Style code and document

R/convert_output.R

@@ -2190,9 +2190,9 @@ convert_output <- function(
   # temporarily add call to local csv so I can test
   # con_file <- glue::glue("~/GitHub/stockplotr/inst/resources/{model}_var_names.csv")
   var_names_sheet <- utils::read.csv(con_file, na.strings = "")
-  
+
   if (tolower(model) == "bam") {
-    var_names_sheet <- var_names_sheet |> 
+    var_names_sheet <- var_names_sheet |>
       dplyr::mutate(label = tolower(label))
   }
 

R/plot_biomass.R

@@ -84,10 +84,10 @@ plot_biomass <- function(
     cli::cli_alert_warning("Scale amount is not applicable when relative = TRUE. Resetting scale_amount to 1.")
     scale_amount <- 1
   }
-  
+
   orig_group <- group
   orig_facet <- facet
-  
+
   # Filter data for biomass
   # TODO: determine method to ID that first point in the timeseries is actually Bunfished ref pt
   prepared_data <- filter_data(
@@ -177,63 +177,91 @@ plot_biomass <- function(
   ### Make RDA ----
   if (make_rda) {
     if (relative) {
-      rel.B.min <- calc_kqs(returned_kq = "rel.B.min",
-                            final = final)
-      rel.B.max <- calc_kqs(returned_kq = "rel.B.max",
-                            final = final)
+      rel.B.min <- calc_kqs(
+        returned_kq = "rel.B.min",
+        final = final
+      )
+      rel.B.max <- calc_kqs(
+        returned_kq = "rel.B.max",
+        final = final
+      )
 
       # calculate & export key quantities
       export_kqs(rel.B.min, rel.B.max)
 
       # Add key quantities to captions/alt text
       insert_kqs(rel.B.min, rel.B.max)
     } else {
-      B.min <- calc_kqs(returned_kq = "B.min",
-                        prepared_data = prepared_data)
-      B.max <- calc_kqs(returned_kq = "B.max",
-                        prepared_data = prepared_data)
+      B.min <- calc_kqs(
+        returned_kq = "B.min",
+        prepared_data = prepared_data
+      )
+      B.max <- calc_kqs(
+        returned_kq = "B.max",
+        prepared_data = prepared_data
+      )
 
       export_kqs(B.min, B.max)
       insert_kqs(B.min, B.max)
     }
 
     B.ref.pt <- as.character(ref_line)
     B.units <- as.character(unit_label)
-    B.start.year <- calc_kqs(returned_kq = "B.start.year",
-                             prepared_data = prepared_data)
-    B.end.year <- calc_kqs(returned_kq = "B.end.year", 
-                           prepared_data = prepared_data)
-    B.terminal.year <- calc_kqs(returned_kq = "B.terminal.year",
-                                dat = dat,
-                                relative = relative)
+    B.start.year <- calc_kqs(
+      returned_kq = "B.start.year",
+      prepared_data = prepared_data
+    )
+    B.end.year <- calc_kqs(
+      returned_kq = "B.end.year",
+      prepared_data = prepared_data
+    )
+    B.terminal.year <- calc_kqs(
+      returned_kq = "B.terminal.year",
+      dat = dat,
+      relative = relative
+    )
 
     # SS3, FIMS
     if ("spawning_biomass_msy" %in% dat$label) {
-      sb_msy <- calc_kqs(returned_kq = "sb_msy",
-                         dat = dat)
-      b_sb_msy <- calc_kqs(returned_kq = "b_sb_msy",
-                           dat = dat)
-      B.msy <- calc_kqs(returned_kq = "B.msy",
-                        dat = dat)
+      sb_msy <- calc_kqs(
+        returned_kq = "sb_msy",
+        dat = dat
+      )
+      b_sb_msy <- calc_kqs(
+        returned_kq = "b_sb_msy",
+        dat = dat
+      )
+      B.msy <- calc_kqs(
+        returned_kq = "B.msy",
+        dat = dat
+      )
       B.msy.min <- NA
       B.msy.max <- NA
-    # BAM
+      # BAM
     } else if ("bmsy" %in% dat$label) {
-      B.msy <- calc_kqs(returned_kq = "B.msy", 
-                        dat = dat)
-      B.msy.min_uncert <- calc_kqs(returned_kq = "B.msy.min_uncert",
-                                   dat = dat)
-      B.msy.min <- calc_kqs(returned_kq = "B.msy.min", 
-                            dat = dat)
-      B.msy.max <- calc_kqs(returned_kq = "B.msy.max",
-                            dat = dat)
-    # Rceattle
+      B.msy <- calc_kqs(
+        returned_kq = "B.msy",
+        dat = dat
+      )
+      B.msy.min_uncert <- calc_kqs(
+        returned_kq = "B.msy.min_uncert",
+        dat = dat
+      )
+      B.msy.min <- calc_kqs(
+        returned_kq = "B.msy.min",
+        dat = dat
+      )
+      B.msy.max <- calc_kqs(
+        returned_kq = "B.msy.max",
+        dat = dat
+      )
+      # Rceattle
     } else {
       B.msy <- NA
       B.msy.min <- NA
       B.msy.max <- NA
     }
-    
+
     export_kqs(
       B.ref.pt,
       B.units,

