs-warning-bg-subtle);--bs-list-group-border-color: var(--bs-warning-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-warning-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-warning-border-subtle);--bs-list-group-active-color: var(--bs-warning-bg-subtle);--bs-list-group-active-bg: var(--bs-warning-text-emphasis);--bs-list-group-active-border-color: var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color: var(--bs-danger-text-emphasis);--bs-list-group-bg: var(--bs-danger-bg-subtle);--bs-list-group-border-color: var(--bs-danger-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-danger-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-danger-border-subtle);--bs-list-group-active-color: var(--bs-danger-bg-subtle);--bs-list-group-active-bg: var(--bs-danger-text-emphasis);--bs-list-group-active-border-color: var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color: var(--bs-light-text-emphasis);--bs-list-group-bg: var(--bs-light-bg-subtle);--bs-list-group-border-color: var(--bs-light-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-light-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-light-border-subtle);--bs-list-group-active-color: var(--bs-light-bg-subtle);--bs-list-group-active-bg: var(--bs-light-text-emphasis);--bs-list-group-active-border-color: var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color: var(--bs-dark-text-emphasis);--bs-list-group-bg: var(--bs-dark-bg-subtle);--bs-list-group-border-color: var(--bs-dark-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-dark-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-dark-border-subtle);--bs-list-group-active-color: var(--bs-dark-bg-subtle);--bs-list-group-active-bg: var(--bs-dark-text-emphasis);--bs-list-group-active-border-color: var(--bs-dark-text-emphasis)}.btn-close{--bs-btn-close-color: #fff;--bs-btn-close-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23fff'%3e%3cpath d='M.293.293a1 1 0 0 1 1.414 0L8 6.586 14.293.293a1 1 0 1 1 1.414 1.414L9.414 8l6.293 6.293a1 1 0 0 1-1.414 1.414L8 9.414l-6.293 6.293a1 1 0 0 1-1.414-1.414L6.586 8 .293 1.707a1 1 0 0 1 0-1.414z'/%3e%3c/svg%3e");--bs-btn-close-opacity: 0.4;--bs-btn-close-hover-opacity: 1;--bs-btn-close-focus-shadow: 0 0 0 0.25rem rgba(55, 90, 127, 0.25);--bs-btn-close-focus-opacity: 1;--bs-btn-close-disabled-opacity: 0.25;--bs-btn-close-white-filter: invert(1) grayscale(100%) brightness(200%);box-sizing:content-box;width:1em;height:1em;padding:.25em .25em;color:var(--bs-btn-close-color);background:rgba(0,0,0,0) var(--bs-btn-close-bg) center/1em auto no-repeat;border:0;border-radius:.25rem;opacity:var(--bs-btn-close-opacity)}.btn-close:hover{color:var(--bs-btn-close-color);text-decoration:none;opacity:var(--bs-btn-close-hover-opacity)}.btn-close:focus{outline:0;box-shadow:var(--bs-btn-close-focus-shadow);opacity:var(--bs-btn-close-focus-opacity)}.btn-close:disabled,.btn-close.disabled{pointer-events:none;user-select:none;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;-o-user-select:none;opacity:var(--bs-btn-close-disabled-opacity)}.btn-close-white{filter:var(--bs-btn-close-white-filter)}[data-bs-theme=dark] .btn-close{filter:var(--bs-btn-close-white-filter)}.toast{--bs-toast-zindex: 1090;--bs-toast-padding-x: 0.75rem;--bs-toast-padding-y: 0.5rem;--bs-toast-spacing: 1.5rem;--bs-toast-max-width: 350px;--bs-toast-font-size:0.875rem;--bs-toast-color: ;--bs-toast-bg: #434343;--bs-toast-border-width: 1px;--bs-toast-border-color: rgba(0, 0, 0, 0.175);--bs-toast-border-radius: 0.25rem;--bs-toast-box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15);--bs-toast-header-color: rgba(255, 255, 255, 0.75);--bs-toast-header-bg: #2d2d2d;--bs-toast-header-border-color: rgba(0, 0, 0, 0.175);width:var(--bs-toast-max-width);max-width:100%;font-size:var(--bs-toast-font-size);color:var(--bs-toast-color);pointer-events:auto;background-color:var(--bs-toast-bg);background-clip:padding-box;border:var(--bs-toast-border-width) solid var(--bs-toast-border-color);box-shadow:var(--bs-toast-box-shadow);border-radius:var(--bs-toast-border-radius)}.toast.showing{opacity:0}.toast:not(.show){display:none}.toast-container{--bs-toast-zindex: 1090;position:absolute;z-index:var(--bs-toast-zindex);width:max-content;width:-webkit-max-content;width:-moz-max-content;width:-ms-max-content;width:-o-max-content;max-width:100%;pointer-events:none}.toast-container>:not(:last-child){margin-bottom:var(--bs-toast-spacing)}.toast-header{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;padding:var(--bs-toast-padding-y) var(--bs-toast-padding-x);color:var(--bs-toast-header-color);background-color:var(--bs-toast-header-bg);background-clip:padding-box;border-bottom:var(--bs-toast-border-width) solid var(--bs-toast-header-border-color);border-top-left-radius:calc(var(--bs-toast-border-radius) - var(--bs-toast-border-width));border-top-right-radius:calc(var(--bs-toast-border-radius) - var(--bs-toast-border-width))}.toast-header .btn-close{margin-right:calc(-0.5*var(--bs-toast-padding-x));margin-left:var(--bs-toast-padding-x)}.toast-body{padding:var(--bs-toast-padding-x);word-wrap:break-word}.modal{--bs-modal-zindex: 1055;--bs-modal-width: 500px;--bs-modal-padding: 1rem;--bs-modal-margin: 0.5rem;--bs-modal-color: ;--bs-modal-bg: #2d2d2d;--bs-modal-border-color: #434343;--bs-modal-border-width: 1px;--bs-modal-border-radius: 0.5rem;--bs-modal-box-shadow: 0 0.125rem 0.25rem rgba(0, 0, 0, 0.075);--bs-modal-inner-border-radius: calc(0.5rem - 1px);--bs-modal-header-padding-x: 1rem;--bs-modal-header-padding-y: 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+* we also add `bright-[color]-` synonyms for the `-[color]-intense` classes since
+* that seems to be what ansi_up emits
+*
+*/.ansi-black-fg{color:#3e424d}.ansi-black-bg{background-color:#3e424d}.ansi-black-intense-black,.ansi-bright-black-fg{color:#282c36}.ansi-black-intense-black,.ansi-bright-black-bg{background-color:#282c36}.ansi-red-fg{color:#e75c58}.ansi-red-bg{background-color:#e75c58}.ansi-red-intense-red,.ansi-bright-red-fg{color:#b22b31}.ansi-red-intense-red,.ansi-bright-red-bg{background-color:#b22b31}.ansi-green-fg{color:#00a250}.ansi-green-bg{background-color:#00a250}.ansi-green-intense-green,.ansi-bright-green-fg{color:#007427}.ansi-green-intense-green,.ansi-bright-green-bg{background-color:#007427}.ansi-yellow-fg{color:#ddb62b}.ansi-yellow-bg{background-color:#ddb62b}.ansi-yellow-intense-yellow,.ansi-bright-yellow-fg{color:#b27d12}.ansi-yellow-intense-yellow,.ansi-bright-yellow-bg{background-color:#b27d12}.ansi-blue-fg{color:#208ffb}.ansi-blue-bg{background-color:#208ffb}.ansi-blue-intense-blue,.ansi-bright-blue-fg{color:#0065ca}.ansi-blue-intense-blue,.ansi-bright-blue-bg{background-color:#0065ca}.ansi-magenta-fg{color:#d160c4}.ansi-magenta-bg{background-color:#d160c4}.ansi-magenta-intense-magenta,.ansi-bright-magenta-fg{color:#a03196}.ansi-magenta-intense-magenta,.ansi-bright-magenta-bg{background-color:#a03196}.ansi-cyan-fg{color:#60c6c8}.ansi-cyan-bg{background-color:#60c6c8}.ansi-cyan-intense-cyan,.ansi-bright-cyan-fg{color:#258f8f}.ansi-cyan-intense-cyan,.ansi-bright-cyan-bg{background-color:#258f8f}.ansi-white-fg{color:#c5c1b4}.ansi-white-bg{background-color:#c5c1b4}.ansi-white-intense-white,.ansi-bright-white-fg{color:#a1a6b2}.ansi-white-intense-white,.ansi-bright-white-bg{background-color:#a1a6b2}.ansi-default-inverse-fg{color:#fff}.ansi-default-inverse-bg{background-color:#000}.ansi-bold{font-weight:bold}.ansi-underline{text-decoration:underline}:root{--quarto-body-bg: 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.quarto-categories{margin-top:.75em}@media(min-width: 992px){.quarto-title-banner{padding-top:2.5em;padding-bottom:2.5em}}@media(max-width: 991.98px){.quarto-title-banner{padding-top:1em;padding-bottom:1em}}@media(max-width: 767.98px){body.hypothesis-enabled #title-block-header>*{padding-right:20px}}main.quarto-banner-title-block>section:first-child>h2,main.quarto-banner-title-block>section:first-child>.h2,main.quarto-banner-title-block>section:first-child>h3,main.quarto-banner-title-block>section:first-child>.h3,main.quarto-banner-title-block>section:first-child>h4,main.quarto-banner-title-block>section:first-child>.h4{margin-top:0}.quarto-title .quarto-categories{display:flex;flex-wrap:wrap;row-gap:.5em;column-gap:.4em;padding-bottom:.5em;margin-top:.75em}.quarto-title .quarto-categories .quarto-category{padding:.25em .75em;font-size:.65em;text-transform:uppercase;border:solid 1px;border-radius:.25rem;opacity:.6}.quarto-title .quarto-categories .quarto-category a{color:inherit}.quarto-title-meta-container{display:grid;grid-template-columns:1fr auto}.quarto-title-meta-column-end{display:flex;flex-direction:column;padding-left:1em}.quarto-title-meta-column-end a .bi{margin-right:.3em}#title-block-header.quarto-title-block.default .quarto-title-meta{display:grid;grid-template-columns:repeat(2, 1fr);grid-column-gap:1em}#title-block-header.quarto-title-block.default .quarto-title .title{margin-bottom:0}#title-block-header.quarto-title-block.default .quarto-title-author-orcid img{margin-top:-0.2em;height:.8em;width:.8em}#title-block-header.quarto-title-block.default .quarto-title-author-email{opacity:.7}#title-block-header.quarto-title-block.default .quarto-description p:last-of-type{margin-bottom:0}#title-block-header.quarto-title-block.default .quarto-title-meta-contents p,#title-block-header.quarto-title-block.default .quarto-title-authors p,#title-block-header.quarto-title-block.default .quarto-title-affiliations p{margin-bottom:.1em}#title-block-header.quarto-title-block.default .quarto-title-meta-heading{text-transform:uppercase;margin-top:1em;font-size:.8em;opacity:.8;font-weight:400}#title-block-header.quarto-title-block.default .quarto-title-meta-contents{font-size:.9em}#title-block-header.quarto-title-block.default .quarto-title-meta-contents p.affiliation:last-of-type{margin-bottom:.1em}#title-block-header.quarto-title-block.default p.affiliation{margin-bottom:.1em}#title-block-header.quarto-title-block.default .keywords,#title-block-header.quarto-title-block.default .description,#title-block-header.quarto-title-block.default .abstract{margin-top:0}#title-block-header.quarto-title-block.default .keywords>p,#title-block-header.quarto-title-block.default .description>p,#title-block-header.quarto-title-block.default .abstract>p{font-size:.9em}#title-block-header.quarto-title-block.default .keywords>p:last-of-type,#title-block-header.quarto-title-block.default .description>p:last-of-type,#title-block-header.quarto-title-block.default .abstract>p:last-of-type{margin-bottom:0}#title-block-header.quarto-title-block.default .keywords .block-title,#title-block-header.quarto-title-block.default .description .block-title,#title-block-header.quarto-title-block.default .abstract .block-title{margin-top:1em;text-transform:uppercase;font-size:.8em;opacity:.8;font-weight:400}#title-block-header.quarto-title-block.default .quarto-title-meta-author{display:grid;grid-template-columns:minmax(max-content, 1fr) 1fr;grid-column-gap:1em}.quarto-title-tools-only{display:flex;justify-content:right}
diff --git a/docs/site_libs/bootstrap/bootstrap-icons.css b/docs/site_libs/bootstrap/bootstrap-icons.css
index 94f1940..285e444 100644
--- a/docs/site_libs/bootstrap/bootstrap-icons.css
+++ b/docs/site_libs/bootstrap/bootstrap-icons.css
@@ -1,8 +1,14 @@
+/*!
+ * Bootstrap Icons v1.11.1 (https://icons.getbootstrap.com/)
+ * Copyright 2019-2023 The Bootstrap Authors
+ * Licensed under MIT (https://github.com/twbs/icons/blob/main/LICENSE)
+ */
+
 @font-face {
   font-display: block;
   font-family: "bootstrap-icons";
   src: 
-url("./bootstrap-icons.woff?2ab2cbbe07fcebb53bdaa7313bb290f2") format("woff");
+url("./bootstrap-icons.woff?2820a3852bdb9a5832199cc61cec4e65") format("woff");
 }
 
