Style code and document

R/save_all_plots.R

@@ -70,7 +70,7 @@ save_all_plots <- function(
   # imported from plot_landings
   landings_unit_label = "mt",
   # imported from plot_recruitment_deviations- zero unique arguments
-  # imported from plot_stock_recruitment 
+  # imported from plot_stock_recruitment
   spawning_biomass_label = "mt",
   spawning_biomass_scale_amount = 1,
   # imported from plot_spawning_biomass

R/utils.R

@@ -48,8 +48,8 @@ check_year <- function(
 # Sourced from r4ss
 get_ncol <- function(file, skip = 0) {
   nummax <- max(utils::count.fields(file,
-                                    skip = skip, quote = "",
-                                    comment.char = ""
+    skip = skip, quote = "",
+    comment.char = ""
   )) + 1
   nummax
 }
@@ -87,41 +87,41 @@ SS3_extract_df <- function(dat, label) {
   if (!inherits(dat, "data.table")) {
     dat <- data.table::as.data.table(dat)
   }
-  
+
   # Identify all values to be treated as NA
   na_values <- c("", "-", "#")
-  
+
   # Find the row indices using a vectorized approach
   start_row <- which(apply(dat, 1, function(row) any(row == label)))[2]
-  
+
   # If the label is not found or is not the second instance, return NA
   if (is.na(start_row)) {
     cli::cli_alert_warning("Label not found in data frame or less than two instances were found.")
     return(NA)
   }
-  
+
   # Find the next blank row after the starting row
   next_blank_rows <- which(apply(dat, 1, function(row) all(is.na(row) | row %in% na_values)))
-  
+
   # Find the first blank row that appears after the start_row
   end_row <- next_blank_rows[which(next_blank_rows > start_row)[1]]
   if (is.na(end_row) || length(end_row) == 0) {
     end_row <- nrow(dat)
   }
-  
+
   # Extract the subset
   clean_dt <- data.table::as.data.table(dat[start_row:(end_row - 1), ])
-  
+
   # Efficiently replace specified values with NA
   for (j in names(clean_dt)) {
     data.table::set(clean_dt, i = which(clean_dt[[j]] %in% na_values), j = j, value = NA)
   }
-  
+
   # Identify columns to remove and subset the data.table
   # This is the key optimization for removing NA columns
   cols_to_keep <- which(sapply(clean_dt, function(x) !all(is.na(x))))
   clean_dt <- clean_dt |> dplyr::select(dplyr::all_of(c(names(cols_to_keep))))
-  
+
   # Return data
   as.data.frame(clean_dt)
 }
@@ -138,10 +138,10 @@ SS3_extract_fleet <- function(dat, vers) {
   }
   # Locate the row containing the specified value from the df
   value_row <- switch(i,
-                      "1" = which(apply(dat, 1, function(row) any(row == "Fleet")))[1],
-                      "2" = which(apply(dat, 1, function(row) any(row == "Fleet_name")))[1]
+    "1" = which(apply(dat, 1, function(row) any(row == "Fleet")))[1],
+    "2" = which(apply(dat, 1, function(row) any(row == "Fleet_name")))[1]
   )
-  
+
   # If the parameter value is not found, return NA
   if (is.na(value_row)) {
     cli::cli_alert_warning("Label not found in data frame.")
@@ -154,25 +154,25 @@ SS3_extract_fleet <- function(dat, vers) {
   }
   # Combine the rows surrounding the selected metric from the output table
   rows <- c(value_row, next_blank)
-  
+
   # Extract the metric using the rows from above as a guide and clean up empty columns
   clean_df <- dat[rows[1]:(rows[2] - 1), ] |>
     naniar::replace_with_na_all(condition = ~ .x == "")
   fleets <- switch(i,
-                   "1" = {
-                     fleet_names <- Filter(function(x) !all(is.na(x)), clean_df)[-1, 9]$X9
-                     fleet_length <- seq_along(fleet_names)
-                     names(fleet_names) <- fleet_length
-                     fleet_length
-                   },
-                   {
-                     clean_df <- dat[rows[1]:(rows[2] - 1), ] |>
-                       naniar::replace_with_na_all(condition = ~ .x == "")
-                     fleet_info <- Filter(function(x) !all(is.na(x)), clean_df)[-1, ]
-                     stats::setNames(fleet_info[[ncol(fleet_info)]], fleet_info[[1]])
-                   }
+    "1" = {
+      fleet_names <- Filter(function(x) !all(is.na(x)), clean_df)[-1, 9]$X9
+      fleet_length <- seq_along(fleet_names)
+      names(fleet_names) <- fleet_length
+      fleet_length
+    },
+    {
+      clean_df <- dat[rows[1]:(rows[2] - 1), ] |>
+        naniar::replace_with_na_all(condition = ~ .x == "")
+      fleet_info <- Filter(function(x) !all(is.na(x)), clean_df)[-1, ]
+      stats::setNames(fleet_info[[ncol(fleet_info)]], fleet_info[[1]])
+    }
   )
-  
+
   if (any(grepl("[0-9]+", fleets))) {
     # Set fleets to another object to use as names of new vector
     # This follows the same naming convention of i = 2