[Hotfix] change the default era to NULL in multiple functions

R/plot_biomass_at_age.R

@@ -56,6 +56,7 @@ plot_biomass_at_age <- function(
     label_name = "^biomass",
     geom = "point",
     group = "age",
+    era = "time",
     scale_amount = scale_amount,
     interactive = interactive
   )

R/plot_fishing_mortality.R

@@ -34,7 +34,7 @@ plot_fishing_mortality <- function(
   group = NULL,
   facet = NULL,
   ref_line = "msy",
-  era = "time",
+  era = NULL,
   module = NULL,
   make_rda = FALSE,
   figures_dir = getwd(),

R/plot_recruitment.R

@@ -33,7 +33,7 @@ plot_recruitment <- function(
   dat,
   unit_label = "mt",
   scale_amount = 1,
-  era = "time",
+  era = NULL,
   group = NULL,
   facet = NULL,
   # relative = FALSE,
@@ -129,25 +129,25 @@ plot_recruitment <- function(
   }
 
   # Plot vertical lines if era is not filtering
-  if (is.null(era)) {
-    # Find unique era
-    eras <- unique(filter_data$era)
-    if (length(eras) > 1) {
-      year_vlines <- c()
-      for (i in 2:length(eras)) {
-        erax <- filter_data |>
-          dplyr::filter(era == eras[i]) |>
-          dplyr::pull(year) |>
-          min(na.rm = TRUE)
-        year_vlines <- c(year_vlines, erax)
-      }
-    }
-    final <- final +
-      ggplot2::geom_vline(
-        xintercept = year_vlines,
-        color = "#999999"
-      )
-  }
+  # if (is.null(era)) {
+  #   # Find unique era
+  #   eras <- unique(filter_data$era)
+  #   if (length(eras) > 1) {
+  #     year_vlines <- c()
+  #     for (i in 2:length(eras)) {
+  #       erax <- filter_data |>
+  #         dplyr::filter(era == eras[i]) |>
+  #         dplyr::pull(year) |>
+  #         min(na.rm = TRUE)
+  #       year_vlines <- c(year_vlines, erax)
+  #     }
+  #   }
+  #   final <- final +
+  #     ggplot2::geom_vline(
+  #       xintercept = year_vlines,
+  #       color = "#999999"
+  #     )
+  # }
 
   # Make RDA
   if (make_rda) {

R/plot_recruitment_deviations.R

@@ -33,7 +33,7 @@
 plot_recruitment_deviations <- function(
   dat,
   module = NULL,
-  era = "time",
+  era = NULL,
   interactive = TRUE,
   make_rda = FALSE,
   figures_dir = getwd(),
@@ -78,29 +78,29 @@ plot_recruitment_deviations <- function(
     theme_noaa()
 
   # Plot vertical lines if era is not filtering
-  if (is.null(era)) {
-    # Find unique era
-    eras <- unique(filter_data$era)
-    if (length(eras) > 1) {
-      # era1 <- filter_data |>
-      #   dplyr::filter(era == eras[1]) |>
-      #   dplyr::pull(year) |>
-      #   max(na.rm = TRUE)
-      year_vlines <- c()
-      for (i in 2:length(eras)) {
-        erax <- filter_data |>
-          dplyr::filter(era == eras[i]) |>
-          dplyr::pull(year) |>
-          min(na.rm = TRUE)
-        year_vlines <- c(year_vlines, erax)
-      }
-    }
-    final <- final +
-      ggplot2::geom_vline(
-        xintercept = year_vlines,
-        color = "#999999"
-      )
-  }
+  # if (is.null(era)) {
+  #   # Find unique era
+  #   eras <- unique(filter_data$era)
+  #   if (length(eras) > 1) {
+  #     # era1 <- filter_data |>
+  #     #   dplyr::filter(era == eras[1]) |>
+  #     #   dplyr::pull(year) |>
+  #     #   max(na.rm = TRUE)
+  #     year_vlines <- c()
+  #     for (i in 2:length(eras)) {
+  #       erax <- filter_data |>
+  #         dplyr::filter(era == eras[i]) |>
+  #         dplyr::pull(year) |>
+  #         min(na.rm = TRUE)
+  #       year_vlines <- c(year_vlines, erax)
+  #     }
+  #   }
+  #   final <- final +
+  #     ggplot2::geom_vline(
+  #       xintercept = year_vlines,
+  #       color = "#999999"
+  #     )
+  # }
 