 .bi::before,
@@ -441,7 +447,6 @@ url("./bootstrap-icons.woff?2ab2cbbe07fcebb53bdaa7313bb290f2") format("woff");
 .bi-cloud-fog2::before { content: "\f2a2"; }
 .bi-cloud-hail-fill::before { content: "\f2a3"; }
 .bi-cloud-hail::before { content: "\f2a4"; }
-.bi-cloud-haze-1::before { content: "\f2a5"; }
 .bi-cloud-haze-fill::before { content: "\f2a6"; }
 .bi-cloud-haze::before { content: "\f2a7"; }
 .bi-cloud-haze2-fill::before { content: "\f2a8"; }
@@ -1437,21 +1442,16 @@ url("./bootstrap-icons.woff?2ab2cbbe07fcebb53bdaa7313bb290f2") format("woff");
 .bi-dpad::before { content: "\f687"; }
 .bi-ear-fill::before { content: "\f688"; }
 .bi-ear::before { content: "\f689"; }
-.bi-envelope-check-1::before { content: "\f68a"; }
 .bi-envelope-check-fill::before { content: "\f68b"; }
 .bi-envelope-check::before { content: "\f68c"; }
-.bi-envelope-dash-1::before { content: "\f68d"; }
 .bi-envelope-dash-fill::before { content: "\f68e"; }
 .bi-envelope-dash::before { content: "\f68f"; }
-.bi-envelope-exclamation-1::before { content: "\f690"; }
 .bi-envelope-exclamation-fill::before { content: "\f691"; }
 .bi-envelope-exclamation::before { content: "\f692"; }
 .bi-envelope-plus-fill::before { content: "\f693"; }
 .bi-envelope-plus::before { content: "\f694"; }
-.bi-envelope-slash-1::before { content: "\f695"; }
 .bi-envelope-slash-fill::before { content: "\f696"; }
 .bi-envelope-slash::before { content: "\f697"; }
-.bi-envelope-x-1::before { content: "\f698"; }
 .bi-envelope-x-fill::before { content: "\f699"; }
 .bi-envelope-x::before { content: "\f69a"; }
 .bi-explicit-fill::before { content: "\f69b"; }
@@ -1461,8 +1461,6 @@ url("./bootstrap-icons.woff?2ab2cbbe07fcebb53bdaa7313bb290f2") format("woff");
 .bi-list-columns-reverse::before { content: "\f69f"; }
 .bi-list-columns::before { content: "\f6a0"; }
 .bi-meta::before { content: "\f6a1"; }
-.bi-mortorboard-fill::before { content: "\f6a2"; }
-.bi-mortorboard::before { content: "\f6a3"; }
 .bi-nintendo-switch::before { content: "\f6a4"; }
 .bi-pc-display-horizontal::before { content: "\f6a5"; }
 .bi-pc-display::before { content: "\f6a6"; }
@@ -1481,7 +1479,6 @@ url("./bootstrap-icons.woff?2ab2cbbe07fcebb53bdaa7313bb290f2") format("woff");
 .bi-send-check::before { content: "\f6b3"; }
 .bi-send-dash-fill::before { content: "\f6b4"; }
 .bi-send-dash::before { content: "\f6b5"; }
-.bi-send-exclamation-1::before { content: "\f6b6"; }
 .bi-send-exclamation-fill::before { content: "\f6b7"; }
 .bi-send-exclamation::before { content: "\f6b8"; }
 .bi-send-fill::before { content: "\f6b9"; }
@@ -1493,7 +1490,6 @@ url("./bootstrap-icons.woff?2ab2cbbe07fcebb53bdaa7313bb290f2") format("woff");
 .bi-send-x::before { content: "\f6bf"; }
 .bi-send::before { content: "\f6c0"; }
 .bi-steam::before { content: "\f6c1"; }
-.bi-terminal-dash-1::before { content: "\f6c2"; }
 .bi-terminal-dash::before { content: "\f6c3"; }
 .bi-terminal-plus::before { content: "\f6c4"; }
 .bi-terminal-split::before { content: "\f6c5"; }
@@ -1523,7 +1519,6 @@ url("./bootstrap-icons.woff?2ab2cbbe07fcebb53bdaa7313bb290f2") format("woff");
 .bi-usb-symbol::before { content: "\f6dd"; }
 .bi-usb::before { content: "\f6de"; }
 .bi-boombox-fill::before { content: "\f6df"; }
-.bi-displayport-1::before { content: "\f6e0"; }
 .bi-displayport::before { content: "\f6e1"; }
 .bi-gpu-card::before { content: "\f6e2"; }
 .bi-memory::before { content: "\f6e3"; }
@@ -1536,8 +1531,6 @@ url("./bootstrap-icons.woff?2ab2cbbe07fcebb53bdaa7313bb290f2") format("woff");
 .bi-pci-card::before { content: "\f6ea"; }
 .bi-router-fill::before { content: "\f6eb"; }
 .bi-router::before { content: "\f6ec"; }
-.bi-ssd-fill::before { content: "\f6ed"; }
-.bi-ssd::before { content: "\f6ee"; }
 .bi-thunderbolt-fill::before { content: "\f6ef"; }
 .bi-thunderbolt::before { content: "\f6f0"; }
 .bi-usb-drive-fill::before { content: "\f6f1"; }
@@ -1644,7 +1637,6 @@ url("./bootstrap-icons.woff?2ab2cbbe07fcebb53bdaa7313bb290f2") format("woff");
 .bi-filetype-pdf::before { content: "\f756"; }
 .bi-filetype-php::before { content: "\f757"; }
 .bi-filetype-png::before { content: "\f758"; }
-.bi-filetype-ppt-1::before { content: "\f759"; }
 .bi-filetype-ppt::before { content: "\f75a"; }
 .bi-filetype-psd::before { content: "\f75b"; }
 .bi-filetype-py::before { content: "\f75c"; }
@@ -1660,7 +1652,6 @@ url("./bootstrap-icons.woff?2ab2cbbe07fcebb53bdaa7313bb290f2") format("woff");
 .bi-filetype-txt::before { content: "\f766"; }
 .bi-filetype-wav::before { content: "\f767"; }
 .bi-filetype-woff::before { content: "\f768"; }
-.bi-filetype-xls-1::before { content: "\f769"; }
 .bi-filetype-xls::before { content: "\f76a"; }
 .bi-filetype-xml::before { content: "\f76b"; }
 .bi-filetype-yml::before { content: "\f76c"; }
@@ -1703,56 +1694,38 @@ url("./bootstrap-icons.woff?2ab2cbbe07fcebb53bdaa7313bb290f2") format("woff");
 .bi-filetype-json::before { content: "\f791"; }
 .bi-filetype-pptx::before { content: "\f792"; }
 .bi-filetype-xlsx::before { content: "\f793"; }
-.bi-1-circle-1::before { content: "\f794"; }
-.bi-1-circle-fill-1::before { content: "\f795"; }
 .bi-1-circle-fill::before { content: "\f796"; }
 .bi-1-circle::before { content: "\f797"; }
 .bi-1-square-fill::before { content: "\f798"; }
 .bi-1-square::before { content: "\f799"; }
-.bi-2-circle-1::before { content: "\f79a"; }
-.bi-2-circle-fill-1::before { content: "\f79b"; }
 .bi-2-circle-fill::before { content: "\f79c"; }
 .bi-2-circle::before { content: "\f79d"; }
 .bi-2-square-fill::before { content: "\f79e"; }
 .bi-2-square::before { content: "\f79f"; }
-.bi-3-circle-1::before { content: "\f7a0"; }
-.bi-3-circle-fill-1::before { content: "\f7a1"; }
 .bi-3-circle-fill::before { content: "\f7a2"; }
 .bi-3-circle::before { content: "\f7a3"; }
 .bi-3-square-fill::before { content: "\f7a4"; }
 .bi-3-square::before { content: "\f7a5"; }
-.bi-4-circle-1::before { content: "\f7a6"; }
-.bi-4-circle-fill-1::before { content: "\f7a7"; }
 .bi-4-circle-fill::before { content: "\f7a8"; }
 .bi-4-circle::before { content: "\f7a9"; }
 .bi-4-square-fill::before { content: "\f7aa"; }
 .bi-4-square::before { content: "\f7ab"; }
-.bi-5-circle-1::before { content: "\f7ac"; }
-.bi-5-circle-fill-1::before { content: "\f7ad"; }
 .bi-5-circle-fill::before { content: "\f7ae"; }
 .bi-5-circle::before { content: "\f7af"; }
 .bi-5-square-fill::before { content: "\f7b0"; }
 .bi-5-square::before { content: "\f7b1"; }
-.bi-6-circle-1::before { content: "\f7b2"; }
-.bi-6-circle-fill-1::before { content: "\f7b3"; }
 .bi-6-circle-fill::before { content: "\f7b4"; }
 .bi-6-circle::before { content: "\f7b5"; }
 .bi-6-square-fill::before { content: "\f7b6"; }
 .bi-6-square::before { content: "\f7b7"; }
-.bi-7-circle-1::before { content: "\f7b8"; }
-.bi-7-circle-fill-1::before { content: "\f7b9"; }
 .bi-7-circle-fill::before { content: "\f7ba"; }
 .bi-7-circle::before { content: "\f7bb"; }
 .bi-7-square-fill::before { content: "\f7bc"; }
 .bi-7-square::before { content: "\f7bd"; }
-.bi-8-circle-1::before { content: "\f7be"; }
-.bi-8-circle-fill-1::before { content: "\f7bf"; }
 .bi-8-circle-fill::before { content: "\f7c0"; }
 .bi-8-circle::before { content: "\f7c1"; }
 .bi-8-square-fill::before { content: "\f7c2"; }
 .bi-8-square::before { content: "\f7c3"; }
-.bi-9-circle-1::before { content: "\f7c4"; }
-.bi-9-circle-fill-1::before { content: "\f7c5"; }
 .bi-9-circle-fill::before { content: "\f7c6"; }
 .bi-9-circle::before { content: "\f7c7"; }
 .bi-9-square-fill::before { content: "\f7c8"; }
@@ -1771,8 +1744,6 @@ url("./bootstrap-icons.woff?2ab2cbbe07fcebb53bdaa7313bb290f2") format("woff");
 .bi-browser-edge::before { content: "\f7d5"; }
 .bi-browser-firefox::before { content: "\f7d6"; }
 .bi-browser-safari::before { content: "\f7d7"; }
-.bi-c-circle-1::before { content: "\f7d8"; }
-.bi-c-circle-fill-1::before { content: "\f7d9"; }
 .bi-c-circle-fill::before { content: "\f7da"; }
 .bi-c-circle::before { content: "\f7db"; }
 .bi-c-square-fill::before { content: "\f7dc"; }
@@ -1783,8 +1754,6 @@ url("./bootstrap-icons.woff?2ab2cbbe07fcebb53bdaa7313bb290f2") format("woff");
 .bi-car-front::before { content: "\f7e1"; }
 .bi-cassette-fill::before { content: "\f7e2"; }
 .bi-cassette::before { content: "\f7e3"; }
-.bi-cc-circle-1::before { content: "\f7e4"; }
-.bi-cc-circle-fill-1::before { content: "\f7e5"; }
 .bi-cc-circle-fill::before { content: "\f7e6"; }
 .bi-cc-circle::before { content: "\f7e7"; }
 .bi-cc-square-fill::before { content: "\f7e8"; }
@@ -1803,8 +1772,6 @@ url("./bootstrap-icons.woff?2ab2cbbe07fcebb53bdaa7313bb290f2") format("woff");
 .bi-filetype-sql::before { content: "\f7f5"; }
 .bi-fire::before { content: "\f7f6"; }
 .bi-google-play::before { content: "\f7f7"; }
-.bi-h-circle-1::before { content: "\f7f8"; }
-.bi-h-circle-fill-1::before { content: "\f7f9"; }
 .bi-h-circle-fill::before { content: "\f7fa"; }
 .bi-h-circle::before { content: "\f7fb"; }
 .bi-h-square-fill::before { content: "\f7fc"; }
@@ -1813,8 +1780,6 @@ url("./bootstrap-icons.woff?2ab2cbbe07fcebb53bdaa7313bb290f2") format("woff");
 .bi-lungs-fill::before { content: "\f7ff"; }
 .bi-lungs::before { content: "\f800"; }
 .bi-microsoft-teams::before { content: "\f801"; }
-.bi-p-circle-1::before { content: "\f802"; }
-.bi-p-circle-fill-1::before { content: "\f803"; }
 .bi-p-circle-fill::before { content: "\f804"; }
 .bi-p-circle::before { content: "\f805"; }
 .bi-p-square-fill::before { content: "\f806"; }
@@ -1823,8 +1788,6 @@ url("./bootstrap-icons.woff?2ab2cbbe07fcebb53bdaa7313bb290f2") format("woff");
 .bi-pass::before { content: "\f809"; }
 .bi-prescription::before { content: "\f80a"; }
 .bi-prescription2::before { content: "\f80b"; }
-.bi-r-circle-1::before { content: "\f80c"; }
-.bi-r-circle-fill-1::before { content: "\f80d"; }
 .bi-r-circle-fill::before { content: "\f80e"; }
 .bi-r-circle::before { content: "\f80f"; }
 .bi-r-square-fill::before { content: "\f810"; }
@@ -2016,3 +1979,100 @@ url("./bootstrap-icons.woff?2ab2cbbe07fcebb53bdaa7313bb290f2") format("woff");
 .bi-sina-weibo::before { content: "\f8ca"; }
 .bi-tencent-qq::before { content: "\f8cb"; }
 .bi-wikipedia::before { content: "\f8cc"; }
+.bi-alphabet-uppercase::before { content: "\f2a5"; }
+.bi-alphabet::before { content: "\f68a"; }
+.bi-amazon::before { content: "\f68d"; }
+.bi-arrows-collapse-vertical::before { content: "\f690"; }
+.bi-arrows-expand-vertical::before { content: "\f695"; }
+.bi-arrows-vertical::before { content: "\f698"; }
+.bi-arrows::before { content: "\f6a2"; }
+.bi-ban-fill::before { content: "\f6a3"; }
+.bi-ban::before { content: "\f6b6"; }
+.bi-bing::before { content: "\f6c2"; }
+.bi-cake::before { content: "\f6e0"; }
+.bi-cake2::before { content: "\f6ed"; }
+.bi-cookie::before { content: "\f6ee"; }
+.bi-copy::before { content: "\f759"; }
+.bi-crosshair::before { content: "\f769"; }
+.bi-crosshair2::before { content: "\f794"; }
+.bi-emoji-astonished-fill::before { content: "\f795"; }
+.bi-emoji-astonished::before { content: "\f79a"; }
+.bi-emoji-grimace-fill::before { content: "\f79b"; }
+.bi-emoji-grimace::before { content: "\f7a0"; }
+.bi-emoji-grin-fill::before { content: "\f7a1"; }
+.bi-emoji-grin::before { content: "\f7a6"; }
+.bi-emoji-surprise-fill::before { content: "\f7a7"; }
+.bi-emoji-surprise::before { content: "\f7ac"; }
+.bi-emoji-tear-fill::before { content: "\f7ad"; }
+.bi-emoji-tear::before { content: "\f7b2"; }
+.bi-envelope-arrow-down-fill::before { content: "\f7b3"; }
+.bi-envelope-arrow-down::before { content: "\f7b8"; }
+.bi-envelope-arrow-up-fill::before { content: "\f7b9"; }
+.bi-envelope-arrow-up::before { content: "\f7be"; }
+.bi-feather::before { content: "\f7bf"; }
+.bi-feather2::before { content: "\f7c4"; }
+.bi-floppy-fill::before { content: "\f7c5"; }
+.bi-floppy::before { content: "\f7d8"; }
+.bi-floppy2-fill::before { content: "\f7d9"; }
+.bi-floppy2::before { content: "\f7e4"; }
+.bi-gitlab::before { content: "\f7e5"; }
+.bi-highlighter::before { content: "\f7f8"; }
+.bi-marker-tip::before { content: "\f802"; }
+.bi-nvme-fill::before { content: "\f803"; }
+.bi-nvme::before { content: "\f80c"; }
+.bi-opencollective::before { content: "\f80d"; }
+.bi-pci-card-network::before { content: "\f8cd"; }
+.bi-pci-card-sound::before { content: "\f8ce"; }
+.bi-radar::before { content: "\f8cf"; }
+.bi-send-arrow-down-fill::before { content: "\f8d0"; }
+.bi-send-arrow-down::before { content: "\f8d1"; }
+.bi-send-arrow-up-fill::before { content: "\f8d2"; }
+.bi-send-arrow-up::before { content: "\f8d3"; }
+.bi-sim-slash-fill::before { content: "\f8d4"; }
+.bi-sim-slash::before { content: "\f8d5"; }
+.bi-sourceforge::before { content: "\f8d6"; }
+.bi-substack::before { content: "\f8d7"; }
+.bi-threads-fill::before { content: "\f8d8"; }
+.bi-threads::before { content: "\f8d9"; }
+.bi-transparency::before { content: "\f8da"; }
+.bi-twitter-x::before { content: "\f8db"; }
+.bi-type-h4::before { content: "\f8dc"; }
+.bi-type-h5::before { content: "\f8dd"; }
+.bi-type-h6::before { content: "\f8de"; }
+.bi-backpack-fill::before { content: "\f8df"; }
+.bi-backpack::before { content: "\f8e0"; }
+.bi-backpack2-fill::before { content: "\f8e1"; }
+.bi-backpack2::before { content: "\f8e2"; }
+.bi-backpack3-fill::before { content: "\f8e3"; }
+.bi-backpack3::before { content: "\f8e4"; }
+.bi-backpack4-fill::before { content: "\f8e5"; }
+.bi-backpack4::before { content: "\f8e6"; }
+.bi-brilliance::before { content: "\f8e7"; }
+.bi-cake-fill::before { content: "\f8e8"; }
+.bi-cake2-fill::before { content: "\f8e9"; }
+.bi-duffle-fill::before { content: "\f8ea"; }
+.bi-duffle::before { content: "\f8eb"; }
+.bi-exposure::before { content: "\f8ec"; }
+.bi-gender-neuter::before { content: "\f8ed"; }
+.bi-highlights::before { content: "\f8ee"; }
+.bi-luggage-fill::before { content: "\f8ef"; }
+.bi-luggage::before { content: "\f8f0"; }
+.bi-mailbox-flag::before { content: "\f8f1"; }
+.bi-mailbox2-flag::before { content: "\f8f2"; }
+.bi-noise-reduction::before { content: "\f8f3"; }
+.bi-passport-fill::before { content: "\f8f4"; }
+.bi-passport::before { content: "\f8f5"; }
+.bi-person-arms-up::before { content: "\f8f6"; }
+.bi-person-raised-hand::before { content: "\f8f7"; }
+.bi-person-standing-dress::before { content: "\f8f8"; }
+.bi-person-standing::before { content: "\f8f9"; }
+.bi-person-walking::before { content: "\f8fa"; }
+.bi-person-wheelchair::before { content: "\f8fb"; }
+.bi-shadows::before { content: "\f8fc"; }
+.bi-suitcase-fill::before { content: "\f8fd"; }
+.bi-suitcase-lg-fill::before { content: "\f8fe"; }
+.bi-suitcase-lg::before { content: "\f8ff"; }
+.bi-suitcase::before { content: "\f900"; }
+.bi-suitcase2-fill::before { content: "\f901"; }
+.bi-suitcase2::before { content: "\f902"; }
+.bi-vignette::before { content: "\f903"; }
diff --git a/docs/site_libs/bootstrap/bootstrap-icons.woff b/docs/site_libs/bootstrap/bootstrap-icons.woff
index 18d21d4..dbeeb05 100644
Binary files a/docs/site_libs/bootstrap/bootstrap-icons.woff and b/docs/site_libs/bootstrap/bootstrap-icons.woff differ
diff --git a/docs/site_libs/bootstrap/bootstrap.min.css b/docs/site_libs/bootstrap/bootstrap.min.css
index f7ed565..6c5ac06 100644
--- a/docs/site_libs/bootstrap/bootstrap.min.css
+++ b/docs/site_libs/bootstrap/bootstrap.min.css
@@ -1,10 +1,12 @@
 /*!
- * Bootstrap v5.1.3 (https://getbootstrap.com/)
- * Copyright 2011-2021 The Bootstrap Authors
- * Copyright 2011-2021 Twitter, Inc.
+ * Bootstrap  v5.3.1 (https://getbootstrap.com/)
+ * Copyright 2011-2023 The Bootstrap Authors
  * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)
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#fff;--bs-navbar-nav-link-padding-x: 0.5rem;--bs-navbar-toggler-padding-y: 0.25;--bs-navbar-toggler-padding-x: 0;--bs-navbar-toggler-font-size: 1.25rem;--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='%23ccd1d5' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e");--bs-navbar-toggler-border-color: rgba(204, 209, 213, 0);--bs-navbar-toggler-border-radius: 0.25rem;--bs-navbar-toggler-focus-width: 0.25rem;--bs-navbar-toggler-transition: box-shadow 0.15s ease-in-out;position:relative;display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;align-items:center;-webkit-align-items:center;justify-content:space-between;-webkit-justify-content:space-between;padding:var(--bs-navbar-padding-y) 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992px){.navbar-expand-lg{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-lg .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-lg .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-lg .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-lg .navbar-nav-scroll{overflow:visible}.navbar-expand-lg .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-lg .navbar-toggler{display:none}.navbar-expand-lg .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-lg .offcanvas .offcanvas-header{display:none}.navbar-expand-lg .offcanvas 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.offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-xxl .offcanvas .offcanvas-header{display:none}.navbar-expand-xxl .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}.navbar-expand{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand .navbar-nav .dropdown-menu{position:absolute}.navbar-expand .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand .navbar-nav-scroll{overflow:visible}.navbar-expand .navbar-collapse{display:flex !important;display:-webkit-flex 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6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e")}.breadcrumb{--bs-breadcrumb-padding-x: 0.75rem;--bs-breadcrumb-padding-y: 0.375rem;--bs-breadcrumb-margin-bottom: 1rem;--bs-breadcrumb-bg: ;--bs-breadcrumb-border-radius: 0.25rem;--bs-breadcrumb-divider-color: rgba(33, 37, 41, 0.75);--bs-breadcrumb-item-padding-x: 0.5rem;--bs-breadcrumb-item-active-color: rgba(33, 37, 41, 0.75);display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding:var(--bs-breadcrumb-padding-y) var(--bs-breadcrumb-padding-x);margin-bottom:var(--bs-breadcrumb-margin-bottom);font-size:var(--bs-breadcrumb-font-size);list-style:none;background-color:var(--bs-breadcrumb-bg);border-radius:var(--bs-breadcrumb-border-radius)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item::before{float:left;padding-right:var(--bs-breadcrumb-item-padding-x);color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider, ">") /* rtl: var(--bs-breadcrumb-divider, ">") */}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x: 0.75rem;--bs-pagination-padding-y: 0.375rem;--bs-pagination-font-size:1rem;--bs-pagination-color: #fff;--bs-pagination-bg: #18bc9c;--bs-pagination-border-width: 0;--bs-pagination-border-color: transparent;--bs-pagination-border-radius: 0.25rem;--bs-pagination-hover-color: #fff;--bs-pagination-hover-bg: #0f7864;--bs-pagination-hover-border-color: transparent;--bs-pagination-focus-color: #13967d;--bs-pagination-focus-bg: #ecf0f1;--bs-pagination-focus-box-shadow: 0 0 0 0.25rem rgba(44, 62, 80, 0.25);--bs-pagination-active-color: #fff;--bs-pagination-active-bg: #0f7864;--bs-pagination-active-border-color: transparent;--bs-pagination-disabled-color: #ecf0f1;--bs-pagination-disabled-bg: #3be6c4;--bs-pagination-disabled-border-color: 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reduce){.page-link{transition:none}}.page-link:hover{z-index:2;color:var(--bs-pagination-hover-color);background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color)}.page-link:focus{z-index:3;color:var(--bs-pagination-focus-color);background-color:var(--bs-pagination-focus-bg);outline:0;box-shadow:var(--bs-pagination-focus-box-shadow)}.page-link.active,.active>.page-link{z-index:3;color:var(--bs-pagination-active-color);background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color)}.page-link.disabled,.disabled>.page-link{color:var(--bs-pagination-disabled-color);pointer-events:none;background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color)}.page-item:not(:first-child) .page-link{margin-left:calc(0*-1)}.page-item:first-child .page-link{border-top-left-radius:var(--bs-pagination-border-radius);border-bottom-left-radius:var(--bs-pagination-border-radius)}.page-item:last-child .page-link{border-top-right-radius:var(--bs-pagination-border-radius);border-bottom-right-radius:var(--bs-pagination-border-radius)}.pagination-lg{--bs-pagination-padding-x: 1.5rem;--bs-pagination-padding-y: 0.75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius: 0.5rem}.pagination-sm{--bs-pagination-padding-x: 0.5rem;--bs-pagination-padding-y: 0.25rem;--bs-pagination-font-size:0.875rem;--bs-pagination-border-radius: 0.2em}.badge{--bs-badge-padding-x: 0.65em;--bs-badge-padding-y: 0.35em;--bs-badge-font-size:0.75em;--bs-badge-font-weight: 700;--bs-badge-color: #fff;--bs-badge-border-radius: 0.25rem;display:inline-block;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;color:var(--bs-badge-color);text-align:center;white-space:nowrap;vertical-align:baseline;border-radius:var(--bs-badge-border-radius)}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg: transparent;--bs-alert-padding-x: 1rem;--bs-alert-padding-y: 1rem;--bs-alert-margin-bottom: 1rem;--bs-alert-color: inherit;--bs-alert-border-color: transparent;--bs-alert-border: 1px solid var(--bs-alert-border-color);--bs-alert-border-radius: 0.25rem;--bs-alert-link-color: inherit;position:relative;padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border);border-radius:var(--bs-alert-border-radius)}.alert-heading{color:inherit}.alert-link{font-weight:700;color:var(--bs-alert-link-color)}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:1.25rem 1rem}.alert-default{--bs-alert-color: var(--bs-default-text-emphasis);--bs-alert-bg: var(--bs-default-bg-subtle);--bs-alert-border-color: var(--bs-default-border-subtle);--bs-alert-link-color: var(--bs-default-text-emphasis)}.alert-primary{--bs-alert-color: var(--bs-primary-text-emphasis);--bs-alert-bg: var(--bs-primary-bg-subtle);--bs-alert-border-color: var(--bs-primary-border-subtle);--bs-alert-link-color: var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color: var(--bs-secondary-text-emphasis);--bs-alert-bg: var(--bs-secondary-bg-subtle);--bs-alert-border-color: var(--bs-secondary-border-subtle);--bs-alert-link-color: var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color: var(--bs-success-text-emphasis);--bs-alert-bg: var(--bs-success-bg-subtle);--bs-alert-border-color: var(--bs-success-border-subtle);--bs-alert-link-color: var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color: var(--bs-info-text-emphasis);--bs-alert-bg: var(--bs-info-bg-subtle);--bs-alert-border-color: var(--bs-info-border-subtle);--bs-alert-link-color: var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color: var(--bs-warning-text-emphasis);--bs-alert-bg: var(--bs-warning-bg-subtle);--bs-alert-border-color: var(--bs-warning-border-subtle);--bs-alert-link-color: var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color: var(--bs-danger-text-emphasis);--bs-alert-bg: var(--bs-danger-bg-subtle);--bs-alert-border-color: var(--bs-danger-border-subtle);--bs-alert-link-color: var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color: var(--bs-light-text-emphasis);--bs-alert-bg: var(--bs-light-bg-subtle);--bs-alert-border-color: var(--bs-light-border-subtle);--bs-alert-link-color: var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color: var(--bs-dark-text-emphasis);--bs-alert-bg: var(--bs-dark-bg-subtle);--bs-alert-border-color: var(--bs-dark-border-subtle);--bs-alert-link-color: var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:1rem}}.progress,.progress-stacked{--bs-progress-height: 1rem;--bs-progress-font-size:0.75rem;--bs-progress-bg: #ecf0f1;--bs-progress-border-radius: 0.25rem;--bs-progress-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.075);--bs-progress-bar-color: #fff;--bs-progress-bar-bg: #2c3e50;--bs-progress-bar-transition: width 0.6s ease;display:flex;display:-webkit-flex;height:var(--bs-progress-height);overflow:hidden;font-size:var(--bs-progress-font-size);background-color:var(--bs-progress-bg);border-radius:var(--bs-progress-border-radius)}.progress-bar{display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;justify-content:center;-webkit-justify-content:center;overflow:hidden;color:var(--bs-progress-bar-color);text-align:center;white-space:nowrap;background-color:var(--bs-progress-bar-bg);transition:var(--bs-progress-bar-transition)}@media(prefers-reduced-motion: reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:1s linear infinite progress-bar-stripes}@media(prefers-reduced-motion: reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color: #212529;--bs-list-group-bg: #fff;--bs-list-group-border-color: #dee2e6;--bs-list-group-border-width: 1px;--bs-list-group-border-radius: 0.25rem;--bs-list-group-item-padding-x: 1rem;--bs-list-group-item-padding-y: 0.5rem;--bs-list-group-action-color: rgba(33, 37, 41, 0.75);--bs-list-group-action-hover-color: #000;--bs-list-group-action-hover-bg: #ecf0f1;--bs-list-group-action-active-color: #212529;--bs-list-group-action-active-bg: #ecf0f1;--bs-list-group-disabled-color: rgba(33, 37, 41, 0.75);--bs-list-group-disabled-bg: #ecf0f1;--bs-list-group-active-color: #fff;--bs-list-group-active-bg: #2c3e50;--bs-list-group-active-border-color: 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";counter-increment:section}.list-group-item-action{width:100%;color:var(--bs-list-group-action-color);text-align:inherit}.list-group-item-action:hover,.list-group-item-action:focus{z-index:1;color:var(--bs-list-group-action-hover-color);text-decoration:none;background-color:var(--bs-list-group-action-hover-bg)}.list-group-item-action:active{color:var(--bs-list-group-action-active-color);background-color:var(--bs-list-group-action-active-bg)}.list-group-item{position:relative;display:block;padding:var(--bs-list-group-item-padding-y) var(--bs-list-group-item-padding-x);color:var(--bs-list-group-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-list-group-bg);border:var(--bs-list-group-border-width) solid 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576px){.list-group-horizontal-sm{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-sm>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-sm>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 768px){.list-group-horizontal-md{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-md>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-md>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-md>.list-group-item.active{margin-top:0}.list-group-horizontal-md>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-md>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 992px){.list-group-horizontal-lg{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-lg>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-lg>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1200px){.list-group-horizontal-xl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1400px){.list-group-horizontal-xxl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xxl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xxl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xxl>.list-group-item.active{margin-top:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}.list-group-flush{border-radius:0}.list-group-flush>.list-group-item{border-width:0 0 var(--bs-list-group-border-width)}.list-group-flush>.list-group-item:last-child{border-bottom-width:0}.list-group-item-default{--bs-list-group-color: var(--bs-default-text-emphasis);--bs-list-group-bg: var(--bs-default-bg-subtle);--bs-list-group-border-color: var(--bs-default-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-default-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-default-border-subtle);--bs-list-group-active-color: var(--bs-default-bg-subtle);--bs-list-group-active-bg: var(--bs-default-text-emphasis);--bs-list-group-active-border-color: var(--bs-default-text-emphasis)}.list-group-item-primary{--bs-list-group-color: var(--bs-primary-text-emphasis);--bs-list-group-bg: var(--bs-primary-bg-subtle);--bs-list-group-border-color: var(--bs-primary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-primary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-primary-border-subtle);--bs-list-group-active-color: var(--bs-primary-bg-subtle);--bs-list-group-active-bg: var(--bs-primary-text-emphasis);--bs-list-group-active-border-color: var(--bs-primary-text-emphasis)}.list-group-item-secondary{--bs-list-group-color: var(--bs-secondary-text-emphasis);--bs-list-group-bg: var(--bs-secondary-bg-subtle);--bs-list-group-border-color: var(--bs-secondary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-secondary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-secondary-border-subtle);--bs-list-group-active-color: var(--bs-secondary-bg-subtle);--bs-list-group-active-bg: var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color: var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color: var(--bs-success-text-emphasis);--bs-list-group-bg: var(--bs-success-bg-subtle);--bs-list-group-border-color: var(--bs-success-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-success-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-success-border-subtle);--bs-list-group-active-color: var(--bs-success-bg-subtle);--bs-list-group-active-bg: var(--bs-success-text-emphasis);--bs-list-group-active-border-color: var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color: var(--bs-info-text-emphasis);--bs-list-group-bg: var(--bs-info-bg-subtle);--bs-list-group-border-color: var(--bs-info-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-info-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-info-border-subtle);--bs-list-group-active-color: var(--bs-info-bg-subtle);--bs-list-group-active-bg: var(--bs-info-text-emphasis);--bs-list-group-active-border-color: var(--bs-info-text-emphasis)}.list-group-item-warning{--bs-list-group-color: var(--bs-warning-text-emphasis);--bs-list-group-bg: var(--bs-warning-bg-subtle);--bs-list-group-border-color: var(--bs-warning-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-warning-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-warning-border-subtle);--bs-list-group-active-color: var(--bs-warning-bg-subtle);--bs-list-group-active-bg: var(--bs-warning-text-emphasis);--bs-list-group-active-border-color: var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color: var(--bs-danger-text-emphasis);--bs-list-group-bg: var(--bs-danger-bg-subtle);--bs-list-group-border-color: var(--bs-danger-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-danger-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-danger-border-subtle);--bs-list-group-active-color: var(--bs-danger-bg-subtle);--bs-list-group-active-bg: var(--bs-danger-text-emphasis);--bs-list-group-active-border-color: var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color: var(--bs-light-text-emphasis);--bs-list-group-bg: var(--bs-light-bg-subtle);--bs-list-group-border-color: var(--bs-light-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-light-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-light-border-subtle);--bs-list-group-active-color: var(--bs-light-bg-subtle);--bs-list-group-active-bg: var(--bs-light-text-emphasis);--bs-list-group-active-border-color: var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color: var(--bs-dark-text-emphasis);--bs-list-group-bg: var(--bs-dark-bg-subtle);--bs-list-group-border-color: var(--bs-dark-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-dark-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-dark-border-subtle);--bs-list-group-active-color: var(--bs-dark-bg-subtle);--bs-list-group-active-bg: var(--bs-dark-text-emphasis);--bs-list-group-active-border-color: var(--bs-dark-text-emphasis)}.btn-close{--bs-btn-close-color: #fff;--bs-btn-close-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23fff'%3e%3cpath d='M.293.293a1 1 0 0 1 1.414 0L8 6.586 14.293.293a1 1 0 1 1 1.414 1.414L9.414 8l6.293 6.293a1 1 0 0 1-1.414 1.414L8 9.414l-6.293 6.293a1 1 0 0 1-1.414-1.414L6.586 8 .293 1.707a1 1 0 0 1 0-1.414z'/%3e%3c/svg%3e");--bs-btn-close-opacity: 0.4;--bs-btn-close-hover-opacity: 1;--bs-btn-close-focus-shadow: 0 0 0 0.25rem rgba(44, 62, 80, 0.25);--bs-btn-close-focus-opacity: 1;--bs-btn-close-disabled-opacity: 0.25;--bs-btn-close-white-filter: invert(1) grayscale(100%) brightness(200%);box-sizing:content-box;width:1em;height:1em;padding:.25em .25em;color:var(--bs-btn-close-color);background:rgba(0,0,0,0) var(--bs-btn-close-bg) center/1em auto no-repeat;border:0;border-radius:.25rem;opacity:var(--bs-btn-close-opacity)}.btn-close:hover{color:var(--bs-btn-close-color);text-decoration:none;opacity:var(--bs-btn-close-hover-opacity)}.btn-close:focus{outline:0;box-shadow:var(--bs-btn-close-focus-shadow);opacity:var(--bs-btn-close-focus-opacity)}.btn-close:disabled,.btn-close.disabled{pointer-events:none;user-select:none;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;-o-user-select:none;opacity:var(--bs-btn-close-disabled-opacity)}.btn-close-white{filter:var(--bs-btn-close-white-filter)}[data-bs-theme=dark] .btn-close{filter:var(--bs-btn-close-white-filter)}.toast{--bs-toast-zindex: 1090;--bs-toast-padding-x: 0.75rem;--bs-toast-padding-y: 0.5rem;--bs-toast-spacing: 1.5rem;--bs-toast-max-width: 350px;--bs-toast-font-size:0.875rem;--bs-toast-color: ;--bs-toast-bg: rgba(255, 255, 255, 0.85);--bs-toast-border-width: 1px;--bs-toast-border-color: rgba(0, 0, 0, 0.175);--bs-toast-border-radius: 0.25rem;--bs-toast-box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15);--bs-toast-header-color: rgba(33, 37, 41, 0.75);--bs-toast-header-bg: rgba(255, 255, 255, 0.85);--bs-toast-header-border-color: rgba(0, 0, 0, 0.175);width:var(--bs-toast-max-width);max-width:100%;font-size:var(--bs-toast-font-size);color:var(--bs-toast-color);pointer-events:auto;background-color:var(--bs-toast-bg);background-clip:padding-box;border:var(--bs-toast-border-width) solid var(--bs-toast-border-color);box-shadow:var(--bs-toast-box-shadow);border-radius:var(--bs-toast-border-radius)}.toast.showing{opacity:0}.toast:not(.show){display:none}.toast-container{--bs-toast-zindex: 