From faf0372595154f7443e36fefc6f546ab03718007 Mon Sep 17 00:00:00 2001 From: Alberto Mateo Urdiales <48100888+amateo250@users.noreply.github.com> Date: Wed, 3 Sep 2025 15:04:20 +0200 Subject: [PATCH 1/2] Version 2025 by Alberto --- .Rhistory | 467 +- Case_definition.qmd | 50 +- Data_import_cleaning.qmd | 52 +- IntroCourse_inCop.Rproj | 1 + Lab_and_Descriptive.qmd | 12 +- README.md | 12 +- Stratified.qmd | 40 +- Univariate.qmd | 46 +- _quarto.yml | 2 +- data/Spetses_clean1_2024.rds | Bin 8563 -> 8745 bytes data/Spetses_clean2_2024.rds | Bin 8563 -> 8781 bytes data/spetses_school.csv | 796 ++-- data/spetses_school_codebook.xlsx | Bin 24443 -> 27402 bytes docs-preview/Case_definition.html | 1522 +++++++ docs-preview/Data_import_cleaning.html | 3166 ++++++++++++++ .../figure-html/age_exploration-1.png | Bin 0 -> 11519 bytes docs-preview/Lab_and_Descriptive.html | 3273 ++++++++++++++ .../figure-html/epicurve date-time-1.png | Bin 0 -> 18227 bytes .../figure-html/epicurve strata-1.png | Bin 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mode 100644 docs-preview/site_libs/quarto-search/quarto-search.js create mode 100644 docs-preview/styles.css create mode 100644 docs/Lab_and_Descriptive_files/figure-html/unnamed-chunk-4-1.png delete mode 100644 docs/Lab_and_Descriptive_files/figure-html/unnamed-chunk-7-1.png create mode 100644 test.R 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/_quarto.yml b/_quarto.yml index a4a726e..5e987fb 100644 --- a/_quarto.yml +++ b/_quarto.yml @@ -1,6 +1,6 @@ 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z{{{SadGc?-q}QMnQ+$KJDE_;0>Ng+;=AVTk|E;6?_bdFns^m8+0}cq?Oc3G@541}{ z4)QUhfq1|N?UPc2M$J$_N%AN!Whj3gs5GD~OVSsQ=+6=UXCnJApkG%O+P{nXf706j z?$W3_?!Bl#Q(X_{EY+#Ccyu1hmhr$7W+4W#DBWOf1~gq^bZ0cUN$7qk~!=P550Js zpa*kAh&^Etyahh_U&H(_XYOyLibVc+g%{Tj=>^@;oERcm6cqP2)PFfge?!d{`xABB z;+MtKUOlzjOq4e?3(k2}iNY!F{$|M;0{jrQM8{mT*g8|6azKPlT= zB5?ix?fP$oRE7U*R diff --git a/docs-preview/Case_definition.html b/docs-preview/Case_definition.html new file mode 100644 index 0000000..8afab09 --- /dev/null +++ b/docs-preview/Case_definition.html @@ -0,0 +1,1522 @@ + + + + + + + + + +Case Definition – Spetses Introductory Course + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

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1. Learning outcomes

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At the end of the session, participants will be able to:

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  • Apply case definition criteria to a dataset using logical arguments in R
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2. Story/plot description

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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.

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3. Questions/Assignments

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If 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).

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To be sure we all start from the same case definition, let’s agree that a case was defined as a person who:

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Non cases (not-ill) were defined as people who:

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3.1 Install packages (if needed) and load libraries

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# 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)
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3.2 Import your data

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# Import the clean data set:
+copdata <- rio::import(here::here("data", "Spetses_clean1_2024.rds"), trust = TRUE)
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3.3 Identify the variables you need to apply the case definition criteria.

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The 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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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:

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a) Ate a meal at the school dinner

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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.

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filter by those with meal == 1.

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What are some of the implications this decision may lead to? (excluding any people from the cohort who didn’t eat at the dinner)

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+Show the code +
copdata <- copdata %>% 
+  filter(meal == 1)
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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:

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- 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.

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- 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.

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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.

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b) Fell ill after the start of the meal

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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.

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Note that we the concept of having eaten a meal is already included as per one of the steps above.

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Create a new gastrosymptoms column in your line list with mutate() and case_when().

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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)

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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).

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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.

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copdata <- copdata %>% 
+  mutate(gastrosymptoms = case_when(
+    # Those had diarrhoea...
+    diarrhoea == "1" |
+      #or bloody diarrhoea...
+    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)
+    )
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c) Fell ill within the time period of interest

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  • 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).
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  • Dinner 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).
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# Start with copdata:
+copdata <- copdata %>% 
+  # Create new column for meal date and time:
+  mutate(meal_datetime = lubridate::ymd_hm("2024-10-05 18:00"))
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  • You can calculate the incubation period for each participant by subtracting onset_datetime - meal_datetime.
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  • Then, 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.
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Be aware of the type of variable incubation is (check class()), as well as of missing valuesNA.

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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

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copdata <- copdata %>% 
+  mutate(incubation = onset_datetime - meal_datetime,
+         incubation = as.numeric (incubation))
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+median(as.numeric(copdata$incubation), na.rm = TRUE)
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[1] 15
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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!

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We see that the median incubation time is 15 hours. This is useful information, as incubation periods tend to be relatively pathogen-specific.

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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 (onset_datetime) at least one symptom (diarrhoea, bloody or vomiting) 48h after meal_datetime to become a case.

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What are some of the implications this decision may lead to? (implications of this case definition)

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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.

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d) Finally, with this information you can create a new case column to hold the binary (1/0) case definition variable.

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copdata <- copdata %>% 
+  mutate(case = case_when(
+    # Those who had symptoms <48h from the meal are cases (1)
+    gastrosymptoms == 1 & 
+      onset_datetime >= meal_datetime &
+      onset_datetime <= (meal_datetime + days(2)) ~ 1,
+    # Those who had symptoms >48h from the meal are non-cases (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 = 0)
+  )
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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…).

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Moreover, 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”.

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The two situations above are a reminder that you need to be both careful and aware of the implications of your data analysis decisions.

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What do you think are the risks of mis-classifying cases as non-cases in your analysis?

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We will have bias either towards or away from the null, depending on the proportions of subjects misclassified.

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# Tabulate cases:
+janitor::tabyl(dat = copdata, case)
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Let’s have a look at how many people ate a meal, had symptoms, and were considered as cases after applying our case definition:

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copdata %>% 
+  summarise(atemeal = sum(meal == 1),
+            hadsympt = sum(gastrosymptoms == 1),
+            nb_cases = sum(case == 1)
+            )
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4. Export clean data

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Finally, we can save the cleaned data set as a new file Spetses_clean2_YOURINITIALS, under the data folder, before we proceeding with descriptive analysis.

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rio::export(x = copdata, 
+            file = here::here("data", "Spetses_clean2_2024.rds"))
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Data import and cleaning

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1. Learning outcomes

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At the end of the session, participants will be able to:

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  • Import datasets in R
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  • Apply basic data management techniques to explore and familiarise themselves with the dataset
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  • Apply strategies for data cleaning
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2. Story/plot description

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You and your team have carried out hundreds of interviews in a very short time. Well done!

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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.

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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.

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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).

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Open R Studio and set up a new R project, and create a data and a scripts folder.

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3. Tips you could follow

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3.1. Install packages and load libraries

+
    +
  • 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 masking functions.
    • +
+
+
+Show the code +
# 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 
+)
+
+
+
+
+

3.2. Import your data

+
    +
  • You can find the spetses_school.csv file in EVA. Feel free to explore the codebook as well.
    • +
+
+
+Show the code +
# Import the raw data set:
+copdata <- import(here::here("data", "spetses_school.csv"))
+
+
+
+
+

3.3. Explore and clean your data

+
    +

  • Have a look at the structure of your data. Here you have some questions that may help you get started:

    +
      +

    • How many observations and variables does the dataset contain?

      • +

    • What types of variables do you have and what types of values are recorded?

      • +

    • Do any of the values of the other variables look implausible? Which ones and why? What will you do about it?

      • +
    • +

  • Go ahead and clean your data as you see fit.

    • +
+
+ +
+
+

You 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

+
+
+
+
+
+Show the code +
head(copdata)
+
+
+
+ +
+
+
+Show the code +
dim(copdata)
+
+
+
[1] 397  41
+
+
+Show the code +
str(copdata)
+
+
+
'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 ...
+ $ 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 ...
+ $ 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 ...
+ $ 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" "" "" "" ...
+
+
+Show the code +
skim(copdata)
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Data summary
Namecopdata
Number of rows397
Number of columns41
_______________________
Column type frequency:
character3
numeric38
________________________
Group variablesNone
+

Variable type: character

+ ++++++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
skim_variablen_missingcomplete_rateminmaxemptyn_uniquewhitespace
id018803970
sex0146020
dayonset010817540
+

Variable type: numeric

+ +++++++++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
skim_variablen_missingcomplete_ratemeansdp0p25p50p75p100hist
age01.0018.579.928161718180▇▁▁▁▁
group01.000.960.2001111▁▁▁▁▇
class360.911.940.8411233▇▁▆▁▇
ill01.000.690.4700111▃▁▁▁▇
diarrhoea1410.640.820.3901111▂▁▁▁▇
bloody2000.500.030.1700001▇▁▁▁▁
vomiting1790.550.300.4600011▇▁▁▁▃
abdo1520.620.850.3501111▂▁▁▁▇
nausea1700.570.760.4301111▂▁▁▁▇
fever2230.440.260.4400011▇▁▁▁▃
headache1740.560.630.4800111▅▁▁▁▇
jointpain2070.480.160.3700001▇▁▁▁▂
starthour1770.5512.494.923991521▁▇▁▆▃
meal90.980.970.1701111▁▁▁▁▇
feta160.960.710.4500111▃▁▁▁▇
fetaD160.961.321.0000223▆▅▁▇▂
sardines170.960.670.4700111▃▁▁▁▇
sardinesD170.961.341.0400223▆▂▁▇▂
eggplant300.920.590.4900111▆▁▁▁▇
eggplantD300.921.141.0500123▇▂▁▇▂
veal150.960.890.3101111▁▁▁▁▇
vealD140.961.830.9101223▂▃▁▇▃
pasta150.960.880.3201111▁▁▁▁▇
pastaD150.961.810.9101223▂▃▁▇▃
tomsal240.940.570.5000111▆▁▁▁▇
tomsalD240.941.081.0600123▇▂▁▆▂
bread180.950.910.2901111▁▁▁▁▇
breadD180.951.750.7102223▁▂▁▇▁
dessert420.890.420.4900011▇▁▁▁▆
dessertD420.890.831.0600023▇▁▁▃▁
champagne250.940.870.3401111▁▁▁▁▇
champagneD250.941.370.9301123▂▇▁▂▃
beer300.920.780.4201111▂▁▁▁▇
beerD350.911.941.2301333▃▂▁▂▇
redwine500.870.230.4200001▇▁▁▁▂
redwineD520.870.450.9200003▇▁▁▁▁
whitewine310.920.730.4500111▃▁▁▁▇
whitewineD360.911.581.2100233▆▅▁▅▇
+
+
+Show the code +
names(copdata)
+
+
+
 [1] "id"         "sex"        "age"        "group"      "class"     
+ [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"
+
+
+

Let’s explore and manage some variables in detail.

+
+

Age

+

Through 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.

+
+
+Show the code +
# Have a look at the histogram  
+hist(copdata$age)
+
+
+
+
+

+
+
+
+
+Show the code +
# Create cross-tab with the group variable:  
+tabyl(dat = copdata, 
+               var1 = age, 
+               var2 = group)
+
+
+
+ +
+
+
+

Note 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:

+
+
+Show the code +
# Convert group to a factor and label 0 as teacher, 1 as student:
+copdata <- copdata %>% 
+  mutate(group = factor(group, 
+                        labels = c("teacher", "student")))
+
+
+

Now, have a look at your cross-tab again:

+
+
+Show the code +
tabyl(dat = copdata, 
+               var1 = age, 
+               var2 = group)
+
+
+
+ +
+
+
+

With this table, we can more easily identify ages that are likely to be typographic errors. Specifically:

+
    +

  • There is one teacher aged 16 (likely digit reversal - should be 61)

    • +

  • There is one student aged 8 (likely missing a digit - should be 18)

    • +

  • There is one student aged 180 (likely has an extra digit - should be 18)

    • +
+

Assuming 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:

+
+
+Show the code +
# 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
+             )                    
+         )
+
+
+
+
+

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.

+
+
+
    +
  • Create summary table for the dose response columns (those finishing with a capital “D”) to have a look at their distribution.
    • +
+
+ +
+
+

Use gtsummary::tbl_summary().

+
+
+
+
+
+Show the code +
#| 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:   
+  tbl_summary(missing = "no") 
+
+  # Print the summary table: 
+drtable
+
+
+
+ + + ++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
CharacteristicN = 3971
fetaD
+
    0110 (29%)
    178 (20%)
    2155 (41%)
    338 (10.0%)
sardinesD
+
    0125 (33%)
    135 (9.2%)
    2184 (48%)
    336 (9.5%)
eggplantD
+
    0151 (41%)
    141 (11%)
    2147 (40%)
    328 (7.6%)
vealD
+
    042 (11%)
    170 (18%)
    2184 (48%)
    387 (23%)
pastaD
+
    044 (12%)
    170 (18%)
    2183 (48%)
    385 (22%)
tomsalD
+
    0162 (43%)
    152 (14%)
    2126 (34%)
    333 (8.8%)
breadD
+
    035 (9.2%)
    151 (13%)
    2267 (70%)
    326 (6.9%)
dessertD
+
    0206 (58%)
    133 (9.3%)
    287 (25%)
    329 (8.2%)
champagneD
+
    049 (13%)
    1208 (56%)
    245 (12%)
    370 (19%)
beerD
+
    081 (22%)
    141 (11%)
    257 (16%)
    3183 (51%)
redwineD
+
    0266 (77%)
    131 (9.0%)
    221 (6.1%)
    327 (7.8%)
whitewineD
+
    0100 (28%)
    173 (20%)
    267 (19%)
    3121 (34%)
1 n (%)
+ +
+
+
+
+
+
+

3.4. Modify variables format

+

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.

+
+
+

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.

+
+
+

You can use mutate to modify the type of the following variables:

+ + ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Table 1: Variable types to modify
Variable nameOriginalDesiredHint
sexcharacterfactormutate(), as.factor()
classintegerfactormutate(), as.factor()
illintegerfactormutate(), as.factor()
All the clinical symptom variablesintegerfactormutate(across()), as.factor()
All the food variables representing the amount of specific foods eaten (those finishing with a capital “D”)integerfactormutate(across()), as.factor()
dayonsetcharacterdatelubridate::dmy()
starthour and dayonset together

integer (starthour)

+

date (dayonset)

POSIXct, POSIXtlubridate::ymd_h() could have inside stringr::str_glue() with dayonset and starthour
+
+

Sex, class and ill

+

Let’s start transforming one-by-one the first three variables in the table: sex, class and ill.

+
+
+Show the code +
copdata <- copdata %>% 
+  mutate(
+    sex = as.factor(sex),
+    class = as.factor(class),
+    ill = as.factor(ill))
+
+
+
+
+

Symptoms and Food variables

+

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.

+
    +
  1. For the variables that are clinical symptoms, we will list them one by one and show you the use of mutate(across( )).
    2. +
+
+
+Show the code +
copdata <- copdata %>% 
+  mutate(
+    # clinical symptoms
+    across(.cols = c(diarrhoea, bloody, vomiting,
+             abdo, nausea, fever,headache, jointpain), 
+           .fns = ~ as.factor(.)
+           )
+    )
+
+
+
    +
  1. For 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.
    2. +
+
+
+Show the code +
# 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 %>% 
+    select(
+      ends_with("D", ignore.case = FALSE)) %>% 
+    names()
+
+# Another way of doing it:
+# food_dose <- c("fetaD", "sardinesD", "eggplantD", "pastaD", 
+#                 "vealD", "greeksalD", "dessertD", "breadD", 
+#                 "champagneD", "beerD", "redwineD", "whitewineD")
+
+
+copdata <- copdata %>% 
+  mutate(
+    # food dose variables
+    across(.cols = all_of(food_dose), 
+           .fns = ~as.factor(.)))
+
+
+
+ +
+
+

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

+
+ +
+
+

Why may you want to transform the variable starthour to date-time?

+
+
+
+
+
+

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.

+
+
+

You 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”).

+
+
+Show the code +
# Have a look at how the data is stored
+head(copdata$dayonset)
+
+
+
[1] "6oct2024" ""         ""         ""         "6oct2024" "7oct2024"
+
+
+Show the code +
class(copdata$dayonset)
+
+
+
[1] "character"
+
+
+Show the code +
# Update copdata:
+copdata <- copdata %>% 
+  # Change column to date class:
+  mutate(
+    dayonset = lubridate::dmy(dayonset))
+
+# Check class of updated column:
+class(copdata$dayonset)
+
+
+
[1] "Date"
+
+
+Show the code +
# Have a look at your data now:
+head(copdata$dayonset)
+
+
+
[1] "2024-10-06" NA           NA           NA           "2024-10-06"
+[6] "2024-10-07"
+
+
+

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 lubridate::ymd_h() function.

+

Before 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.

+

We can check if we have any missing values by cross-tabulating starthour with dayonset:

+
+
+Show the code +
# Cross-tabulate dayonset with starthour:
+tabyl(dat = copdata, 
+               var1 = starthour, 
+               var2 = dayonset)
+
+
+
+ +
+
+
+

This 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:

+
+
+Show the code +
copdata <- copdata %>% 
+  # Combine dayonset and starthour in a new date time variable:
+  mutate(onset_datetime = 
+           ymd_h(
+             str_glue("{dayonset}, {starthour}"), 
+                                           # Deal with missing starthour:
+                                           truncated = 2))
+
+
+
Warning: There was 1 warning in `mutate()`.
+ℹ In argument: `onset_datetime = ymd_h(str_glue("{dayonset}, {starthour}"),
+  truncated = 2)`.
+Caused by warning:
+!  175 failed to parse.
+
+
+

Note 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.

+

The 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.

+

Now we can check that everything in the new combined date-time variable has parsed correctly:

+
+
+Show the code +
head(copdata$dayonset)
+
+
+
[1] "2024-10-06" NA           NA           NA           "2024-10-06"
+[6] "2024-10-07"
+
+
+Show the code +
head(copdata$starthour)
+
+
+
[1]  9 NA NA NA 15 15
+
+
+Show the code +
head(copdata$onset_datetime)
+
+
+
[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"
+
+
+
+
+
+

3.5. Export clean data

+

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.

+

Save your clean data as a new file “Spetses_clean1_YOURINITIALS.rds”, under the data folder of your Rproject.

+
+ +
+
+

Use rio::export().

+
+
+
+
+
+Show the code +
export(x = copdata, 
+            file = here::here("data", "Spetses_clean1_2024.rds"))
+
+
+
+
+

+
+
+ + + + +
+
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/docs-preview/Data_import_cleaning_files/figure-html/age_exploration-1.png b/docs-preview/Data_import_cleaning_files/figure-html/age_exploration-1.png new file mode 100644 index 0000000000000000000000000000000000000000..6d4223a81377611a77da06ff800deccd2fc3e4c9 GIT binary patch literal 11519 zcmeHt2UJsAw=M}K3Iaz&QCbicumDy{D2a*!iWEhWZUqz(q)G{a!vPMWfFeao2!hf< zDG{VZ4+2620uc~G6$6AG2sH`0YkU52-}(PL?i=roJMOsm-S>yE_GYa)=UQ{FIlnpA zW=ENu8j1=_3-j^ui5eRjT;$^u#PIR)f7rwidg?3pOo0o>+{E$>xbpF#`1qXoP$)hW zXyf>tocNqTO9Z{33+jeGPAF6y)HpezoWKnc6&Ht!qd5Ui6!?M>Cujuv#5u(goxl*0 z=tKl9FAl{Q2bi6}*9l?*bmAjAp@?zd>jVvgVIbz` zg-E1%R110vxkwj7ayNoB=if5r895y@hR{b8=SO!m^L$j#Zg`AzFn6&bzaQly}3MU z7ZKAt+v_`FdbRJ^i&HuB$?`|5e+%*F&Am#bBbpOs*+SN&{;Fd1*lF1_#JNvHPY*Gc zheh~|g|KM85b`+;Ok%SNMiC}xw3omaB_OHrGz7j^!aT$PE*xj{KVooVaJ6^1F|rOR zFoX{|wIXo*5TZQcx{$TVZ0t_fYo~B>g&sXk*isIEgmzt z*`>Hni7?)-EFvI2!7|=L{HD)ANeQyPXQFgu580OwwC~$Ps9H}6F)i4tOK^@>@ma^H z3r~cfUV7Wt9;2Dms{?E8iU@I-I4aM z!L5|pcjI*$1Zol2m1Gk8M=T0gABx>ZvD-YM$*h2-i?O*2$6v?tyvNl`NZdb}iiY^H zEA|gGD8tJGy~Y}3O{pYy>Rg5t*^Xq379StTYDSfyH_VFzZC+UP#yh$v3?;9!CYQaP z*=?i#(=p^gq#5amPK9&8%l4TjH$A&r-sDZY65Fb==E>lp@|fN*EJ{Z%KRBj?#k9jM zWXe;z#!K}rUE7J$l)f-KWK!a8RU~_4eVZa5P%{x$BtNQ^JXr1!KIyMT{$i41F1=AH zO-UkWZ1_}dj7)nsA0?2kw4FlXDz9E1y<}a@y8x3T*$mQa#b>6H5v-XYCSCh%bd%To zYiUZwc&UcvRJ$*#TT{PKu>886O@#M?2VOO?c6_JhAz-i>xQ$NJ>l(Kr;jBr;=z%W! z9fPnDTnU2Z>%Umxn1b__rqIZ??i|{}NlA2B{&hJSI4byxeSkwzRI?IjVHI5pU|f@q+3#ZDsPdw z#he(~--OR#yebx?Rf~kYZ9-ge>6|Gl*aqh)Y!soDZm5u23yZWV`G9ZmC}^6JH(VSLt$zjB*kuetVUn z7sV#0=DgD{(5+}3zbKR6xG#}gy?BG^B9nA)S0|&iUKv^UxPYX1;}?pZ+jfe@5*^27 zBb^ZYyYmkhoY@uj(uUrB(T$uZP3g7uNmvnXiCpx4+t@P8sR_%XA2^sYM>;`hBBzq8 zYliA#Ea+`XX1VKMonq&lro8gRE|Vp?GzB!CoF~U!8G0kH+W102;J6hH;a#cG)NptE z4xMi2#RN-{BkY$FlruezBrapQwIv`CmM)lYx54nF7r@-Ac*Kxy)5BVIT&{SIOt(et zBHdQj&8&!7^>xFMT4S)Z+QrE{q1x+7a!zn*Gd;jPwe#mLp&TdHSI}0MEb~s)-lZpY za1&C=wqQ5Ek?lk$4q>xe0;Jn5e@fRlTj?g3wWR!ZB=3ZNQsN@U6~;Xp9O%lFaoIqA z`d&M>#4g&tZe72rLm9aB6~xp0Ce;W(sT`)IL;1n9VDDcg*jdeTN1c&?!kt*!Oye^4W@#g0%7jsAlRs1{i)|j9-tNK{ZRy#ZL|f z|H5DhBA)Kpt|I=BbhY346vlQIR}g08T#x3^mm-&a9(QLLuuo=>LBZ{MK5c_FP?S>^ zf^WrN8+~RgYoI6#nFjH6kQBf&Ll>)hFz=WwEqY%uy=gV_ABJp+b9bs0?SH2^zCq|H=bx%kTJ{7AoUkiXCz#m zk%V?>%wYYtrfs){{%cqF#Mlb1bZFxE#a;Ph`&!7+_(YhV+T2V3=EU9h4q}G~vV%D` z=l5&{Vk-nu)xYE#eXfoqvt--vV|i?jV9m(IwiVYT&u)HHP-&(4@qGl<71G+pw(p%3 zoUOyY-qM((4phX|+UH)xM^Y8%57^?*-_A`*O13|?Ks4OEI~1CA@<}W_PLIjlP#+k{zs4byqrf0N&@xE2<_wiH|y7a&u=9} z_HJwYE0ksH`NA(ZFIGi(`!vre9eE(_q5`3$m|{^4YR)RrK1lDbO?Ka)E6;~{7pbcF zw)D&J@{M4k96u1a6ll}ugQ^arzj(9AF2>8UBiHKh2di~u-nIUIdkJr)yfaU=S2z3M zB{39U@=?gxU2@huHI|-EdY|z{3Qw9KTg_Lu-1y(R&)*ls zJGfH*RX5XpJ3y<{_3{aAhZUnTWc&%GYE6`T8NR#|iW4?)&~I3r=t z|Il*oKUkTnB!!4A7tc9wVX4K6O`|j5!nd|jc-78u;Z3FiRm; 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+
+ +
+ +
+ + + + +
+ +
+
+

Laboratory data and descriptive analysis

+
+ + + +
+ + + + +
+ + + +
+ + +
+

1. Learning outcomes

+

At the end of the session, participants will be able to:

+
    +
  • Merge two datasets using a common column.
    • +
  • Discuss which descriptive statistics would you use to describe the cases
    • +
  • Describe the cases by person and place (this is an outbreak in a high school, so we won’t explore “place” this time!)
    • +
  • Generate preliminary hypotheses bases on the descriptions.
    • +
+
+
+

2. Story/plot description

+

You have received the laboratory results for the samples collected from some of the cases. The lab conducted an RT-PCR panel targeting various gastrointestinal pathogens and also gathered additional metadata. Your task is to merge the questionnaire data with the data you have received from the lab, using the unique identifiers that both datasets share (id column), and explore that lab data. What does it tell you?

+

Once your dataset is clean and the lab data merged, you can 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.

+
+
+

3. Questions/Assignments

+

If you closed your R project, please open it again and load the clean dataset. We recommend you use the Spetses_clean2_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).

+
+
+

3.1 Install packages (if needed) and load libraries

+
+
+Show the code +
# 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)
+
+
+
+
+

3.2 Import your data

+
+
+Show the code +
# Import the clean data set:
+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.
+
+
+
+
+

3.3 Merge and explore the lab data. Explore it as you wish!

+
+
+Show the code +
copdatalab <- left_join(copdata, lab, 
+                        by = "id")
+# Tabulate:
+janitor::tabyl(dat = copdatalab, "RT-PCR_ecoli_etec")
+
+
+
+ +
+
+
+
+
+

3.4 Describe the outbreak in terms of time.

+

You 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.

+
+

a) Create a histogram to visualize the incubation period (geom_histogram is a good function to use for this!).

+
+ +
+
+

What can you infer from the incubation periods in the histogram?

+
+
+
+
+
+Show the code +
#| label: inc_time
+
+# Create a dataset with only cases
+cases <- copdata %>% 
+  filter(case == 1)
+
+incplot <- cases %>% 
+  # Create an empty ggplot frame:
+  ggplot() +
+  # Add a histogram of incubation:
+  geom_histogram(
+    mapping = aes(x = incubation), 
+    # Set bin widths to 6 hours:
+    binwidth = 6) +
+  # Adapt scale to better fit data
+  scale_x_continuous(breaks = seq(0, 48, 6)) + 
+  # Label x and y axes:
+  labs(x = "Incubation period in 6-hour bins",
+       y = "Number of cases")
+
+# Print plot:
+incplot
+
+
+
+
+

+
+
+
+
+
+
+

b) Create an epicurve for the date and time of onset (using onset_datetime), limiting the input data to cases.

+
    +
  • Use scale_x_datetime() and chose a value for date_breaks() based on your calculated incubation period.
    • +
  • Label your x and y axes using labs().
    • +
+
+ +
+
+

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 date_breaks argument.

+
+
+
+
+
+Show the code +
# Create a vector with sequences every 6h from the first to the last case
+breaks_6h <- seq(from = min(cases$onset_datetime, na.rm = TRUE),
+                 to = max(cases$onset_datetime, na.rm = TRUE),
+                 by = "6 hours")
+
+# Fetch cases data:
+epicurve_datetime <- cases %>%  
+  # Add factor onset_datetime to ggplot aesthetic:
+  ggplot(
+    mapping = aes(x = onset_datetime)) + 
+  # Add geom_histogram:
+  geom_histogram(
+    # Apply the vector of requences created above
+    breaks = breaks_6h) +
+  # Adapt scale to data and adjust axis label angle:
+  scale_x_datetime(
+    date_breaks = "6 hours",
+    labels = label_date_short()) +
+  # Update x and y axis labels:
+  labs(x = "Date and time of onset symptoms", 
+       y = "Number of cases") +
+  # Remove unnecessary grid lines:
+  theme_bw()
+
+# Print epicurve:
+epicurve_datetime
+
+
+
+
+

+
+
+
+
+
    +
  1. Building up on your previous epicurve, create another epicurve to compare between sexes and additionally investigate how teachers versus students were distributed.
    2. +
+
    +
  • Use fill = group
    • +
  • Use sex as your facets for facet_wrap()
    • +
+
+ +
+
+

fill 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.

+
+
+
+
+ +
+
+

If 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.

+
+
+
+
+
+Show the code +
epicurve_strata <- cases %>% 
+  # Add factor onset_day to ggplot aesthetic:
+  ggplot(
+    mapping = aes(x = onset_datetime, fill = group)) + 
+  # Add nicer fill colours:
+  scale_fill_manual(values = c("darkred", "lightblue")) +
+    # Add geom_histogram:
+  geom_histogram(
+    # Apply the vector of requences created above
+    breaks = breaks_6h) +
+  # Adjust x axis scales to a suitable unit:
+  scale_x_datetime(
+    date_breaks = "6 hours", 
+    labels = label_date_short()) +
+  # Update x and y axis labels:
+  labs(x = "Date and time of onset", 
+       y = "Number of cases", 
+       fill = "Group", 
+       title = "Epicurve of the outbreak, stratified by sex",
+       subtitle = str_glue("Spetses, October 2024, N = {sum(copdata$case)}")) +
+  # Stratify by sex:
+  facet_wrap(facets = "sex",
+             ncol = 2) +
+  # Add theme:
+  theme_bw()
+
+# Print epicurve:
+epicurve_strata
+
+
+
+
+

+
+
+
+
+
+ +
+
+

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?

+
+
+
+
+ +
+
+

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.

+

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.

+

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.

+

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!

+
+
+
+
+
+
+

3.5. Describe the outbreak in terms of person.

+

You 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.

+
+

a) Create a cross-tabulation of case with group.

+
+
+Show the code +
copdata %>% 
+  janitor::tabyl(case, group) %>% 
+  adorn_totals() %>% 
+  adorn_percentages() %>% 
+  adorn_pct_formatting()
+
+
+
+ +
+
+
+
+
+

b) Create a cross-tabulation of case with sex.

+
+
+Show the code +
copdata %>% 
+  janitor::tabyl(case, sex) %>% 
+  adorn_totals() %>% 
+  adorn_percentages() %>% 
+  adorn_pct_formatting()
+
+
+
+ +
+
+
+
+ +
+
+

What do you think of these results?

+
+
+
+
+ +
+
+

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.

+
+
+
+
+ +
+
+

If 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().

+
+
+
+
+
+

c) Extra - Age-sex pyramid of cases.

+

Hint: Change show_midpoint = FALSE to TRUE to see skewedness in the data patterns more easily.

+
+
+Show the code +
copdata <- copdata %>% 
+  # Create age categories:
+  mutate(age_cat = epikit::age_categories(
+    # Name of age column:
+    x = age, 
+    # Define the age categories:
+    breakers = c(0, 10, 16, 18, 20, 50, 70)
+    )
+  )
+
+
+# Check age categories:
+janitor::tabyl(copdata, age_cat)
+
+
+
+ +
+
+
+Show the code +
# Pipe copdata:
+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
+
+
+
+
+

+
+
+
+
+
+
+
+

3.6 Explore the distribution of all clinical signs (symptoms).

+
+

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.

+
+
+Show the code +
# 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
+
+
+
+ + + ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
CharacteristicOverall N = 3771Non-case N = 1611Case N = 2161
Diarrhoea
+
+
+
    046 (18%)40 (100%)6 (2.8%)
    1206 (82%)0 (0%)206 (97%)
Dysentary
+
+
+
    0189 (97%)42 (100%)147 (97%)
    15 (2.6%)0 (0%)5 (3.3%)
Vomiting
+
+
+
    0149 (69%)42 (100%)107 (62%)
    166 (31%)0 (0%)66 (38%)
Abdominal pain
+
+
+
    035 (14%)6 (12%)29 (15%)
    1207 (86%)44 (88%)163 (85%)
Nausea
+
+
+
    055 (25%)12 (26%)43 (24%)
    1169 (75%)34 (74%)135 (76%)
Fever
+
+
+
    0127 (74%)32 (80%)95 (73%)
    144 (26%)8 (20%)36 (27%)
Headache
+
+
+
    083 (38%)11 (25%)72 (41%)
    1137 (62%)33 (75%)104 (59%)
Joint pain
+
+
+
    0159 (85%)32 (84%)127 (85%)
    129 (15%)6 (16%)23 (15%)
1 n (%)
+ +
+
+
+
+ +
+
+

Do you think the symptoms selected for the case definition were the right ones, or would you change anything?

+
+
+
+
+ +
+
+

If 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.

+
+
+
+
+
+

b) Extra - Bar plot of symptoms stratified by case definition.

+
+
+Show the code +
# 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)
+
+
+
`summarise()` has grouped output by 'Symptoms'. You can override using the
+`.groups` argument.
+
+
+Show the code +
# Print plot:
+symptom_bar
+
+
+
+
+

+
+
+
+
+
+
+
+

3.7 Attack proportions

+
+

a) Calculate the overall attack proportions (percentage of cases among the total observed individuals). You could use tabyl().

+
+
+Show the code +
# 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
+
+
+
[1] "57.3%"
+
+
+
+ +
+
+

What can you infer from the overall attack proportion about this outbreak and possible vehicles/exposures?

+
+
+
+
+ +
+
+

The overall attack proportion is 57.3%. This means that more than half of the people who ate a meal were cases!

+
+
+
+
+
+

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.

+
+
+Show the code +
# 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
+
+
+
+ + + ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
CharacteristicOverall N = 3771Non-case N = 1611Case N = 2161
class
+
+
+
    1131 (100%)63 (48%)68 (52%)
    2101 (100%)44 (44%)57 (56%)
    3111 (100%)38 (34%)73 (66%)
    Unknown341618
group
+
+
+
    teacher15 (100%)9 (60%)6 (40%)
    student362 (100%)152 (42%)210 (58%)
sex
+
+
+
    female213 (100%)96 (45%)117 (55%)
    male164 (100%)65 (40%)99 (60%)
1 n (%)
+ +
+
+
+
+
+
+

3.8 Draft the relevant paragraph(s) and table(s) and/or figure(s) in your outbreak report (Methods and results).

+
+ +
+
+

To 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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+
+ +
+ +
+ + + + +
+ +
+
+
+
+ + + +
+ + + + +
+ + + +
+ + +
+

1. Learning outcomes

+

At the end of the session, participants will be able to:

+
    +

  • Consider the effect of confounding and effect modification on the association between exposure and disease,

    • +

  • Perform stratified analysis using the Mantel-Haenszel approach

    • +
+

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.

+
+
+

2. Story/plot description

+

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.

+
+
+

3. Questions/Assignments

+
+
+

3.1. Confounders and effect modification

+

Discuss how to identify potential confounders and effect modification. Draw dummy tables before coding to have clear what you want to achieve.

+
+
+

3.2. Install packages and load libraries

+
+
+Show the code +
# 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)
+
+
+
+
+

3.3. Import your data

+
+
+Show the code +
# Import the raw data set: 
+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, 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 veal and champagne, stratified by pasta, but you may decide to look at other food items as well.

+
+ +
+
+

Use the function csinter() of the EpiStats package.

+
+
+
+
+

a) Veal as exposure of interest, stratified by having eaten pasta

+

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?

+
+
+Show the code +
stratall <- copdata %>% 
+  # Mutate across to convert cases to numeric:
+  mutate(across(.cols = case, 
+                .fns = ~ as.numeric(.)))
+
+# Pass data to the csinter function:
+pastastrata <- csinter(x = stratall, 
+                       cases = "case", 
+                       exposure = "veal", 
+                       by = "pasta")
+
+pastastrata
+
+
+
$df1
+  CSInter case - veal by(pasta) Total Cases Risk %          P.est. Stats
+1                     pasta = 1   338  <NA>     NA Risk difference  0.10
+2                       Exposed   330   198  60.00      Risk Ratio  1.20
+3                     Unexposed     8     4  50.00 Attrib.risk.exp  0.17
+4                                  NA  <NA>     NA Attrib.risk.pop  0.16
+5                     pasta = 0    36  <NA>     NA Risk difference  0.02
+6                       Exposed     8     3  37.50      Risk Ratio  1.05
+7                     Unexposed    28    10  35.71 Attrib.risk.exp  0.05
+8                                  NA  <NA>     NA Attrib.risk.pop  0.01
+9           Missing / Missing %     3  0.8%     NA            <NA>    NA
+  95%CI.ll 95%CI.ul
+1    -0.25     0.45
+2     0.60     2.41
+3    -0.68     0.59
+4       NA       NA
+5    -0.36     0.40
+6     0.38     2.92
+7    -1.65     0.66
+8       NA       NA
+9       NA       NA
+
+$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 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
+
+
+

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)):

+
+
+Show the code +
# Perform Wilcoxon rank sum test on veal and pasta:
+wilcox.test(veal ~ pasta, 
+            data = copdata)
+
+
+

+    Wilcoxon rank sum test with continuity correction
+
+data:  veal by pasta
+W = 1496, p-value < 2.2e-16
+alternative hypothesis: true location shift is not equal to 0
+
+
+
+
+

b) Champagne as exposure of interest, stratified by having eaten pasta

+
+
+Show the code +
# Pass data to the csinter function:
+champstrata <- csinter(x = stratall, 
+                       cases = "case", 
+                       exposure = "champagne", 
+                       by = "pasta")
+
+
+
+ +
+
+

Are there any indications to make you think there may be effect modification and/or confounding?

+
+
+
+

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 “pastastrata”.

+

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 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 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!

+ + + + +
+
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/docs-preview/Univariate.html b/docs-preview/Univariate.html new file mode 100644 index 0000000..1b7db88 --- /dev/null +++ b/docs-preview/Univariate.html @@ -0,0 +1,3893 @@ + + + + + + + + + +Univariable analysis – Spetses Introductory Course + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ +
+ + + + +
+ +
+
+

Univariable analysis

+
+ + + +
+ + + + +
+ + + +
+ + +
+

1. Learning outcomes

+

At the end of the session, participants will be able to:

+
    +

  • Perform hypothesis tests.

    • +

  • Estimate risk ratios (also called relative risk) for categorical data.

    • +

  • Interpret the univariable results.

    • +

  • (Optional) Investigate dose-response relationships in categorical data.

    • +
+
+
+

2. Story/plot description

+

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.

+

Now, you will practice performing hypothesis testing and calculating RR in R to investigate the associations of suspicious food items with the disease.

+

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):

+
    +

  • For continuous variables:

    +
      +

    • shapiro.test() (for checking if normally distributed)

      • +

    • t.test() (for normal distributions)

      • +

    • wilcox.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.)

      • +
    • +

  • For categorical variables:

    +
      +

    • chisq.test() (if all comparisons include at least 5 cases)

      • +

    • fisher.test() (if there are < 5 cases for any comparison)

      • +
    • +
+
+
+

3. Questions/Assignments

+
+
+

3.1. Install packages and load libraries

+
+
+Show the code +
# 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)
+
+
+
+
+

3.2. Import your data

+
+
+Show the code +
# Import the raw data set:
+copdata <- rio::import(here::here("data", "Spetses_clean2_2024.rds"), trust = T)
+
+
+
+
+

3.3. Hypothesis tests for other variables

+

Check if the following variables are associated with being a case: age, sex, class and group.

+
+

a) age

+

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<0.05 means that we reject the null hypothesis that the data follow the normal distribution. For age, the p-value is <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 <20 years.

+
+
+Show the code +
# Check if age overall follows a normal distribution:
+shapiro.test(copdata$age)
+
+
+

+    Shapiro-Wilk normality test
+
+data:  copdata$age
+W = 0.31302, p-value < 2.2e-16
+
+
+Show the code +
# Can simply have a look at
+hist(copdata$age)
+
+
+
+
+

+
+
+
+
+Show the code +
# Looking only at the students:
+students <- copdata %>% 
+  filter(group == "student")
+hist(students$age)
+
+
+
+
+

+
+
+
+
+

Age overall (nor within the students’ group) is not normally distributed.

+

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>0.05 we do not reject the null hypothesis.

+
+
+Show the code +
# Perform Wilcoxon rank sum test on age and sex:
+wilcox.test(age ~ case, 
+            data = copdata)
+
+
+

+    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
+
+
+
+
+

b) sex

+
+
+Show the code +
copdata %>% 
+  select(sex, case) %>% 
+  tbl_summary(by = case) %>% 
+  add_p()
+
+
+
+ + + ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Characteristic0
+N = 1611		1
+N = 2161		p-value2
sex
+
+		0.3
    female96 (60%)117 (54%)
+
    male65 (40%)99 (46%)
+
1 n (%)
2 Pearson’s Chi-squared test
+ +
+
+
+
+ +
+
+

What do these results tell you - is there an association between sex and being a case?

+

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?

+
+
+
+
+
+

c) class

+
+
+Show the code +
copdata %>% 
+  select(class, case) %>% 
+  tbl_summary(by = case) %>% 
+  add_p()
+
+
+
+ + + ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Characteristic0
+N = 1611		1
+N = 2161		p-value2
class
+
+		0.090
    163 (43%)68 (34%)
+
    244 (30%)57 (29%)
+
    338 (26%)73 (37%)
+
    Unknown1618
+
1 n (%)
2 Pearson’s Chi-squared test
+ +
+
+
+

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.

+

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.

+
+
+

d) group

+
+
+Show the code +
copdata %>% 
+  select(group, case) %>% 
+  tbl_summary(by = case) %>% 
+  add_p()
+
+
+
+ + + ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Characteristic0
+N = 1611		1
+N = 2161		p-value2
group
+
+		0.2
    teacher9 (5.6%)6 (2.8%)
+
    student152 (94%)210 (97%)
+
1 n (%)
2 Pearson’s Chi-squared test
+ +
+
+
+

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.

+
+
+

Let’s do all together

+
+
+Show the code +
copdata %>% 
+  select(sex, class, group, case) %>% 
+  tbl_summary(by = case) %>% 
+  add_p()
+
+
+
+ + + ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Characteristic0
+N = 1611		1
+N = 2161		p-value2
sex
+
+		0.3
    female96 (60%)117 (54%)
+
    male65 (40%)99 (46%)
+
class
+
+		0.090
    163 (43%)68 (34%)
+
    244 (30%)57 (29%)
+
    338 (26%)73 (37%)
+
    Unknown1618
+
group
+
+		0.2
    teacher9 (5.6%)6 (2.8%)
+
    student152 (94%)210 (97%)
+
1 n (%)
2 Pearson’s Chi-squared test
+ +
+
+
+
+
+
+

3.4. Risk Ratios

+

The 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.

+
+

a) Calculate 95% CI Risk Ratios for food

+

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 CSTable()), or all at once (see hint below).

+
+ +
+
+

Create a food_vars vector containing food variables of interest and use the function CSTable() of the EpiStats package.

+

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.

+
+
+
+
+
+Show the code +
# You could use the EpiStats package for each food item
+CS(copdata, "case", "feta")
+
+
+
$df1
+          Cases Non Cases Total Risk
+Exposed     156       115   271 0.58
+Unexposed    60        42   102 0.59
+Total       216       157   373 0.58
+
+$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>chi2                  0.826       NA       NA
+
+
+Show the code +
CS(copdata, "case", "sardines")
+
+
+
$df1
+          Cases Non Cases Total Risk
+Exposed     150       105   255 0.59
+Unexposed    65        52   117 0.56
+Total       215       157   372 0.58
+
+$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>chi2                  0.553       NA       NA
+
+
+Show the code +
CS(copdata, "case", "eggplant")
+
+
+
$df1
+          Cases Non Cases Total Risk
+Exposed     123        93   216 0.57
+Unexposed    83        60   143 0.58
+Total       206       153   359 0.57
+
+$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>chi2                  0.837       NA       NA
+
+
+Show the code +
CS(copdata, "case", "pasta")
+
+
+
$df1
+          Cases Non Cases Total Risk
+Exposed     202       136   338 0.60
+Unexposed    13        23    36 0.36
+Total       215       159   374 0.57
+
+$df2
+                Point estimate 95%CI.ll 95%CI.ul
+Risk difference           0.24     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>chi2                  0.006       NA       NA
+
+
+
+
+Show the code +
# 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)
+
+
+
$df
+          Tot.Exp Cases.Exp AR.Exp% Tot.Unexp Cases.Unexp AR.Unexp%   RR CI.ll
+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
+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
+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)
+pasta      2.58   0.006
+veal       2.32   0.018
+champagne  1.89   0.044
+tomsal     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
+
+
+
+
+

b) Prepare the RR table for publication

+
+ +
+
+

Use flextable() and set_header_labels().

+
+
+
+
+
+Show the code +
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"))
+
+
+
+ +
+
+

· What can you infer from this table?

+

· 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?

+

· 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 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 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) Pasta

+

Using epitools::riskratio function:

+
+
+Show the code +
epitools::riskratio(copdata$pastaD,                     
+                    copdata$case,                     
+                    conf.level = 0.95)
+
+
+
$data
+         Outcome
+Predictor   0   1 Total
+    0      23  13    36
+    1      32  38    70
+    2      76 107   183
+    3      28  57    85
+    Total 159 215   374
+
+$measure
+         risk ratio with 95% C.I.
+Predictor estimate     lower    upper
+        0 1.000000        NA       NA
+        1 1.503297 0.9257878 2.441057
+        2 1.619168 1.0310538 2.542742
+        3 1.857014 1.1730804 2.939696
+
+$p.value
+         two-sided
+Predictor  midp.exact fisher.exact chi.square
+        0          NA           NA         NA
+        1 0.081364161  0.100893180 0.07613202
+        2 0.015337953  0.017069765 0.01373981
+        3 0.002010616  0.002370887 0.00162311
+
+$correction
+[1] FALSE
+
+attr(,"method")
+[1] "Unconditional MLE & normal approximation (Wald) CI"
+
+
+

Using a binomial regression:

+
+
+Show the code +
# 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
+
+
+
+ +
+
+
+
+ +
+
+

What do these results tell you?

+
+
+
+

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) Veal

+

Using epitools::riskratio function:

+
+
+Show the code +
epitools::riskratio(copdata$vealD,
+                    copdata$case,
+                    conf.level = 0.95)
+
+
+
$data
+         Outcome
+Predictor   0   1 Total
+    0      19  15    34
+    1      30  40    70
+    2      78 106   184
+    3      33  54    87
+    Total 160 215   375
+
+$measure
+         risk ratio with 95% C.I.
+Predictor estimate     lower    upper
+        0 1.000000        NA       NA
+        1 1.295238 0.8431805 1.989659
+        2 1.305797 0.8769752 1.944304
+        3 1.406897 0.9314233 2.125090
+
+$p.value
+         two-sided
+Predictor midp.exact fisher.exact chi.square
+        0         NA           NA         NA
+        1 0.22186384    0.2951163 0.21193013
+        2 0.15357032    0.1884220 0.14587265
+        3 0.07956697    0.1017336 0.07298506
+
+$correction
+[1] FALSE
+
+attr(,"method")
+[1] "Unconditional MLE & normal approximation (Wald) CI"
+
+
+

Using a binomial regression:

+
+
+Show the code +
# 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"))
+
+# To get exponentiated:
+binom_vealD_exp <- glm(case ~ vealD, data = copdata, 
+                       family = binomial(link = "log")) %>% 
+  tidy(exponentiate = TRUE, 
+       conf.int = TRUE)
+
+binom_vealD_exp
+
+
+
+ +
+
+
+
+
+

c) Champagne

+

Using epitools::riskratio function:

+
+
+Show the code +
epitools::riskratio(copdata$champagneD,                     
+                    copdata$case,                     
+                    conf.level = 0.95)
+
+
+
$data
+         Outcome
+Predictor   0   1 Total
+    0      27  21    48
+    1      93 111   204
+    2      12  32    44
+    3      24  44    68
+    Total 156 208   364
+
+$measure
+         risk ratio with 95% C.I.
+Predictor estimate     lower    upper
+        0 1.000000        NA       NA
+        1 1.243697 0.8812613 1.755193
+        2 1.662338 1.1502014 2.402507
+        3 1.478992 1.0260296 2.131923
+
+$p.value
+         two-sided
+Predictor  midp.exact fisher.exact  chi.square
+        0          NA           NA          NA
+        1 0.188922591  0.201367850 0.183280359
+        2 0.005584928  0.006247816 0.004961941
+        3 0.027627736  0.036334120 0.025117641
+
+$correction
+[1] FALSE
+
+attr(,"method")
+[1] "Unconditional MLE & normal approximation (Wald) CI"
+
+
+

Using a binomial regression:

+
+
+Show the code +
# 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
+
+
+
+ +
+
+
+
+
+
+

3.6 Summary

+

As a summary of what you’ve done above, answer these questions:

+
    +

  • Is the respondents’ sex associated with being a case?

    • +

  • Is the school class associated with being a case?

    • +

  • Which foods increase the risk of being a case?

    • +

  • (Optional) Is there a dose-response relationship between the food items and being a case?

    • +

  • 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?

    • +
+ + + + +
+ +
+ +
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Spetses Introductory Course

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Background:

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This 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.