   # Make RDA
   if (make_rda) {

R/plot_spawning_biomass.R

@@ -236,7 +236,7 @@ plot_spawning_biomass <- function(
       ) + theme_noaa()
     }
   }
-
+  
   ### Make RDA ----
   if (make_rda) {
     if (relative) {

R/plot_stock_recruitment.R

@@ -26,14 +26,14 @@
 #'   interactive = FALSE,
 #'   spawning_biomass_label = "metric tons",
 #'   recruitment_label = "metric tons",
-#'   module = "SPAWN_RECRUIT"
+#'   module = "DERIVED_QUANTITIES"
 #' )
 plot_stock_recruitment <- function(
   dat,
   spawning_biomass_label = "mt",
   recruitment_label = "mt",
   interactive = TRUE,
-  # era = "time",
+  era = NULL,
   module = NULL,
   scale_amount = 1,
   make_rda = FALSE,
@@ -42,45 +42,80 @@ plot_stock_recruitment <- function(
   # Extract recruitment
   recruitment <- filter_data(
     dat = dat,
+    # TODO: change string to ^recruitment in naming convention change PR
     label_name = "recruitment",
-    era = "time",
+    era = era,
     geom = "point",
     scale_amount = scale_amount,
     interactive = interactive,
     module = module
+  ) |>
+    # filter for year !na
+    dplyr::filter(
+      !is.na(year),
+      # filter out rec devs if in data
+      !grepl("deviations", label)
+    )
+
+  process_rec <- process_data(
+    recruitment
   )
+
+  rec_proc <- process_rec[[1]]
+  group <- process_rec[[2]]
+  facet <- process_rec[[3]]
+
   if (length(unique(recruitment$label)) > 1) {
-    recruitment <- recruitment |>
+    rec_proc <- rec_proc |>
       tidyr::pivot_wider(
-        id_cols = c(year, model, group_var, estimate_lower, estimate_upper),
+        id_cols = dplyr::any_of(c("year", "model", "group_var", facet)),
         names_from = label,
-        values_from = estimate
+        values_from = c(estimate, estimate_lower, estimate_upper)
       )
+    # rename columns to remove "estimate"
+    colnames(rec_proc) <- gsub("estimate_", "", colnames(rec_proc))
   } else {
-    recruitment <- recruitment |>
-      dplyr::rename(predicted_recruitment = estimate) |>
-      dplyr::select(-c(label))
+    rec_proc <-  rec_proc|>
+      dplyr::rename(
+        predicted_recruitment = estimate,
+        lower_predicted_recruitment = estimate_lower,
+        upper_predicted_recruitment = estimate_upper
+      ) |>
+      dplyr::select(-label)
   }
 
-  if (any(grepl("^recruitment$", colnames(recruitment)))) {
-    recruitment <- dplyr::rename(recruitment, predicted_recruitment = recruitment)
-  }
+  # if (any(grepl("^recruitment$", colnames(recruitment)))) {
+  #   # TODO: adjust naming to recruitment_predicted in naming convention change PR
+  #   recruitment <- dplyr::rename(recruitment, predicted_recruitment = recruitment)
+  # }
 
   # Extract spawning biomass
   sb <- filter_data(
     dat = dat,
     label_name = "spawning biomass",
     geom = "point",
-    era = "time",
+    era = era,
     scale_amount = scale_amount,
     interactive = interactive,
     module = module
   ) |>
-    dplyr::rename(spawning_biomass = estimate) |>
-    dplyr::select(-c(label))
+    dplyr::filter(!is.na(year))
 