1090;position:absolute;z-index:var(--bs-toast-zindex);width:max-content;width:-webkit-max-content;width:-moz-max-content;width:-ms-max-content;width:-o-max-content;max-width:100%;pointer-events:none}.toast-container>:not(:last-child){margin-bottom:var(--bs-toast-spacing)}.toast-header{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;padding:var(--bs-toast-padding-y) var(--bs-toast-padding-x);color:var(--bs-toast-header-color);background-color:var(--bs-toast-header-bg);background-clip:padding-box;border-bottom:var(--bs-toast-border-width) solid var(--bs-toast-header-border-color);border-top-left-radius:calc(var(--bs-toast-border-radius) - var(--bs-toast-border-width));border-top-right-radius:calc(var(--bs-toast-border-radius) - var(--bs-toast-border-width))}.toast-header .btn-close{margin-right:calc(-0.5*var(--bs-toast-padding-x));margin-left:var(--bs-toast-padding-x)}.toast-body{padding:var(--bs-toast-padding-x);word-wrap:break-word}.modal{--bs-modal-zindex: 1055;--bs-modal-width: 500px;--bs-modal-padding: 1rem;--bs-modal-margin: 0.5rem;--bs-modal-color: ;--bs-modal-bg: #fff;--bs-modal-border-color: rgba(0, 0, 0, 0.175);--bs-modal-border-width: 1px;--bs-modal-border-radius: 0.5rem;--bs-modal-box-shadow: 0 0.125rem 0.25rem rgba(0, 0, 0, 0.075);--bs-modal-inner-border-radius: calc(0.5rem - 1px);--bs-modal-header-padding-x: 1rem;--bs-modal-header-padding-y: 1rem;--bs-modal-header-padding: 1rem 1rem;--bs-modal-header-border-color: #dee2e6;--bs-modal-header-border-width: 1px;--bs-modal-title-line-height: 1.5;--bs-modal-footer-gap: 0.5rem;--bs-modal-footer-bg: ;--bs-modal-footer-border-color: #dee2e6;--bs-modal-footer-border-width: 1px;position:fixed;top:0;left:0;z-index:var(--bs-modal-zindex);display:none;width:100%;height:100%;overflow-x:hidden;overflow-y:auto;outline:0}.modal-dialog{position:relative;width:auto;margin:var(--bs-modal-margin);pointer-events:none}.modal.fade .modal-dialog{transition:transform .3s ease-out;transform:translate(0, -50px)}@media(prefers-reduced-motion: reduce){.modal.fade .modal-dialog{transition:none}}.modal.show .modal-dialog{transform:none}.modal.modal-static .modal-dialog{transform:scale(1.02)}.modal-dialog-scrollable{height:calc(100% - var(--bs-modal-margin)*2)}.modal-dialog-scrollable .modal-content{max-height:100%;overflow:hidden}.modal-dialog-scrollable .modal-body{overflow-y:auto}.modal-dialog-centered{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;min-height:calc(100% - var(--bs-modal-margin)*2)}.modal-content{position:relative;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;width:100%;color:var(--bs-modal-color);pointer-events:auto;background-color:var(--bs-modal-bg);background-clip:padding-box;border:var(--bs-modal-border-width) solid var(--bs-modal-border-color);border-radius:var(--bs-modal-border-radius);outline:0}.modal-backdrop{--bs-backdrop-zindex: 1050;--bs-backdrop-bg: #000;--bs-backdrop-opacity: 0.5;position:fixed;top:0;left:0;z-index:var(--bs-backdrop-zindex);width:100vw;height:100vh;background-color:var(--bs-backdrop-bg)}.modal-backdrop.fade{opacity:0}.modal-backdrop.show{opacity:var(--bs-backdrop-opacity)}.modal-header{display:flex;display:-webkit-flex;flex-shrink:0;-webkit-flex-shrink:0;align-items:center;-webkit-align-items:center;justify-content:space-between;-webkit-justify-content:space-between;padding:var(--bs-modal-header-padding);border-bottom:var(--bs-modal-header-border-width) solid var(--bs-modal-header-border-color);border-top-left-radius:var(--bs-modal-inner-border-radius);border-top-right-radius:var(--bs-modal-inner-border-radius)}.modal-header .btn-close{padding:calc(var(--bs-modal-header-padding-y)*.5) calc(var(--bs-modal-header-padding-x)*.5);margin:calc(-0.5*var(--bs-modal-header-padding-y)) calc(-0.5*var(--bs-modal-header-padding-x)) calc(-0.5*var(--bs-modal-header-padding-y)) auto}.modal-title{margin-bottom:0;line-height:var(--bs-modal-title-line-height)}.modal-body{position:relative;flex:1 1 auto;-webkit-flex:1 1 auto;padding:var(--bs-modal-padding)}.modal-footer{display:flex;display:-webkit-flex;flex-shrink:0;-webkit-flex-shrink:0;flex-wrap:wrap;-webkit-flex-wrap:wrap;align-items:center;-webkit-align-items:center;justify-content:flex-end;-webkit-justify-content:flex-end;padding:calc(var(--bs-modal-padding) - var(--bs-modal-footer-gap)*.5);background-color:var(--bs-modal-footer-bg);border-top:var(--bs-modal-footer-border-width) solid var(--bs-modal-footer-border-color);border-bottom-right-radius:var(--bs-modal-inner-border-radius);border-bottom-left-radius:var(--bs-modal-inner-border-radius)}.modal-footer>*{margin:calc(var(--bs-modal-footer-gap)*.5)}@media(min-width: 576px){.modal{--bs-modal-margin: 1.75rem;--bs-modal-box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15)}.modal-dialog{max-width:var(--bs-modal-width);margin-right:auto;margin-left:auto}.modal-sm{--bs-modal-width: 300px}}@media(min-width: 992px){.modal-lg,.modal-xl{--bs-modal-width: 800px}}@media(min-width: 1200px){.modal-xl{--bs-modal-width: 1140px}}.modal-fullscreen{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen .modal-header,.modal-fullscreen .modal-footer{border-radius:0}.modal-fullscreen .modal-body{overflow-y:auto}@media(max-width: 575.98px){.modal-fullscreen-sm-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-sm-down .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-sm-down .modal-header,.modal-fullscreen-sm-down .modal-footer{border-radius:0}.modal-fullscreen-sm-down .modal-body{overflow-y:auto}}@media(max-width: 767.98px){.modal-fullscreen-md-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-md-down .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-md-down .modal-header,.modal-fullscreen-md-down .modal-footer{border-radius:0}.modal-fullscreen-md-down .modal-body{overflow-y:auto}}@media(max-width: 991.98px){.modal-fullscreen-lg-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-lg-down .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-lg-down .modal-header,.modal-fullscreen-lg-down .modal-footer{border-radius:0}.modal-fullscreen-lg-down .modal-body{overflow-y:auto}}@media(max-width: 1199.98px){.modal-fullscreen-xl-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-xl-down .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-xl-down .modal-header,.modal-fullscreen-xl-down .modal-footer{border-radius:0}.modal-fullscreen-xl-down .modal-body{overflow-y:auto}}@media(max-width: 1399.98px){.modal-fullscreen-xxl-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-xxl-down .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-xxl-down .modal-header,.modal-fullscreen-xxl-down .modal-footer{border-radius:0}.modal-fullscreen-xxl-down .modal-body{overflow-y:auto}}.tooltip{--bs-tooltip-zindex: 1080;--bs-tooltip-max-width: 200px;--bs-tooltip-padding-x: 0.5rem;--bs-tooltip-padding-y: 0.25rem;--bs-tooltip-margin: ;--bs-tooltip-font-size:0.875rem;--bs-tooltip-color: #fff;--bs-tooltip-bg: #000;--bs-tooltip-border-radius: 0.25rem;--bs-tooltip-opacity: 0.9;--bs-tooltip-arrow-width: 0.8rem;--bs-tooltip-arrow-height: 0.4rem;z-index:var(--bs-tooltip-zindex);display:block;margin:var(--bs-tooltip-margin);font-family:Lato,-apple-system,BlinkMacSystemFont,"Segoe UI",Roboto,"Helvetica Neue",Arial,sans-serif,"Apple Color Emoji","Segoe UI Emoji","Segoe UI Symbol";font-style:normal;font-weight:400;line-height:1.5;text-align:left;text-align:start;text-decoration:none;text-shadow:none;text-transform:none;letter-spacing:normal;word-break:normal;white-space:normal;word-spacing:normal;line-break:auto;font-size:var(--bs-tooltip-font-size);word-wrap:break-word;opacity:0}.tooltip.show{opacity:var(--bs-tooltip-opacity)}.tooltip .tooltip-arrow{display:block;width:var(--bs-tooltip-arrow-width);height:var(--bs-tooltip-arrow-height)}.tooltip .tooltip-arrow::before{position:absolute;content:"";border-color:rgba(0,0,0,0);border-style:solid}.bs-tooltip-top .tooltip-arrow,.bs-tooltip-auto[data-popper-placement^=top] .tooltip-arrow{bottom:calc(-1*var(--bs-tooltip-arrow-height))}.bs-tooltip-top 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var(--bg-gradient-start, 36%), #fd7e14 var(--bg-gradient-end, 180%)) #848e8b;color:#fff}.bg-gradient-cyan-yellow{--bslib-color-fg: #fff;--bslib-color-bg: #809a8b;background:linear-gradient(var(--bg-gradient-deg, 140deg), #3498db var(--bg-gradient-start, 36%), #f39c12 var(--bg-gradient-end, 180%)) #809a8b;color:#fff}.bg-gradient-cyan-green{--bslib-color-fg: #fff;--bslib-color-bg: #29a6c2;background:linear-gradient(var(--bg-gradient-deg, 140deg), #3498db var(--bg-gradient-start, 36%), #18bc9c var(--bg-gradient-end, 180%)) #29a6c2;color:#fff}.bg-gradient-cyan-teal{--bslib-color-fg: #fff;--bslib-color-bg: #2cacc0;background:linear-gradient(var(--bg-gradient-deg, 140deg), #3498db var(--bg-gradient-start, 36%), #20c997 var(--bg-gradient-end, 180%)) #2cacc0;color:#fff}:root{--bslib-spacer: 1rem;--bslib-mb-spacer: var(--bslib-spacer, 1rem)}.bslib-mb-spacing{margin-bottom:var(--bslib-mb-spacer)}.bslib-gap-spacing{gap:var(--bslib-mb-spacer)}.bslib-gap-spacing>.bslib-mb-spacing,.bslib-gap-spacing>.form-group,.bslib-gap-spacing>p,.bslib-gap-spacing>pre{margin-bottom:0}.html-fill-container>.html-fill-item.bslib-mb-spacing{margin-bottom:0}.tab-content>.tab-pane.html-fill-container{display:none}.tab-content>.active.html-fill-container{display:flex}.tab-content.html-fill-container{padding:0}.accordion .accordion-header{font-size:calc(1.29rem + 0.48vw);margin-top:0;margin-bottom:.5rem;font-weight:500;line-height:1.2;color:var(--bs-heading-color);margin-bottom:0}@media(min-width: 1200px){.accordion .accordion-header{font-size:1.65rem}}.accordion .accordion-icon:not(:empty){margin-right:.75rem;display:flex}.accordion .accordion-button:not(.collapsed){box-shadow:none}.accordion .accordion-button:not(.collapsed):focus{box-shadow:var(--bs-accordion-btn-focus-box-shadow)}.bslib-card{overflow:auto}.bslib-card 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+* we also add `bright-[color]-` synonyms for the `-[color]-intense` classes since
+* that seems to be what ansi_up emits
+*
+*/.ansi-black-fg{color:#3e424d}.ansi-black-bg{background-color:#3e424d}.ansi-black-intense-black,.ansi-bright-black-fg{color:#282c36}.ansi-black-intense-black,.ansi-bright-black-bg{background-color:#282c36}.ansi-red-fg{color:#e75c58}.ansi-red-bg{background-color:#e75c58}.ansi-red-intense-red,.ansi-bright-red-fg{color:#b22b31}.ansi-red-intense-red,.ansi-bright-red-bg{background-color:#b22b31}.ansi-green-fg{color:#00a250}.ansi-green-bg{background-color:#00a250}.ansi-green-intense-green,.ansi-bright-green-fg{color:#007427}.ansi-green-intense-green,.ansi-bright-green-bg{background-color:#007427}.ansi-yellow-fg{color:#ddb62b}.ansi-yellow-bg{background-color:#ddb62b}.ansi-yellow-intense-yellow,.ansi-bright-yellow-fg{color:#b27d12}.ansi-yellow-intense-yellow,.ansi-bright-yellow-bg{background-color:#b27d12}.ansi-blue-fg{color:#208ffb}.ansi-blue-bg{background-color:#208ffb}.ansi-blue-intense-blue,.ansi-bright-blue-fg{color:#0065ca}.ansi-blue-intense-blue,.ansi-bright-blue-bg{background-color:#0065ca}.ansi-magenta-fg{color:#d160c4}.ansi-magenta-bg{background-color:#d160c4}.ansi-magenta-intense-magenta,.ansi-bright-magenta-fg{color:#a03196}.ansi-magenta-intense-magenta,.ansi-bright-magenta-bg{background-color:#a03196}.ansi-cyan-fg{color:#60c6c8}.ansi-cyan-bg{background-color:#60c6c8}.ansi-cyan-intense-cyan,.ansi-bright-cyan-fg{color:#258f8f}.ansi-cyan-intense-cyan,.ansi-bright-cyan-bg{background-color:#258f8f}.ansi-white-fg{color:#c5c1b4}.ansi-white-bg{background-color:#c5c1b4}.ansi-white-intense-white,.ansi-bright-white-fg{color:#a1a6b2}.ansi-white-intense-white,.ansi-bright-white-bg{background-color:#a1a6b2}.ansi-default-inverse-fg{color:#fff}.ansi-default-inverse-bg{background-color:#000}.ansi-bold{font-weight:bold}.ansi-underline{text-decoration:underline}:root{--quarto-body-bg: 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td.gt_row{border-bottom-width:1px;border-bottom-color:var(--quarto-border-color);border-top-width:0px}table.gt_table tbody.gt_table_body{border-top-width:1px;border-bottom-width:1px;border-bottom-color:var(--quarto-border-color);border-top-color:currentColor}div.columns{display:initial;gap:initial}div.column{display:inline-block;overflow-x:initial;vertical-align:top;width:50%}.code-annotation-tip-content{word-wrap:break-word}.code-annotation-container-hidden{display:none !important}dl.code-annotation-container-grid{display:grid;grid-template-columns:min-content auto}dl.code-annotation-container-grid dt{grid-column:1}dl.code-annotation-container-grid dd{grid-column:2}pre.sourceCode.code-annotation-code{padding-right:0}code.sourceCode .code-annotation-anchor{z-index:100;position:relative;float:right;background-color:rgba(0,0,0,0)}input[type=checkbox]{margin-right:.5ch}:root{--mermaid-bg-color: #fff;--mermaid-edge-color: #6c757d;--mermaid-node-fg-color: #212529;--mermaid-fg-color: 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ol{margin-bottom:1em}ul>li:not(:has(>p))>ul,ol>li:not(:has(>p))>ul,ul>li:not(:has(>p))>ol,ol>li:not(:has(>p))>ol{margin-bottom:0}ul>li:not(:has(>p))>ul>li:has(>p),ol>li:not(:has(>p))>ul>li:has(>p),ul>li:not(:has(>p))>ol>li:has(>p),ol>li:not(:has(>p))>ol>li:has(>p){margin-top:1rem}body{margin:0}main.page-columns>header>h1.title,main.page-columns>header>.title.h1{margin-bottom:0}@media(min-width: 992px){body .page-columns{display:grid;gap:0;grid-template-columns:[screen-start] 1.5em [screen-start-inset] 5fr [page-start page-start-inset] 35px [body-start-outset] 35px [body-start] 1.5em [body-content-start] minmax(500px, calc(850px - 3em)) [body-content-end] 1.5em [body-end] 35px [body-end-outset] minmax(75px, 145px) [page-end-inset] 35px [page-end] 5fr [screen-end-inset] 1.5em [screen-end]}body.fullcontent:not(.floating):not(.docked) .page-columns{display:grid;gap:0;grid-template-columns:[screen-start] 1.5em [screen-start-inset] 5fr [page-start page-start-inset] 35px [body-start-outset] 35px [body-start] 1.5em [body-content-start] minmax(500px, calc(850px - 3em)) [body-content-end] 1.5em [body-end] 35px [body-end-outset] 35px [page-end-inset page-end] 5fr [screen-end-inset] 1.5em}body.slimcontent:not(.floating):not(.docked) .page-columns{display:grid;gap:0;grid-template-columns:[screen-start] 1.5em [screen-start-inset] 5fr [page-start page-start-inset] 35px [body-start-outset] 35px [body-start] 1.5em [body-content-start] minmax(500px, calc(850px - 3em)) [body-content-end] 1.5em [body-end] 50px [body-end-outset] minmax(0px, 200px) [page-end-inset] 35px [page-end] 5fr [screen-end-inset] 1.5em [screen-end]}body.listing:not(.floating):not(.docked) .page-columns{display:grid;gap:0;grid-template-columns:[screen-start] 1.5em [screen-start-inset page-start] minmax(50px, 100px) [page-start-inset] 50px [body-start-outset] 50px [body-start] 1.5em [body-content-start] minmax(500px, calc(850px - 3em)) [body-content-end] 3em [body-end] 50px [body-end-outset] minmax(0px, 250px) [page-end-inset] minmax(50px, 100px) [page-end] 1fr [screen-end-inset] 1.5em [screen-end]}body:not(.floating):not(.docked) .page-columns.toc-left{display:grid;gap:0;grid-template-columns:[screen-start] 1.5em [screen-start-inset] 5fr [page-start] 35px [page-start-inset] minmax(0px, 175px) [body-start-outset] 35px [body-start] 1.5em [body-content-start] minmax(450px, calc(800px - 3em)) [body-content-end] 1.5em [body-end] 50px [body-end-outset] minmax(0px, 200px) [page-end-inset] 50px [page-end] 5fr [screen-end-inset] 1.5em [screen-end]}body:not(.floating):not(.docked) .page-columns.toc-left .page-columns{display:grid;gap:0;grid-template-columns:[screen-start] 1.5em [screen-start-inset] 5fr [page-start] 35px [page-start-inset] minmax(0px, 175px) [body-start-outset] 35px [body-start] 1.5em [body-content-start] minmax(450px, calc(800px - 3em)) [body-content-end] 1.5em [body-end] 50px [body-end-outset] minmax(0px, 200px) [page-end-inset] 50px [page-end] 5fr [screen-end-inset] 1.5em 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Q="x"===y?mt:bt,G="x"===y?gt:_t,Z=E[w],J=Z+g[Q],tt=Z-g[G],et=oe(f?ne(J,K):J,Z,f?ie(tt,X):tt);E[w]=et,C[w]=et-Z}}e.modifiersData[n]=C}},requiresIfExists:["offset"]};function Me(t,e,i){void 0===i&&(i=!1);var n=zt(e);zt(e)&&function(t){var e=t.getBoundingClientRect();e.width,t.offsetWidth,e.height,t.offsetHeight}(e);var s,o,r=Gt(e),a=Vt(t),l={scrollLeft:0,scrollTop:0},c={x:0,y:0};return(n||!n&&!i)&&(("body"!==Rt(e)||we(r))&&(l=(s=e)!==Wt(s)&&zt(s)?{scrollLeft:(o=s).scrollLeft,scrollTop:o.scrollTop}:ve(s)),zt(e)?((c=Vt(e)).x+=e.clientLeft,c.y+=e.clientTop):r&&(c.x=ye(r))),{x:a.left+l.scrollLeft-c.x,y:a.top+l.scrollTop-c.y,width:a.width,height:a.height}}function He(t){var e=new Map,i=new Set,n=[];function s(t){i.add(t.name),[].concat(t.requires||[],t.requiresIfExists||[]).forEach((function(t){if(!i.has(t)){var n=e.get(t);n&&s(n)}})),n.push(t)}return t.forEach((function(t){e.set(t.name,t)})),t.forEach((function(t){i.has(t.name)||s(t)})),n}var Be={placement:"bottom",modifiers:[],strategy:"absolute"};function Re(){for(var t=arguments.length,e=new Array(t),i=0;i<t;i++)e[i]=arguments[i];return!e.some((function(t){return!(t&&"function"==typeof t.getBoundingClientRect)}))}function We(t){void 0===t&&(t={});var e=t,i=e.defaultModifiers,n=void 0===i?[]:i,s=e.defaultOptions,o=void 0===s?Be:s;return function(t,e,i){void 0===i&&(i=o);var s,r,a={placement:"bottom",orderedModifiers:[],options:Object.assign({},Be,o),modifiersData:{},elements:{reference:t,popper:e},attributes:{},styles:{}},l=[],c=!1,h={state:a,setOptions:function(i){var s="function"==typeof i?i(a.options):i;d(),a.options=Object.assign({},o,a.options,s),a.scrollParents={reference:$t(t)?Ae(t):t.contextElement?Ae(t.contextElement):[],popper:Ae(e)};var r,c,u=function(t){var e=He(t);return Bt.reduce((function(t,i){return t.concat(e.filter((function(t){return t.phase===i})))}),[])}((r=[].concat(n,a.options.modifiers),c=r.reduce((function(t,e){var i=t[e.name];return t[e.name]=i?Object.assign({},i,e,{options:Object.assign({},i.options,e.options),data:Object.assign({},i.data,e.data)}):e,t}),{}),Object.keys(c).map((function(t){return c[t]}))));return a.orderedModifiers=u.filter((function(t){return t.enabled})),a.orderedModifiers.forEach((function(t){var e=t.name,i=t.options,n=void 0===i?{}:i,s=t.effect;if("function"==typeof s){var o=s({state:a,name:e,instance:h,options:n});l.push(o||function(){})}})),h.update()},forceUpdate:function(){if(!c){var t=a.elements,e=t.reference,i=t.popper;if(Re(e,i)){a.rects={reference:Me(e,te(i),"fixed"===a.options.strategy),popper:Kt(i)},a.reset=!1,a.placement=a.options.placement,a.orderedModifiers.forEach((function(t){return a.modifiersData[t.name]=Object.assign({},t.data)}));for(var n=0;n<a.orderedModifiers.length;n++)if(!0!==a.reset){var s=a.orderedModifiers[n],o=s.fn,r=s.options,l=void 0===r?{}:r,d=s.name;"function"==typeof o&&(a=o({state:a,options:l,name:d,instance:h})||a)}else a.reset=!1,n=-1}}},update:(s=function(){return new Promise((function(t){h.forceUpdate(),t(a)}))},function(){return r||(r=new Promise((function(t){Promise.resolve().then((function(){r=void 0,t(s())}))}))),r}),destroy:function(){d(),c=!0}};if(!Re(t,e))return h;function d(){l.forEach((function(t){return t()})),l=[]}return h.setOptions(i).then((function(t){!c&&i.onFirstUpdate&&i.onFirstUpdate(t)})),h}}var $e=We(),ze=We({defaultModifiers:[pe,Pe,ue,Ft]}),qe=We({defaultModifiers:[pe,Pe,ue,Ft,Ie,xe,je,le,Ne]});const Fe=Object.freeze({__proto__:null,popperGenerator:We,detectOverflow:ke,createPopperBase:$e,createPopper:qe,createPopperLite:ze,top:mt,bottom:gt,right:_t,left:bt,auto:vt,basePlacements:yt,start:wt,end:Et,clippingParents:At,viewport:Tt,popper:Ot,reference:Ct,variationPlacements:kt,placements:Lt,beforeRead:xt,read:Dt,afterRead:St,beforeMain:Nt,main:It,afterMain:Pt,beforeWrite:jt,write:Mt,afterWrite:Ht,modifierPhases:Bt,applyStyles:Ft,arrow:le,computeStyles:ue,eventListeners:pe,flip:xe,hide:Ne,offset:Ie,popperOffsets:Pe,preventOverflow:je}),Ue="dropdown",Ve="Escape",Ke="Space",Xe="ArrowUp",Ye="ArrowDown",Qe=new RegExp("ArrowUp|ArrowDown|Escape"),Ge="click.bs.dropdown.data-api",Ze="keydown.bs.dropdown.data-api",Je="show",ti='[data-bs-toggle="dropdown"]',ei=".dropdown-menu",ii=m()?"top-end":"top-start",ni=m()?"top-start":"top-end",si=m()?"bottom-end":"bottom-start",oi=m()?"bottom-start":"bottom-end",ri=m()?"left-start":"right-start",ai=m()?"right-start":"left-start",li={offset:[0,2],boundary:"clippingParents",reference:"toggle",display:"dynamic",popperConfig:null,autoClose:!0},ci={offset:"(array|string|function)",boundary:"(string|element)",reference:"(string|element|object)",display:"string",popperConfig:"(null|object|function)",autoClose:"(boolean|string)"};class hi extends B{constructor(t,e){super(t),this._popper=null,this._config=this._getConfig(e),this._menu=this._getMenuElement(),this._inNavbar=this._detectNavbar()}static get Default(){return li}static get DefaultType(){return ci}static get NAME(){return Ue}toggle(){return this._isShown()?this.hide():this.show()}show(){if(c(this._element)||this._isShown(this._menu))return;const t={relatedTarget:this._element};if(j.trigger(this._element,"show.bs.dropdown",t).defaultPrevented)return;const e=hi.getParentFromElement(this._element);this._inNavbar?U.setDataAttribute(this._menu,"popper","none"):this._createPopper(e),"ontouchstart"in document.documentElement&&!e.closest(".navbar-nav")&&[].concat(...document.body.children).forEach((t=>j.on(t,"mouseover",d))),this._element.focus(),this._element.setAttribute("aria-expanded",!0),this._menu.classList.add(Je),this._element.classList.add(Je),j.trigger(this._element,"shown.bs.dropdown",t)}hide(){if(c(this._element)||!this._isShown(this._menu))return;const t={relatedTarget:this._element};this._completeHide(t)}dispose(){this._popper&&this._popper.destroy(),super.dispose()}update(){this._inNavbar=this._detectNavbar(),this._popper&&this._popper.update()}_completeHide(t){j.trigger(this._element,"hide.bs.dropdown",t).defaultPrevented||("ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach((t=>j.off(t,"mouseover",d))),this._popper&&this._popper.destroy(),this._menu.classList.remove(Je),this._element.classList.remove(Je),this._element.setAttribute("aria-expanded","false"),U.removeDataAttribute(this._menu,"popper"),j.trigger(this._element,"hidden.bs.dropdown",t))}_getConfig(t){if(t={...this.constructor.Default,...U.getDataAttributes(this._element),...t},a(Ue,t,this.constructor.DefaultType),"object"==typeof t.reference&&!o(t.reference)&&"function"!=typeof t.reference.getBoundingClientRect)throw new TypeError(`${Ue.toUpperCase()}: Option "reference" provided type "object" without a required "getBoundingClientRect" method.`);return t}_createPopper(t){if(void 0===Fe)throw new TypeError("Bootstrap's dropdowns require Popper (https://popper.js.org)");let e=this._element;"parent"===this._config.reference?e=t:o(this._config.reference)?e=r(this._config.reference):"object"==typeof this._config.reference&&(e=this._config.reference);const i=this._getPopperConfig(),n=i.modifiers.find((t=>"applyStyles"===t.name&&!1===t.enabled));this._popper=qe(e,this._menu,i),n&&U.setDataAttribute(this._menu,"popper","static")}_isShown(t=this._element){return t.classList.contains(Je)}_getMenuElement(){return V.next(this._element,ei)[0]}_getPlacement(){const t=this._element.parentNode;if(t.classList.contains("dropend"))return ri;if(t.classList.contains("dropstart"))return ai;const e="end"===getComputedStyle(this._menu).getPropertyValue("--bs-position").trim();return t.classList.contains("dropup")?e?ni:ii:e?oi:si}_detectNavbar(){return null!==this._element.closest(".navbar")}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return"static"===this._config.display&&(t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,..."function"==typeof this._config.popperConfig?this._config.popperConfig(t):this._config.popperConfig}}_selectMenuItem({key:t,target:e}){const i=V.find(".dropdown-menu .dropdown-item:not(.disabled):not(:disabled)",this._menu).filter(l);i.length&&v(i,e,t===Ye,!i.includes(e)).focus()}static jQueryInterface(t){return this.each((function(){const e=hi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}static clearMenus(t){if(t&&(2===t.button||"keyup"===t.type&&"Tab"!==t.key))return;const e=V.find(ti);for(let i=0,n=e.length;i<n;i++){const n=hi.getInstance(e[i]);if(!n||!1===n._config.autoClose)continue;if(!n._isShown())continue;const s={relatedTarget:n._element};if(t){const e=t.composedPath(),i=e.includes(n._menu);if(e.includes(n._element)||"inside"===n._config.autoClose&&!i||"outside"===n._config.autoClose&&i)continue;if(n._menu.contains(t.target)&&("keyup"===t.type&&"Tab"===t.key||/input|select|option|textarea|form/i.test(t.target.tagName)))continue;"click"===t.type&&(s.clickEvent=t)}n._completeHide(s)}}static getParentFromElement(t){return n(t)||t.parentNode}static dataApiKeydownHandler(t){if(/input|textarea/i.test(t.target.tagName)?t.key===Ke||t.key!==Ve&&(t.key!==Ye&&t.key!==Xe||t.target.closest(ei)):!Qe.test(t.key))return;const e=this.classList.contains(Je);if(!e&&t.key===Ve)return;if(t.preventDefault(),t.stopPropagation(),c(this))return;const i=this.matches(ti)?this:V.prev(this,ti)[0],n=hi.getOrCreateInstance(i);if(t.key!==Ve)return t.key===Xe||t.key===Ye?(e||n.show(),void n._selectMenuItem(t)):void(e&&t.key!==Ke||hi.clearMenus());n.hide()}}j.on(document,Ze,ti,hi.dataApiKeydownHandler),j.on(document,Ze,ei,hi.dataApiKeydownHandler),j.on(document,Ge,hi.clearMenus),j.on(document,"keyup.bs.dropdown.data-api",hi.clearMenus),j.on(document,Ge,ti,(function(t){t.preventDefault(),hi.getOrCreateInstance(this).toggle()})),g(hi);const di=".fixed-top, .fixed-bottom, .is-fixed, .sticky-top",ui=".sticky-top";class fi{constructor(){this._element=document.body}getWidth(){const t=document.documentElement.clientWidth;return Math.abs(window.innerWidth-t)}hide(){const t=this.getWidth();this._disableOverFlow(),this._setElementAttributes(this._element,"paddingRight",(e=>e+t)),this._setElementAttributes(di,"paddingRight",(e=>e+t)),this._setElementAttributes(ui,"marginRight",(e=>e-t))}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t)[e];t.style[e]=`${i(Number.parseFloat(s))}px`}))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,"paddingRight"),this._resetElementAttributes(di,"paddingRight"),this._resetElementAttributes(ui,"marginRight")}_saveInitialAttribute(t,e){const i=t.style[e];i&&U.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=U.getDataAttribute(t,e);void 0===i?t.style.removeProperty(e):(U.removeDataAttribute(t,e),t.style[e]=i)}))}_applyManipulationCallback(t,e){o(t)?e(t):V.find(t,this._element).forEach(e)}isOverflowing(){return this.getWidth()>0}}const pi={className:"modal-backdrop",isVisible:!0,isAnimated:!1,rootElement:"body",clickCallback:null},mi={className:"string",isVisible:"boolean",isAnimated:"boolean",rootElement:"(element|string)",clickCallback:"(function|null)"},gi="show",_i="mousedown.bs.backdrop";class bi{constructor(t){this._config=this._getConfig(t),this._isAppended=!1,this._element=null}show(t){this._config.isVisible?(this._append(),this._config.isAnimated&&u(this._getElement()),this._getElement().classList.add(gi),this._emulateAnimation((()=>{_(t)}))):_(t)}hide(t){this._config.isVisible?(this._getElement().classList.remove(gi),this._emulateAnimation((()=>{this.dispose(),_(t)}))):_(t)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_getConfig(t){return(t={...pi,..."object"==typeof t?t:{}}).rootElement=r(t.rootElement),a("backdrop",t,mi),t}_append(){this._isAppended||(this._config.rootElement.append(this._getElement()),j.on(this._getElement(),_i,(()=>{_(this._config.clickCallback)})),this._isAppended=!0)}dispose(){this._isAppended&&(j.off(this._element,_i),this._element.remove(),this._isAppended=!1)}_emulateAnimation(t){b(t,this._getElement(),this._config.isAnimated)}}const vi={trapElement:null,autofocus:!0},yi={trapElement:"element",autofocus:"boolean"},wi=".bs.focustrap",Ei="backward";class Ai{constructor(t){this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}activate(){const{trapElement:t,autofocus:e}=this._config;this._isActive||(e&&t.focus(),j.off(document,wi),j.on(document,"focusin.bs.focustrap",(t=>this._handleFocusin(t))),j.on(document,"keydown.tab.bs.focustrap",(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,j.off(document,wi))}_handleFocusin(t){const{target:e}=t,{trapElement:i}=this._config;if(e===document||e===i||i.contains(e))return;const n=V.focusableChildren(i);0===n.length?i.focus():this._lastTabNavDirection===Ei?n[n.length-1].focus():n[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?Ei:"forward")}_getConfig(t){return t={...vi,..."object"==typeof t?t:{}},a("focustrap",t,yi),t}}const Ti="modal",Oi="Escape",Ci={backdrop:!0,keyboard:!0,focus:!0},ki={backdrop:"(boolean|string)",keyboard:"boolean",focus:"boolean"},Li="hidden.bs.modal",xi="show.bs.modal",Di="resize.bs.modal",Si="click.dismiss.bs.modal",Ni="keydown.dismiss.bs.modal",Ii="mousedown.dismiss.bs.modal",Pi="modal-open",ji="show",Mi="modal-static";class Hi extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._dialog=V.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._ignoreBackdropClick=!1,this._isTransitioning=!1,this._scrollBar=new fi}static get Default(){return Ci}static get NAME(){return Ti}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||j.trigger(this._element,xi,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isAnimated()&&(this._isTransitioning=!0),this._scrollBar.hide(),document.body.classList.add(Pi),this._adjustDialog(),this._setEscapeEvent(),this._setResizeEvent(),j.on(this._dialog,Ii,(()=>{j.one(this._element,"mouseup.dismiss.bs.modal",(t=>{t.target===this._element&&(this._ignoreBackdropClick=!0)}))})),this._showBackdrop((()=>this._showElement(t))))}hide(){if(!this._isShown||this._isTransitioning)return;if(j.trigger(this._element,"hide.bs.modal").defaultPrevented)return;this._isShown=!1;const t=this._isAnimated();t&&(this._isTransitioning=!0),this._setEscapeEvent(),this._setResizeEvent(),this._focustrap.deactivate(),this._element.classList.remove(ji),j.off(this._element,Si),j.off(this._dialog,Ii),this._queueCallback((()=>this._hideModal()),this._element,t)}dispose(){[window,this._dialog].forEach((t=>j.off(t,".bs.modal"))),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new bi({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new Ai({trapElement:this._element})}_getConfig(t){return t={...Ci,...U.getDataAttributes(this._element),..."object"==typeof t?t:{}},a(Ti,t,ki),t}_showElement(t){const e=this._isAnimated(),i=V.findOne(".modal-body",this._dialog);this._element.parentNode&&this._element.parentNode.nodeType===Node.ELEMENT_NODE||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0,i&&(i.scrollTop=0),e&&u(this._element),this._element.classList.add(ji),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,j.trigger(this._element,"shown.bs.modal",{relatedTarget:t})}),this._dialog,e)}_setEscapeEvent(){this._isShown?j.on(this._element,Ni,(t=>{this._config.keyboard&&t.key===Oi?(t.preventDefault(),this.hide()):this._config.keyboard||t.key!==Oi||this._triggerBackdropTransition()})):j.off(this._element,Ni)}_setResizeEvent(){this._isShown?j.on(window,Di,(()=>this._adjustDialog())):j.off(window,Di)}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(Pi),this._resetAdjustments(),this._scrollBar.reset(),j.trigger(this._element,Li)}))}_showBackdrop(t){j.on(this._element,Si,(t=>{this._ignoreBackdropClick?this._ignoreBackdropClick=!1:t.target===t.currentTarget&&(!0===this._config.backdrop?this.hide():"static"===this._config.backdrop&&this._triggerBackdropTransition())})),this._backdrop.show(t)}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(j.trigger(this._element,"hidePrevented.bs.modal").defaultPrevented)return;const{classList:t,scrollHeight:e,style:i}=this._element,n=e>document.documentElement.clientHeight;!n&&"hidden"===i.overflowY||t.contains(Mi)||(n||(i.overflowY="hidden"),t.add(Mi),this._queueCallback((()=>{t.remove(Mi),n||this._queueCallback((()=>{i.overflowY=""}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;(!i&&t&&!m()||i&&!t&&m())&&(this._element.style.paddingLeft=`${e}px`),(i&&!t&&!m()||!i&&t&&m())&&(this._element.style.paddingRight=`${e}px`)}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=Hi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}j.on(document,"click.bs.modal.data-api",'[data-bs-toggle="modal"]',(function(t){const e=n(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),j.one(e,xi,(t=>{t.defaultPrevented||j.one(e,Li,(()=>{l(this)&&this.focus()}))}));const i=V.findOne(".modal.show");i&&Hi.getInstance(i).hide(),Hi.getOrCreateInstance(e).toggle(this)})),R(Hi),g(Hi);const Bi="offcanvas",Ri={backdrop:!0,keyboard:!0,scroll:!1},Wi={backdrop:"boolean",keyboard:"boolean",scroll:"boolean"},$i="show",zi=".offcanvas.show",qi="hidden.bs.offcanvas";class Fi extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get NAME(){return Bi}static get Default(){return Ri}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||j.trigger(this._element,"show.bs.offcanvas",{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._element.style.visibility="visible",this._backdrop.show(),this._config.scroll||(new fi).hide(),this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add($i),this._queueCallback((()=>{this._config.scroll||this._focustrap.activate(),j.trigger(this._element,"shown.bs.offcanvas",{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(j.trigger(this._element,"hide.bs.offcanvas").defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.remove($i),this._backdrop.hide(),this._queueCallback((()=>{this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._element.style.visibility="hidden",this._config.scroll||(new fi).reset(),j.trigger(this._element,qi)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_getConfig(t){return t={...Ri,...U.getDataAttributes(this._element),..."object"==typeof t?t:{}},a(Bi,t,Wi),t}_initializeBackDrop(){return new bi({className:"offcanvas-backdrop",isVisible:this._config.backdrop,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:()=>this.hide()})}_initializeFocusTrap(){return new Ai({trapElement:this._element})}_addEventListeners(){j.on(this._element,"keydown.dismiss.bs.offcanvas",(t=>{this._config.keyboard&&"Escape"===t.key&&this.hide()}))}static jQueryInterface(t){return this.each((function(){const e=Fi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}j.on(document,"click.bs.offcanvas.data-api",'[data-bs-toggle="offcanvas"]',(function(t){const e=n(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),c(this))return;j.one(e,qi,(()=>{l(this)&&this.focus()}));const i=V.findOne(zi);i&&i!