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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:

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  • Data_import_cleaning.qmd
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  • Case_definition.qmd
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  • Lab_and_Descriptive.qmd
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  • Univariate.qmd
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  • Stratified.qmd
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+ + + + + \ No newline at end of file diff --git a/docs-preview/search.json b/docs-preview/search.json new file mode 100644 index 0000000..14f5b76 --- /dev/null +++ b/docs-preview/search.json @@ -0,0 +1,317 @@ +[ + { + "objectID": "Univariate.html", + "href": "Univariate.html", + "title": "Univariable analysis", + "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." + }, + { + "objectID": "Univariate.html#learning-outcomes", + "href": "Univariate.html#learning-outcomes", + "title": "Univariable analysis", + "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." + }, + { + "objectID": "Univariate.html#storyplot-description", + "href": "Univariate.html#storyplot-description", + "title": "Univariable analysis", + "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 < 5 cases for any comparison)" + }, + { + "objectID": "Univariate.html#questionsassignments", + "href": "Univariate.html#questionsassignments", + "title": "Univariable analysis", + "section": "3. Questions/Assignments", + "text": "3. Questions/Assignments" + }, + { + "objectID": "Univariate.html#install-packages-and-load-libraries", + "href": "Univariate.html#install-packages-and-load-libraries", + "title": "Univariable analysis", + "section": "3.1. Install packages and load libraries", + "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)" + }, + { + "objectID": "Univariate.html#import-your-data", + "href": "Univariate.html#import-your-data", + "title": "Univariable analysis", + "section": "3.2. Import your data", + "text": "3.2. Import your data\n\n\nShow the code\n# Import the raw data set:\ncopdata <- rio::import(here::here(\"data\", \"Spetses_clean2_2024.rds\"), trust = T)" + }, + { + "objectID": "Univariate.html#hypothesis-tests-for-other-variables", + "href": "Univariate.html#hypothesis-tests-for-other-variables", + "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<0.05 means that we reject the null hypothesis that the data follow the normal distribution. For age, the p-value is <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 <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 < 2.2e-16\n\n\nShow the code\n# Can simply have a look at\nhist(copdata$age)\n\n\n\n\n\n\n\n\n\nShow the code\n# Looking only at the students:\nstudents <- copdata %>% \n filter(group == \"student\")\nhist(students$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>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 %>% \n select(sex, case) %>% \n tbl_summary(by = case) %>% \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 %>% \n select(class, case) %>% \n tbl_summary(by = case) %>% \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 %>% \n select(group, case) %>% \n tbl_summary(by = case) %>% \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 %>% \n select(sex, class, group, case) %>% \n tbl_summary(by = case) %>% \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" + }, + { + "objectID": "Univariate.html#risk-ratios", + "href": "Univariate.html#risk-ratios", + "title": "Univariable analysis", + "section": "3.4. Risk Ratios", + "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>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>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>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>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 <- 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 <- CSTable(copdata, \"case\", food_vars) %>% \n as.data.frame() %>% \n rownames_to_column() %>% \n flextable() %>% \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)" + }, + { + "objectID": "Univariate.html#optional-dose-response", + "href": "Univariate.html#optional-dose-response", + "title": "Univariable analysis", + "section": "3.5. (Optional) Dose Response", + "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$correction\n[1] FALSE\n\nattr(,\"method\")\n[1] \"Unconditional MLE & normal approximation (Wald) CI\"\n\n\nUsing a binomial regression:\n\n\nShow the code\n# Let's get the results directly exponentiated\nbinom_pastaD_exp <- glm(case ~ pastaD, data = copdata, \n family = binomial(link = \"log\")) %>% \n tidy(exponentiate = TRUE, \n conf.int = TRUE)\n\nbinom_pastaD_exp\n\n\n\n \n\n\n\n\n\n\n\n\n\nLet’s stop and… think!\n\n\n\n\n\nWhat do these results tell you?\n\n\n\nResults suggest a dose response relationship of having eaten pasta, pointing towards pasta as the potential vehicle. The higher the amount of pasta they ate, the stronger is the association (RR) with getting ill/being a case.\n\n\nb) Veal\nUsing epitools::riskratio function:\n\n\nShow the code\nepitools::riskratio(copdata$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$correction\n[1] FALSE\n\nattr(,\"method\")\n[1] \"Unconditional MLE & normal approximation (Wald) CI\"\n\n\nUsing a binomial regression:\n\n\nShow the code\n# Binomial regression for RRs. \n# The outcome needs to be exponentiated so we can interpret it properly!\nbinom_vealD <- glm(case ~ vealD, data = copdata, \n family = binomial(link = \"log\"))\n\n# To get exponentiated:\nbinom_vealD_exp <- glm(case ~ vealD, data = copdata, \n family = binomial(link = \"log\")) %>% \n tidy(exponentiate = TRUE, \n conf.int = TRUE)\n\nbinom_vealD_exp\n\n\n\n \n\n\n\n\n\nc) Champagne\nUsing epitools::riskratio function:\n\n\nShow the code\nepitools::riskratio(copdata$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 <- glm(case ~ champagneD, data = copdata, \n family = binomial(link = \"log\")) %>% \n tidy(exponentiate = TRUE, \n conf.int = TRUE)\n\nbinom_champagneD_exp" + }, + { + "objectID": "Univariate.html#summary", + "href": "Univariate.html#summary", + "title": "Univariable analysis", + "section": "3.6 Summary", + "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?" + }, + { + "objectID": "Lab_and_Descriptive.html", + "href": "Lab_and_Descriptive.html", + "title": "Laboratory data and descriptive analysis", + "section": "", + "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." + }, + { + "objectID": "Lab_and_Descriptive.html#learning-outcomes", + "href": "Lab_and_Descriptive.html#learning-outcomes", + "title": "Laboratory data and descriptive analysis", + "section": "", + "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." + }, + { + "objectID": "Lab_and_Descriptive.html#storyplot-description", + "href": "Lab_and_Descriptive.html#storyplot-description", + "title": "Laboratory data and descriptive analysis", + "section": "2. Story/plot description", + "text": "2. Story/plot description\nYou have received the laboratory results for the samples collected from some of the cases. The lab conducted an RT-PCR panel targeting various gastrointestinal pathogens and also gathered additional metadata. Your task is to merge the questionnaire data with the data you have received from the lab, using the unique identifiers that both datasets share (id column), and explore that lab data. What does it tell you?\nOnce your dataset is clean and the lab data merged, you can 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." + }, + { + "objectID": "Lab_and_Descriptive.html#questionsassignments", + "href": "Lab_and_Descriptive.html#questionsassignments", + "title": "Laboratory data and descriptive analysis", + "section": "3. Questions/Assignments", + "text": "3. Questions/Assignments\nIf you closed your R project, please open it again and load the clean dataset. We recommend you use the Spetses_clean2_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)." + }, + { + "objectID": "Lab_and_Descriptive.html#install-packages-if-needed-and-load-libraries", + "href": "Lab_and_Descriptive.html#install-packages-if-needed-and-load-libraries", + "title": "Laboratory data and descriptive analysis", + "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)" + }, + { + "objectID": "Lab_and_Descriptive.html#import-your-data", + "href": "Lab_and_Descriptive.html#import-your-data", + "title": "Laboratory data and descriptive analysis", + "section": "3.2 Import your data", + "text": "3.2 Import your data\n\n\nShow the code\n# Import the clean data set:\ncopdata <- rio::import(here::here(\"data\", \"Spetses_clean2_2024.rds\"), trust = T) \nlab <- rio::import(here::here(\"data\", \"Lab data.xlsx\"),\n skip = 1) # skip the first row." + }, + { + "objectID": "Lab_and_Descriptive.html#merge-and-explore-the-lab-data.-explore-it-as-you-wish", + "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\nShow the code\ncopdatalab <- left_join(copdata, lab, \n by = \"id\")\n# Tabulate:\njanitor::tabyl(dat = copdatalab, \"RT-PCR_ecoli_etec\")" + }, + { + "objectID": "Lab_and_Descriptive.html#describe-the-outbreak-in-terms-of-time.", + "href": "Lab_and_Descriptive.html#describe-the-outbreak-in-terms-of-time.", + "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\nShow the code\n#| label: inc_time\n\n# Create a dataset with only cases\ncases <- copdata %>% \n filter(case == 1)\n\nincplot <- cases %>% \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 <- 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 <- cases %>% \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 <- cases %>% \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!" + }, + { + "objectID": "Lab_and_Descriptive.html#describe-the-outbreak-in-terms-of-person.", + "href": "Lab_and_Descriptive.html#describe-the-outbreak-in-terms-of-person.", + "title": "Laboratory data and descriptive analysis", + "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\nShow the code\ncopdata %>% \n janitor::tabyl(case, group) %>% \n adorn_totals() %>% \n adorn_percentages() %>% \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 %>% \n janitor::tabyl(case, sex) %>% \n adorn_totals() %>% \n adorn_percentages() %>% \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 <- copdata %>% \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 <- copdata %>% \n # Filter for cases only:\n filter(case == TRUE) %>% \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" + }, + { + "objectID": "Lab_and_Descriptive.html#explore-the-distribution-of-all-clinical-signs-symptoms.", + "href": "Lab_and_Descriptive.html#explore-the-distribution-of-all-clinical-signs-symptoms.", + "title": "Laboratory data and descriptive analysis", + "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\nShow the code\n# Create summary table:\ntabsymptoms <- copdata %>% \n # Select person characteristics to summarise:\n select(case, diarrhoea, bloody, vomiting,\n abdo, nausea, fever,headache, jointpain) %>% \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(.))) %>%\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(.))) %>% \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 ) %>% \n \n # Add totals:\n add_overall() %>% \n # Make variable names bold and italics:\n bold_labels() %>% \n italicize_labels() %>% \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 <- 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 <- ggplot2::as_labeller(c(\"0\" = \"Non-case\", \n \"1\" = \"Case\"))\n# Select variables and cases:\nsymptom_bar <- copdata %>% \n # Select symptom columns:\n select(case, c(all_of(symptoms))) %>%\n # Drop NAs:\n drop_na() %>% \n # Reshape (pivot longer):\n pivot_longer(!case, \n names_to = \"Symptoms\", \n values_drop_na = TRUE) %>% \n # Keep only TRUE values:\n filter(value == \"1\") %>% \n \n # Group by symptoms and case:\n group_by(Symptoms, case) %>% \n # Count for each symptom by case:\n dplyr::summarise(count = n()) %>% \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" + }, + { + "objectID": "Lab_and_Descriptive.html#attack-proportions", + "href": "Lab_and_Descriptive.html#attack-proportions", + "title": "Laboratory data and descriptive analysis", + "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\nShow the code\n# Create table of case status:\ntotal_ap <- tabyl(copdata, case) %>% \n # Add row totals:\n adorn_totals(where = \"row\") %>% \n # Add percentages with 1 digit after the decimal point:\n adorn_pct_formatting(digits = 1) %>% \n # Filter to rows where case is TRUE:\n filter(case == \"1\") %>% \n # Select the column percent:\n select(percent) %>% \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 <- copdata %>% \n # Select columns:\n select (case, class, group, sex) %>% \n \n # Create table:\n tbl_summary(\n # Stratified by case\n by = case,\n # with row percentages\n percent = \"row\") %>%\n \n # Add totals:\n add_overall() %>%\n \n # Make variable names bold and italics:\n bold_labels() %>% \n italicize_labels() %>% \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 (%)" + }, + { + "objectID": "Lab_and_Descriptive.html#draft-the-relevant-paragraphs-and-tables-andor-figures-in-your-outbreak-report-methods-and-results.", + "href": "Lab_and_Descriptive.html#draft-the-relevant-paragraphs-and-tables-andor-figures-in-your-outbreak-report-methods-and-results.", + "title": "Laboratory data and descriptive analysis", + "section": "3.8 Draft the relevant paragraph(s) and table(s) and/or figure(s) in your outbreak report (Methods and results).", + "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\")" + }, + { + "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\nImport datasets in R\nApply basic data management techniques to explore and familiarise themselves with the dataset\nApply strategies for data cleaning" + }, + { + "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 <- 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 <- copdata %>% \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 <- copdata %>% \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 <- copdata %>% \n # Select all the columns with column names that end in upper case 'D': \n select(ends_with(\"D\", ignore.case = FALSE)) %>% \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 (%)" + }, + { + "objectID": "Data_import_cleaning.html#modify-variables-format", + "href": "Data_import_cleaning.html#modify-variables-format", + "title": "Data import and cleaning", + "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\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 <- copdata %>% \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 <- copdata %>% \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 <- copdata %>% \n select(\n ends_with(\"D\", ignore.case = FALSE)) %>% \n names()\n\n# Another way of doing it:\n# food_dose <- c(\"fetaD\", \"sardinesD\", \"eggplantD\", \"pastaD\", \n# \"vealD\", \"greeksalD\", \"dessertD\", \"breadD\", \n# \"champagneD\", \"beerD\", \"redwineD\", \"whitewineD\")\n\n\ncopdata <- copdata %>% \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$dayonset)\n\n\n[1] \"character\"\n\n\nShow the code\n# Update copdata:\ncopdata <- copdata %>% \n # Change column to date class:\n mutate(\n dayonset = lubridate::dmy(dayonset))\n\n# Check class of updated column:\nclass(copdata$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 <- copdata %>% \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\"" + }, + { + "objectID": "Data_import_cleaning.html#export-clean-data", + "href": "Data_import_cleaning.html#export-clean-data", + "title": "Data import and cleaning", + "section": "3.5. Export clean data", + "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\"))" + }, + { + "objectID": "Case_definition.html", + "href": "Case_definition.html", + "title": "Case Definition", + "section": "", + "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" + }, + { + "objectID": "Case_definition.html#learning-outcomes", + "href": "Case_definition.html#learning-outcomes", + "title": "Case Definition", + "section": "", + "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" + }, + { + "objectID": "Case_definition.html#storyplot-description", + "href": "Case_definition.html#storyplot-description", + "title": "Case Definition", + "section": "2. Story/plot description", + "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." + }, + { + "objectID": "Case_definition.html#questionsassignments", + "href": "Case_definition.html#questionsassignments", + "title": "Case Definition", + "section": "3. Questions/Assignments", + "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" + }, + { + "objectID": "Case_definition.html#install-packages-if-needed-and-load-libraries", + "href": "Case_definition.html#install-packages-if-needed-and-load-libraries", + "title": "Case Definition", + "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)" + }, + { + "objectID": "Case_definition.html#import-your-data", + "href": "Case_definition.html#import-your-data", + "title": "Case Definition", + "section": "3.2 Import your data", + "text": "3.2 Import your data\n\n\nShow the code\n# Import the clean data set:\ncopdata <- rio::import(here::here(\"data\", \"Spetses_clean1_2024.rds\"), trust = TRUE)" + }, + { + "objectID": "Case_definition.html#identify-the-variables-you-need-to-apply-the-case-definition-criteria.", + "href": "Case_definition.html#identify-the-variables-you-need-to-apply-the-case-definition-criteria.", + "title": "Case Definition", + "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, ill, onset_datetime, diarrhoea, bloody and vomiting." + }, + { + "objectID": "Case_definition.html#create-a-new-case-column-to-hold-the-binary-case-definition-variable.-lets-think-about-how-to-do-this-little-by-little", + "href": "Case_definition.html#create-a-new-case-column-to-hold-the-binary-case-definition-variable.-lets-think-about-how-to-do-this-little-by-little", + "title": "Case Definition", + "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 <- copdata %>% \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. => 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. => 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 <- copdata %>% \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 <- copdata %>% \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 <- copdata %>% \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 <- copdata %>% \n mutate(case = case_when(\n # Those who had symptoms <48h from the meal are cases (1)\n gastrosymptoms == 1 & \n onset_datetime >= meal_datetime &\n onset_datetime <= (meal_datetime + days(2)) ~ 1,\n # Those who had symptoms >48h from the meal are non-cases (FALSE)\n gastrosymptoms == 1 & \n onset_datetime > (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 %>% \n summarise(atemeal = sum(meal == 1),\n hadsympt = sum(gastrosymptoms == 1),\n nb_cases = sum(case == 1)\n )" + }, + { + "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": "Stratified.html", + "href": "Stratified.html", + "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." + }, + { + "objectID": "Stratified.html#learning-outcomes", + "href": "Stratified.html#learning-outcomes", + "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." + }, + { + "objectID": "Stratified.html#storyplot-description", + "href": "Stratified.html#storyplot-description", + "title": "Stratified analysis", + "section": "2. Story/plot description", + "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." + }, + { + "objectID": "Stratified.html#questionsassignments", + "href": "Stratified.html#questionsassignments", + "title": "Stratified analysis", + "section": "3. Questions/Assignments", + "text": "3. Questions/Assignments" + }, + { + "objectID": "Stratified.html#confounders-and-effect-modification", + "href": "Stratified.html#confounders-and-effect-modification", + "title": "Stratified analysis", + "section": "3.1. Confounders and effect modification", + "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." + }, + { + "objectID": "Stratified.html#install-packages-and-load-libraries", + "href": "Stratified.html#install-packages-and-load-libraries", + "title": "Stratified analysis", + "section": "3.2. Install packages and load libraries", + "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)" + }, + { + "objectID": "Stratified.html#import-your-data", + "href": "Stratified.html#import-your-data", + "title": "Stratified analysis", + "section": "3.3. Import your data", + "text": "3.3. Import your data\n\n\nShow the code\n# Import the raw data set: \ncopdata <- rio::import(here::here(\"data\", \"Spetses_clean2_2024.rds\"), trust = T)" + }, + { + "objectID": "Stratified.html#consider-and-assess-for-confounding-andor-effect-modification", + "href": "Stratified.html#consider-and-assess-for-confounding-andor-effect-modification", + "title": "Stratified analysis", + "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, 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 <- copdata %>% \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 <- 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 <NA> 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 <NA> NA Attrib.risk.pop 0.16\n5 pasta = 0 36 <NA> 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 <NA> NA Attrib.risk.pop 0.01\n9 Missing / Missing % 3 0.8% NA <NA> 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 < 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 <- 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 >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!" + } +] \ No newline at end of file diff --git a/docs-preview/site_libs/bootstrap/bootstrap-dark.min.css b/docs-preview/site_libs/bootstrap/bootstrap-dark.min.css new file mode 100644 index 0000000..be23bc0 --- /dev/null +++ b/docs-preview/site_libs/bootstrap/bootstrap-dark.min.css @@ -0,0 +1,12 @@ +/*! + * Bootstrap v5.3.1 (https://getbootstrap.com/) + * Copyright 2011-2023 The Bootstrap Authors + * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE) + */@import"https://fonts.googleapis.com/css2?family=Lato:ital,wght@0,400;0,700;1,400&display=swap";:root,[data-bs-theme=light]{--bs-blue: #375a7f;--bs-indigo: #6610f2;--bs-purple: #6f42c1;--bs-pink: #e83e8c;--bs-red: #e74c3c;--bs-orange: #fd7e14;--bs-yellow: #f39c12;--bs-green: #00bc8c;--bs-teal: 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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='%23dee2e6' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e");--bs-navbar-toggler-border-color: rgba(222, 226, 230, 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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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: #375a7f;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;padding-left:0;margin-bottom:0;border-radius:var(--bs-list-group-border-radius)}.list-group-numbered{list-style-type:none;counter-reset:section}.list-group-numbered>.list-group-item::before{content:counters(section, ".") ". ";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 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: 1rem;--bs-modal-header-padding: 1rem 1rem;--bs-modal-header-border-color: #434343;--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: #434343;--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: #222;--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 .tooltip-arrow::before,.bs-tooltip-auto[data-popper-placement^=top] .tooltip-arrow::before{top:-1px;border-width:var(--bs-tooltip-arrow-height) calc(var(--bs-tooltip-arrow-width)*.5) 0;border-top-color:var(--bs-tooltip-bg)}.bs-tooltip-end .tooltip-arrow,.bs-tooltip-auto[data-popper-placement^=right] .tooltip-arrow{left:calc(-1*var(--bs-tooltip-arrow-height));width:var(--bs-tooltip-arrow-height);height:var(--bs-tooltip-arrow-width)}.bs-tooltip-end .tooltip-arrow::before,.bs-tooltip-auto[data-popper-placement^=right] .tooltip-arrow::before{right:-1px;border-width:calc(var(--bs-tooltip-arrow-width)*.5) var(--bs-tooltip-arrow-height) calc(var(--bs-tooltip-arrow-width)*.5) 0;border-right-color:var(--bs-tooltip-bg)}.bs-tooltip-bottom .tooltip-arrow,.bs-tooltip-auto[data-popper-placement^=bottom] .tooltip-arrow{top:calc(-1*var(--bs-tooltip-arrow-height))}.bs-tooltip-bottom .tooltip-arrow::before,.bs-tooltip-auto[data-popper-placement^=bottom] .tooltip-arrow::before{bottom:-1px;border-width:0 calc(var(--bs-tooltip-arrow-width)*.5) var(--bs-tooltip-arrow-height);border-bottom-color:var(--bs-tooltip-bg)}.bs-tooltip-start .tooltip-arrow,.bs-tooltip-auto[data-popper-placement^=left] 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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 .card-body+.card-body{padding-top:0}.bslib-card .card-body{overflow:auto}.bslib-card .card-body p{margin-top:0}.bslib-card .card-body p:last-child{margin-bottom:0}.bslib-card .card-body{max-height:var(--bslib-card-body-max-height, none)}.bslib-card[data-full-screen=true]>.card-body{max-height:var(--bslib-card-body-max-height-full-screen, none)}.bslib-card .card-header .form-group{margin-bottom:0}.bslib-card .card-header .selectize-control{margin-bottom:0}.bslib-card .card-header .selectize-control .item{margin-right:1.15rem}.bslib-card .card-footer{margin-top:auto}.bslib-card .bslib-navs-card-title{display:flex;flex-wrap:wrap;justify-content:space-between;align-items:center}.bslib-card .bslib-navs-card-title .nav{margin-left:auto}.bslib-card .bslib-sidebar-layout:not([data-bslib-sidebar-border=true]){border:none}.bslib-card .bslib-sidebar-layout:not([data-bslib-sidebar-border-radius=true]){border-top-left-radius:0;border-top-right-radius:0}[data-full-screen=true]{position:fixed;inset:3.5rem 1rem 1rem;height:auto !important;max-height:none !important;width:auto !important;z-index:1070}.bslib-full-screen-enter{display:none;position:absolute;bottom:var(--bslib-full-screen-enter-bottom, 0.2rem);right:var(--bslib-full-screen-enter-right, 0);top:var(--bslib-full-screen-enter-top);left:var(--bslib-full-screen-enter-left);color:var(--bslib-color-fg, var(--bs-card-color));background-color:var(--bslib-color-bg, var(--bs-card-bg, var(--bs-body-bg)));border:var(--bs-card-border-width) solid var(--bslib-color-fg, var(--bs-card-border-color));box-shadow:0 2px 4px rgba(0,0,0,.15);margin:.2rem .4rem;padding:.55rem !important;font-size:.8rem;cursor:pointer;opacity:.7;z-index:1070}.bslib-full-screen-enter:hover{opacity:1}.card[data-full-screen=false]:hover>*>.bslib-full-screen-enter{display:block}.bslib-has-full-screen 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2rem;--bs-nav-link-padding-y: 0.5rem;--bs-nav-link-font-weight: ;--bs-nav-link-color: #18bc9c;--bs-nav-link-hover-color: #13967d;--bs-nav-link-disabled-color: #6c757d;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 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30'%3e%3cpath stroke='%23ccd1d5' 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='%23ccd1d5' 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: #fff;--bs-card-img-overlay-padding: 1rem;--bs-card-group-margin: 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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");--bs-accordion-btn-focus-border-color: #969fa8;--bs-accordion-btn-focus-box-shadow: 0 0 0 0.25rem rgba(44, 62, 80, 0.25);--bs-accordion-body-padding-x: 1.25rem;--bs-accordion-body-padding-y: 1rem;--bs-accordion-active-color: #121920;--bs-accordion-active-bg: #d5d8dc}.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='%23808b96'%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='%23808b96'%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: ;--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: 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: #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: #2c3e50;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;padding-left:0;margin-bottom:0;border-radius:var(--bs-list-group-border-radius)}.list-group-numbered{list-style-type:none;counter-reset:section}.list-group-numbered>.list-group-item::before{content:counters(section, ".") ". ";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 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(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 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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 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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;iNumber.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 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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:"