+   process_sb <- process_data(
+     sb
+   )
+   sb_proc <- process_sb[[1]] |>
+     dplyr::rename(
+       spawning_biomass = estimate,
+       lower_spawning_biomass = estimate_lower,
+       upper_spawning_biomass = estimate_upper
+     ) |>
+     dplyr::select(-label)
+   # group <- process_sb[[2]]
+   # facet <- process_sb[[3]]
+
   # Merge recruitment and spawning biomass data
-  sr <- dplyr::left_join(sb, recruitment)
+  sr <- dplyr::left_join(sb_proc, rec_proc, by = c("year", "model", "group_var"))
 
   # Labs
   recruitment_lab <- label_magnitude(
@@ -100,6 +135,7 @@ plot_stock_recruitment <- function(
   final <- plot_timeseries(
     dat = sr,
     x = "spawning_biomass",
+    # TODO: change name to recruitment_predicted in naming convention change PR
     y = "predicted_recruitment",
     geom = "point",
     color = "black",

R/save_all_plots.R

@@ -252,7 +252,7 @@ save_all_plots <- function(
       cli::cli_alert_danger("plot_stock_recruitment failed to run.")
       cli::cli_alert("Tip: check that your arguments are correct.")
       cli::cli_li("spawning_biomass_label = {spawning_biomass_label}")
-      cli::cli_li("recruitment_label = {recruitment_label}")
+      cli::cli_li("recruitment_label = {recruitment_unit_label}")
       print(e)
     }
   )

R/utils_plot.R

@@ -649,7 +649,7 @@ filter_data <- function(
   dat,
   label_name,
   module = NULL,
-  era = "time",
+  era = NULL,
   geom,
   group = NULL,
   facet = NULL,

data-raw/DATASET.R

@@ -1,7 +1,7 @@
 ## code to prepare `DATASET` dataset goes here
 # Path to SS3 output file
 EX_REPORT_PATH <- file.path("inst", "extdata", "Report.sso")
-# Install asar
+# Install stockplotr
 devtools::install_github("nmfs-ost/stockplotr")
 # Convert output
 example_data <- stockplotr::convert_output(EX_REPORT_PATH)

tests/testthat/test-export_rda.R

@@ -22,8 +22,8 @@ test_that("export_rda works for figures", {
   )
 
   # make a simple plot
-  library(ggplot2)
-  final <- ggplot2::ggplot(Orange, aes(circumference, age)) +
+  # library(ggplot2)
+  final <- ggplot2::ggplot(Orange, ggplot2::aes(circumference, age)) +
     ggplot2::geom_point()
 
   # export rda
@@ -43,7 +43,7 @@ test_that("export_rda works for figures", {
 
   # erase temporary testing files
   file.remove(fs::path(getwd(), "captions_alt_text.csv"))
-  file.remove(fs::path(getwd(), "key_quantities.csv"))
+  # file.remove(fs::path(getwd(), "key_quantities.csv")) # file is not made in this test
   unlink(fs::path(getwd(), "figures"), recursive = T)
 })
 
@@ -85,6 +85,6 @@ test_that("export_rda works for tables", {
 
   # erase temporary testing files
   file.remove(fs::path(getwd(), "captions_alt_text.csv"))
-  file.remove(fs::path(getwd(), "key_quantities.csv"))
+  # file.remove(fs::path(getwd(), "key_quantities.csv")) # file is not made in this test ?
   unlink(fs::path(getwd(), "tables"), recursive = T)
 })

tests/testthat/test-plot_landings.R

@@ -21,7 +21,6 @@ test_that("plot_landings generates plots without errors", {
 test_that("rda file made when indicated", {
   # export rda
   plot_landings(out_new,
-    end_year = 2024,
     make_rda = TRUE,
     unit_label = "metric tons",
     figures_dir = getwd()