==e&&Fi.getInstance(i).hide(),Fi.getOrCreateInstance(e).toggle(this)})),j.on(window,"load.bs.offcanvas.data-api",(()=>V.find(zi).forEach((t=>Fi.getOrCreateInstance(t).show())))),R(Fi),g(Fi);const Ui=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Vi=/^(?:(?:https?|mailto|ftp|tel|file|sms):|[^#&/:?]*(?:[#/?]|$))/i,Ki=/^data:(?:image\/(?:bmp|gif|jpeg|jpg|png|tiff|webp)|video\/(?:mpeg|mp4|ogg|webm)|audio\/(?:mp3|oga|ogg|opus));base64,[\d+/a-z]+=*$/i,Xi=(t,e)=>{const i=t.nodeName.toLowerCase();if(e.includes(i))return!Ui.has(i)||Boolean(Vi.test(t.nodeValue)||Ki.test(t.nodeValue));const n=e.filter((t=>t instanceof RegExp));for(let t=0,e=n.length;t<e;t++)if(n[t].test(i))return!0;return!1};function Yi(t,e,i){if(!t.length)return t;if(i&&"function"==typeof i)return i(t);const n=(new window.DOMParser).parseFromString(t,"text/html"),s=[].concat(...n.body.querySelectorAll("*"));for(let t=0,i=s.length;t<i;t++){const i=s[t],n=i.nodeName.toLowerCase();if(!Object.keys(e).includes(n)){i.remove();continue}const o=[].concat(...i.attributes),r=[].concat(e["*"]||[],e[n]||[]);o.forEach((t=>{Xi(t,r)||i.removeAttribute(t.nodeName)}))}return n.body.innerHTML}const Qi="tooltip",Gi=new Set(["sanitize","allowList","sanitizeFn"]),Zi={animation:"boolean",template:"string",title:"(string|element|function)",trigger:"string",delay:"(number|object)",html:"boolean",selector:"(string|boolean)",placement:"(string|function)",offset:"(array|string|function)",container:"(string|element|boolean)",fallbackPlacements:"array",boundary:"(string|element)",customClass:"(string|function)",sanitize:"boolean",sanitizeFn:"(null|function)",allowList:"object",popperConfig:"(null|object|function)"},Ji={AUTO:"auto",TOP:"top",RIGHT:m()?"left":"right",BOTTOM:"bottom",LEFT:m()?"right":"left"},tn={animation:!0,template:'<div class="tooltip" role="tooltip"><div class="tooltip-arrow"></div><div class="tooltip-inner"></div></div>',trigger:"hover focus",title:"",delay:0,html:!1,selector:!1,placement:"top",offset:[0,0],container:!1,fallbackPlacements:["top","right","bottom","left"],boundary:"clippingParents",customClass:"",sanitize:!0,sanitizeFn:null,allowList:{"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},popperConfig:null},en={HIDE:"hide.bs.tooltip",HIDDEN:"hidden.bs.tooltip",SHOW:"show.bs.tooltip",SHOWN:"shown.bs.tooltip",INSERTED:"inserted.bs.tooltip",CLICK:"click.bs.tooltip",FOCUSIN:"focusin.bs.tooltip",FOCUSOUT:"focusout.bs.tooltip",MOUSEENTER:"mouseenter.bs.tooltip",MOUSELEAVE:"mouseleave.bs.tooltip"},nn="fade",sn="show",on="show",rn="out",an=".tooltip-inner",ln=".modal",cn="hide.bs.modal",hn="hover",dn="focus";class un extends B{constructor(t,e){if(void 0===Fe)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t),this._isEnabled=!0,this._timeout=0,this._hoverState="",this._activeTrigger={},this._popper=null,this._config=this._getConfig(e),this.tip=null,this._setListeners()}static get Default(){return tn}static get NAME(){return Qi}static get Event(){return en}static get DefaultType(){return Zi}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(t){if(this._isEnabled)if(t){const e=this._initializeOnDelegatedTarget(t);e._activeTrigger.click=!e._activeTrigger.click,e._isWithActiveTrigger()?e._enter(null,e):e._leave(null,e)}else{if(this.getTipElement().classList.contains(sn))return void this._leave(null,this);this._enter(null,this)}}dispose(){clearTimeout(this._timeout),j.off(this._element.closest(ln),cn,this._hideModalHandler),this.tip&&this.tip.remove(),this._disposePopper(),super.dispose()}show(){if("none"===this._element.style.display)throw new Error("Please use show on visible elements");if(!this.isWithContent()||!this._isEnabled)return;const t=j.trigger(this._element,this.constructor.Event.SHOW),e=h(this._element),i=null===e?this._element.ownerDocument.documentElement.contains(this._element):e.contains(this._element);if(t.defaultPrevented||!i)return;"tooltip"===this.constructor.NAME&&this.tip&&this.getTitle()!==this.tip.querySelector(an).innerHTML&&(this._disposePopper(),this.tip.remove(),this.tip=null);const n=this.getTipElement(),s=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME);n.setAttribute("id",s),this._element.setAttribute("aria-describedby",s),this._config.animation&&n.classList.add(nn);const o="function"==typeof this._config.placement?this._config.placement.call(this,n,this._element):this._config.placement,r=this._getAttachment(o);this._addAttachmentClass(r);const{container:a}=this._config;H.set(n,this.constructor.DATA_KEY,this),this._element.ownerDocument.documentElement.contains(this.tip)||(a.append(n),j.trigger(this._element,this.constructor.Event.INSERTED)),this._popper?this._popper.update():this._popper=qe(this._element,n,this._getPopperConfig(r)),n.classList.add(sn);const l=this._resolvePossibleFunction(this._config.customClass);l&&n.classList.add(...l.split(" ")),"ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach((t=>{j.on(t,"mouseover",d)}));const c=this.tip.classList.contains(nn);this._queueCallback((()=>{const t=this._hoverState;this._hoverState=null,j.trigger(this._element,this.constructor.Event.SHOWN),t===rn&&this._leave(null,this)}),this.tip,c)}hide(){if(!this._popper)return;const t=this.getTipElement();if(j.trigger(this._element,this.constructor.Event.HIDE).defaultPrevented)return;t.classList.remove(sn),"ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach((t=>j.off(t,"mouseover",d))),this._activeTrigger.click=!1,this._activeTrigger.focus=!1,this._activeTrigger.hover=!1;const e=this.tip.classList.contains(nn);this._queueCallback((()=>{this._isWithActiveTrigger()||(this._hoverState!==on&&t.remove(),this._cleanTipClass(),this._element.removeAttribute("aria-describedby"),j.trigger(this._element,this.constructor.Event.HIDDEN),this._disposePopper())}),this.tip,e),this._hoverState=""}update(){null!==this._popper&&this._popper.update()}isWithContent(){return Boolean(this.getTitle())}getTipElement(){if(this.tip)return this.tip;const t=document.createElement("div");t.innerHTML=this._config.template;const e=t.children[0];return this.setContent(e),e.classList.remove(nn,sn),this.tip=e,this.tip}setContent(t){this._sanitizeAndSetContent(t,this.getTitle(),an)}_sanitizeAndSetContent(t,e,i){const n=V.findOne(i,t);e||!n?this.setElementContent(n,e):n.remove()}setElementContent(t,e){if(null!==t)return o(e)?(e=r(e),void(this._config.html?e.parentNode!==t&&(t.innerHTML="",t.append(e)):t.textContent=e.textContent)):void(this._config.html?(this._config.sanitize&&(e=Yi(e,this._config.allowList,this._config.sanitizeFn)),t.innerHTML=e):t.textContent=e)}getTitle(){const t=this._element.getAttribute("data-bs-original-title")||this._config.title;return this._resolvePossibleFunction(t)}updateAttachment(t){return"right"===t?"end":"left"===t?"start":t}_initializeOnDelegatedTarget(t,e){return e||this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_resolvePossibleFunction(t){return"function"==typeof t?t.call(this._element):t}_getPopperConfig(t){const e={placement:t,modifiers:[{name:"flip",options:{fallbackPlacements:this._config.fallbackPlacements}},{name:"offset",options:{offset:this._getOffset()}},{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"arrow",options:{element:`.${this.constructor.NAME}-arrow`}},{name:"onChange",enabled:!0,phase:"afterWrite",fn:t=>this._handlePopperPlacementChange(t)}],onFirstUpdate:t=>{t.options.placement!==t.placement&&this._handlePopperPlacementChange(t)}};return{...e,..."function"==typeof this._config.popperConfig?this._config.popperConfig(e):this._config.popperConfig}}_addAttachmentClass(t){this.getTipElement().classList.add(`${this._getBasicClassPrefix()}-${this.updateAttachment(t)}`)}_getAttachment(t){return Ji[t.toUpperCase()]}_setListeners(){this._config.trigger.split(" ").forEach((t=>{if("click"===t)j.on(this._element,this.constructor.Event.CLICK,this._config.selector,(t=>this.toggle(t)));else if("manual"!==t){const e=t===hn?this.constructor.Event.MOUSEENTER:this.constructor.Event.FOCUSIN,i=t===hn?this.constructor.Event.MOUSELEAVE:this.constructor.Event.FOCUSOUT;j.on(this._element,e,this._config.selector,(t=>this._enter(t))),j.on(this._element,i,this._config.selector,(t=>this._leave(t)))}})),this._hideModalHandler=()=>{this._element&&this.hide()},j.on(this._element.closest(ln),cn,this._hideModalHandler),this._config.selector?this._config={...this._config,trigger:"manual",selector:""}:this._fixTitle()}_fixTitle(){const t=this._element.getAttribute("title"),e=typeof this._element.getAttribute("data-bs-original-title");(t||"string"!==e)&&(this._element.setAttribute("data-bs-original-title",t||""),!t||this._element.getAttribute("aria-label")||this._element.textContent||this._element.setAttribute("aria-label",t),this._element.setAttribute("title",""))}_enter(t,e){e=this._initializeOnDelegatedTarget(t,e),t&&(e._activeTrigger["focusin"===t.type?dn:hn]=!0),e.getTipElement().classList.contains(sn)||e._hoverState===on?e._hoverState=on:(clearTimeout(e._timeout),e._hoverState=on,e._config.delay&&e._config.delay.show?e._timeout=setTimeout((()=>{e._hoverState===on&&e.show()}),e._config.delay.show):e.show())}_leave(t,e){e=this._initializeOnDelegatedTarget(t,e),t&&(e._activeTrigger["focusout"===t.type?dn:hn]=e._element.contains(t.relatedTarget)),e._isWithActiveTrigger()||(clearTimeout(e._timeout),e._hoverState=rn,e._config.delay&&e._config.delay.hide?e._timeout=setTimeout((()=>{e._hoverState===rn&&e.hide()}),e._config.delay.hide):e.hide())}_isWithActiveTrigger(){for(const t in this._activeTrigger)if(this._activeTrigger[t])return!0;return!1}_getConfig(t){const e=U.getDataAttributes(this._element);return Object.keys(e).forEach((t=>{Gi.has(t)&&delete e[t]})),(t={...this.constructor.Default,...e,..."object"==typeof t&&t?t:{}}).container=!1===t.container?document.body:r(t.container),"number"==typeof t.delay&&(t.delay={show:t.delay,hide:t.delay}),"number"==typeof t.title&&(t.title=t.title.toString()),"number"==typeof t.content&&(t.content=t.content.toString()),a(Qi,t,this.constructor.DefaultType),t.sanitize&&(t.template=Yi(t.template,t.allowList,t.sanitizeFn)),t}_getDelegateConfig(){const t={};for(const e in this._config)this.constructor.Default[e]!==this._config[e]&&(t[e]=this._config[e]);return t}_cleanTipClass(){const t=this.getTipElement(),e=new RegExp(`(^|\\s)${this._getBasicClassPrefix()}\\S+`,"g"),i=t.getAttribute("class").match(e);null!==i&&i.length>0&&i.map((t=>t.trim())).forEach((e=>t.classList.remove(e)))}_getBasicClassPrefix(){return"bs-tooltip"}_handlePopperPlacementChange(t){const{state:e}=t;e&&(this.tip=e.elements.popper,this._cleanTipClass(),this._addAttachmentClass(this._getAttachment(e.placement)))}_disposePopper(){this._popper&&(this._popper.destroy(),this._popper=null)}static jQueryInterface(t){return this.each((function(){const e=un.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}g(un);const fn={...un.Default,placement:"right",offset:[0,8],trigger:"click",content:"",template:'<div class="popover" role="tooltip"><div class="popover-arrow"></div><h3 class="popover-header"></h3><div class="popover-body"></div></div>'},pn={...un.DefaultType,content:"(string|element|function)"},mn={HIDE:"hide.bs.popover",HIDDEN:"hidden.bs.popover",SHOW:"show.bs.popover",SHOWN:"shown.bs.popover",INSERTED:"inserted.bs.popover",CLICK:"click.bs.popover",FOCUSIN:"focusin.bs.popover",FOCUSOUT:"focusout.bs.popover",MOUSEENTER:"mouseenter.bs.popover",MOUSELEAVE:"mouseleave.bs.popover"};class gn extends un{static get Default(){return fn}static get NAME(){return"popover"}static get Event(){return mn}static get DefaultType(){return pn}isWithContent(){return this.getTitle()||this._getContent()}setContent(t){this._sanitizeAndSetContent(t,this.getTitle(),".popover-header"),this._sanitizeAndSetContent(t,this._getContent(),".popover-body")}_getContent(){return this._resolvePossibleFunction(this._config.content)}_getBasicClassPrefix(){return"bs-popover"}static jQueryInterface(t){return this.each((function(){const e=gn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}g(gn);const _n="scrollspy",bn={offset:10,method:"auto",target:""},vn={offset:"number",method:"string",target:"(string|element)"},yn="active",wn=".nav-link, .list-group-item, .dropdown-item",En="position";class An extends B{constructor(t,e){super(t),this._scrollElement="BODY"===this._element.tagName?window:this._element,this._config=this._getConfig(e),this._offsets=[],this._targets=[],this._activeTarget=null,this._scrollHeight=0,j.on(this._scrollElement,"scroll.bs.scrollspy",(()=>this._process())),this.refresh(),this._process()}static get Default(){return bn}static get NAME(){return _n}refresh(){const t=this._scrollElement===this._scrollElement.window?"offset":En,e="auto"===this._config.method?t:this._config.method,n=e===En?this._getScrollTop():0;this._offsets=[],this._targets=[],this._scrollHeight=this._getScrollHeight(),V.find(wn,this._config.target).map((t=>{const s=i(t),o=s?V.findOne(s):null;if(o){const t=o.getBoundingClientRect();if(t.width||t.height)return[U[e](o).top+n,s]}return null})).filter((t=>t)).sort(((t,e)=>t[0]-e[0])).forEach((t=>{this._offsets.push(t[0]),this._targets.push(t[1])}))}dispose(){j.off(this._scrollElement,".bs.scrollspy"),super.dispose()}_getConfig(t){return(t={...bn,...U.getDataAttributes(this._element),..."object"==typeof t&&t?t:{}}).target=r(t.target)||document.documentElement,a(_n,t,vn),t}_getScrollTop(){return this._scrollElement===window?this._scrollElement.pageYOffset:this._scrollElement.scrollTop}_getScrollHeight(){return this._scrollElement.scrollHeight||Math.max(document.body.scrollHeight,document.documentElement.scrollHeight)}_getOffsetHeight(){return this._scrollElement===window?window.innerHeight:this._scrollElement.getBoundingClientRect().height}_process(){const t=this._getScrollTop()+this._config.offset,e=this._getScrollHeight(),i=this._config.offset+e-this._getOffsetHeight();if(this._scrollHeight!==e&&this.refresh(),t>=i){const t=this._targets[this._targets.length-1];this._activeTarget!==t&&this._activate(t)}else{if(this._activeTarget&&t<this._offsets[0]&&this._offsets[0]>0)return this._activeTarget=null,void this._clear();for(let e=this._offsets.length;e--;)this._activeTarget!==this._targets[e]&&t>=this._offsets[e]&&(void 0===this._offsets[e+1]||t<this._offsets[e+1])&&this._activate(this._targets[e])}}_activate(t){this._activeTarget=t,this._clear();const e=wn.split(",").map((e=>`${e}[data-bs-target="${t}"],${e}[href="${t}"]`)),i=V.findOne(e.join(","),this._config.target);i.classList.add(yn),i.classList.contains("dropdown-item")?V.findOne(".dropdown-toggle",i.closest(".dropdown")).classList.add(yn):V.parents(i,".nav, .list-group").forEach((t=>{V.prev(t,".nav-link, .list-group-item").forEach((t=>t.classList.add(yn))),V.prev(t,".nav-item").forEach((t=>{V.children(t,".nav-link").forEach((t=>t.classList.add(yn)))}))})),j.trigger(this._scrollElement,"activate.bs.scrollspy",{relatedTarget:t})}_clear(){V.find(wn,this._config.target).filter((t=>t.classList.contains(yn))).forEach((t=>t.classList.remove(yn)))}static jQueryInterface(t){return this.each((function(){const e=An.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}j.on(window,"load.bs.scrollspy.data-api",(()=>{V.find('[data-bs-spy="scroll"]').forEach((t=>new An(t)))})),g(An);const Tn="active",On="fade",Cn="show",kn=".active",Ln=":scope > li > .active";class xn extends B{static get NAME(){return"tab"}show(){if(this._element.parentNode&&this._element.parentNode.nodeType===Node.ELEMENT_NODE&&this._element.classList.contains(Tn))return;let t;const e=n(this._element),i=this._element.closest(".nav, .list-group");if(i){const e="UL"===i.nodeName||"OL"===i.nodeName?Ln:kn;t=V.find(e,i),t=t[t.length-1]}const s=t?j.trigger(t,"hide.bs.tab",{relatedTarget:this._element}):null;if(j.trigger(this._element,"show.bs.tab",{relatedTarget:t}).defaultPrevented||null!==s&&s.defaultPrevented)return;this._activate(this._element,i);const o=()=>{j.trigger(t,"hidden.bs.tab",{relatedTarget:this._element}),j.trigger(this._element,"shown.bs.tab",{relatedTarget:t})};e?this._activate(e,e.parentNode,o):o()}_activate(t,e,i){const n=(!e||"UL"!==e.nodeName&&"OL"!==e.nodeName?V.children(e,kn):V.find(Ln,e))[0],s=i&&n&&n.classList.contains(On),o=()=>this._transitionComplete(t,n,i);n&&s?(n.classList.remove(Cn),this._queueCallback(o,t,!0)):o()}_transitionComplete(t,e,i){if(e){e.classList.remove(Tn);const t=V.findOne(":scope > .dropdown-menu .active",e.parentNode);t&&t.classList.remove(Tn),"tab"===e.getAttribute("role")&&e.setAttribute("aria-selected",!1)}t.classList.add(Tn),"tab"===t.getAttribute("role")&&t.setAttribute("aria-selected",!0),u(t),t.classList.contains(On)&&t.classList.add(Cn);let n=t.parentNode;if(n&&"LI"===n.nodeName&&(n=n.parentNode),n&&n.classList.contains("dropdown-menu")){const e=t.closest(".dropdown");e&&V.find(".dropdown-toggle",e).forEach((t=>t.classList.add(Tn))),t.setAttribute("aria-expanded",!0)}i&&i()}static jQueryInterface(t){return this.each((function(){const e=xn.getOrCreateInstance(this);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}j.on(document,"click.bs.tab.data-api",'[data-bs-toggle="tab"], [data-bs-toggle="pill"], [data-bs-toggle="list"]',(function(t){["A","AREA"].includes(this.tagName)&&t.preventDefault(),c(this)||xn.getOrCreateInstance(this).show()})),g(xn);const Dn="toast",Sn="hide",Nn="show",In="showing",Pn={animation:"boolean",autohide:"boolean",delay:"number"},jn={animation:!0,autohide:!0,delay:5e3};class Mn extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._timeout=null,this._hasMouseInteraction=!1,this._hasKeyboardInteraction=!1,this._setListeners()}static get DefaultType(){return Pn}static get Default(){return jn}static get NAME(){return Dn}show(){j.trigger(this._element,"show.bs.toast").defaultPrevented||(this._clearTimeout(),this._config.animation&&this._element.classList.add("fade"),this._element.classList.remove(Sn),u(this._element),this._element.classList.add(Nn),this._element.classList.add(In),this._queueCallback((()=>{this._element.classList.remove(In),j.trigger(this._element,"shown.bs.toast"),this._maybeScheduleHide()}),this._element,this._config.animation))}hide(){this._element.classList.contains(Nn)&&(j.trigger(this._element,"hide.bs.toast").defaultPrevented||(this._element.classList.add(In),this._queueCallback((()=>{this._element.classList.add(Sn),this._element.classList.remove(In),this._element.classList.remove(Nn),j.trigger(this._element,"hidden.bs.toast")}),this._element,this._config.animation)))}dispose(){this._clearTimeout(),this._element.classList.contains(Nn)&&this._element.classList.remove(Nn),super.dispose()}_getConfig(t){return t={...jn,...U.getDataAttributes(this._element),..."object"==typeof 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e=t.state,i=t.name,n=e.rects.reference,s=e.rects.popper,o=e.modifiersData.preventOverflow,r=ii(e,{elementContext:"reference"}),a=ii(e,{altBoundary:!0}),l=oi(r,n),c=oi(a,s,o),h=ri(l),d=ri(c);e.modifiersData[i]={referenceClippingOffsets:l,popperEscapeOffsets:c,isReferenceHidden:h,hasPopperEscaped:d},e.attributes.popper=Object.assign({},e.attributes.popper,{"data-popper-reference-hidden":h,"data-popper-escaped":d})}},li={name:"offset",enabled:!0,phase:"main",requires:["popperOffsets"],fn:function(t){var e=t.state,i=t.options,n=t.name,s=i.offset,o=void 0===s?[0,0]:s,r=ee.reduce((function(t,i){return t[i]=function(t,e,i){var n=be(t),s=[Vt,zt].indexOf(n)>=0?-1:1,o="function"==typeof i?i(Object.assign({},e,{placement:t})):i,r=o[0],a=o[1];return r=r||0,a=(a||0)*s,[Vt,qt].indexOf(n)>=0?{x:a,y:r}:{x:r,y:a}}(i,e.rects,o),t}),{}),a=r[e.placement],l=a.x,c=a.y;null!=e.modifiersData.popperOffsets&&(e.modifiersData.popperOffsets.x+=l,e.modifiersData.popperOffsets.y+=c),e.modifiersData[n]=r}},ci={name:"popperOffsets",enabled:!0,phase:"read",fn:function(t){var e=t.state,i=t.name;e.modifiersData[i]=ei({reference:e.rects.reference,element:e.rects.popper,strategy:"absolute",placement:e.placement})},data:{}},hi={name:"preventOverflow",enabled:!0,phase:"main",fn:function(t){var e=t.state,i=t.options,n=t.name,s=i.mainAxis,o=void 0===s||s,r=i.altAxis,a=void 0!==r&&r,l=i.boundary,c=i.rootBoundary,h=i.altBoundary,d=i.padding,u=i.tether,f=void 0===u||u,p=i.tetherOffset,m=void 0===p?0:p,g=ii(e,{boundary:l,rootBoundary:c,padding:d,altBoundary:h}),_=be(e.placement),b=Fe(e.placement),v=!b,y=Ie(_),w="x"===y?"y":"x",A=e.modifiersData.popperOffsets,E=e.rects.reference,T=e.rects.popper,C="function"==typeof m?m(Object.assign({},e.rects,{placement:e.placement})):m,O="number"==typeof C?{mainAxis:C,altAxis:C}:Object.assign({mainAxis:0,altAxis:0},C),x=e.modifiersData.offset?e.modifiersData.offset[e.placement]:null,k={x:0,y:0};if(A){if(o){var L,S="y"===y?zt:Vt,D="y"===y?Rt:qt,$="y"===y?"height":"width",I=A[y],N=I+g[S],P=I-g[D],M=f?-T[$]/2:0,j=b===Xt?E[$]:T[$],F=b===Xt?-T[$]:-E[$],H=e.elements.arrow,W=f&&H?Ce(H):{width:0,height:0},B=e.modifiersData["arrow#persistent"]?e.modifiersData["arrow#persistent"].padding:{top:0,right:0,bottom:0,left:0},z=B[S],R=B[D],q=Ne(0,E[$],W[$]),V=v?E[$]/2-M-q-z-O.mainAxis:j-q-z-O.mainAxis,K=v?-E[$]/2+M+q+R+O.mainAxis:F+q+R+O.mainAxis,Q=e.elements.arrow&&$e(e.elements.arrow),X=Q?"y"===y?Q.clientTop||0:Q.clientLeft||0:0,Y=null!=(L=null==x?void 0:x[y])?L:0,U=I+K-Y,G=Ne(f?ye(N,I+V-Y-X):N,I,f?ve(P,U):P);A[y]=G,k[y]=G-I}if(a){var J,Z="x"===y?zt:Vt,tt="x"===y?Rt:qt,et=A[w],it="y"===w?"height":"width",nt=et+g[Z],st=et-g[tt],ot=-1!==[zt,Vt].indexOf(_),rt=null!=(J=null==x?void 0:x[w])?J:0,at=ot?nt:et-E[it]-T[it]-rt+O.altAxis,lt=ot?et+E[it]+T[it]-rt-O.altAxis:st,ct=f&&ot?function(t,e,i){var n=Ne(t,e,i);return n>i?i:n}(at,et,lt):Ne(f?at:nt,et,f?lt:st);A[w]=ct,k[w]=ct-et}e.modifiersData[n]=k}},requiresIfExists:["offset"]};function di(t,e,i){void 0===i&&(i=!1);var n,s,o=me(e),r=me(e)&&function(t){var e=t.getBoundingClientRect(),i=we(e.width)/t.offsetWidth||1,n=we(e.height)/t.offsetHeight||1;return 1!==i||1!==n}(e),a=Le(e),l=Te(t,r,i),c={scrollLeft:0,scrollTop:0},h={x:0,y:0};return(o||!o&&!i)&&(("body"!==ue(e)||Ue(a))&&(c=(n=e)!==fe(n)&&me(n)?{scrollLeft:(s=n).scrollLeft,scrollTop:s.scrollTop}:Xe(n)),me(e)?((h=Te(e,!0)).x+=e.clientLeft,h.y+=e.clientTop):a&&(h.x=Ye(a))),{x:l.left+c.scrollLeft-h.x,y:l.top+c.scrollTop-h.y,width:l.width,height:l.height}}function ui(t){var e=new Map,i=new Set,n=[];function s(t){i.add(t.name),[].concat(t.requires||[],t.requiresIfExists||[]).forEach((function(t){if(!i.has(t)){var n=e.get(t);n&&s(n)}})),n.push(t)}return t.forEach((function(t){e.set(t.name,t)})),t.forEach((function(t){i.has(t.name)||s(t)})),n}var fi={placement:"bottom",modifiers:[],strategy:"absolute"};function pi(){for(var t=arguments.length,e=new Array(t),i=0;i<t;i++)e[i]=arguments[i];return!e.some((function(t){return!(t&&"function"==typeof t.getBoundingClientRect)}))}function mi(t){void 0===t&&(t={});var e=t,i=e.defaultModifiers,n=void 0===i?[]:i,s=e.defaultOptions,o=void 0===s?fi:s;return function(t,e,i){void 0===i&&(i=o);var s,r,a={placement:"bottom",orderedModifiers:[],options:Object.assign({},fi,o),modifiersData:{},elements:{reference:t,popper:e},attributes:{},styles:{}},l=[],c=!1,h={state:a,setOptions:function(i){var s="function"==typeof i?i(a.options):i;d(),a.options=Object.assign({},o,a.options,s),a.scrollParents={reference:pe(t)?Je(t):t.contextElement?Je(t.contextElement):[],popper:Je(e)};var r,c,u=function(t){var e=ui(t);return de.reduce((function(t,i){return t.concat(e.filter((function(t){return t.phase===i})))}),[])}((r=[].concat(n,a.options.modifiers),c=r.reduce((function(t,e){var i=t[e.name];return t[e.name]=i?Object.assign({},i,e,{options:Object.assign({},i.options,e.options),data:Object.assign({},i.data,e.data)}):e,t}),{}),Object.keys(c).map((function(t){return c[t]}))));return a.orderedModifiers=u.filter((function(t){return t.enabled})),a.orderedModifiers.forEach((function(t){var e=t.name,i=t.options,n=void 0===i?{}:i,s=t.effect;if("function"==typeof s){var o=s({state:a,name:e,instance:h,options:n});l.push(o||function(){})}})),h.update()},forceUpdate:function(){if(!c){var t=a.elements,e=t.reference,i=t.popper;if(pi(e,i)){a.rects={reference:di(e,$e(i),"fixed"===a.options.strategy),popper:Ce(i)},a.reset=!1,a.placement=a.options.placement,a.orderedModifiers.forEach((function(t){return a.modifiersData[t.name]=Object.assign({},t.data)}));for(var n=0;n<a.orderedModifiers.length;n++)if(!0!==a.reset){var s=a.orderedModifiers[n],o=s.fn,r=s.options,l=void 0===r?{}:r,d=s.name;"function"==typeof o&&(a=o({state:a,options:l,name:d,instance:h})||a)}else a.reset=!1,n=-1}}},update:(s=function(){return new Promise((function(t){h.forceUpdate(),t(a)}))},function(){return r||(r=new Promise((function(t){Promise.resolve().then((function(){r=void 0,t(s())}))}))),r}),destroy:function(){d(),c=!0}};if(!pi(t,e))return h;function d(){l.forEach((function(t){return t()})),l=[]}return h.setOptions(i).then((function(t){!c&&i.onFirstUpdate&&i.onFirstUpdate(t)})),h}}var gi=mi(),_i=mi({defaultModifiers:[Re,ci,Be,_e]}),bi=mi({defaultModifiers:[Re,ci,Be,_e,li,si,hi,je,ai]});const vi=Object.freeze(Object.defineProperty({__proto__:null,afterMain:ae,afterRead:se,afterWrite:he,applyStyles:_e,arrow:je,auto:Kt,basePlacements:Qt,beforeMain:oe,beforeRead:ie,beforeWrite:le,bottom:Rt,clippingParents:Ut,computeStyles:Be,createPopper:bi,createPopperBase:gi,createPopperLite:_i,detectOverflow:ii,end:Yt,eventListeners:Re,flip:si,hide:ai,left:Vt,main:re,modifierPhases:de,offset:li,placements:ee,popper:Jt,popperGenerator:mi,popperOffsets:ci,preventOverflow:hi,read:ne,reference:Zt,right:qt,start:Xt,top:zt,variationPlacements:te,viewport:Gt,write:ce},Symbol.toStringTag,{value:"Module"})),yi="dropdown",wi=".bs.dropdown",Ai=".data-api",Ei="ArrowUp",Ti="ArrowDown",Ci=`hide${wi}`,Oi=`hidden${wi}`,xi=`show${wi}`,ki=`shown${wi}`,Li=`click${wi}${Ai}`,Si=`keydown${wi}${Ai}`,Di=`keyup${wi}${Ai}`,$i="show",Ii='[data-bs-toggle="dropdown"]:not(.disabled):not(:disabled)',Ni=`${Ii}.${$i}`,Pi=".dropdown-menu",Mi=p()?"top-end":"top-start",ji=p()?"top-start":"top-end",Fi=p()?"bottom-end":"bottom-start",Hi=p()?"bottom-start":"bottom-end",Wi=p()?"left-start":"right-start",Bi=p()?"right-start":"left-start",zi={autoClose:!0,boundary:"clippingParents",display:"dynamic",offset:[0,2],popperConfig:null,reference:"toggle"},Ri={autoClose:"(boolean|string)",boundary:"(string|element)",display:"string",offset:"(array|string|function)",popperConfig:"(null|object|function)",reference:"(string|element|object)"};class qi extends W{constructor(t,e){super(t,e),this._popper=null,this._parent=this._element.parentNode,this._menu=z.next(this._element,Pi)[0]||z.prev(this._element,Pi)[0]||z.findOne(Pi,this._parent),this._inNavbar=this._detectNavbar()}static get Default(){return zi}static get DefaultType(){return Ri}static get NAME(){return yi}toggle(){return this._isShown()?this.hide():this.show()}show(){if(l(this._element)||this._isShown())return;const t={relatedTarget:this._element};if(!N.trigger(this._element,xi,t).defaultPrevented){if(this._createPopper(),"ontouchstart"in document.documentElement&&!this._parent.closest(".navbar-nav"))for(const t of[].concat(...document.body.children))N.on(t,"mouseover",h);this._element.focus(),this._element.setAttribute("aria-expanded",!0),this._menu.classList.add($i),this._element.classList.add($i),N.trigger(this._element,ki,t)}}hide(){if(l(this._element)||!this._isShown())return;const t={relatedTarget:this._element};this._completeHide(t)}dispose(){this._popper&&this._popper.destroy(),super.dispose()}update(){this._inNavbar=this._detectNavbar(),this._popper&&this._popper.update()}_completeHide(t){if(!N.trigger(this._element,Ci,t).defaultPrevented){if("ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.off(t,"mouseover",h);this._popper&&this._popper.destroy(),this._menu.classList.remove($i),this._element.classList.remove($i),this._element.setAttribute("aria-expanded","false"),F.removeDataAttribute(this._menu,"popper"),N.trigger(this._element,Oi,t)}}_getConfig(t){if("object"==typeof(t=super._getConfig(t)).reference&&!o(t.reference)&&"function"!=typeof t.reference.getBoundingClientRect)throw new TypeError(`${yi.toUpperCase()}: Option "reference" provided type "object" without a required "getBoundingClientRect" method.`);return t}_createPopper(){if(void 0===vi)throw new TypeError("Bootstrap's dropdowns require Popper (https://popper.js.org)");let t=this._element;"parent"===this._config.reference?t=this._parent:o(this._config.reference)?t=r(this._config.reference):"object"==typeof this._config.reference&&(t=this._config.reference);const e=this._getPopperConfig();this._popper=bi(t,this._menu,e)}_isShown(){return this._menu.classList.contains($i)}_getPlacement(){const t=this._parent;if(t.classList.contains("dropend"))return Wi;if(t.classList.contains("dropstart"))return Bi;if(t.classList.contains("dropup-center"))return"top";if(t.classList.contains("dropdown-center"))return"bottom";const e="end"===getComputedStyle(this._menu).getPropertyValue("--bs-position").trim();return t.classList.contains("dropup")?e?ji:Mi:e?Hi:Fi}_detectNavbar(){return null!==this._element.closest(".navbar")}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return(this._inNavbar||"static"===this._config.display)&&(F.setDataAttribute(this._menu,"popper","static"),t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,...g(this._config.popperConfig,[t])}}_selectMenuItem({key:t,target:e}){const i=z.find(".dropdown-menu .dropdown-item:not(.disabled):not(:disabled)",this._menu).filter((t=>a(t)));i.length&&b(i,e,t===Ti,!i.includes(e)).focus()}static jQueryInterface(t){return this.each((function(){const e=qi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}static clearMenus(t){if(2===t.button||"keyup"===t.type&&"Tab"!==t.key)return;const e=z.find(Ni);for(const i of e){const e=qi.getInstance(i);if(!e||!1===e._config.autoClose)continue;const n=t.composedPath(),s=n.includes(e._menu);if(n.includes(e._element)||"inside"===e._config.autoClose&&!s||"outside"===e._config.autoClose&&s)continue;if(e._menu.contains(t.target)&&("keyup"===t.type&&"Tab"===t.key||/input|select|option|textarea|form/i.test(t.target.tagName)))continue;const o={relatedTarget:e._element};"click"===t.type&&(o.clickEvent=t),e._completeHide(o)}}static dataApiKeydownHandler(t){const e=/input|textarea/i.test(t.target.tagName),i="Escape"===t.key,n=[Ei,Ti].includes(t.key);if(!n&&!i)return;if(e&&!i)return;t.preventDefault();const s=this.matches(Ii)?this:z.prev(this,Ii)[0]||z.next(this,Ii)[0]||z.findOne(Ii,t.delegateTarget.parentNode),o=qi.getOrCreateInstance(s);if(n)return t.stopPropagation(),o.show(),void o._selectMenuItem(t);o._isShown()&&(t.stopPropagation(),o.hide(),s.focus())}}N.on(document,Si,Ii,qi.dataApiKeydownHandler),N.on(document,Si,Pi,qi.dataApiKeydownHandler),N.on(document,Li,qi.clearMenus),N.on(document,Di,qi.clearMenus),N.on(document,Li,Ii,(function(t){t.preventDefault(),qi.getOrCreateInstance(this).toggle()})),m(qi);const Vi="backdrop",Ki="show",Qi=`mousedown.bs.${Vi}`,Xi={className:"modal-backdrop",clickCallback:null,isAnimated:!1,isVisible:!0,rootElement:"body"},Yi={className:"string",clickCallback:"(function|null)",isAnimated:"boolean",isVisible:"boolean",rootElement:"(element|string)"};class Ui extends H{constructor(t){super(),this._config=this._getConfig(t),this._isAppended=!1,this._element=null}static get Default(){return Xi}static get DefaultType(){return Yi}static get NAME(){return Vi}show(t){if(!this._config.isVisible)return void g(t);this._append();const e=this._getElement();this._config.isAnimated&&d(e),e.classList.add(Ki),this._emulateAnimation((()=>{g(t)}))}hide(t){this._config.isVisible?(this._getElement().classList.remove(Ki),this._emulateAnimation((()=>{this.dispose(),g(t)}))):g(t)}dispose(){this._isAppended&&(N.off(this._element,Qi),this._element.remove(),this._isAppended=!1)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_configAfterMerge(t){return t.rootElement=r(t.rootElement),t}_append(){if(this._isAppended)return;const t=this._getElement();this._config.rootElement.append(t),N.on(t,Qi,(()=>{g(this._config.clickCallback)})),this._isAppended=!0}_emulateAnimation(t){_(t,this._getElement(),this._config.isAnimated)}}const Gi=".bs.focustrap",Ji=`focusin${Gi}`,Zi=`keydown.tab${Gi}`,tn="backward",en={autofocus:!0,trapElement:null},nn={autofocus:"boolean",trapElement:"element"};class sn extends H{constructor(t){super(),this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}static get Default(){return en}static get DefaultType(){return nn}static get NAME(){return"focustrap"}activate(){this._isActive||(this._config.autofocus&&this._config.trapElement.focus(),N.off(document,Gi),N.on(document,Ji,(t=>this._handleFocusin(t))),N.on(document,Zi,(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,N.off(document,Gi))}_handleFocusin(t){const{trapElement:e}=this._config;if(t.target===document||t.target===e||e.contains(t.target))return;const i=z.focusableChildren(e);0===i.length?e.focus():this._lastTabNavDirection===tn?i[i.length-1].focus():i[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?tn:"forward")}}const on=".fixed-top, .fixed-bottom, .is-fixed, .sticky-top",rn=".sticky-top",an="padding-right",ln="margin-right";class cn{constructor(){this._element=document.body}getWidth(){const t=document.documentElement.clientWidth;return Math.abs(window.innerWidth-t)}hide(){const t=this.getWidth();this._disableOverFlow(),this._setElementAttributes(this._element,an,(e=>e+t)),this._setElementAttributes(on,an,(e=>e+t)),this._setElementAttributes(rn,ln,(e=>e-t))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,an),this._resetElementAttributes(on,an),this._resetElementAttributes(rn,ln)}isOverflowing(){return this.getWidth()>0}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t).getPropertyValue(e);t.style.setProperty(e,`${i(Number.parseFloat(s))}px`)}))}_saveInitialAttribute(t,e){const i=t.style.getPropertyValue(e);i&&F.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=F.getDataAttribute(t,e);null!==i?(F.removeDataAttribute(t,e),t.style.setProperty(e,i)):t.style.removeProperty(e)}))}_applyManipulationCallback(t,e){if(o(t))e(t);else for(const i of z.find(t,this._element))e(i)}}const hn=".bs.modal",dn=`hide${hn}`,un=`hidePrevented${hn}`,fn=`hidden${hn}`,pn=`show${hn}`,mn=`shown${hn}`,gn=`resize${hn}`,_n=`click.dismiss${hn}`,bn=`mousedown.dismiss${hn}`,vn=`keydown.dismiss${hn}`,yn=`click${hn}.data-api`,wn="modal-open",An="show",En="modal-static",Tn={backdrop:!0,focus:!0,keyboard:!0},Cn={backdrop:"(boolean|string)",focus:"boolean",keyboard:"boolean"};class On extends W{constructor(t,e){super(t,e),this._dialog=z.