"},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:'',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 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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 "${t}"`);e[t]()}}))}}m(cs);const hs={...cs.Default,content:"",offset:[0,8],placement:"right",template:'',trigger:"click"},ds={...cs.DefaultType,content:"(null|string|element|function)"};class us extends cs{static get Default(){return hs}static get DefaultType(){return ds}static get 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e=this._observableSections.get(t.target.hash);if(e){t.preventDefault();const i=this._rootElement||window,n=e.offsetTop-this._element.offsetTop;if(i.scrollTo)return void i.scrollTo({top:n,behavior:"smooth"});i.scrollTop=n}})))}_getNewObserver(){const t={root:this._rootElement,threshold:this._config.threshold,rootMargin:this._config.rootMargin};return new IntersectionObserver((t=>this._observerCallback(t)),t)}_observerCallback(t){const e=t=>this._targetLinks.get(`#${t.target.id}`),i=t=>{this._previousScrollData.visibleEntryTop=t.target.offsetTop,this._process(e(t))},n=(this._rootElement||document.documentElement).scrollTop,s=n>=this._previousScrollData.parentScrollTop;this._previousScrollData.parentScrollTop=n;for(const o of 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-preview/site_libs/clipboard/clipboard.min.js b/docs-preview/site_libs/clipboard/clipboard.min.js new file mode 100644 index 0000000..1103f81 --- /dev/null +++ b/docs-preview/site_libs/clipboard/clipboard.min.js @@ -0,0 +1,7 @@ +/*! + * clipboard.js v2.0.11 + * https://clipboardjs.com/ + * + * Licensed MIT © Zeno Rocha + */ +!function(t,e){"object"==typeof exports&&"object"==typeof module?module.exports=e():"function"==typeof define&&define.amd?define([],e):"object"==typeof exports?exports.ClipboardJS=e():t.ClipboardJS=e()}(this,function(){return n={686:function(t,e,n){"use strict";n.d(e,{default:function(){return b}});var e=n(279),i=n.n(e),e=n(370),u=n.n(e),e=n(817),r=n.n(e);function c(t){try{return document.execCommand(t)}catch(t){return}}var a=function(t){t=r()(t);return c("cut"),t};function o(t,e){var n,o,t=(n=t,o="rtl"===document.documentElement.getAttribute("dir"),(t=document.createElement("textarea")).style.fontSize="12pt",t.style.border="0",t.style.padding="0",t.style.margin="0",t.style.position="absolute",t.style[o?"right":"left"]="-9999px",o=window.pageYOffset||document.documentElement.scrollTop,t.style.top="".concat(o,"px"),t.setAttribute("readonly",""),t.value=n,t);return e.container.appendChild(t),e=r()(t),c("copy"),t.remove(),e}var f=function(t){var e=1>> 0; + + // 4. If isCallable(callback) is false, throw a TypeError exception. + // See: http://es5.github.com/#x9.11 + if (typeof callback !== "function") { + throw new TypeError(callback + ' is not a function'); + } + + // 5. If thisArg was supplied, let T be thisArg; else let + // T be undefined. + if (arguments.length > 1) { + T = thisArg; + } + + // 6. Let k be 0 + k = 0; + + // 7. Repeat, while k < len + while (k < len) { + + var kValue; + + // a. Let Pk be ToString(k). + // This is implicit for LHS operands of the in operator + // b. Let kPresent be the result of calling the HasProperty + // internal method of O with argument Pk. + // This step can be combined with c + // c. If kPresent is true, then + if (k in O) { + + // i. Let kValue be the result of calling the Get internal + // method of O with argument Pk. + kValue = O[k]; + + // ii. Call the Call internal method of callback with T as + // the this value and argument list containing kValue, k, and O. + callback.call(T, kValue, k, O); + } + // d. Increase k by 1. + k++; + } + // 8. return undefined + }; +} + +// Production steps of ECMA-262, Edition 5, 15.4.4.19 +// Reference: http://es5.github.io/#x15.4.4.19 +if (!Array.prototype.map) { + + Array.prototype.map = function(callback, thisArg) { + + var T, A, k; + + if (this == null) { + throw new TypeError(' this is null or not defined'); + } + + // 1. Let O be the result of calling ToObject passing the |this| + // value as the argument. + var O = Object(this); + + // 2. Let lenValue be the result of calling the Get internal + // method of O with the argument "length". + // 3. Let len be ToUint32(lenValue). + var len = O.length >>> 0; + + // 4. If IsCallable(callback) is false, throw a TypeError exception. + // See: http://es5.github.com/#x9.11 + if (typeof callback !== 'function') { + throw new TypeError(callback + ' is not a function'); + } + + // 5. If thisArg was supplied, let T be thisArg; else let T be undefined. + if (arguments.length > 1) { + T = thisArg; + } + + // 6. Let A be a new array created as if by the expression new Array(len) + // where Array is the standard built-in constructor with that name and + // len is the value of len. + A = new Array(len); + + // 7. Let k be 0 + k = 0; + + // 8. Repeat, while k < len + while (k < len) { + + var kValue, mappedValue; + + // a. Let Pk be ToString(k). + // This is implicit for LHS operands of the in operator + // b. Let kPresent be the result of calling the HasProperty internal + // method of O with argument Pk. + // This step can be combined with c + // c. If kPresent is true, then + if (k in O) { + + // i. Let kValue be the result of calling the Get internal + // method of O with argument Pk. + kValue = O[k]; + + // ii. Let mappedValue be the result of calling the Call internal + // method of callback with T as the this value and argument + // list containing kValue, k, and O. + mappedValue = callback.call(T, kValue, k, O); + + // iii. Call the DefineOwnProperty internal method of A with arguments + // Pk, Property Descriptor + // { Value: mappedValue, + // Writable: true, + // Enumerable: true, + // Configurable: true }, + // and false. + + // In browsers that support Object.defineProperty, use the following: + // Object.defineProperty(A, k, { + // value: mappedValue, + // writable: true, + // enumerable: true, + // configurable: true + // }); + + // For best browser support, use the following: + A[k] = mappedValue; + } + // d. Increase k by 1. + k++; + } + + // 9. return A + return A; + }; +} + +var PagedTable = function (pagedTable) { + var me = this; + + var source = function(pagedTable) { + var sourceElems = [].slice.call(pagedTable.children).filter(function(e) { + return e.hasAttribute("data-pagedtable-source"); + }); + + if (sourceElems === null || sourceElems.length !== 1) { + throw("A single data-pagedtable-source was not found"); + } + + return JSON.parse(sourceElems[0].innerHTML); + }(pagedTable); + + var options = function(source) { + var options = typeof(source.options) !== "undefined" && + source.options !== null ? source.options : {}; + + var columns = typeof(options.columns) !== "undefined" ? options.columns : {}; + var rows = typeof(options.rows) !== "undefined" ? options.rows : {}; + + var positiveIntOrNull = function(value) { + return parseInt(value) >= 0 ? parseInt(value) : null; + }; + + return { + pages: positiveIntOrNull(options.pages), + rows: { + min: positiveIntOrNull(rows.min), + max: positiveIntOrNull(rows.max), + total: positiveIntOrNull(rows.total) + }, + columns: { + min: positiveIntOrNull(columns.min), + max: positiveIntOrNull(columns.max), + total: positiveIntOrNull(columns.total) + } + }; + }(source); + + var Measurer = function() { + + // set some default initial values that will get adjusted in runtime + me.measures = { + padding: 12, + character: 8, + height: 15, + defaults: true + }; + + me.calculate = function(measuresCell) { + if (!me.measures.defaults) + return; + + var measuresCellStyle = window.getComputedStyle(measuresCell, null); + + var newPadding = parsePadding(measuresCellStyle.paddingLeft) + + parsePadding(measuresCellStyle.paddingRight); + + var sampleString = "ABCDEFGHIJ0123456789"; + var newCharacter = Math.ceil(measuresCell.clientWidth / sampleString.length); + + if (newPadding <= 0 || newCharacter <= 0) + return; + + me.measures.padding = newPadding; + me.measures.character = newCharacter; + me.measures.height = measuresCell.clientHeight; + me.measures.defaults = false; + }; + + return me; + }; + + var Page = function(data, options) { + var me = this; + + var defaults = { + max: 7, + rows: 10 + }; + + var totalPages = function() { + return Math.ceil(data.length / me.rows); + }; + + me.number = 0; + me.max = options.pages !== null ? options.pages : defaults.max; + me.visible = me.max; + me.rows = options.rows.min !== null ? options.rows.min : defaults.rows; + me.total = totalPages(); + + me.setRows = function(newRows) { + me.rows = newRows; + me.total = totalPages(); + }; + + me.setPageNumber = function(newPageNumber) { + if (newPageNumber < 0) newPageNumber = 0; + if (newPageNumber >= me.total) newPageNumber = me.total - 1; + + me.number = newPageNumber; + }; + + me.setVisiblePages = function(visiblePages) { + me.visible = Math.min(me.max, visiblePages); + me.setPageNumber(me.number); + }; + + me.getVisiblePageRange = function() { + var start = me.number - Math.max(Math.floor((me.visible - 1) / 2), 0); + var end = me.number + Math.floor(me.visible / 2) + 1; + var pageCount = me.total; + + if (start < 0) { + var diffToStart = 0 - start; + start += diffToStart; + end += diffToStart; + } + + if (end > pageCount) { + var diffToEnd = end - pageCount; + start -= diffToEnd; + end -= diffToEnd; + } + + start = start < 0 ? 0 : start; + end = end >= pageCount ? pageCount : end; + + var first = false; + var last = false; + + if (start > 0 && me.visible > 1) { + start = start + 1; + first = true; + } + + if (end < pageCount && me.visible > 2) { + end = end - 1; + last = true; + } + + return { + first: first, + start: start, + end: end, + last: last + }; + }; + + me.getRowStart = function() { + var rowStart = page.number * page.rows; + if (rowStart < 0) + rowStart = 0; + + return rowStart; + }; + + me.getRowEnd = function() { + var rowStart = me.getRowStart(); + return Math.min(rowStart + me.rows, data.length); + }; + + me.getPaddingRows = function() { + var rowStart = me.getRowStart(); + var rowEnd = me.getRowEnd(); + return data.length > me.rows ? me.rows - (rowEnd - rowStart) : 0; + }; + }; + + var Columns = function(data, columns, options) { + var me = this; + + me.defaults = { + min: 5 + }; + + me.number = 0; + me.visible = 0; + me.total = columns.length; + me.subset = []; + me.padding = 0; + me.min = options.columns.min !== null ? options.columns.min : me.defaults.min; + me.max = options.columns.max !== null ? options.columns.max : null; + me.widths = {}; + + var widthsLookAhead = Math.max(100, options.rows.min); + var paddingColChars = 10; + + me.emptyNames = function() { + columns.forEach(function(column) { + if (columns.label !== null && columns.label !== "") + return false; + }); + + return true; + }; + + var parsePadding = function(value) { + return parseInt(value) >= 0 ? parseInt(value) : 0; + }; + + me.calculateWidths = function(measures) { + columns.forEach(function(column) { + var maxChars = Math.max( + column.label.toString().length, + column.type.toString().length + ); + + for (var idxRow = 0; idxRow < Math.min(widthsLookAhead, data.length); idxRow++) { + maxChars = Math.max(maxChars, data[idxRow][column.name.toString()].length); + } + + me.widths[column.name] = { + // width in characters + chars: maxChars, + // width for the inner html columns + inner: maxChars * measures.character, + // width adding outer styles like padding + outer: maxChars * measures.character + measures.padding + }; + }); + }; + + me.getWidth = function() { + var widthOuter = 0; + for (var idxCol = 0; idxCol < me.subset.length; idxCol++) { + var columnName = me.subset[idxCol].name; + widthOuter = widthOuter + me.widths[columnName].outer; + } + + widthOuter = widthOuter + me.padding * paddingColChars * measurer.measures.character; + + if (me.hasMoreLeftColumns()) { + widthOuter = widthOuter + columnNavigationWidthPX + measurer.measures.padding; + } + + if (me.hasMoreRightColumns()) { + widthOuter = widthOuter + columnNavigationWidthPX + measurer.measures.padding; + } + + return widthOuter; + }; + + me.updateSlice = function() { + if (me.number + me.visible >= me.total) + me.number = me.total - me.visible; + + if (me.number < 0) me.number = 0; + + me.subset = columns.slice(me.number, Math.min(me.number + me.visible, me.total)); + + me.subset = me.subset.map(function(column) { + Object.keys(column).forEach(function(colKey) { + column[colKey] = column[colKey] === null ? "" : column[colKey].toString(); + }); + + column.width = null; + return column; + }); + }; + + me.setVisibleColumns = function(columnNumber, newVisibleColumns, paddingCount) { + me.number = columnNumber; + me.visible = newVisibleColumns; + me.padding = paddingCount; + + me.updateSlice(); + }; + + me.incColumnNumber = function(increment) { + me.number = me.number + increment; + }; + + me.setColumnNumber = function(newNumber) { + me.number = newNumber; + }; + + me.setPaddingCount = function(newPadding) { + me.padding = newPadding; + }; + + me.getPaddingCount = function() { + return me.padding; + }; + + me.hasMoreLeftColumns = function() { + return me.number > 0; + }; + + me.hasMoreRightColumns = function() { + return me.number + me.visible < me.total; + }; + + me.updateSlice(0); + return me; + }; + + var data = source.data; + var page = new Page(data, options); + var measurer = new Measurer(data, options); + var columns = new Columns(data, source.columns, options); + + var table = null; + var tableDiv = null; + var header = null; + var footer = null; + var tbody = null; + + // Caches pagedTable.clientWidth, specially for webkit + var cachedPagedTableClientWidth = null; + + var onChangeCallbacks = []; + + var clearSelection = function() { + if(document.selection && document.selection.empty) { + document.selection.empty(); + } else if(window.getSelection) { + var sel = window.getSelection(); + sel.removeAllRanges(); + } + }; + + var columnNavigationWidthPX = 5; + + var renderColumnNavigation = function(increment, backwards) { + var arrow = document.createElement("div"); + arrow.setAttribute("style", + "border-top: " + columnNavigationWidthPX + "px solid transparent;" + + "border-bottom: " + columnNavigationWidthPX + "px solid transparent;" + + "border-" + (backwards ? "right" : "left") + ": " + columnNavigationWidthPX + "px solid;"); + + var header = document.createElement("th"); + header.appendChild(arrow); + header.setAttribute("style", + "cursor: pointer;" + + "vertical-align: middle;" + + "min-width: " + columnNavigationWidthPX + "px;" + + "width: " + columnNavigationWidthPX + "px;"); + + header.onclick = function() { + columns.incColumnNumber(backwards ? -1 : increment); + + me.animateColumns(backwards); + renderFooter(); + + clearSelection(); + triggerOnChange(); + }; + + return header; + }; + + var maxColumnWidth = function(width) { + var padding = 80; + var columnMax = Math.max(cachedPagedTableClientWidth - padding, 0); + + return parseInt(width) > 0 ? + Math.min(columnMax, parseInt(width)) + "px" : + columnMax + "px"; + }; + + var clearHeader = function() { + var thead = pagedTable.querySelectorAll("thead")[0]; + thead.innerHTML = ""; + }; + + var renderHeader = function(clear) { + cachedPagedTableClientWidth = pagedTable.clientWidth; + + var fragment = document.createDocumentFragment(); + + header = document.createElement("tr"); + fragment.appendChild(header); + + if (columns.number > 0) + header.appendChild(renderColumnNavigation(-columns.visible, true)); + + columns.subset = columns.subset.map(function(columnData) { + var column = document.createElement("th"); + column.setAttribute("align", columnData.align); + column.style.textAlign = columnData.align; + + column.style.maxWidth = maxColumnWidth(null); + if (columnData.width) { + column.style.minWidth = + column.style.maxWidth = maxColumnWidth(columnData.width); + } + + var columnName = document.createElement("div"); + columnName.setAttribute("class", "pagedtable-header-name"); + if (columnData.label === "") { + columnName.innerHTML = " "; + } + else { + columnName.appendChild(document.createTextNode(columnData.label)); + } + column.appendChild(columnName); + + var columnType = document.createElement("div"); + columnType.setAttribute("class", "pagedtable-header-type"); + if (columnData.type === "") { + columnType.innerHTML = " "; + } + else { + columnType.appendChild(document.createTextNode("<" + columnData.type + ">")); + } + column.appendChild(columnType); + + header.appendChild(column); + + columnData.element = column; + + return columnData; + }); + + for (var idx = 0; idx < columns.getPaddingCount(); idx++) { + var paddingCol = document.createElement("th"); + paddingCol.setAttribute("class", "pagedtable-padding-col"); + header.appendChild(paddingCol); + } + + if (columns.number + columns.visible < columns.total) + header.appendChild(renderColumnNavigation(columns.visible, false)); + + if (typeof(clear) == "undefined" || clear) clearHeader(); + var thead = pagedTable.querySelectorAll("thead")[0]; + thead.appendChild(fragment); + }; + + me.animateColumns = function(backwards) { + var thead = pagedTable.querySelectorAll("thead")[0]; + + var headerOld = thead.querySelectorAll("tr")[0]; + var tbodyOld = table.querySelectorAll("tbody")[0]; + + me.fitColumns(backwards); + + renderHeader(false); + + header.style.opacity = "0"; + header.style.transform = backwards ? "translateX(-30px)" : "translateX(30px)"; + header.style.transition = "transform 200ms linear, opacity 200ms"; + header.style.transitionDelay = "0"; + + renderBody(false); + + if (headerOld) { + headerOld.style.position = "absolute"; + headerOld.style.transform = "translateX(0px)"; + headerOld.style.opacity = "1"; + headerOld.style.transition = "transform 100ms linear, opacity 100ms"; + headerOld.setAttribute("class", "pagedtable-remove-head"); + if (headerOld.style.transitionEnd) { + headerOld.addEventListener("transitionend", function() { + var headerOldByClass = thead.querySelector(".pagedtable-remove-head"); + if (headerOldByClass) thead.removeChild(headerOldByClass); + }); + } + else { + thead.removeChild(headerOld); + } + } + + if (tbodyOld) table.removeChild(tbodyOld); + + tbody.style.opacity = "0"; + tbody.style.transition = "transform 200ms linear, opacity 200ms"; + tbody.style.transitionDelay = "0ms"; + + // force relayout + window.getComputedStyle(header).opacity; + window.getComputedStyle(tbody).opacity; + + if (headerOld) { + headerOld.style.transform = backwards ? "translateX(20px)" : "translateX(-30px)"; + headerOld.style.opacity = "0"; + } + + header.style.transform = "translateX(0px)"; + header.style.opacity = "1"; + + tbody.style.opacity = "1"; + } + + me.onChange = function(callback) { + onChangeCallbacks.push(callback); + }; + + var triggerOnChange = function() { + onChangeCallbacks.forEach(function(onChange) { + onChange(); + }); + }; + + var clearBody = function() { + if (tbody) { + table.removeChild(tbody); + tbody = null; + } + }; + + var renderBody = function(clear) { + cachedPagedTableClientWidth = pagedTable.clientWidth + + var fragment = document.createDocumentFragment(); + + var pageData = data.slice(page.getRowStart(), page.getRowEnd()); + + pageData.forEach(function(dataRow, idxRow) { + var htmlRow = document.createElement("tr"); + htmlRow.setAttribute("class", (idxRow % 2 !==0) ? "even" : "odd"); + + if (columns.hasMoreLeftColumns()) + htmlRow.appendChild(document.createElement("td")); + + columns.subset.forEach(function(columnData) { + var cellName = columnData.name; + var dataCell = dataRow[cellName]; + var htmlCell = document.createElement("td"); + + if (dataCell === "NA") htmlCell.setAttribute("class", "pagedtable-na-cell"); + if (dataCell === "__NA__") dataCell = "NA"; + + var cellText = document.createTextNode(dataCell); + htmlCell.appendChild(cellText); + if (dataCell.length > 50) { + htmlCell.setAttribute("title", dataCell); + } + htmlCell.setAttribute("align", columnData.align); + htmlCell.style.textAlign = columnData.align; + htmlCell.style.maxWidth = maxColumnWidth(null); + if (columnData.width) { + htmlCell.style.minWidth = htmlCell.style.maxWidth = maxColumnWidth(columnData.width); + } + htmlRow.appendChild(htmlCell); + }); + + for (var idx = 0; idx < columns.getPaddingCount(); idx++) { + var paddingCol = document.createElement("td"); + paddingCol.setAttribute("class", "pagedtable-padding-col"); + htmlRow.appendChild(paddingCol); + } + + if (columns.hasMoreRightColumns()) + htmlRow.appendChild(document.createElement("td")); + + fragment.appendChild(htmlRow); + }); + + for (var idxPadding = 0; idxPadding < page.getPaddingRows(); idxPadding++) { + var paddingRow = document.createElement("tr"); + + var paddingCellRow = document.createElement("td"); + paddingCellRow.innerHTML = " "; + paddingCellRow.setAttribute("colspan", "100%"); + paddingRow.appendChild(paddingCellRow); + + fragment.appendChild(paddingRow); + } + + if (typeof(clear) == "undefined" || clear) clearBody(); + tbody = document.createElement("tbody"); + tbody.appendChild(fragment); + + table.appendChild(tbody); + }; + + var getLabelInfo = function() { + var pageStart = page.getRowStart(); + var pageEnd = page.getRowEnd(); + var totalRows = data.length; + + var totalRowsLabel = options.rows.total ? options.rows.total : totalRows; + var totalRowsLabelFormat = totalRowsLabel.toString().replace(/(\d)(?=(\d\d\d)+(?!\d))/g, '$1,'); + + var infoText = (pageStart + 1) + "-" + pageEnd + " of " + totalRowsLabelFormat + " rows"; + if (totalRows < page.rows) { + infoText = totalRowsLabel + " row" + (totalRows != 1 ? "s" : ""); + } + if (columns.total > columns.visible) { + var totalColumnsLabel = options.columns.total ? options.columns.total : columns.total; + + infoText = infoText + " | " + (columns.number + 1) + "-" + + (Math.min(columns.number + columns.visible, columns.total)) + + " of " + totalColumnsLabel + " columns"; + } + + return infoText; + }; + + var clearFooter = function() { + footer = pagedTable.querySelectorAll("div.pagedtable-footer")[0]; + footer.innerHTML = ""; + + return footer; + }; + + var createPageLink = function(idxPage) { + var pageLink = document.createElement("a"); + pageLinkClass = idxPage === page.number ? "pagedtable-index pagedtable-index-current" : "pagedtable-index"; + pageLink.setAttribute("class", pageLinkClass); + pageLink.setAttribute("data-page-index", idxPage); + pageLink.onclick = function() { + page.setPageNumber(parseInt(this.getAttribute("data-page-index"))); + renderBody(); + renderFooter(); + + triggerOnChange(); + }; + + pageLink.appendChild(document.createTextNode(idxPage + 1)); + + return pageLink; + } + + var renderFooter = function() { + footer = clearFooter(); + + var next = document.createElement("a"); + next.appendChild(document.createTextNode("Next")); + next.onclick = function() { + page.setPageNumber(page.number + 1); + renderBody(); + renderFooter(); + + triggerOnChange(); + }; + if (data.length > page.rows) footer.appendChild(next); + + var pageNumbers = document.createElement("div"); + pageNumbers.setAttribute("class", "pagedtable-indexes"); + + var pageRange = page.getVisiblePageRange(); + + if (pageRange.first) { + var pageLink = createPageLink(0); + pageNumbers.appendChild(pageLink); + + var pageSeparator = document.createElement("div"); + pageSeparator.setAttribute("class", "pagedtable-index-separator-left"); + pageSeparator.appendChild(document.createTextNode("...")) + pageNumbers.appendChild(pageSeparator); + } + + for (var idxPage = pageRange.start; idxPage < pageRange.end; idxPage++) { + var pageLink = createPageLink(idxPage); + + pageNumbers.appendChild(pageLink); + } + + if (pageRange.last) { + var pageSeparator = document.createElement("div"); + pageSeparator.setAttribute("class", "pagedtable-index-separator-right"); + pageSeparator.appendChild(document.createTextNode("...")) + pageNumbers.appendChild(pageSeparator); + + var pageLink = createPageLink(page.total - 1); + pageNumbers.appendChild(pageLink); + } + + if (data.length > page.rows) footer.appendChild(pageNumbers); + + var previous = document.createElement("a"); + previous.appendChild(document.createTextNode("Previous")); + previous.onclick = function() { + page.setPageNumber(page.number - 1); + renderBody(); + renderFooter(); + + triggerOnChange(); + }; + if (data.length > page.rows) footer.appendChild(previous); + + var infoLabel = document.createElement("div"); + infoLabel.setAttribute("class", "pagedtable-info"); + infoLabel.setAttribute("title", getLabelInfo()); + infoLabel.appendChild(document.createTextNode(getLabelInfo())); + footer.appendChild(infoLabel); + + var enabledClass = "pagedtable-index-nav"; + var disabledClass = "pagedtable-index-nav pagedtable-index-nav-disabled"; + previous.setAttribute("class", page.number <= 0 ? disabledClass : enabledClass); + next.setAttribute("class", (page.number + 1) * page.rows >= data.length ? disabledClass : enabledClass); + }; + + var measuresCell = null; + + var renderMeasures = function() { + var measuresTable = document.createElement("table"); + measuresTable.style.visibility = "hidden"; + measuresTable.style.position = "absolute"; + measuresTable.style.whiteSpace = "nowrap"; + measuresTable.style.height = "auto"; + measuresTable.style.width = "auto"; + + var measuresRow = document.createElement("tr"); + measuresTable.appendChild(measuresRow); + + measuresCell = document.createElement("td"); + var sampleString = "ABCDEFGHIJ0123456789"; + measuresCell.appendChild(document.createTextNode(sampleString)); + + measuresRow.appendChild(measuresCell); + + tableDiv.appendChild(measuresTable); + } + + me.init = function() { + tableDiv = document.createElement("div"); + pagedTable.appendChild(tableDiv); + var pagedTableClass = data.length > 0 ? + "pagedtable pagedtable-not-empty" : + "pagedtable pagedtable-empty"; + + if (columns.total == 0 || (columns.emptyNames() && data.length == 0)) { + pagedTableClass = pagedTableClass + " pagedtable-empty-columns"; + } + + tableDiv.setAttribute("class", pagedTableClass); + + renderMeasures(); + measurer.calculate(measuresCell); + columns.calculateWidths(measurer.measures); + + table = document.createElement("table"); + table.setAttribute("cellspacing", "0"); + table.setAttribute("class", "table table-condensed"); + tableDiv.appendChild(table); + + table.appendChild(document.createElement("thead")); + + var footerDiv = document.createElement("div"); + footerDiv.setAttribute("class", "pagedtable-footer"); + tableDiv.appendChild(footerDiv); + + // if the host has not yet provided horizontal space, render hidden + if (tableDiv.clientWidth <= 0) { + tableDiv.style.opacity = "0"; + } + + me.render(); + + // retry seizing columns later if the host has not provided space + function retryFit() { + if (tableDiv.clientWidth <= 0) { + setTimeout(retryFit, 100); + } else { + me.render(); + triggerOnChange(); + } + } + if (tableDiv.clientWidth <= 0) { + retryFit(); + } + }; + + var registerWidths = function() { + columns.subset = columns.subset.map(function(column) { + column.width = columns.widths[column.name].inner; + return column; + }); + }; + + var parsePadding = function(value) { + return parseInt(value) >= 0 ? parseInt(value) : 0; + }; + + me.fixedHeight = function() { + return options.rows.max != null; + } + + me.fitRows = function() { + if (me.fixedHeight()) + return; + + measurer.calculate(measuresCell); + + var rows = options.rows.min !== null ? options.rows.min : 0; + var headerHeight = header !== null && header.offsetHeight > 0 ? header.offsetHeight : 0; + var footerHeight = footer !== null && footer.offsetHeight > 0 ? footer.offsetHeight : 0; + + if (pagedTable.offsetHeight > 0) { + var availableHeight = pagedTable.offsetHeight - headerHeight - footerHeight; + rows = Math.floor((availableHeight) / measurer.measures.height); + } + + rows = options.rows.min !== null ? Math.max(options.rows.min, rows) : rows; + + page.setRows(rows); + } + + // The goal of this function is to add as many columns as possible + // starting from left-to-right, when the right most limit is reached + // it tries to add columns from the left as well. + // + // When startBackwards is true columns are added from right-to-left + me.fitColumns = function(startBackwards) { + measurer.calculate(measuresCell); + columns.calculateWidths(measurer.measures); + + if (tableDiv.clientWidth > 0) { + tableDiv.style.opacity = 1; + } + + var visibleColumns = tableDiv.clientWidth <= 0 ? Math.max(columns.min, 1) : 1; + var columnNumber = columns.number; + var paddingCount = 0; + + // track a list of added columns as we build the visible ones to allow us + // to remove columns when they don't fit anymore. + var columnHistory = []; + + var lastTableHeight = 0; + var backwards = startBackwards; + + var tableDivStyle = window.getComputedStyle(tableDiv, null); + var tableDivPadding = parsePadding(tableDivStyle.paddingLeft) + + parsePadding(tableDivStyle.paddingRight); + + var addPaddingCol = false; + var currentWidth = 0; + + while (true) { + columns.setVisibleColumns(columnNumber, visibleColumns, paddingCount); + currentWidth = columns.getWidth(); + + if (tableDiv.clientWidth - tableDivPadding < currentWidth) { + break; + } + + columnHistory.push({ + columnNumber: columnNumber, + visibleColumns: visibleColumns, + paddingCount: paddingCount + }); + + if (columnHistory.length > 100) { + console.error("More than 100 tries to fit columns, aborting"); + break; + } + + if (columns.max !== null && + columns.visible + columns.getPaddingCount() >= columns.max) { + break; + } + + // if we run out of right-columns + if (!backwards && columnNumber + columns.visible >= columns.total) { + // if we started adding right-columns, try adding left-columns + if (!startBackwards && columnNumber > 0) { + backwards = true; + } + else if (columns.min === null || visibleColumns + columns.getPaddingCount() >= columns.min) { + break; + } + else { + paddingCount = paddingCount + 1; + } + } + + // if we run out of left-columns + if (backwards && columnNumber == 0) { + // if we started adding left-columns, try adding right-columns + if (startBackwards && columnNumber + columns.visible < columns.total) { + backwards = false; + } + else if (columns.min === null || visibleColumns + columns.getPaddingCount() >= columns.min) { + break; + } + else { + paddingCount = paddingCount + 1; + } + } + + // when moving backwards try fitting left columns first + if (backwards && columnNumber > 0) { + columnNumber = columnNumber - 1; + } + + if (columnNumber + visibleColumns < columns.total) { + visibleColumns = visibleColumns + 1; + } + } + + var lastRenderableColumn = { + columnNumber: columnNumber, + visibleColumns: visibleColumns, + paddingCount: paddingCount + }; + + if (columnHistory.length > 0) { + lastRenderableColumn = columnHistory[columnHistory.length - 1]; + } + + columns.setVisibleColumns( + lastRenderableColumn.columnNumber, + lastRenderableColumn.visibleColumns, + lastRenderableColumn.paddingCount); + + if (pagedTable.offsetWidth > 0) { + page.setVisiblePages(Math.max(Math.ceil(1.0 * (pagedTable.offsetWidth - 250) / 40), 2)); + } + + registerWidths(); + }; + + me.fit = function(startBackwards) { + me.fitRows(); + me.fitColumns(startBackwards); + } + + me.render = function() { + me.fitColumns(false); + + // render header/footer to measure height accurately + renderHeader(); + renderFooter(); + + me.fitRows(); + renderBody(); + + // re-render footer to match new rows + renderFooter(); + } + + var resizeLastWidth = -1; + var resizeLastHeight = -1; + var resizeNewWidth = -1; + var resizeNewHeight = -1; + var resizePending = false; + + me.resize = function(newWidth, newHeight) { + + function resizeDelayed() { + resizePending = false; + + if ( + (resizeNewWidth !== resizeLastWidth) || + (!me.fixedHeight() && resizeNewHeight !== resizeLastHeight) + ) { + resizeLastWidth = resizeNewWidth; + resizeLastHeight = resizeNewHeight; + + setTimeout(resizeDelayed, 200); + resizePending = true; + } else { + me.render(); + triggerOnChange(); + + resizeLastWidth = -1; + resizeLastHeight = -1; + } + } + + resizeNewWidth = newWidth; + resizeNewHeight = newHeight; + + if (!resizePending) resizeDelayed(); + }; +}; + +var PagedTableDoc; +(function (PagedTableDoc) { + var allPagedTables = []; + + PagedTableDoc.initAll = function() { + allPagedTables = []; + + var pagedTables = [].slice.call(document.querySelectorAll('[data-pagedtable="false"],[data-pagedtable=""]')); + pagedTables.forEach(function(pagedTable, idx) { + pagedTable.setAttribute("data-pagedtable", "true"); + pagedTable.setAttribute("pagedtable-page", 0); + pagedTable.setAttribute("class", "pagedtable-wrapper"); + + var pagedTableInstance = new PagedTable(pagedTable); + pagedTableInstance.init(); + + allPagedTables.push(pagedTableInstance); + }); + }; + + PagedTableDoc.resizeAll = function() { + allPagedTables.forEach(function(pagedTable) { + pagedTable.render(); + }); + }; + + window.addEventListener("resize", PagedTableDoc.resizeAll); + + return PagedTableDoc; +})(PagedTableDoc || (PagedTableDoc = {})); + +window.onload = function() { + PagedTableDoc.initAll(); +}; diff --git a/docs-preview/site_libs/quarto-html/anchor.min.js b/docs-preview/site_libs/quarto-html/anchor.min.js new file mode 100644 index 0000000..5ac814d --- /dev/null +++ b/docs-preview/site_libs/quarto-html/anchor.min.js @@ -0,0 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0000000..343d2f8 --- /dev/null +++ b/docs-preview/site_libs/quarto-html/quarto-syntax-highlighting-dark.css @@ -0,0 +1,189 @@ +/* quarto syntax highlight colors */ +:root { + --quarto-hl-al-color: #f07178; + --quarto-hl-an-color: #d4d0ab; + --quarto-hl-at-color: #00e0e0; + --quarto-hl-bn-color: #d4d0ab; + --quarto-hl-bu-color: #abe338; + --quarto-hl-ch-color: #abe338; + --quarto-hl-co-color: #f8f8f2; + --quarto-hl-cv-color: #ffd700; + --quarto-hl-cn-color: #ffd700; + --quarto-hl-cf-color: #ffa07a; + --quarto-hl-dt-color: #ffa07a; + --quarto-hl-dv-color: #d4d0ab; + --quarto-hl-do-color: #f8f8f2; + --quarto-hl-er-color: #f07178; + --quarto-hl-ex-color: #00e0e0; + --quarto-hl-fl-color: #d4d0ab; + --quarto-hl-fu-color: #ffa07a; + --quarto-hl-im-color: #abe338; + --quarto-hl-in-color: #d4d0ab; + --quarto-hl-kw-color: #ffa07a; + --quarto-hl-op-color: #ffa07a; + --quarto-hl-ot-color: #00e0e0; + --quarto-hl-pp-color: #dcc6e0; + --quarto-hl-re-color: #00e0e0; + --quarto-hl-sc-color: #abe338; + --quarto-hl-ss-color: #abe338; + --quarto-hl-st-color: #abe338; + --quarto-hl-va-color: #00e0e0; + --quarto-hl-vs-color: #abe338; + --quarto-hl-wa-color: #dcc6e0; +} + +/* other quarto variables */ +:root { + --quarto-font-monospace: SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace; +} + +code span.al { + background-color: #2a0f15; + font-weight: bold; + color: #f07178; +} + +code span.an { + color: #d4d0ab; +} + +code span.at { + color: #00e0e0; +} + +code span.bn { + color: #d4d0ab; +} + +code span.bu { + color: #abe338; +} + +code span.ch { + color: #abe338; +} + +code span.co { + font-style: italic; + color: #f8f8f2; +} + +code span.cv { + color: #ffd700; +} + +code span.cn { + color: #ffd700; +} + +code span.cf { + font-weight: bold; + color: #ffa07a; +} + +code span.dt { + color: #ffa07a; +} + +code span.dv { + color: #d4d0ab; +} + +code span.do { + color: #f8f8f2; +} + +code span.er { + color: #f07178; + text-decoration: underline; +} + +code span.ex { + font-weight: bold; + color: #00e0e0; +} + +code span.fl { + color: #d4d0ab; +} + +code span.fu { + color: #ffa07a; +} + +code span.im { + color: #abe338; +} + +code span.in { + color: #d4d0ab; +} + +code span.kw { + font-weight: bold; + color: #ffa07a; +} + +pre > code.sourceCode > span { + color: #f8f8f2; +} + +code span { + color: #f8f8f2; +} + +code.sourceCode > span { + color: #f8f8f2; +} + +div.sourceCode, +div.sourceCode pre.sourceCode { + color: #f8f8f2; +} + +code span.op { + color: #ffa07a; +} + +code span.ot { + color: #00e0e0; +} + +code span.pp { + color: #dcc6e0; +} + +code span.re { + background-color: #f8f8f2; + color: #00e0e0; +} + +code span.sc { + color: #abe338; +} + +code span.ss { + color: #abe338; +} + +code span.st { + color: #abe338; +} + +code span.va { + color: #00e0e0; +} + +code span.vs { + color: #abe338; +} + +code span.wa { + color: #dcc6e0; +} + +.prevent-inlining { + content: " code.sourceCode > span { + color: #003B4F; +} + +code span { + color: #003B4F; +} + +code.sourceCode > span { + color: #003B4F; +} + +div.sourceCode, +div.sourceCode pre.sourceCode { + color: #003B4F; +} + +code span.ot { + color: #003B4F; + font-style: inherit; +} + +code span.at { + color: #657422; + font-style: inherit; +} + +code span.ss { + color: #20794D; + font-style: inherit; +} + +code span.an { + color: #5E5E5E; + font-style: inherit; +} + +code span.fu { + color: #4758AB; + font-style: inherit; +} + +code span.st { + color: #20794D; + font-style: inherit; +} + +code span.cf { + color: #003B4F; + font-weight: bold; + font-style: inherit; +} + +code span.op { + color: #5E5E5E; + font-style: inherit; +} + +code span.er { + color: #AD0000; + font-style: inherit; +} + +code span.bn { + color: #AD0000; + font-style: inherit; +} + +code span.al { + color: #AD0000; + font-style: inherit; +} + +code span.va { + color: #111111; + font-style: inherit; +} + +code span.bu { + font-style: inherit; +} + +code span.ex { + font-style: inherit; +} + +code span.pp { + color: #AD0000; + font-style: inherit; +} + +code span.in { + color: #5E5E5E; + font-style: inherit; +} + +code span.vs { + color: #20794D; + font-style: inherit; +} + +code span.wa { + color: #5E5E5E; + font-style: italic; +} + +code span.do { + color: #5E5E5E; + font-style: italic; +} + +code span.im { + color: #00769E; + font-style: inherit; +} + +code span.ch { + color: #20794D; + font-style: inherit; +} + +code span.dt { + color: #AD0000; + font-style: inherit; +} + +code span.fl { + color: #AD0000; + font-style: inherit; +} + +code span.co { + color: #5E5E5E; + font-style: inherit; +} + +code span.cv { + color: #5E5E5E; + font-style: italic; +} + +code span.cn { + color: #8f5902; + font-style: inherit; +} + +code span.sc { + color: #5E5E5E; + font-style: inherit; +} + +code span.dv { + color: #AD0000; + font-style: inherit; +} + +code span.kw { + color: #003B4F; + font-weight: bold; + font-style: inherit; +} + +.prevent-inlining { + content: " { + // Find any conflicting margin elements and add margins to the + // top to prevent overlap + const marginChildren = window.document.querySelectorAll( + ".column-margin.column-container > *, .margin-caption, .aside" + ); + + let lastBottom = 0; + for (const marginChild of marginChildren) { + if (marginChild.offsetParent !== null) { + // clear the top margin so we recompute it + marginChild.style.marginTop = null; + const top = marginChild.getBoundingClientRect().top + window.scrollY; + if (top < lastBottom) { + 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"]); + lastBottom = top + marginChild.getBoundingClientRect().height + marginTop; + } + } +}; + +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(); + if ( + window.document.body.getBoundingClientRect().width < 990 && + isReaderMode() + ) { + quartoToggleReader(); + } + }, 50) + ); + resizeObserver.observe(window.document.body); + } + + const tocEl = window.document.querySelector('nav.toc-active[role="doc-toc"]'); + const sidebarEl = window.document.getElementById("quarto-sidebar"); + const leftTocEl = window.document.getElementById("quarto-sidebar-toc-left"); + const marginSidebarEl = window.document.getElementById( + "quarto-margin-sidebar" + ); + // function to determine whether the element has a previous sibling that is active + const prevSiblingIsActiveLink = (el) => { + const sibling = el.previousElementSibling; + if (sibling && sibling.tagName === "A") { + return sibling.classList.contains("active"); + } else { + return false; + } + }; + + // fire slideEnter for bootstrap tab activations (for htmlwidget resize behavior) + function fireSlideEnter(e) { + const event = window.document.createEvent("Event"); + event.initEvent("slideenter", true, true); + window.document.dispatchEvent(event); + } + const tabs = window.document.querySelectorAll('a[data-bs-toggle="tab"]'); + tabs.forEach((tab) => { + tab.addEventListener("shown.bs.tab", fireSlideEnter); + }); + + // fire slideEnter for tabby tab activations (for htmlwidget resize behavior) + document.addEventListener("tabby", fireSlideEnter, false); + + // Track scrolling and mark TOC links as active + // get table of contents and sidebar (bail if we don't have at least one) + const tocLinks = tocEl + ? [...tocEl.querySelectorAll("a[data-scroll-target]")] + : []; + const makeActive = (link) => tocLinks[link].classList.add("active"); + const removeActive = (link) => tocLinks[link].classList.remove("active"); + const removeAllActive = () => + [...Array(tocLinks.length).keys()].forEach((link) => removeActive(link)); + + // activate the anchor for a section associated with this TOC entry + tocLinks.forEach((link) => { + link.addEventListener("click", () => { + if (link.href.indexOf("#") !== -1) { + const anchor = link.href.split("#")[1]; + const heading = window.document.querySelector( + `[data-anchor-id="${anchor}"]` + ); + if (heading) { + // Add the class + heading.classList.add("reveal-anchorjs-link"); + + // function to show the anchor + const handleMouseout = () => { + heading.classList.remove("reveal-anchorjs-link"); + heading.removeEventListener("mouseout", handleMouseout); + }; + + // add a function to clear the anchor when the user mouses out of it + heading.addEventListener("mouseout", handleMouseout); + } + } + }); + }); + + const sections = tocLinks.map((link) => { + const target = link.getAttribute("data-scroll-target"); + if (target.startsWith("#")) { + return window.document.getElementById(decodeURI(`${target.slice(1)}`)); + } else { + return window.document.querySelector(decodeURI(`${target}`)); + } + }); + + const sectionMargin = 200; + let currentActive = 0; + // track whether we've initialized state the first time + let init = false; + + const updateActiveLink = () => { + // The index from bottom to top (e.g. reversed list) + let sectionIndex = -1; + if ( + window.innerHeight + window.pageYOffset >= + window.document.body.offsetHeight + ) { + // This is the no-scroll case where last section should be the active one + sectionIndex = 0; + } else { + // This finds the last section visible on screen that should be made active + sectionIndex = [...sections].reverse().findIndex((section) => { + if (section) { + return window.pageYOffset >= section.offsetTop - sectionMargin; + } else { + return false; + } + }); + } + if (sectionIndex > -1) { + const current = sections.length - sectionIndex - 1; + if (current !== currentActive) { + removeAllActive(); + currentActive = current; + makeActive(current); + if (init) { + window.dispatchEvent(sectionChanged); + } + init = true; + } + } + }; + + const inHiddenRegion = (top, bottom, hiddenRegions) => { + for (const region of hiddenRegions) { + if (top <= region.bottom && bottom >= region.top) { + return true; + } + } + return false; + }; + + const categorySelector = "header.quarto-title-block .quarto-category"; + const activateCategories = (href) => { + // Find any categories + // Surround them with a link pointing back to: + // #category=Authoring + try { + const categoryEls = window.document.querySelectorAll(categorySelector); + for (const categoryEl of categoryEls) { + const categoryText = categoryEl.textContent; + if (categoryText) { + const link = `${href}#category=${encodeURIComponent(categoryText)}`; + const linkEl = window.document.createElement("a"); + linkEl.setAttribute("href", link); + for (const child of categoryEl.childNodes) { + linkEl.append(child); + } + categoryEl.appendChild(linkEl); + } + } + } catch { + // Ignore errors + } + }; + function hasTitleCategories() { + return window.document.querySelector(categorySelector) !== null; + } + + function offsetRelativeUrl(url) { + const offset = getMeta("quarto:offset"); + return offset ? offset + url : url; + } + + function offsetAbsoluteUrl(url) { + const offset = getMeta("quarto:offset"); + const baseUrl = new URL(offset, window.location); + + const projRelativeUrl = url.replace(baseUrl, ""); + if (projRelativeUrl.startsWith("/")) { + return projRelativeUrl; + } else { + return "/" + projRelativeUrl; + } + } + + // read a meta tag value + function getMeta(metaName) { + const metas = window.document.getElementsByTagName("meta"); + for (let i = 0; i < metas.length; i++) { + if (metas[i].getAttribute("name") === metaName) { + return metas[i].getAttribute("content"); + } + } + return ""; + } + + async function findAndActivateCategories() { + const currentPagePath = offsetAbsoluteUrl(window.location.href); + const response = await fetch(offsetRelativeUrl("listings.json")); + if (response.status == 200) { + return response.json().then(function (listingPaths) { + const listingHrefs = []; + for (const listingPath of listingPaths) { + const pathWithoutLeadingSlash = listingPath.listing.substring(1); + for (const item of listingPath.items) { + if ( + item === currentPagePath || + item === currentPagePath + "index.html" + ) { + // Resolve this path against the offset to be sure + // we already are using the correct path to the listing + // (this adjusts the listing urls to be rooted against + // whatever root the page is actually running against) + const relative = offsetRelativeUrl(pathWithoutLeadingSlash); + const baseUrl = window.location; + const resolvedPath = new URL(relative, baseUrl); + listingHrefs.push(resolvedPath.pathname); + break; + } + } + } + + // Look up the tree for a nearby linting and use that if we find one + const nearestListing = findNearestParentListing( + offsetAbsoluteUrl(window.location.pathname), + listingHrefs + ); + if (nearestListing) { + activateCategories(nearestListing); + } else { + // See if the referrer is a listing page for this item + const referredRelativePath = offsetAbsoluteUrl(document.referrer); + const referrerListing = listingHrefs.find((listingHref) => { + const isListingReferrer = + listingHref === referredRelativePath || + listingHref === referredRelativePath + "index.html"; + return isListingReferrer; + }); + + if (referrerListing) { + // Try to use the referrer if possible + activateCategories(referrerListing); + } else if (listingHrefs.length > 0) { + // Otherwise, just fall back to the first listing + activateCategories(listingHrefs[0]); + } + } + }); + } + } + if (hasTitleCategories()) { + findAndActivateCategories(); + } + + const findNearestParentListing = (href, listingHrefs) => { + if (!href || !listingHrefs) { + return undefined; + } + // Look up the tree for a nearby linting and use that if we find one + const relativeParts = href.substring(1).split("/"); + while (relativeParts.length > 0) { + const path = relativeParts.join("/"); + for (const listingHref of listingHrefs) { + if (listingHref.startsWith(path)) { + return listingHref; + } + } + relativeParts.pop(); + } + + return undefined; + }; + + const manageSidebarVisiblity = (el, placeholderDescriptor) => { + let isVisible = true; + let elRect; + + return (hiddenRegions) => { + if (el === null) { + return; + } + + // Find the last element of the TOC + const lastChildEl = el.lastElementChild; + + if (lastChildEl) { + // Converts the sidebar to a menu + const convertToMenu = () => { + for (const child of el.children) { + child.style.opacity = 0; + child.style.overflow = "hidden"; + child.style.pointerEvents = "none"; + } + + nexttick(() => { + const toggleContainer = window.document.createElement("div"); + toggleContainer.style.width = "100%"; + toggleContainer.classList.add("zindex-over-content"); + toggleContainer.classList.add("quarto-sidebar-toggle"); + toggleContainer.classList.add("headroom-target"); // Marks this to be managed by headeroom + toggleContainer.id = placeholderDescriptor.id; + toggleContainer.style.position = "fixed"; + + const toggleIcon = window.document.createElement("i"); + toggleIcon.classList.add("quarto-sidebar-toggle-icon"); + toggleIcon.classList.add("bi"); + toggleIcon.classList.add("bi-caret-down-fill"); + + const toggleTitle = window.document.createElement("div"); + const titleEl = window.document.body.querySelector( + placeholderDescriptor.titleSelector + ); + if (titleEl) { + toggleTitle.append( + titleEl.textContent || titleEl.innerText, + toggleIcon + ); + } + toggleTitle.classList.add("zindex-over-content"); + toggleTitle.classList.add("quarto-sidebar-toggle-title"); + toggleContainer.append(toggleTitle); + + const toggleContents = window.document.createElement("div"); + toggleContents.classList = el.classList; + toggleContents.classList.add("zindex-over-content"); + toggleContents.classList.add("quarto-sidebar-toggle-contents"); + for (const child of el.children) { + if (child.id === "toc-title") { + continue; + } + + const clone = child.cloneNode(true); + clone.style.opacity = 1; + clone.style.pointerEvents = null; + clone.style.display = null; + toggleContents.append(clone); + } + toggleContents.style.height = "0px"; + const positionToggle = () => { + // position the element (top left of parent, same width as parent) + if (!elRect) { + elRect = el.getBoundingClientRect(); + } + toggleContainer.style.left = `${elRect.left}px`; + toggleContainer.style.top = `${elRect.top}px`; + toggleContainer.style.width = `${elRect.width}px`; + }; + positionToggle(); + + toggleContainer.append(toggleContents); + el.parentElement.prepend(toggleContainer); + + // Process clicks + let tocShowing = false; + // Allow the caller to control whether this is dismissed + // when it is clicked (e.g. sidebar navigation supports + // opening and closing the nav tree, so don't dismiss on click) + const clickEl = placeholderDescriptor.dismissOnClick + ? toggleContainer + : toggleTitle; + + const closeToggle = () => { + if (tocShowing) { + toggleContainer.classList.remove("expanded"); + toggleContents.style.height = "0px"; + tocShowing = false; + } + }; + + // Get rid of any expanded toggle if the user scrolls + window.document.addEventListener( + "scroll", + throttle(() => { + closeToggle(); + }, 50) + ); + + // Handle positioning of the toggle + window.addEventListener( + "resize", + throttle(() => { + elRect = undefined; + positionToggle(); + }, 50) + ); + + window.addEventListener("quarto-hrChanged", () => { + elRect = undefined; + }); + + // Process the click + clickEl.onclick = () => { + if (!tocShowing) { + toggleContainer.classList.add("expanded"); + toggleContents.style.height = null; + tocShowing = true; + } else { + closeToggle(); + } + }; + }); + }; + + // Converts a sidebar from a menu back to a sidebar + const convertToSidebar = () => { + for (const child of el.children) { + child.style.opacity = 1; + child.style.overflow = null; + child.style.pointerEvents = null; + } + + const placeholderEl = window.document.getElementById( + placeholderDescriptor.id + ); + if (placeholderEl) { + placeholderEl.remove(); + } + + el.classList.remove("rollup"); + }; + + if (isReaderMode()) { + convertToMenu(); + isVisible = false; + } else { + // Find the top and bottom o the element that is being managed + const elTop = el.offsetTop; + const elBottom = + elTop + lastChildEl.offsetTop + lastChildEl.offsetHeight; + + if (!isVisible) { + // If the element is current not visible reveal if there are + // no conflicts with overlay regions + if (!inHiddenRegion(elTop, elBottom, hiddenRegions)) { + convertToSidebar(); + isVisible = true; + } + } else { + // If the element is visible, hide it if it conflicts with overlay regions + // and insert a placeholder toggle (or if we're in reader mode) + if (inHiddenRegion(elTop, elBottom, hiddenRegions)) { + convertToMenu(); + isVisible = false; + } + } + } + } + }; + }; + + const tabEls = document.querySelectorAll('a[data-bs-toggle="tab"]'); + for (const tabEl of tabEls) { + const id = tabEl.getAttribute("data-bs-target"); + if (id) { + const columnEl = document.querySelector( + `${id} .column-margin, .tabset-margin-content` + ); + if (columnEl) + tabEl.addEventListener("shown.bs.tab", function (event) { + const el = event.srcElement; + if (el) { + const visibleCls = `${el.id}-margin-content`; + // walk up until we find a parent tabset + let panelTabsetEl = el.parentElement; + while (panelTabsetEl) { + if (panelTabsetEl.classList.contains("panel-tabset")) { + break; + } + panelTabsetEl = panelTabsetEl.parentElement; + } + + if (panelTabsetEl) { + const prevSib = panelTabsetEl.previousElementSibling; + if ( + prevSib && + prevSib.classList.contains("tabset-margin-container") + ) { + const childNodes = prevSib.querySelectorAll( + ".tabset-margin-content" + ); + for (const childEl of childNodes) { + if (childEl.classList.contains(visibleCls)) { + childEl.classList.remove("collapse"); + } else { + childEl.classList.add("collapse"); + } + } + } + } + } + + layoutMarginEls(); + }); + } + } + + // Manage the visibility of the toc and the sidebar + const marginScrollVisibility = manageSidebarVisiblity(marginSidebarEl, { + id: "quarto-toc-toggle", + titleSelector: "#toc-title", + dismissOnClick: true, + }); + const sidebarScrollVisiblity = manageSidebarVisiblity(sidebarEl, { + id: "quarto-sidebarnav-toggle", + titleSelector: ".title", + dismissOnClick: false, + }); + let tocLeftScrollVisibility; + if (leftTocEl) { + tocLeftScrollVisibility = manageSidebarVisiblity(leftTocEl, { + id: "quarto-lefttoc-toggle", + titleSelector: "#toc-title", + dismissOnClick: true, + }); + } + + // Find the first element that uses formatting in special columns + const conflictingEls = window.document.body.querySelectorAll( + '[class^="column-"], [class*=" column-"], aside, [class*="margin-caption"], [class*=" margin-caption"], [class*="margin-ref"], [class*=" margin-ref"]' + ); + + // Filter all the possibly conflicting elements into ones + // the do conflict on the left or ride side + const arrConflictingEls = Array.from(conflictingEls); + const leftSideConflictEls = arrConflictingEls.filter((el) => { + if (el.tagName === "ASIDE") { + return false; + } + return Array.from(el.classList).find((className) => { + return ( + className !== "column-body" && + className.startsWith("column-") && + !className.endsWith("right") && + !className.endsWith("container") && + className !== "column-margin" + ); + }); + }); + const rightSideConflictEls = arrConflictingEls.filter((el) => { + if (el.tagName === "ASIDE") { + return true; + } + + const hasMarginCaption = Array.from(el.classList).find((className) => { + return className == "margin-caption"; + }); + if (hasMarginCaption) { + return true; + } + + return Array.from(el.classList).find((className) => { + return ( + className !== "column-body" && + !className.endsWith("container") && + className.startsWith("column-") && + !className.endsWith("left") + ); + }); + }); + + const kOverlapPaddingSize = 10; + function toRegions(els) { + return els.map((el) => { + const boundRect = el.getBoundingClientRect(); + const top = + boundRect.top + + document.documentElement.scrollTop - + kOverlapPaddingSize; + return { + top, + bottom: top + el.scrollHeight + 2 * kOverlapPaddingSize, + }; + }); + } + + let hasObserved = false; + const visibleItemObserver = (els) => { + let visibleElements = [...els]; + const intersectionObserver = new IntersectionObserver( + (entries, _observer) => { + entries.forEach((entry) => { + if (entry.isIntersecting) { + if (visibleElements.indexOf(entry.target) === -1) { + visibleElements.push(entry.target); + } + } else { + visibleElements = visibleElements.filter((visibleEntry) => { + return visibleEntry !== entry; + }); + } + }); + + if (!hasObserved) { + hideOverlappedSidebars(); + } + hasObserved = true; + }, + {} + ); + els.forEach((el) => { + intersectionObserver.observe(el); + }); + + return { + getVisibleEntries: () => { + return visibleElements; + }, + }; + }; + + const rightElementObserver = visibleItemObserver(rightSideConflictEls); + const leftElementObserver = visibleItemObserver(leftSideConflictEls); + + const hideOverlappedSidebars = () => { + marginScrollVisibility(toRegions(rightElementObserver.getVisibleEntries())); + sidebarScrollVisiblity(toRegions(leftElementObserver.getVisibleEntries())); + if (tocLeftScrollVisibility) { + tocLeftScrollVisibility( + toRegions(leftElementObserver.getVisibleEntries()) + ); + } + }; + + window.quartoToggleReader = () => { + // Applies a slow class (or removes it) + // to update the transition speed + const slowTransition = (slow) => { + const manageTransition = (id, slow) => { + const el = document.getElementById(id); + if (el) { + if (slow) { + el.classList.add("slow"); + } else { + el.classList.remove("slow"); + } + } + }; + + manageTransition("TOC", slow); + manageTransition("quarto-sidebar", slow); + }; + const readerMode = !isReaderMode(); + setReaderModeValue(readerMode); + + // If we're entering reader mode, slow the transition + if (readerMode) { + slowTransition(readerMode); + } + highlightReaderToggle(readerMode); + hideOverlappedSidebars(); + + // If we're exiting reader mode, restore the non-slow transition + if (!readerMode) { + slowTransition(!readerMode); + } + }; + + const highlightReaderToggle = (readerMode) => { + const els = document.querySelectorAll(".quarto-reader-toggle"); + if (els) { + els.forEach((el) => { + if (readerMode) { + el.classList.add("reader"); + } else { + el.classList.remove("reader"); + } + }); + } + }; + + const setReaderModeValue = (val) => { + if (window.location.protocol !== "file:") { + window.localStorage.setItem("quarto-reader-mode", val); + } else { + localReaderMode = val; + } + }; + + const isReaderMode = () => { + if (window.location.protocol !== "file:") { + return window.localStorage.getItem("quarto-reader-mode") === "true"; + } else { + return localReaderMode; + } + }; + let localReaderMode = null; + + const tocOpenDepthStr = tocEl?.getAttribute("data-toc-expanded"); + const tocOpenDepth = tocOpenDepthStr ? Number(tocOpenDepthStr) : 1; + + // Walk the TOC and collapse/expand nodes + // Nodes are expanded if: + // - they are top level + // - they have children that are 'active' links + // - they are directly below an link that is 'active' + const walk = (el, depth) => { + // Tick depth when we enter a UL + if (el.tagName === "UL") { + depth = depth + 1; + } + + // It this is active link + let isActiveNode = false; + if (el.tagName === "A" && el.classList.contains("active")) { + isActiveNode = true; + } + + // See if there is an active child to this element + let hasActiveChild = false; + for (child of el.children) { + hasActiveChild = walk(child, depth) || hasActiveChild; + } + + // 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 || + hasActiveChild || + prevSiblingIsActiveLink(el) + ) { + el.classList.remove("collapse"); + } else { + el.classList.add("collapse"); + } + + // untick depth when we leave a UL + depth = depth - 1; + } + return hasActiveChild || isActiveNode; + }; + + // walk the TOC and expand / collapse any items that should be shown + if (tocEl) { + updateActiveLink(); + walk(tocEl, 0); + } + + // Throttle the scroll event and walk peridiocally + window.document.addEventListener( + "scroll", + throttle(() => { + if (tocEl) { + updateActiveLink(); + walk(tocEl, 0); + } + if (!isReaderMode()) { + hideOverlappedSidebars(); + } + }, 5) + ); + window.addEventListener( + "resize", + throttle(() => { + if (tocEl) { + updateActiveLink(); + walk(tocEl, 0); + } + if (!isReaderMode()) { + hideOverlappedSidebars(); + } + }, 10) + ); + hideOverlappedSidebars(); + highlightReaderToggle(isReaderMode()); +}); + +// grouped tabsets +window.addEventListener("pageshow", (_event) => { + function getTabSettings() { + const data = localStorage.getItem("quarto-persistent-tabsets-data"); + if (!data) { + localStorage.setItem("quarto-persistent-tabsets-data", "{}"); + return {}; + } + if (data) { + return JSON.parse(data); + } + } + + function setTabSettings(data) { + localStorage.setItem( + "quarto-persistent-tabsets-data", + JSON.stringify(data) + ); + } + + function setTabState(groupName, groupValue) { + const data = getTabSettings(); + data[groupName] = groupValue; + setTabSettings(data); + } + + function toggleTab(tab, active) { + const tabPanelId = tab.getAttribute("aria-controls"); + const tabPanel = document.getElementById(tabPanelId); + if (active) { + tab.classList.add("active"); + tabPanel.classList.add("active"); + } else { + tab.classList.remove("active"); + tabPanel.classList.remove("active"); + } + } + + function toggleAll(selectedGroup, selectorsToSync) { + for (const [thisGroup, tabs] of Object.entries(selectorsToSync)) { + const active = selectedGroup === thisGroup; + for (const tab of tabs) { + toggleTab(tab, active); + } + } + } + + function findSelectorsToSyncByLanguage() { + const result = {}; + const tabs = Array.from( + document.querySelectorAll(`div[data-group] a[id^='tabset-']`) + ); + for (const item of tabs) { + const div = item.parentElement.parentElement.parentElement; + const group = div.getAttribute("data-group"); + if (!result[group]) { + result[group] = {}; + } + const selectorsToSync = result[group]; + const value = item.innerHTML; + if (!selectorsToSync[value]) { + selectorsToSync[value] = []; + } + selectorsToSync[value].push(item); + } + return result; + } + + function setupSelectorSync() { + const selectorsToSync = findSelectorsToSyncByLanguage(); + Object.entries(selectorsToSync).forEach(([group, tabSetsByValue]) => { + Object.entries(tabSetsByValue).forEach(([value, items]) => { + items.forEach((item) => { + item.addEventListener("click", (_event) => { + setTabState(group, value); + toggleAll(value, selectorsToSync[group]); + }); + }); + }); + }); + return selectorsToSync; + } + + const selectorsToSync 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0000000..38cc430 --- /dev/null +++ b/docs-preview/site_libs/quarto-nav/quarto-nav.js @@ -0,0 +1,325 @@ +const headroomChanged = new CustomEvent("quarto-hrChanged", { + detail: {}, + bubbles: true, + cancelable: false, + 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; + const scrollUpBuffer = 35; + const btn = document.getElementById("quarto-back-to-top"); + const hideBackToTop = () => { + btn.style.display = "none"; + }; + const showBackToTop = () => { + btn.style.display = "inline-block"; + }; + if (btn) { + window.document.addEventListener( + "scroll", + function () { + const currentScrollTop = + window.pageYOffset || document.documentElement.scrollTop; + + // Shows and hides the button 'intelligently' as the user scrolls + if (currentScrollTop - scrollDownBuffer > lastScrollTop) { + hideBackToTop(); + lastScrollTop = currentScrollTop <= 0 ? 0 : currentScrollTop; + } else if (currentScrollTop < lastScrollTop - scrollUpBuffer) { + showBackToTop(); + lastScrollTop = currentScrollTop <= 0 ? 0 : currentScrollTop; + } + + // Show the button at the bottom, hides it at the top + if (currentScrollTop <= 0) { + hideBackToTop(); + } else if ( + window.innerHeight + currentScrollTop >= + document.body.offsetHeight + ) { + showBackToTop(); + } + }, + false + ); + } + + function throttle(func, wait) { + var timeout; + return function () { + const context = this; + const args = arguments; + const later = function () { + clearTimeout(timeout); + timeout = null; + func.apply(context, args); + }; + + if (!timeout) { + timeout = setTimeout(later, wait); + } + }; + } + + function headerOffset() { + // Set an offset if there is are fixed top navbar + const headerEl = window.document.querySelector("header.fixed-top"); + if (headerEl) { + return headerEl.clientHeight; + } else { + return 0; + } + } + + function footerOffset() { + const footerEl = window.document.querySelector("footer.footer"); + if (footerEl) { + return footerEl.clientHeight; + } else { + return 0; + } + } + + function dashboardOffset() { + const dashboardNavEl = window.document.getElementById( + "quarto-dashboard-header" + ); + if (dashboardNavEl !== null) { + return dashboardNavEl.clientHeight; + } else { + return 0; + } + } + + function updateDocumentOffsetWithoutAnimation() { + updateDocumentOffset(false); + } + + function updateDocumentOffset(animated) { + // set body offset + const topOffset = headerOffset(); + const bodyOffset = topOffset + footerOffset() + dashboardOffset(); + const bodyEl = window.document.body; + bodyEl.setAttribute("data-bs-offset", topOffset); + bodyEl.style.paddingTop = topOffset + "px"; + + // deal with sidebar offsets + const sidebars = window.document.querySelectorAll( + ".sidebar, .headroom-target" + ); + sidebars.forEach((sidebar) => { + if (!animated) { + sidebar.classList.add("notransition"); + // Remove the no transition class after the animation has time to complete + setTimeout(function () { + sidebar.classList.remove("notransition"); + }, 201); + } + + if (window.Headroom && sidebar.classList.contains("sidebar-unpinned")) { + sidebar.style.top = "0"; + sidebar.style.maxHeight = "100vh"; + } else { + sidebar.style.top = topOffset + "px"; + sidebar.style.maxHeight = "calc(100vh - " + topOffset + "px)"; + } + }); + + // allow space for footer + const mainContainer = window.document.querySelector(".quarto-container"); + if (mainContainer) { + mainContainer.style.minHeight = "calc(100vh - " + bodyOffset + "px)"; + } + + // link offset + let linkStyle = window.document.querySelector("#quarto-target-style"); + if (!linkStyle) { + linkStyle = window.document.createElement("style"); + linkStyle.setAttribute("id", "quarto-target-style"); + window.document.head.appendChild(linkStyle); + } + while (linkStyle.firstChild) { + linkStyle.removeChild(linkStyle.firstChild); + } + if (topOffset > 0) { + linkStyle.appendChild( + window.document.createTextNode(` + section:target::before { + content: ""; + display: block; + height: ${topOffset}px; + margin: -${topOffset}px 0 0; + }`) + ); + } + if (init) { + window.dispatchEvent(headroomChanged); + } + init = true; + } + + // initialize headroom + var header = window.document.querySelector("#quarto-header"); + if (header && window.Headroom) { + const headroom = new window.Headroom(header, { + tolerance: 5, + onPin: function () { + const sidebars = window.document.querySelectorAll( + ".sidebar, .headroom-target" + ); + sidebars.forEach((sidebar) => { + sidebar.classList.remove("sidebar-unpinned"); + }); + updateDocumentOffset(); + }, + onUnpin: function () { + const sidebars = window.document.querySelectorAll( + ".sidebar, .headroom-target" + ); + sidebars.forEach((sidebar) => { + sidebar.classList.add("sidebar-unpinned"); + }); + updateDocumentOffset(); + }, + }); 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links if we aren't on the filesystem + if (window.location.protocol !== "file:") { + 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/, "/"); + } + } + + // Fixup any sharing links that require urls + // Append url to any sharing urls + const sharingLinks = window.document.querySelectorAll( + "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]; + const href = sharingLink.getAttribute("href"); + if (href) { + sharingLink.setAttribute( + "href", + href.replace("|url|", window.location.href) + ); + } + } + + // Scroll the active navigation item into view, if necessary + const navSidebar = window.document.querySelector("nav#quarto-sidebar"); + if (navSidebar) { + // Find the active item + const activeItem = navSidebar.querySelector("li.sidebar-item a.active"); + if (activeItem) { + // Wait for the scroll height and height to resolve by observing size changes on the + // nav element that is scrollable + const resizeObserver = new ResizeObserver((_entries) => { + // The bottom of the element + const elBottom = activeItem.offsetTop; + const viewBottom = navSidebar.scrollTop + navSidebar.clientHeight; + + // The element height and scroll height are the same, then we are still loading + if (viewBottom !== navSidebar.scrollHeight) { + // Determine if the item isn't visible and scroll to it + if (elBottom >= viewBottom) { + navSidebar.scrollTop = elBottom; + } + + // stop observing now since we've completed the scroll + resizeObserver.unobserve(navSidebar); + } + }); + resizeObserver.observe(navSidebar); + } + } + } +}); diff --git a/docs-preview/site_libs/quarto-search/autocomplete.umd.js b/docs-preview/site_libs/quarto-search/autocomplete.umd.js new file mode 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t=!e.endsWith(this.pattern);return{isMatch:t,score:t?0:1,indices:[0,e.length-1]}}}],[{key:"type",get:function(){return"inverse-suffix-exact"}},{key:"multiRegex",get:function(){return/^!"(.*)"\$$/}},{key:"singleRegex",get:function(){return/^!(.*)\$$/}}]),n}(z),G=function(e){a(n,e);var t=l(n);function n(e){var i,o=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{},c=o.location,a=void 0===c?I.location:c,s=o.threshold,u=void 0===s?I.threshold:s,h=o.distance,l=void 0===h?I.distance:h,f=o.includeMatches,d=void 0===f?I.includeMatches:f,v=o.findAllMatches,g=void 0===v?I.findAllMatches:v,y=o.minMatchCharLength,p=void 0===y?I.minMatchCharLength:y,m=o.isCaseSensitive,k=void 0===m?I.isCaseSensitive:m,M=o.ignoreLocation,b=void 0===M?I.ignoreLocation:M;return r(this,n),(i=t.call(this,e))._bitapSearch=new T(e,{location:a,threshold:u,distance:l,includeMatches:d,findAllMatches:g,minMatchCharLength:p,isCaseSensitive:k,ignoreLocation:b}),i}return o(n,[{key:"search",value:function(e){return 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a/docs-preview/site_libs/quarto-search/quarto-search.js b/docs-preview/site_libs/quarto-search/quarto-search.js new file mode 100644 index 0000000..d788a95 --- /dev/null +++ b/docs-preview/site_libs/quarto-search/quarto-search.js @@ -0,0 +1,1290 @@ +const kQueryArg = "q"; +const kResultsArg = "show-results"; + +// If items don't provide a URL, then both the navigator and the onSelect +// function aren't called (and therefore, the default implementation is used) +// +// We're using this sentinel URL to signal to those handlers that this +// item is a more item (along with the type) and can be handled appropriately +const kItemTypeMoreHref = "0767FDFD-0422-4E5A-BC8A-3BE11E5BBA05"; + +window.document.addEventListener("DOMContentLoaded", function (_event) { + // Ensure that search is available on this page. If it isn't, + // should return early and not do anything + var searchEl = window.document.getElementById("quarto-search"); + if (!searchEl) return; + + const { autocomplete } = window["@algolia/autocomplete-js"]; + + let quartoSearchOptions = {}; + let language = {}; + const searchOptionEl = window.document.getElementById( + "quarto-search-options" + ); + if (searchOptionEl) { + const jsonStr = searchOptionEl.textContent; + quartoSearchOptions = JSON.parse(jsonStr); + language = quartoSearchOptions.language; + } + + // note the search mode + if (quartoSearchOptions.type === "overlay") { + searchEl.classList.add("type-overlay"); + } else { + searchEl.classList.add("type-textbox"); + } + + // Used to determine highlighting behavior for this page + // A `q` query param is expected when the user follows a search + // to this page + const currentUrl = new URL(window.location); + const query = currentUrl.searchParams.get(kQueryArg); + const showSearchResults = currentUrl.searchParams.get(kResultsArg); + const mainEl = window.document.querySelector("main"); + + // highlight matches on the page + if (query && mainEl) { + // perform any highlighting + highlight(escapeRegExp(query), mainEl); + + // fix up the URL to remove the q query param + const replacementUrl = new URL(window.location); + replacementUrl.searchParams.delete(kQueryArg); + window.history.replaceState({}, "", replacementUrl); + } + + // function to clear highlighting on the page when the search query changes + // (e.g. if the user edits the query or clears it) + let highlighting = true; + const resetHighlighting = (searchTerm) => { + if (mainEl && highlighting && query && searchTerm !== query) { + clearHighlight(query, mainEl); + highlighting = false; + } + }; + + // Clear search highlighting when the user scrolls sufficiently + const resetFn = () => { + resetHighlighting(""); + window.removeEventListener("quarto-hrChanged", resetFn); + window.removeEventListener("quarto-sectionChanged", resetFn); + }; + + // Register this event after the initial scrolling and settling of events + // on the page + window.addEventListener("quarto-hrChanged", resetFn); + window.addEventListener("quarto-sectionChanged", resetFn); + + // Responsively switch to overlay mode if the search is present on the navbar + // Note that switching the sidebar to overlay mode requires more coordinate (not just + // the media query since we generate different HTML for sidebar overlays than we do + // for sidebar input UI) + const detachedMediaQuery = + quartoSearchOptions.type === "overlay" ? "all" : "(max-width: 991px)"; + + // If configured, include the analytics client to send insights + const plugins = configurePlugins(quartoSearchOptions); + + let lastState = null; + const { setIsOpen, setQuery, setCollections } = autocomplete({ + container: searchEl, + detachedMediaQuery: detachedMediaQuery, + defaultActiveItemId: 0, + panelContainer: "#quarto-search-results", + panelPlacement: quartoSearchOptions["panel-placement"], + debug: false, + openOnFocus: true, + plugins, + classNames: { + form: "d-flex", + }, + placeholder: language["search-text-placeholder"], + translations: { + clearButtonTitle: language["search-clear-button-title"], + detachedCancelButtonText: language["search-detached-cancel-button-title"], + submitButtonTitle: language["search-submit-button-title"], + }, + initialState: { + query, + }, + getItemUrl({ item }) { + return item.href; + }, + onStateChange({ state }) { + // If this is a file URL, note that + + // Perhaps reset highlighting + resetHighlighting(state.query); + + // If the panel just opened, ensure the panel is positioned properly + if (state.isOpen) { + if (lastState && !lastState.isOpen) { + setTimeout(() => { + positionPanel(quartoSearchOptions["panel-placement"]); + }, 150); + } + } + + // Perhaps show the copy link + showCopyLink(state.query, quartoSearchOptions); + + lastState = state; + }, + reshape({ sources, state }) { + return sources.map((source) => { + try { + const items = source.getItems(); + + // Validate the items + validateItems(items); + + // group the items by document + const groupedItems = new Map(); + items.forEach((item) => { + const hrefParts = item.href.split("#"); + const baseHref = hrefParts[0]; + const isDocumentItem = hrefParts.length === 1; + + const items = groupedItems.get(baseHref); + if (!items) { + groupedItems.set(baseHref, [item]); + } else { + // If the href for this item matches the document + // exactly, place this item first as it is the item that represents + // the document itself + if (isDocumentItem) { + items.unshift(item); + } else { + items.push(item); + } + groupedItems.set(baseHref, items); + } + }); + + const reshapedItems = []; + let count = 1; + for (const [_key, value] of groupedItems) { + const firstItem = value[0]; + reshapedItems.push({ + ...firstItem, + type: kItemTypeDoc, + }); + + const collapseMatches = quartoSearchOptions["collapse-after"]; + const collapseCount = + typeof collapseMatches === "number" ? collapseMatches : 1; + + if (value.length > 1) { + const target = `search-more-${count}`; + const isExpanded = + state.context.expanded && + state.context.expanded.includes(target); + + const remainingCount = value.length - collapseCount; + + for (let i = 1; i < value.length; i++) { + if (collapseMatches && i === collapseCount) { + reshapedItems.push({ + target, + title: isExpanded + ? language["search-hide-matches-text"] + : remainingCount === 1 + ? `${remainingCount} ${language["search-more-match-text"]}` + : `${remainingCount} ${language["search-more-matches-text"]}`, + type: kItemTypeMore, + href: kItemTypeMoreHref, + }); + } + + if (isExpanded || !collapseMatches || i < collapseCount) { + reshapedItems.push({ + ...value[i], + type: kItemTypeItem, + target, + }); + } + } + } + count += 1; + } + + return { + ...source, + getItems() { + return reshapedItems; + }, + }; + } catch (error) { + // Some form of error occurred + return { + ...source, + getItems() { + return [ + { + title: error.name || "An Error Occurred While Searching", + text: + error.message || + "An unknown error occurred while attempting to perform the requested search.", + type: kItemTypeError, + }, + ]; + }, + }; + } + }); + }, + navigator: { + navigate({ itemUrl }) { + if (itemUrl !== offsetURL(kItemTypeMoreHref)) { + window.location.assign(itemUrl); + } + }, + navigateNewTab({ itemUrl }) { + if (itemUrl !== offsetURL(kItemTypeMoreHref)) { + const windowReference = window.open(itemUrl, "_blank", "noopener"); + if (windowReference) { + windowReference.focus(); + } + } + }, + navigateNewWindow({ itemUrl }) { + if (itemUrl !== offsetURL(kItemTypeMoreHref)) { + window.open(itemUrl, "_blank", "noopener"); + } + }, + }, + getSources({ state, setContext, setActiveItemId, refresh }) { + return [ + { + sourceId: "documents", + getItemUrl({ item }) { + if (item.href) { + return offsetURL(item.href); + } else { + return undefined; + } + }, + onSelect({ + item, + state, + setContext, + setIsOpen, + setActiveItemId, + refresh, + }) { + if (item.type === kItemTypeMore) { + toggleExpanded(item, state, setContext, setActiveItemId, refresh); + + // Toggle more + setIsOpen(true); + } + }, + getItems({ query }) { + if (query === null || query === "") { + return []; + } + + const limit = quartoSearchOptions.limit; + if (quartoSearchOptions.algolia) { + return algoliaSearch(query, limit, quartoSearchOptions.algolia); + } else { + // Fuse search options + const fuseSearchOptions = { + isCaseSensitive: false, + shouldSort: true, + minMatchCharLength: 2, + limit: limit, + }; + + return readSearchData().then(function (fuse) { + return fuseSearch(query, fuse, fuseSearchOptions); + }); + } + }, + templates: { + noResults({ createElement }) { + const hasQuery = lastState.query; + + return createElement( + "div", + { + class: `quarto-search-no-results${ + hasQuery ? "" : " no-query" + }`, + }, + language["search-no-results-text"] + ); + }, + header({ items, createElement }) { + // count the documents + const count = items.filter((item) => { + return item.type === kItemTypeDoc; + }).length; + + if (count > 0) { + return createElement( + "div", + { class: "search-result-header" }, + `${count} ${language["search-matching-documents-text"]}` + ); + } else { + return createElement( + "div", + { class: "search-result-header-no-results" }, + `` + ); + } + }, + footer({ _items, createElement }) { + if ( + quartoSearchOptions.algolia && + quartoSearchOptions.algolia["show-logo"] + ) { + const libDir = quartoSearchOptions.algolia["libDir"]; + const logo = createElement("img", { + src: offsetURL( + `${libDir}/quarto-search/search-by-algolia.svg` + ), + class: "algolia-search-logo", + }); + return createElement( + "a", + { href: "http://www.algolia.com/" }, + logo + ); + } + }, + + item({ item, createElement }) { + return renderItem( + item, + createElement, + state, + setActiveItemId, + setContext, + refresh, + quartoSearchOptions + ); + }, + }, + }, + ]; + }, + }); + + window.quartoOpenSearch = () => { + setIsOpen(false); + setIsOpen(true); + 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") { + const inputEl = window.document.querySelector( + "#quarto-search .aa-Autocomplete" + ); + if (inputEl) { + inputEl.removeAttribute("aria-labelledby"); + } + } + + 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 = 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); + focusSearchInput(); + } +}); + +function configurePlugins(quartoSearchOptions) { + const autocompletePlugins = []; + const algoliaOptions = quartoSearchOptions.algolia; + if ( + algoliaOptions && + algoliaOptions["analytics-events"] && + algoliaOptions["search-only-api-key"] && + algoliaOptions["application-id"] + ) { + const apiKey = algoliaOptions["search-only-api-key"]; + const appId = algoliaOptions["application-id"]; + + // Aloglia insights may not be loaded because they require cookie consent + // Use deferred loading so events will start being recorded when/if consent + // is granted. + const algoliaInsightsDeferredPlugin = deferredLoadPlugin(() => { + if ( + window.aa && + window["@algolia/autocomplete-plugin-algolia-insights"] + ) { + window.aa("init", { + appId, + apiKey, + useCookie: true, + }); + + const { createAlgoliaInsightsPlugin } = + window["@algolia/autocomplete-plugin-algolia-insights"]; + // Register the insights client + const algoliaInsightsPlugin = createAlgoliaInsightsPlugin({ + insightsClient: window.aa, + onItemsChange({ insights, insightsEvents }) { + 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; + }); + + for (const event of events) { + insights.viewedObjectIDs(event); + } + }, + }); + return algoliaInsightsPlugin; + } + }); + + // Add the plugin + autocompletePlugins.push(algoliaInsightsDeferredPlugin); + return autocompletePlugins; + } +} + +// For plugins that may not load immediately, create a wrapper +// plugin and forward events and plugin data once the plugin +// is initialized. This is useful for cases like cookie consent +// which may prevent the analytics insights event plugin from initializing +// immediately. +function deferredLoadPlugin(createPlugin) { + let plugin = undefined; + let subscribeObj = undefined; + const wrappedPlugin = () => { + if (!plugin && subscribeObj) { + plugin = createPlugin(); + if (plugin && plugin.subscribe) { + plugin.subscribe(subscribeObj); + } + } + return plugin; + }; + + return { + subscribe: (obj) => { + subscribeObj = obj; + }, + onStateChange: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.onStateChange) { + plugin.onStateChange(obj); + } + }, + onSubmit: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.onSubmit) { + plugin.onSubmit(obj); + } + }, + onReset: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.onReset) { + plugin.onReset(obj); + } + }, + getSources: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.getSources) { + return plugin.getSources(obj); + } else { + return Promise.resolve([]); + } + }, + data: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.data) { + plugin.data(obj); + } + }, + }; +} + +function validateItems(items) { + // Validate the first item + if (items.length > 0) { + const item = items[0]; + const missingFields = []; + if (item.href == undefined) { + missingFields.push("href"); + } + if (!item.title == undefined) { + missingFields.push("title"); + } + if (!item.text == undefined) { + missingFields.push("text"); + } + + if (missingFields.length === 1) { + throw { + name: `Error: Search index is missing the ${missingFields[0]} field.`, + message: `The items being returned for this search do not include all the required fields. Please ensure that your index items include the ${missingFields[0]} field or use index-fields in your _quarto.yml file to specify the field names.`, + }; + } else if (missingFields.length > 1) { + const missingFieldList = missingFields + .map((field) => { + return `${field}`; + }) + .join(", "); + + throw { + name: `Error: Search index is missing the following fields: ${missingFieldList}.`, + message: `The items being returned for this search do not include all the required fields. Please ensure that your index items includes the following fields: ${missingFieldList}, or use index-fields in your _quarto.yml file to specify the field names.`, + }; + } + } +} + +let lastQuery = null; +function showCopyLink(query, options) { + const language = options.language; + lastQuery = query; + // Insert share icon + const inputSuffixEl = window.document.body.querySelector( + ".aa-Form .aa-InputWrapperSuffix" + ); + + if (inputSuffixEl) { + let copyButtonEl = window.document.body.querySelector( + ".aa-Form .aa-InputWrapperSuffix .aa-CopyButton" + ); + + if (copyButtonEl === null) { + copyButtonEl = window.document.createElement("button"); + copyButtonEl.setAttribute("class", "aa-CopyButton"); + copyButtonEl.setAttribute("type", "button"); + copyButtonEl.setAttribute("title", language["search-copy-link-title"]); + copyButtonEl.onmousedown = (e) => { + e.preventDefault(); + e.stopPropagation(); + }; + + const linkIcon = "bi-clipboard"; + const checkIcon = "bi-check2"; + + const shareIconEl = window.document.createElement("i"); + shareIconEl.setAttribute("class", `bi ${linkIcon}`); + copyButtonEl.appendChild(shareIconEl); + inputSuffixEl.prepend(copyButtonEl); + + const clipboard = new window.ClipboardJS(".aa-CopyButton", { + text: function (_trigger) { + const copyUrl = new URL(window.location); + copyUrl.searchParams.set(kQueryArg, lastQuery); + copyUrl.searchParams.set(kResultsArg, "1"); + return copyUrl.toString(); + }, + }); + clipboard.on("success", function (e) { + // Focus the input + + // button target + const button = e.trigger; + const icon = button.querySelector("i.bi"); + + // flash "checked" + icon.classList.add(checkIcon); + icon.classList.remove(linkIcon); + setTimeout(function () { + icon.classList.remove(checkIcon); + icon.classList.add(linkIcon); + }, 1000); + }); + } + + // If there is a query, show the link icon + if (copyButtonEl) { + if (lastQuery && options["copy-button"]) { + copyButtonEl.style.display = "flex"; + } else { + copyButtonEl.style.display = "none"; + } + } + } +} + +/* 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) { + 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")); + if (response.status == 200) { + return response.json().then(function (searchDocs) { + searchDocs.forEach(function (searchDoc) { + fuse.add(searchDoc); + }); + fuseIndex = fuse; + return fuseIndex; + }); + } else { + return Promise.reject( + new Error( + "Unexpected status from search index request: " + response.status + ) + ); + } + } + + return fuseIndex; +} + +function inputElement() { + return window.document.body.querySelector(".aa-Form .aa-Input"); +} + +function focusSearchInput() { + setTimeout(() => { + const inputEl = inputElement(); + if (inputEl) { + inputEl.focus(); + } + }, 50); +} + +/* Panels */ +const kItemTypeDoc = "document"; +const kItemTypeMore = "document-more"; +const kItemTypeItem = "document-item"; +const kItemTypeError = "error"; + +function renderItem( + item, + createElement, + state, + setActiveItemId, + setContext, + refresh, + quartoSearchOptions +) { + switch (item.type) { + case kItemTypeDoc: + return createDocumentCard( + createElement, + "file-richtext", + item.title, + item.section, + item.text, + item.href, + item.crumbs, + quartoSearchOptions + ); + case kItemTypeMore: + return createMoreCard( + createElement, + item, + state, + setActiveItemId, + setContext, + refresh + ); + case kItemTypeItem: + return createSectionCard( + createElement, + item.section, + item.text, + item.href + ); + case kItemTypeError: + return createErrorCard(createElement, item.title, item.text); + default: + return undefined; + } +} + +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" }, + titleContents + ); + + const textEls = []; + if (section) { + const sectionEl = createElement( + "p", + { class: "search-result-section" }, + section + ); + textEls.push(sectionEl); + } + const descEl = createElement("p", { + class: "search-result-text", + dangerouslySetInnerHTML: { + __html: text, + }, + }); + textEls.push(descEl); + + const textContainerEl = createElement( + "div", + { class: "search-result-text-container" }, + textEls + ); + + const containerEl = createElement( + "div", + { + class: "search-result-container", + }, + [titleContainerEl, textContainerEl] + ); + + const linkEl = createElement( + "a", + { + href: offsetURL(href), + class: "search-result-link", + }, + containerEl + ); + + const classes = ["search-result-doc", "search-item"]; + if (!section) { + classes.push("document-selectable"); + } + + return createElement( + "div", + { + class: classes.join(" "), + }, + linkEl + ); +} + +function createMoreCard( + createElement, + item, + state, + setActiveItemId, + setContext, + refresh +) { + const moreCardEl = createElement( + "div", + { + class: "search-result-more search-item", + onClick: (e) => { + // Handle expanding the sections by adding the expanded + // section to the list of expanded sections + toggleExpanded(item, state, setContext, setActiveItemId, refresh); + e.stopPropagation(); + }, + }, + item.title + ); + + return moreCardEl; +} + +function toggleExpanded(item, state, setContext, setActiveItemId, refresh) { + const expanded = state.context.expanded || []; + if (expanded.includes(item.target)) { + setContext({ + expanded: expanded.filter((target) => target !== item.target), + }); + } else { + setContext({ expanded: [...expanded, item.target] }); + } + + refresh(); + setActiveItemId(item.__autocomplete_id); +} + +function createSectionCard(createElement, section, text, href) { + const sectionEl = createSection(createElement, section, text, href); + return createElement( + "div", + { + class: "search-result-doc-section search-item", + }, + sectionEl + ); +} + +function createSection(createElement, title, text, href) { + const descEl = createElement("p", { + class: "search-result-text", + dangerouslySetInnerHTML: { + __html: text, + }, + }); + + const titleEl = createElement("p", { class: "search-result-section" }, title); + const linkEl = createElement( + "a", + { + href: offsetURL(href), + class: "search-result-link", + }, + [titleEl, descEl] + ); + return linkEl; +} + +function createErrorCard(createElement, title, text) { + const descEl = createElement("p", { + class: "search-error-text", + dangerouslySetInnerHTML: { + __html: text, + }, + }); + + const titleEl = createElement("p", { + class: "search-error-title", + dangerouslySetInnerHTML: { + __html: ` ${title}`, + }, + }); + const errorEl = createElement("div", { class: "search-error" }, [ + titleEl, + descEl, + ]); + return errorEl; +} + +function positionPanel(pos) { + const panelEl = window.document.querySelector( + "#quarto-search-results .aa-Panel" + ); + const inputEl = window.document.querySelector( + "#quarto-search .aa-Autocomplete" + ); + + if (panelEl && inputEl) { + panelEl.style.top = `${Math.round(panelEl.offsetTop)}px`; + if (pos === "start") { + panelEl.style.left = `${Math.round(inputEl.left)}px`; + } else { + panelEl.style.right = `${Math.round(inputEl.offsetRight)}px`; + } + } +} + +/* Highlighting */ +// highlighting functions +function highlightMatch(query, text) { + if (text) { + const start = text.toLowerCase().indexOf(query.toLowerCase()); + if (start !== -1) { + const startMark = ""; + const endMark = ""; + + const end = start + query.length; + text = + text.slice(0, start) + + startMark + + text.slice(start, end) + + endMark + + text.slice(end); + const startInfo = clipStart(text, start); + const endInfo = clipEnd( + text, + startInfo.position + startMark.length + endMark.length + ); + text = + startInfo.prefix + + text.slice(startInfo.position, endInfo.position) + + endInfo.suffix; + + return text; + } else { + return text; + } + } else { + return text; + } +} + +function clipStart(text, pos) { + const clipStart = pos - 50; + if (clipStart < 0) { + // This will just return the start of the string + return { + position: 0, + prefix: "", + }; + } else { + // We're clipping before the start of the string, walk backwards to the first space. + const spacePos = findSpace(text, pos, -1); + return { + position: spacePos.position, + prefix: "", + }; + } +} + +function clipEnd(text, pos) { + const clipEnd = pos + 200; + if (clipEnd > text.length) { + return { + position: text.length, + suffix: "", + }; + } else { + const spacePos = findSpace(text, clipEnd, 1); + return { + position: spacePos.position, + suffix: spacePos.clipped ? "…" : "", + }; + } +} + +function findSpace(text, start, step) { + let stepPos = start; + while (stepPos > -1 && stepPos < text.length) { + const char = text[stepPos]; + if (char === " " || char === "," || char === ":") { + return { + position: step === 1 ? stepPos : stepPos - step, + clipped: stepPos > 1 && stepPos < text.length, + }; + } + stepPos = stepPos + step; + } + + return { + position: stepPos - step, + clipped: false, + }; +} + +// removes highlighting as implemented by the mark tag +function clearHighlight(searchterm, el) { + const childNodes = el.childNodes; + for (let i = childNodes.length - 1; i >= 0; i--) { + const node = childNodes[i]; + if (node.nodeType === Node.ELEMENT_NODE) { + if ( + node.tagName === "MARK" && + node.innerText.toLowerCase() === searchterm.toLowerCase() + ) { + el.replaceChild(document.createTextNode(node.innerText), node); + } else { + clearHighlight(searchterm, node); + } + } + } +} + +function escapeRegExp(string) { + return string.replace(/[.*+?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string +} + +// highlight matches +function highlight(term, el) { + const termRegex = new RegExp(term, "ig"); + const childNodes = el.childNodes; + + // walk back to front avoid mutating elements in front of us + for (let i = childNodes.length - 1; i >= 0; i--) { + const node = childNodes[i]; + + if (node.nodeType === Node.TEXT_NODE) { + // Search text nodes for text to highlight + const text = node.nodeValue; + + let startIndex = 0; + let matchIndex = text.search(termRegex); + if (matchIndex > -1) { + const markFragment = document.createDocumentFragment(); + while (matchIndex > -1) { + const prefix = text.slice(startIndex, matchIndex); + markFragment.appendChild(document.createTextNode(prefix)); + + const mark = document.createElement("mark"); + mark.appendChild( + document.createTextNode( + text.slice(matchIndex, matchIndex + term.length) + ) + ); + markFragment.appendChild(mark); + + startIndex = matchIndex + term.length; + matchIndex = text.slice(startIndex).search(new RegExp(term, "ig")); + if (matchIndex > -1) { + matchIndex = startIndex + matchIndex; + } + } + if (startIndex < text.length) { + markFragment.appendChild( + document.createTextNode(text.slice(startIndex, text.length)) + ); + } + + el.replaceChild(markFragment, node); + } + } else if (node.nodeType === Node.ELEMENT_NODE) { + // recurse through elements + highlight(term, node); + } + } +} + +/* Link Handling */ +// get the offset from this page for a given site root relative url +function offsetURL(url) { + var offset = getMeta("quarto:offset"); + return offset ? offset + url : url; +} + +// read a meta tag value +function getMeta(metaName) { + var metas = window.document.getElementsByTagName("meta"); + for (let i = 0; i < metas.length; i++) { + if (metas[i].getAttribute("name") === metaName) { + return metas[i].getAttribute("content"); + } + } + return ""; +} + +function algoliaSearch(query, limit, algoliaOptions) { + const { getAlgoliaResults } = window["@algolia/autocomplete-preset-algolia"]; + + const applicationId = algoliaOptions["application-id"]; + const searchOnlyApiKey = algoliaOptions["search-only-api-key"]; + const indexName = algoliaOptions["index-name"]; + const indexFields = algoliaOptions["index-fields"]; + const searchClient = window.algoliasearch(applicationId, searchOnlyApiKey); + const searchParams = algoliaOptions["params"]; + const searchAnalytics = !!algoliaOptions["analytics-events"]; + + return getAlgoliaResults({ + searchClient, + queries: [ + { + indexName: indexName, + query, + params: { + hitsPerPage: limit, + clickAnalytics: searchAnalytics, + ...searchParams, + }, + }, + ], + transformResponse: (response) => { + if (!indexFields) { + return response.hits.map((hit) => { + return hit.map((item) => { + return { + ...item, + text: highlightMatch(query, item.text), + }; + }); + }); + } else { + const remappedHits = response.hits.map((hit) => { + return hit.map((item) => { + const newItem = { ...item }; + ["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; + }); + }); + return remappedHits; + } + }, + }); +} + +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]; + const sep = baseUrl.search("\\?") > 0 ? "&" : "?"; + anchorParts[0] = baseUrl + sep + name + "=" + value; + return anchorParts.join("#"); + }; + + return { + title: result.item.title, + 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/docs-preview/styles.css b/docs-preview/styles.css new file mode 100644 index 0000000..2ddf50c --- /dev/null +++ b/docs-preview/styles.css @@ -0,0 +1 @@ +/* css styles */ diff --git a/docs/Case_definition.html b/docs/Case_definition.html index 8c5afef..2c048c9 100644 --- a/docs/Case_definition.html +++ b/docs/Case_definition.html @@ -2,12 +2,12 @@ - + -Spetses Introductory Course - Case Definition +Case Definition – Spetses Introductory Course - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + +
CharacteristicN = 3971
fetaD
    0110 (29%)
    178 (20%)
    2155 (41%)
    338 (10.0%)
sardinesD
    0125 (33%)
    135 (9.2%)
    2184 (48%)
    336 (9.5%)
eggplantD
    0151 (41%)
    141 (11%)
    2147 (40%)
    328 (7.6%)
moussakaD
    042 (11%)
    170 (18%)
    2184 (48%)
    387 (23%)
orzoD
    044 (12%)
    170 (18%)
    2183 (48%)
    385 (22%)
greeksalD
    0162 (43%)
    152 (14%)
    2126 (34%)
    333 (8.8%)
breadD
    035 (9.2%)
    151 (13%)
    2267 (70%)
    326 (6.9%)
dessertD
    0206 (58%)
    133 (9.3%)
    287 (25%)
    329 (8.2%)
champagneD
    049 (13%)
    1208 (56%)
    245 (12%)
    370 (19%)
beerD
    081 (22%)
    141 (11%)
    257 (16%)
    3183 (51%)
redwineD
    0266 (77%)
    131 (9.0%)
    221 (6.1%)
    327 (7.8%)
whitewineD
    0100 (28%)
    173 (20%)
    267 (19%)
    3121 (34%)
1 n (%)
++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
CharacteristicN = 3971
fetaD
+
    0110 (29%)
    178 (20%)
    2155 (41%)
    338 (10.0%)
sardinesD
+
    0125 (33%)
    135 (9.2%)
    2184 (48%)
    336 (9.5%)
eggplantD
+
    0151 (41%)
    141 (11%)
    2147 (40%)
    328 (7.6%)
vealD
+
    042 (11%)
    170 (18%)
    2184 (48%)
    387 (23%)
pastaD
+
    044 (12%)
    170 (18%)
    2183 (48%)
    385 (22%)
tomsalD
+
    0162 (43%)
    152 (14%)
    2126 (34%)
    333 (8.8%)
breadD
+
    035 (9.2%)
    151 (13%)
    2267 (70%)
    326 (6.9%)
dessertD
+
    0206 (58%)
    133 (9.3%)
    287 (25%)
    329 (8.2%)
champagneD
+
    049 (13%)
    1208 (56%)
    245 (12%)
    370 (19%)
beerD
+
    081 (22%)
    141 (11%)
    257 (16%)
    3183 (51%)
redwineD
+
    0266 (77%)
    131 (9.0%)
    221 (6.1%)
    327 (7.8%)
whitewineD
+
    0100 (28%)
    173 (20%)
    267 (19%)
    3121 (34%)
1 n (%)
+ @@ -1708,11 +1879,11 @@

