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

R/plot_biomass.R

@@ -37,6 +37,7 @@ plot_biomass <- function(
   group = NULL,
   facet = NULL,
   ref_line = "msy",
+  era = NULL,
   unit_label = "metric tons",
   module = NULL,
   scale_amount = 1,
@@ -74,55 +75,48 @@ plot_biomass <- function(
     scale_amount <- 1
   }
 
-  # Filter data for spawning biomass
+  # Filter data for biomass
+  # TODO: determine method to ID that first point in the timeseries is actually Bunfished ref pt
   prepared_data <- filter_data(
     dat = dat,
-    label_name = "^biomass$",
+    label_name = ifelse(relative, "biomass_biomass_unfished|biomass_ratio", "^biomass$"), # what exactly is biomass_ratio?
     geom = geom,
     group = group,
     facet = facet,
+    era = era,
     module = module,
     scale_amount = scale_amount,
     interactive = interactive
   )
-
-  # check if all 3 are present and subset for one or two
-  if (length(unique(prepared_data$label)) > 1 & any(grepl("biomass$", unique(prepared_data$label)))) {
-    # cli::cli_alert_info("> 1 label name. Selecting total biomass only.")
-    prepared_data <- prepared_data |>
-      dplyr::filter(
-        grepl("biomass$", label)
-      )
+  if (relative) {
+    if (nrow(prepared_data) == 0) {
+      cli::cli_abort("No data found for relative biomass. Please check that your data contains a label for 'biomass_biomass_unfished'.")
+      stop()
+    }
+  } else {
+    # check if all 3 are present and subset for one or two
+    if (length(unique(prepared_data$label)) > 1 & any(grepl("biomass$", unique(prepared_data$label)))) {
+      # cli::cli_alert_info("> 1 label name. Selecting total biomass only.")
+      prepared_data <- prepared_data |>
+        dplyr::filter(
+          grepl("biomass$", label)
+        )
+    }
   }
-
+    
   # Process data for indexing/grouping
   # TODO: check and add into process_data step to summarize when theres >1 label
   processing <- process_data(
     prepared_data,
     group,
     facet
   )
-
+  
   # variable <- processing[[1]]
   prepared_data <- processing[[1]]
   group <- processing[[2]]
   if (!is.null(processing[[3]])) facet <- processing[[3]]
-
-  # Calculate estimate if relative
-  if (relative) {
-    if (!is.null(names(ref_line))) {
-      ref_line_val <- ref_line[[1]]
-      # ref_line <- names(ref_line)
-    } else {
-      ref_line_val <- calculate_reference_point(
-        dat = rp_dat,
-        reference_name = glue::glue("^biomass_", ref_line)
-      ) / scale_amount
-    }
-    if (is.na(ref_line_val)) cli::cli_abort("Reference value not found. Cannot plot relative values.")
-    prepared_data <- prepared_data |>
-      dplyr::mutate(estimate = estimate / ref_line_val)
-  }
+
 
   plt <- plot_timeseries(
     dat = prepared_data,
@@ -135,16 +129,37 @@ plot_biomass <- function(
   )
   # Add reference line
   # getting data set - an ifelse statement in the fxn wasn't working
-
-  final <- reference_line(
-    plot = plt,
-    dat = rp_dat,
-    label_name = "biomass",
-    reference = ref_line,
-    relative = relative,
-    scale_amount = scale_amount
-  ) +
-    theme_noaa()
+  if (relative) {
+    # don't add any reference line here and just add theme for final plot
+    final <- plt + theme_noaa()
+  } else {
+    if ("unfished" %in% c(names(ref_line), ref_line)) {
+      # find the minimum x axis value from the plot
+      min_year <- min <- ggplot2::ggplot_build(plt)@data[[2]] |>
+        as.data.frame() |>
+        dplyr::pull(y) |>
+        min() |>
+        round(digits = 2)
+      # find the reference point value for unfished
+      ref_point <- calculate_reference_point(
+        dat = stockplotr::example_data,
+        reference_name = "biomass_unfished"
+      ) / scale_amount
+      # add point to plot and add theme
+      final <- plt +
+        ggplot2::geom_point(ggplot2::aes(x = min_year - 1, y = ref_point)) + # should I keep -1 or set as first year?
+        theme_noaa()
+    } else {
+      final <- reference_line(
+        plot = plt,
+        dat = rp_dat,
+        label_name = "biomass",
+        reference = ref_line,
+        scale_amount = scale_amount
+      ) +
+        theme_noaa()
+    }
+  }
 