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._isTransitioning=!1,this._scrollBar=new cn,this._addEventListeners()}static get Default(){return Tn}static get DefaultType(){return Cn}static get NAME(){return"modal"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||N.trigger(this._element,pn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isTransitioning=!0,this._scrollBar.hide(),document.body.classList.add(wn),this._adjustDialog(),this._backdrop.show((()=>this._showElement(t))))}hide(){this._isShown&&!this._isTransitioning&&(N.trigger(this._element,dn).defaultPrevented||(this._isShown=!1,this._isTransitioning=!0,this._focustrap.deactivate(),this._element.classList.remove(An),this._queueCallback((()=>this._hideModal()),this._element,this._isAnimated())))}dispose(){N.off(window,hn),N.off(this._dialog,hn),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new Ui({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_showElement(t){document.body.contains(this._element)||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0;const e=z.findOne(".modal-body",this._dialog);e&&(e.scrollTop=0),d(this._element),this._element.classList.add(An),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,N.trigger(this._element,mn,{relatedTarget:t})}),this._dialog,this._isAnimated())}_addEventListeners(){N.on(this._element,vn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():this._triggerBackdropTransition())})),N.on(window,gn,(()=>{this._isShown&&!this._isTransitioning&&this._adjustDialog()})),N.on(this._element,bn,(t=>{N.one(this._element,_n,(e=>{this._element===t.target&&this._element===e.target&&("static"!==this._config.backdrop?this._config.backdrop&&this.hide():this._triggerBackdropTransition())}))}))}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(wn),this._resetAdjustments(),this._scrollBar.reset(),N.trigger(this._element,fn)}))}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(N.trigger(this._element,un).defaultPrevented)return;const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._element.style.overflowY;"hidden"===e||this._element.classList.contains(En)||(t||(this._element.style.overflowY="hidden"),this._element.classList.add(En),this._queueCallback((()=>{this._element.classList.remove(En),this._queueCallback((()=>{this._element.style.overflowY=e}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;if(i&&!t){const t=p()?"paddingLeft":"paddingRight";this._element.style[t]=`${e}px`}if(!i&&t){const t=p()?"paddingRight":"paddingLeft";this._element.style[t]=`${e}px`}}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=On.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}N.on(document,yn,'[data-bs-toggle="modal"]',(function(t){const e=z.getElementFromSelector(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),N.one(e,pn,(t=>{t.defaultPrevented||N.one(e,fn,(()=>{a(this)&&this.focus()}))}));const i=z.findOne(".modal.show");i&&On.getInstance(i).hide(),On.getOrCreateInstance(e).toggle(this)})),R(On),m(On);const xn=".bs.offcanvas",kn=".data-api",Ln=`load${xn}${kn}`,Sn="show",Dn="showing",$n="hiding",In=".offcanvas.show",Nn=`show${xn}`,Pn=`shown${xn}`,Mn=`hide${xn}`,jn=`hidePrevented${xn}`,Fn=`hidden${xn}`,Hn=`resize${xn}`,Wn=`click${xn}${kn}`,Bn=`keydown.dismiss${xn}`,zn={backdrop:!0,keyboard:!0,scroll:!1},Rn={backdrop:"(boolean|string)",keyboard:"boolean",scroll:"boolean"};class qn extends W{constructor(t,e){super(t,e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get Default(){return zn}static get DefaultType(){return Rn}static get NAME(){return"offcanvas"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||N.trigger(this._element,Nn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._backdrop.show(),this._config.scroll||(new cn).hide(),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add(Dn),this._queueCallback((()=>{this._config.scroll&&!this._config.backdrop||this._focustrap.activate(),this._element.classList.add(Sn),this._element.classList.remove(Dn),N.trigger(this._element,Pn,{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(N.trigger(this._element,Mn).defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.add($n),this._backdrop.hide(),this._queueCallback((()=>{this._element.classList.remove(Sn,$n),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._config.scroll||(new cn).reset(),N.trigger(this._element,Fn)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_initializeBackDrop(){const t=Boolean(this._config.backdrop);return new Ui({className:"offcanvas-backdrop",isVisible:t,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:t?()=>{"static"!==this._config.backdrop?this.hide():N.trigger(this._element,jn)}:null})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_addEventListeners(){N.on(this._element,Bn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():N.trigger(this._element,jn))}))}static jQueryInterface(t){return this.each((function(){const e=qn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}N.on(document,Wn,'[data-bs-toggle="offcanvas"]',(function(t){const e=z.getElementFromSelector(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),l(this))return;N.one(e,Fn,(()=>{a(this)&&this.focus()}));const i=z.findOne(In);i&&i!==e&&qn.getInstance(i).hide(),qn.getOrCreateInstance(e).toggle(this)})),N.on(window,Ln,(()=>{for(const t of z.find(In))qn.getOrCreateInstance(t).show()})),N.on(window,Hn,(()=>{for(const t of z.find("[aria-modal][class*=show][class*=offcanvas-]"))"fixed"!==getComputedStyle(t).position&&qn.getOrCreateInstance(t).hide()})),R(qn),m(qn);const Vn={"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},Kn=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Qn=/^(?!javascript:)(?:[a-z0-9+.-]+:|[^&:/?#]*(?:[/?#]|$))/i,Xn=(t,e)=>{const i=t.nodeName.toLowerCase();return e.includes(i)?!Kn.has(i)||Boolean(Qn.test(t.nodeValue)):e.filter((t=>t instanceof RegExp)).some((t=>t.test(i)))},Yn={allowList:Vn,content:{},extraClass:"",html:!1,sanitize:!0,sanitizeFn:null,template:"<div></div>"},Un={allowList:"object",content:"object",extraClass:"(string|function)",html:"boolean",sanitize:"boolean",sanitizeFn:"(null|function)",template:"string"},Gn={entry:"(string|element|function|null)",selector:"(string|element)"};class Jn extends H{constructor(t){super(),this._config=this._getConfig(t)}static get Default(){return Yn}static get DefaultType(){return Un}static get NAME(){return"TemplateFactory"}getContent(){return Object.values(this._config.content).map((t=>this._resolvePossibleFunction(t))).filter(Boolean)}hasContent(){return this.getContent().length>0}changeContent(t){return this._checkContent(t),this._config.content={...this._config.content,...t},this}toHtml(){const t=document.createElement("div");t.innerHTML=this._maybeSanitize(this._config.template);for(const[e,i]of Object.entries(this._config.content))this._setContent(t,i,e);const e=t.children[0],i=this._resolvePossibleFunction(this._config.extraClass);return i&&e.classList.add(...i.split(" ")),e}_typeCheckConfig(t){super._typeCheckConfig(t),this._checkContent(t.content)}_checkContent(t){for(const[e,i]of Object.entries(t))super._typeCheckConfig({selector:e,entry:i},Gn)}_setContent(t,e,i){const n=z.findOne(i,t);n&&((e=this._resolvePossibleFunction(e))?o(e)?this._putElementInTemplate(r(e),n):this._config.html?n.innerHTML=this._maybeSanitize(e):n.textContent=e:n.remove())}_maybeSanitize(t){return this._config.sanitize?function(t,e,i){if(!t.length)return t;if(i&&"function"==typeof i)return i(t);const n=(new window.DOMParser).parseFromString(t,"text/html"),s=[].concat(...n.body.querySelectorAll("*"));for(const t of s){const i=t.nodeName.toLowerCase();if(!Object.keys(e).includes(i)){t.remove();continue}const n=[].concat(...t.attributes),s=[].concat(e["*"]||[],e[i]||[]);for(const e of n)Xn(e,s)||t.removeAttribute(e.nodeName)}return n.body.innerHTML}(t,this._config.allowList,this._config.sanitizeFn):t}_resolvePossibleFunction(t){return g(t,[this])}_putElementInTemplate(t,e){if(this._config.html)return e.innerHTML="",void e.append(t);e.textContent=t.textContent}}const Zn=new Set(["sanitize","allowList","sanitizeFn"]),ts="fade",es="show",is=".modal",ns="hide.bs.modal",ss="hover",os="focus",rs={AUTO:"auto",TOP:"top",RIGHT:p()?"left":"right",BOTTOM:"bottom",LEFT:p()?"right":"left"},as={allowList:Vn,animation:!0,boundary:"clippingParents",container:!1,customClass:"",delay:0,fallbackPlacements:["top","right","bottom","left"],html:!1,offset:[0,6],placement:"top",popperConfig:null,sanitize:!0,sanitizeFn:null,selector:!1,template:'<div class="tooltip" role="tooltip"><div class="tooltip-arrow"></div><div class="tooltip-inner"></div></div>',title:"",trigger:"hover focus"},ls={allowList:"object",animation:"boolean",boundary:"(string|element)",container:"(string|element|boolean)",customClass:"(string|function)",delay:"(number|object)",fallbackPlacements:"array",html:"boolean",offset:"(array|string|function)",placement:"(string|function)",popperConfig:"(null|object|function)",sanitize:"boolean",sanitizeFn:"(null|function)",selector:"(string|boolean)",template:"string",title:"(string|element|function)",trigger:"string"};class cs extends W{constructor(t,e){if(void 0===vi)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t,e),this._isEnabled=!0,this._timeout=0,this._isHovered=null,this._activeTrigger={},this._popper=null,this._templateFactory=null,this._newContent=null,this.tip=null,this._setListeners(),this._config.selector||this._fixTitle()}static get Default(){return as}static get DefaultType(){return ls}static get NAME(){return"tooltip"}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(){this._isEnabled&&(this._activeTrigger.click=!this._activeTrigger.click,this._isShown()?this._leave():this._enter())}dispose(){clearTimeout(this._timeout),N.off(this._element.closest(is),ns,this._hideModalHandler),this._element.getAttribute("data-bs-original-title")&&this._element.setAttribute("title",this._element.getAttribute("data-bs-original-title")),this._disposePopper(),super.dispose()}show(){if("none"===this._element.style.display)throw new Error("Please use show on visible elements");if(!this._isWithContent()||!this._isEnabled)return;const t=N.trigger(this._element,this.constructor.eventName("show")),e=(c(this._element)||this._element.ownerDocument.documentElement).contains(this._element);if(t.defaultPrevented||!e)return;this._disposePopper();const i=this._getTipElement();this._element.setAttribute("aria-describedby",i.getAttribute("id"));const{container:n}=this._config;if(this._element.ownerDocument.documentElement.contains(this.tip)||(n.append(i),N.trigger(this._element,this.constructor.eventName("inserted"))),this._popper=this._createPopper(i),i.classList.add(es),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.on(t,"mouseover",h);this._queueCallback((()=>{N.trigger(this._element,this.constructor.eventName("shown")),!1===this._isHovered&&this._leave(),this._isHovered=!1}),this.tip,this._isAnimated())}hide(){if(this._isShown()&&!N.trigger(this._element,this.constructor.eventName("hide")).defaultPrevented){if(this._getTipElement().classList.remove(es),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.off(t,"mouseover",h);this._activeTrigger.click=!1,this._activeTrigger[os]=!1,this._activeTrigger[ss]=!1,this._isHovered=null,this._queueCallback((()=>{this._isWithActiveTrigger()||(this._isHovered||this._disposePopper(),this._element.removeAttribute("aria-describedby"),N.trigger(this._element,this.constructor.eventName("hidden")))}),this.tip,this._isAnimated())}}update(){this._popper&&this._popper.update()}_isWithContent(){return Boolean(this._getTitle())}_getTipElement(){return this.tip||(this.tip=this._createTipElement(this._newContent||this._getContentForTemplate())),this.tip}_createTipElement(t){const e=this._getTemplateFactory(t).toHtml();if(!e)return null;e.classList.remove(ts,es),e.classList.add(`bs-${this.constructor.NAME}-auto`);const i=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME).toString();return e.setAttribute("id",i),this._isAnimated()&&e.classList.add(ts),e}setContent(t){this._newContent=t,this._isShown()&&(this._disposePopper(),this.show())}_getTemplateFactory(t){return this._templateFactory?this._templateFactory.changeContent(t):this._templateFactory=new Jn({...this._config,content:t,extraClass:this._resolvePossibleFunction(this._config.customClass)}),this._templateFactory}_getContentForTemplate(){return{".tooltip-inner":this._getTitle()}}_getTitle(){return this._resolvePossibleFunction(this._config.title)||this._element.getAttribute("data-bs-original-title")}_initializeOnDelegatedTarget(t){return this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_isAnimated(){return this._config.animation||this.tip&&this.tip.classList.contains(ts)}_isShown(){return this.tip&&this.tip.classList.contains(es)}_createPopper(t){const e=g(this._config.placement,[this,t,this._element]),i=rs[e.toUpperCase()];return bi(this._element,t,this._getPopperConfig(i))}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_resolvePossibleFunction(t){return g(t,[this._element])}_getPopperConfig(t){const e={placement:t,modifiers:[{name:"flip",options:{fallbackPlacements:this._config.fallbackPlacements}},{name:"offset",options:{offset:this._getOffset()}},{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"arrow",options:{element:`.${this.constructor.NAME}-arrow`}},{name:"preSetPlacement",enabled:!0,phase:"beforeMain",fn:t=>{this._getTipElement().setAttribute("data-popper-placement",t.state.placement)}}]};return{...e,...g(this._config.popperConfig,[e])}}_setListeners(){const t=this._config.trigger.split(" ");for(const e of t)if("click"===e)N.on(this._element,this.constructor.eventName("click"),this._config.selector,(t=>{this._initializeOnDelegatedTarget(t).toggle()}));else if("manual"!==e){const t=e===ss?this.constructor.eventName("mouseenter"):this.constructor.eventName("focusin"),i=e===ss?this.constructor.eventName("mouseleave"):this.constructor.eventName("focusout");N.on(this._element,t,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusin"===t.type?os:ss]=!0,e._enter()})),N.on(this._element,i,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusout"===t.type?os:ss]=e._element.contains(t.relatedTarget),e._leave()}))}this._hideModalHandler=()=>{this._element&&this.hide()},N.on(this._element.closest(is),ns,this._hideModalHandler)}_fixTitle(){const t=this._element.getAttribute("title");t&&(this._element.getAttribute("aria-label")||this._element.textContent.trim()||this._element.setAttribute("aria-label",t),this._element.setAttribute("data-bs-original-title",t),this._element.removeAttribute("title"))}_enter(){this._isShown()||this._isHovered?this._isHovered=!0:(this._isHovered=!0,this._setTimeout((()=>{this._isHovered&&this.show()}),this._config.delay.show))}_leave(){this._isWithActiveTrigger()||(this._isHovered=!1,this._setTimeout((()=>{this._isHovered||this.hide()}),this._config.delay.hide))}_setTimeout(t,e){clearTimeout(this._timeout),this._timeout=setTimeout(t,e)}_isWithActiveTrigger(){return Object.values(this._activeTrigger).includes(!0)}_getConfig(t){const e=F.getDataAttributes(this._element);for(const t of Object.keys(e))Zn.has(t)&&delete e[t];return t={...e,..."object"==typeof t&&t?t:{}},t=this._mergeConfigObj(t),t=this._configAfterMerge(t),this._typeCheckConfig(t),t}_configAfterMerge(t){return t.container=!1===t.container?document.body:r(t.container),"number"==typeof t.delay&&(t.delay={show:t.delay,hide:t.delay}),"number"==typeof t.title&&(t.title=t.title.toString()),"number"==typeof t.content&&(t.content=t.content.toString()),t}_getDelegateConfig(){const t={};for(const[e,i]of Object.entries(this._config))this.constructor.Default[e]!==i&&(t[e]=i);return t.selector=!1,t.trigger="manual",t}_disposePopper(){this._popper&&(this._popper.destroy(),this._popper=null),this.tip&&(this.tip.remove(),this.tip=null)}static jQueryInterface(t){return this.each((function(){const e=cs.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}m(cs);const hs={...cs.Default,content:"",offset:[0,8],placement:"right",template:'<div class="popover" role="tooltip"><div class="popover-arrow"></div><h3 class="popover-header"></h3><div class="popover-body"></div></div>',trigger:"click"},ds={...cs.DefaultType,content:"(null|string|element|function)"};class us extends cs{static get Default(){return hs}static get DefaultType(){return ds}static get NAME(){return"popover"}_isWithContent(){return this._getTitle()||this._getContent()}_getContentForTemplate(){return{".popover-header":this._getTitle(),".popover-body":this._getContent()}}_getContent(){return this._resolvePossibleFunction(this._config.content)}static jQueryInterface(t){return this.each((function(){const e=us.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}m(us);const fs=".bs.scrollspy",ps=`activate${fs}`,ms=`click${fs}`,gs=`load${fs}.data-api`,_s="active",bs="[href]",vs=".nav-link",ys=`${vs}, .nav-item > ${vs}, .list-group-item`,ws={offset:null,rootMargin:"0px 0px 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t){if(!o.isIntersecting){this._activeTarget=null,this._clearActiveClass(e(o));continue}const t=o.target.offsetTop>=this._previousScrollData.visibleEntryTop;if(s&&t){if(i(o),!n)return}else s||t||i(o)}}_initializeTargetsAndObservables(){this._targetLinks=new Map,this._observableSections=new Map;const t=z.find(bs,this._config.target);for(const e of t){if(!e.hash||l(e))continue;const t=z.findOne(decodeURI(e.hash),this._element);a(t)&&(this._targetLinks.set(decodeURI(e.hash),e),this._observableSections.set(e.hash,t))}}_process(t){this._activeTarget!==t&&(this._clearActiveClass(this._config.target),this._activeTarget=t,t.classList.add(_s),this._activateParents(t),N.trigger(this._element,ps,{relatedTarget:t}))}_activateParents(t){if(t.classList.contains("dropdown-item"))z.findOne(".dropdown-toggle",t.closest(".dropdown")).classList.add(_s);else for(const e of z.parents(t,".nav, .list-group"))for(const t of z.prev(e,ys))t.classList.add(_s)}_clearActiveClass(t){t.classList.remove(_s);const e=z.find(`${bs}.${_s}`,t);for(const t of e)t.classList.remove(_s)}static jQueryInterface(t){return this.each((function(){const e=Es.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t]()}}))}}N.on(window,gs,(()=>{for(const t of z.find('[data-bs-spy="scroll"]'))Es.getOrCreateInstance(t)})),m(Es);const Ts=".bs.tab",Cs=`hide${Ts}`,Os=`hidden${Ts}`,xs=`show${Ts}`,ks=`shown${Ts}`,Ls=`click${Ts}`,Ss=`keydown${Ts}`,Ds=`load${Ts}`,$s="ArrowLeft",Is="ArrowRight",Ns="ArrowUp",Ps="ArrowDown",Ms="Home",js="End",Fs="active",Hs="fade",Ws="show",Bs=":not(.dropdown-toggle)",zs='[data-bs-toggle="tab"], [data-bs-toggle="pill"], [data-bs-toggle="list"]',Rs=`.nav-link${Bs}, .list-group-item${Bs}, [role="tab"]${Bs}, ${zs}`,qs=`.${Fs}[data-bs-toggle="tab"], .${Fs}[data-bs-toggle="pill"], .${Fs}[data-bs-toggle="list"]`;class Vs extends W{constructor(t){super(t),this._parent=this._element.closest('.list-group, .nav, [role="tablist"]'),this._parent&&(this._setInitialAttributes(this._parent,this._getChildren()),N.on(this._element,Ss,(t=>this._keydown(t))))}static get NAME(){return"tab"}show(){const t=this._element;if(this._elemIsActive(t))return;const e=this._getActiveElem(),i=e?N.trigger(e,Cs,{relatedTarget:t}):null;N.trigger(t,xs,{relatedTarget:e}).defaultPrevented||i&&i.defaultPrevented||(this._deactivate(e,t),this._activate(t,e))}_activate(t,e){t&&(t.classList.add(Fs),this._activate(z.getElementFromSelector(t)),this._queueCallback((()=>{"tab"===t.getAttribute("role")?(t.removeAttribute("tabindex"),t.setAttribute("aria-selected",!0),this._toggleDropDown(t,!0),N.trigger(t,ks,{relatedTarget:e})):t.classList.add(Ws)}),t,t.classList.contains(Hs)))}_deactivate(t,e){t&&(t.classList.remove(Fs),t.blur(),this._deactivate(z.getElementFromSelector(t)),this._queueCallback((()=>{"tab"===t.getAttribute("role")?(t.setAttribute("aria-selected",!1),t.setAttribute("tabindex","-1"),this._toggleDropDown(t,!1),N.trigger(t,Os,{relatedTarget:e})):t.classList.remove(Ws)}),t,t.classList.contains(Hs)))}_keydown(t){if(![$s,Is,Ns,Ps,Ms,js].includes(t.key))return;t.stopPropagation(),t.preventDefault();const e=this._getChildren().filter((t=>!l(t)));let i;if([Ms,js].includes(t.key))i=e[t.key===Ms?0:e.length-1];else{const n=[Is,Ps].includes(t.key);i=b(e,t.target,n,!0)}i&&(i.focus({preventScroll:!0}),Vs.getOrCreateInstance(i).show())}_getChildren(){return z.find(Rs,this._parent)}_getActiveElem(){return this._getChildren().find((t=>this._elemIsActive(t)))||null}_setInitialAttributes(t,e){this._setAttributeIfNotExists(t,"role","tablist");for(const t of e)this._setInitialAttributesOnChild(t)}_setInitialAttributesOnChild(t){t=this._getInnerElement(t);const e=this._elemIsActive(t),i=this._getOuterElement(t);t.setAttribute("aria-selected",e),i!==t&&this._setAttributeIfNotExists(i,"role","presentation"),e||t.setAttribute("tabindex","-1"),this._setAttributeIfNotExists(t,"role","tab"),this._setInitialAttributesOnTargetPanel(t)}_setInitialAttributesOnTargetPanel(t){const e=z.getElementFromSelector(t);e&&(this._setAttributeIfNotExists(e,"role","tabpanel"),t.id&&this._setAttributeIfNotExists(e,"aria-labelledby",`${t.id}`))}_toggleDropDown(t,e){const i=this._getOuterElement(t);if(!i.classList.contains("dropdown"))return;const n=(t,n)=>{const s=z.findOne(t,i);s&&s.classList.toggle(n,e)};n(".dropdown-toggle",Fs),n(".dropdown-menu",Ws),i.setAttribute("aria-expanded",e)}_setAttributeIfNotExists(t,e,i){t.hasAttribute(e)||t.setAttribute(e,i)}_elemIsActive(t){return t.classList.contains(Fs)}_getInnerElement(t){return t.matches(Rs)?t:z.findOne(Rs,t)}_getOuterElement(t){return t.closest(".nav-item, .list-group-item")||t}static jQueryInterface(t){return this.each((function(){const e=Vs.getOrCreateInstance(this);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t]()}}))}}N.on(document,Ls,zs,(function(t){["A","AREA"].includes(this.tagName)&&t.preventDefault(),l(this)||Vs.getOrCreateInstance(this).show()})),N.on(window,Ds,(()=>{for(const t of z.find(qs))Vs.getOrCreateInstance(t)})),m(Vs);const Ks=".bs.toast",Qs=`mouseover${Ks}`,Xs=`mouseout${Ks}`,Ys=`focusin${Ks}`,Us=`focusout${Ks}`,Gs=`hide${Ks}`,Js=`hidden${Ks}`,Zs=`show${Ks}`,to=`shown${Ks}`,eo="hide",io="show",no="showing",so={animation:"boolean",autohide:"boolean",delay:"number"},oo={animation:!0,autohide:!0,delay:5e3};class ro extends W{constructor(t,e){super(t,e),this._timeout=null,this._hasMouseInteraction=!1,this._hasKeyboardInteraction=!1,this._setListeners()}static get Default(){return oo}static get DefaultType(){return so}static get NAME(){return"toast"}show(){N.trigger(this._element,Zs).defaultPrevented||(this._clearTimeout(),this._config.animation&&this._element.classList.add("fade"),this._element.classList.remove(eo),d(this._element),this._element.classList.add(io,no),this._queueCallback((()=>{this._element.classList.remove(no),N.trigger(this._element,to),this._maybeScheduleHide()}),this._element,this._config.animation))}hide(){this.isShown()&&(N.trigger(this._element,Gs).defaultPrevented||(this._element.classList.add(no),this._queueCallback((()=>{this._element.classList.add(eo),this._element.classList.remove(no,io),N.trigger(this._element,Js)}),this._element,this._config.animation)))}dispose(){this._clearTimeout(),this.isShown()&&this._element.classList.remove(io),super.dispose()}isShown(){return this._element.classList.contains(io)}_maybeScheduleHide(){this._config.autohide&&(this._hasMouseInteraction||this._hasKeyboardInteraction||(this._timeout=setTimeout((()=>{this.hide()}),this._config.delay)))}_onInteraction(t,e){switch(t.type){case"mouseover":case"mouseout":this._hasMouseInteraction=e;break;case"focusin":case"focusout":this._hasKeyboardInteraction=e}if(e)return void this._clearTimeout();const i=t.relatedTarget;this._element===i||this._element.contains(i)||this._maybeScheduleHide()}_setListeners(){N.on(this._element,Qs,(t=>this._onInteraction(t,!0))),N.on(this._element,Xs,(t=>this._onInteraction(t,!1))),N.on(this._element,Ys,(t=>this._onInteraction(t,!0))),N.on(this._element,Us,(t=>this._onInteraction(t,!1)))}_clearTimeout(){clearTimeout(this._timeout),this._timeout=null}static jQueryInterface(t){return this.each((function(){const e=ro.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}return R(ro),m(ro),{Alert:Q,Button:Y,Carousel:xt,Collapse:Bt,Dropdown:qi,Modal:On,Offcanvas:qn,Popover:us,ScrollSpy:Es,Tab:Vs,Toast:ro,Tooltip:cs}}));
 //# sourceMappingURL=bootstrap.bundle.min.js.map
\ No newline at end of file
diff --git a/docs/site_libs/quarto-html/anchor.min.js b/docs/site_libs/quarto-html/anchor.min.js
index 1c2b86f..5ac814d 100644
--- a/docs/site_libs/quarto-html/anchor.min.js
+++ b/docs/site_libs/quarto-html/anchor.min.js
@@ -1,9 +1,9 @@
 // @license magnet:?xt=urn:btih:d3d9a9a6595521f9666a5e94cc830dab83b65699&dn=expat.txt Expat
 //
-// AnchorJS - v4.3.1 - 2021-04-17
+// AnchorJS - v5.0.0 - 2023-01-18
 // https://www.bryanbraun.com/anchorjs/
-// Copyright (c) 2021 Bryan Braun; Licensed MIT
+// Copyright (c) 2023 Bryan Braun; Licensed MIT
 //
 // @license magnet:?xt=urn:btih:d3d9a9a6595521f9666a5e94cc830dab83b65699&dn=expat.txt Expat
-!function(A,e){"use strict";"function"==typeof define&&define.amd?define([],e):"object"==typeof module&&module.exports?module.exports=e():(A.AnchorJS=e(),A.anchors=new A.AnchorJS)}(this,function(){"use strict";return function(A){function d(A){A.icon=Object.prototype.hasOwnProperty.call(A,"icon")?A.icon:"",A.visible=Object.prototype.hasOwnProperty.call(A,"visible")?A.visible:"hover",A.placement=Object.prototype.hasOwnProperty.call(A,"placement")?A.placement:"right",A.ariaLabel=Object.prototype.hasOwnProperty.call(A,"ariaLabel")?A.ariaLabel:"Anchor",A.class=Object.prototype.hasOwnProperty.call(A,"class")?A.class:"",A.base=Object.prototype.hasOwnProperty.call(A,"base")?A.base:"",A.truncate=Object.prototype.hasOwnProperty.call(A,"truncate")?Math.floor(A.truncate):64,A.titleText=Object.prototype.hasOwnProperty.call(A,"titleText")?A.titleText:""}function w(A){var e;if("string"==typeof A||A instanceof String)e=[].slice.call(document.querySelectorAll(A));else{if(!(Array.isArray(A)||A instanceof NodeList))throw new TypeError("The selector provided to AnchorJS was invalid.");e=[].slice.call(A)}return e}this.options=A||{},this.elements=[],d(this.options),this.isTouchDevice=function(){return Boolean("ontouchstart"in window||window.TouchEvent||window.DocumentTouch&&document instanceof DocumentTouch)},this.add=function(A){var e,t,o,i,n,s,a,c,r,l,h,u,p=[];if(d(this.options),"touch"===(l=this.options.visible)&&(l=this.isTouchDevice()?"always":"hover"),0===(e=w(A=A||"h2, h3, h4, h5, h6")).length)return this;for(null===document.head.querySelector("style.anchorjs")&&((u=document.createElement("style")).className="anchorjs",u.appendChild(document.createTextNode("")),void 0===(A=document.head.querySelector('[rel="stylesheet"],style'))?document.head.appendChild(u):document.head.insertBefore(u,A),u.sheet.insertRule(".anchorjs-link{opacity:0;text-decoration:none;-webkit-font-smoothing:antialiased;-moz-osx-font-smoothing:grayscale}",u.sheet.cssRules.length),u.sheet.insertRule(":hover>.anchorjs-link,.anchorjs-link:focus{opacity:1}",u.sheet.cssRules.length),u.sheet.insertRule("[data-anchorjs-icon]::after{content:attr(data-anchorjs-icon)}",u.sheet.cssRules.length),u.sheet.insertRule('@font-face{font-family:anchorjs-icons;src:url(data:n/a;base64,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) format("truetype")}',u.sheet.cssRules.length)),u=document.querySelectorAll("[id]"),t=[].map.call(u,function(A){return A.id}),i=0;i<e.length;i++)if(this.hasAnchorJSLink(e[i]))p.push(i);else{if(e[i].hasAttribute("id"))o=e[i].getAttribute("id");else if(e[i].hasAttribute("data-anchor-id"))o=e[i].getAttribute("data-anchor-id");else{for(c=a=this.urlify(e[i].textContent),s=0;n=t.indexOf(c=void 0!==n?a+"-"+s:c),s+=1,-1!==n;);n=void 0,t.push(c),e[i].setAttribute("id",c),o=c}(r=document.createElement("a")).className="anchorjs-link "+this.options.class,r.setAttribute("aria-label",this.options.ariaLabel),r.setAttribute("data-anchorjs-icon",this.options.icon),this.options.titleText&&(r.title=this.options.titleText),h=document.querySelector("base")?window.location.pathname+window.location.search:"",h=this.options.base||h,r.href=h+"#"+o,"always"===l&&(r.style.opacity="1"),""===this.options.icon&&(r.style.font="1em/1 anchorjs-icons","left"===this.options.placement&&(r.style.lineHeight="inherit")),"left"===this.options.placement?(r.style.position="absolute",r.style.marginLeft="-1em",r.style.paddingRight=".5em",e[i].insertBefore(r,e[i].firstChild)):(r.style.paddingLeft=".375em",e[i].appendChild(r))}for(i=0;i<p.length;i++)e.splice(p[i]-i,1);return this.elements=this.elements.concat(e),this},this.remove=function(A){for(var e,t,o=w(A),i=0;i<o.length;i++)(t=o[i].querySelector(".anchorjs-link"))&&(-1!==(e=this.elements.indexOf(o[i]))&&this.elements.splice(e,1),o[i].removeChild(t));return this},this.removeAll=function(){this.remove(this.elements)},this.urlify=function(A){var e=document.createElement("textarea");return e.innerHTML=A,A=e.value,this.options.truncate||d(this.options),A.trim().replace(/'/gi,"").replace(/[& +$,:;=?@"#{}|^~[`%!'<>\]./()*\\\n\t\b\v\u00A0]/g,"-").replace(/-{2,}/g,"-").substring(0,this.options.truncate).replace(/^-+|-+$/gm,"").toLowerCase()},this.hasAnchorJSLink=function(A){var e=A.firstChild&&-1<(" "+A.firstChild.className+" ").indexOf(" anchorjs-link "),A=A.lastChild&&-1<(" "+A.lastChild.className+" ").indexOf(" anchorjs-link ");return e||A||!1}}});
+!function(A,e){"use strict";"function"==typeof define&&define.amd?define([],e):"object"==typeof module&&module.exports?module.exports=e():(A.AnchorJS=e(),A.anchors=new A.AnchorJS)}(globalThis,function(){"use strict";return function(A){function u(A){A.icon=Object.prototype.hasOwnProperty.call(A,"icon")?A.icon:"",A.visible=Object.prototype.hasOwnProperty.call(A,"visible")?A.visible:"hover",A.placement=Object.prototype.hasOwnProperty.call(A,"placement")?A.placement:"right",A.ariaLabel=Object.prototype.hasOwnProperty.call(A,"ariaLabel")?A.ariaLabel:"Anchor",A.class=Object.prototype.hasOwnProperty.call(A,"class")?A.class:"",A.base=Object.prototype.hasOwnProperty.call(A,"base")?A.base:"",A.truncate=Object.prototype.hasOwnProperty.call(A,"truncate")?Math.floor(A.truncate):64,A.titleText=Object.prototype.hasOwnProperty.call(A,"titleText")?A.titleText:""}function d(A){var e;if("string"==typeof A||A instanceof String)e=[].slice.call(document.querySelectorAll(A));else{if(!(Array.isArray(A)||A instanceof NodeList))throw new TypeError("The selector provided to AnchorJS was invalid.");e=[].slice.call(A)}return e}this.options=A||{},this.elements=[],u(this.options),this.add=function(A){var e,t,o,i,n,s,a,r,l,c,h,p=[];if(u(this.options),0!==(e=d(A=A||"h2, h3, h4, h5, h6")).length){for(null===document.head.querySelector("style.anchorjs")&&((A=document.createElement("style")).className="anchorjs",A.appendChild(document.createTextNode("")),void 0===(h=document.head.querySelector('[rel="stylesheet"],style'))?document.head.appendChild(A):document.head.insertBefore(A,h),A.sheet.insertRule(".anchorjs-link{opacity:0;text-decoration:none;-webkit-font-smoothing:antialiased;-moz-osx-font-smoothing:grayscale}",A.sheet.cssRules.length),A.sheet.insertRule(":hover>.anchorjs-link,.anchorjs-link:focus{opacity:1}",A.sheet.cssRules.length),A.sheet.insertRule("[data-anchorjs-icon]::after{content:attr(data-anchorjs-icon)}",A.sheet.cssRules.length),A.sheet.insertRule('@font-face{font-family:anchorjs-icons;src:url(data:n/a;base64,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) format("truetype")}',A.sheet.cssRules.length)),h=document.querySelectorAll("[id]"),t=[].map.call(h,function(A){return A.id}),i=0;i<e.length;i++)if(this.hasAnchorJSLink(e[i]))p.push(i);else{if(e[i].hasAttribute("id"))o=e[i].getAttribute("id");else if(e[i].hasAttribute("data-anchor-id"))o=e[i].getAttribute("data-anchor-id");else{for(r=a=this.urlify(e[i].textContent),s=0;n=t.indexOf(r=void 0!==n?a+"-"+s:r),s+=1,-1!==n;);n=void 0,t.push(r),e[i].setAttribute("id",r),o=r}(l=document.createElement("a")).className="anchorjs-link "+this.options.class,l.setAttribute("aria-label",this.options.ariaLabel),l.setAttribute("data-anchorjs-icon",this.options.icon),this.options.titleText&&(l.title=this.options.titleText),c=document.querySelector("base")?window.location.pathname+window.location.search:"",c=this.options.base||c,l.href=c+"#"+o,"always"===this.options.visible&&(l.style.opacity="1"),""===this.options.icon&&(l.style.font="1em/1 anchorjs-icons","left"===this.options.placement)&&(l.style.lineHeight="inherit"),"left"===this.options.placement?(l.style.position="absolute",l.style.marginLeft="-1.25em",l.style.paddingRight=".25em",l.style.paddingLeft=".25em",e[i].insertBefore(l,e[i].firstChild)):(l.style.marginLeft=".1875em",l.style.paddingRight=".1875em",l.style.paddingLeft=".1875em",e[i].appendChild(l))}for(i=0;i<p.length;i++)e.splice(p[i]-i,1);this.elements=this.elements.concat(e)}return this},this.remove=function(A){for(var e,t,o=d(A),i=0;i<o.length;i++)(t=o[i].querySelector(".anchorjs-link"))&&(-1!==(e=this.elements.indexOf(o[i]))&&this.elements.splice(e,1),o[i].removeChild(t));return this},this.removeAll=function(){this.remove(this.elements)},this.urlify=function(A){var e=document.createElement("textarea");return e.innerHTML=A,A=e.value,this.options.truncate||u(this.options),A.trim().replace(/'/gi,"").replace(/[& +$,:;=?@"#{}|^~[`%!'<>\]./()*\\\n\t\b\v\u00A0]/g,"-").replace(/-{2,}/g,"-").substring(0,this.options.truncate).replace(/^-+|-+$/gm,"").toLowerCase()},this.hasAnchorJSLink=function(A){var e=A.firstChild&&-1<(" "+A.firstChild.className+" ").indexOf(" anchorjs-link "),A=A.lastChild&&-1<(" "+A.lastChild.className+" ").indexOf(" anchorjs-link ");return e||A||!1}}});
 // @license-end
\ No newline at end of file
diff --git a/docs/site_libs/quarto-html/popper.min.js b/docs/site_libs/quarto-html/popper.min.js
index 2269d66..e3726d7 100644
--- a/docs/site_libs/quarto-html/popper.min.js
+++ b/docs/site_libs/quarto-html/popper.min.js
@@ -1,6 +1,6 @@
 /**
- * @popperjs/core v2.11.4 - MIT License
+ * @popperjs/core v2.11.7 - MIT License
  */
 