3.4. Modify variab

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.

-

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.

+

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.

You can use mutate to modify the type of the following variables:

- +
@@ -1742,24 +1913,30 @@

3.4. Modify variab

- + - - + + - + + + + + + + - + @@ -1768,16 +1945,17 @@

3.4. Modify variab

Table 1: Variable types to modify
mutate(), as.factor()
All the clinical symptom variablesill integerlogicalmutate(across()), as.logical()factormutate(), as.factor()
All the food variables representing the amount of specific foods eaten (those finishing with a capital “D”)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”)integerfactormutate(across()), as.factor()
dayonset character date lubridate::dmy()
starthour and dayonset together

integer (starthour)

date (dayonset)
-
-

Sex, group and class

-

Let’s start transforming one-by-one the first two variables in the table: sex, and class.

+
+

Sex, class and ill

+

Let’s start transforming one-by-one the first three variables in the table: sex, class and ill.

-
-Code +
+Show the code 
copdata <- copdata %>% 
-  dplyr::mutate(
+  mutate(
     sex = as.factor(sex),
-    class = as.factor(class))
+ class = as.factor(class), + ill = as.factor(ill))
@@ -1788,14 +1966,14 @@

Symptoms and F
  • For the variables that are clinical symptoms, we will list them one by one and show you the use of mutate(across( )).
    • -
    -Code +
    +Show the code 
    copdata <- copdata %>% 
-  dplyr::mutate(
+  mutate(
     # clinical symptoms
     across(.cols = c(diarrhoea, bloody, vomiting,
              abdo, nausea, fever,headache, jointpain), 
-           .fns = ~ as.logical(.)
+           .fns = ~ as.factor(.)
            )
     )
    @@ -1804,23 +1982,23 @@

    Symptoms and F
  • For 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.
    • -
    -Code +
    +Show the code 
    # 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(.)))
    @@ -1838,7 +2016,7 @@

    Symptoms and F

    -

    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.

    @@ -1863,32 +2041,32 @@

    Date and time vari
    -

    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.

    +

    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.

    You 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”).

    -
    -Code +
    +Show the code 
    # Have a look at how the data is stored
 head(copdata$dayonset)
    [1] "6oct2024" ""         ""         ""         "6oct2024" "7oct2024"
    -
    -Code +
    +Show the code 
    class(copdata$dayonset)
    [1] "character"
    -
    -Code +
    +Show the code 
    # Update copdata:
 copdata <- copdata %>% 
   # Change column to date class:
-  dplyr::mutate(
+  mutate(
     dayonset = lubridate::dmy(dayonset))
 
 # Check class of updated column:
@@ -1897,8 +2075,8 @@ 
    Date and time vari
 
    
 
    [1] "Date"
    
 
    
-
    
-Code
+
    
+Show the code
 
    # Have a look at your data now:
 head(copdata$dayonset)
    
 
    
@@ -1911,15 +2089,14 @@ 
    Date and time vari
 
    Before 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.
    
 
    We can check if we have any missing values by cross-tabulating starthour with dayonset:
    
 
    
-
    
-Code
+
    
+Show the code
 
    # Cross-tabulate dayonset with starthour:
-janitor::tabyl(dat = copdata, 
+tabyl(dat = copdata, 
                var1 = starthour, 
                var2 = dayonset)
    
 
    
 
    
-
 
    
   
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+
 
 
 
+
 
 
 
@@ -107,55 +115,56 @@
 
 
    
   
    
-    
    
@@ -225,8 +233,10 @@ 
    On this page
    
   
    
   
 
+
 
 
+
 
    
 
    1. Learning outcomes
    
 
    At the end of the session, participants will be able to:
    
@@ -249,8 +259,8 @@ 
    3. Questions/Assignme
 
    
 
    3.1 Install packages (if needed) and load libraries
    
 
    
-
    
-Code
+
    
+Show the code
 
    # Load the required libraries into the current R session:
 pacman::p_load(rio, 
                here, 
@@ -271,10 +281,10 @@ 
    
 
    3.2 Import your data
    
 
    
-
    
-Code
+
    
+Show the code
 
    # 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.
    
 
    
@@ -283,15 +293,14 @@ 
    3.2 Import your data
    
 
    
 
    3.3 Merge and explore the lab data. Explore it as you wish!
    
 
    
-
    
-Code
+
    
+Show the code
 
    copdatalab <- left_join(copdata, lab, 
                         by = "id")
 # Tabulate:
 janitor::tabyl(dat = copdatalab, "RT-PCR_ecoli_etec")
    
 
    
 
    
-
 
    
   
 
    
 
    
@@ -547,8 +567,8 @@ 
    
 
    b) Create a cross-tabulation of case with sex.
    
 
    
-
    
-Code
+
    
+Show the code
 
    copdata %>% 
   janitor::tabyl(case, sex) %>% 
   adorn_totals() %>% 
@@ -556,10 +576,9 @@ 
      adorn_pct_formatting() 
    
 
    
 
    
-
 
    
   
 
    
 
    
@@ -617,8 +636,8 @@ 
    c) Extra - Age-sex pyramid of cases.
    
 
    Hint: Change show_midpoint = FALSE to TRUE to see skewedness in the data patterns more easily.
    
 
    
-
    
-Code
+
    
+Show the code
 
    copdata <- copdata %>% 
   # Create age categories:
   mutate(age_cat = epikit::age_categories(
@@ -634,15 +653,14 @@ 
    c) Ext
 janitor::tabyl(copdata, age_cat)
    
 
    
 
    
-
 
    
   
 
    
 
    
-
    
-Code
+
    
+Show the code
 
    # Pipe copdata:
 agesex <- copdata %>% 
   # Filter for cases only:
@@ -661,7 +679,11 @@ 
    c) Ext
 agesex
    
 
    
 
    
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@@ -671,8 +693,8 @@ 
    
 
    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.
    
 
    
-
    
-Code
+
    
+Show the code
 
    # Create summary table:
 tabsymptoms <- copdata %>% 
     # Select person characteristics to summarise:
@@ -728,24 +750,23 @@ 
    tabsymptoms
    
 
    
 
    
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    CharacteristicOverall
-N = 3771Non-case
-N = 1611Case
-N = 2161
    Diarrhoea
        FALSE46 (18%)40 (100%)6 (2.8%)
        TRUE206 (82%)0 (0%)206 (97%)
    Dysentary
        FALSE189 (97%)42 (100%)147 (97%)
        TRUE5 (2.6%)0 (0%)5 (3.3%)
    Vomiting
        FALSE149 (69%)42 (100%)107 (62%)
        TRUE66 (31%)0 (0%)66 (38%)
    Abdominal pain
        FALSE35 (14%)6 (12%)29 (15%)
        TRUE207 (86%)44 (88%)163 (85%)
    Nausea
        FALSE55 (25%)12 (26%)43 (24%)
        TRUE169 (75%)34 (74%)135 (76%)
    Fever
        FALSE127 (74%)32 (80%)95 (73%)
        TRUE44 (26%)8 (20%)36 (27%)
    Headache
        FALSE83 (38%)11 (25%)72 (41%)
        TRUE137 (62%)33 (75%)104 (59%)
    Joint pain
        FALSE159 (85%)32 (84%)127 (85%)
        TRUE29 (15%)6 (16%)23 (15%)
    1 n (%)
    
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    CharacteristicOverall N = 3771Non-case N = 1611Case N = 2161
    Diarrhoea
    
+
    
+
    
+
        046 (18%)40 (100%)6 (2.8%)
        1206 (82%)0 (0%)206 (97%)
    Dysentary
    
+
    
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        0189 (97%)42 (100%)147 (97%)
        15 (2.6%)0 (0%)5 (3.3%)
    Vomiting
    
+
    
+
    
+
        0149 (69%)42 (100%)107 (62%)
        166 (31%)0 (0%)66 (38%)
    Abdominal pain
    
+
    
+
    
+
        035 (14%)6 (12%)29 (15%)
        1207 (86%)44 (88%)163 (85%)
    Nausea
    
+
    
+
    
+
        055 (25%)12 (26%)43 (24%)
        1169 (75%)34 (74%)135 (76%)
    Fever
    
+
    
+
    
+
        0127 (74%)32 (80%)95 (73%)
        144 (26%)8 (20%)36 (27%)
    Headache
    
+
    
+
    
+
        083 (38%)11 (25%)72 (41%)
        1137 (62%)33 (75%)104 (59%)
    Joint pain
    
+
    
+
    
+
        0159 (85%)32 (84%)127 (85%)
        129 (15%)6 (16%)23 (15%)
    1 n (%)
    
+
 
    
 
    
 
    
@@ -1329,8 +1431,8 @@ 
    
 
    b) Extra - Bar plot of symptoms stratified by case definition.
    
 
    
-
    
-Code
+
    
+Show the code
 
    # Create list of symptom variables:
 symptoms <- c("diarrhoea", 
               "bloody", 
@@ -1342,8 +1444,8 @@ 
                  "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:
@@ -1355,7 +1457,7 @@ 
                   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) %>% 
@@ -1384,13 +1486,17 @@ 
    
-Code
+
    
+Show the code
 
    # Print plot:
 symptom_bar
    
 
    
 
    
-
    
+
    
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+
    
 
    
 
    
 
    
@@ -1400,8 +1506,8 @@ 
    3.7 Attack proportions<
 
    
 
    a) Calculate the overall attack proportions (percentage of cases among the total observed individuals). You could use tabyl().
    
 
    
-
    
-Code
+
    
+Show the code
 
    # Create table of case status:
 total_ap <- tabyl(copdata, case) %>% 
  # Add row totals:
@@ -1409,7 +1515,7 @@ 
      # 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:
@@ -1458,8 +1564,8 @@ 
    
 
    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.
    
 
    
-
    
-Code
+
    
+Show the code
 
    # Table to calculate attack proportions:
 attack_prop <- copdata %>% 
   # Select columns:
@@ -1492,24 +1598,23 @@ 
    attack_prop
    
 
    
 
    
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    CharacteristicOverall N = 3771Non-case N = 1611Case N = 2161
    class
        1131 (100%)63 (48%)68 (52%)
        2101 (100%)44 (44%)57 (56%)
        3111 (100%)38 (34%)73 (66%)
        Unknown341618
    group
        teacher15 (100%)9 (60%)6 (40%)
        student362 (100%)152 (42%)210 (58%)
    sex
        female213 (100%)96 (45%)117 (55%)
        male164 (100%)65 (40%)99 (60%)
    1 n (%)
    
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    CharacteristicOverall N = 3771Non-case N = 1611Case N = 2161
    class
    
+
    
+
    
+
        1131 (100%)63 (48%)68 (52%)
        2101 (100%)44 (44%)57 (56%)
        3111 (100%)38 (34%)73 (66%)
        Unknown341618
    group
    
+
    
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        teacher15 (100%)9 (60%)6 (40%)
        student362 (100%)152 (42%)210 (58%)
    sex
    
+
    
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        female213 (100%)96 (45%)117 (55%)
        male164 (100%)65 (40%)99 (60%)
    1 n (%)
    
+
 
    
 
    
 
    
@@ -2069,6 +2220,33 @@ 
     {
+        return filterRegex.test(href) || localhostRegex.test(href) || mailtoRegex.test(href);
+    }
+    // Inspect non-navigation links and adorn them if external
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+            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 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 @@ 
     {
+        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 @@ 
    ## 3.2 Import your data
 
 ```{r, Import_data}
-# 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.
+# Import the clean data set:
+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.
 ```
 
 ## 3.3 Merge and explore the lab data. Explore it as you wish!
 
 ```{r, merge_lab_data}
-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")
 ```
 
 ## 3.4 Describe the outbreak in terms of time.
@@ -2502,7 +2869,7 @@ 
    
 # Create a dataset with only cases
 cases <- copdata %>% 
-  filter(case == TRUE)
+  filter(case == 1)
 
 incplot <- cases %>% 
   # Create an empty ggplot frame:
@@ -2771,57 +3138,57 @@ 
    
 ```{r symptom_barplot}
 
-# Create list of symptom variables:
-symptoms <- c("diarrhoea", 
-              "bloody", 
-              "vomiting", 
-              "abdo", 
-              "nausea", 
-              "fever", 
-              "headache", 
-              "jointpain")
+# 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(`FALSE` = "Non-case", 
-                                   `TRUE` = "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 == TRUE) %>% 
- 
-   # 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)
+# 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
+# Print plot:
+symptom_bar
 ```
 
 ## 3.7 Attack proportions
@@ -2836,7 +3203,7 @@ 
      # 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:
@@ -2896,10 +3263,11 @@ 
    ::: {.callout-tip title="Need a little bit of help?" collapse="true"}
 To 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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zyz%~F#VK<*%y5$qkp49{W>=DxDNJfRi*~Hxe*%QCV(KRDeT^%VK=`Df+FJ7+u(9!) z<_1DMt5@@is0F?Qwt@`S598UBjggqRVSUyMCL^uDxPKS%$@}xz)iS&yrXEOeNZ_qE z1N5diL8>zy47Q8r5o?5==HI-7e_hKPYU@Ks1w1yF{Uxoy&Oj8(bU{#u$<72~*{QPC o)QgTnA^;Wt{7)kNXY0~Ahv6?2`oM(*9^f!NY;ow7(YfFL7e#yUi~s-t diff --git a/docs/Stratified.html b/docs/Stratified.html index 9f69d3d..f269d6e 100644 --- a/docs/Stratified.html +++ b/docs/Stratified.html @@ -2,12 +2,12 @@ - + -Spetses Introductory Course - Stratified analysis +Stratified analysis – Spetses Introductory Course - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + +
    CharacteristicFALSE, N = 1611TRUE, N = 2161p-value2
    sex0.3
        female96 (60%)117 (54%)
        male65 (40%)99 (46%)
    1 n (%)
    2 Pearson’s Chi-squared test
    ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    Characteristic0
    +N = 1611    		1
    +N = 2161    		p-value2
    sex
    +
    +    		0.3
        female96 (60%)117 (54%)
    +
        male65 (40%)99 (46%)
    +
    1 n (%)
    2 Pearson’s Chi-squared test
    + @@ -845,32 +889,31 @@

    b) sex

    c) class

    -
    -Code +
    +Show the code 
    copdata %>% 
   select(class, case) %>% 
   tbl_summary(by = case) %>% 
   add_p()
    - -
    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + +
    CharacteristicFALSE, N = 1611TRUE, N = 2161p-value2
    class0.090
        163 (43%)68 (34%)
        244 (30%)57 (29%)
        338 (26%)73 (37%)
        Unknown1618
    1 n (%)
    2 Pearson’s Chi-squared test
    ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    Characteristic0
    +N = 1611    		1
    +N = 2161    		p-value2
    class
    +
    +    		0.090
        163 (43%)68 (34%)
    +
        244 (30%)57 (29%)
    +
        338 (26%)73 (37%)
    +
        Unknown1618
    +
    1 n (%)
    2 Pearson’s Chi-squared test
    +
    @@ -1345,32 +1421,31 @@

    c) class

    d) group

    -
    -Code +
    +Show the code 
    copdata %>% 
   select(group, case) %>% 
   tbl_summary(by = case) %>% 
   add_p()
    - -
    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + +
    CharacteristicFALSE, N = 1611TRUE, N = 2161p-value2
    group0.2
        teacher9 (5.6%)6 (2.8%)
        student152 (94%)210 (97%)
    1 n (%)
    2 Pearson’s Chi-squared test
    ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    Characteristic0
    +N = 1611    		1
    +N = 2161    		p-value2
    group
    +
    +    		0.2
        teacher9 (5.6%)6 (2.8%)
    +
        student152 (94%)210 (97%)
    +
    1 n (%)
    2 Pearson’s Chi-squared test
    +
    @@ -1836,32 +1938,31 @@

    d) group

    Let’s do all together

    -
    -Code +
    +Show the code 
    copdata %>% 
   select(sex, class, group, case) %>% 
   tbl_summary(by = case) %>% 
   add_p()
    - -
    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + +
    CharacteristicFALSE, N = 1611TRUE, N = 2161p-value2
    sex0.3
        female96 (60%)117 (54%)
        male65 (40%)99 (46%)
    class0.090
        163 (43%)68 (34%)
        244 (30%)57 (29%)
        338 (26%)73 (37%)
        Unknown1618
    group0.2
        teacher9 (5.6%)6 (2.8%)
        student152 (94%)210 (97%)
    1 n (%)
    2 Pearson’s Chi-squared test
    ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    Characteristic0
    +N = 1611    		1
    +N = 2161    		p-value2
    sex
    +
    +    		0.3
        female96 (60%)117 (54%)
    +
        male65 (40%)99 (46%)
    +
    class
    +
    +    		0.090
        163 (43%)68 (34%)
    +
        244 (30%)57 (29%)
    +
        338 (26%)73 (37%)
    +
        Unknown1618
    +
    group
    +
    +    		0.2
        teacher9 (5.6%)6 (2.8%)
    +
        student152 (94%)210 (97%)
    +
    1 n (%)
    2 Pearson’s Chi-squared test
    +
    @@ -2380,8 +2534,8 @@

    a)
    -
    -Code +
    +Show the code 
    # You could use the EpiStats package for each food item
 CS(copdata, "case", "feta")
    @@ -2401,8 +2555,8 @@

    a) chi2(1) 0.05 NA NA Pr>chi2 0.826 NA NA

    -
    -Code +
    +Show the code 
    CS(copdata, "case", "sardines")
    @@ -2421,8 +2575,8 @@

    a) chi2(1) 0.35 NA NA Pr>chi2 0.553 NA NA

    -
    -Code +
    +Show the code 
    CS(copdata, "case", "eggplant")
    @@ -2441,33 +2595,33 @@

    a) chi2(1) 0.04 NA NA Pr>chi2 0.837 NA NA

    -
    -Code -
    CS(copdata, "case", "moussaka")
    +
    +Show the code +
    CS(copdata, "case", "pasta")
    $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>chi2                  0.018       NA       NA
    +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>chi2 0.006 NA NA
    -
    -Code +
    +Show the code 
    # 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!
@@ -2476,10 +2630,10 @@ 
    a)
 
    
 
    $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 @@ 
    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 @@ 
    b)
    -
    -Code +
    +Show the code 
    rr_tbl <- CSTable(copdata, "case", food_vars) %>% 
   as.data.frame() %>% 
   rownames_to_column() %>% 
@@ -2561,48 +2715,48 @@ 
    b)
    -

    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!

    -

    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)

    +

    The interesting results here are that the food items that are most suspicious are veal, pasta 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 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) Veal

    Using epitools::riskratio function:

    -
    -Code -
    epitools::riskratio(copdata$orzoD,
+
    
+Show the code
+
    epitools::riskratio(copdata$vealD,
                     copdata$case,
                     conf.level = 0.95)
    
 
    
 
    
 
    $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      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.
 Predictor estimate     lower    upper
         0 1.000000        NA       NA
-        1 1.503297 0.9257878 2.441057
-        2 1.619168 1.0310538 2.542742
-        3 1.857014 1.1730804 2.939696
+        1 1.295238 0.8431805 1.989659
+        2 1.305797 0.8769752 1.944304
+        3 1.406897 0.9314233 2.125090
 
 $p.value
          two-sided
-Predictor  midp.exact fisher.exact chi.square
-        0          NA           NA         NA
-        1 0.081364161  0.100893180 0.07613202
-        2 0.015337953  0.017069765 0.01373981
-        3 0.002010616  0.002370887 0.00162311
+Predictor midp.exact fisher.exact chi.square
+        0         NA           NA         NA
+        1 0.22186384    0.2951163 0.21193013
+        2 0.15357032    0.1884220 0.14587265
+        3 0.07956697    0.1017336 0.07298506
 
 $correction
 [1] FALSE
@@ -2613,26 +2767,25 @@ 
    a) Orzo
    
 
    
 
    Using a binomial regression:
    
 
    
-
    
-Code
+
    
+Show the code
 
    # Binomial regression for RRs. 
 # The outcome needs to be exponentiated so we can interpret it properly!
-binom_orzoD <- glm(case ~ orzoD, data = copdata, 
+binom_vealD <- glm(case ~ vealD, data = copdata, 
              family = binomial(link = "log"))
 
 # To get exponentiated:
-binom_orzoD_exp <- glm(case ~ orzoD, data = copdata, 
+binom_vealD_exp <- glm(case ~ vealD, data = copdata, 
                        family = binomial(link = "log")) %>% 
   tidy(exponentiate = TRUE, 
        conf.int = TRUE)
 
-binom_orzoD_exp
    
+binom_vealD_exp
    -
    @@ -2653,43 +2806,43 @@

    a) Orzo

    -

    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 veal, pointing towards veal as the potential vehicle. The higher the amount of veal they ate, the stronger is the association (RR) with getting ill/being a case.

    -
    -

    b) Moussaka

    +
    +

    b) Pasta

    Using epitools::riskratio function:

    -
    -Code -
    epitools::riskratio(copdata$moussakaD,                     
+
    
+Show the code
+
    epitools::riskratio(copdata$pastaD,                     
                     copdata$case,                     
                     conf.level = 0.95)
    
 
    
 
    
 
    $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      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.
 Predictor estimate     lower    upper
         0 1.000000        NA       NA
-        1 1.295238 0.8431805 1.989659
-        2 1.305797 0.8769752 1.944304
-        3 1.406897 0.9314233 2.125090
+        1 1.503297 0.9257878 2.441057
+        2 1.619168 1.0310538 2.542742
+        3 1.857014 1.1730804 2.939696
 
 $p.value
          two-sided
-Predictor midp.exact fisher.exact chi.square
-        0         NA           NA         NA
-        1 0.22186384    0.2951163 0.21193013
-        2 0.15357032    0.1884220 0.14587265
-        3 0.07956697    0.1017336 0.07298506
+Predictor  midp.exact fisher.exact chi.square
+        0          NA           NA         NA
+        1 0.081364161  0.100893180 0.07613202
+        2 0.015337953  0.017069765 0.01373981
+        3 0.002010616  0.002370887 0.00162311
 
 $correction
 [1] FALSE
@@ -2700,21 +2853,20 @@ 
    b) Moussaka
    
 
    
 
    Using a binomial regression:
    
 
    
-
    
-Code
+
    
+Show the code
 
    # Let's get the results directly exponentiated
-binom_moussakaD_exp <- glm(case ~ moussakaD, data = copdata, 
+binom_pastaD_exp <- glm(case ~ pastaD, data = copdata, 
                        family = binomial(link = "log")) %>% 
   tidy(exponentiate = TRUE, 
        conf.int = TRUE)
 
-binom_moussakaD_exp
    
+binom_pastaD_exp
    -
    @@ -2724,8 +2876,8 @@

    b) Moussaka

    c) Champagne

    Using epitools::riskratio function:

    -
    -Code +
    +Show the code 
    epitools::riskratio(copdata$champagneD,                     
                     copdata$case,                     
                     conf.level = 0.95)
    @@ -2733,12 +2885,12 @@

    c) Champagne

    $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 @@ 
    c) Champagne

    Using a binomial regression:

    -
    -Code +
    +Show the code 
    # Let's get the results directly exponentiated
 binom_champagneD_exp <- glm(case ~ champagneD, data = copdata, 
                        family = binomial(link = "log")) %>% 
@@ -2776,7 +2928,6 @@ 
    c) Champagne
    
 binom_champagneD_exp
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    At the end of the session, participants will be able to:

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    2. Story/plot description

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    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.

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    3. Questions/Assignments

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    If 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).

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    # 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)
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    3.2 Import your data

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-copdata <- rio::import(here::here("data", "Spetses_clean1_2024.rds"), trust = TRUE)
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    3.3 Identify the variables you need to apply the case definition criteria.

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    The 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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    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:

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    a) Ate a meal at the school dinner

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    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.

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    filter by those with meal == 1.

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    What are some of the implications this decision may lead to? (excluding any people from the cohort who didn’t eat at the dinner)

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-  filter(meal == 1)
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    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:

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    - 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.

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    - 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.

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    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.

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    b) Fell ill after the start of the meal

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    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.

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    Note that we the concept of having eaten a meal is already included as per one of the steps above.

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    Create a new gastrosymptoms column in your line list with mutate() and case_when().

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    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)

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    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).

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    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.

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    copdata <- copdata %>% 
-  mutate(gastrosymptoms = case_when(
-    # Those had diarrhoea...
-    diarrhoea == "1" |
-      #or bloody diarrhoea...
-    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)
-    )
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    c) Fell ill within the time period of interest

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    • 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).
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    # Start with copdata:
-copdata <- copdata %>% 
-  # Create new column for meal date and time:
-  mutate(meal_datetime = lubridate::ymd_hm("2024-10-05 18:00"))
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    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

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    copdata <- copdata %>% 
-  mutate(incubation = onset_datetime - meal_datetime,
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    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!

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    We see that the median incubation time is 15 hours. This is useful information, as incubation periods tend to be relatively pathogen-specific.

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    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 (onset_datetime) at least one symptom (diarrhoea, bloody or vomiting) 48h after meal_datetime to become a case.

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    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.

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    d) Finally, with this information you can create a new case column to hold the binary (1/0) case definition variable.

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    copdata <- copdata %>% 
-  mutate(case = case_when(
-    # Those who had symptoms <48h from the meal are cases (1)
-    gastrosymptoms == 1 & 
-      onset_datetime >= meal_datetime &
-      onset_datetime <= (meal_datetime + days(2)) ~ 1,
-    # Those who had symptoms >48h from the meal are non-cases (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 = 0)
-  )
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    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…).

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    Moreover, 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”.

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    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.

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    Open R Studio and set up a new R project, and create a data and a scripts folder.

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    # Check if the 'pacman' package is installed, if not install it:
-if (!requireNamespace("pacman", quietly = TRUE)) install.packages("pacman")
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-pacman::p_load(rio, 
-               here, 
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-               plyr,
-               janitor,
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-               flextable,
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    [1] 397  41
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    '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 ...
- $ 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 ...
- $ 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 ...
- $ 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" "" "" "" ...
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    Data summary
    Namecopdata
    Number of rows397
    Number of columns41
    _______________________
    Column type frequency:
    character3
    numeric38
    ________________________
    Group variablesNone
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    Variable type: character

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    skim_variablen_missingcomplete_rateminmaxemptyn_uniquewhitespace
    id018803970
    sex0146020
    dayonset010817540
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    Variable type: numeric

    - ------------- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    skim_variablen_missingcomplete_ratemeansdp0p25p50p75p100hist
    age01.0018.579.928161718180▇▁▁▁▁
    group01.000.960.2001111▁▁▁▁▇
    class360.911.940.8411233▇▁▆▁▇
    ill01.000.690.4700111▃▁▁▁▇
    diarrhoea1410.640.820.3901111▂▁▁▁▇
    bloody2000.500.030.1700001▇▁▁▁▁
    vomiting1790.550.300.4600011▇▁▁▁▃
    abdo1520.620.850.3501111▂▁▁▁▇
    nausea1700.570.760.4301111▂▁▁▁▇
    fever2230.440.260.4400011▇▁▁▁▃
    headache1740.560.630.4800111▅▁▁▁▇
    jointpain2070.480.160.3700001▇▁▁▁▂
    starthour1770.5512.494.923991521▁▇▁▆▃
    meal90.980.970.1701111▁▁▁▁▇
    feta160.960.710.4500111▃▁▁▁▇
    fetaD160.961.321.0000223▆▅▁▇▂
    sardines170.960.670.4700111▃▁▁▁▇
    sardinesD170.961.341.0400223▆▂▁▇▂
    eggplant300.920.590.4900111▆▁▁▁▇
    eggplantD300.921.141.0500123▇▂▁▇▂
    veal150.960.890.3101111▁▁▁▁▇
    vealD140.961.830.9101223▂▃▁▇▃
    pasta150.960.880.3201111▁▁▁▁▇
    pastaD150.961.810.9101223▂▃▁▇▃
    tomsal240.940.570.5000111▆▁▁▁▇
    tomsalD240.941.081.0600123▇▂▁▆▂
    bread180.950.910.2901111▁▁▁▁▇
    breadD180.951.750.7102223▁▂▁▇▁
    dessert420.890.420.4900011▇▁▁▁▆
    dessertD420.890.831.0600023▇▁▁▃▁
    champagne250.940.870.3401111▁▁▁▁▇
    champagneD250.941.370.9301123▂▇▁▂▃
    beer300.920.780.4201111▂▁▁▁▇
    beerD350.911.941.2301333▃▂▁▂▇
    redwine500.870.230.4200001▇▁▁▁▂
    redwineD520.870.450.9200003▇▁▁▁▁
    whitewine310.920.730.4500111▃▁▁▁▇
    whitewineD360.911.581.2100233▆▅▁▅▇
    -
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    -Show the code -
    names(copdata)
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     [1] "id"         "sex"        "age"        "group"      "class"     
- [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"
    -
    -
    -

    Let’s explore and manage some variables in detail.

    -
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    Age

    -

    Through 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.

    -
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    -Show the code -
    # Have a look at the histogram  
-hist(copdata$age)
    -
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    -

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    -Show the code -
    # Create cross-tab with the group variable:  
-tabyl(dat = copdata, 
-               var1 = age, 
-               var2 = group)
    -
    -
    -
    - -
    -
    -
    -

    Note 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:

    -
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    -Show the code -
    # Convert group to a factor and label 0 as teacher, 1 as student:
-copdata <- copdata %>% 
-  mutate(group = factor(group, 
-                        labels = c("teacher", "student")))
    -
    -
    -

    Now, have a look at your cross-tab again:

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    -Show the code -
    tabyl(dat = copdata, 
-               var1 = age, 
-               var2 = group)
    -
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    -
    - -
    -
    -
    -

    With this table, we can more easily identify ages that are likely to be typographic errors. Specifically:

    -
      -

    • There is one teacher aged 16 (likely digit reversal - should be 61)

      • -

    • There is one student aged 8 (likely missing a digit - should be 18)

      • -

    • There is one student aged 180 (likely has an extra digit - should be 18)

      • -
    -

    Assuming 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:

    -
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    -Show the code -
    # 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
-             )                    
-         )
    -
    -
    -
    -
    -

    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.

    -
    -
    -
      -
    • Create summary table for the dose response columns (those finishing with a capital “D”) to have a look at their distribution.
      • -
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    Use gtsummary::tbl_summary().

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    -Show the code -
    #| 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:   
-  tbl_summary(missing = "no") 
-
-  # Print the summary table: 
-drtable
    -
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    - - - ---- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    CharacteristicN = 3971
    fetaD
    -
        0110 (29%)
        178 (20%)
        2155 (41%)
        338 (10.0%)
    sardinesD
    -
        0125 (33%)
        135 (9.2%)
        2184 (48%)
        336 (9.5%)
    eggplantD
    -
        0151 (41%)
        141 (11%)
        2147 (40%)
        328 (7.6%)
    vealD
    -
        042 (11%)
        170 (18%)
        2184 (48%)
        387 (23%)
    pastaD
    -
        044 (12%)
        170 (18%)
        2183 (48%)
        385 (22%)
    tomsalD
    -
        0162 (43%)
        152 (14%)
        2126 (34%)
        333 (8.8%)
    breadD
    -
        035 (9.2%)
        151 (13%)
        2267 (70%)
        326 (6.9%)
    dessertD
    -
        0206 (58%)
        133 (9.3%)
        287 (25%)
        329 (8.2%)
    champagneD
    -
        049 (13%)
        1208 (56%)
        245 (12%)
        370 (19%)
    beerD
    -
        081 (22%)
        141 (11%)
        257 (16%)
        3183 (51%)
    redwineD
    -
        0266 (77%)
        131 (9.0%)
        221 (6.1%)
        327 (7.8%)
    whitewineD
    -
        0100 (28%)
        173 (20%)
        267 (19%)
        3121 (34%)
    1 n (%)
    - -
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    3.4. Modify variables format

    -

    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.

    -
    -
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    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.

    -
    -
    -

    You can use mutate to modify the type of the following variables:

    - - ------ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    Table 1: Variable types to modify
    Variable nameOriginalDesiredHint
    sexcharacterfactormutate(), as.factor()
    classintegerfactormutate(), as.factor()
    illintegerfactormutate(), as.factor()
    All the clinical symptom variablesintegerfactormutate(across()), as.factor()
    All the food variables representing the amount of specific foods eaten (those finishing with a capital “D”)integerfactormutate(across()), as.factor()
    dayonsetcharacterdatelubridate::dmy()
    starthour and dayonset together

    integer (starthour)

    -

    date (dayonset)

    		POSIXct, POSIXtlubridate::ymd_h() could have inside stringr::str_glue() with dayonset and starthour
    -
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    Sex, class and ill

    -

    Let’s start transforming one-by-one the first three variables in the table: sex, class and ill.

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    -Show the code -
    copdata <- copdata %>% 
-  mutate(
-    sex = as.factor(sex),
-    class = as.factor(class),
-    ill = as.factor(ill))
    -
    -
    -
    -
    -

    Symptoms and Food variables

    -

    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.

    -
      -
    1. For the variables that are clinical symptoms, we will list them one by one and show you the use of mutate(across( )).
      2. -
    -
    -
    -Show the code -
    copdata <- copdata %>% 
-  mutate(
-    # clinical symptoms
-    across(.cols = c(diarrhoea, bloody, vomiting,
-             abdo, nausea, fever,headache, jointpain), 
-           .fns = ~ as.factor(.)
-           )
-    )
    -
    -
    -
      -
    1. For 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.
      2. -
    -
    -
    -Show the code -
    # 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 %>% 
-    select(
-      ends_with("D", ignore.case = FALSE)) %>% 
-    names()
-
-# Another way of doing it:
-# food_dose <- c("fetaD", "sardinesD", "eggplantD", "pastaD", 
-#                 "vealD", "greeksalD", "dessertD", "breadD", 
-#                 "champagneD", "beerD", "redwineD", "whitewineD")
-
-
-copdata <- copdata %>% 
-  mutate(
-    # food dose variables
-    across(.cols = all_of(food_dose), 
-           .fns = ~as.factor(.)))
    -
    -
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    - -
    -
    -

    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.

    -
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    Date and time variables

    -
    - -
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    Why may you want to transform the variable starthour to date-time?

    -
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    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.

    -
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    -

    You 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”).

    -
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    -Show the code -
    # Have a look at how the data is stored
-head(copdata$dayonset)
    -
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    -
    [1] "6oct2024" ""         ""         ""         "6oct2024" "7oct2024"
    -
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    -Show the code -
    class(copdata$dayonset)
    -
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    [1] "character"
    -
    -
    -Show the code -
    # Update copdata:
-copdata <- copdata %>% 
-  # Change column to date class:
-  mutate(
-    dayonset = lubridate::dmy(dayonset))
-
-# Check class of updated column:
-class(copdata$dayonset)
    -
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    -
    [1] "Date"
    -
    -
    -Show the code -
    # Have a look at your data now:
-head(copdata$dayonset)
    -
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    -
    [1] "2024-10-06" NA           NA           NA           "2024-10-06"
-[6] "2024-10-07"
    -
    -
    -

    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 lubridate::ymd_h() function.

    -

    Before 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.

    -

    We can check if we have any missing values by cross-tabulating starthour with dayonset:

    -
    -
    -Show the code -
    # Cross-tabulate dayonset with starthour:
-tabyl(dat = copdata, 
-               var1 = starthour, 
-               var2 = dayonset)
    -
    -
    -
    - -
    -
    -
    -

    This 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:

    -
    -
    -Show the code -
    copdata <- copdata %>% 
-  # Combine dayonset and starthour in a new date time variable:
-  mutate(onset_datetime = 
-           ymd_h(
-             str_glue("{dayonset}, {starthour}"), 
-                                           # Deal with missing starthour:
-                                           truncated = 2))
    -
    -
    -
    Warning: There was 1 warning in `mutate()`.
-ℹ In argument: `onset_datetime = ymd_h(str_glue("{dayonset}, {starthour}"),
-  truncated = 2)`.
-Caused by warning:
-!  175 failed to parse.
    -
    -
    -

    Note 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.

    -

    The 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.

    -

    Now we can check that everything in the new combined date-time variable has parsed correctly:

    -
    -
    -Show the code -
    head(copdata$dayonset)
    -
    -
    -
    [1] "2024-10-06" NA           NA           NA           "2024-10-06"
-[6] "2024-10-07"
    -
    -
    -Show the code -
    head(copdata$starthour)
    -
    -
    -
    [1]  9 NA NA NA 15 15
    -
    -
    -Show the code -
    head(copdata$onset_datetime)
    -
    -
    -
    [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"
    -
    -
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    -
    -
    -

    3.5. Export clean data

    -

    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.

    -

    Save your clean data as a new file “Spetses_clean1_YOURINITIALS.rds”, under the data folder of your Rproject.

    -
    - -
    -
    -

    Use rio::export().

    -
    -
    -
    -
    -
    -Show the code -
    export(x = copdata, 
-            file = here::here("data", "Spetses_clean1_2024.rds"))
    -
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    -
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    - - - - -
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    Laboratory data and descriptive analysis

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    1. Learning outcomes

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    At the end of the session, participants will be able to:

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    • Merge two datasets using a common column.
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    • Discuss which descriptive statistics would you use to describe the cases
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    • Describe the cases by person and place (this is an outbreak in a high school, so we won’t explore “place” this time!)
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    • Generate preliminary hypotheses bases on the descriptions.
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    2. Story/plot description

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    You have received the laboratory results for the samples collected from some of the cases. The lab conducted an RT-PCR panel targeting various gastrointestinal pathogens and also gathered additional metadata. Your task is to merge the questionnaire data with the data you have received from the lab, using the unique identifiers that both datasets share (id column), and explore that lab data. What does it tell you?

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    Once your dataset is clean and the lab data merged, you can 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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    3. Questions/Assignments

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    If you closed your R project, please open it again and load the clean dataset. We recommend you use the Spetses_clean2_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).

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    3.1 Install packages (if needed) and load libraries

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    # 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)
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    3.2 Import your data

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    # Import the clean data set:
-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.
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    3.3 Merge and explore the lab data. Explore it as you wish!

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    copdatalab <- left_join(copdata, lab, 
-                        by = "id")
-# Tabulate:
-janitor::tabyl(dat = copdatalab, "RT-PCR_ecoli_etec")
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    3.4 Describe the outbreak in terms of time.

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    You 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.

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    a) Create a histogram to visualize the incubation period (geom_histogram is a good function to use for this!).

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    What can you infer from the incubation periods in the histogram?

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    #| label: inc_time
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-# Create a dataset with only cases
-cases <- copdata %>% 
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-incplot <- cases %>% 
-  # Create an empty ggplot frame:
-  ggplot() +
-  # Add a histogram of incubation:
-  geom_histogram(
-    mapping = aes(x = incubation), 
-    # Set bin widths to 6 hours:
-    binwidth = 6) +
-  # Adapt scale to better fit data
-  scale_x_continuous(breaks = seq(0, 48, 6)) + 
-  # Label x and y axes:
-  labs(x = "Incubation period in 6-hour bins",
-       y = "Number of cases")
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-incplot
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    b) Create an epicurve for the date and time of onset (using onset_datetime), limiting the input data to cases.

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    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 date_breaks argument.

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    # Create a vector with sequences every 6h from the first to the last case
-breaks_6h <- seq(from = min(cases$onset_datetime, na.rm = TRUE),
-                 to = max(cases$onset_datetime, na.rm = TRUE),
-                 by = "6 hours")
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-# Fetch cases data:
-epicurve_datetime <- cases %>%  
-  # Add factor onset_datetime to ggplot aesthetic:
-  ggplot(
-    mapping = aes(x = onset_datetime)) + 
-  # Add geom_histogram:
-  geom_histogram(
-    # Apply the vector of requences created above
-    breaks = breaks_6h) +
-  # Adapt scale to data and adjust axis label angle:
-  scale_x_datetime(
-    date_breaks = "6 hours",
-    labels = label_date_short()) +
-  # Update x and y axis labels:
-  labs(x = "Date and time of onset symptoms", 
-       y = "Number of cases") +
-  # Remove unnecessary grid lines:
-  theme_bw()
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-epicurve_datetime
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    1. Building up on your previous epicurve, create another epicurve to compare between sexes and additionally investigate how teachers versus students were distributed.
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    fill 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.

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    If 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.

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    epicurve_strata <- cases %>% 
-  # Add factor onset_day to ggplot aesthetic:
-  ggplot(
-    mapping = aes(x = onset_datetime, fill = group)) + 
-  # Add nicer fill colours:
-  scale_fill_manual(values = c("darkred", "lightblue")) +
-    # Add geom_histogram:
-  geom_histogram(
-    # Apply the vector of requences created above
-    breaks = breaks_6h) +
-  # Adjust x axis scales to a suitable unit:
-  scale_x_datetime(
-    date_breaks = "6 hours", 
-    labels = label_date_short()) +
-  # Update x and y axis labels:
-  labs(x = "Date and time of onset", 
-       y = "Number of cases", 
-       fill = "Group", 
-       title = "Epicurve of the outbreak, stratified by sex",
-       subtitle = str_glue("Spetses, October 2024, N = {sum(copdata$case)}")) +
-  # Stratify by sex:
-  facet_wrap(facets = "sex",
-             ncol = 2) +
-  # Add theme:
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-epicurve_strata
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    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?

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    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.

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    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.

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    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.

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    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!

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    3.5. Describe the outbreak in terms of person.

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    You 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.

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    a) Create a cross-tabulation of case with group.

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    b) Create a cross-tabulation of case with sex.

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    What do you think of these results?

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    - -
    -
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    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.

    -
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    - -
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    If 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().

    -
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    -
    -
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    -

    c) Extra - Age-sex pyramid of cases.

    -

    Hint: Change show_midpoint = FALSE to TRUE to see skewedness in the data patterns more easily.

    -
    -
    -Show the code -
    copdata <- copdata %>% 
-  # Create age categories:
-  mutate(age_cat = epikit::age_categories(
-    # Name of age column:
-    x = age, 
-    # Define the age categories:
-    breakers = c(0, 10, 16, 18, 20, 50, 70)
-    )
-  )
-
-
-# Check age categories:
-janitor::tabyl(copdata, age_cat)
    -
    -
    -
    - -
    -
    -
    -Show the code -
    # Pipe copdata:
-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
    -
    -
    -
    -
    -

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    3.6 Explore the distribution of all clinical signs (symptoms).

    -
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    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.

    -
    -
    -Show the code -
    # 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
    -
    -
    -
    - - - ------ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    CharacteristicOverall N = 3771Non-case N = 1611Case N = 2161
    Diarrhoea
    -
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    -
        046 (18%)40 (100%)6 (2.8%)
        1206 (82%)0 (0%)206 (97%)
    Dysentary
    -
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        0189 (97%)42 (100%)147 (97%)
        15 (2.6%)0 (0%)5 (3.3%)
    Vomiting
    -
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    -
        0149 (69%)42 (100%)107 (62%)
        166 (31%)0 (0%)66 (38%)
    Abdominal pain
    -
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        035 (14%)6 (12%)29 (15%)
        1207 (86%)44 (88%)163 (85%)
    Nausea
    -
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        055 (25%)12 (26%)43 (24%)
        1169 (75%)34 (74%)135 (76%)
    Fever
    -
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        0127 (74%)32 (80%)95 (73%)
        144 (26%)8 (20%)36 (27%)
    Headache
    -
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        083 (38%)11 (25%)72 (41%)
        1137 (62%)33 (75%)104 (59%)
    Joint pain
    -
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        0159 (85%)32 (84%)127 (85%)
        129 (15%)6 (16%)23 (15%)
    1 n (%)
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    Do you think the symptoms selected for the case definition were the right ones, or would you change anything?

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    If 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.

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    b) Extra - Bar plot of symptoms stratified by case definition.

    -
    -
    -Show the code -
    # 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)
    -
    -
    -
    `summarise()` has grouped output by 'Symptoms'. You can override using the
-`.groups` argument.
    -
    -
    -Show the code -
    # Print plot:
-symptom_bar
    -
    -
    -
    -
    -

    -
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    3.7 Attack proportions

    -
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    a) Calculate the overall attack proportions (percentage of cases among the total observed individuals). You could use tabyl().

    -
    -
    -Show the code -
    # 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
    -
    -
    -
    [1] "57.3%"
    -
    -
    -
    - -
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    What can you infer from the overall attack proportion about this outbreak and possible vehicles/exposures?

    -
    -
    -
    -
    - -
    -
    -

    The overall attack proportion is 57.3%. This means that more than half of the people who ate a meal were cases!

    -
    -
    -
    -
    -
    -

    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.