R/plot_fishing_mortality.R

@@ -87,26 +87,35 @@ plot_fishing_mortality <- function(
 
   ### Make RDA ----
   if (make_rda) {
-    F.min <- calc_kqs(returned_kq = "F.min",
-                      prepared_data = prepared_data)
-    F.max <- calc_kqs(returned_kq = "F.max",
-                      prepared_data = prepared_data)
+    F.min <- calc_kqs(
+      returned_kq = "F.min",
+      prepared_data = prepared_data
+    )
+    F.max <- calc_kqs(
+      returned_kq = "F.max",
+      prepared_data = prepared_data
+    )
 
     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)
-    F.terminal.year <- calc_kqs(returned_kq = "F.terminal.year",
+    F.terminal.year <- calc_kqs(
+      returned_kq = "F.terminal.year",
+      dat = dat
+    )
+
+    F.target <- calc_kqs(
+      returned_kq = "F.target",
+      dat = dat
+    )
+    f.limit <- calc_kqs(
+      returned_kq = "f.limit",
       dat = dat
     )
-
-    F.target <- calc_kqs(returned_kq = "F.target",
-                         dat = dat)
-    f.limit <- calc_kqs(returned_kq = "f.limit",
-                        dat = dat)
-
+
     export_kqs(
       F.ref.pt,
       F.start.year,

R/plot_spawning_biomass.R

@@ -242,13 +242,17 @@ plot_spawning_biomass <- function(
   ### Make RDA ----
   if (make_rda) {
     if (relative) {
-      rel.ssb.min <- calc_kqs(returned_kq = "rel.ssb.min",
-                              final = final)
-
-
-      rel.ssb.max <- calc_kqs(returned_kq = "rel.ssb.max",
-                              final = final)
-
+      rel.ssb.min <- calc_kqs(
+        returned_kq = "rel.ssb.min",
+        final = final
+      )
+
+
+      rel.ssb.max <- calc_kqs(
+        returned_kq = "rel.ssb.max",
+        final = final
+      )
+
       # calculate & export key quantities
       export_kqs(rel.ssb.min, rel.ssb.max)
 

R/plot_stock_recruitment.R

@@ -178,8 +178,10 @@ plot_stock_recruitment <- function(
   # Make RDA
   if (make_rda) {
     # Obtain relevant key quantities for captions/alt text
-    sr.age.min <- calc_kqs(returned_kq = "sr.age.min",
-                           dat = dat)
+    sr.age.min <- calc_kqs(
+      returned_kq = "sr.age.min",
+      dat = dat
+    )
     sr.ssb.units <- spawning_biomass_label
     sr.ssb.min <- min(sr$spawning_biomass, na.rm = TRUE) |> round(digits = 3)
     sr.ssb.max <- max(sr$spawning_biomass, na.rm = TRUE) |> round(digits = 3)

R/process_data.R

@@ -533,7 +533,7 @@ process_table <- function(
       dplyr::rename(
         !!mod_uncert_lab := uncertainty
       ) |>
-    # set values to strings to include trailing zeros from rounding and     # format large estimate values with commas
+      # set values to strings to include trailing zeros from rounding and     # format large estimate values with commas
       dplyr::mutate(estimate = formatC(estimate, format = "f", digits = digits, big.mark = ",")) |>
       tidyr::pivot_wider(
         id_cols = dplyr::all_of(c(stringr::str_to_title(mod_cols))),