-!function(e,t){"object"==typeof exports&&"undefined"!=typeof module?t(exports):"function"==typeof define&&define.amd?define(["exports"],t):t((e="undefined"!=typeof globalThis?globalThis:e||self).Popper={})}(this,(function(e){"use strict";function t(e){if(null==e)return window;if("[object Window]"!==e.toString()){var t=e.ownerDocument;return t&&t.defaultView||window}return e}function n(e){return e instanceof t(e).Element||e instanceof Element}function r(e){return e instanceof t(e).HTMLElement||e instanceof HTMLElement}function o(e){return"undefined"!=typeof ShadowRoot&&(e instanceof t(e).ShadowRoot||e instanceof ShadowRoot)}var i=Math.max,a=Math.min,s=Math.round;function f(e,t){void 0===t&&(t=!1);var n=e.getBoundingClientRect(),o=1,i=1;if(r(e)&&t){var a=e.offsetHeight,f=e.offsetWidth;f>0&&(o=s(n.width)/f||1),a>0&&(i=s(n.height)/a||1)}return{width:n.width/o,height:n.height/i,top:n.top/i,right:n.right/o,bottom:n.bottom/i,left:n.left/o,x:n.left/o,y:n.top/i}}function c(e){var 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ae={name:"offset",enabled:!0,phase:"main",requires:["popperOffsets"],fn:function(e){var t=e.state,n=e.options,r=e.name,o=n.offset,i=void 0===o?[0,0]:o,a=S.reduce((function(e,n){return e[n]=function(e,t,n){var r=C(e),o=[P,D].indexOf(r)>=0?-1:1,i="function"==typeof n?n(Object.assign({},t,{placement:e})):n,a=i[0],s=i[1];return a=a||0,s=(s||0)*o,[P,L].indexOf(r)>=0?{x:s,y:a}:{x:a,y:s}}(n,t.rects,i),e}),{}),s=a[t.placement],f=s.x,c=s.y;null!=t.modifiersData.popperOffsets&&(t.modifiersData.popperOffsets.x+=f,t.modifiersData.popperOffsets.y+=c),t.modifiersData[r]=a}},se={left:"right",right:"left",bottom:"top",top:"bottom"};function fe(e){return e.replace(/left|right|bottom|top/g,(function(e){return se[e]}))}var ce={start:"end",end:"start"};function pe(e){return e.replace(/start|end/g,(function(e){return ce[e]}))}function ue(e,t){void 0===t&&(t={});var n=t,r=n.placement,o=n.boundary,i=n.rootBoundary,a=n.padding,s=n.flipVariations,f=n.allowedAutoPlacements,c=void 0===f?S:f,p=U(r),u=p?s?R:R.filter((function(e){return U(e)===p})):k,l=u.filter((function(e){return c.indexOf(e)>=0}));0===l.length&&(l=u);var d=l.reduce((function(t,n){return t[n]=J(e,{placement:n,boundary:o,rootBoundary:i,padding:a})[C(n)],t}),{});return Object.keys(d).sort((function(e,t){return d[e]-d[t]}))}var le={name:"flip",enabled:!0,phase:"main",fn:function(e){var t=e.state,n=e.options,r=e.name;if(!t.modifiersData[r]._skip){for(var o=n.mainAxis,i=void 0===o||o,a=n.altAxis,s=void 0===a||a,f=n.fallbackPlacements,c=n.padding,p=n.boundary,u=n.rootBoundary,l=n.altBoundary,d=n.flipVariations,h=void 0===d||d,m=n.allowedAutoPlacements,v=t.options.placement,y=C(v),g=f||(y===v||!h?[fe(v)]:function(e){if(C(e)===M)return[];var t=fe(e);return[pe(e),t,pe(t)]}(v)),b=[v].concat(g).reduce((function(e,n){return e.concat(C(n)===M?ue(t,{placement:n,boundary:p,rootBoundary:u,padding:c,flipVariations:h,allowedAutoPlacements:m}):n)}),[]),x=t.rects.reference,w=t.rects.popper,O=new Map,j=!0,E=b[0],k=0;k<b.length;k++){var B=b[k],H=C(B),T=U(B)===W,R=[D,A].indexOf(H)>=0,S=R?"width":"height",V=J(t,{placement:B,boundary:p,rootBoundary:u,altBoundary:l,padding:c}),q=R?T?L:P:T?A:D;x[S]>w[S]&&(q=fe(q));var N=fe(q),I=[];if(i&&I.push(V[H]<=0),s&&I.push(V[q]<=0,V[N]<=0),I.every((function(e){return e}))){E=B,j=!1;break}O.set(B,I)}if(j)for(var _=function(e){var t=b.find((function(t){var n=O.get(t);if(n)return n.slice(0,e).every((function(e){return e}))}));if(t)return E=t,"break"},F=h?3:1;F>0;F--){if("break"===_(F))break}t.placement!==E&&(t.modifiersData[r]._skip=!0,t.placement=E,t.reset=!0)}},requiresIfExists:["offset"],data:{_skip:!1}};function de(e,t,n){return i(e,a(t,n))}var he={name:"preventOverflow",enabled:!0,phase:"main",fn:function(e){var t=e.state,n=e.options,r=e.name,o=n.mainAxis,s=void 0===o||o,f=n.altAxis,c=void 0!==f&&f,p=n.boundary,u=n.rootBoundary,l=n.altBoundary,d=n.padding,h=n.tether,m=void 0===h||h,v=n.tetherOffset,y=void 0===v?0:v,b=J(t,{boundary:p,rootBoundary:u,padding:d,altBoundary:l}),x=C(t.placement),w=U(t.placement),O=!w,j=z(x),M="x"===j?"y":"x",k=t.modifiersData.popperOffsets,B=t.rects.reference,H=t.rects.popper,T="function"==typeof y?y(Object.assign({},t.rects,{placement:t.placement})):y,R="number"==typeof T?{mainAxis:T,altAxis:T}:Object.assign({mainAxis:0,altAxis:0},T),S=t.modifiersData.offset?t.modifiersData.offset[t.placement]:null,V={x:0,y:0};if(k){if(s){var q,N="y"===j?D:P,I="y"===j?A:L,_="y"===j?"height":"width",F=k[j],X=F+b[N],Y=F-b[I],G=m?-H[_]/2:0,K=w===W?B[_]:H[_],Q=w===W?-H[_]:-B[_],Z=t.elements.arrow,$=m&&Z?g(Z):{width:0,height:0},ee=t.modifiersData["arrow#persistent"]?t.modifiersData["arrow#persistent"].padding:{top:0,right:0,bottom:0,left:0},te=ee[N],ne=ee[I],re=de(0,B[_],$[_]),oe=O?B[_]/2-G-re-te-R.mainAxis:K-re-te-R.mainAxis,ie=O?-B[_]/2+G+re+ne+R.mainAxis:Q+re+ne+R.mainAxis,ae=t.elements.arrow&&E(t.elements.arrow),se=ae?"y"===j?ae.clientTop||0:ae.clientLeft||0:0,fe=null!=(q=null==S?void 0:S[j])?q:0,ce=F+ie-fe,pe=de(m?a(X,F+oe-fe-se):X,F,m?i(Y,ce):Y);k[j]=pe,V[j]=pe-F}if(c){var ue,le="x"===j?D:P,he="x"===j?A:L,me=k[M],ve="y"===M?"height":"width",ye=me+b[le],ge=me-b[he],be=-1!==[D,P].indexOf(x),xe=null!=(ue=null==S?void 0:S[M])?ue:0,we=be?ye:me-B[ve]-H[ve]-xe+R.altAxis,Oe=be?me+B[ve]+H[ve]-xe-R.altAxis:ge,je=m&&be?function(e,t,n){var r=de(e,t,n);return r>n?n:r}(we,me,Oe):de(m?we:ye,me,m?Oe:ge);k[M]=je,V[M]=je-me}t.modifiersData[r]=V}},requiresIfExists:["offset"]};var me={name:"arrow",enabled:!0,phase:"main",fn:function(e){var t,n=e.state,r=e.name,o=e.options,i=n.elements.arrow,a=n.modifiersData.popperOffsets,s=C(n.placement),f=z(s),c=[P,L].indexOf(s)>=0?"height":"width";if(i&&a){var p=function(e,t){return Y("number"!=typeof(e="function"==typeof e?e(Object.assign({},t.rects,{placement:t.placement})):e)?e:G(e,k))}(o.padding,n),u=g(i),l="y"===f?D:P,d="y"===f?A:L,h=n.rects.reference[c]+n.rects.reference[f]-a[f]-n.rects.popper[c],m=a[f]-n.rects.reference[f],v=E(i),y=v?"y"===f?v.clientHeight||0:v.clientWidth||0:0,b=h/2-m/2,x=p[l],w=y-u[c]-p[d],O=y/2-u[c]/2+b,j=de(x,O,w),M=f;n.modifiersData[r]=((t={})[M]=j,t.centerOffset=j-O,t)}},effect:function(e){var t=e.state,n=e.options.element,r=void 0===n?"[data-popper-arrow]":n;null!=r&&("string"!=typeof r||(r=t.elements.popper.querySelector(r)))&&N(t.elements.popper,r)&&(t.elements.arrow=r)},requires:["popperOffsets"],requiresIfExists:["preventOverflow"]};function ve(e,t,n){return void 0===n&&(n={x:0,y:0}),{top:e.top-t.height-n.y,right:e.right-t.width+n.x,bottom:e.bottom-t.height+n.y,left:e.left-t.width-n.x}}function ye(e){return[D,L,A,P].some((function(t){return e[t]>=0}))}var ge={name:"hide",enabled:!0,phase:"main",requiresIfExists:["preventOverflow"],fn:function(e){var t=e.state,n=e.name,r=t.rects.reference,o=t.rects.popper,i=t.modifiersData.preventOverflow,a=J(t,{elementContext:"reference"}),s=J(t,{altBoundary:!0}),f=ve(a,r),c=ve(s,o,i),p=ye(f),u=ye(c);t.modifiersData[n]={referenceClippingOffsets:f,popperEscapeOffsets:c,isReferenceHidden:p,hasPopperEscaped:u},t.attributes.popper=Object.assign({},t.attributes.popper,{"data-popper-reference-hidden":p,"data-popper-escaped":u})}},be=Z({defaultModifiers:[ee,te,oe,ie]}),xe=[ee,te,oe,ie,ae,le,he,me,ge],we=Z({defaultModifiers:xe});e.applyStyles=ie,e.arrow=me,e.computeStyles=oe,e.createPopper=we,e.createPopperLite=be,e.defaultModifiers=xe,e.detectOverflow=J,e.eventListeners=ee,e.flip=le,e.hide=ge,e.offset=ae,e.popperGenerator=Z,e.popperOffsets=te,e.preventOverflow=he,Object.defineProperty(e,"__esModule",{value:!0})}));
 