    -
    -
    -Show the code -
    # 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
    -
    -
    -
    - - - ------ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    CharacteristicOverall N = 3771Non-case N = 1611Case N = 2161
    class
    -
    -
    -
        1131 (100%)63 (48%)68 (52%)
        2101 (100%)44 (44%)57 (56%)
        3111 (100%)38 (34%)73 (66%)
        Unknown341618
    group
    -
    -
    -
        teacher15 (100%)9 (60%)6 (40%)
        student362 (100%)152 (42%)210 (58%)
    sex
    -
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        female213 (100%)96 (45%)117 (55%)
        male164 (100%)65 (40%)99 (60%)
    1 n (%)
    - -
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    -

    3.8 Draft the relevant paragraph(s) and table(s) and/or figure(s) in your outbreak report (Methods and results).

    -
    - -
    -
    -

    To 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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    - - - - -
    - -
    - -
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z=0Q$R_<|9;u%&%>fXe7;OP{{Cqv5p37*-X^Rty4cGL%~#o{n&slxxk>$DFDVV2x2S z(D#*PUxWd%jEeC(^e%40nFE%68w$l0l26KOe?V5UbaiWmN;E&{J9a}GP8^ga($4tH zAnA1jj6JGy60dz}j>nyA8AfAKuitI=CJ&0&6k7(H2~ zH~E28b$I41eA{L)9CO@Bbu8S!K`QzZ`s^pJt?o0}5UYRo^?O=-$?YrR$lry~*V!MN z51)}9aAHNa-g$9E9V_kgQaor9WkN37&7p~LAJ!cd1YTOR?;pRu=AVAgjfnEO?uH6> zDT@CFJqRHu>SJuFV6_W`-7hbqd>sp%fNUs5;G-a5K}T7u+kNqqId|s}&vXFsOloH_ z`|8(F3iUUm2ZSk<9ufwr<_Cb{;*lDqyJlY$ZMyNNtQ90B`a5~5GCqb=P@caZ&+WSJ z{l!TZ;m}l z_ZF9ZC~=P-r`8Qs^i`2>fDgN0cD;heDjf`Rdo@$NwxP`x%=Vj{tbJ>Nr;s!XE8fW| zE3N_>3B%r^C83BoY*bL%AX&_cf=bfZgT}Ca<(8YiYSA}Yq~{y23PbN-jA^p zWQ@NR_6DJ?DyWQ-s%ELvdw~WouS12<+*-XBbPMq~0^!b?up?!Pv)N}e);(VV*#L?yBe%!|WCsc{ z>YgL2D86O^YOBQn+DEmNj5#@_SFp}`^G%)pTDFFym+Ito@|Y5_D3y~?1x`1>i&T%4 zKG9YotW4BR#j*0J5R|uMLwgwTo+aQ~O;A40Fs?J3%PzG02FcSHpy7t8PJWl<>)7iL zm97#D1=<{ - - - - - - - - -Stratified analysis – Spetses Introductory Course - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    At the end of the session, participants will be able to:

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    • Consider the effect of confounding and effect modification on the association between exposure and disease,

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    • Perform stratified analysis using the Mantel-Haenszel approach

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    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.

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    2. Story/plot description

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    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).

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    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.

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    3. Questions/Assignments

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    3.1. Confounders and effect modification

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    Discuss 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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    3.2. Install packages and load libraries

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    -Show the code -
    # 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)
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    3.3. Import your data

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    # Import the raw data set: 
-copdata <- rio::import(here::here("data", "Spetses_clean2_2024.rds"), trust = T)
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    3.4. Consider and assess for confounding and/or effect modification

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    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.

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    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.

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    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.

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    Use the function csinter() of the EpiStats package.

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    a) Veal as exposure of interest, stratified by having eaten pasta

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    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?

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    stratall <- copdata %>% 
-  # Mutate across to convert cases to numeric:
-  mutate(across(.cols = case, 
-                .fns = ~ as.numeric(.)))
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-# Pass data to the csinter function:
-pastastrata <- csinter(x = stratall, 
-                       cases = "case", 
-                       exposure = "veal", 
-                       by = "pasta")
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-pastastrata
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    $df1
-  CSInter case - veal by(pasta) Total Cases Risk %          P.est. Stats
-1                     pasta = 1   338  <NA>     NA Risk difference  0.10
-2                       Exposed   330   198  60.00      Risk Ratio  1.20
-3                     Unexposed     8     4  50.00 Attrib.risk.exp  0.17
-4                                  NA  <NA>     NA Attrib.risk.pop  0.16
-5                     pasta = 0    36  <NA>     NA Risk difference  0.02
-6                       Exposed     8     3  37.50      Risk Ratio  1.05
-7                     Unexposed    28    10  35.71 Attrib.risk.exp  0.05
-8                                  NA  <NA>     NA Attrib.risk.pop  0.01
-9           Missing / Missing %     3  0.8%     NA            <NA>    NA
-  95%CI.ll 95%CI.ul
-1    -0.25     0.45
-2     0.60     2.41
-3    -0.68     0.59
-4       NA       NA
-5    -0.36     0.40
-6     0.38     2.92
-7    -1.65     0.66
-8       NA       NA
-9       NA       NA
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-$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 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
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    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)):

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    # Perform Wilcoxon rank sum test on veal and pasta:
-wilcox.test(veal ~ pasta, 
-            data = copdata)
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-    Wilcoxon rank sum test with continuity correction
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-data:  veal by pasta
-W = 1496, p-value < 2.2e-16
-alternative hypothesis: true location shift is not equal to 0
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    b) Champagne as exposure of interest, stratified by having eaten pasta

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    # Pass data to the csinter function:
-champstrata <- csinter(x = stratall, 
-                       cases = "case", 
-                       exposure = "champagne", 
-                       by = "pasta")
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    Are there any indications to make you think there may be effect modification and/or confounding?

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    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).

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    Above, we uses ccinter to stratify and save the object as “pastastrata”.

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    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.

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    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.

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    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!

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    At the end of the session, participants will be able to:

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    • Perform hypothesis tests.

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    • Interpret the univariable results.

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    2. Story/plot description

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    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.

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    Now, you will practice performing hypothesis testing and calculating RR in R to investigate the associations of suspicious food items with the disease.

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    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):

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    • For continuous variables:

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      • shapiro.test() (for checking if normally distributed)

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      • t.test() (for normal distributions)

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      • wilcox.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.)

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    • For categorical variables:

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      • chisq.test() (if all comparisons include at least 5 cases)

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      • fisher.test() (if there are < 5 cases for any comparison)

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    3. Questions/Assignments

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    3.1. Install packages and load libraries

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    # 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)
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    3.2. Import your data

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    # Import the raw data set:
-copdata <- rio::import(here::here("data", "Spetses_clean2_2024.rds"), trust = T)
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    3.3. Hypothesis tests for other variables

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    Check if the following variables are associated with being a case: age, sex, class and group.

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    a) age

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    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<0.05 means that we reject the null hypothesis that the data follow the normal distribution. For age, the p-value is <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 <20 years.

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    # Check if age overall follows a normal distribution:
-shapiro.test(copdata$age)
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-W = 0.31302, p-value < 2.2e-16
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    # Can simply have a look at
-hist(copdata$age)
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    # Looking only at the students:
-students <- copdata %>% 
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-hist(students$age)
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    Age overall (nor within the students’ group) is not normally distributed.

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    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>0.05 we do not reject the null hypothesis.

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    # Perform Wilcoxon rank sum test on age and sex:
-wilcox.test(age ~ case, 
-            data = copdata)
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-    Wilcoxon rank sum test with continuity correction
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-data:  age by case
-W = 15934, p-value = 0.1512
-alternative hypothesis: true location shift is not equal to 0
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    b) sex

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    copdata %>% 
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    Characteristic0
    -N = 1611    		1
    -N = 2161    		p-value2
    sex
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    What do these results tell you - is there an association between sex and being a case?

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    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?

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    c) class

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    copdata %>% 
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    -N = 1611    		1
    -N = 2161    		p-value2
    class
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        163 (43%)68 (34%)
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    2 Pearson’s Chi-squared test
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    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.

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    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.

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    d) group

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    copdata %>% 
-  select(group, case) %>% 
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    -N = 1611    		1
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    group
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        teacher9 (5.6%)6 (2.8%)
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    2 Pearson’s Chi-squared test
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    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.

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    Let’s do all together

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    copdata %>% 
-  select(sex, class, group, case) %>% 
-  tbl_summary(by = case) %>% 
-  add_p()
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    - - - ------ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    -N = 1611    		1
    -N = 2161    		p-value2
    sex
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        female96 (60%)117 (54%)
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    class
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        163 (43%)68 (34%)
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        Unknown1618
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        teacher9 (5.6%)6 (2.8%)
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    2 Pearson’s Chi-squared test
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    3.4. Risk Ratios

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    The 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.

    -
    -

    a) Calculate 95% CI Risk Ratios for food

    -

    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 CSTable()), or all at once (see hint below).

    -
    - -
    -
    -

    Create a food_vars vector containing food variables of interest and use the function CSTable() of the EpiStats package.

    -

    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.

    -
    -
    -
    -
    -
    -Show the code -
    # You could use the EpiStats package for each food item
-CS(copdata, "case", "feta")
    -
    -
    -
    $df1
-          Cases Non Cases Total Risk
-Exposed     156       115   271 0.58
-Unexposed    60        42   102 0.59
-Total       216       157   373 0.58
-
-$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>chi2                  0.826       NA       NA
    -
    -
    -Show the code -
    CS(copdata, "case", "sardines")
    -
    -
    -
    $df1
-          Cases Non Cases Total Risk
-Exposed     150       105   255 0.59
-Unexposed    65        52   117 0.56
-Total       215       157   372 0.58
-
-$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>chi2                  0.553       NA       NA
    -
    -
    -Show the code -
    CS(copdata, "case", "eggplant")
    -
    -
    -
    $df1
-          Cases Non Cases Total Risk
-Exposed     123        93   216 0.57
-Unexposed    83        60   143 0.58
-Total       206       153   359 0.57
-
-$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>chi2                  0.837       NA       NA
    -
    -
    -Show the code -
    CS(copdata, "case", "pasta")
    -
    -
    -
    $df1
-          Cases Non Cases Total Risk
-Exposed     202       136   338 0.60
-Unexposed    13        23    36 0.36
-Total       215       159   374 0.57
-
-$df2
-                Point estimate 95%CI.ll 95%CI.ul
-Risk difference           0.24     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>chi2                  0.006       NA       NA
    -
    -
    -
    -
    -Show the code -
    # 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)
    -
    -
    -
    $df
-          Tot.Exp Cases.Exp AR.Exp% Tot.Unexp Cases.Unexp AR.Unexp%   RR CI.ll
-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
-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
-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)
-pasta      2.58   0.006
-veal       2.32   0.018
-champagne  1.89   0.044
-tomsal     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
    -
    -
    -
    -
    -

    b) Prepare the RR table for publication

    -
    - -
    -
    -

    Use flextable() and set_header_labels().

    -
    -
    -
    -
    -
    -Show the code -
    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"))
    -
    -
    -
    - -
    -
    -

    · What can you infer from this table?

    -

    · 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?

    -

    · 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 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 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) Pasta

    -

    Using epitools::riskratio function:

    -
    -
    -Show the code -
    epitools::riskratio(copdata$pastaD,                     
-                    copdata$case,                     
-                    conf.level = 0.95)
    -
    -
    -
    $data
-         Outcome
-Predictor   0   1 Total
-    0      23  13    36
-    1      32  38    70
-    2      76 107   183
-    3      28  57    85
-    Total 159 215   374
-
-$measure
-         risk ratio with 95% C.I.
-Predictor estimate     lower    upper
-        0 1.000000        NA       NA
-        1 1.503297 0.9257878 2.441057
-        2 1.619168 1.0310538 2.542742
-        3 1.857014 1.1730804 2.939696
-
-$p.value
-         two-sided
-Predictor  midp.exact fisher.exact chi.square
-        0          NA           NA         NA
-        1 0.081364161  0.100893180 0.07613202
-        2 0.015337953  0.017069765 0.01373981
-        3 0.002010616  0.002370887 0.00162311
-
-$correction
-[1] FALSE
-
-attr(,"method")
-[1] "Unconditional MLE & normal approximation (Wald) CI"
    -
    -
    -

    Using a binomial regression:

    -
    -
    -Show the code -
    # 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
    -
    -
    -
    - -
    -
    -
    -
    - -
    -
    -

    What do these results tell you?

    -
    -
    -
    -

    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) Veal

    -

    Using epitools::riskratio function:

    -
    -
    -Show the code -
    epitools::riskratio(copdata$vealD,
-                    copdata$case,
-                    conf.level = 0.95)
    -
    -
    -
    $data
-         Outcome
-Predictor   0   1 Total
-    0      19  15    34
-    1      30  40    70
-    2      78 106   184
-    3      33  54    87
-    Total 160 215   375
-
-$measure
-         risk ratio with 95% C.I.
-Predictor estimate     lower    upper
-        0 1.000000        NA       NA
-        1 1.295238 0.8431805 1.989659
-        2 1.305797 0.8769752 1.944304
-        3 1.406897 0.9314233 2.125090
-
-$p.value
-         two-sided
-Predictor midp.exact fisher.exact chi.square
-        0         NA           NA         NA
-        1 0.22186384    0.2951163 0.21193013
-        2 0.15357032    0.1884220 0.14587265
-        3 0.07956697    0.1017336 0.07298506
-
-$correction
-[1] FALSE
-
-attr(,"method")
-[1] "Unconditional MLE & normal approximation (Wald) CI"
    -
    -
    -

    Using a binomial regression:

    -
    -
    -Show the code -
    # 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"))
-
-# To get exponentiated:
-binom_vealD_exp <- glm(case ~ vealD, data = copdata, 
-                       family = binomial(link = "log")) %>% 
-  tidy(exponentiate = TRUE, 
-       conf.int = TRUE)
-
-binom_vealD_exp
    -
    -
    -
    - -
    -
    -
    -
    -
    -

    c) Champagne

    -

    Using epitools::riskratio function:

    -
    -
    -Show the code -
    epitools::riskratio(copdata$champagneD,                     
-                    copdata$case,                     
-                    conf.level = 0.95)
    -
    -
    -
    $data
-         Outcome
-Predictor   0   1 Total
-    0      27  21    48
-    1      93 111   204
-    2      12  32    44
-    3      24  44    68
-    Total 156 208   364
-
-$measure
-         risk ratio with 95% C.I.
-Predictor estimate     lower    upper
-        0 1.000000        NA       NA
-        1 1.243697 0.8812613 1.755193
-        2 1.662338 1.1502014 2.402507
-        3 1.478992 1.0260296 2.131923
-
-$p.value
-         two-sided
-Predictor  midp.exact fisher.exact  chi.square
-        0          NA           NA          NA
-        1 0.188922591  0.201367850 0.183280359
-        2 0.005584928  0.006247816 0.004961941
-        3 0.027627736  0.036334120 0.025117641
-
-$correction
-[1] FALSE
-
-attr(,"method")
-[1] "Unconditional MLE & normal approximation (Wald) CI"
    -
    -
    -

    Using a binomial regression:

    -
    -
    -Show the code -
    # 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
    -
    -
    -
    - -
    -
    -
    -
    -
    -
    -

    3.6 Summary

    -

    As a summary of what you’ve done above, answer these questions:

    -
      -

    • Is the respondents’ sex associated with being a case?

      • -

    • Is the school class associated with being a case?

      • -

    • Which foods increase the risk of being a case?

      • -

    • (Optional) Is there a dose-response relationship between the food items and being a case?

      • -

    • 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?

      • -
    - - - - -
    - -
    - -
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-Spetses Introductory Course - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    Spetses Introductory Course

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    Background:

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    This 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.

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    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:

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    • Data_import_cleaning.qmd
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    • Lab_and_Descriptive.qmd
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    • Univariate.qmd
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    • Stratified.qmd
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    - - - - - \ No newline at end of file diff --git a/docs-preview/search.json b/docs-preview/search.json deleted file mode 100644 index 14f5b76..0000000 --- a/docs-preview/search.json +++ /dev/null @@ -1,317 +0,0 @@ -[ - { - "objectID": "Univariate.html", - "href": "Univariate.html", - "title": "Univariable analysis", - "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." - }, - { - "objectID": "Univariate.html#learning-outcomes", - "href": "Univariate.html#learning-outcomes", - "title": "Univariable analysis", - "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." - }, - { - "objectID": "Univariate.html#storyplot-description", - "href": "Univariate.html#storyplot-description", - "title": "Univariable analysis", - "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 < 5 cases for any comparison)" - }, - { - "objectID": "Univariate.html#questionsassignments", - "href": "Univariate.html#questionsassignments", - "title": "Univariable analysis", - "section": "3. Questions/Assignments", - "text": "3. Questions/Assignments" - }, - { - "objectID": "Univariate.html#install-packages-and-load-libraries", - "href": "Univariate.html#install-packages-and-load-libraries", - "title": "Univariable analysis", - "section": "3.1. Install packages and load libraries", - "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)" - }, - { - "objectID": "Univariate.html#import-your-data", - "href": "Univariate.html#import-your-data", - "title": "Univariable analysis", - "section": "3.2. Import your data", - "text": "3.2. Import your data\n\n\nShow the code\n# Import the raw data set:\ncopdata <- rio::import(here::here(\"data\", \"Spetses_clean2_2024.rds\"), trust = T)" - }, - { - "objectID": "Univariate.html#hypothesis-tests-for-other-variables", - "href": "Univariate.html#hypothesis-tests-for-other-variables", - "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<0.05 means that we reject the null hypothesis that the data follow the normal distribution. For age, the p-value is <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 <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 < 2.2e-16\n\n\nShow the code\n# Can simply have a look at\nhist(copdata$age)\n\n\n\n\n\n\n\n\n\nShow the code\n# Looking only at the students:\nstudents <- copdata %>% \n filter(group == \"student\")\nhist(students$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>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 %>% \n select(sex, case) %>% \n tbl_summary(by = case) %>% \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 %>% \n select(class, case) %>% \n tbl_summary(by = case) %>% \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 %>% \n select(group, case) %>% \n tbl_summary(by = case) %>% \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 %>% \n select(sex, class, group, case) %>% \n tbl_summary(by = case) %>% \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" - }, - { - "objectID": "Univariate.html#risk-ratios", - "href": "Univariate.html#risk-ratios", - "title": "Univariable analysis", - "section": "3.4. Risk Ratios", - "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>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>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>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>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 <- 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 <- CSTable(copdata, \"case\", food_vars) %>% \n as.data.frame() %>% \n rownames_to_column() %>% \n flextable() %>% \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)" - }, - { - "objectID": "Univariate.html#optional-dose-response", - "href": "Univariate.html#optional-dose-response", - "title": "Univariable analysis", - "section": "3.5. (Optional) Dose Response", - "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$correction\n[1] FALSE\n\nattr(,\"method\")\n[1] \"Unconditional MLE & normal approximation (Wald) CI\"\n\n\nUsing a binomial regression:\n\n\nShow the code\n# Let's get the results directly exponentiated\nbinom_pastaD_exp <- glm(case ~ pastaD, data = copdata, \n family = binomial(link = \"log\")) %>% \n tidy(exponentiate = TRUE, \n conf.int = TRUE)\n\nbinom_pastaD_exp\n\n\n\n \n\n\n\n\n\n\n\n\n\nLet’s stop and… think!\n\n\n\n\n\nWhat do these results tell you?\n\n\n\nResults suggest a dose response relationship of having eaten pasta, pointing towards pasta as the potential vehicle. The higher the amount of pasta they ate, the stronger is the association (RR) with getting ill/being a case.\n\n\nb) Veal\nUsing epitools::riskratio function:\n\n\nShow the code\nepitools::riskratio(copdata$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$correction\n[1] FALSE\n\nattr(,\"method\")\n[1] \"Unconditional MLE & normal approximation (Wald) CI\"\n\n\nUsing a binomial regression:\n\n\nShow the code\n# Binomial regression for RRs. \n# The outcome needs to be exponentiated so we can interpret it properly!\nbinom_vealD <- glm(case ~ vealD, data = copdata, \n family = binomial(link = \"log\"))\n\n# To get exponentiated:\nbinom_vealD_exp <- glm(case ~ vealD, data = copdata, \n family = binomial(link = \"log\")) %>% \n tidy(exponentiate = TRUE, \n conf.int = TRUE)\n\nbinom_vealD_exp\n\n\n\n \n\n\n\n\n\nc) Champagne\nUsing epitools::riskratio function:\n\n\nShow the code\nepitools::riskratio(copdata$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 <- glm(case ~ champagneD, data = copdata, \n family = binomial(link = \"log\")) %>% \n tidy(exponentiate = TRUE, \n conf.int = TRUE)\n\nbinom_champagneD_exp" - }, - { - "objectID": "Univariate.html#summary", - "href": "Univariate.html#summary", - "title": "Univariable analysis", - "section": "3.6 Summary", - "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?" - }, - { - "objectID": "Lab_and_Descriptive.html", - "href": "Lab_and_Descriptive.html", - "title": "Laboratory data and descriptive analysis", - "section": "", - "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." - }, - { - "objectID": "Lab_and_Descriptive.html#learning-outcomes", - "href": "Lab_and_Descriptive.html#learning-outcomes", - "title": "Laboratory data and descriptive analysis", - "section": "", - "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." - }, - { - "objectID": "Lab_and_Descriptive.html#storyplot-description", - "href": "Lab_and_Descriptive.html#storyplot-description", - "title": "Laboratory data and descriptive analysis", - "section": "2. Story/plot description", - "text": "2. Story/plot description\nYou have received the laboratory results for the samples collected from some of the cases. The lab conducted an RT-PCR panel targeting various gastrointestinal pathogens and also gathered additional metadata. Your task is to merge the questionnaire data with the data you have received from the lab, using the unique identifiers that both datasets share (id column), and explore that lab data. What does it tell you?\nOnce your dataset is clean and the lab data merged, you can 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." - }, - { - "objectID": "Lab_and_Descriptive.html#questionsassignments", - "href": "Lab_and_Descriptive.html#questionsassignments", - "title": "Laboratory data and descriptive analysis", - "section": "3. Questions/Assignments", - "text": "3. Questions/Assignments\nIf you closed your R project, please open it again and load the clean dataset. We recommend you use the Spetses_clean2_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)." - }, - { - "objectID": "Lab_and_Descriptive.html#install-packages-if-needed-and-load-libraries", - "href": "Lab_and_Descriptive.html#install-packages-if-needed-and-load-libraries", - "title": "Laboratory data and descriptive analysis", - "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)" - }, - { - "objectID": "Lab_and_Descriptive.html#import-your-data", - "href": "Lab_and_Descriptive.html#import-your-data", - "title": "Laboratory data and descriptive analysis", - "section": "3.2 Import your data", - "text": "3.2 Import your data\n\n\nShow the code\n# Import the clean data set:\ncopdata <- rio::import(here::here(\"data\", \"Spetses_clean2_2024.rds\"), trust = T) \nlab <- rio::import(here::here(\"data\", \"Lab data.xlsx\"),\n skip = 1) # skip the first row." - }, - { - "objectID": "Lab_and_Descriptive.html#merge-and-explore-the-lab-data.-explore-it-as-you-wish", - "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\nShow the code\ncopdatalab <- left_join(copdata, lab, \n by = \"id\")\n# Tabulate:\njanitor::tabyl(dat = copdatalab, \"RT-PCR_ecoli_etec\")" - }, - { - "objectID": "Lab_and_Descriptive.html#describe-the-outbreak-in-terms-of-time.", - "href": "Lab_and_Descriptive.html#describe-the-outbreak-in-terms-of-time.", - "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\nShow the code\n#| label: inc_time\n\n# Create a dataset with only cases\ncases <- copdata %>% \n filter(case == 1)\n\nincplot <- cases %>% \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 <- 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 <- cases %>% \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 <- cases %>% \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!" - }, - { - "objectID": "Lab_and_Descriptive.html#describe-the-outbreak-in-terms-of-person.", - "href": "Lab_and_Descriptive.html#describe-the-outbreak-in-terms-of-person.", - "title": "Laboratory data and descriptive analysis", - "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\nShow the code\ncopdata %>% \n janitor::tabyl(case, group) %>% \n adorn_totals() %>% \n adorn_percentages() %>% \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 %>% \n janitor::tabyl(case, sex) %>% \n adorn_totals() %>% \n adorn_percentages() %>% \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 <- copdata %>% \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 <- copdata %>% \n # Filter for cases only:\n filter(case == TRUE) %>% \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" - }, - { - "objectID": "Lab_and_Descriptive.html#explore-the-distribution-of-all-clinical-signs-symptoms.", - "href": "Lab_and_Descriptive.html#explore-the-distribution-of-all-clinical-signs-symptoms.", - "title": "Laboratory data and descriptive analysis", - "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\nShow the code\n# Create summary table:\ntabsymptoms <- copdata %>% \n # Select person characteristics to summarise:\n select(case, diarrhoea, bloody, vomiting,\n abdo, nausea, fever,headache, jointpain) %>% \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(.))) %>%\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(.))) %>% \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 ) %>% \n \n # Add totals:\n add_overall() %>% \n # Make variable names bold and italics:\n bold_labels() %>% \n italicize_labels() %>% \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 <- 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 <- ggplot2::as_labeller(c(\"0\" = \"Non-case\", \n \"1\" = \"Case\"))\n# Select variables and cases:\nsymptom_bar <- copdata %>% \n # Select symptom columns:\n select(case, c(all_of(symptoms))) %>%\n # Drop NAs:\n drop_na() %>% \n # Reshape (pivot longer):\n pivot_longer(!case, \n names_to = \"Symptoms\", \n values_drop_na = TRUE) %>% \n # Keep only TRUE values:\n filter(value == \"1\") %>% \n \n # Group by symptoms and case:\n group_by(Symptoms, case) %>% \n # Count for each symptom by case:\n dplyr::summarise(count = n()) %>% \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" - }, - { - "objectID": "Lab_and_Descriptive.html#attack-proportions", - "href": "Lab_and_Descriptive.html#attack-proportions", - "title": "Laboratory data and descriptive analysis", - "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\nShow the code\n# Create table of case status:\ntotal_ap <- tabyl(copdata, case) %>% \n # Add row totals:\n adorn_totals(where = \"row\") %>% \n # Add percentages with 1 digit after the decimal point:\n adorn_pct_formatting(digits = 1) %>% \n # Filter to rows where case is TRUE:\n filter(case == \"1\") %>% \n # Select the column percent:\n select(percent) %>% \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 <- copdata %>% \n # Select columns:\n select (case, class, group, sex) %>% \n \n # Create table:\n tbl_summary(\n # Stratified by case\n by = case,\n # with row percentages\n percent = \"row\") %>%\n \n # Add totals:\n add_overall() %>%\n \n # Make variable names bold and italics:\n bold_labels() %>% \n italicize_labels() %>% \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 (%)" - }, - { - "objectID": "Lab_and_Descriptive.html#draft-the-relevant-paragraphs-and-tables-andor-figures-in-your-outbreak-report-methods-and-results.", - "href": "Lab_and_Descriptive.html#draft-the-relevant-paragraphs-and-tables-andor-figures-in-your-outbreak-report-methods-and-results.", - "title": "Laboratory data and descriptive analysis", - "section": "3.8 Draft the relevant paragraph(s) and table(s) and/or figure(s) in your outbreak report (Methods and results).", - "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\")" - }, - { - "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\nImport datasets in R\nApply basic data management techniques to explore and familiarise themselves with the dataset\nApply strategies for data cleaning" - }, - { - "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 <- 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 <- copdata %>% \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 <- copdata %>% \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 <- copdata %>% \n # Select all the columns with column names that end in upper case 'D': \n select(ends_with(\"D\", ignore.case = FALSE)) %>% \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 (%)" - }, - { - "objectID": "Data_import_cleaning.html#modify-variables-format", - "href": "Data_import_cleaning.html#modify-variables-format", - "title": "Data import and cleaning", - "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\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 <- copdata %>% \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 <- copdata %>% \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 <- copdata %>% \n select(\n ends_with(\"D\", ignore.case = FALSE)) %>% \n names()\n\n# Another way of doing it:\n# food_dose <- c(\"fetaD\", \"sardinesD\", \"eggplantD\", \"pastaD\", \n# \"vealD\", \"greeksalD\", \"dessertD\", \"breadD\", \n# \"champagneD\", \"beerD\", \"redwineD\", \"whitewineD\")\n\n\ncopdata <- copdata %>% \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$dayonset)\n\n\n[1] \"character\"\n\n\nShow the code\n# Update copdata:\ncopdata <- copdata %>% \n # Change column to date class:\n mutate(\n dayonset = lubridate::dmy(dayonset))\n\n# Check class of updated column:\nclass(copdata$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 <- copdata %>% \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\"" - }, - { - "objectID": "Data_import_cleaning.html#export-clean-data", - "href": "Data_import_cleaning.html#export-clean-data", - "title": "Data import and cleaning", - "section": "3.5. Export clean data", - "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\"))" - }, - { - "objectID": "Case_definition.html", - "href": "Case_definition.html", - "title": "Case Definition", - "section": "", - "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" - }, - { - "objectID": "Case_definition.html#learning-outcomes", - "href": "Case_definition.html#learning-outcomes", - "title": "Case Definition", - "section": "", - "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" - }, - { - "objectID": "Case_definition.html#storyplot-description", - "href": "Case_definition.html#storyplot-description", - "title": "Case Definition", - "section": "2. Story/plot description", - "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." - }, - { - "objectID": "Case_definition.html#questionsassignments", - "href": "Case_definition.html#questionsassignments", - "title": "Case Definition", - "section": "3. Questions/Assignments", - "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" - }, - { - "objectID": "Case_definition.html#install-packages-if-needed-and-load-libraries", - "href": "Case_definition.html#install-packages-if-needed-and-load-libraries", - "title": "Case Definition", - "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)" - }, - { - "objectID": "Case_definition.html#import-your-data", - "href": "Case_definition.html#import-your-data", - "title": "Case Definition", - "section": "3.2 Import your data", - "text": "3.2 Import your data\n\n\nShow the code\n# Import the clean data set:\ncopdata <- rio::import(here::here(\"data\", \"Spetses_clean1_2024.rds\"), trust = TRUE)" - }, - { - "objectID": "Case_definition.html#identify-the-variables-you-need-to-apply-the-case-definition-criteria.", - "href": "Case_definition.html#identify-the-variables-you-need-to-apply-the-case-definition-criteria.", - "title": "Case Definition", - "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, ill, onset_datetime, diarrhoea, bloody and vomiting." - }, - { - "objectID": "Case_definition.html#create-a-new-case-column-to-hold-the-binary-case-definition-variable.-lets-think-about-how-to-do-this-little-by-little", - "href": "Case_definition.html#create-a-new-case-column-to-hold-the-binary-case-definition-variable.-lets-think-about-how-to-do-this-little-by-little", - "title": "Case Definition", - "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 <- copdata %>% \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. => 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. => 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 <- copdata %>% \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 <- copdata %>% \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 <- copdata %>% \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 <- copdata %>% \n mutate(case = case_when(\n # Those who had symptoms <48h from the meal are cases (1)\n gastrosymptoms == 1 & \n onset_datetime >= meal_datetime &\n onset_datetime <= (meal_datetime + days(2)) ~ 1,\n # Those who had symptoms >48h from the meal are non-cases (FALSE)\n gastrosymptoms == 1 & \n onset_datetime > (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 %>% \n summarise(atemeal = sum(meal == 1),\n hadsympt = sum(gastrosymptoms == 1),\n nb_cases = sum(case == 1)\n )" - }, - { - "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": "Stratified.html", - "href": "Stratified.html", - "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." - }, - { - "objectID": "Stratified.html#learning-outcomes", - "href": "Stratified.html#learning-outcomes", - "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." - }, - { - "objectID": "Stratified.html#storyplot-description", - "href": "Stratified.html#storyplot-description", - "title": "Stratified analysis", - "section": "2. Story/plot description", - "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." - }, - { - "objectID": "Stratified.html#questionsassignments", - "href": "Stratified.html#questionsassignments", - "title": "Stratified analysis", - "section": "3. Questions/Assignments", - "text": "3. Questions/Assignments" - }, - { - "objectID": "Stratified.html#confounders-and-effect-modification", - "href": "Stratified.html#confounders-and-effect-modification", - "title": "Stratified analysis", - "section": "3.1. Confounders and effect modification", - "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." - }, - { - "objectID": "Stratified.html#install-packages-and-load-libraries", - "href": "Stratified.html#install-packages-and-load-libraries", - "title": "Stratified analysis", - "section": "3.2. Install packages and load libraries", - "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)" - }, - { - "objectID": "Stratified.html#import-your-data", - "href": "Stratified.html#import-your-data", - "title": "Stratified analysis", - "section": "3.3. Import your data", - "text": "3.3. Import your data\n\n\nShow the code\n# Import the raw data set: \ncopdata <- rio::import(here::here(\"data\", \"Spetses_clean2_2024.rds\"), trust = T)" - }, - { - "objectID": "Stratified.html#consider-and-assess-for-confounding-andor-effect-modification", - "href": "Stratified.html#consider-and-assess-for-confounding-andor-effect-modification", - "title": "Stratified analysis", - "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, 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 <- copdata %>% \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 <- 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 <NA> 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 <NA> NA Attrib.risk.pop 0.16\n5 pasta = 0 36 <NA> 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 <NA> NA Attrib.risk.pop 0.01\n9 Missing / Missing % 3 0.8% NA <NA> 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 < 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 <- 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 >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!" - } -] \ No newline at end of file diff --git a/docs-preview/site_libs/bootstrap/bootstrap-dark.min.css b/docs-preview/site_libs/bootstrap/bootstrap-dark.min.css deleted file mode 100644 index be23bc0..0000000 --- a/docs-preview/site_libs/bootstrap/bootstrap-dark.min.css +++ /dev/null @@ -1,12 +0,0 @@ -/*! - * Bootstrap v5.3.1 (https://getbootstrap.com/) - * Copyright 2011-2023 The Bootstrap Authors - * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE) - */@import"https://fonts.googleapis.com/css2?family=Lato:ital,wght@0,400;0,700;1,400&display=swap";:root,[data-bs-theme=light]{--bs-blue: #375a7f;--bs-indigo: #6610f2;--bs-purple: #6f42c1;--bs-pink: #e83e8c;--bs-red: #e74c3c;--bs-orange: #fd7e14;--bs-yellow: #f39c12;--bs-green: #00bc8c;--bs-teal: 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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='%23dee2e6' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e");--bs-navbar-toggler-border-color: rgba(222, 226, 230, 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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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: #375a7f;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;padding-left:0;margin-bottom:0;border-radius:var(--bs-list-group-border-radius)}.list-group-numbered{list-style-type:none;counter-reset:section}.list-group-numbered>.list-group-item::before{content:counters(section, ".") ". ";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 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: 1rem;--bs-modal-header-padding: 1rem 1rem;--bs-modal-header-border-color: #434343;--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: #434343;--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: #222;--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 .tooltip-arrow::before,.bs-tooltip-auto[data-popper-placement^=top] .tooltip-arrow::before{top:-1px;border-width:var(--bs-tooltip-arrow-height) calc(var(--bs-tooltip-arrow-width)*.5) 0;border-top-color:var(--bs-tooltip-bg)}.bs-tooltip-end .tooltip-arrow,.bs-tooltip-auto[data-popper-placement^=right] .tooltip-arrow{left:calc(-1*var(--bs-tooltip-arrow-height));width:var(--bs-tooltip-arrow-height);height:var(--bs-tooltip-arrow-width)}.bs-tooltip-end .tooltip-arrow::before,.bs-tooltip-auto[data-popper-placement^=right] .tooltip-arrow::before{right:-1px;border-width:calc(var(--bs-tooltip-arrow-width)*.5) var(--bs-tooltip-arrow-height) calc(var(--bs-tooltip-arrow-width)*.5) 0;border-right-color:var(--bs-tooltip-bg)}.bs-tooltip-bottom .tooltip-arrow,.bs-tooltip-auto[data-popper-placement^=bottom] .tooltip-arrow{top:calc(-1*var(--bs-tooltip-arrow-height))}.bs-tooltip-bottom .tooltip-arrow::before,.bs-tooltip-auto[data-popper-placement^=bottom] .tooltip-arrow::before{bottom:-1px;border-width:0 calc(var(--bs-tooltip-arrow-width)*.5) var(--bs-tooltip-arrow-height);border-bottom-color:var(--bs-tooltip-bg)}.bs-tooltip-start .tooltip-arrow,.bs-tooltip-auto[data-popper-placement^=left] 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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 .card-body+.card-body{padding-top:0}.bslib-card .card-body{overflow:auto}.bslib-card .card-body p{margin-top:0}.bslib-card .card-body p:last-child{margin-bottom:0}.bslib-card .card-body{max-height:var(--bslib-card-body-max-height, none)}.bslib-card[data-full-screen=true]>.card-body{max-height:var(--bslib-card-body-max-height-full-screen, none)}.bslib-card .card-header .form-group{margin-bottom:0}.bslib-card .card-header .selectize-control{margin-bottom:0}.bslib-card .card-header .selectize-control .item{margin-right:1.15rem}.bslib-card .card-footer{margin-top:auto}.bslib-card .bslib-navs-card-title{display:flex;flex-wrap:wrap;justify-content:space-between;align-items:center}.bslib-card .bslib-navs-card-title .nav{margin-left:auto}.bslib-card .bslib-sidebar-layout:not([data-bslib-sidebar-border=true]){border:none}.bslib-card .bslib-sidebar-layout:not([data-bslib-sidebar-border-radius=true]){border-top-left-radius:0;border-top-right-radius:0}[data-full-screen=true]{position:fixed;inset:3.5rem 1rem 1rem;height:auto !important;max-height:none !important;width:auto !important;z-index:1070}.bslib-full-screen-enter{display:none;position:absolute;bottom:var(--bslib-full-screen-enter-bottom, 0.2rem);right:var(--bslib-full-screen-enter-right, 0);top:var(--bslib-full-screen-enter-top);left:var(--bslib-full-screen-enter-left);color:var(--bslib-color-fg, var(--bs-card-color));background-color:var(--bslib-color-bg, var(--bs-card-bg, var(--bs-body-bg)));border:var(--bs-card-border-width) solid var(--bslib-color-fg, var(--bs-card-border-color));box-shadow:0 2px 4px rgba(0,0,0,.15);margin:.2rem .4rem;padding:.55rem !important;font-size:.8rem;cursor:pointer;opacity:.7;z-index:1070}.bslib-full-screen-enter:hover{opacity:1}.card[data-full-screen=false]:hover>*>.bslib-full-screen-enter{display:block}.bslib-has-full-screen 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content: "\f2ae"; } -.bi-cloud-moon-fill::before { content: "\f2af"; } -.bi-cloud-moon::before { content: "\f2b0"; } -.bi-cloud-plus-fill::before { content: "\f2b1"; } -.bi-cloud-plus::before { content: "\f2b2"; } -.bi-cloud-rain-fill::before { content: "\f2b3"; } -.bi-cloud-rain-heavy-fill::before { content: "\f2b4"; } -.bi-cloud-rain-heavy::before { content: "\f2b5"; } -.bi-cloud-rain::before { content: "\f2b6"; } -.bi-cloud-slash-fill::before { content: "\f2b7"; } -.bi-cloud-slash::before { content: "\f2b8"; } -.bi-cloud-sleet-fill::before { content: "\f2b9"; } -.bi-cloud-sleet::before { content: "\f2ba"; } -.bi-cloud-snow-fill::before { content: "\f2bb"; } -.bi-cloud-snow::before { content: "\f2bc"; } -.bi-cloud-sun-fill::before { content: "\f2bd"; } -.bi-cloud-sun::before { content: "\f2be"; } -.bi-cloud-upload-fill::before { content: "\f2bf"; } -.bi-cloud-upload::before { content: "\f2c0"; } -.bi-cloud::before { content: "\f2c1"; } -.bi-clouds-fill::before { content: "\f2c2"; } 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{ content: "\f2d8"; } -.bi-credit-card-2-front-fill::before { content: "\f2d9"; } -.bi-credit-card-2-front::before { content: "\f2da"; } -.bi-credit-card-fill::before { content: "\f2db"; } -.bi-credit-card::before { content: "\f2dc"; } -.bi-crop::before { content: "\f2dd"; } -.bi-cup-fill::before { content: "\f2de"; } -.bi-cup-straw::before { content: "\f2df"; } -.bi-cup::before { content: "\f2e0"; } -.bi-cursor-fill::before { content: "\f2e1"; } -.bi-cursor-text::before { content: "\f2e2"; } -.bi-cursor::before { content: "\f2e3"; } -.bi-dash-circle-dotted::before { content: "\f2e4"; } -.bi-dash-circle-fill::before { content: "\f2e5"; } -.bi-dash-circle::before { content: "\f2e6"; } -.bi-dash-square-dotted::before { content: "\f2e7"; } -.bi-dash-square-fill::before { content: "\f2e8"; } -.bi-dash-square::before { content: "\f2e9"; } -.bi-dash::before { content: "\f2ea"; } -.bi-diagram-2-fill::before { content: "\f2eb"; } -.bi-diagram-2::before { content: "\f2ec"; } 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-.bi-distribute-horizontal::before { content: "\f303"; } -.bi-distribute-vertical::before { content: "\f304"; } -.bi-door-closed-fill::before { content: "\f305"; } -.bi-door-closed::before { content: "\f306"; } -.bi-door-open-fill::before { content: "\f307"; } -.bi-door-open::before { content: "\f308"; } -.bi-dot::before { content: "\f309"; } -.bi-download::before { content: "\f30a"; } -.bi-droplet-fill::before { content: "\f30b"; } -.bi-droplet-half::before { content: "\f30c"; } -.bi-droplet::before { content: "\f30d"; } -.bi-earbuds::before { content: "\f30e"; } -.bi-easel-fill::before { content: "\f30f"; } -.bi-easel::before { content: "\f310"; } -.bi-egg-fill::before { content: "\f311"; } -.bi-egg-fried::before { content: "\f312"; } -.bi-egg::before { content: "\f313"; } -.bi-eject-fill::before { content: "\f314"; } -.bi-eject::before { content: "\f315"; } -.bi-emoji-angry-fill::before { content: "\f316"; } -.bi-emoji-angry::before { content: "\f317"; } 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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 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.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: #18bc9c;--bs-nav-link-hover-color: #13967d;--bs-nav-link-disabled-color: #6c757d;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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.nav-link:focus{border-bottom-color:currentcolor}.nav-underline .nav-link.active,.nav-underline .show>.nav-link{font-weight:700;color:var(--bs-nav-underline-link-active-color);border-bottom-color:currentcolor}.nav-fill>.nav-link,.nav-fill .nav-item{flex:1 1 auto;-webkit-flex:1 1 auto;text-align:center}.nav-justified>.nav-link,.nav-justified .nav-item{flex-basis:0;-webkit-flex-basis:0;flex-grow:1;-webkit-flex-grow:1;text-align:center}.nav-fill .nav-item .nav-link,.nav-justified .nav-item .nav-link{width:100%}.tab-content>.tab-pane{display:none}.tab-content>.active{display:block}.navbar{--bs-navbar-padding-x: 0;--bs-navbar-padding-y: 1rem;--bs-navbar-color: #ccd1d5;--bs-navbar-hover-color: rgba(255, 255, 255, 0.8);--bs-navbar-disabled-color: rgba(204, 209, 213, 0.75);--bs-navbar-active-color: #fff;--bs-navbar-brand-padding-y: 0.3125rem;--bs-navbar-brand-margin-end: 1rem;--bs-navbar-brand-font-size: 1.25rem;--bs-navbar-brand-color: #ccd1d5;--bs-navbar-brand-hover-color: #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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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='%23808b96'%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='%23808b96'%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: ;--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: 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: #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: #2c3e50;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;padding-left:0;margin-bottom:0;border-radius:var(--bs-list-group-border-radius)}.list-group-numbered{list-style-type:none;counter-reset:section}.list-group-numbered>.list-group-item::before{content:counters(section, ".") ". ";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 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(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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calc(var(--bs-tooltip-arrow-width)*.5) var(--bs-tooltip-arrow-height);border-bottom-color:var(--bs-tooltip-bg)}.bs-tooltip-start .tooltip-arrow,.bs-tooltip-auto[data-popper-placement^=left] .tooltip-arrow{right:calc(-1*var(--bs-tooltip-arrow-height));width:var(--bs-tooltip-arrow-height);height:var(--bs-tooltip-arrow-width)}.bs-tooltip-start .tooltip-arrow::before,.bs-tooltip-auto[data-popper-placement^=left] .tooltip-arrow::before{left:-1px;border-width:calc(var(--bs-tooltip-arrow-width)*.5) 0 calc(var(--bs-tooltip-arrow-width)*.5) var(--bs-tooltip-arrow-height);border-left-color:var(--bs-tooltip-bg)}.tooltip-inner{max-width:var(--bs-tooltip-max-width);padding:var(--bs-tooltip-padding-y) var(--bs-tooltip-padding-x);color:var(--bs-tooltip-color);text-align:center;background-color:var(--bs-tooltip-bg);border-radius:var(--bs-tooltip-border-radius)}.popover{--bs-popover-zindex: 1070;--bs-popover-max-width: 276px;--bs-popover-font-size:0.875rem;--bs-popover-bg: #fff;--bs-popover-border-width: 1px;--bs-popover-border-color: rgba(0, 0, 0, 0.175);--bs-popover-border-radius: 0.5rem;--bs-popover-inner-border-radius: calc(0.5rem - 1px);--bs-popover-box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15);--bs-popover-header-padding-x: 1rem;--bs-popover-header-padding-y: 0.5rem;--bs-popover-header-font-size:1rem;--bs-popover-header-color: inherit;--bs-popover-header-bg: #ecf0f1;--bs-popover-body-padding-x: 1rem;--bs-popover-body-padding-y: 1rem;--bs-popover-body-color: #212529;--bs-popover-arrow-width: 1rem;--bs-popover-arrow-height: 0.5rem;--bs-popover-arrow-border: var(--bs-popover-border-color);z-index:var(--bs-popover-zindex);display:block;max-width:var(--bs-popover-max-width);font-family:Lato,-apple-system,BlinkMacSystemFont,"Segoe UI",Roboto,"Helvetica Neue",Arial,sans-serif,"Apple Color Emoji","Segoe UI Emoji","Segoe UI 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i=`scroll${e[0].toUpperCase()+e.slice(1)}`;this._queueCallback((()=>{this._isTransitioning=!1,this._element.classList.remove(Mt),this._element.classList.add(Pt,Nt),this._element.style[e]="",N.trigger(this._element,St)}),this._element,!0),this._element.style[e]=`${this._element[i]}px`}hide(){if(this._isTransitioning||!this._isShown())return;if(N.trigger(this._element,Dt).defaultPrevented)return;const t=this._getDimension();this._element.style[t]=`${this._element.getBoundingClientRect()[t]}px`,d(this._element),this._element.classList.add(Mt),this._element.classList.remove(Pt,Nt);for(const t of this._triggerArray){const e=z.getElementFromSelector(t);e&&!this._isShown(e)&&this._addAriaAndCollapsedClass([t],!1)}this._isTransitioning=!0,this._element.style[t]="",this._queueCallback((()=>{this._isTransitioning=!1,this._element.classList.remove(Mt),this._element.classList.add(Pt),N.trigger(this._element,$t)}),this._element,!0)}_isShown(t=this._element){return 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s(t){i.add(t.name),[].concat(t.requires||[],t.requiresIfExists||[]).forEach((function(t){if(!i.has(t)){var n=e.get(t);n&&s(n)}})),n.push(t)}return t.forEach((function(t){e.set(t.name,t)})),t.forEach((function(t){i.has(t.name)||s(t)})),n}var fi={placement:"bottom",modifiers:[],strategy:"absolute"};function pi(){for(var t=arguments.length,e=new Array(t),i=0;iNumber.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return(this._inNavbar||"static"===this._config.display)&&(F.setDataAttribute(this._menu,"popper","static"),t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,...g(this._config.popperConfig,[t])}}_selectMenuItem({key:t,target:e}){const i=z.find(".dropdown-menu 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e=/input|textarea/i.test(t.target.tagName),i="Escape"===t.key,n=[Ei,Ti].includes(t.key);if(!n&&!i)return;if(e&&!i)return;t.preventDefault();const s=this.matches(Ii)?this:z.prev(this,Ii)[0]||z.next(this,Ii)[0]||z.findOne(Ii,t.delegateTarget.parentNode),o=qi.getOrCreateInstance(s);if(n)return t.stopPropagation(),o.show(),void o._selectMenuItem(t);o._isShown()&&(t.stopPropagation(),o.hide(),s.focus())}}N.on(document,Si,Ii,qi.dataApiKeydownHandler),N.on(document,Si,Pi,qi.dataApiKeydownHandler),N.on(document,Li,qi.clearMenus),N.on(document,Di,qi.clearMenus),N.on(document,Li,Ii,(function(t){t.preventDefault(),qi.getOrCreateInstance(this).toggle()})),m(qi);const Vi="backdrop",Ki="show",Qi=`mousedown.bs.${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:"