   ### Make RDA ----
   if (make_rda) {

R/plot_biomass_at_age.R

@@ -49,6 +49,7 @@ 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

@@ -31,9 +31,8 @@ plot_fishing_mortality <- function(
   group = NULL,
   facet = NULL,
   ref_line = "msy",
-  era = "time",
+  era = NULL,
   module = NULL,
-  relative = FALSE,
   make_rda = FALSE,
   figures_dir = getwd(),
   interactive = TRUE,
@@ -62,16 +61,6 @@ plot_fishing_mortality <- function(
   group <- processed_data[[2]]
   facet <- processed_data[[3]]
 
-  # Extract reference point unless explicit
-  # if (!is.null(names(ref_line))) {
-  #   ref_line_val <- ref_line[[1]]
-  # } else {
-  #   reference_point <- calculate_reference_point(
-  #     dat = dat,
-  #     reference_name = glue::glue("fishing_mortality_", ref_line)
-  #   )
-  # }
-
   # Create base plot
   plt <- plot_timeseries(
     dat = prepared_data,
@@ -89,39 +78,18 @@ plot_fishing_mortality <- function(
     # era = "time",
     label_name = "fishing_mortality",
     reference = ref_line,
-    relative = relative,
     scale_amount = 1
   ) + theme_noaa()
 
   ### Make RDA ----
   if (make_rda) {
-    if (relative) {
-      # Obtain relevant key quantities for captions/alt text
-      # pulling out the 2nd df in 'data' works for several datasets
-      rel.F.min <- ggplot2::ggplot_build(final)@data[[2]] |>
-        as.data.frame() |>
-        dplyr::pull(y) |>
-        min() |>
-        round(digits = 2)
-      rel.F.max <- ggplot2::ggplot_build(final)@data[[2]] |>
-        as.data.frame() |>
-        dplyr::pull(y) |>
-        max() |>
-        round(digits = 2)
-
-      # calculate & export key quantities
-      export_kqs(rel.F.min, rel.F.max)
-
-      # Add key quantities to captions/alt text
-      insert_kqs(rel.F.min, rel.F.max)
-    } else {
-      F.min <- min(prepared_data$estimate) |> round(digits = 3)
-      F.max <- max(prepared_data$estimate) |> round(digits = 3)
-
-      export_kqs(F.min, F.max)
-      insert_kqs(F.min, F.max)
-    }
-
+
+    F.min <- min(prepared_data$estimate) |> round(digits = 3)
+    F.max <- max(prepared_data$estimate) |> round(digits = 3)
+
+    export_kqs(F.min, F.max)
+    insert_kqs(F.min, F.max)
+
     F.ref.pt <- as.character(ref_line)
     F.start.year <- min(prepared_data$year)
     F.end.year <- max(prepared_data$year)
@@ -140,7 +108,7 @@ plot_fishing_mortality <- function(
 
     create_rda(
       object = final,
-      topic_label = ifelse(relative, "relative_fishing_mortality", "fishing_mortality"),
+      topic_label = "fishing_mortality", # ifelse(relative, "relative_fishing_mortality", "fishing_mortality"),
       fig_or_table = "figure",
       dat = dat,
       dir = figures_dir,

R/plot_indices.R

@@ -63,7 +63,7 @@ plot_indices <- function(
     prepared_data <- prepared_data |>
       dplyr::filter(fleet %in% focus)
   }
-
+  
   processed_data <- process_data(
     dat = prepared_data,
     group = group,
@@ -109,7 +109,7 @@ plot_indices <- function(
     facet_formula <- stats::reformulate(facet)
     plt <- plt + ggplot2::facet_wrap(facet_formula, scales = "free")
   }
-
+  
   ### Make RDA ----
   if (make_rda) {
     # Obtain relevant key quantities for captions/alt text

R/plot_recruitment.R

@@ -28,7 +28,7 @@ plot_recruitment <- function(
   dat,
   unit_label = "mt",
   scale_amount = 1,
-  era = "time",
+  era = NULL,
   group = NULL,
   facet = NULL,
   # relative = FALSE,
@@ -124,25 +124,25 @@ 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) {