diff --git a/docs/site_libs/quarto-html/quarto-syntax-highlighting.css b/docs/site_libs/quarto-html/quarto-syntax-highlighting.css
index d9fd98f..b30ce57 100644
--- a/docs/site_libs/quarto-html/quarto-syntax-highlighting.css
+++ b/docs/site_libs/quarto-html/quarto-syntax-highlighting.css
@@ -85,6 +85,7 @@ code span.st {
 
 code span.cf {
   color: #003B4F;
+  font-weight: bold;
   font-style: inherit;
 }
 
@@ -193,6 +194,7 @@ code span.dv {
 
 code span.kw {
   color: #003B4F;
+  font-weight: bold;
   font-style: inherit;
 }
 
diff --git a/docs/site_libs/quarto-html/quarto.js b/docs/site_libs/quarto-html/quarto.js
index c3935c7..39e6869 100644
--- a/docs/site_libs/quarto-html/quarto.js
+++ b/docs/site_libs/quarto-html/quarto.js
@@ -9,7 +9,7 @@ const layoutMarginEls = () => {
   // Find any conflicting margin elements and add margins to the
   // top to prevent overlap
   const marginChildren = window.document.querySelectorAll(
-    ".column-margin.column-container > * "
+    ".column-margin.column-container > *, .margin-caption, .aside"
   );
 
   let lastBottom = 0;
@@ -18,25 +18,14 @@ const layoutMarginEls = () => {
       // clear the top margin so we recompute it
       marginChild.style.marginTop = null;
       const top = marginChild.getBoundingClientRect().top + window.scrollY;
-      console.log({
-        childtop: marginChild.getBoundingClientRect().top,
-        scroll: window.scrollY,
-        top,
-        lastBottom,
-      });
       if (top < lastBottom) {
-        const margin = lastBottom - top;
+        const marginChildStyle = window.getComputedStyle(marginChild);
+        const marginBottom = parseFloat(marginChildStyle["marginBottom"]);
+        const margin = lastBottom - top + marginBottom;
         marginChild.style.marginTop = `${margin}px`;
       }
       const styles = window.getComputedStyle(marginChild);
       const marginTop = parseFloat(styles["marginTop"]);
-
-      console.log({
-        top,
-        height: marginChild.getBoundingClientRect().height,
-        marginTop,
-        total: top + marginChild.getBoundingClientRect().height + marginTop,
-      });
       lastBottom = top + marginChild.getBoundingClientRect().height + marginTop;
     }
   }
@@ -46,7 +35,15 @@ window.document.addEventListener("DOMContentLoaded", function (_event) {
   // Recompute the position of margin elements anytime the body size changes
   if (window.ResizeObserver) {
     const resizeObserver = new window.ResizeObserver(
-      throttle(layoutMarginEls, 50)
+      throttle(() => {
+        layoutMarginEls();
+        if (
+          window.document.body.getBoundingClientRect().width < 990 &&
+          isReaderMode()
+        ) {
+          quartoToggleReader();
+        }
+      }, 50)
     );
     resizeObserver.observe(window.document.body);
   }
@@ -97,7 +94,7 @@ window.document.addEventListener("DOMContentLoaded", function (_event) {
       if (link.href.indexOf("#") !== -1) {
         const anchor = link.href.split("#")[1];
         const heading = window.document.querySelector(
-          `[data-anchor-id=${anchor}]`
+          `[data-anchor-id="${anchor}"]`
         );
         if (heading) {
           // Add the class
@@ -137,8 +134,10 @@ window.document.addEventListener("DOMContentLoaded", function (_event) {
       window.innerHeight + window.pageYOffset >=
       window.document.body.offsetHeight
     ) {
+      // This is the no-scroll case where last section should be the active one
       sectionIndex = 0;
     } else {
+      // This finds the last section visible on screen that should be made active
       sectionIndex = [...sections].reverse().findIndex((section) => {
         if (section) {
           return window.pageYOffset >= section.offsetTop - sectionMargin;
@@ -320,6 +319,7 @@ window.document.addEventListener("DOMContentLoaded", function (_event) {
           for (const child of el.children) {
             child.style.opacity = 0;
             child.style.overflow = "hidden";
+            child.style.pointerEvents = "none";
           }
 
           nexttick(() => {
@@ -361,6 +361,7 @@ window.document.addEventListener("DOMContentLoaded", function (_event) {
 
               const clone = child.cloneNode(true);
               clone.style.opacity = 1;
+              clone.style.pointerEvents = null;
               clone.style.display = null;
               toggleContents.append(clone);
             }
@@ -435,6 +436,7 @@ window.document.addEventListener("DOMContentLoaded", function (_event) {
           for (const child of el.children) {
             child.style.opacity = 1;
             child.style.overflow = null;
+            child.style.pointerEvents = null;
           }
 
           const placeholderEl = window.document.getElementById(
@@ -742,6 +744,7 @@ window.document.addEventListener("DOMContentLoaded", function (_event) {
     // Process the collapse state if this is an UL
     if (el.tagName === "UL") {
       if (tocOpenDepth === -1 && depth > 1) {
+        // toc-expand: false
         el.classList.add("collapse");
       } else if (
         depth <= tocOpenDepth ||
@@ -760,10 +763,9 @@ window.document.addEventListener("DOMContentLoaded", function (_event) {
   };
 