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")),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:'',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 "${t}"`);e[t]()}}))}}m(cs);const hs={...cs.Default,content:"",offset:[0,8],placement:"right",template:'',trigger:"click"},ds={...cs.DefaultType,content:"(null|string|element|function)"};class us extends cs{static get Default(){return hs}static get DefaultType(){return ds}static get 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e=this._observableSections.get(t.target.hash);if(e){t.preventDefault();const i=this._rootElement||window,n=e.offsetTop-this._element.offsetTop;if(i.scrollTo)return void i.scrollTo({top:n,behavior:"smooth"});i.scrollTop=n}})))}_getNewObserver(){const t={root:this._rootElement,threshold:this._config.threshold,rootMargin:this._config.rootMargin};return new IntersectionObserver((t=>this._observerCallback(t)),t)}_observerCallback(t){const e=t=>this._targetLinks.get(`#${t.target.id}`),i=t=>{this._previousScrollData.visibleEntryTop=t.target.offsetTop,this._process(e(t))},n=(this._rootElement||document.documentElement).scrollTop,s=n>=this._previousScrollData.parentScrollTop;this._previousScrollData.parentScrollTop=n;for(const o of 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 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options.columns.min : me.defaults.min; - me.max = options.columns.max !== null ? options.columns.max : null; - me.widths = {}; - - var widthsLookAhead = Math.max(100, options.rows.min); - var paddingColChars = 10; - - me.emptyNames = function() { - columns.forEach(function(column) { - if (columns.label !== null && columns.label !== "") - return false; - }); - - return true; - }; - - var parsePadding = function(value) { - return parseInt(value) >= 0 ? parseInt(value) : 0; - }; - - me.calculateWidths = function(measures) { - columns.forEach(function(column) { - var maxChars = Math.max( - column.label.toString().length, - column.type.toString().length - ); - - for (var idxRow = 0; idxRow < Math.min(widthsLookAhead, data.length); idxRow++) { - maxChars = Math.max(maxChars, data[idxRow][column.name.toString()].length); - } - - me.widths[column.name] = { - // width in characters - chars: maxChars, - // width for the inner html columns - inner: maxChars * measures.character, - // width adding outer styles like padding - outer: maxChars * measures.character + measures.padding - }; - }); - }; - - me.getWidth = function() { - var widthOuter = 0; - for (var idxCol = 0; idxCol < me.subset.length; idxCol++) { - var columnName = me.subset[idxCol].name; - widthOuter = widthOuter + me.widths[columnName].outer; - } - - widthOuter = widthOuter + me.padding * paddingColChars * measurer.measures.character; - - if (me.hasMoreLeftColumns()) { - widthOuter = widthOuter + columnNavigationWidthPX + measurer.measures.padding; - } - - if (me.hasMoreRightColumns()) { - widthOuter = widthOuter + columnNavigationWidthPX + measurer.measures.padding; - } - - return widthOuter; - }; - - me.updateSlice = function() { - if (me.number + me.visible >= me.total) - me.number = me.total - me.visible; - - if (me.number < 0) me.number = 0; - - me.subset = columns.slice(me.number, Math.min(me.number + me.visible, me.total)); - - me.subset = me.subset.map(function(column) { - Object.keys(column).forEach(function(colKey) { - column[colKey] = column[colKey] === null ? "" : column[colKey].toString(); - }); - - column.width = null; - return column; - }); - }; - - me.setVisibleColumns = function(columnNumber, newVisibleColumns, paddingCount) { - me.number = columnNumber; - me.visible = newVisibleColumns; - me.padding = paddingCount; - - me.updateSlice(); - }; - - me.incColumnNumber = function(increment) { - me.number = me.number + increment; - }; - - me.setColumnNumber = function(newNumber) { - me.number = newNumber; - }; - - me.setPaddingCount = function(newPadding) { - me.padding = newPadding; - }; - - me.getPaddingCount = function() { - return me.padding; - }; - - me.hasMoreLeftColumns = function() { - return me.number > 0; - }; - - me.hasMoreRightColumns = function() { - return me.number + me.visible < me.total; - }; - - me.updateSlice(0); - return me; - }; - - var data = source.data; - var page = new Page(data, options); - var measurer = new Measurer(data, options); - var columns = new Columns(data, source.columns, options); - - var table = null; - var tableDiv = null; - var header = null; - var footer = null; - var tbody = null; - - // Caches pagedTable.clientWidth, specially for webkit - var cachedPagedTableClientWidth = null; - - var onChangeCallbacks = []; - - var clearSelection = function() { - if(document.selection && document.selection.empty) { - document.selection.empty(); - } else if(window.getSelection) { - var sel = window.getSelection(); - sel.removeAllRanges(); - } - }; - - var columnNavigationWidthPX = 5; - - var renderColumnNavigation = function(increment, backwards) { - var arrow = document.createElement("div"); - arrow.setAttribute("style", - "border-top: " + columnNavigationWidthPX + "px solid transparent;" + - "border-bottom: " + columnNavigationWidthPX + "px solid transparent;" + - "border-" + (backwards ? 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"translateX(-30px)" : "translateX(30px)"; - header.style.transition = "transform 200ms linear, opacity 200ms"; - header.style.transitionDelay = "0"; - - renderBody(false); - - if (headerOld) { - headerOld.style.position = "absolute"; - headerOld.style.transform = "translateX(0px)"; - headerOld.style.opacity = "1"; - headerOld.style.transition = "transform 100ms linear, opacity 100ms"; - headerOld.setAttribute("class", "pagedtable-remove-head"); - if (headerOld.style.transitionEnd) { - headerOld.addEventListener("transitionend", function() { - var headerOldByClass = thead.querySelector(".pagedtable-remove-head"); - if (headerOldByClass) thead.removeChild(headerOldByClass); - }); - } - else { - thead.removeChild(headerOld); - } - } - - if (tbodyOld) table.removeChild(tbodyOld); - - tbody.style.opacity = "0"; - tbody.style.transition = "transform 200ms linear, opacity 200ms"; - tbody.style.transitionDelay = "0ms"; - - // force relayout - window.getComputedStyle(header).opacity; - window.getComputedStyle(tbody).opacity; - - if (headerOld) { - headerOld.style.transform = backwards ? "translateX(20px)" : "translateX(-30px)"; - headerOld.style.opacity = "0"; - } - - header.style.transform = "translateX(0px)"; - header.style.opacity = "1"; - - tbody.style.opacity = "1"; - } - - me.onChange = function(callback) { - onChangeCallbacks.push(callback); - }; - - var triggerOnChange = function() { - onChangeCallbacks.forEach(function(onChange) { - onChange(); - }); - }; - - var clearBody = function() { - if (tbody) { - table.removeChild(tbody); - tbody = null; - } - }; - - var renderBody = function(clear) { - cachedPagedTableClientWidth = pagedTable.clientWidth - - var fragment = document.createDocumentFragment(); - - var pageData = data.slice(page.getRowStart(), page.getRowEnd()); - - pageData.forEach(function(dataRow, idxRow) { - var htmlRow = document.createElement("tr"); - htmlRow.setAttribute("class", (idxRow % 2 !==0) ? "even" : "odd"); - - if (columns.hasMoreLeftColumns()) - htmlRow.appendChild(document.createElement("td")); - - columns.subset.forEach(function(columnData) { - var cellName = columnData.name; - var dataCell = dataRow[cellName]; - var htmlCell = document.createElement("td"); - - if (dataCell === "NA") htmlCell.setAttribute("class", "pagedtable-na-cell"); - if (dataCell === "__NA__") dataCell = "NA"; - - var cellText = document.createTextNode(dataCell); - htmlCell.appendChild(cellText); - if (dataCell.length > 50) { - htmlCell.setAttribute("title", dataCell); - } - htmlCell.setAttribute("align", columnData.align); - htmlCell.style.textAlign = columnData.align; - htmlCell.style.maxWidth = maxColumnWidth(null); - if (columnData.width) { - htmlCell.style.minWidth = htmlCell.style.maxWidth = maxColumnWidth(columnData.width); - } - htmlRow.appendChild(htmlCell); - }); - - for (var idx = 0; idx < columns.getPaddingCount(); idx++) { - var paddingCol = document.createElement("td"); - paddingCol.setAttribute("class", "pagedtable-padding-col"); - htmlRow.appendChild(paddingCol); - } - - if (columns.hasMoreRightColumns()) - htmlRow.appendChild(document.createElement("td")); - - fragment.appendChild(htmlRow); - }); - - for (var idxPadding = 0; idxPadding < page.getPaddingRows(); idxPadding++) { - var paddingRow = document.createElement("tr"); - - var paddingCellRow = document.createElement("td"); - paddingCellRow.innerHTML = " "; - paddingCellRow.setAttribute("colspan", "100%"); - paddingRow.appendChild(paddingCellRow); - - fragment.appendChild(paddingRow); - } - - if (typeof(clear) == "undefined" || clear) clearBody(); - tbody = document.createElement("tbody"); - tbody.appendChild(fragment); - - table.appendChild(tbody); - }; - - var getLabelInfo = function() { - var pageStart = page.getRowStart(); - var pageEnd = page.getRowEnd(); - var totalRows = data.length; - - var totalRowsLabel = options.rows.total ? options.rows.total : totalRows; - var totalRowsLabelFormat = totalRowsLabel.toString().replace(/(\d)(?=(\d\d\d)+(?!\d))/g, '$1,'); - - var infoText = (pageStart + 1) + "-" + pageEnd + " of " + totalRowsLabelFormat + " rows"; - if (totalRows < page.rows) { - infoText = totalRowsLabel + " row" + (totalRows != 1 ? "s" : ""); - } - if (columns.total > columns.visible) { - var totalColumnsLabel = options.columns.total ? options.columns.total : columns.total; - - infoText = infoText + " | " + (columns.number + 1) + "-" + - (Math.min(columns.number + columns.visible, columns.total)) + - " of " + totalColumnsLabel + " columns"; - } - - return infoText; - }; - - var clearFooter = function() { - footer = pagedTable.querySelectorAll("div.pagedtable-footer")[0]; - footer.innerHTML = ""; - - return footer; - }; - - var createPageLink = function(idxPage) { - var pageLink = document.createElement("a"); - pageLinkClass = idxPage === page.number ? "pagedtable-index pagedtable-index-current" : "pagedtable-index"; - pageLink.setAttribute("class", pageLinkClass); - pageLink.setAttribute("data-page-index", idxPage); - pageLink.onclick = function() { - page.setPageNumber(parseInt(this.getAttribute("data-page-index"))); - renderBody(); - renderFooter(); - - triggerOnChange(); - }; - - pageLink.appendChild(document.createTextNode(idxPage + 1)); - - return pageLink; - } - - var renderFooter = function() { - footer = clearFooter(); - - var next = document.createElement("a"); - next.appendChild(document.createTextNode("Next")); - next.onclick = function() { - page.setPageNumber(page.number + 1); - renderBody(); - renderFooter(); - - triggerOnChange(); - }; - if (data.length > page.rows) footer.appendChild(next); - - var pageNumbers = document.createElement("div"); - pageNumbers.setAttribute("class", "pagedtable-indexes"); - - var pageRange = page.getVisiblePageRange(); - - if (pageRange.first) { - var pageLink = createPageLink(0); - pageNumbers.appendChild(pageLink); - - var pageSeparator = document.createElement("div"); - pageSeparator.setAttribute("class", "pagedtable-index-separator-left"); - pageSeparator.appendChild(document.createTextNode("...")) - pageNumbers.appendChild(pageSeparator); - } - - for (var idxPage = pageRange.start; idxPage < pageRange.end; idxPage++) { - var pageLink = createPageLink(idxPage); - - pageNumbers.appendChild(pageLink); - } - - if (pageRange.last) { - var pageSeparator = document.createElement("div"); - pageSeparator.setAttribute("class", "pagedtable-index-separator-right"); - pageSeparator.appendChild(document.createTextNode("...")) - pageNumbers.appendChild(pageSeparator); - - var pageLink = createPageLink(page.total - 1); - pageNumbers.appendChild(pageLink); - } - - if (data.length > page.rows) footer.appendChild(pageNumbers); - - var previous = document.createElement("a"); - previous.appendChild(document.createTextNode("Previous")); - previous.onclick = function() { - page.setPageNumber(page.number - 1); - renderBody(); - renderFooter(); - - triggerOnChange(); - }; - if (data.length > page.rows) footer.appendChild(previous); - - var infoLabel = document.createElement("div"); - infoLabel.setAttribute("class", "pagedtable-info"); - infoLabel.setAttribute("title", getLabelInfo()); - infoLabel.appendChild(document.createTextNode(getLabelInfo())); - footer.appendChild(infoLabel); - - var enabledClass = "pagedtable-index-nav"; - var disabledClass = "pagedtable-index-nav pagedtable-index-nav-disabled"; - previous.setAttribute("class", page.number <= 0 ? disabledClass : enabledClass); - next.setAttribute("class", (page.number + 1) * page.rows >= data.length ? disabledClass : enabledClass); - }; - - var measuresCell = null; - - var renderMeasures = function() { - var measuresTable = document.createElement("table"); - measuresTable.style.visibility = "hidden"; - measuresTable.style.position = "absolute"; - measuresTable.style.whiteSpace = "nowrap"; - measuresTable.style.height = "auto"; - measuresTable.style.width = "auto"; - - var measuresRow = document.createElement("tr"); - measuresTable.appendChild(measuresRow); - - measuresCell = document.createElement("td"); - var sampleString = "ABCDEFGHIJ0123456789"; - measuresCell.appendChild(document.createTextNode(sampleString)); - - measuresRow.appendChild(measuresCell); - - tableDiv.appendChild(measuresTable); - } - - me.init = function() { - tableDiv = document.createElement("div"); - pagedTable.appendChild(tableDiv); - var pagedTableClass = data.length > 0 ? - "pagedtable pagedtable-not-empty" : - "pagedtable pagedtable-empty"; - - if (columns.total == 0 || (columns.emptyNames() && data.length == 0)) { - pagedTableClass = pagedTableClass + " pagedtable-empty-columns"; - } - - tableDiv.setAttribute("class", pagedTableClass); - - renderMeasures(); - measurer.calculate(measuresCell); - columns.calculateWidths(measurer.measures); - - table = document.createElement("table"); - table.setAttribute("cellspacing", "0"); - table.setAttribute("class", "table table-condensed"); - tableDiv.appendChild(table); - - table.appendChild(document.createElement("thead")); - - var footerDiv = document.createElement("div"); - footerDiv.setAttribute("class", "pagedtable-footer"); - tableDiv.appendChild(footerDiv); - - // if the host has not yet provided horizontal space, render hidden - if (tableDiv.clientWidth <= 0) { - tableDiv.style.opacity = "0"; - } - - me.render(); - - // retry seizing columns later if the host has not provided space - function retryFit() { - if (tableDiv.clientWidth <= 0) { - setTimeout(retryFit, 100); - } else { - me.render(); - triggerOnChange(); - } - } - if (tableDiv.clientWidth <= 0) { - retryFit(); - } - }; - - var registerWidths = function() { - columns.subset = columns.subset.map(function(column) { - column.width = columns.widths[column.name].inner; - return column; - }); - }; - - var parsePadding = function(value) { - return parseInt(value) >= 0 ? parseInt(value) : 0; - }; - - me.fixedHeight = function() { - return options.rows.max != null; - } - - me.fitRows = function() { - if (me.fixedHeight()) - return; - - measurer.calculate(measuresCell); - - var rows = options.rows.min !== null ? options.rows.min : 0; - var headerHeight = header !== null && header.offsetHeight > 0 ? header.offsetHeight : 0; - var footerHeight = footer !== null && footer.offsetHeight > 0 ? footer.offsetHeight : 0; - - if (pagedTable.offsetHeight > 0) { - var availableHeight = pagedTable.offsetHeight - headerHeight - footerHeight; - rows = Math.floor((availableHeight) / measurer.measures.height); - } - - rows = options.rows.min !== null ? Math.max(options.rows.min, rows) : rows; - - page.setRows(rows); - } - - // The goal of this function is to add as many columns as possible - // starting from left-to-right, when the right most limit is reached - // it tries to add columns from the left as well. - // - // When startBackwards is true columns are added from right-to-left - me.fitColumns = function(startBackwards) { - measurer.calculate(measuresCell); - columns.calculateWidths(measurer.measures); - - if (tableDiv.clientWidth > 0) { - tableDiv.style.opacity = 1; - } - - var visibleColumns = tableDiv.clientWidth <= 0 ? Math.max(columns.min, 1) : 1; - var columnNumber = columns.number; - var paddingCount = 0; - - // track a list of added columns as we build the visible ones to allow us - // to remove columns when they don't fit anymore. - var columnHistory = []; - - var lastTableHeight = 0; - var backwards = startBackwards; - - var tableDivStyle = window.getComputedStyle(tableDiv, null); - var tableDivPadding = parsePadding(tableDivStyle.paddingLeft) + - parsePadding(tableDivStyle.paddingRight); - - var addPaddingCol = false; - var currentWidth = 0; - - while (true) { - columns.setVisibleColumns(columnNumber, visibleColumns, paddingCount); - currentWidth = columns.getWidth(); - - if (tableDiv.clientWidth - tableDivPadding < currentWidth) { - break; - } - - columnHistory.push({ - columnNumber: columnNumber, - visibleColumns: visibleColumns, - paddingCount: paddingCount - }); - - if (columnHistory.length > 100) { - console.error("More than 100 tries to fit columns, aborting"); - break; - } - - if (columns.max !== null && - columns.visible + columns.getPaddingCount() >= columns.max) { - break; - } - - // if we run out of right-columns - if (!backwards && columnNumber + columns.visible >= columns.total) { - // if we started adding right-columns, try adding left-columns - if (!startBackwards && columnNumber > 0) { - backwards = true; - } - else if (columns.min === null || visibleColumns + columns.getPaddingCount() >= columns.min) { - break; - } - else { - paddingCount = paddingCount + 1; - } - } - - // if we run out of left-columns - if (backwards && columnNumber == 0) { - // if we started adding left-columns, try adding right-columns - if (startBackwards && columnNumber + columns.visible < columns.total) { - backwards = false; - } - else if (columns.min === null || visibleColumns + columns.getPaddingCount() >= columns.min) { - break; - } - else { - paddingCount = paddingCount + 1; - } - } - - // when moving backwards try fitting left columns first - if (backwards && columnNumber 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new rows - renderFooter(); - } - - var resizeLastWidth = -1; - var resizeLastHeight = -1; - var resizeNewWidth = -1; - var resizeNewHeight = -1; - var resizePending = false; - - me.resize = function(newWidth, newHeight) { - - function resizeDelayed() { - resizePending = false; - - if ( - (resizeNewWidth !== resizeLastWidth) || - (!me.fixedHeight() && resizeNewHeight !== resizeLastHeight) - ) { - resizeLastWidth = resizeNewWidth; - resizeLastHeight = resizeNewHeight; - - setTimeout(resizeDelayed, 200); - resizePending = true; - } else { - me.render(); - triggerOnChange(); - - resizeLastWidth = -1; - resizeLastHeight = -1; - } - } - - resizeNewWidth = newWidth; - resizeNewHeight = newHeight; - - if (!resizePending) resizeDelayed(); - }; -}; - -var PagedTableDoc; -(function (PagedTableDoc) { - var allPagedTables = []; - - PagedTableDoc.initAll = function() { - allPagedTables = []; - - var pagedTables = 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343d2f8..0000000 --- a/docs-preview/site_libs/quarto-html/quarto-syntax-highlighting-dark.css +++ /dev/null @@ -1,189 +0,0 @@ -/* quarto syntax highlight colors */ -:root { - --quarto-hl-al-color: #f07178; - --quarto-hl-an-color: #d4d0ab; - --quarto-hl-at-color: #00e0e0; - --quarto-hl-bn-color: #d4d0ab; - --quarto-hl-bu-color: #abe338; - --quarto-hl-ch-color: #abe338; - --quarto-hl-co-color: #f8f8f2; - --quarto-hl-cv-color: #ffd700; - --quarto-hl-cn-color: #ffd700; - --quarto-hl-cf-color: #ffa07a; - --quarto-hl-dt-color: #ffa07a; - --quarto-hl-dv-color: #d4d0ab; - --quarto-hl-do-color: #f8f8f2; - --quarto-hl-er-color: #f07178; - --quarto-hl-ex-color: #00e0e0; - --quarto-hl-fl-color: #d4d0ab; - --quarto-hl-fu-color: #ffa07a; - --quarto-hl-im-color: #abe338; - --quarto-hl-in-color: #d4d0ab; - --quarto-hl-kw-color: #ffa07a; - --quarto-hl-op-color: #ffa07a; - --quarto-hl-ot-color: #00e0e0; - --quarto-hl-pp-color: #dcc6e0; - --quarto-hl-re-color: #00e0e0; - --quarto-hl-sc-color: #abe338; - --quarto-hl-ss-color: #abe338; - --quarto-hl-st-color: #abe338; - --quarto-hl-va-color: #00e0e0; - --quarto-hl-vs-color: #abe338; - --quarto-hl-wa-color: #dcc6e0; -} - -/* other quarto variables */ -:root { - --quarto-font-monospace: SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace; -} - -code span.al { - background-color: #2a0f15; - font-weight: bold; - color: #f07178; -} - -code span.an { - color: #d4d0ab; -} - -code span.at { - color: #00e0e0; -} - -code span.bn { - color: #d4d0ab; -} - -code span.bu { - color: #abe338; -} - -code span.ch { - color: #abe338; -} - -code span.co { - font-style: italic; - color: #f8f8f2; -} - -code span.cv { - color: #ffd700; -} - -code span.cn { - color: #ffd700; -} - -code span.cf { - font-weight: bold; - color: #ffa07a; -} - -code span.dt { - color: #ffa07a; -} - -code span.dv { - color: #d4d0ab; -} - -code span.do { - color: #f8f8f2; -} - -code span.er { - color: #f07178; - text-decoration: underline; -} - -code span.ex { - font-weight: bold; - color: #00e0e0; -} - -code span.fl { - color: #d4d0ab; -} - -code span.fu { - color: #ffa07a; -} - -code span.im { - color: #abe338; -} - -code span.in { - color: #d4d0ab; -} - -code span.kw { - font-weight: bold; - color: #ffa07a; -} - -pre > code.sourceCode > span { - color: #f8f8f2; -} - -code span { - color: #f8f8f2; -} - -code.sourceCode > span { - color: #f8f8f2; -} - -div.sourceCode, -div.sourceCode pre.sourceCode { - color: #f8f8f2; -} - -code span.op { - color: #ffa07a; -} - -code span.ot { - color: #00e0e0; -} - -code span.pp { - color: #dcc6e0; -} - -code span.re { - background-color: #f8f8f2; - color: #00e0e0; -} - -code span.sc { - color: #abe338; -} - -code span.ss { - color: #abe338; -} - -code span.st { - color: #abe338; -} - -code span.va { - color: #00e0e0; -} - -code span.vs { - color: #abe338; -} - -code span.wa { - color: #dcc6e0; -} - -.prevent-inlining { - content: " code.sourceCode > span { - color: #003B4F; -} - -code span { - color: #003B4F; -} - -code.sourceCode > span { - color: #003B4F; -} - -div.sourceCode, -div.sourceCode pre.sourceCode { - color: #003B4F; -} - -code span.ot { - color: #003B4F; - font-style: inherit; -} - -code span.at { - color: #657422; - font-style: inherit; -} - -code span.ss { - color: #20794D; - font-style: inherit; -} - -code span.an { - color: #5E5E5E; - font-style: inherit; -} - -code span.fu { - color: #4758AB; - font-style: inherit; -} - -code span.st { - color: #20794D; - font-style: inherit; -} - -code span.cf { - color: #003B4F; - font-weight: bold; - font-style: inherit; -} - -code span.op { - color: #5E5E5E; - font-style: inherit; -} - -code span.er { - color: #AD0000; - font-style: inherit; -} - -code span.bn { - color: #AD0000; - font-style: inherit; -} - -code span.al { - color: #AD0000; - font-style: inherit; -} - -code span.va { - color: #111111; - font-style: inherit; -} - -code span.bu { - font-style: inherit; -} - -code span.ex { - font-style: inherit; -} - -code span.pp { - color: #AD0000; - font-style: inherit; -} - -code span.in { - color: #5E5E5E; - font-style: inherit; -} - -code span.vs { - color: #20794D; - font-style: inherit; -} - -code span.wa { - color: #5E5E5E; - font-style: italic; -} - -code span.do { - color: #5E5E5E; - font-style: italic; -} - -code span.im { - color: #00769E; - font-style: inherit; -} - -code span.ch { - color: #20794D; - font-style: inherit; -} - -code span.dt { - color: #AD0000; - font-style: inherit; -} - -code span.fl { - color: #AD0000; - font-style: inherit; -} - -code span.co { - color: #5E5E5E; - font-style: inherit; -} - -code span.cv { - color: #5E5E5E; - font-style: italic; -} - -code span.cn { - color: #8f5902; - font-style: inherit; -} - -code span.sc { - color: #5E5E5E; - font-style: inherit; -} - -code span.dv { - color: #AD0000; - font-style: inherit; -} - -code span.kw { - color: #003B4F; - font-weight: bold; - font-style: inherit; -} - -.prevent-inlining { - content: " { - // Find any conflicting margin elements and add margins to the - // top to prevent overlap - const marginChildren = window.document.querySelectorAll( - ".column-margin.column-container > *, .margin-caption, .aside" - ); - - let lastBottom = 0; - for (const marginChild of marginChildren) { - if (marginChild.offsetParent !== null) { - // clear the top margin so we recompute it - marginChild.style.marginTop = null; - const top = marginChild.getBoundingClientRect().top + window.scrollY; - if (top < lastBottom) { - 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"]); - lastBottom = top + marginChild.getBoundingClientRect().height + marginTop; - } - } -}; - -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(); - if ( - window.document.body.getBoundingClientRect().width < 990 && - isReaderMode() - ) { - quartoToggleReader(); - } - }, 50) - ); - resizeObserver.observe(window.document.body); - } - - const tocEl = window.document.querySelector('nav.toc-active[role="doc-toc"]'); - const sidebarEl = window.document.getElementById("quarto-sidebar"); - const leftTocEl = window.document.getElementById("quarto-sidebar-toc-left"); - const marginSidebarEl = window.document.getElementById( - "quarto-margin-sidebar" - ); - // function to determine whether the element has a previous sibling that is active - const prevSiblingIsActiveLink = (el) => { - const sibling = el.previousElementSibling; - if (sibling && sibling.tagName === "A") { - return sibling.classList.contains("active"); - } else { - return false; - } - }; - - // fire slideEnter for bootstrap tab activations (for htmlwidget resize behavior) - function fireSlideEnter(e) { - const event = window.document.createEvent("Event"); - event.initEvent("slideenter", true, true); - window.document.dispatchEvent(event); - } - const tabs = window.document.querySelectorAll('a[data-bs-toggle="tab"]'); - tabs.forEach((tab) => { - tab.addEventListener("shown.bs.tab", fireSlideEnter); - }); - - // fire slideEnter for tabby tab activations (for htmlwidget resize behavior) - document.addEventListener("tabby", fireSlideEnter, false); - - // Track scrolling and mark TOC links as active - // get table of contents and sidebar (bail if we don't have at least one) - const tocLinks = tocEl - ? [...tocEl.querySelectorAll("a[data-scroll-target]")] - : []; - const makeActive = (link) => tocLinks[link].classList.add("active"); - const removeActive = (link) => tocLinks[link].classList.remove("active"); - const removeAllActive = () => - [...Array(tocLinks.length).keys()].forEach((link) => removeActive(link)); - - // activate the anchor for a section associated with this TOC entry - tocLinks.forEach((link) => { - link.addEventListener("click", () => { - if (link.href.indexOf("#") !== -1) { - const anchor = link.href.split("#")[1]; - const heading = window.document.querySelector( - `[data-anchor-id="${anchor}"]` - ); - if (heading) { - // Add the class - heading.classList.add("reveal-anchorjs-link"); - - // function to show the anchor - const handleMouseout = () => { - heading.classList.remove("reveal-anchorjs-link"); - heading.removeEventListener("mouseout", handleMouseout); - }; - - // add a function to clear the anchor when the user mouses out of it - heading.addEventListener("mouseout", handleMouseout); - } - } - }); - }); - - const sections = tocLinks.map((link) => { - const target = link.getAttribute("data-scroll-target"); - if (target.startsWith("#")) { - return window.document.getElementById(decodeURI(`${target.slice(1)}`)); - } else { - return window.document.querySelector(decodeURI(`${target}`)); - } - }); - - const sectionMargin = 200; - let currentActive = 0; - // track whether we've initialized state the first time - let init = false; - - const updateActiveLink = () => { - // The index from bottom to top (e.g. reversed list) - let sectionIndex = -1; - if ( - window.innerHeight + window.pageYOffset >= - window.document.body.offsetHeight - ) { - // This is the no-scroll case where last section should be the active one - sectionIndex = 0; - } else { - // This finds the last section visible on screen that should be made active - sectionIndex = [...sections].reverse().findIndex((section) => { - if (section) { - return window.pageYOffset >= section.offsetTop - sectionMargin; - } else { - return false; - } - }); - } - if (sectionIndex > -1) { - const current = sections.length - sectionIndex - 1; - if (current !== currentActive) { - removeAllActive(); - currentActive = current; - makeActive(current); - if (init) { - window.dispatchEvent(sectionChanged); - } - init = true; - } - } - }; - - const inHiddenRegion = (top, bottom, hiddenRegions) => { - for (const region of hiddenRegions) { - if (top <= region.bottom && bottom >= region.top) { - return true; - } - } - return false; - }; - - const categorySelector = "header.quarto-title-block .quarto-category"; - const activateCategories = (href) => { - // Find any categories - // Surround them with a link pointing back to: - // #category=Authoring - try { - const categoryEls = window.document.querySelectorAll(categorySelector); - for (const categoryEl of categoryEls) { - const categoryText = categoryEl.textContent; - if (categoryText) { - const link = `${href}#category=${encodeURIComponent(categoryText)}`; - const linkEl = window.document.createElement("a"); - linkEl.setAttribute("href", link); - for (const child of categoryEl.childNodes) { - linkEl.append(child); - } - categoryEl.appendChild(linkEl); - } - } - } catch { - // Ignore errors - } - }; - function hasTitleCategories() { - return window.document.querySelector(categorySelector) !== null; - } - - function offsetRelativeUrl(url) { - const offset = getMeta("quarto:offset"); - return offset ? offset + url : url; - } - - function offsetAbsoluteUrl(url) { - const offset = getMeta("quarto:offset"); - const baseUrl = new URL(offset, window.location); - - const projRelativeUrl = url.replace(baseUrl, ""); - if (projRelativeUrl.startsWith("/")) { - return projRelativeUrl; - } else { - return "/" + projRelativeUrl; - } - } - - // read a meta tag value - function getMeta(metaName) { - const metas = window.document.getElementsByTagName("meta"); - for (let i = 0; i < metas.length; i++) { - if (metas[i].getAttribute("name") === metaName) { - return metas[i].getAttribute("content"); - } - } - return ""; - } - - async function findAndActivateCategories() { - const currentPagePath = offsetAbsoluteUrl(window.location.href); - const response = await fetch(offsetRelativeUrl("listings.json")); - if (response.status == 200) { - return response.json().then(function (listingPaths) { - const listingHrefs = []; - for (const listingPath of listingPaths) { - const pathWithoutLeadingSlash = listingPath.listing.substring(1); - for (const item of listingPath.items) { - if ( - item === currentPagePath || - item === currentPagePath + "index.html" - ) { - // Resolve this path against the offset to be sure - // we already are using the correct path to the listing - // (this adjusts the listing urls to be rooted against - // whatever root the page is actually running against) - const relative = offsetRelativeUrl(pathWithoutLeadingSlash); - const baseUrl = window.location; - const resolvedPath = new URL(relative, baseUrl); - listingHrefs.push(resolvedPath.pathname); - break; - } - } - } - - // Look up the tree for a nearby linting and use that if we find one - const nearestListing = findNearestParentListing( - offsetAbsoluteUrl(window.location.pathname), - listingHrefs - ); - if (nearestListing) { - activateCategories(nearestListing); - } else { - // See if the referrer is a listing page for this item - const referredRelativePath = offsetAbsoluteUrl(document.referrer); - const referrerListing = listingHrefs.find((listingHref) => { - const isListingReferrer = - listingHref === referredRelativePath || - listingHref === referredRelativePath + "index.html"; - return isListingReferrer; - }); - - if (referrerListing) { - // Try to use the referrer if possible - activateCategories(referrerListing); - } else if (listingHrefs.length > 0) { - // Otherwise, just fall back to the first listing - activateCategories(listingHrefs[0]); - } - } - }); - } - } - if (hasTitleCategories()) { - findAndActivateCategories(); - } - - const findNearestParentListing = (href, listingHrefs) => { - if (!href || !listingHrefs) { - return undefined; - } - // Look up the tree for a nearby linting and use that if we find one - const relativeParts = href.substring(1).split("/"); - while (relativeParts.length > 0) { - const path = relativeParts.join("/"); - for (const listingHref of listingHrefs) { - if (listingHref.startsWith(path)) { - return listingHref; - } - } - relativeParts.pop(); - } - - return undefined; - }; - - const manageSidebarVisiblity = (el, placeholderDescriptor) => { - let isVisible = true; - let elRect; - - return (hiddenRegions) => { - if (el === null) { - return; - } - - // Find the last element of the TOC - const lastChildEl = el.lastElementChild; - - if (lastChildEl) { - // Converts the sidebar to a menu - const convertToMenu = () => { - for (const child of el.children) { - child.style.opacity = 0; - child.style.overflow = "hidden"; - child.style.pointerEvents = "none"; - } - - nexttick(() => { - const toggleContainer = window.document.createElement("div"); - toggleContainer.style.width = "100%"; - toggleContainer.classList.add("zindex-over-content"); - toggleContainer.classList.add("quarto-sidebar-toggle"); - toggleContainer.classList.add("headroom-target"); // Marks this to be managed by headeroom - toggleContainer.id = placeholderDescriptor.id; - toggleContainer.style.position = "fixed"; - - const toggleIcon = window.document.createElement("i"); - toggleIcon.classList.add("quarto-sidebar-toggle-icon"); - toggleIcon.classList.add("bi"); - toggleIcon.classList.add("bi-caret-down-fill"); - - const toggleTitle = window.document.createElement("div"); - const titleEl = window.document.body.querySelector( - placeholderDescriptor.titleSelector - ); - if (titleEl) { - toggleTitle.append( - titleEl.textContent || titleEl.innerText, - toggleIcon - ); - } - toggleTitle.classList.add("zindex-over-content"); - toggleTitle.classList.add("quarto-sidebar-toggle-title"); - toggleContainer.append(toggleTitle); - - const toggleContents = window.document.createElement("div"); - toggleContents.classList = el.classList; - toggleContents.classList.add("zindex-over-content"); - toggleContents.classList.add("quarto-sidebar-toggle-contents"); - for (const child of el.children) { - if (child.id === "toc-title") { - continue; - } - - const clone = child.cloneNode(true); - clone.style.opacity = 1; - clone.style.pointerEvents = null; - clone.style.display = null; - toggleContents.append(clone); - } - toggleContents.style.height = "0px"; - const positionToggle = () => { - // position the element (top left of parent, same width as parent) - if (!elRect) { - elRect = el.getBoundingClientRect(); - } - toggleContainer.style.left = `${elRect.left}px`; - toggleContainer.style.top = `${elRect.top}px`; - toggleContainer.style.width = `${elRect.width}px`; - }; - positionToggle(); - - toggleContainer.append(toggleContents); - el.parentElement.prepend(toggleContainer); - - // Process clicks - let tocShowing = false; - // Allow the caller to control whether this is dismissed - // when it is clicked (e.g. sidebar navigation supports - // opening and closing the nav tree, so don't dismiss on click) - const clickEl = placeholderDescriptor.dismissOnClick - ? toggleContainer - : toggleTitle; - - const closeToggle = () => { - if (tocShowing) { - toggleContainer.classList.remove("expanded"); - toggleContents.style.height = "0px"; - tocShowing = false; - } - }; - - // Get rid of any expanded toggle if the user scrolls - window.document.addEventListener( - "scroll", - throttle(() => { - closeToggle(); - }, 50) - ); - - // Handle positioning of the toggle - window.addEventListener( - "resize", - throttle(() => { - elRect = undefined; - positionToggle(); - }, 50) - ); - - window.addEventListener("quarto-hrChanged", () => { - elRect = undefined; - }); - - // Process the click - clickEl.onclick = () => { - if (!tocShowing) { - toggleContainer.classList.add("expanded"); - toggleContents.style.height = null; - tocShowing = true; - } else { - closeToggle(); - } - }; - }); - }; - - // Converts a sidebar from a menu back to a sidebar - const convertToSidebar = () => { - for (const child of el.children) { - child.style.opacity = 1; - child.style.overflow = null; - child.style.pointerEvents = null; - } - - const placeholderEl = window.document.getElementById( - placeholderDescriptor.id - ); - if (placeholderEl) { - placeholderEl.remove(); - } - - el.classList.remove("rollup"); - }; - - if (isReaderMode()) { - convertToMenu(); - isVisible = false; - } else { - // Find the top and bottom o the element that is being managed - const elTop = el.offsetTop; - const elBottom = - elTop + lastChildEl.offsetTop + lastChildEl.offsetHeight; - - if (!isVisible) { - // If the element is current not visible reveal if there are - // no conflicts with overlay regions - if (!inHiddenRegion(elTop, elBottom, hiddenRegions)) { - convertToSidebar(); - isVisible = true; - } - } else { - // If the element is visible, hide it if it conflicts with overlay regions - // and insert a placeholder toggle (or if we're in reader mode) - if (inHiddenRegion(elTop, elBottom, hiddenRegions)) { - convertToMenu(); - isVisible = false; - } - } - } - } - }; - }; - - const tabEls = document.querySelectorAll('a[data-bs-toggle="tab"]'); - for (const tabEl of tabEls) { - const id = tabEl.getAttribute("data-bs-target"); - if (id) { - const columnEl = document.querySelector( - `${id} .column-margin, .tabset-margin-content` - ); - if (columnEl) - tabEl.addEventListener("shown.bs.tab", function (event) { - const el = event.srcElement; - if (el) { - const visibleCls = `${el.id}-margin-content`; - // walk up until we find a parent tabset - let panelTabsetEl = el.parentElement; - while (panelTabsetEl) { - if (panelTabsetEl.classList.contains("panel-tabset")) { - break; - } - panelTabsetEl = panelTabsetEl.parentElement; - } - - if (panelTabsetEl) { - const prevSib = panelTabsetEl.previousElementSibling; - if ( - prevSib && - prevSib.classList.contains("tabset-margin-container") - ) { - const childNodes = prevSib.querySelectorAll( - ".tabset-margin-content" - ); - for (const childEl of childNodes) { - if (childEl.classList.contains(visibleCls)) { - childEl.classList.remove("collapse"); - } else { - childEl.classList.add("collapse"); - } - } - } - } - } - - layoutMarginEls(); - }); - } - } - - // Manage the visibility of the toc and the sidebar - const marginScrollVisibility = manageSidebarVisiblity(marginSidebarEl, { - id: "quarto-toc-toggle", - titleSelector: "#toc-title", - dismissOnClick: true, - }); - const sidebarScrollVisiblity = manageSidebarVisiblity(sidebarEl, { - id: "quarto-sidebarnav-toggle", - titleSelector: ".title", - dismissOnClick: false, - }); - let tocLeftScrollVisibility; - if (leftTocEl) { - tocLeftScrollVisibility = manageSidebarVisiblity(leftTocEl, { - id: "quarto-lefttoc-toggle", - titleSelector: "#toc-title", - dismissOnClick: true, - }); - } - - // Find the first element that uses formatting in special columns - const conflictingEls = window.document.body.querySelectorAll( - '[class^="column-"], [class*=" column-"], aside, [class*="margin-caption"], [class*=" margin-caption"], [class*="margin-ref"], [class*=" margin-ref"]' - ); - - // Filter all the possibly conflicting elements into ones - // the do conflict on the left or ride side - const arrConflictingEls = Array.from(conflictingEls); - const leftSideConflictEls = arrConflictingEls.filter((el) => { - if (el.tagName === "ASIDE") { - return false; - } - return Array.from(el.classList).find((className) => { - return ( - className !== "column-body" && - className.startsWith("column-") && - !className.endsWith("right") && - !className.endsWith("container") && - className !== "column-margin" - ); - }); - }); - const rightSideConflictEls = arrConflictingEls.filter((el) => { - if (el.tagName === "ASIDE") { - return true; - } - - const hasMarginCaption = Array.from(el.classList).find((className) => { - return className == "margin-caption"; - }); - if (hasMarginCaption) { - return true; - } - - return Array.from(el.classList).find((className) => { - return ( - className !== "column-body" && - !className.endsWith("container") && - className.startsWith("column-") && - !className.endsWith("left") - ); - }); - }); - - const kOverlapPaddingSize = 10; - function toRegions(els) { - return els.map((el) => { - const boundRect = el.getBoundingClientRect(); - const top = - boundRect.top + - document.documentElement.scrollTop - - kOverlapPaddingSize; - return { - top, - bottom: top + el.scrollHeight + 2 * kOverlapPaddingSize, - }; - }); - } - - let hasObserved = false; - const visibleItemObserver = (els) => { - let visibleElements = [...els]; - const intersectionObserver = new IntersectionObserver( - (entries, _observer) => { - entries.forEach((entry) => { - if (entry.isIntersecting) { - if (visibleElements.indexOf(entry.target) === -1) { - visibleElements.push(entry.target); - } - } else { - visibleElements = visibleElements.filter((visibleEntry) => { - return visibleEntry !== entry; - }); - } - }); - - if (!hasObserved) { - hideOverlappedSidebars(); - } - hasObserved = true; - }, - {} - ); - els.forEach((el) => { - intersectionObserver.observe(el); - }); - - return { - getVisibleEntries: () => { - return visibleElements; - }, - }; - }; - - const rightElementObserver = visibleItemObserver(rightSideConflictEls); - const leftElementObserver = visibleItemObserver(leftSideConflictEls); - - const hideOverlappedSidebars = () => { - marginScrollVisibility(toRegions(rightElementObserver.getVisibleEntries())); - sidebarScrollVisiblity(toRegions(leftElementObserver.getVisibleEntries())); - if (tocLeftScrollVisibility) { - tocLeftScrollVisibility( - toRegions(leftElementObserver.getVisibleEntries()) - ); - } - }; - - window.quartoToggleReader = () => { - // Applies a slow class (or removes it) - // to update the transition speed - const slowTransition = (slow) => { - const manageTransition = (id, slow) => { - const el = document.getElementById(id); - if (el) { - if (slow) { - el.classList.add("slow"); - } else { - el.classList.remove("slow"); - } - } - }; - - manageTransition("TOC", slow); - manageTransition("quarto-sidebar", slow); - }; - const readerMode = !isReaderMode(); - setReaderModeValue(readerMode); - - // If we're entering reader mode, slow the transition - if (readerMode) { - slowTransition(readerMode); - } - highlightReaderToggle(readerMode); - hideOverlappedSidebars(); - - // If we're exiting reader mode, restore the non-slow transition - if (!readerMode) { - slowTransition(!readerMode); - } - }; - - const highlightReaderToggle = (readerMode) => { - const els = document.querySelectorAll(".quarto-reader-toggle"); - if (els) { - els.forEach((el) => { - if (readerMode) { - el.classList.add("reader"); - } else { - el.classList.remove("reader"); - } - }); - } - }; - - const setReaderModeValue = (val) => { - if (window.location.protocol !== "file:") { - window.localStorage.setItem("quarto-reader-mode", val); - } else { - localReaderMode = val; - } - }; - - const isReaderMode = () => { - if (window.location.protocol !== "file:") { - return window.localStorage.getItem("quarto-reader-mode") === "true"; - } else { - return localReaderMode; - } - }; - let localReaderMode = null; - - const tocOpenDepthStr = tocEl?.getAttribute("data-toc-expanded"); - const tocOpenDepth = tocOpenDepthStr ? Number(tocOpenDepthStr) : 1; - - // Walk the TOC and collapse/expand nodes - // Nodes are expanded if: - // - they are top level - // - they have children that are 'active' links - // - they are directly below an link that is 'active' - const walk = (el, depth) => { - // Tick depth when we enter a UL - if (el.tagName === "UL") { - depth = depth + 1; - } - - // It this is active link - let isActiveNode = false; - if (el.tagName === "A" && el.classList.contains("active")) { - isActiveNode = true; - } - - // See if there is an active child to this element - let hasActiveChild = false; - for (child of el.children) { - hasActiveChild = walk(child, depth) || hasActiveChild; - } - - // 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 || - hasActiveChild || - prevSiblingIsActiveLink(el) - ) { - el.classList.remove("collapse"); - } else { - el.classList.add("collapse"); - } - - // untick depth when we leave a UL - depth = depth - 1; - } - return hasActiveChild || isActiveNode; - }; - - // walk the TOC and expand / collapse any items that should be shown - if (tocEl) { - updateActiveLink(); - walk(tocEl, 0); - } - - // Throttle the scroll event and walk peridiocally - window.document.addEventListener( - "scroll", - throttle(() => { - if (tocEl) { - updateActiveLink(); - walk(tocEl, 0); - } - if (!isReaderMode()) { - hideOverlappedSidebars(); - } - }, 5) - ); - window.addEventListener( - "resize", - throttle(() => { - if (tocEl) { - updateActiveLink(); - walk(tocEl, 0); - } - if (!isReaderMode()) { - hideOverlappedSidebars(); - } - }, 10) - ); - hideOverlappedSidebars(); - highlightReaderToggle(isReaderMode()); -}); - -// grouped tabsets -window.addEventListener("pageshow", (_event) => { - function getTabSettings() { - const data = localStorage.getItem("quarto-persistent-tabsets-data"); - if (!data) { - 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a/docs-preview/site_libs/quarto-search/quarto-search.js b/docs-preview/site_libs/quarto-search/quarto-search.js deleted file mode 100644 index d788a95..0000000 --- a/docs-preview/site_libs/quarto-search/quarto-search.js +++ /dev/null @@ -1,1290 +0,0 @@ -const kQueryArg = "q"; -const kResultsArg = "show-results"; - -// If items don't provide a URL, then both the navigator and the onSelect -// function aren't called (and therefore, the default implementation is used) -// -// We're using this sentinel URL to signal to those handlers that this -// item is a more item (along with the type) and can be handled appropriately -const kItemTypeMoreHref = "0767FDFD-0422-4E5A-BC8A-3BE11E5BBA05"; - -window.document.addEventListener("DOMContentLoaded", function (_event) { - // Ensure that search is available on this page. If it isn't, - // should return early and not do anything - var searchEl = window.document.getElementById("quarto-search"); - if (!searchEl) return; - - const { autocomplete } = window["@algolia/autocomplete-js"]; - - let quartoSearchOptions = {}; - let language = {}; - const searchOptionEl = window.document.getElementById( - "quarto-search-options" - ); - if (searchOptionEl) { - const jsonStr = searchOptionEl.textContent; - quartoSearchOptions = JSON.parse(jsonStr); - language = quartoSearchOptions.language; - } - - // note the search mode - if (quartoSearchOptions.type === "overlay") { - searchEl.classList.add("type-overlay"); - } else { - searchEl.classList.add("type-textbox"); - } - - // Used to determine highlighting behavior for this page - // A `q` query param is expected when the user follows a search - // to this page - const currentUrl = new URL(window.location); - const query = currentUrl.searchParams.get(kQueryArg); - const showSearchResults = currentUrl.searchParams.get(kResultsArg); - const mainEl = window.document.querySelector("main"); - - // highlight matches on the page - if (query && mainEl) { - // perform any highlighting - highlight(escapeRegExp(query), mainEl); - - // fix up the URL to remove the q query param - const replacementUrl = new URL(window.location); - replacementUrl.searchParams.delete(kQueryArg); - window.history.replaceState({}, "", replacementUrl); - } - - // function to clear highlighting on the page when the search query changes - // (e.g. if the user edits the query or clears it) - let highlighting = true; - const resetHighlighting = (searchTerm) => { - if (mainEl && highlighting && query && searchTerm !== query) { - clearHighlight(query, mainEl); - highlighting = false; - } - }; - - // Clear search highlighting when the user scrolls sufficiently - const resetFn = () => { - resetHighlighting(""); - window.removeEventListener("quarto-hrChanged", resetFn); - window.removeEventListener("quarto-sectionChanged", resetFn); - }; - - // Register this event after the initial scrolling and settling of events - // on the page - window.addEventListener("quarto-hrChanged", resetFn); - window.addEventListener("quarto-sectionChanged", resetFn); - - // Responsively switch to overlay mode if the search is present on the navbar - // Note that switching the sidebar to overlay mode requires more coordinate (not just - // the media query since we generate different HTML for sidebar overlays than we do - // for sidebar input UI) - const detachedMediaQuery = - quartoSearchOptions.type === "overlay" ? "all" : "(max-width: 991px)"; - - // If configured, include the analytics client to send insights - const plugins = configurePlugins(quartoSearchOptions); - - let lastState = null; - const { setIsOpen, setQuery, setCollections } = autocomplete({ - container: searchEl, - detachedMediaQuery: detachedMediaQuery, - defaultActiveItemId: 0, - panelContainer: "#quarto-search-results", - panelPlacement: quartoSearchOptions["panel-placement"], - debug: false, - openOnFocus: true, - plugins, - classNames: { - form: "d-flex", - }, - placeholder: language["search-text-placeholder"], - translations: { - clearButtonTitle: language["search-clear-button-title"], - detachedCancelButtonText: language["search-detached-cancel-button-title"], - submitButtonTitle: language["search-submit-button-title"], - }, - initialState: { - query, - }, - getItemUrl({ item }) { - return item.href; - }, - onStateChange({ state }) { - // If this is a file URL, note that - - // Perhaps reset highlighting - resetHighlighting(state.query); - - // If the panel just opened, ensure the panel is positioned properly - if (state.isOpen) { - if (lastState && !lastState.isOpen) { - setTimeout(() => { - positionPanel(quartoSearchOptions["panel-placement"]); - }, 150); - } - } - - // Perhaps show the copy link - showCopyLink(state.query, quartoSearchOptions); - - lastState = state; - }, - reshape({ sources, state }) { - return sources.map((source) => { - try { - const items = source.getItems(); - - // Validate the items - validateItems(items); - - // group the items by document - const groupedItems = new Map(); - items.forEach((item) => { - const hrefParts = item.href.split("#"); - const baseHref = hrefParts[0]; - const isDocumentItem = hrefParts.length === 1; - - const items = groupedItems.get(baseHref); - if (!items) { - groupedItems.set(baseHref, [item]); - } else { - // If the href for this item matches the document - // exactly, place this item first as it is the item that represents - // the document itself - if (isDocumentItem) { - items.unshift(item); - } else { - items.push(item); - } - groupedItems.set(baseHref, items); - } - }); - - const reshapedItems = []; - let count = 1; - for (const [_key, value] of groupedItems) { - const firstItem = value[0]; - reshapedItems.push({ - ...firstItem, - type: kItemTypeDoc, - }); - - const collapseMatches = quartoSearchOptions["collapse-after"]; - const collapseCount = - typeof collapseMatches === "number" ? collapseMatches : 1; - - if (value.length > 1) { - const target = `search-more-${count}`; - const isExpanded = - state.context.expanded && - state.context.expanded.includes(target); - - const remainingCount = value.length - collapseCount; - - for (let i = 1; i < value.length; i++) { - if (collapseMatches && i === collapseCount) { - reshapedItems.push({ - target, - title: isExpanded - ? language["search-hide-matches-text"] - : remainingCount === 1 - ? `${remainingCount} ${language["search-more-match-text"]}` - : `${remainingCount} ${language["search-more-matches-text"]}`, - type: kItemTypeMore, - href: kItemTypeMoreHref, - }); - } - - if (isExpanded || !collapseMatches || i < collapseCount) { - reshapedItems.push({ - ...value[i], - type: kItemTypeItem, - target, - }); - } - } - } - count += 1; - } - - return { - ...source, - getItems() { - return reshapedItems; - }, - }; - } catch (error) { - // Some form of error occurred - return { - ...source, - getItems() { - return [ - { - title: error.name || "An Error Occurred While Searching", - text: - error.message || - "An unknown error occurred while attempting to perform the requested search.", - type: kItemTypeError, - }, - ]; - }, - }; - } - }); - }, - navigator: { - navigate({ itemUrl }) { - if (itemUrl !== offsetURL(kItemTypeMoreHref)) { - window.location.assign(itemUrl); - } - }, - navigateNewTab({ itemUrl }) { - if (itemUrl !== offsetURL(kItemTypeMoreHref)) { - const windowReference = window.open(itemUrl, "_blank", "noopener"); - if (windowReference) { - windowReference.focus(); - } - } - }, - navigateNewWindow({ itemUrl }) { - if (itemUrl !== offsetURL(kItemTypeMoreHref)) { - window.open(itemUrl, "_blank", "noopener"); - } - }, - }, - getSources({ state, setContext, setActiveItemId, refresh }) { - return [ - { - sourceId: "documents", - getItemUrl({ item }) { - if (item.href) { - return offsetURL(item.href); - } else { - return undefined; - } - }, - onSelect({ - item, - state, - setContext, - setIsOpen, - setActiveItemId, - refresh, - }) { - if (item.type === kItemTypeMore) { - toggleExpanded(item, state, setContext, setActiveItemId, refresh); - - // Toggle more - setIsOpen(true); - } - }, - getItems({ query }) { - if (query === null || query === "") { - return []; - } - - const limit = quartoSearchOptions.limit; - if (quartoSearchOptions.algolia) { - return algoliaSearch(query, limit, quartoSearchOptions.algolia); - } else { - // Fuse search options - const fuseSearchOptions = { - isCaseSensitive: false, - shouldSort: true, - minMatchCharLength: 2, - limit: limit, - }; - - return readSearchData().then(function (fuse) { - return fuseSearch(query, fuse, fuseSearchOptions); - }); - } - }, - templates: { - noResults({ createElement }) { - const hasQuery = lastState.query; - - return createElement( - "div", - { - class: `quarto-search-no-results${ - hasQuery ? "" : " no-query" - }`, - }, - language["search-no-results-text"] - ); - }, - header({ items, createElement }) { - // count the documents - const count = items.filter((item) => { - return item.type === kItemTypeDoc; - }).length; - - if (count > 0) { - return createElement( - "div", - { class: "search-result-header" }, - `${count} ${language["search-matching-documents-text"]}` - ); - } else { - return createElement( - "div", - { class: "search-result-header-no-results" }, - `` - ); - } - }, - footer({ _items, createElement }) { - if ( - quartoSearchOptions.algolia && - quartoSearchOptions.algolia["show-logo"] - ) { - const libDir = quartoSearchOptions.algolia["libDir"]; - const logo = createElement("img", { - src: offsetURL( - `${libDir}/quarto-search/search-by-algolia.svg` - ), - class: "algolia-search-logo", - }); - return createElement( - "a", - { href: "http://www.algolia.com/" }, - logo - ); - } - }, - - item({ item, createElement }) { - return renderItem( - item, - createElement, - state, - setActiveItemId, - setContext, - refresh, - quartoSearchOptions - ); - }, - }, - }, - ]; - }, - }); - - window.quartoOpenSearch = () => { - setIsOpen(false); - setIsOpen(true); - 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") { - const inputEl = window.document.querySelector( - "#quarto-search .aa-Autocomplete" - ); - if (inputEl) { - inputEl.removeAttribute("aria-labelledby"); - } - } - - 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 = 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); - focusSearchInput(); - } -}); - -function configurePlugins(quartoSearchOptions) { - const autocompletePlugins = []; - const algoliaOptions = quartoSearchOptions.algolia; - if ( - algoliaOptions && - algoliaOptions["analytics-events"] && - algoliaOptions["search-only-api-key"] && - algoliaOptions["application-id"] - ) { - const apiKey = algoliaOptions["search-only-api-key"]; - const appId = algoliaOptions["application-id"]; - - // Aloglia insights may not be loaded because they require cookie consent - // Use deferred loading so events will start being recorded when/if consent - // is granted. - const algoliaInsightsDeferredPlugin = deferredLoadPlugin(() => { - if ( - window.aa && - window["@algolia/autocomplete-plugin-algolia-insights"] - ) { - window.aa("init", { - appId, - apiKey, - useCookie: true, - }); - - const { createAlgoliaInsightsPlugin } = - window["@algolia/autocomplete-plugin-algolia-insights"]; - // Register the insights client - const algoliaInsightsPlugin = createAlgoliaInsightsPlugin({ - insightsClient: window.aa, - onItemsChange({ insights, insightsEvents }) { - 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; - }); - - for (const event of events) { - insights.viewedObjectIDs(event); - } - }, - }); - return algoliaInsightsPlugin; - } - }); - - // Add the plugin - autocompletePlugins.push(algoliaInsightsDeferredPlugin); - return autocompletePlugins; - } -} - -// For plugins that may not load immediately, create a wrapper -// plugin and forward events and plugin data once the plugin -// is initialized. This is useful for cases like cookie consent -// which may prevent the analytics insights event plugin from initializing -// immediately. -function deferredLoadPlugin(createPlugin) { - let plugin = undefined; - let subscribeObj = undefined; - const wrappedPlugin = () => { - if (!plugin && subscribeObj) { - plugin = createPlugin(); - if (plugin && plugin.subscribe) { - plugin.subscribe(subscribeObj); - } - } - return plugin; - }; - - return { - subscribe: (obj) => { - subscribeObj = obj; - }, - onStateChange: (obj) => { - const plugin = wrappedPlugin(); - if (plugin && plugin.onStateChange) { - plugin.onStateChange(obj); - } - }, - onSubmit: (obj) => { - const plugin = wrappedPlugin(); - if (plugin && plugin.onSubmit) { - plugin.onSubmit(obj); - } - }, - onReset: (obj) => { - const plugin = wrappedPlugin(); - if (plugin && plugin.onReset) { - plugin.onReset(obj); - } - }, - getSources: (obj) => { - const plugin = wrappedPlugin(); - if (plugin && plugin.getSources) { - return plugin.getSources(obj); - } else { - return Promise.resolve([]); - } - }, - data: (obj) => { - const plugin = wrappedPlugin(); - if (plugin && plugin.data) { - plugin.data(obj); - } - }, - }; -} - -function validateItems(items) { - // Validate the first item - if (items.length > 0) { - const item = items[0]; - const missingFields = []; - if (item.href == undefined) { - missingFields.push("href"); - } - if (!item.title == undefined) { - missingFields.push("title"); - } - if (!item.text == undefined) { - missingFields.push("text"); - } - - if (missingFields.length === 1) { - throw { - name: `Error: Search index is missing the ${missingFields[0]} field.`, - message: `The items being returned for this search do not include all the required fields. Please ensure that your index items include the ${missingFields[0]} field or use index-fields in your _quarto.yml file to specify the field names.`, - }; - } else if (missingFields.length > 1) { - const missingFieldList = missingFields - .map((field) => { - return `${field}`; - }) - .join(", "); - - throw { - name: `Error: Search index is missing the following fields: ${missingFieldList}.`, - message: `The items being returned for this search do not include all the required fields. Please ensure that your index items includes the following fields: ${missingFieldList}, or use index-fields in your _quarto.yml file to specify the field names.`, - }; - } - } -} - -let lastQuery = null; -function showCopyLink(query, options) { - const language = options.language; - lastQuery = query; - // Insert share icon - const inputSuffixEl = window.document.body.querySelector( - ".aa-Form .aa-InputWrapperSuffix" - ); - - if (inputSuffixEl) { - let copyButtonEl = window.document.body.querySelector( - ".aa-Form .aa-InputWrapperSuffix .aa-CopyButton" - ); - - if (copyButtonEl === null) { - copyButtonEl = window.document.createElement("button"); - copyButtonEl.setAttribute("class", "aa-CopyButton"); - copyButtonEl.setAttribute("type", "button"); - copyButtonEl.setAttribute("title", language["search-copy-link-title"]); - copyButtonEl.onmousedown = (e) => { - e.preventDefault(); - e.stopPropagation(); - }; - - const linkIcon = "bi-clipboard"; - const checkIcon = "bi-check2"; - - const shareIconEl = window.document.createElement("i"); - shareIconEl.setAttribute("class", `bi ${linkIcon}`); - copyButtonEl.appendChild(shareIconEl); - inputSuffixEl.prepend(copyButtonEl); - - const clipboard = new window.ClipboardJS(".aa-CopyButton", { - text: function (_trigger) { - const copyUrl = new URL(window.location); - copyUrl.searchParams.set(kQueryArg, lastQuery); - copyUrl.searchParams.set(kResultsArg, "1"); - return copyUrl.toString(); - }, - }); - clipboard.on("success", function (e) { - // Focus the input - - // button target - const button = e.trigger; - const icon = button.querySelector("i.bi"); - - // flash "checked" - icon.classList.add(checkIcon); - icon.classList.remove(linkIcon); - setTimeout(function () { - icon.classList.remove(checkIcon); - icon.classList.add(linkIcon); - }, 1000); - }); - } - - // If there is a query, show the link icon - if (copyButtonEl) { - if (lastQuery && options["copy-button"]) { - copyButtonEl.style.display = "flex"; - } else { - copyButtonEl.style.display = "none"; - } - } - } -} - -/* 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) { - 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")); - if (response.status == 200) { - return response.json().then(function (searchDocs) { - searchDocs.forEach(function (searchDoc) { - fuse.add(searchDoc); - }); - fuseIndex = fuse; - return fuseIndex; - }); - } else { - return Promise.reject( - new Error( - "Unexpected status from search index request: " + response.status - ) - ); - } - } - - return fuseIndex; -} - -function inputElement() { - return window.document.body.querySelector(".aa-Form .aa-Input"); -} - -function focusSearchInput() { - setTimeout(() => { - const inputEl = inputElement(); - if (inputEl) { - inputEl.focus(); - } - }, 50); -} - -/* Panels */ -const kItemTypeDoc = "document"; -const kItemTypeMore = "document-more"; -const kItemTypeItem = "document-item"; -const kItemTypeError = "error"; - -function renderItem( - item, - createElement, - state, - setActiveItemId, - setContext, - refresh, - quartoSearchOptions -) { - switch (item.type) { - case kItemTypeDoc: - return createDocumentCard( - createElement, - "file-richtext", - item.title, - item.section, - item.text, - item.href, - item.crumbs, - quartoSearchOptions - ); - case kItemTypeMore: - return createMoreCard( - createElement, - item, - state, - setActiveItemId, - setContext, - refresh - ); - case kItemTypeItem: - return createSectionCard( - createElement, - item.section, - item.text, - item.href - ); - case kItemTypeError: - return createErrorCard(createElement, item.title, item.text); - default: - return undefined; - } -} - -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" }, - titleContents - ); - - const textEls = []; - if (section) { - const sectionEl = createElement( - "p", - { class: "search-result-section" }, - section - ); - textEls.push(sectionEl); - } - const descEl = createElement("p", { - class: "search-result-text", - dangerouslySetInnerHTML: { - __html: text, - }, - }); - textEls.push(descEl); - - const textContainerEl = createElement( - "div", - { class: "search-result-text-container" }, - textEls - ); - - const containerEl = createElement( - "div", - { - class: "search-result-container", - }, - [titleContainerEl, textContainerEl] - ); - - const linkEl = createElement( - "a", - { - href: offsetURL(href), - class: "search-result-link", - }, - containerEl - ); - - const classes = ["search-result-doc", "search-item"]; - if (!section) { - classes.push("document-selectable"); - } - - return createElement( - "div", - { - class: classes.join(" "), - }, - linkEl - ); -} - -function createMoreCard( - createElement, - item, - state, - setActiveItemId, - setContext, - refresh -) { - const moreCardEl = createElement( - "div", - { - class: "search-result-more search-item", - onClick: (e) => { - // Handle expanding the sections by adding the expanded - // section to the list of expanded sections - toggleExpanded(item, state, setContext, setActiveItemId, refresh); - e.stopPropagation(); - }, - }, - item.title - ); - - return moreCardEl; -} - -function toggleExpanded(item, state, setContext, setActiveItemId, refresh) { - const expanded = state.context.expanded || []; - if (expanded.includes(item.target)) { - setContext({ - expanded: expanded.filter((target) => target !== item.target), - }); - } else { - setContext({ expanded: [...expanded, item.target] }); - } - - refresh(); - setActiveItemId(item.__autocomplete_id); -} - -function createSectionCard(createElement, section, text, href) { - const sectionEl = createSection(createElement, section, text, href); - return createElement( - "div", - { - class: "search-result-doc-section search-item", - }, - sectionEl - ); -} - -function createSection(createElement, title, text, href) { - const descEl = createElement("p", { - class: "search-result-text", - dangerouslySetInnerHTML: { - __html: text, - }, - }); - - const titleEl = createElement("p", { class: "search-result-section" }, title); - const linkEl = createElement( - "a", - { - href: offsetURL(href), - class: "search-result-link", - }, - [titleEl, descEl] - ); - return linkEl; -} - -function createErrorCard(createElement, title, text) { - const descEl = createElement("p", { - class: "search-error-text", - dangerouslySetInnerHTML: { - __html: text, - }, - }); - - const titleEl = createElement("p", { - class: "search-error-title", - dangerouslySetInnerHTML: { - __html: ` ${title}`, - }, - }); - const errorEl = createElement("div", { class: "search-error" }, [ - titleEl, - descEl, - ]); - return errorEl; -} - -function positionPanel(pos) { - const panelEl = window.document.querySelector( - "#quarto-search-results .aa-Panel" - ); - const inputEl = window.document.querySelector( - "#quarto-search .aa-Autocomplete" - ); - - if (panelEl && inputEl) { - panelEl.style.top = `${Math.round(panelEl.offsetTop)}px`; - if (pos === "start") { - panelEl.style.left = `${Math.round(inputEl.left)}px`; - } else { - panelEl.style.right = `${Math.round(inputEl.offsetRight)}px`; - } - } -} - -/* Highlighting */ -// highlighting functions -function highlightMatch(query, text) { - if (text) { - const start = text.toLowerCase().indexOf(query.toLowerCase()); - if (start !== -1) { - const startMark = ""; - const endMark = ""; - - const end = start + query.length; - text = - text.slice(0, start) + - startMark + - text.slice(start, end) + - endMark + - text.slice(end); - const startInfo = clipStart(text, start); - const endInfo = clipEnd( - text, - startInfo.position + startMark.length + endMark.length - ); - text = - startInfo.prefix + - text.slice(startInfo.position, endInfo.position) + - endInfo.suffix; - - return text; - } else { - return text; - } - } else { - return text; - } -} - -function clipStart(text, pos) { - const clipStart = pos - 50; - if (clipStart < 0) { - // This will just return the start of the string - return { - position: 0, - prefix: "", - }; - } else { - // We're clipping before the start of the string, walk backwards to the first space. - const spacePos = findSpace(text, pos, -1); - return { - position: spacePos.position, - prefix: "", - }; - } -} - -function clipEnd(text, pos) { - const clipEnd = pos + 200; - if (clipEnd > text.length) { - return { - position: text.length, - suffix: "", - }; - } else { - const spacePos = findSpace(text, clipEnd, 1); - return { - position: spacePos.position, - suffix: spacePos.clipped ? "…" : "", - }; - } -} - -function findSpace(text, start, step) { - let stepPos = start; - while (stepPos > -1 && stepPos < text.length) { - const char = text[stepPos]; - if (char === " " || char === "," || char === ":") { - return { - position: step === 1 ? stepPos : stepPos - step, - clipped: stepPos > 1 && stepPos < text.length, - }; - } - stepPos = stepPos + step; - } - - return { - position: stepPos - step, - clipped: false, - }; -} - -// removes highlighting as implemented by the mark tag -function clearHighlight(searchterm, el) { - const childNodes = el.childNodes; - for (let i = childNodes.length - 1; i >= 0; i--) { - const node = childNodes[i]; - if (node.nodeType === Node.ELEMENT_NODE) { - if ( - node.tagName === "MARK" && - node.innerText.toLowerCase() === searchterm.toLowerCase() - ) { - el.replaceChild(document.createTextNode(node.innerText), node); - } else { - clearHighlight(searchterm, node); - } - } - } -} - -function escapeRegExp(string) { - return string.replace(/[.*+?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string -} - -// highlight matches -function highlight(term, el) { - const termRegex = new RegExp(term, "ig"); - const childNodes = el.childNodes; - - // walk back to front avoid mutating elements in front of us - for (let i = childNodes.length - 1; i >= 0; i--) { - const node = childNodes[i]; - - if (node.nodeType === Node.TEXT_NODE) { - // Search text nodes for text to highlight - const text = node.nodeValue; - - let startIndex = 0; - let matchIndex = text.search(termRegex); - if (matchIndex > -1) { - const markFragment = document.createDocumentFragment(); - while (matchIndex > -1) { - const prefix = text.slice(startIndex, matchIndex); - markFragment.appendChild(document.createTextNode(prefix)); - - const mark = document.createElement("mark"); - mark.appendChild( - document.createTextNode( - text.slice(matchIndex, matchIndex + term.length) - ) - ); - markFragment.appendChild(mark); - - startIndex = matchIndex + term.length; - matchIndex = text.slice(startIndex).search(new RegExp(term, "ig")); - if (matchIndex > -1) { - matchIndex = startIndex + matchIndex; - } - } - if (startIndex < text.length) { - markFragment.appendChild( - document.createTextNode(text.slice(startIndex, text.length)) - ); - } - - el.replaceChild(markFragment, node); - } - } else if (node.nodeType === Node.ELEMENT_NODE) { - // recurse through elements - highlight(term, node); - } - } -} - -/* Link Handling */ -// get the offset from this page for a given site root relative url -function offsetURL(url) { - var offset = getMeta("quarto:offset"); - return offset ? offset + url : url; -} - -// read a meta tag value -function getMeta(metaName) { - var metas = window.document.getElementsByTagName("meta"); - for (let i = 0; i < metas.length; i++) { - if (metas[i].getAttribute("name") === metaName) { - return metas[i].getAttribute("content"); - } - } - return ""; -} - -function algoliaSearch(query, limit, algoliaOptions) { - const { getAlgoliaResults } = window["@algolia/autocomplete-preset-algolia"]; - - const applicationId = algoliaOptions["application-id"]; - const searchOnlyApiKey = algoliaOptions["search-only-api-key"]; - const indexName = algoliaOptions["index-name"]; - const indexFields = algoliaOptions["index-fields"]; - const searchClient = window.algoliasearch(applicationId, searchOnlyApiKey); - const searchParams = algoliaOptions["params"]; - const searchAnalytics = !!algoliaOptions["analytics-events"]; - - return getAlgoliaResults({ - searchClient, - queries: [ - { - indexName: indexName, - query, - params: { - hitsPerPage: limit, - clickAnalytics: searchAnalytics, - ...searchParams, - }, - }, - ], - transformResponse: (response) => { - if (!indexFields) { - return response.hits.map((hit) => { - return hit.map((item) => { - return { - ...item, - text: highlightMatch(query, item.text), - }; - }); - }); - } else { - const remappedHits = response.hits.map((hit) => { - return hit.map((item) => { - const newItem = { ...item }; - ["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; - }); - }); - return remappedHits; - } - }, - }); -} - -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]; - const sep = baseUrl.search("\\?") > 0 ? "&" : "?"; - anchorParts[0] = baseUrl + sep + name + "=" + value; - return anchorParts.join("#"); - }; - - return { - title: result.item.title, - 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/docs-preview/styles.css b/docs-preview/styles.css deleted file mode 100644 index 2ddf50c..0000000 --- a/docs-preview/styles.css +++ /dev/null @@ -1 +0,0 @@ -/* css styles */ diff --git a/docs/.nojekyll b/docs/.nojekyll deleted file mode 100644 index e69de29..0000000 diff --git a/docs/Case_definition.html b/docs/Case_definition.html index 2c048c9..8afab09 100644 --- a/docs/Case_definition.html +++ b/docs/Case_definition.html @@ -370,7 +370,7 @@