R/table_index.R

@@ -48,18 +48,17 @@
 #' )
 #' }
 table_index <- function(
-    dat,
-    era = NULL,
-    interactive = TRUE,
-    group = NULL,
-    method = "sum",
-    module = NULL,
-    label = NULL,
-    digits = 2,
-    make_rda = FALSE,
-    tables_dir = getwd()
-    ) {
-
+  dat,
+  era = NULL,
+  interactive = TRUE,
+  group = NULL,
+  method = "sum",
+  module = NULL,
+  label = NULL,
+  digits = 2,
+  make_rda = FALSE,
+  tables_dir = getwd()
+) {
   # TODO: do group and facet need to be uncommented and updated?
   # Filter data for landings
   prepared_data <- filter_data(
@@ -71,37 +70,39 @@ table_index <- function(
     scale_amount = 1,
     interactive = interactive
   ) |>
-    dplyr::mutate(estimate = round(as.numeric(estimate), digits = digits),
-                  uncertainty = round(as.numeric(uncertainty), digits = digits))
-
+    dplyr::mutate(
+      estimate = round(as.numeric(estimate), digits = digits),
+      uncertainty = round(as.numeric(uncertainty), digits = digits)
+    )
+
   # Add check if there is any data
   if (nrow(prepared_data) == 0) {
     cli::cli_abort("No index data found.")
   }
-  
+
   # get uncertainty label by model
   uncert_lab <- prepared_data |>
     dplyr::filter(!is.na(uncertainty_label)) |>
     dplyr::group_by(model) |>
     dplyr::reframe(unique_uncert = unique(uncertainty_label)) # changed to reframe -- may cause errors
   uncert_lab <- stats::setNames(uncert_lab$unique_uncert, uncert_lab$model)
   # if (length(unique(uncert_lab)) == 1) uncert_lab <- unique(uncert_lab) # might need this line
-  
+
   # This needs to be adjusted when comparing different models and diff error
   if (length(uncert_lab) > 1 & length(unique(uncert_lab)) == 1 | length(names(uncert_lab)) == 1) { # prepared_data$model
     # cli::cli_alert_warning("More than one value for uncertainty exists: {uncert_lab}")
     uncert_lab <- uncert_lab[[1]]
     # cli::cli_alert_warning("The first value ({uncert_lab}) will be chosen.")
   }
-  
+
   if (is.na(uncert_lab)) uncert_lab <- "uncertainty"
-  
+
   # get fleet names
   # TODO: change from fleets to id_group AFTER the process data step and adjust throughout the table based on indexing
   fleets <- unique(prepared_data$fleet) |>
     # sort numerically even if fleets are 100% characters
     stringr::str_sort(numeric = TRUE)
-  
+
   # TODO: fix this so that fleet names aren't removed if, e.g., group = "fleet"
   table_data_info <- process_table(
     dat = prepared_data,
@@ -113,44 +114,43 @@ table_index <- function(
   table_data <- table_data_info[[1]]
   indexed_vars <- table_data_info[[2]]
   id_col_vals <- table_data_info[[3]]
-  
+
   # id_group_vals <- sapply(id_cols, function(x) unique(prepared_data[[x]]), simplify = FALSE)
   # TODO: add check if there is a index column for every error column -- if not remove the error (can keep index)
-  
+
   # if (uncert_lab != "") uncert_lab <- glue::glue("({uncert_lab})")
-  
+
   # merge error and index columns and rename
   df_list <- merge_error(
     table_data,
     id_col_vals,
     unit_label = "", # should this be CPUE?
     uncert_lab
   )
-  
+
   # transform dfs into tables
   final <- lapply(df_list, function(df) {
     df |>
       gt::gt() |>
       add_theme()
   })
-  
+
   # export figure to rda if argument = T
   if (make_rda == TRUE) {
-
     # Caption contains no key quantities for index table
     # So, export captions/alt text csv if absent
     if (!file.exists(fs::path(getwd(), "captions_alt_text.csv"))) {
       caps_alttext <- utils::read.csv(
         system.file("resources", "captions_alt_text_template.csv", package = "stockplotr")
       )
-    # export df with captions and alt text to csv
-    utils::write.csv(
-      x = caps_alttext,
-      file = fs::path(getwd(), "captions_alt_text.csv"),
-      row.names = FALSE
-      )    
+      # export df with captions and alt text to csv
+      utils::write.csv(
+        x = caps_alttext,
+        file = fs::path(getwd(), "captions_alt_text.csv"),
+        row.names = FALSE
+      )
     }
-  
+
     if (length(df_list) == 1) {
       create_rda(
         object = final$label,
@@ -168,7 +168,7 @@ table_index <- function(
     cli::cli_alert_warning("Multiple tables cannot be exported at this time.")
     cli::cli_alert_info("We are currently developing this feature.")
   }
-  
+
   # Send table(s) to viewer
   if (!is.data.frame(table_data)) {
     for (t in final) {