   // walk the TOC and expand / collapse any items that should be shown
-
   if (tocEl) {
-    walk(tocEl, 0);
     updateActiveLink();
+    walk(tocEl, 0);
   }
 
   // Throttle the scroll event and walk peridiocally
@@ -782,6 +784,10 @@ window.document.addEventListener("DOMContentLoaded", function (_event) {
   window.addEventListener(
     "resize",
     throttle(() => {
+      if (tocEl) {
+        updateActiveLink();
+        walk(tocEl, 0);
+      }
       if (!isReaderMode()) {
         hideOverlappedSidebars();
       }
diff --git a/docs/site_libs/quarto-nav/quarto-nav.js b/docs/site_libs/quarto-nav/quarto-nav.js
index 3b21201..38cc430 100644
--- a/docs/site_libs/quarto-nav/quarto-nav.js
+++ b/docs/site_libs/quarto-nav/quarto-nav.js
@@ -5,9 +5,45 @@ const headroomChanged = new CustomEvent("quarto-hrChanged", {
   composed: false,
 });
 
+const announceDismiss = () => {
+  const annEl = window.document.getElementById("quarto-announcement");
+  if (annEl) {
+    annEl.remove();
+
+    const annId = annEl.getAttribute("data-announcement-id");
+    window.localStorage.setItem(`quarto-announce-${annId}`, "true");
+  }
+};
+
+const announceRegister = () => {
+  const annEl = window.document.getElementById("quarto-announcement");
+  if (annEl) {
+    const annId = annEl.getAttribute("data-announcement-id");
+    const isDismissed =
+      window.localStorage.getItem(`quarto-announce-${annId}`) || false;
+    if (isDismissed) {
+      announceDismiss();
+      return;
+    } else {
+      annEl.classList.remove("hidden");
+    }
+
+    const actionEl = annEl.querySelector(".quarto-announcement-action");
+    if (actionEl) {
+      actionEl.addEventListener("click", function (e) {
+        e.preventDefault();
+        // Hide the bar immediately
+        announceDismiss();
+      });
+    }
+  }
+};
+
 window.document.addEventListener("DOMContentLoaded", function () {
   let init = false;
 
+  announceRegister();
+
   // Manage the back to top button, if one is present.
   let lastScrollTop = window.pageYOffset || document.documentElement.scrollTop;
   const scrollDownBuffer = 5;
@@ -85,6 +121,17 @@ window.document.addEventListener("DOMContentLoaded", function () {
     }
   }
 
+  function dashboardOffset() {
+    const dashboardNavEl = window.document.getElementById(
+      "quarto-dashboard-header"
+    );
+    if (dashboardNavEl !== null) {
+      return dashboardNavEl.clientHeight;
+    } else {
+      return 0;
+    }
+  }
+
   function updateDocumentOffsetWithoutAnimation() {
     updateDocumentOffset(false);
   }
@@ -92,7 +139,7 @@ window.document.addEventListener("DOMContentLoaded", function () {
   function updateDocumentOffset(animated) {
     // set body offset
     const topOffset = headerOffset();
-    const bodyOffset = topOffset + footerOffset();
+    const bodyOffset = topOffset + footerOffset() + dashboardOffset();
     const bodyEl = window.document.body;
     bodyEl.setAttribute("data-bs-offset", topOffset);
     bodyEl.style.paddingTop = topOffset + "px";
@@ -205,9 +252,9 @@ window.document.addEventListener("DOMContentLoaded", function () {
   // Observe size changed for the header
   const headerEl = window.document.querySelector("header.fixed-top");
   if (headerEl && window.ResizeObserver) {
-    const observer = new window.ResizeObserver(
-      updateDocumentOffsetWithoutAnimation
-    );
+    const observer = new window.ResizeObserver(() => {
+      setTimeout(updateDocumentOffsetWithoutAnimation, 0);
+    });
     observer.observe(headerEl, {
       attributes: true,
       childList: true,
@@ -226,6 +273,7 @@ window.document.addEventListener("DOMContentLoaded", function () {
     const links = window.document.querySelectorAll("a");
     for (let i = 0; i < links.length; i++) {
       if (links[i].href) {
+        links[i].dataset.originalHref = links[i].href;
         links[i].href = links[i].href.replace(/\/index\.html/, "/");
       }
     }
@@ -233,7 +281,7 @@ window.document.addEventListener("DOMContentLoaded", function () {
     // Fixup any sharing links that require urls
     // Append url to any sharing urls
     const sharingLinks = window.document.querySelectorAll(
-      "a.sidebar-tools-main-item"
+      "a.sidebar-tools-main-item, a.quarto-navigation-tool, a.quarto-navbar-tools, a.quarto-navbar-tools-item"
     );
     for (let i = 0; i < sharingLinks.length; i++) {
       const sharingLink = sharingLinks[i];
diff --git a/docs/site_libs/quarto-search/autocomplete.umd.js b/docs/site_libs/quarto-search/autocomplete.umd.js
index 619c57c..ae0063a 100644
--- a/docs/site_libs/quarto-search/autocomplete.umd.js
+++ b/docs/site_libs/quarto-search/autocomplete.umd.js
@@ -1,3 +1,3 @@
-/*! @algolia/autocomplete-js 1.7.3 | MIT License | © Algolia, Inc. and contributors | https://github.com/algolia/autocomplete */
-!function(e,t){"object"==typeof exports&&"undefined"!=typeof module?t(exports):"function"==typeof define&&define.amd?define(["exports"],t):t((e="undefined"!=typeof globalThis?globalThis:e||self)["@algolia/autocomplete-js"]={})}(this,(function(e){"use strict";function t(e,t){var n=Object.keys(e);if(Object.getOwnPropertySymbols){var r=Object.getOwnPropertySymbols(e);t&&(r=r.filter((function(t){return Object.getOwnPropertyDescriptor(e,t).enumerable}))),n.push.apply(n,r)}return n}function n(e){for(var n=1;n<arguments.length;n++){var r=null!=arguments[n]?arguments[n]:{};n%2?t(Object(r),!0).forEach((function(t){o(e,t,r[t])})):Object.getOwnPropertyDescriptors?Object.defineProperties(e,Object.getOwnPropertyDescriptors(r)):t(Object(r)).forEach((function(t){Object.defineProperty(e,t,Object.getOwnPropertyDescriptor(r,t))}))}return e}function r(e){return r="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(e){return typeof e}:function(e){return e&&"function"==typeof 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m.createElement("section",{key:t,className:u.source,"data-autocomplete-source-id":l.sourceId},l.templates.header&&m.createElement("div",{className:u.sourceHeader},l.templates.header({components:p,createElement:m.createElement,Fragment:m.Fragment,items:c,source:l,state:f,html:a})),l.templates.noResults&&0===c.length?m.createElement("div",{className:u.sourceNoResults},l.templates.noResults({components:p,createElement:m.createElement,Fragment:m.Fragment,source:l,state:f,html:a})):m.createElement("ul",i({className:u.list},s.getListProps(n({state:f,props:r.getListProps({source:l})},o))),c.map((function(e){var t=r.getItemProps({item:e,source:l});return m.createElement("li",i({key:t.id,className:u.item},s.getItemProps(n({state:f,props:t},o))),l.templates.item({components:p,createElement:m.createElement,Fragment:m.Fragment,item:e,state:f,html:a}))}))),l.templates.footer&&m.createElement("div",{className:u.sourceFooter},l.templates.footer({components:p,createElement:m.createElement,Fragment:m.Fragment,items:c,source:l,state:f,html:a})))})),d=m.createElement(m.Fragment,null,m.createElement("div",{className:u.panelLayout},v),m.createElement("div",{className:"aa-GradientBottom"})),y=v.reduce((function(e,t){return e[t.props["data-autocomplete-source-id"]]=t,e}),{});e(n(n({children:d,state:f,sections:v,elements:y},m),{},{components:p,html:a},o),l.panel)}else c.contains(l.panel)&&c.removeChild(l.panel)}(r,t)}function C(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:{};l();var t=O.value.renderer,n=t.components,r=u(t,gr);g.current=qt(r,O.value.core,{components:Bt(n,(function(e){return!e.value.hasOwnProperty("__autocomplete_componentName")})),initialState:j.current},e),v(),c(),S.value.refresh().then((function(){D(j.current)}))}function k(e){requestAnimationFrame((function(){var t=O.value.core.environment.document.body.contains(A.value.detachedOverlay);e!==t&&(e?(O.value.core.environment.document.body.appendChild(A.value.detachedOverlay),O.value.core.environment.document.body.classList.add("aa-Detached"),A.value.input.focus()):(O.value.core.environment.document.body.removeChild(A.value.detachedOverlay),O.value.core.environment.document.body.classList.remove("aa-Detached")))}))}return a((function(){var e=S.value.getEnvironmentProps({formElement:A.value.form,panelElement:A.value.panel,inputElement:A.value.input});return Ht(O.value.core.environment,e),function(){Ht(O.value.core.environment,Object.keys(e).reduce((function(e,t){return n(n({},e),{},o({},t,void 0))}),{}))}})),a((function(){var e=_.value?O.value.core.environment.document.body:O.value.renderer.panelContainer,t=_.value?A.value.detachedOverlay:A.value.panel;return _.value&&j.current.isOpen&&k(!0),D(j.current),function(){e.contains(t)&&e.removeChild(t)}})),a((function(){var e=O.value.renderer.container;return e.appendChild(A.value.root),function(){e.removeChild(A.value.root)}})),a((function(){var e=p((function(e){D(e.state)}),0);return h.current=function(t){var n=t.state,r=t.prevState;(_.value&&r.isOpen!==n.isOpen&&k(n.isOpen),_.value||!n.isOpen||r.isOpen||E(),n.query!==r.query)&&O.value.core.environment.document.querySelectorAll(".aa-Panel--scrollable").forEach((function(e){0!==e.scrollTop&&(e.scrollTop=0)}));e({state:n})},function(){h.current=void 0}})),a((function(){var e=p((function(){var e=_.value;_.value=O.value.core.environment.matchMedia(O.value.renderer.detachedMediaQuery).matches,e!==_.value?C({}):requestAnimationFrame(E)}),20);return O.value.core.environment.addEventListener("resize",e),function(){O.value.core.environment.removeEventListener("resize",e)}})),a((function(){if(!_.value)return function(){};function e(e){A.value.detachedContainer.classList.toggle("aa-DetachedContainer--modal",e)}function t(t){e(t.matches)}var n=O.value.core.environment.matchMedia(getComputedStyle(O.value.core.environment.document.documentElement).getPropertyValue("--aa-detached-modal-media-query"));e(n.matches);var r=Boolean(n.addEventListener);return r?n.addEventListener("change",t):n.addListener(t),function(){r?n.removeEventListener("change",t):n.removeListener(t)}})),a((function(){return requestAnimationFrame(E),function(){}})),n(n({},w),{},{update:C,destroy:function(){l()}})},e.getAlgoliaFacets=function(e){var t=hr({transformResponse:function(e){return e.facetHits}}),r=e.queries.map((function(e){return n(n({},e),{},{type:"facet"})}));return t(n(n({},e),{},{queries:r}))},e.getAlgoliaResults=Or,Object.defineProperty(e,"__esModule",{value:!0})}));
 
diff --git a/docs/site_libs/quarto-search/quarto-search.js b/docs/site_libs/quarto-search/quarto-search.js
index f5d852d..d788a95 100644
--- a/docs/site_libs/quarto-search/quarto-search.js
+++ b/docs/site_libs/quarto-search/quarto-search.js
@@ -43,7 +43,7 @@ window.document.addEventListener("DOMContentLoaded", function (_event) {
   const mainEl = window.document.querySelector("main");
 
   // highlight matches on the page
-  if (query !== null && mainEl) {
+  if (query && mainEl) {
     // perform any highlighting
     highlight(escapeRegExp(query), mainEl);
 
@@ -57,7 +57,7 @@ window.document.addEventListener("DOMContentLoaded", function (_event) {
   // (e.g. if the user edits the query or clears it)
   let highlighting = true;
   const resetHighlighting = (searchTerm) => {
-    if (mainEl && highlighting && query !== null && searchTerm !== query) {
+    if (mainEl && highlighting && query && searchTerm !== query) {
       clearHighlight(query, mainEl);
       highlighting = false;
     }
@@ -98,6 +98,7 @@ window.document.addEventListener("DOMContentLoaded", function (_event) {
     classNames: {
       form: "d-flex",
     },
+    placeholder: language["search-text-placeholder"],
     translations: {
       clearButtonTitle: language["search-clear-button-title"],
       detachedCancelButtonText: language["search-detached-cancel-button-title"],
@@ -110,6 +111,8 @@ window.document.addEventListener("DOMContentLoaded", function (_event) {
       return item.href;
     },
     onStateChange({ state }) {
+      // If this is a file URL, note that
+
       // Perhaps reset highlighting
       resetHighlighting(state.query);
 
@@ -359,7 +362,8 @@ window.document.addEventListener("DOMContentLoaded", function (_event) {
                 state,
                 setActiveItemId,
                 setContext,
-                refresh
+                refresh,
+                quartoSearchOptions
               );
             },
           },
@@ -374,6 +378,32 @@ window.document.addEventListener("DOMContentLoaded", function (_event) {
     focusSearchInput();
   };
 
+  document.addEventListener("keyup", (event) => {
+    const { key } = event;
+    const kbds = quartoSearchOptions["keyboard-shortcut"];
+    const focusedEl = document.activeElement;
+
+    const isFormElFocused = [
+      "input",
+      "select",
+      "textarea",
+      "button",
+      "option",
+    ].find((tag) => {
+      return focusedEl.tagName.toLowerCase() === tag;
+    });
+
+    if (
+      kbds &&
+      kbds.includes(key) &&
+      !isFormElFocused &&
+      !document.activeElement.isContentEditable
+    ) {
+      event.preventDefault();
+      window.quartoOpenSearch();
+    }
+  });
+
   // Remove the labeleledby attribute since it is pointing
   // to a non-existent label
   if (quartoSearchOptions.type === "overlay") {
@@ -385,11 +415,30 @@ window.document.addEventListener("DOMContentLoaded", function (_event) {
     }
   }
 
+  function throttle(func, wait) {
+    let waiting = false;
+    return function () {
+      if (!waiting) {
+        func.apply(this, arguments);
+        waiting = true;
+        setTimeout(function () {
+          waiting = false;
+        }, wait);
+      }
+    };
+  }
+
   // If the main document scrolls dismiss the search results
   // (otherwise, since they're floating in the document they can scroll with the document)
-  window.document.body.onscroll = () => {
-    setIsOpen(false);
-  };
+  window.document.body.onscroll = throttle(() => {
+    // Only do this if we're not detached
+    // Bug #7117
+    // This will happen when the keyboard is shown on ios (resulting in a scroll)
+    // which then closed the search UI
+    if (!window.matchMedia(detachedMediaQuery).matches) {
+      setIsOpen(false);
+    }
+  }, 50);
 
   if (showSearchResults) {
     setIsOpen(true);
@@ -429,15 +478,27 @@ function configurePlugins(quartoSearchOptions) {
         const algoliaInsightsPlugin = createAlgoliaInsightsPlugin({
           insightsClient: window.aa,
           onItemsChange({ insights, insightsEvents }) {
-            const events = insightsEvents.map((event) => {
-              const maxEvents = event.objectIDs.slice(0, 20);
-              return {
-                ...event,
-                objectIDs: maxEvents,
-              };
+            const events = insightsEvents.flatMap((event) => {
+              // This API limits the number of items per event to 20
+              const chunkSize = 20;
+              const itemChunks = [];
+              const eventItems = event.items;
+              for (let i = 0; i < eventItems.length; i += chunkSize) {
+                itemChunks.push(eventItems.slice(i, i + chunkSize));
+              }
+              // Split the items into multiple events that can be sent
+              const events = itemChunks.map((items) => {
+                return {
+                  ...event,
+                  items,
+                };
+              });
+              return events;
             });
 
-            insights.viewedObjectIDs(...events);
+            for (const event of events) {
+              insights.viewedObjectIDs(event);
+            }
           },
         });
         return algoliaInsightsPlugin;
@@ -613,20 +674,30 @@ function showCopyLink(query, options) {
 /* Search Index Handling */
 // create the index
 var fuseIndex = undefined;
+var shownWarning = false;
+
+// fuse index options
+const kFuseIndexOptions = {
+  keys: [
+    { name: "title", weight: 20 },
+    { name: "section", weight: 20 },
+    { name: "text", weight: 10 },
+  ],
+  ignoreLocation: true,
+  threshold: 0.1,
+};
+
 async function readSearchData() {
   // Initialize the search index on demand
   if (fuseIndex === undefined) {
-    // create fuse index
-    const options = {
-      keys: [
-        { name: "title", weight: 20 },
-        { name: "section", weight: 20 },
-        { name: "text", weight: 10 },
-      ],
-      ignoreLocation: true,
-      threshold: 0.1,
-    };
-    const fuse = new window.Fuse([], options);
+    if (window.location.protocol === "file:" && !shownWarning) {
+      window.alert(
+        "Search requires JavaScript features disabled when running in file://... URLs. In order to use search, please run this document in a web server."
+      );
+      shownWarning = true;
+      return;
+    }
+    const fuse = new window.Fuse([], kFuseIndexOptions);
 
     // fetch the main search.json
     const response = await fetch(offsetURL("search.json"));
@@ -646,6 +717,7 @@ async function readSearchData() {
       );
     }
   }
+
   return fuseIndex;
 }
 
@@ -674,7 +746,8 @@ function renderItem(
   state,
   setActiveItemId,
   setContext,
-  refresh
+  refresh,
+  quartoSearchOptions
 ) {
   switch (item.type) {
     case kItemTypeDoc:
@@ -684,7 +757,9 @@ function renderItem(
         item.title,
         item.section,
         item.text,
-        item.href
+        item.href,
+        item.crumbs,
+        quartoSearchOptions
       );
     case kItemTypeMore:
       return createMoreCard(
@@ -709,15 +784,46 @@ function renderItem(
   }
 }
 
-function createDocumentCard(createElement, icon, title, section, text, href) {
+function createDocumentCard(
+  createElement,
+  icon,
+  title,
+  section,
+  text,
+  href,
+  crumbs,
+  quartoSearchOptions
+) {
   const iconEl = createElement("i", {
     class: `bi bi-${icon} search-result-icon`,
   });
   const titleEl = createElement("p", { class: "search-result-title" }, title);
+  const titleContents = [iconEl, titleEl];
+  const showParent = quartoSearchOptions["show-item-context"];
+  if (crumbs && showParent) {
+    let crumbsOut = undefined;
+    const crumbClz = ["search-result-crumbs"];
+    if (showParent === "root") {
+      crumbsOut = crumbs.length > 1 ? crumbs[0] : undefined;
+    } else if (showParent === "parent") {
+      crumbsOut = crumbs.length > 1 ? crumbs[crumbs.length - 2] : undefined;
+    } else {
+      crumbsOut = crumbs.length > 1 ? crumbs.join(" > ") : undefined;
+      crumbClz.push("search-result-crumbs-wrap");
+    }
+
+    const crumbEl = createElement(
+      "p",
+      { class: crumbClz.join(" ") },
+      crumbsOut
+    );
+    titleContents.push(crumbEl);
+  }
+
   const titleContainerEl = createElement(
     "div",
     { class: "search-result-title-container" },
-    [iconEl, titleEl]
+    titleContents
   );
 
   const textEls = [];
@@ -1099,17 +1205,19 @@ function algoliaSearch(query, limit, algoliaOptions) {
         const remappedHits = response.hits.map((hit) => {
           return hit.map((item) => {
             const newItem = { ...item };
-            ["href", "section", "title", "text"].forEach((keyName) => {
-              const mappedName = indexFields[keyName];
-              if (
-                mappedName &&
-                item[mappedName] !== undefined &&
-                mappedName !== keyName
-              ) {
-                newItem[keyName] = item[mappedName];
-                delete newItem[mappedName];
+            ["href", "section", "title", "text", "crumbs"].forEach(
+              (keyName) => {
+                const mappedName = indexFields[keyName];
+                if (
+                  mappedName &&
+                  item[mappedName] !== undefined &&
+                  mappedName !== keyName
+                ) {
+                  newItem[keyName] = item[mappedName];
+                  delete newItem[mappedName];
+                }
               }
-            });
+            );
             newItem.text = highlightMatch(query, newItem.text);
             return newItem;
           });
@@ -1120,8 +1228,34 @@ function algoliaSearch(query, limit, algoliaOptions) {
   });
 }
 
-function fuseSearch(query, fuse, fuseOptions) {
-  return fuse.search(query, fuseOptions).map((result) => {
+let subSearchTerm = undefined;
+let subSearchFuse = undefined;
+const kFuseMaxWait = 125;
+
+async function fuseSearch(query, fuse, fuseOptions) {
+  let index = fuse;
+  // Fuse.js using the Bitap algorithm for text matching which runs in
+  // O(nm) time (no matter the structure of the text). In our case this
+  // means that long search terms mixed with large index gets very slow
+  //
+  // This injects a subIndex that will be used once the terms get long enough
+  // Usually making this subindex is cheap since there will typically be
+  // a subset of results matching the existing query
+  if (subSearchFuse !== undefined && query.startsWith(subSearchTerm)) {
+    // Use the existing subSearchFuse
+    index = subSearchFuse;
+  } else if (subSearchFuse !== undefined) {
+    // The term changed, discard the existing fuse
+    subSearchFuse = undefined;
+    subSearchTerm = undefined;
+  }
+
+  // Search using the active fuse
+  const then = performance.now();
+  const resultsRaw = await index.search(query, fuseOptions);
+  const now = performance.now();
+
+  const results = resultsRaw.map((result) => {
     const addParam = (url, name, value) => {
       const anchorParts = url.split("#");
       const baseUrl = anchorParts[0];
@@ -1135,6 +1269,22 @@ function fuseSearch(query, fuse, fuseOptions) {
       section: result.item.section,
       href: addParam(result.item.href, kQueryArg, query),
       text: highlightMatch(query, result.item.text),
+      crumbs: result.item.crumbs,
     };
   });
+
+  // If we don't have a subfuse and the query is long enough, go ahead
+  // and create a subfuse to use for subsequent queries
+  if (
+    now - then > kFuseMaxWait &&
+    subSearchFuse === undefined &&
+    resultsRaw.length < fuseOptions.limit
+  ) {
+    subSearchTerm = query;
+    subSearchFuse = new window.Fuse([], kFuseIndexOptions);
+    resultsRaw.forEach((rr) => {
+      subSearchFuse.add(rr.item);
+    });
+  }
+  return results;
 }
diff --git a/test.R b/test.R
new file mode 100644
index 0000000..bc4091c
--- /dev/null
+++ b/test.R
@@ -0,0 +1,59 @@
+# Check if the 'pacman' package is installed, if not install it:
+if (!requireNamespace("pacman", quietly = TRUE)) install.packages("pacman")
+
+# Load the required libraries into the current R session:
+pacman::p_load(rio, 
+               here, 
+               tidyverse, 
+               skimr,
+               plyr,
+               janitor,
+               lubridate,
+               gtsummary, 
+               flextable,
+               officer,
+               epikit, 
+               apyramid, 
+               scales)
+
+
+# Import the raw data set:
+copdata <- rio::import(here::here("data", "spetses_school.csv"))
+# Create cross-tab with the group variable:  
+janitor::tabyl(dat = copdata, 
+               var1 = age, 
+               var2 = group)
+
+# Convert group to a factor and label 0 as teacher, 1 as student:
+copdata <- copdata %>% 
+  mutate(group = factor(group, 
+                        labels = c("teacher", "student")))
+
+# Update incorrect ages to the correct values with case_when:  
+copdata <- copdata %>%       
+  mutate(age =                         
+           case_when(                            
+             # Where respondent is 16 and a teacher, change age to 61:
+             age == 16 & group == "teacher" ~ 61,              
+             # where respondent is 8 or 180 and a student, change age to 18:
+             age == 8 & group == "student" ~ 18,              
+             age == 180 & group == "student" ~ 18,              
+             # Keep remaining values as is:              
+             .default = as.numeric(age)   
+             # if .default is not working, try:
+             # TRUE ~ age
+           )                    
+  ) 
+#| label: check_dose_cols
+#| tbl-cap: no caption
+
+# Create summary table for dose response columns: 
+drtable <- copdata %>%       
+  # Select all the columns with column names that end in upper case 'D':   
+  select(ends_with("D", ignore.case = FALSE)) %>%       
+  # Create the summary table, excluding missing values:   
+  gtsummary::tbl_summary(missing = "no") 
+
+# Print the summary table: 
+drtable
+