    a) Ate a

    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.

    -

    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.

    @@ -1374,7 +1374,7 @@

    4. Export clean data

     ### 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. @@ -1387,7 +1387,7 @@

    4. Export clean data

    ::: ::: {.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. ::: diff --git a/docs/Data_import_cleaning.html b/docs/Data_import_cleaning.html index 71a5063..38e3914 100644 --- a/docs/Data_import_cleaning.html +++ b/docs/Data_import_cleaning.html @@ -1147,23 +1147,23 @@

    Age

    drtable
    -
    - diff --git a/docs/Lab_and_Descriptive.html b/docs/Lab_and_Descriptive.html index 6e532df..29969bf 100644 --- a/docs/Lab_and_Descriptive.html +++ b/docs/Lab_and_Descriptive.html @@ -750,23 +750,23 @@

    tabsymptoms

    -
    - @@ -1598,23 +1598,23 @@

    attack_prop

    -
    - diff --git a/docs/SimOb_RInject09.html b/docs/SimOb_RInject09.html deleted file mode 100644 index f865346..0000000 --- a/docs/SimOb_RInject09.html +++ /dev/null @@ -1,2766 +0,0 @@ - - - - - - - - - -Copenhagen Introductory Course - Data import and cleaning - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - -
    - -
    - - - - -
    - -
    -
    -

    Data import and cleaning

    -
    - - - -
    - - - - -
    - - -
    - -
    -

    1. Learning outcomes

    -

    At the end of the session, participants will be able to:

    -
      -
    • Import datasets in R
      • -
    • Apply basic data management techniques to explore and familiarise themselves with the dataset
      • -
    • Apply strategies for data cleaning
      • -
    -
    -
    -

    2. Story/plot description

    -

    You and your team have carried out hundreds of interviews in a very short time. Well done!

    -

    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.

    -

    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.

    -

    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).

    -

    Open R Studio and set up a new R project, and create a data and a scripts folder.

    -
    -
    -

    3. Tips you could follow

    -
    -

    3.1. Install packages and load libraries

    -
      -
    • 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 masking functions.
      • -
    -
    -
    -Code -
    # 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)
    -
    -
    -
    -
    -

    3.2. Import your data

    -
      -
    • You can find the spetses_school.csv file in EVA. Feel free to explore the codebook as well.
      • -
    -
    -
    -Code -
    # Import the raw data set:
-copdata <- rio::import(here::here("data", "spetses_school.csv"))
    -
    -
    -
    -
    -

    3.3. Explore and clean your data

    -
      -

    • Have a look at the structure of your data. Here you have some questions that may help you get started:

      -
        -

      • How many observations and variables does the dataset contain?

        • -

      • What types of variables do you have and what types of values are recorded?

        • -

      • Do any of the values of the other variables look implausible? Which ones and why? What will you do about it?

        • -
      • -

    • Go ahead and clean your data as you see fit.

      • -
    -
    - -
    -
    -

    You 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

    -
    -
    -
    -
    -
    -Code -
    head(copdata)
    -
    -
    - -
    - -
    -
    -
    -Code -
    dim(copdata)
    -
    -
    -
    [1] 397  40
    -
    -
    -Code -
    str(copdata)
    -
    -
    -
    '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 -
    skimr::skim(copdata)
    -
    -
    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    Data summary
    Namecopdata
    Number of rows397
    Number of columns40
    _______________________
    Column type frequency:
    character3
    numeric37
    ________________________
    Group variablesNone
    -

    Variable type: character

    - ---------- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    skim_variablen_missingcomplete_rateminmaxemptyn_uniquewhitespace
    id018803970
    sex0146020
    dayonset010817540
    -

    Variable type: numeric

    - ------------- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    skim_variablen_missingcomplete_ratemeansdp0p25p50p75p100hist
    age01.0018.579.928161718180▇▁▁▁▁
    group01.000.960.2001111▁▁▁▁▇
    class360.911.940.8411233▇▁▆▁▇
    diarrhoea1410.640.820.3901111▂▁▁▁▇
    bloody2000.500.030.1700001▇▁▁▁▁
    vomiting1790.550.300.4600011▇▁▁▁▃
    abdo1520.620.850.3501111▂▁▁▁▇
    nausea1700.570.760.4301111▂▁▁▁▇
    fever2230.440.260.4400011▇▁▁▁▃
    headache1740.560.630.4800111▅▁▁▁▇
    jointpain2070.480.160.3700001▇▁▁▁▂
    starthour1770.5512.494.923991521▁▇▁▆▃
    meal90.980.970.1701111▁▁▁▁▇
    feta160.960.710.4500111▃▁▁▁▇
    fetaD160.961.321.0000223▆▅▁▇▂
    sardines170.960.670.4700111▃▁▁▁▇
    sardinesD170.961.341.0400223▆▂▁▇▂
    eggplant300.920.590.4900111▆▁▁▁▇
    eggplantD300.921.141.0500123▇▂▁▇▂
    moussaka150.960.890.3101111▁▁▁▁▇
    moussakaD140.961.830.9101223▂▃▁▇▃
    orzo150.960.880.3201111▁▁▁▁▇
    orzoD150.961.810.9101223▂▃▁▇▃
    greeksal240.940.570.5000111▆▁▁▁▇
    greeksalD240.941.081.0600123▇▂▁▆▂
    bread180.950.910.2901111▁▁▁▁▇
    breadD180.951.750.7102223▁▂▁▇▁
    dessert420.890.420.4900011▇▁▁▁▆
    dessertD420.890.831.0600023▇▁▁▃▁
    champagne250.940.870.3401111▁▁▁▁▇
    champagneD250.941.370.9301123▂▇▁▂▃
    beer300.920.780.4201111▂▁▁▁▇
    beerD350.911.941.2301333▃▂▁▂▇
    redwine500.870.230.4200001▇▁▁▁▂
    redwineD520.870.450.9200003▇▁▁▁▁
    whitewine310.920.730.4500111▃▁▁▁▇
    whitewineD360.911.581.2100233▆▅▁▅▇
    -
    -
    -Code -
    names(copdata)
    -
    -
    -
     [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"
    -
    -
    -

    Let’s explore and manage some variables in detail.

    -
    -

    Age

    -

    Through 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.

    -
    -
    -Code -
    # Have a look at the histogram  
-hist(copdata$age)
    -
    -
    -

    -
    -
    -Code -
    # Create cross-tab with the group variable:  
-janitor::tabyl(dat = copdata, 
-               var1 = age, 
-               var2 = group)
    -
    -
    - -
    - -
    -
    -
    -

    Note 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:

    -
    -
    -Code -
    # Convert group to a factor and label 0 as teacher, 1 as student:
-copdata <- copdata %>% 
-  mutate(group = factor(group, 
-                        labels = c("teacher", "student")))
    -
    -
    -

    Now, have a look at your cross-tab again:

    -
    -
    -Code -
    janitor::tabyl(dat = copdata, 
-               var1 = age, 
-               var2 = group)
    -
    -
    - -
    - -
    -
    -
    -

    With this table, we can more easily identify ages that are likely to be typographic errors. Specifically:

    -
      -

    • There is one teacher aged 16 (likely digit reversal - should be 61)

      • -

    • There is one student aged 8 (likely missing a digit - should be 18)

      • -

    • There is one student aged 180 (likely has an extra digit - should be 18)

      • -
    -

    Assuming 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:

    -
    -
    -Code -
    # 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
-             )                    
-         )
    -
    -
    -
    -
    -

    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.

    -
    -
    -
      -
    • Create summary table for the dose response columns (those finishing with a capital “D”) to have a look at their distribution.
      • -
    -
    - -
    -
    -

    Use gtsummary::tbl_summary().

    -
    -
    -
    -
    -
    -Code -
    #| 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
    -
    -
    - -
    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    CharacteristicN = 3971
    fetaD
        0110 (29%)
        178 (20%)
        2155 (41%)
        338 (10.0%)
    sardinesD
        0125 (33%)
        135 (9.2%)
        2184 (48%)
        336 (9.5%)
    eggplantD
        0151 (41%)
        141 (11%)
        2147 (40%)
        328 (7.6%)
    moussakaD
        042 (11%)
        170 (18%)
        2184 (48%)
        387 (23%)
    orzoD
        044 (12%)
        170 (18%)
        2183 (48%)
        385 (22%)
    greeksalD
        0162 (43%)
        152 (14%)
        2126 (34%)
        333 (8.8%)
    breadD
        035 (9.2%)
        151 (13%)
        2267 (70%)
        326 (6.9%)
    dessertD
        0206 (58%)
        133 (9.3%)
        287 (25%)
        329 (8.2%)
    champagneD
        049 (13%)
        1208 (56%)
        245 (12%)
        370 (19%)
    beerD
        081 (22%)
        141 (11%)
        257 (16%)
        3183 (51%)
    redwineD
        0266 (77%)
        131 (9.0%)
        221 (6.1%)
        327 (7.8%)
    whitewineD
        0100 (28%)
        173 (20%)
        267 (19%)
        3121 (34%)
    1 n (%)
    -
    -
    -
    -
    -
    -
    -

    3.4. Modify variables format

    -

    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.

    -
    -
    -

    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.

    -
    -
    -

    You can use mutate to modify the type of the following variables:

    - - ------ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    Table 1: Variable types to modify
    Variable nameOriginalDesiredHint
    sexcharacterfactormutate(), as.factor()
    classintegerfactormutate(), as.factor()
    All the clinical symptom variablesintegerlogicalmutate(across()), as.logical()
    All the food variables representing the amount of specific foods eaten (those finishing with a capital “D”)integerfactormutate(across()), as.factor()
    dayonsetcharacterdatelubridate::dmy()
    starthour and dayonset together

    integer (starthour)

    -

    date (dayonset)

    		POSIXct, POSIXtlubridate::ymd_h() could have inside stringr::str_glue() with dayonset and starthour
    -
    -

    Sex, group and class

    -

    Let’s start transforming one-by-one the first two variables in the table: sex, and class.

    -
    -
    -Code -
    copdata <- copdata %>% 
-  dplyr::mutate(
-    sex = as.factor(sex),
-    class = as.factor(class))
    -
    -
    -
    -
    -

    Symptoms and Food variables

    -

    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.

    -
      -
    1. For the variables that are clinical symptoms, we will list them one by one and show you the use of mutate(across( )).
      2. -
    -
    -
    -Code -
    copdata <- copdata %>% 
-  dplyr::mutate(
-    # clinical symptoms
-    across(.cols = c(diarrhoea, bloody, vomiting,
-             abdo, nausea, fever,headache, jointpain), 
-           .fns = ~ as.logical(.)
-           )
-    )
    -
    -
    -
      -
    1. For 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.
      2. -
    -
    -
    -Code -
    # 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(
-      ends_with("D", ignore.case = FALSE)) %>% 
-    names()
-
-# Another way of doing it:
-# food_dose <- c("fetaD", "sardinesD", "eggplantD", "moussakaD", 
-#                 "orzoD", "greeksalD", "dessertD", "breadD", 
-#                 "champagneD", "beerD", "redwineD", "whitewineD")
-
-
-copdata <- copdata %>% 
-  dplyr::mutate(
-    # food dose variables
-    across(.cols = all_of(food_dose), 
-           .fns = ~as.factor(.)))
    -
    -
    -
    - -
    -
    -

    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.

    -
    -
    -
    -
    -
    -

    Date and time variables

    -
    - -
    -
    -

    Why may you want to transform the variable starthour to date-time?

    -
    -
    -
    -
    -
    -

    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.

    -
    -
    -

    You 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”).

    -
    -
    -Code -
    # Have a look at how the data is stored
-head(copdata$dayonset)
    -
    -
    -
    [1] "6oct2024" ""         ""         ""         "6oct2024" "7oct2024"
    -
    -
    -Code -
    class(copdata$dayonset)
    -
    -
    -
    [1] "character"
    -
    -
    -Code -
    # Update copdata:
-copdata <- copdata %>% 
-  # Change column to date class:
-  dplyr::mutate(
-    dayonset = lubridate::dmy(dayonset))
-
-# Check class of updated column:
-class(copdata$dayonset)
    -
    -
    -
    [1] "Date"
    -
    -
    -Code -
    # Have a look at your data now:
-head(copdata$dayonset)
    -
    -
    -
    [1] "2024-10-06" NA           NA           NA           "2024-10-06"
-[6] "2024-10-07"
    -
    -
    -

    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 lubridate::ymd_h() function.

    -

    Before 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.

    -

    We can check if we have any missing values by cross-tabulating starthour with dayonset:

    -
    -
    -Code -
    # Cross-tabulate dayonset with starthour:
-janitor::tabyl(dat = copdata, 
-               var1 = starthour, 
-               var2 = dayonset)
    -
    -
    - -
    - -
    -
    -
    -

    This 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:

    -
    -
    -Code -
    copdata <- copdata %>% 
-  # Combine dayonset and starthour in a new date time variable:
-  mutate(onset_datetime = 
-           lubridate::ymd_h(
-             str_glue("{dayonset}, {starthour}"), 
-                                           # Deal with missing starthour:
-                                           truncated = 2))
    -
    -
    -
    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.
    -
    -
    -

    Note 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.

    -

    The 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.

    -

    Now we can check that everything in the new combined date-time variable has parsed correctly:

    -
    -
    -Code -
    head(copdata$dayonset)
    -
    -
    -
    [1] "2024-10-06" NA           NA           NA           "2024-10-06"
-[6] "2024-10-07"
    -
    -
    -Code -
    head(copdata$starthour)
    -
    -
    -
    [1]  9 NA NA NA 15 15
    -
    -
    -Code -
    head(copdata$onset_datetime)
    -
    -
    -
    [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"
    -
    -
    -
    -
    -
    -

    3.5. Export clean data

    -

    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.

    -

    Save your clean data as a new file “Copenhagen_clean1_YOURINITIALS.rds”, under the data folder of your Rproject.

    -
    - -
    -
    -

    Use rio::export().

    -
    -
    -
    -
    -
    -Code -
    rio::export(x = copdata, 
-            file = here::here("data", "Copenhagen_clean1_2024.rds"))
    -
    -
    -
    -
    -

    -
    -
    - - - - -
    -
    - -
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    1. Learning outcomes

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    At the end of the session, participants will be able to:

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    • Apply case definition criteria to a dataset using logical arguments in R
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    2. Story/plot description

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    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.

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    3. Questions/Assignments

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    If 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).

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    To be sure we all start from the same case definition, let’s agree that a case was defined as a person who:

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    • attended the school dinner on 11 November 2006 (i.e. is on the linelist)
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    • ate a meal at the school dinner (i.e. was exposed)
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    • fell ill after the start of the meal
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    • fell ill within the time period of interest after the school dinner
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    • suffered from diarrhoea with or without blood, or vomiting
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    Non cases (not-ill) were defined as people who:

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    • ate a meal at the school dinner (i.e. were exposed)
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    • did not develop diarrhoea (with or without blood) or vomiting
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    3.1 Install packages (if needed) and load libraries

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    # Load the required libraries into the current R session:
-pacman::p_load(rio, 
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-               skimr,
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-               gtsummary, 
-               flextable,
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    3.2 Import your data

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    # Import the clean data set:
-copdata <- rio::import(here::here("data", "Copenhagen_clean1_2024.rds"))
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    3.3 Identify the variables you need to apply the case definition criteria.

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    The variables we need from the dataset to apply the above case definition are: meal, onset_datetime, diarrhoea, bloody and vomiting.

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    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:

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    a) Ate a meal at the school dinner

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    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.

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    filter by those with meal == TRUE.

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    What are some of the implications this decision may lead to? (excluding any people from the cohort who didn’t eat at the dinner)

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    copdata <- copdata %>% 
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    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:

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    - 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.

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    - 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.

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    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.

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    b) Fell ill after the start of the meal

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    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.

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    Note that we the concept of having eaten a meal is already included as per one of the steps above.

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    Create a new gastrosymptoms column in your line list with mutate() and case_when().

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    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)

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    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).

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    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.

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    copdata <- copdata %>% 
-  mutate(gastrosymptoms = case_when(
-    # Those had diarrhoea...
-    diarrhoea == TRUE |
-      #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)
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    c) Fell ill within the time period of interest

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    • 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).
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    • Dinner 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).
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    # Start with copdata:
-copdata <- copdata %>% 
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    • You can calculate the incubation period for each participant by subtracting onset_datetime - meal_datetime.
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    Be aware of the type of variable incubation is (check class()), as well as of missing valuesNA.

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    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

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    copdata <- copdata %>% 
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    [1] 15
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    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!

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    We see that the median incubation time is 15 hours. This is useful information, as incubation periods tend to be relatively pathogen-specific.

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    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 (onset_datetime) at least one symptom (diarrhoea, bloody or vomiting) 48h after meal_datetime to become a case.

    -
    - -
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    What are some of the implications this decision may lead to? (implications of this case definition)

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    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.

    -
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    -

    d) Finally, with this information you can create a new case column to hold the binary (TRUE/FALSE) case definition variable.

    -
    -
    -Code -
    copdata <- copdata %>% 
-  mutate(case = case_when(
-    # Those who had symptoms <48h from the meal are cases (TRUE)
-    gastrosymptoms == TRUE & 
-      onset_datetime >= meal_datetime &
-      onset_datetime <= (meal_datetime + days(2)) ~ TRUE,
-    # Those who had symptoms >48h from the meal are non-cases (FALSE)
-    gastrosymptoms == TRUE & 
-      onset_datetime > (meal_datetime + days(2)) ~ FALSE,
-    # 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)
-  )
    -
    -
    -

    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…).

    -

    Moreover, 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”.

    -

    The two situations above are a reminder that you need to be both careful and aware of the implications of your data analysis decisions.

    -
    - -
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    What do you think are the risks of mis-classifying cases as non-cases in your analysis?

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    -

    We will have bias either towards or away from the null, depending on the proportions of subjects misclassified.

    -
    -
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    -
    -Code -
    # Tabulate cases:
-janitor::tabyl(dat = copdata, case)
    -
    -
    - -
    - -
    -
    -
    -

    Let’s have a look at how many people ate a meal, had symptoms, and were considered as cases after applying our case definition:

    -
    -
    -Code -
    copdata %>% 
-  summarise(atemeal = sum(meal == TRUE),
-            hadsympt = sum(gastrosymptoms == TRUE),
-            nb_cases = sum(case == TRUE)
-            )
    -
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    4. Export clean data

    -

    Finally, we can save the cleaned data set as a new file Copenhagen_clean2_YOURINITIALS, under the data folder, before we proceeding with descriptive analysis.

    -
    -
    -Code -
    rio::export(x = copdata, 
-            file = here::here("data", "Copenhagen_clean2_2024.rds"))
    -
    -
    - - - - -
    - -
    - -
    - - - - \ No newline at end of file diff --git a/docs/SimOb_RInject11.html b/docs/SimOb_RInject11.html deleted file mode 100644 index 21d9281..0000000 --- a/docs/SimOb_RInject11.html +++ /dev/null @@ -1,2895 +0,0 @@ - - - - - - - - - -Copenhagen Introductory Course - Laboratory data and descriptive analysis - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    Laboratory data and descriptive analysis

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    1. Learning outcomes

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    At the end of the session, participants will be able to:

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    • Merge two datasets using a common column.
      • -
    • Discuss which descriptive statistics would you use to describe the cases
      • -
    • Describe the cases by person and place (this is an outbreak in a high school, so we won’t explore “place” this time!)
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    • Generate preliminary hypotheses bases on the descriptions.
      • -
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    2. Story/plot description

    -

    You 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.

    -

    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.

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    3. Questions/Assignments

    -

    If 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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    3.1 Install packages (if needed) and load libraries

    -
    -
    -Code -
    # 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)
    -
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    3.2 Import your data

    -
    -
    -Code -
    # Import the clean data set:
-copdata <- rio::import(here::here("data", "Copenhagen_clean2_2024.rds")) 
-lab <- rio::import(here::here("data", "lab_pos.xlsx"))
    -
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    -
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    3.3 Merge and explore the lab data

    -
    -
    -Code -
    copdatalab <- left_join(copdata, lab, by = "id")
-# Tabulate:
-janitor::tabyl(dat = copdatalab, result)
    -
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    3.4 Describe the outbreak in terms of time.

    -

    You 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.

    -
    -

    a) Create a histogram to visualize the incubation period (geom_histogram is a good function to use for this!).

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    What can you infer from the incubation periods in the histogram?

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    -Code -
    #| label: inc_time
-
-# Create a dataset with only cases
-cases <- copdata %>% 
-  filter(case == TRUE)
-
-incplot <- cases %>% 
-  # Create an empty ggplot frame:
-  ggplot() +
-  # Add a histogram of incubation:
-  geom_histogram(
-    mapping = aes(x = incubation), 
-    # Set bin widths to 6 hours:
-    binwidth = 6) +
-  # Adapt scale to better fit data
-  scale_x_continuous(breaks = seq(0, 48, 6)) + 
-  # Label x and y axes:
-  labs(x = "Incubation period in 6-hour bins",
-       y = "Number of cases")
-
-# Print plot:
-incplot
    -
    -
    -

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    b) Create an epicurve for the date and time of onset (using onset_datetime), limiting the input data to cases.

    -
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    • Use scale_x_datetime() and chose a value for date_breaks() based on your calculated incubation period.
      • -
    • Label your x and y axes using labs().
      • -
    -
    - -
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    -

    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 date_breaks argument.

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    -Code -
    # Create a vector with sequences every 6h from the first to the last case
-breaks_6h <- seq(from = min(cases$onset_datetime, na.rm = TRUE),
-                 to = max(cases$onset_datetime, na.rm = TRUE),
-                 by = "6 hours")
-
-# Fetch cases data:
-epicurve_datetime <- cases %>%  
-  # Add factor onset_datetime to ggplot aesthetic:
-  ggplot(
-    mapping = aes(x = onset_datetime)) + 
-  # Add geom_histogram:
-  geom_histogram(
-    # Apply the vector of requences created above
-    breaks = breaks_6h) +
-  # Adapt scale to data and adjust axis label angle:
-  scale_x_datetime(
-    date_breaks = "6 hours",
-    labels = label_date_short()) +
-  # Update x and y axis labels:
-  labs(x = "Date and time of onset symptoms", 
-       y = "Number of cases") +
-  # Remove unnecessary grid lines:
-  theme_bw()
-
-# Print epicurve:
-epicurve_datetime
    -
    -
    -

    -
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      -
    1. Building up on your previous epicurve, create another epicurve to compare between sexes and additionally investigate how teachers versus students were distributed.
      2. -
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      -
    • Use fill = group
      • -
    • Use sex as your facets for facet_wrap()
      • -
    -
    - -
    -
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    fill 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.

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    If 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.

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    -Code -
    epicurve_strata <- cases %>% 
-  # Add factor onset_day to ggplot aesthetic:
-  ggplot(
-    mapping = aes(x = onset_datetime, fill = group)) + 
-  # Add nicer fill colours:
-  scale_fill_manual(values = c("darkred", "lightblue")) +
-    # Add geom_histogram:
-  geom_histogram(
-    # Apply the vector of requences created above
-    breaks = breaks_6h) +
-  # Adjust x axis scales to a suitable unit:
-  scale_x_datetime(
-    date_breaks = "6 hours", 
-    labels = label_date_short()) +
-  # Update x and y axis labels:
-  labs(x = "Date and time of onset", 
-       y = "Number of cases", 
-       fill = "Group", 
-       title = "Epicurve of the outbreak, stratified by sex",
-       subtitle = str_glue("Spetses, October 2024, N = {sum(copdata$case)}")) +
-  # Stratify by sex:
-  facet_wrap(facets = "sex",
-             ncol = 2) +
-  # Add theme:
-  theme_bw()
-
-# Print epicurve:
-epicurve_strata
    -
    -
    -

    -
    -
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    - -
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    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?

    -
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    - -
    -
    -

    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.

    -

    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.

    -

    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.

    -

    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!

    -
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    3.5. Describe the outbreak in terms of person.

    -

    You 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.

    -
    -

    a) Create a cross-tabulation of case with group.

    -
    -
    -Code -
    copdata %>% 
-  janitor::tabyl(case, group) %>% 
-  adorn_totals() %>% 
-  adorn_percentages() %>% 
-  adorn_pct_formatting()
    -
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    b) Create a cross-tabulation of case with sex.

    -
    -
    -Code -
    copdata %>% 
-  janitor::tabyl(case, sex) %>% 
-  adorn_totals() %>% 
-  adorn_percentages() %>% 
-  adorn_pct_formatting()
    -
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    What do you think of these results?

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    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.

    -
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    If 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().

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    c) Extra - Age-sex pyramid of cases.

    -

    Hint: Change show_midpoint = FALSE to TRUE to see skewedness in the data patterns more easily.

    -
    -
    -Code -
    copdata <- copdata %>% 
-  # Create age categories:
-  mutate(age_cat = epikit::age_categories(
-    # Name of age column:
-    x = age, 
-    # Define the age categories:
-    breakers = c(0, 10, 16, 18, 20, 50, 70)
-    )
-  )
-
-
-# Check age categories:
-janitor::tabyl(copdata, age_cat)
    -
    -
    - -
    - -
    -
    -
    -Code -
    # Pipe copdata:
-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
    -
    -
    -

    -
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    -
    -

    3.6 Explore the distribution of all clinical signs (symptoms).

    -
    -

    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.

    -
    -
    -Code -
    # 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(.))) %>%
-  # 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, 
-    # 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
    -
    -
    - -
    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    CharacteristicOverall -N = 3771Non-case -N = 1611Case -N = 2161
    Diarrhoea
        FALSE46 (18%)40 (100%)6 (2.8%)
        TRUE206 (82%)0 (0%)206 (97%)
    Dysentary
        FALSE189 (97%)42 (100%)147 (97%)
        TRUE5 (2.6%)0 (0%)5 (3.3%)
    Vomiting
        FALSE149 (69%)42 (100%)107 (62%)
        TRUE66 (31%)0 (0%)66 (38%)
    Abdominal pain
        FALSE35 (14%)6 (12%)29 (15%)
        TRUE207 (86%)44 (88%)163 (85%)
    Nausea
        FALSE55 (25%)12 (26%)43 (24%)
        TRUE169 (75%)34 (74%)135 (76%)
    Fever
        FALSE127 (74%)32 (80%)95 (73%)
        TRUE44 (26%)8 (20%)36 (27%)
    Headache
        FALSE83 (38%)11 (25%)72 (41%)
        TRUE137 (62%)33 (75%)104 (59%)
    Joint pain
        FALSE159 (85%)32 (84%)127 (85%)
        TRUE29 (15%)6 (16%)23 (15%)
    1 n (%)
    -
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    Do you think the symptoms selected for the case definition were the right ones, or would you change anything?

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    -
    -
    -
    - -
    -
    -

    If 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.

    -
    -
    -
    -
    -
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    b) Extra - Bar plot of symptoms stratified by case definition.

    -
    -
    -Code -
    # 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(`FALSE` = "Non-case", 
-                                   `TRUE` = "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 == TRUE) %>% 
- 
-   # 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)
    -
    -
    -
    `summarise()` has grouped output by 'Symptoms'. You can override using the
-`.groups` argument.
    -
    -
    -Code -
    # Print plot:
-symptom_bar
    -
    -
    -

    -
    -
    -
    -
    -
    -

    3.7 Attack proportions

    -
    -

    a) Calculate the overall attack proportions (percentage of cases among the total observed individuals). You could use tabyl().

    -
    -
    -Code -
    # 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 == TRUE) %>% 
-  # Select the column percent:
-  select(percent) %>% 
-  # Extract (pull) the value from this cell:
-  pull()
-
-# Print result:
-total_ap
    -
    -
    -
    [1] "57.3%"
    -
    -
    -
    - -
    -
    -

    What can you infer from the overall attack proportion about this outbreak and possible vehicles/exposures?

    -
    -
    -
    -
    - -
    -
    -

    The overall attack proportion is 57.3%. This means that more than half of the people who ate a meal were cases!

    -
    -
    -
    -
    -
    -

    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.

    -
    -
    -Code -
    # 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
    -
    -
    - -
    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    CharacteristicOverall N = 3771Non-case N = 1611Case N = 2161
    class
        1131 (100%)63 (48%)68 (52%)
        2101 (100%)44 (44%)57 (56%)
        3111 (100%)38 (34%)73 (66%)
        Unknown341618
    group
        teacher15 (100%)9 (60%)6 (40%)
        student362 (100%)152 (42%)210 (58%)
    sex
        female213 (100%)96 (45%)117 (55%)
        male164 (100%)65 (40%)99 (60%)
    1 n (%)
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    3.8 Draft the relevant paragraph(s) and table(s) and/or figure(s) in your outbreak report (Methods and results).

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    To 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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+0,0 @@ - - - - - - - - - -Copenhagen Introductory Course - Univariable analysis - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    Univariable analysis

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    1. Learning outcomes

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    At the end of the session, participants will be able to:

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    • Perform hypothesis tests.

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    • Estimate risk ratios (also called relative risk) for categorical data.

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    • Interpret the univariable results.

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    • (Optional) Investigate dose-response relationships in categorical data.2. Story/plot description

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    2. Story/plot description

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    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.

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    Now, you will practice performing hypothesis testing and calculating RR in R to investigate the associations of suspicious food items with the disease.

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    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):

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    • For continuous variables:

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      • shapiro.test() (for checking if normally distributed)

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      • t.test() (for normal distributions)

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      • wilcox.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.)

        • -
      • -

    • For categorical variables:

      -
        -

      • chisq.test() (if all comparisons include at least 5 cases)

        • -

      • fisher.test() (if there are < 5 cases for any comparison)

        • -
      • -
    -
    -
    -

    3. Questions/Assignments

    -
    -
    -

    3.1. Install packages and load libraries

    -
    -
    -Code -
    # 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)
    -
    -
    -
    -
    -

    3.2. Import your data

    -
    -
    -Code -
    # Import the raw data set:
-copdata <- rio::import(here::here("data", "Copenhagen_clean2_2024.rds"))
    -
    -
    -
    -
    -

    3.3. Hypothesis tests for other variables

    -

    Check if the following variables are associated with being a case: age, sex, class and group.

    -
    -

    a) age

    -

    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<0.05 means that we reject the null hypothesis that the data follow the normal distribution. For age, the p-value is <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 <20 years.

    -
    -
    -Code -
    # Check if age overall follows a normal distribution:
-shapiro.test(copdata$age)
    -
    -
    -
    
-    Shapiro-Wilk normality test
-
-data:  copdata$age
-W = 0.31302, p-value < 2.2e-16
    -
    -
    -Code -
    # Can simply have a look at
-hist(copdata$age)
    -
    -
    -

    -
    -
    -Code -
    # Looking only at the students:
-students <- copdata %>% 
-  filter(group == "student")
-hist(students$age)
    -
    -
    -

    -
    -
    -

    Age overall (nor within the students’ group) is not normally distributed.

    -

    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>0.05 we do not reject the null hypothesis.

    -
    -
    -Code -
    # Perform Wilcoxon rank sum test on age and sex:
-wilcox.test(age ~ case, 
-            data = copdata)
    -
    -
    -
    
-    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
    -
    -
    -
    -
    -

    b) sex

    -
    -
    -Code -
    copdata %>% 
-  select(sex, case) %>% 
-  tbl_summary(by = case) %>% 
-  add_p()
    -
    -
    - -
    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    CharacteristicFALSE, N = 1611TRUE, N = 2161p-value2
    sex0.3
        female96 (60%)117 (54%)
        male65 (40%)99 (46%)
    1 n (%)
    2 Pearson’s Chi-squared test
    -
    -
    -
    -
    - -
    -
    -

    What do these results tell you - is there an association between sex and being a case?

    -

    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?

    -
    -
    -
    -
    -
    -

    c) class

    -
    -
    -Code -
    copdata %>% 
-  select(class, case) %>% 
-  tbl_summary(by = case) %>% 
-  add_p()
    -
    -
    - -
    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    CharacteristicFALSE, N = 1611TRUE, N = 2161p-value2
    class0.090
        163 (43%)68 (34%)
        244 (30%)57 (29%)
        338 (26%)73 (37%)
        Unknown1618
    1 n (%)
    2 Pearson’s Chi-squared test
    -
    -
    -
    -

    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.

    -

    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.

    -
    -
    -

    d) group

    -
    -
    -Code -
    copdata %>% 
-  select(group, case) %>% 
-  tbl_summary(by = case) %>% 
-  add_p()
    -
    -
    - -
    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    CharacteristicFALSE, N = 1611TRUE, N = 2161p-value2
    group0.2
        teacher9 (5.6%)6 (2.8%)
        student152 (94%)210 (97%)
    1 n (%)
    2 Pearson’s Chi-squared test
    -
    -
    -
    -

    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.

    -
    -
    -

    Let’s do all together

    -
    -
    -Code -
    copdata %>% 
-  select(sex, class, group, case) %>% 
-  tbl_summary(by = case) %>% 
-  add_p()
    -
    -
    - -
    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    CharacteristicFALSE, N = 1611TRUE, N = 2161p-value2
    sex0.3
        female96 (60%)117 (54%)
        male65 (40%)99 (46%)
    class0.090
        163 (43%)68 (34%)
        244 (30%)57 (29%)
        338 (26%)73 (37%)
        Unknown1618
    group0.2
        teacher9 (5.6%)6 (2.8%)
        student152 (94%)210 (97%)
    1 n (%)
    2 Pearson’s Chi-squared test
    -
    -
    -
    -
    -
    -
    -

    3.4. Risk Ratios

    -

    The 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.

    -
    -

    a) Calculate 95% CI Risk Ratios for food

    -

    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 CSTable()), or all at once (see hint below).

    -
    - -
    -
    -

    Create a food_vars vector containing food variables of interest and use the function CSTable() of the EpiStats package.

    -

    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.

    -
    -
    -
    -
    -
    -Code -
    # You could use the EpiStats package for each food item
-CS(copdata, "case", "feta")
    -
    -
    -
    $df1
-          Cases Non Cases Total Risk
-Exposed     156       115   271 0.58
-Unexposed    60        42   102 0.59
-Total       216       157   373 0.58
-
-$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>chi2                  0.826       NA       NA
    -
    -
    -Code -
    CS(copdata, "case", "sardines")
    -
    -
    -
    $df1
-          Cases Non Cases Total Risk
-Exposed     150       105   255 0.59
-Unexposed    65        52   117 0.56
-Total       215       157   372 0.58
-
-$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>chi2                  0.553       NA       NA
    -
    -
    -Code -
    CS(copdata, "case", "eggplant")
    -
    -
    -
    $df1
-          Cases Non Cases Total Risk
-Exposed     123        93   216 0.57
-Unexposed    83        60   143 0.58
-Total       206       153   359 0.57
-
-$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>chi2                  0.837       NA       NA
    -
    -
    -Code -
    CS(copdata, "case", "moussaka")
    -
    -
    -
    $df1
-          Cases Non Cases Total Risk
-Exposed     201       137   338 0.59
-Unexposed    14        22    36 0.39
-Total       215       159   374 0.57
-
-$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>chi2                  0.018       NA       NA
    -
    -
    -
    -
    -Code -
    # 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",  
-               "champagne", "beer", "redwine", "whitewine")
-
-# ...and using EpiStats::CSTable() to run all variables together!
-CSTable(copdata, "case", food_vars)
    -
    -
    -
    $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
    -
    -
    -
    -
    -

    b) Prepare the RR table for publication

    -
    - -
    -
    -

    Use flextable() and set_header_labels().

    -
    -
    -
    -
    -
    -Code -
    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"))
    -
    -
    -
    - -
    -
    -

    · What can you infer from this table?

    -

    · 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?

    -

    · 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!

    -

    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)

    -
    -
    -
    -

    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

    -
    -
    -Code -
    # 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, 
-                       family = binomial(link = "log")) %>% 
-  tidy(exponentiate = TRUE, 
-       conf.int = TRUE)
-
-binom_orzoD_exp
    -
    -
    - -
    - -
    -
    -
    -
    - -
    -
    -

    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.

    -
    -
    -

    b) Moussaka

    -
    -
    -Code -
    # Let's get the results directly exponentiated
-binom_moussakaD_exp <- glm(case ~ moussakaD, data = copdata, 
-                       family = binomial(link = "log")) %>% 
-  tidy(exponentiate = TRUE, 
-       conf.int = TRUE)
-
-binom_moussakaD_exp
    -
    -
    - -
    - -
    -
    -
    -
    -
    -

    c) Champagne

    -
    -
    -Code -
    # 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
    -
    -
    - -
    - -
    -
    -
    -
    -
    -
    -

    3.6 Summary

    -

    As a summary of what you've done above, answer these questions:

    -
      -

    • Is the respondents' sex associated with being a case?

      • -

    • Is the school class associated with being a case?

      • -

    • Which foods increase the risk of being a case?

      • -

    • (Optional) Is there a dose-response relationship between the food items and being a case?

      • -

    • 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?

      • -
    - - - - -
    - -
    - -
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    1. Learning outcomes

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    At the end of the session, participants will be able to:

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    • Consider the effect of confounding and effect modification on the association between exposure and disease,

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    • Perform stratified analysis using the Mantel-Haenszel approach

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    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.

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    2. Story/plot description

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    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).

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    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.

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    3. Questions/Assignments

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    3.1. Confounders and effect modification

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    Discuss 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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    3.2. Install packages and load libraries

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    # 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)
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    3.3. Import your data

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    # Import the raw data set: 
-copdata <- rio::import(here::here("data", "Copenhagen_clean2_2024.rds"))
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    3.4. Consider and assess for confounding and/or effect modification

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    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.

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    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.

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    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.

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    Use the function csinter() of the EpiStats package.

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    a) Moussaka as exposure of interest, stratified by having eaten orzo

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    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?

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    stratall <- copdata %>% 
-  # Mutate across to convert cases to numeric:
-  mutate(across(.cols = case, 
-                .fns = ~ as.numeric(.)))
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-# Pass data to the csinter function:
-orzostrata <- csinter(x = stratall, 
-                       cases = "case", 
-                       exposure = "moussaka", 
-                       by = "orzo")
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-orzostrata
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    $df1
-  CSInter case - moussaka by(orzo) Total Cases Risk %          P.est. Stats
-1                         orzo = 1   338  <NA>     NA Risk difference  0.10
-2                          Exposed   330   198  60.00      Risk Ratio  1.20
-3                        Unexposed     8     4  50.00 Attrib.risk.exp  0.17
-4                                     NA  <NA>     NA Attrib.risk.pop  0.16
-5                         orzo = 0    36  <NA>     NA Risk difference  0.02
-6                          Exposed     8     3  37.50      Risk Ratio  1.05
-7                        Unexposed    28    10  35.71 Attrib.risk.exp  0.05
-8                                     NA  <NA>     NA Attrib.risk.pop  0.01
-9              Missing / Missing %     3  0.8%     NA            <NA>    NA
-  95%CI.ll 95%CI.ul
-1    -0.25     0.45
-2     0.60     2.41
-3    -0.68     0.59
-4       NA       NA
-5    -0.36     0.40
-6     0.38     2.92
-7    -1.65     0.66
-8       NA       NA
-9       NA       NA
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-$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
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    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)):

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    # Perform Wilcoxon rank sum test on orzo and moussaka:
-wilcox.test(orzo ~ moussaka, 
-            data = copdata)
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-    Wilcoxon rank sum test with continuity correction
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-data:  orzo by moussaka
-W = 1496, p-value < 2.2e-16
-alternative hypothesis: true location shift is not equal to 0
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    b) Champagne as exposure of interest, stratified by having eaten orzo

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    # Pass data to the csinter function:
-champstrata <- csinter(x = stratall, 
-                       cases = "case", 
-                       exposure = "champagne", 
-                       by = "orzo")
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    Are there any indications to make you think there may be effect modification and/or confounding?

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    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).

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    Above, we uses ccinter to stratify and save the object as "orzostrata".

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    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.

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    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.

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    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!

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    - - - - \ No newline at end of file diff --git a/docs/Stratified.html b/docs/Stratified.html index f269d6e..94e7aed 100644 --- a/docs/Stratified.html +++ b/docs/Stratified.html @@ -192,8 +192,8 @@

    On this page

  • 3.3. Import your data
  • 3.4. Consider and assess for confounding and/or effect modification
    • @@ -231,7 +231,7 @@

    1. Learning outcomes

    2. Story/plot description

    -

    From the univariable analysis, it seems that eating orzpo and eating pasta 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.

    @@ -270,18 +270,15 @@

    3.3. Import your data

    Show the code 
    # 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)
    -
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    Warning: Missing `trust` will be set to FALSE by default for RDS in 2.0.0.
    -

    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, veal). You may consider stratifying by veal, 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 pasta and champagne, stratified by veal, 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.

    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.

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    a) Veal

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    a) Pasta

    Using epitools::riskratio function:

    Show the code -
    epitools::riskratio(copdata$vealD,
-                    copdata$case,
+
    epitools::riskratio(copdata$pastaD,                     
+                    copdata$case,                     
                     conf.level = 0.95)
    
 
    $data
          Outcome
 Predictor   0   1 Total
-    0      19  15    34
-    1      30  40    70
-    2      78 106   184
-    3      33  54    87
-    Total 160 215   375
+    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.
 Predictor estimate     lower    upper
         0 1.000000        NA       NA
-        1 1.295238 0.8431805 1.989659
-        2 1.305797 0.8769752 1.944304
-        3 1.406897 0.9314233 2.125090
+        1 1.503297 0.9257878 2.441057
+        2 1.619168 1.0310538 2.542742
+        3 1.857014 1.1730804 2.939696
 
 $p.value
          two-sided
-Predictor midp.exact fisher.exact chi.square
-        0         NA           NA         NA
-        1 0.22186384    0.2951163 0.21193013
-        2 0.15357032    0.1884220 0.14587265
-        3 0.07956697    0.1017336 0.07298506
+Predictor  midp.exact fisher.exact chi.square
+        0          NA           NA         NA
+        1 0.081364161  0.100893180 0.07613202
+        2 0.015337953  0.017069765 0.01373981
+        3 0.002010616  0.002370887 0.00162311
 
 $correction
 [1] FALSE
@@ -2769,23 +2769,18 @@ 
    a) Veal
    
 
    
 
    
 Show the code
-
    # 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"))
-
-# To get exponentiated:
-binom_vealD_exp <- glm(case ~ vealD, data = copdata, 
-                       family = binomial(link = "log")) %>% 
-  tidy(exponentiate = TRUE, 
-       conf.int = TRUE)
-
-binom_vealD_exp
    
+
    # 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
    
 
    
 
    
 
    
   
 
    
 
    
@@ -2806,43 +2801,43 @@ 
    a) Veal
    -

    Results suggest a dose response relationship of having eaten veal, pointing towards veal as the potential vehicle. The higher the amount of veal they 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) Pasta

    +
    +

    b) Veal

    Using epitools::riskratio function:

    Show the code -
    epitools::riskratio(copdata$pastaD,                     
-                    copdata$case,                     
+
    epitools::riskratio(copdata$vealD,
+                    copdata$case,
                     conf.level = 0.95)
    
 
    $data
          Outcome
 Predictor   0   1 Total
-    0      23  13    36
-    1      32  38    70
-    2      76 107   183
-    3      28  57    85
-    Total 159 215   374
+    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.
 Predictor estimate     lower    upper
         0 1.000000        NA       NA
-        1 1.503297 0.9257878 2.441057
-        2 1.619168 1.0310538 2.542742
-        3 1.857014 1.1730804 2.939696
+        1 1.295238 0.8431805 1.989659
+        2 1.305797 0.8769752 1.944304
+        3 1.406897 0.9314233 2.125090
 
 $p.value
          two-sided
-Predictor  midp.exact fisher.exact chi.square
-        0          NA           NA         NA
-        1 0.081364161  0.100893180 0.07613202
-        2 0.015337953  0.017069765 0.01373981
-        3 0.002010616  0.002370887 0.00162311
+Predictor midp.exact fisher.exact chi.square
+        0         NA           NA         NA
+        1 0.22186384    0.2951163 0.21193013
+        2 0.15357032    0.1884220 0.14587265
+        3 0.07956697    0.1017336 0.07298506
 
 $correction
 [1] FALSE
@@ -2855,18 +2850,23 @@ 
    b) Pasta
    
 
    
 
    
 Show the code
-
    # 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
    
+
    # 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"))
+
+# To get exponentiated:
+binom_vealD_exp <- glm(case ~ vealD, data = copdata, 
+                       family = binomial(link = "log")) %>% 
+  tidy(exponentiate = TRUE, 
+       conf.int = TRUE)
+
+binom_vealD_exp
    
 
    
 
    
 
    
   
 
    
 
    
@@ -3785,7 +3785,7 @@ 
    3.6 Summary
    
 · 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 veal, pasta 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!
+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 pasta was "clean" but eaten by all the people who ate the food item that actually was contaminated!(Optional)
 
@@ -3793,59 +3793,59 @@ 
    3.6 Summary
    
 
 Check for a dose-response relationship between the food items with the highest RR values (the top 3) and being a case.
 
-#### a) Veal
+#### a) Pasta
 
 Using `epitools::riskratio` function:
 
 ```{r}
-epitools::riskratio(copdata$vealD,
-                    copdata$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_vealD <- glm(case ~ vealD, data = copdata, 
-             family = binomial(link = "log"))
-
-# To get exponentiated:
-binom_vealD_exp <- glm(case ~ vealD, data = copdata, 
-                       family = binomial(link = "log")) %>% 
-  tidy(exponentiate = TRUE, 
-       conf.int = TRUE)
-
-binom_vealD_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 veal, pointing towards veal as the potential vehicle. The higher the amount of veal they ate, the stronger is the association (RR) with getting ill/being a case.
-
-#### b) Pasta
-
-Using `epitools::riskratio` function:
-
-```{r}
-epitools::riskratio(copdata$pastaD,                     
-                    copdata$case,                     
-                    conf.level = 0.95)
-```
-
-Using a binomial regression:
-
-```{r}
-# Let's get the results directly exponentiated
-binom_pastaD_exp <- glm(case ~ pastaD, 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_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 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) Veal
+
+Using `epitools::riskratio` function:
+
+```{r}
+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"))
+
+# To get exponentiated:
+binom_vealD_exp <- glm(case ~ vealD, data = copdata, 
                        family = binomial(link = "log")) %>% 
   tidy(exponentiate = TRUE, 
        conf.int = TRUE)
 
-binom_pastaD_exp
+binom_vealD_exp
 ```
 
 #### c) Champagne
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-Copenhagen_IC_qmd - About
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diff --git a/docs/search.json b/docs/search.json
index 749296f..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 <- 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 <- copdata %>% \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 <- copdata %>%       \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 <- copdata %>%       \n  # Select all the columns with column names that end in upper case 'D':   \n  select(ends_with(\"D\", ignore.case = FALSE)) %>%       \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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-    "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 <- copdata %>% \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 <- copdata %>% \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 <- copdata %>% \n    dplyr::select(\n      ends_with(\"D\", ignore.case = FALSE)) %>% \n    names()\n\n# Another way of doing it:\n# food_dose <- c(\"fetaD\", \"sardinesD\", \"eggplantD\", \"moussakaD\", \n#                 \"orzoD\", \"greeksalD\", \"dessertD\", \"breadD\", \n#                 \"champagneD\", \"beerD\", \"redwineD\", \"whitewineD\")\n\n\ncopdata <- copdata %>% \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$dayonset)\n\n\n[1] \"character\"\n\n\nCode\n# Update copdata:\ncopdata <- copdata %>% \n  # Change column to date class:\n  dplyr::mutate(\n    dayonset = lubridate::dmy(dayonset))\n\n# Check class of updated column:\nclass(copdata$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 <- copdata %>% \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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-    "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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-    "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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-    "section": "3.3. Import your data",
-    "text": "3.3. Import your data\n\n\nCode\n# Import the raw data set: \ncopdata <- 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 <- copdata %>% \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 <- csinter(x = stratall, \n                       cases = \"case\", \n                       exposure = \"veal\", \n                       by = \"pasta\")\n\npastastrata\n\n\n$df1\n  CSInter case - veal by(pasta) Total Cases Risk %          P.est. Stats\n1                     pasta = 1   338  <NA>     NA Risk difference  0.10\n2                       Exposed   330   198  60.00      Risk Ratio  1.20\n3                     Unexposed     8     4  50.00 Attrib.risk.exp  0.17\n4                                  NA  <NA>     NA Attrib.risk.pop  0.16\n5                     pasta = 0    36  <NA>     NA Risk difference  0.02\n6                       Exposed     8     3  37.50      Risk Ratio  1.05\n7                     Unexposed    28    10  35.71 Attrib.risk.exp  0.05\n8                                  NA  <NA>     NA Attrib.risk.pop  0.01\n9           Missing / Missing %     3  0.8%     NA            <NA>    NA\n  95%CI-ll 95%CI-ul\n1    -0.25     0.45\n2     0.60     2.41\n3    -0.68     0.59\n4       NA       NA\n5    -0.36     0.40\n6     0.38     2.92\n7    -1.65     0.66\n8       NA       NA\n9       NA       NA\n\n$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 < 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 <- 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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-    "title": "Inject 11: Descriptive analysis",
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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 <- rio::import(here::here(\"data\", \"Copenhagen_clean2_2024.rds\")) \nlab <- 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 <- copdata %>% \n  filter(case == TRUE)\n\nincplot <- cases %>% \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 <- 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 <- cases %>%  \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 <- cases %>% \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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-    "text": "4. Person\n\na) Cross-tabulation of cases with group\n\n\nCode\ncopdata %>% \n  janitor::tabyl(case, group) %>% \n  adorn_totals() %>% \n  adorn_percentages() %>% \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 %>% \n  janitor::tabyl(case, sex) %>% \n  adorn_totals() %>% \n  adorn_percentages() %>% \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 <- copdata %>% \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 <- copdata %>% \n  # Filter for cases only:\n  filter(case == TRUE) %>% \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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-    "text": "5. Symptoms\n\nSummary table of symptoms, stratified by case definition\n\n\n\nCode\n# Create summary table:\ntabsymptoms <- copdata %>% \n    # Select person characteristics to summarise:\n  select(case, diarrhoea, bloody, vomiting,\n             abdo, nausea, fever,headache, jointpain) %>% \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(.))) %>%\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(.))) %>% \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  ) %>% \n  \n  # Add totals:\n  add_overall() %>% \n  # Make variable names bold and italics:\n  bold_labels() %>% \n  italicize_labels() %>% \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 <- 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 <- ggplot2::as_labeller(c(`FALSE` = \"Non-case\", \n                                   `TRUE` = \"Case\"))\n# Select variables and cases:\nsymptom_bar <- copdata %>% \n  # Select symptom columns:\n  select(case, c(all_of(symptoms))) %>%\n  # Drop NAs:\n  drop_na() %>% \n  # Reshape (pivot longer):\n  pivot_longer(!case, \n               names_to = \"Symptoms\", \n               values_drop_na = TRUE) %>% \n  # Keep only TRUE values:\n  filter(value == TRUE) %>% \n \n   # Group by symptoms and case:\n  group_by(Symptoms, case) %>% \n  # Count for each symptom by case:\n  dplyr::summarise(count = n()) %>% \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": "3.7 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 <- tabyl(copdata, case) %>% \n # Add row totals:\n  adorn_totals(where = \"row\") %>% \n  # Add percentages with 1 digit after the decimal point:\n  adorn_pct_formatting(digits = 1) %>% \n  # Filter to rows where case is TRUE:\n  filter(case == TRUE) %>% \n  # Select the column percent:\n  select(percent) %>% \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 <- copdata %>% \n  # Select columns:\n  select (case, class, group, sex) %>% \n  \n  # Create table:\n  tbl_summary(\n    # Stratified by case\n    by = case,\n    # with row percentages\n    percent = \"row\") %>%\n  \n  # Add totals:\n  add_overall() %>%\n  \n  # Make variable names bold and italics:\n  bold_labels() %>% \n  italicize_labels() %>% \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 <- 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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-    "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 <- copdata %>% \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 <- copdata %>% \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 <- copdata %>% \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 <- copdata %>% \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 <- copdata %>% \n  mutate(case = case_when(\n    # Those who had symptoms <48h from the meal are cases (TRUE)\n    gastrosymptoms == TRUE & \n      onset_datetime >= meal_datetime &\n      onset_datetime <= (meal_datetime + days(2)) ~ TRUE,\n    # Those who had symptoms >48h from the meal are non-cases (FALSE)\n    gastrosymptoms == TRUE & \n      onset_datetime > (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 %>% \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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+    "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 < 5 cases for any comparison)"
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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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     "title": "Univariable analysis",
     "section": "3.2. Import your data",
-    "text": "3.2. Import your data\n\n\nCode\n# Import the raw data set:\ncopdata <- 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 <- 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<0.05 means that we reject the null hypothesis that the data follow the normal distribution. For age, the p-value is <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 <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 < 2.2e-16\n\n\nCode\n# Can simply have a look at\nhist(copdata$age)\n\n\n\n\n\nCode\n# Looking only at the students:\nstudents <- copdata %>% \n  filter(group == \"student\")\nhist(students$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>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 %>% \n  select(sex, case) %>% \n  tbl_summary(by = case) %>% \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 %>% \n  select(class, case) %>% \n  tbl_summary(by = case) %>% \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 %>% \n  select(group, case) %>% \n  tbl_summary(by = case) %>% \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 %>% \n  select(sex, class, group, case) %>% \n  tbl_summary(by = case) %>% \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<0.05 means that we reject the null hypothesis that the data follow the normal distribution. For age, the p-value is <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 <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 < 2.2e-16\n\n\nShow the code\n# Can simply have a look at\nhist(copdata$age)\n\n\n\n\n\n\n\n\n\nShow the code\n# Looking only at the students:\nstudents <- copdata %>% \n  filter(group == \"student\")\nhist(students$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>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 %>% \n  select(sex, case) %>% \n  tbl_summary(by = case) %>% \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 %>% \n  select(class, case) %>% \n  tbl_summary(by = case) %>% \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 %>% \n  select(group, case) %>% \n  tbl_summary(by = case) %>% \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 %>% \n  select(sex, class, group, case) %>% \n  tbl_summary(by = case) %>% \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>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>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>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>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 <- 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 <- CSTable(copdata, \"case\", food_vars) %>% \n  as.data.frame() %>% \n  rownames_to_column() %>% \n  flextable() %>% \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": "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 <- glm(case ~ orzoD, data = copdata, \n             family = binomial(link = \"log\"))\n\n# To get exponentiated:\nbinom_orzoD_exp <- glm(case ~ orzoD, data = copdata, \n                       family = binomial(link = \"log\")) %>% \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 <- glm(case ~ moussakaD, data = copdata, \n                       family = binomial(link = \"log\")) %>% \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 <- glm(case ~ champagneD, data = copdata, \n                       family = binomial(link = \"log\")) %>% \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"
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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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-    "title": "Case Definition",
-    "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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-    "title": "Case Definition",
-    "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 <- copdata %>% \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. => 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. => 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 <- copdata %>% \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 <- copdata %>% \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 <- copdata %>% \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 <- copdata %>% \n  mutate(case = case_when(\n    # Those who had symptoms <48h from the meal are cases (TRUE)\n    gastrosymptoms == TRUE & \n      onset_datetime >= meal_datetime &\n      onset_datetime <= (meal_datetime + days(2)) ~ TRUE,\n    # Those who had symptoms >48h from the meal are non-cases (FALSE)\n    gastrosymptoms == TRUE & \n      onset_datetime > (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 %>% \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 <- copdata %>% \n  filter(case == TRUE)\n\nincplot <- cases %>% \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 <- 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 <- cases %>%  \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 <- cases %>% \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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-    "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 %>% \n  janitor::tabyl(case, group) %>% \n  adorn_totals() %>% \n  adorn_percentages() %>% \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 %>% \n  janitor::tabyl(case, sex) %>% \n  adorn_totals() %>% \n  adorn_percentages() %>% \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 <- copdata %>% \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 <- copdata %>% \n  # Filter for cases only:\n  filter(case == TRUE) %>% \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": "3.4. 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$df2\n                Point estimate 95%CI.ll 95%CI.ul\nRisk difference          -0.01    -0.12     0.10\nRisk ratio                0.98     0.81     1.19\nPrev. frac. ex.           0.02    -0.19     0.19\nPrev. frac. pop           0.02       NA       NA\nchi2(1)                   0.05       NA       NA\nPr>chi2                  0.826       NA       NA\n\n\nShow the code\nCS(copdata, \"case\", \"sardines\")\n\n\n$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$df2\n                Point estimate 95%CI.ll 95%CI.ul\nRisk difference           0.03    -0.08     0.14\nRisk ratio                1.06     0.87     1.28\nAttr. frac. ex.           0.06    -0.14     0.22\nAttr. frac. pop           0.04       NA       NA\nchi2(1)                   0.35       NA       NA\nPr>chi2                  0.553       NA       NA\n\n\nShow the code\nCS(copdata, \"case\", \"eggplant\")\n\n\n$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$df2\n                Point estimate 95%CI.ll 95%CI.ul\nRisk difference          -0.01    -0.12     0.09\nRisk ratio                0.98     0.82     1.18\nPrev. frac. ex.           0.02    -0.18     0.18\nPrev. frac. pop           0.01       NA       NA\nchi2(1)                   0.04       NA       NA\nPr>chi2                  0.837       NA       NA\n\n\nShow the code\nCS(copdata, \"case\", \"pasta\")\n\n\n$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$df2\n                Point estimate 95%CI.ll 95%CI.ul\nRisk difference           0.24     0.07     0.40\nRisk ratio                1.65     1.06     2.58\nAttr. frac. ex.           0.40     0.06     0.61\nAttr. frac. pop           0.37       NA       NA\nchi2(1)                   7.45       NA       NA\nPr>chi2                  0.006       NA       NA\n\n\n\n\nShow the code\n# You can save time (and probably typos!) by creating a vector for food variables...\nfood_vars <- c(\"feta\", \"sardines\", \"eggplant\", \"pasta\", \n               \"veal\", \"tomsal\", \"dessert\", \"bread\",  \n               \"champagne\", \"beer\", \"redwine\", \"whitewine\")\n\n# ...and using EpiStats::CSTable() to run all variables together!\nCSTable(copdata, \"case\", food_vars)\n\n\n$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 <- CSTable(copdata, \"case\", food_vars) %>% \n  as.data.frame() %>% \n  rownames_to_column() %>% \n  flextable() %>% \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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-    "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 <- copdata %>% \n    # Select person characteristics to summarise:\n  select(case, diarrhoea, bloody, vomiting,\n             abdo, nausea, fever,headache, jointpain) %>% \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(.))) %>%\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(.))) %>% \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  ) %>% \n  \n  # Add totals:\n  add_overall() %>% \n  # Make variable names bold and italics:\n  bold_labels() %>% \n  italicize_labels() %>% \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 <- 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 <- ggplot2::as_labeller(c(`FALSE` = \"Non-case\", \n                                   `TRUE` = \"Case\"))\n# Select variables and cases:\nsymptom_bar <- copdata %>% \n  # Select symptom columns:\n  select(case, c(all_of(symptoms))) %>%\n  # Drop NAs:\n  drop_na() %>% \n  # Reshape (pivot longer):\n  pivot_longer(!case, \n               names_to = \"Symptoms\", \n               values_drop_na = TRUE) %>% \n  # Keep only TRUE values:\n  filter(value == TRUE) %>% \n \n   # Group by symptoms and case:\n  group_by(Symptoms, case) %>% \n  # Count for each symptom by case:\n  dplyr::summarise(count = n()) %>% \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": "3.8 Draft the relevant paragraph(s) and table(s) and/or figure(s) in your outbreak report (Methods and results).",
-    "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 <- left_join(copdata, lab, by = \"id\")\n# Tabulate:\njanitor::tabyl(dat = copdatalab, result)"
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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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-    "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 < 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 <- glm(case ~ orzoD, data = copdata, \n             family = binomial(link = \"log\"))\n\n# To get exponentiated:\nbinom_orzoD_exp <- glm(case ~ orzoD, data = copdata, \n                       family = binomial(link = \"log\")) %>% \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 <- glm(case ~ moussakaD, data = copdata, \n                       family = binomial(link = \"log\")) %>% \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 <- glm(case ~ champagneD, data = copdata, \n                       family = binomial(link = \"log\")) %>% \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 <- glm(case ~ pastaD, data = copdata, \n                       family = binomial(link = \"log\")) %>% \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 <- glm(case ~ vealD, data = copdata, \n             family = binomial(link = \"log\"))\n\n# To get exponentiated:\nbinom_vealD_exp <- glm(case ~ vealD, data = copdata, \n                       family = binomial(link = \"log\")) %>% \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 <- glm(case ~ champagneD, data = copdata, \n                       family = binomial(link = \"log\")) %>% \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 <- copdata %>% \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 <- csinter(x = stratall, \n                       cases = \"case\", \n                       exposure = \"moussaka\", \n                       by = \"orzo\")\n\norzostrata\n\n\n$df1\n  CSInter case - moussaka by(orzo) Total Cases Risk %          P.est. Stats\n1                         orzo = 1   338  <NA>     NA Risk difference  0.10\n2                          Exposed   330   198  60.00      Risk Ratio  1.20\n3                        Unexposed     8     4  50.00 Attrib.risk.exp  0.17\n4                                     NA  <NA>     NA Attrib.risk.pop  0.16\n5                         orzo = 0    36  <NA>     NA Risk difference  0.02\n6                          Exposed     8     3  37.50      Risk Ratio  1.05\n7                        Unexposed    28    10  35.71 Attrib.risk.exp  0.05\n8                                     NA  <NA>     NA Attrib.risk.pop  0.01\n9              Missing / Missing %     3  0.8%     NA            <NA>    NA\n  95%CI.ll 95%CI.ul\n1    -0.25     0.45\n2     0.60     2.41\n3    -0.68     0.59\n4       NA       NA\n5    -0.36     0.40\n6     0.38     2.92\n7    -1.65     0.66\n8       NA       NA\n9       NA       NA\n\n$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 < 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 <- 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 >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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-    "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)"
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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\nShow the code\n# Import the raw data set:\ncopdata <- import(here::here(\"data\", \"spetses_school.csv\"))"
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-    "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 <- copdata %>% \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 <- copdata %>%       \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 <- copdata %>%       \n  # Select all the columns with column names that end in upper case 'D':   \n  select(ends_with(\"D\", ignore.case = FALSE)) %>%       \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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-    "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\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 <- copdata %>% \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 <- copdata %>% \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 <- copdata %>% \n    select(\n      ends_with(\"D\", ignore.case = FALSE)) %>% \n    names()\n\n# Another way of doing it:\n# food_dose <- c(\"fetaD\", \"sardinesD\", \"eggplantD\", \"pastaD\", \n#                 \"vealD\", \"greeksalD\", \"dessertD\", \"breadD\", \n#                 \"champagneD\", \"beerD\", \"redwineD\", \"whitewineD\")\n\n\ncopdata <- copdata %>% \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$dayonset)\n\n\n[1] \"character\"\n\n\nShow the code\n# Update copdata:\ncopdata <- copdata %>% \n  # Change column to date class:\n  mutate(\n    dayonset = lubridate::dmy(dayonset))\n\n# Check class of updated column:\nclass(copdata$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 <- copdata %>% \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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-    "section": "3.5. Export clean data",
-    "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\"))"
+    "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": "3.2 Import your data\n\n\nShow the code\n# Import the clean data set:\ncopdata <- rio::import(here::here(\"data\", \"Spetses_clean2_2024.rds\"), trust = T) \nlab <- 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 <- 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 <- left_join(copdata, lab, \n                        by = \"id\")\n# Tabulate:\njanitor::tabyl(dat = copdatalab, \"RT-PCR_ecoli_etec\")"
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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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-    "href": "Univariate.html",
-    "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."
+    "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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-    "href": "Univariate.html#learning-outcomes",
-    "title": "Univariable analysis",
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+    "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)"
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+    "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 <- import(here::here(\"data\", \"spetses_school.csv\"))"
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+    "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 <- copdata %>% \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 <- copdata %>%       \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 <- copdata %>%       \n  # Select all the columns with column names that end in upper case 'D':   \n  select(ends_with(\"D\", ignore.case = FALSE)) %>%       \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 <- copdata %>% \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 <- copdata %>% \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 <- copdata %>% \n    select(\n      ends_with(\"D\", ignore.case = FALSE)) %>% \n    names()\n\n# Another way of doing it:\n# food_dose <- c(\"fetaD\", \"sardinesD\", \"eggplantD\", \"pastaD\", \n#                 \"vealD\", \"greeksalD\", \"dessertD\", \"breadD\", \n#                 \"champagneD\", \"beerD\", \"redwineD\", \"whitewineD\")\n\n\ncopdata <- copdata %>% \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$dayonset)\n\n\n[1] \"character\"\n\n\nShow the code\n# Update copdata:\ncopdata <- copdata %>% \n  # Change column to date class:\n  mutate(\n    dayonset = lubridate::dmy(dayonset))\n\n# Check class of updated column:\nclass(copdata$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 <- copdata %>% \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 < 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\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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+    "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\nShow the code\n# Import the raw data set:\ncopdata <- rio::import(here::here(\"data\", \"Spetses_clean2_2024.rds\"), trust = T)"
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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<0.05 means that we reject the null hypothesis that the data follow the normal distribution. For age, the p-value is <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 <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 < 2.2e-16\n\n\nShow the code\n# Can simply have a look at\nhist(copdata$age)\n\n\n\n\n\n\n\n\n\nShow the code\n# Looking only at the students:\nstudents <- copdata %>% \n  filter(group == \"student\")\nhist(students$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>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 %>% \n  select(sex, case) %>% \n  tbl_summary(by = case) %>% \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 %>% \n  select(class, case) %>% \n  tbl_summary(by = case) %>% \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 %>% \n  select(group, case) %>% \n  tbl_summary(by = case) %>% \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 %>% \n  select(sex, class, group, case) %>% \n  tbl_summary(by = case) %>% \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": "3.2 Import your data\n\n\nShow the code\n# Import the clean data set:\ncopdata <- 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\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>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>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>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>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 <- 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 <- CSTable(copdata, \"case\", food_vars) %>% \n  as.data.frame() %>% \n  rownames_to_column() %>% \n  flextable() %>% \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 veal, pasta 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": "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) 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$correction\n[1] FALSE\n\nattr(,\"method\")\n[1] \"Unconditional MLE & normal approximation (Wald) CI\"\n\n\nUsing a binomial regression:\n\n\nShow the code\n# Binomial regression for RRs. \n# The outcome needs to be exponentiated so we can interpret it properly!\nbinom_vealD <- glm(case ~ vealD, data = copdata, \n             family = binomial(link = \"log\"))\n\n# To get exponentiated:\nbinom_vealD_exp <- glm(case ~ vealD, data = copdata, \n                       family = binomial(link = \"log\")) %>% \n  tidy(exponentiate = TRUE, \n       conf.int = TRUE)\n\nbinom_vealD_exp\n\n\n\n  \n\n\n\n\n\n\n\n\n\nLet’s stop and… think!\n\n\n\n\n\nWhat do these results tell you?\n\n\n\nResults suggest a dose response relationship of having eaten veal, pointing towards veal as the potential vehicle. The higher the amount of veal they ate, the stronger is the association (RR) with getting ill/being a case.\n\n\nb) Pasta\nUsing epitools::riskratio function:\n\n\nShow the code\nepitools::riskratio(copdata$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$correction\n[1] FALSE\n\nattr(,\"method\")\n[1] \"Unconditional MLE & normal approximation (Wald) CI\"\n\n\nUsing a binomial regression:\n\n\nShow the code\n# Let's get the results directly exponentiated\nbinom_pastaD_exp <- glm(case ~ pastaD, data = copdata, \n                       family = binomial(link = \"log\")) %>% \n  tidy(exponentiate = TRUE, \n       conf.int = TRUE)\n\nbinom_pastaD_exp\n\n\n\n  \n\n\n\n\n\nc) Champagne\nUsing epitools::riskratio function:\n\n\nShow the code\nepitools::riskratio(copdata$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 <- glm(case ~ champagneD, data = copdata, \n                       family = binomial(link = \"log\")) %>% \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 <- copdata %>% \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. => 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. => 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 <- copdata %>% \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 <- copdata %>% \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 <- copdata %>% \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 <- copdata %>% \n  mutate(case = case_when(\n    # Those who had symptoms <48h from the meal are cases (1)\n    gastrosymptoms == 1 & \n      onset_datetime >= meal_datetime &\n      onset_datetime <= (meal_datetime + days(2)) ~ 1,\n    # Those who had symptoms >48h from the meal are non-cases (FALSE)\n    gastrosymptoms == 1 & \n      onset_datetime > (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 %>% \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 pasta 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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     "section": "3.3. Import your data",
-    "text": "3.3. Import your data\n\n\nShow the code\n# Import the raw data set: \ncopdata <- rio::import(here::here(\"data\", \"Spetses_clean2_2024.rds\"))\n\n\nWarning: Missing `trust` will be set to FALSE by default for RDS in 2.0.0."
+    "text": "3.3. Import your data\n\n\nShow the code\n# Import the raw data set: \ncopdata <- 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, veal). You may consider stratifying by veal, 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 pasta and champagne, stratified by veal, 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) Pasta as exposure of interest, stratified by having eaten veal\nIf we stratify the effect of pasta by veal we ask the question: does eating veal modify or confound the association between eating pasta and being a case?\n\n\nShow the code\nstratall <- copdata %>% \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:\nvealstrata <- csinter(x = stratall, \n                       cases = \"case\", \n                       exposure = \"pasta\", \n                       by = \"veal\")\n\nvealstrata\n\n\n$df1\n  CSInter case - pasta by(veal) Total Cases Risk %          P.est. Stats\n1                      veal = 1   338  <NA>     NA Risk difference  0.10\n2                       Exposed   330   198  60.00      Risk Ratio  1.20\n3                     Unexposed     8     4  50.00 Attrib.risk.exp  0.17\n4                                  NA  <NA>     NA Attrib.risk.pop  0.16\n5                      veal = 0    36  <NA>     NA Risk difference  0.02\n6                       Exposed     8     3  37.50      Risk Ratio  1.05\n7                     Unexposed    28    10  35.71 Attrib.risk.exp  0.05\n8                                  NA  <NA>     NA Attrib.risk.pop  0.01\n9           Missing / Missing %     3  0.8%     NA            <NA>    NA\n  95%CI.ll 95%CI.ul\n1    -0.25     0.45\n2     0.60     2.41\n3    -0.68     0.59\n4       NA       NA\n5    -0.36     0.40\n6     0.38     2.92\n7    -1.65     0.66\n8       NA       NA\n9       NA       NA\n\n$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 pasta   NA      NA   1.53     1.01     2.32\n3  MH RR pasta adjusted for veal   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 veal is associated with pasta (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 < 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 veal\n\n\nShow the code\n# Pass data to the csinter function:\nchampstrata <- csinter(x = stratall, \n                       cases = \"case\", \n                       exposure = \"champagne\", \n                       by = \"veal\")\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 veal (the strongest risk factor in the univariable analysis), to examine if veal confounds the association between eating pasta and being a case. Before stratification, we will need to check if veal 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 veal is associated with being a case. If we run a Wilcoxon rank sum test, we will see that veal is also associated with pasta (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 “vealstrata”.\nvealstrata: Within the stratum of the people who ate veal, pasta has no significant effect (RR = 1.20, CI: 0.60 - 2.41). The same holds within the stratum of people who didn’t eat veal (RR = 1.05, CI = 0.38, 2.92). The adjusted MH-RR also suggests that pasta 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 veal confounds the association between pasta and the disease.\nThis result suggest that pasta is not a risk factor of the disease and that the crude observed effect was due to the confounding effect of veal.\nIf you stratify by pasta, you see that pasta does not confound the association between veal and the disease. The same applies if you stratify the exposure to veal by other variables. The above, the higher RR for veal and the dose response relationship we found earlier for veal (remember this was optional) provide additional evidence that there was something going on with the veal 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": "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. 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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-    "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": "3.2 Import your data\n\n\nShow the code\n# Import the clean data set:\ncopdata <- rio::import(here::here(\"data\", \"Spetses_clean1_2024.rds\"), trust = TRUE)"
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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, ill, 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 == 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 <- copdata %>% \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. => 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. => 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\nShow the code\ncopdata <- copdata %>% \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 <- copdata %>% \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 <- copdata %>% \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 <- copdata %>% \n  mutate(case = case_when(\n    # Those who had symptoms <48h from the meal are cases (1)\n    gastrosymptoms == 1 & \n      onset_datetime >= meal_datetime &\n      onset_datetime <= (meal_datetime + days(2)) ~ 1,\n    # Those who had symptoms >48h from the meal are non-cases (FALSE)\n    gastrosymptoms == 1 & \n      onset_datetime > (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 %>% \n  summarise(atemeal = sum(meal == 1),\n            hadsympt = sum(gastrosymptoms == 1),\n            nb_cases = sum(case == 1)\n            )"
-  },
-  {
-    "objectID": "Lab_and_Descriptive.html#merge-and-explore-the-lab-data.-explore-it-as-you-wish",
-    "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\nShow the code\ncopdatalab <- 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 <- copdata %>% \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 <- 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  <NA>     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  <NA>     NA Attrib.risk.pop  0.16\n5                     pasta = 0    36  <NA>     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  <NA>     NA Attrib.risk.pop  0.01\n9           Missing / Missing %     3  0.8%     NA            <NA>    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 < 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 <- 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 >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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+++ b/docs/site_libs/bootstrap/bootstrap-icons.css
@@ -1,8 +1,14 @@
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+ * Copyright 2019-2023 The Bootstrap Authors
+ * Licensed under MIT (https://github.com/twbs/icons/blob/main/LICENSE)
+ */
+
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 .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");
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 .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"; }
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+.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"; }
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+.bi-sim-slash-fill::before { content: "\f8d4"; }
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+.bi-sourceforge::before { content: "\f8d6"; }
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+.bi-transparency::before { content: "\f8da"; }
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diff --git a/docs/site_libs/bootstrap/bootstrap-icons.woff b/docs/site_libs/bootstrap/bootstrap-icons.woff
index 18d21d457558d4dc2e231a8f6ee585fada9c6bab..dbeeb055674125ad78fda0f3d166b36e5cc92336 100644
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diff --git a/docs/site_libs/bootstrap/bootstrap.min.js b/docs/site_libs/bootstrap/bootstrap.min.js
index cc0a255..e8f21f7 100644
--- a/docs/site_libs/bootstrap/bootstrap.min.js
+++ b/docs/site_libs/bootstrap/bootstrap.min.js
@@ -1,7 +1,7 @@
 /*!
-  * Bootstrap v5.1.3 (https://getbootstrap.com/)
-  * Copyright 2011-2021 The Bootstrap Authors (https://github.com/twbs/bootstrap/graphs/contributors)
+  * Bootstrap v5.3.1 (https://getbootstrap.com/)
+  * Copyright 2011-2023 The Bootstrap Authors (https://github.com/twbs/bootstrap/graphs/contributors)
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this.find(e,t).filter((t=>!c(t)&&l(t)))}},K="carousel",X={interval:5e3,keyboard:!0,slide:!1,pause:"hover",wrap:!0,touch:!0},Y={interval:"(number|boolean)",keyboard:"boolean",slide:"(boolean|string)",pause:"(string|boolean)",wrap:"boolean",touch:"boolean"},Q="next",G="prev",Z="left",J="right",tt={ArrowLeft:J,ArrowRight:Z},et="slid.bs.carousel",it="active",nt=".active.carousel-item";class st extends B{constructor(t,e){super(t),this._items=null,this._interval=null,this._activeElement=null,this._isPaused=!1,this._isSliding=!1,this.touchTimeout=null,this.touchStartX=0,this.touchDeltaX=0,this._config=this._getConfig(e),this._indicatorsElement=V.findOne(".carousel-indicators",this._element),this._touchSupported="ontouchstart"in document.documentElement||navigator.maxTouchPoints>0,this._pointerEvent=Boolean(window.PointerEvent),this._addEventListeners()}static get Default(){return X}static get NAME(){return K}next(){this._slide(Q)}nextWhenVisible(){!document.hidden&&l(this._element)&&this.next()}prev(){this._slide(G)}pause(t){t||(this._isPaused=!0),V.findOne(".carousel-item-next, .carousel-item-prev",this._element)&&(s(this._element),this.cycle(!0)),clearInterval(this._interval),this._interval=null}cycle(t){t||(this._isPaused=!1),this._interval&&(clearInterval(this._interval),this._interval=null),this._config&&this._config.interval&&!this._isPaused&&(this._updateInterval(),this._interval=setInterval((document.visibilityState?this.nextWhenVisible:this.next).bind(this),this._config.interval))}to(t){this._activeElement=V.findOne(nt,this._element);const e=this._getItemIndex(this._activeElement);if(t>this._items.length-1||t<0)return;if(this._isSliding)return void j.one(this._element,et,(()=>this.to(t)));if(e===t)return this.pause(),void this.cycle();const i=t>e?Q:G;this._slide(i,this._items[t])}_getConfig(t){return t={...X,...U.getDataAttributes(this._element),..."object"==typeof t?t:{}},a(K,t,Y),t}_handleSwipe(){const t=Math.abs(this.touchDeltaX);if(t<=40)return;const e=t/this.touchDeltaX;this.touchDeltaX=0,e&&this._slide(e>0?J:Z)}_addEventListeners(){this._config.keyboard&&j.on(this._element,"keydown.bs.carousel",(t=>this._keydown(t))),"hover"===this._config.pause&&(j.on(this._element,"mouseenter.bs.carousel",(t=>this.pause(t))),j.on(this._element,"mouseleave.bs.carousel",(t=>this.cycle(t)))),this._config.touch&&this._touchSupported&&this._addTouchEventListeners()}_addTouchEventListeners(){const t=t=>this._pointerEvent&&("pen"===t.pointerType||"touch"===t.pointerType),e=e=>{t(e)?this.touchStartX=e.clientX:this._pointerEvent||(this.touchStartX=e.touches[0].clientX)},i=t=>{this.touchDeltaX=t.touches&&t.touches.length>1?0:t.touches[0].clientX-this.touchStartX},n=e=>{t(e)&&(this.touchDeltaX=e.clientX-this.touchStartX),this._handleSwipe(),"hover"===this._config.pause&&(this.pause(),this.touchTimeout&&clearTimeout(this.touchTimeout),this.touchTimeout=setTimeout((t=>this.cycle(t)),500+this._config.interval))};V.find(".carousel-item img",this._element).forEach((t=>{j.on(t,"dragstart.bs.carousel",(t=>t.preventDefault()))})),this._pointerEvent?(j.on(this._element,"pointerdown.bs.carousel",(t=>e(t))),j.on(this._element,"pointerup.bs.carousel",(t=>n(t))),this._element.classList.add("pointer-event")):(j.on(this._element,"touchstart.bs.carousel",(t=>e(t))),j.on(this._element,"touchmove.bs.carousel",(t=>i(t))),j.on(this._element,"touchend.bs.carousel",(t=>n(t))))}_keydown(t){if(/input|textarea/i.test(t.target.tagName))return;const e=tt[t.key];e&&(t.preventDefault(),this._slide(e))}_getItemIndex(t){return this._items=t&&t.parentNode?V.find(".carousel-item",t.parentNode):[],this._items.indexOf(t)}_getItemByOrder(t,e){const i=t===Q;return v(this._items,e,i,this._config.wrap)}_triggerSlideEvent(t,e){const i=this._getItemIndex(t),n=this._getItemIndex(V.findOne(nt,this._element));return j.trigger(this._element,"slide.bs.carousel",{relatedTarget:t,direction:e,from:n,to:i})}_setActiveIndicatorElement(t){if(this._indicatorsElement){const e=V.findOne(".active",this._indicatorsElement);e.classList.remove(it),e.removeAttribute("aria-current");const i=V.find("[data-bs-target]",this._indicatorsElement);for(let e=0;e{j.trigger(this._element,et,{relatedTarget:o,direction:d,from:s,to:r})};if(this._element.classList.contains("slide")){o.classList.add(h),u(o),n.classList.add(c),o.classList.add(c);const t=()=>{o.classList.remove(c,h),o.classList.add(it),n.classList.remove(it,h,c),this._isSliding=!1,setTimeout(f,0)};this._queueCallback(t,n,!0)}else n.classList.remove(it),o.classList.add(it),this._isSliding=!1,f();a&&this.cycle()}_directionToOrder(t){return[J,Z].includes(t)?m()?t===Z?G:Q:t===Z?Q:G:t}_orderToDirection(t){return[Q,G].includes(t)?m()?t===G?Z:J:t===G?J:Z:t}static carouselInterface(t,e){const i=st.getOrCreateInstance(t,e);let{_config:n}=i;"object"==typeof e&&(n={...n,...e});const s="string"==typeof e?e:n.slide;if("number"==typeof e)i.to(e);else if("string"==typeof s){if(void 0===i[s])throw new TypeError(`No method named "${s}"`);i[s]()}else n.interval&&n.ride&&(i.pause(),i.cycle())}static jQueryInterface(t){return this.each((function(){st.carouselInterface(this,t)}))}static dataApiClickHandler(t){const e=n(this);if(!e||!e.classList.contains("carousel"))return;const i={...U.getDataAttributes(e),...U.getDataAttributes(this)},s=this.getAttribute("data-bs-slide-to");s&&(i.interval=!1),st.carouselInterface(e,i),s&&st.getInstance(e).to(s),t.preventDefault()}}j.on(document,"click.bs.carousel.data-api","[data-bs-slide], [data-bs-slide-to]",st.dataApiClickHandler),j.on(window,"load.bs.carousel.data-api",(()=>{const t=V.find('[data-bs-ride="carousel"]');for(let e=0,i=t.length;et===this._element));null!==s&&o.length&&(this._selector=s,this._triggerArray.push(e))}this._initializeChildren(),this._config.parent||this._addAriaAndCollapsedClass(this._triggerArray,this._isShown()),this._config.toggle&&this.toggle()}static get Default(){return rt}static get NAME(){return ot}toggle(){this._isShown()?this.hide():this.show()}show(){if(this._isTransitioning||this._isShown())return;let t,e=[];if(this._config.parent){const t=V.find(ut,this._config.parent);e=V.find(".collapse.show, .collapse.collapsing",this._config.parent).filter((e=>!t.includes(e)))}const i=V.findOne(this._selector);if(e.length){const n=e.find((t=>i!==t));if(t=n?pt.getInstance(n):null,t&&t._isTransitioning)return}if(j.trigger(this._element,"show.bs.collapse").defaultPrevented)return;e.forEach((e=>{i!==e&&pt.getOrCreateInstance(e,{toggle:!1}).hide(),t||H.set(e,"bs.collapse",null)}));const n=this._getDimension();this._element.classList.remove(ct),this._element.classList.add(ht),this._element.style[n]=0,this._addAriaAndCollapsedClass(this._triggerArray,!0),this._isTransitioning=!0;const s=`scroll${n[0].toUpperCase()+n.slice(1)}`;this._queueCallback((()=>{this._isTransitioning=!1,this._element.classList.remove(ht),this._element.classList.add(ct,lt),this._element.style[n]="",j.trigger(this._element,"shown.bs.collapse")}),this._element,!0),this._element.style[n]=`${this._element[s]}px`}hide(){if(this._isTransitioning||!this._isShown())return;if(j.trigger(this._element,"hide.bs.collapse").defaultPrevented)return;const t=this._getDimension();this._element.style[t]=`${this._element.getBoundingClientRect()[t]}px`,u(this._element),this._element.classList.add(ht),this._element.classList.remove(ct,lt);const e=this._triggerArray.length;for(let t=0;t{this._isTransitioning=!1,this._element.classList.remove(ht),this._element.classList.add(ct),j.trigger(this._element,"hidden.bs.collapse")}),this._element,!0)}_isShown(t=this._element){return t.classList.contains(lt)}_getConfig(t){return(t={...rt,...U.getDataAttributes(this._element),...t}).toggle=Boolean(t.toggle),t.parent=r(t.parent),a(ot,t,at),t}_getDimension(){return this._element.classList.contains("collapse-horizontal")?"width":"height"}_initializeChildren(){if(!this._config.parent)return;const t=V.find(ut,this._config.parent);V.find(ft,this._config.parent).filter((e=>!t.includes(e))).forEach((t=>{const e=n(t);e&&this._addAriaAndCollapsedClass([t],this._isShown(e))}))}_addAriaAndCollapsedClass(t,e){t.length&&t.forEach((t=>{e?t.classList.remove(dt):t.classList.add(dt),t.setAttribute("aria-expanded",e)}))}static jQueryInterface(t){return this.each((function(){const e={};"string"==typeof t&&/show|hide/.test(t)&&(e.toggle=!1);const 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e=t.ownerDocument;return e&&e.defaultView||window}return t}function $t(t){return t instanceof Wt(t).Element||t instanceof Element}function zt(t){return t instanceof Wt(t).HTMLElement||t instanceof HTMLElement}function qt(t){return"undefined"!=typeof ShadowRoot&&(t instanceof Wt(t).ShadowRoot||t instanceof ShadowRoot)}const Ft={name:"applyStyles",enabled:!0,phase:"write",fn:function(t){var e=t.state;Object.keys(e.elements).forEach((function(t){var i=e.styles[t]||{},n=e.attributes[t]||{},s=e.elements[t];zt(s)&&Rt(s)&&(Object.assign(s.style,i),Object.keys(n).forEach((function(t){var e=n[t];!1===e?s.removeAttribute(t):s.setAttribute(t,!0===e?"":e)})))}))},effect:function(t){var e=t.state,i={popper:{position:e.options.strategy,left:"0",top:"0",margin:"0"},arrow:{position:"absolute"},reference:{}};return Object.assign(e.elements.popper.style,i.popper),e.styles=i,e.elements.arrow&&Object.assign(e.elements.arrow.style,i.arrow),function(){Object.keys(e.elements).forEach((function(t){var 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Be={placement:"bottom",modifiers:[],strategy:"absolute"};function Re(){for(var t=arguments.length,e=new Array(t),i=0;ij.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 "${t}"`);e[t]()}}))}static clearMenus(t){if(t&&(2===t.button||"keyup"===t.type&&"Tab"!==t.key))return;const e=V.find(ti);for(let i=0,n=e.length;ie+t)),this._setElementAttributes(di,"paddingRight",(e=>e+t)),this._setElementAttributes(ui,"marginRight",(e=>e-t))}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t)[e];t.style[e]=`${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]+=*$/i,Xi=(t,e)=>{const i=t.nodeName.toLowerCase();if(e.includes(i))return!Ui.has(i)||Boolean(Vi.test(t.nodeValue)||Ki.test(t.nodeValue));const n=e.filter((t=>t instanceof RegExp));for(let t=0,e=n.length;t{Xi(t,r)||i.removeAttribute(t.nodeName)}))}return n.body.innerHTML}const Qi="tooltip",Gi=new Set(["sanitize","allowList","sanitizeFn"]),Zi={animation:"boolean",template:"string",title:"(string|element|function)",trigger:"string",delay:"(number|object)",html:"boolean",selector:"(string|boolean)",placement:"(string|function)",offset:"(array|string|function)",container:"(string|element|boolean)",fallbackPlacements:"array",boundary:"(string|element)",customClass:"(string|function)",sanitize:"boolean",sanitizeFn:"(null|function)",allowList:"object",popperConfig:"(null|object|function)"},Ji={AUTO:"auto",TOP:"top",RIGHT:m()?"left":"right",BOTTOM:"bottom",LEFT:m()?"right":"left"},tn={animation:!0,template:'
      ',trigger:"hover focus",title:"",delay:0,html:!1,selector:!1,placement:"top",offset:[0,0],container:!1,fallbackPlacements:["top","right","bottom","left"],boundary:"clippingParents",customClass:"",sanitize:!0,sanitizeFn:null,allowList:{"*":["class","dir","id","lang","role",/^aria-[\w-]*$/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 "${t}"`);e[t]()}}))}}g(un);const fn={...un.Default,placement:"right",offset:[0,8],trigger:"click",content:"",template:''},pn={...un.DefaultType,content:"(string|element|function)"},mn={HIDE:"hide.bs.popover",HIDDEN:"hidden.bs.popover",SHOW:"show.bs.popover",SHOWN:"shown.bs.popover",INSERTED:"inserted.bs.popover",CLICK:"click.bs.popover",FOCUSIN:"focusin.bs.popover",FOCUSOUT:"focusout.bs.popover",MOUSEENTER:"mouseenter.bs.popover",MOUSELEAVE:"mouseleave.bs.popover"};class gn extends un{static get Default(){return fn}static get NAME(){return"popover"}static get Event(){return mn}static get DefaultType(){return pn}isWithContent(){return this.getTitle()||this._getContent()}setContent(t){this._sanitizeAndSetContent(t,this.getTitle(),".popover-header"),this._sanitizeAndSetContent(t,this._getContent(),".popover-body")}_getContent(){return this._resolvePossibleFunction(this._config.content)}_getBasicClassPrefix(){return"bs-popover"}static jQueryInterface(t){return this.each((function(){const e=gn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${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&&t0)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`${e}[data-bs-target="${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, 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t}))){T=O,E=!1;break}A.set(O,N)}if(E)for(var P=function(t){var e=v.find((function(e){var i=A.get(e);if(i)return i.slice(0,t).every((function(t){return t}))}));if(e)return T=e,"break"},M=p?3:1;M>0&&"break"!==P(M);M--);e.placement!==T&&(e.modifiersData[n]._skip=!0,e.placement=T,e.reset=!0)}},requiresIfExists:["offset"],data:{_skip:!1}};function oi(t,e,i){return void 0===i&&(i={x:0,y:0}),{top:t.top-e.height-i.y,right:t.right-e.width+i.x,bottom:t.bottom-e.height+i.y,left:t.left-e.width-i.x}}function ri(t){return[zt,qt,Rt,Vt].some((function(e){return t[e]>=0}))}const ai={name:"hide",enabled:!0,phase:"main",requiresIfExists:["preventOverflow"],fn:function(t){var 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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;iNumber.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 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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]=`${e}px`}}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const 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W{constructor(t,e){super(t,e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get Default(){return zn}static get DefaultType(){return Rn}static get NAME(){return"offcanvas"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||N.trigger(this._element,Nn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._backdrop.show(),this._config.scroll||(new cn).hide(),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add(Dn),this._queueCallback((()=>{this._config.scroll&&!this._config.backdrop||this._focustrap.activate(),this._element.classList.add(Sn),this._element.classList.remove(Dn),N.trigger(this._element,Pn,{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(N.trigger(this._element,Mn).defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.add($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:"
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")),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:'',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 "${t}"`);e[t]()}}))}}m(cs);const hs={...cs.Default,content:"",offset:[0,8],placement:"right",template:'',trigger:"click"},ds={...cs.DefaultType,content:"(null|string|element|function)"};class us extends cs{static get Default(){return hs}static get DefaultType(){return ds}static get 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e=this._observableSections.get(t.target.hash);if(e){t.preventDefault();const i=this._rootElement||window,n=e.offsetTop-this._element.offsetTop;if(i.scrollTo)return void i.scrollTo({top:n,behavior:"smooth"});i.scrollTop=n}})))}_getNewObserver(){const t={root:this._rootElement,threshold:this._config.threshold,rootMargin:this._config.rootMargin};return new IntersectionObserver((t=>this._observerCallback(t)),t)}_observerCallback(t){const e=t=>this._targetLinks.get(`#${t.target.id}`),i=t=>{this._previousScrollData.visibleEntryTop=t.target.offsetTop,this._process(e(t))},n=(this._rootElement||document.documentElement).scrollTop,s=n>=this._previousScrollData.parentScrollTop;this._previousScrollData.parentScrollTop=n;for(const o of 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(![$s,Is,Ns,Ps,Ms,js].includes(t.key))return;t.stopPropagation(),t.preventDefault();const e=this._getChildren().filter((t=>!l(t)));let i;if([Ms,js].includes(t.key))i=e[t.key===Ms?0:e.length-1];else{const n=[Is,Ps].includes(t.key);i=b(e,t.target,n,!0)}i&&(i.focus({preventScroll:!0}),Vs.getOrCreateInstance(i).show())}_getChildren(){return z.find(Rs,this._parent)}_getActiveElem(){return this._getChildren().find((t=>this._elemIsActive(t)))||null}_setInitialAttributes(t,e){this._setAttributeIfNotExists(t,"role","tablist");for(const t of e)this._setInitialAttributesOnChild(t)}_setInitialAttributesOnChild(t){t=this._getInnerElement(t);const e=this._elemIsActive(t),i=this._getOuterElement(t);t.setAttribute("aria-selected",e),i!==t&&this._setAttributeIfNotExists(i,"role","presentation"),e||t.setAttribute("tabindex","-1"),this._setAttributeIfNotExists(t,"role","tab"),this._setInitialAttributesOnTargetPanel(t)}_setInitialAttributesOnTargetPanel(t){const 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"${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}}));
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\ 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\]./()*\\\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\]./()*\\\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 n=t(e);return{scrollLeft:n.pageXOffset,scrollTop:n.pageYOffset}}function p(e){return e?(e.nodeName||"").toLowerCase():null}function u(e){return((n(e)?e.ownerDocument:e.document)||window.document).documentElement}function l(e){return f(u(e)).left+c(e).scrollLeft}function d(e){return t(e).getComputedStyle(e)}function h(e){var t=d(e),n=t.overflow,r=t.overflowX,o=t.overflowY;return/auto|scroll|overlay|hidden/.test(n+o+r)}function m(e,n,o){void 0===o&&(o=!1);var i,a,d=r(n),m=r(n)&&function(e){var t=e.getBoundingClientRect(),n=s(t.width)/e.offsetWidth||1,r=s(t.height)/e.offsetHeight||1;return 1!==n||1!==r}(n),v=u(n),g=f(e,m),y={scrollLeft:0,scrollTop:0},b={x:0,y:0};return(d||!d&&!o)&&(("body"!==p(n)||h(v))&&(y=(i=n)!==t(i)&&r(i)?{scrollLeft:(a=i).scrollLeft,scrollTop:a.scrollTop}:c(i)),r(n)?((b=f(n,!0)).x+=n.clientLeft,b.y+=n.clientTop):v&&(b.x=l(v))),{x:g.left+y.scrollLeft-b.x,y:g.top+y.scrollTop-b.y,width:g.width,height:g.height}}function v(e){var t=f(e),n=e.offsetWidth,r=e.offsetHeight;return Math.abs(t.width-n)<=1&&(n=t.width),Math.abs(t.height-r)<=1&&(r=t.height),{x:e.offsetLeft,y:e.offsetTop,width:n,height:r}}function g(e){return"html"===p(e)?e:e.assignedSlot||e.parentNode||(o(e)?e.host:null)||u(e)}function y(e){return["html","body","#document"].indexOf(p(e))>=0?e.ownerDocument.body:r(e)&&h(e)?e:y(g(e))}function b(e,n){var r;void 0===n&&(n=[]);var o=y(e),i=o===(null==(r=e.ownerDocument)?void 0:r.body),a=t(o),s=i?[a].concat(a.visualViewport||[],h(o)?o:[]):o,f=n.concat(s);return i?f:f.concat(b(g(s)))}function x(e){return["table","td","th"].indexOf(p(e))>=0}function w(e){return r(e)&&"fixed"!==d(e).position?e.offsetParent:null}function O(e){for(var n=t(e),i=w(e);i&&x(i)&&"static"===d(i).position;)i=w(i);return i&&("html"===p(i)||"body"===p(i)&&"static"===d(i).position)?n:i||function(e){var 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t,n=u(e),r=c(e),o=null==(t=e.ownerDocument)?void 0:t.body,a=i(n.scrollWidth,n.clientWidth,o?o.scrollWidth:0,o?o.clientWidth:0),s=i(n.scrollHeight,n.clientHeight,o?o.scrollHeight:0,o?o.clientHeight:0),f=-r.scrollLeft+l(e),p=-r.scrollTop;return"rtl"===d(o||n).direction&&(f+=i(n.clientWidth,o?o.clientWidth:0)-a),{width:a,height:s,x:f,y:p}}(u(e)))}function I(e,t,o){var s="clippingParents"===t?function(e){var t=b(g(e)),o=["absolute","fixed"].indexOf(d(e).position)>=0&&r(e)?O(e):e;return n(o)?t.filter((function(e){return n(e)&&q(e,o)&&"body"!==p(e)})):[]}(e):[].concat(t),f=[].concat(s,[o]),c=f[0],u=f.reduce((function(t,n){var r=N(e,n);return t.top=i(r.top,t.top),t.right=a(r.right,t.right),t.bottom=a(r.bottom,t.bottom),t.left=i(r.left,t.left),t}),N(e,c));return u.width=u.right-u.left,u.height=u.bottom-u.top,u.x=u.left,u.y=u.top,u}function _(e){return e.split("-")[1]}function F(e){return["top","bottom"].indexOf(e)>=0?"x":"y"}function U(e){var 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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=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 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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=0||(o[n]=e[n]);return o}(e,t);if(Object.getOwnPropertySymbols){var i=Object.getOwnPropertySymbols(e);for(r=0;r=0||Object.prototype.propertyIsEnumerable.call(e,n)&&(o[n]=e[n])}return o}function a(e,t){return function(e){if(Array.isArray(e))return e}(e)||function(e,t){var n=null==e?null:"undefined"!=typeof Symbol&&e[Symbol.iterator]||e["@@iterator"];if(null==n)return;var r,o,i=[],u=!0,a=!1;try{for(n=n.call(e);!(u=(r=n.next()).done)&&(i.push(r.value),!t||i.length!==t);u=!0);}catch(e){a=!0,o=e}finally{try{u||null==n.return||n.return()}finally{if(a)throw o}}return i}(e,t)||l(e,t)||function(){throw new TypeError("Invalid attempt to destructure non-iterable instance.\nIn order to be iterable, non-array objects must have a [Symbol.iterator]() method.")}()}function c(e){return function(e){if(Array.isArray(e))return s(e)}(e)||function(e){if("undefined"!=typeof Symbol&&null!=e[Symbol.iterator]||null!=e["@@iterator"])return Array.from(e)}(e)||l(e)||function(){throw new TypeError("Invalid attempt to spread non-iterable instance.\nIn order to be iterable, non-array objects must have a [Symbol.iterator]() method.")}()}function l(e,t){if(e){if("string"==typeof e)return s(e,t);var 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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;
 }