small edits to functions.

DESCRIPTION

@@ -18,15 +18,16 @@ Imports:
     dplyr (>= 0.3),
     ggplot2 (>= 1.0.0),
     plyr (>= 1.8.0),
-    reshape2,
     stringr,
     git2r,
     methods,
     tidyr,
     RColorBrewer,
     htmlwidgets,
     here,
-    rlang
+    rlang,
+    plotly,
+    tidyverse
 Suggests:
     knitr,
     rmarkdown

R/CalculateAll.R

@@ -98,7 +98,7 @@ CalculateAll = function(conf, layers){
     if ( dplyr::is.grouped_df(scores_g) ){
       cat(sprintf('Ungrouping scores variable returned from %s model...\n', g))
       scores_g <- scores_g %>%
-        ungroup()
+        dplyr::ungroup()
     }
 
     ## error if 'status' or 'trend' are missing

R/CalculateResilienceAll.R

@@ -67,8 +67,8 @@ CalculateResilienceAll = function(layers, conf){
                  paste(
                    unlist(
                      layers$meta %>%
-                       filter(layer %in% r_layers & val_0to1==F) %>%
-                       select(layer)),
+                       dplyr::filter(layer %in% r_layers & val_0to1==F) %>%
+                       dplyr::select(layer)),
                    collapse = ', ')))
   }
 
@@ -144,7 +144,7 @@ CalculateResilienceAll = function(layers, conf){
 
   r_rgn_layers <- scenario_data_year %>%
         dplyr::left_join(r_rgn_layers_data, by=c("year", "layer")) %>%
-        select(region_id, val_num, layer)
+        dplyr::select(region_id, val_num, layer)
 
   ## error check: matrix and region data layers include the same resilience factors
   check <- setdiff(r_layers, r_rgn_layers$layer)

R/PlotFlower.R

@@ -16,20 +16,19 @@
 #' scores.csv; if not provided, defaults to most recent year
 #' @param assessment_name this is the name that will be given to the weighted 
 #' average of all regions (i.e., region_id=0, usually something like: "Global Average")
-#' @scenario_folder name of the scenario folder within the repository 
+#' @param scenario_folder name of the scenario folder within the repository 
 #' (this is the folder with scores.csv, conf and layers folders, etc.)
-#' @scores_file name of the file with the score data used to create the flower plots, 
+#' @param scores_file name of the file with the score data used to create the flower plots, 
 #' typically: "scores.csv"
-#' @dir_fig_save file path to the location the figures (and related csv file) will be 
+#' @param dir_fig_save file path to the location the figures (and related csv file) will be 
 #' saved
-#' @save whether to save csv and png files (otherwise figures are only displayed)
+#' @param save whether to save csv and png files (otherwise figures are only displayed)
 #'
 #' @return png file/s of flowerplots will be saved in the dir_fig_save location; an
 #' additional regions_figs.csv file will be saved that describes region_id, region_name,
 #' and file paths to flower_.png files. 
 #' @export
 #'
-#' @examples
 #'
 #'
 

R/WriteRefPoint.R

@@ -32,6 +32,6 @@ WriteRefPoint <- function(goal, method, ref_pt, scenario_name="eez") {
                      goal   = goal,
                      method = method,
                      reference_point = ref_pt))
-  write_csv(ref_pts, sprintf(here("%s/temp/reference_pts_%s.csv"), scenario_name, scen_year))
+  readr::write_csv(ref_pts, sprintf(here("%s/temp/reference_pts_%s.csv"), scenario_name, scen_year))
 
 }

R/layers_eez_base_updater.R

@@ -11,11 +11,8 @@
 
 layers_eez_base_updater <- function() {
 
-  require(here)
-  require(tidyverse)
-
   # read in the csv
-  layers_eez_base <- read_csv(here("metadata_documentation/layers_eez_base.csv"), col_types = cols())
+  layers_eez_base <- readr::read_csv(here("metadata_documentation/layers_eez_base.csv"), col_types = cols())
 
   # prompt user for current version year (gsub() to remove 'v' if user adds it as well)
   message("")
@@ -46,8 +43,8 @@ layers_eez_base_updater <- function() {
 
 
   # prompt user for layers which have been updated
-  updated_layers <- str_split(readline(prompt = "enter 'all' or layers separated only by commas: "), ",")[[1]] %>% 
-    str_remove_all(" ")
+  updated_layers <- stringr::str_split(readline(prompt = "enter 'all' or layers separated only by commas: "), ",")[[1]] %>% 
+    stringr::str_remove_all(" ")
 
   if (tolower(updated_layers[1]) == "all") {
     updated_layers <- possible_layers
@@ -71,8 +68,8 @@ layers_eez_base_updater <- function() {
       message("")
     }
 
-    updated_layers <- str_split(readline(prompt = "enter 'all' or layers separated only by commas: "), ",")[[1]] %>% 
-      str_remove_all(" ")
+    updated_layers <- stringr::str_split(readline(prompt = "enter 'all' or layers separated only by commas: "), ",")[[1]] %>% 
+      stringr::str_remove_all(" ")
 
     if (tolower(updated_layers[1]) == "all") {
       updated_layers <- possible_layers
@@ -81,13 +78,13 @@ layers_eez_base_updater <- function() {
 
   # create df with updated file paths
   layers_eez_base_updated <- layers_eez_base %>% 
-    mutate(dir = case_when(layer %in% updated_layers ~ 
+    mutate(dir = dplyr::case_when(layer %in% updated_layers ~ 
                              gsub("v20\\d\\d", version_year, dir),
                            TRUE ~ dir))
 
   # vector of dir names which have changed from the original csv
-  updated_dirs <- anti_join(layers_eez_base_updated, layers_eez_base, by = c("layer", "dir")) %>% 
-    rename("dir (UPDATED)" = dir)
+  updated_dirs <- dplyr::anti_join(layers_eez_base_updated, layers_eez_base, by = c("layer", "dir")) %>% 
+    dplyr::rename("dir (UPDATED)" = dir)
 
   # make sure the chosen year and layers elicited changes
   if (nrow(updated_dirs) != 0) { 
@@ -99,7 +96,7 @@ layers_eez_base_updater <- function() {
     overwrite <- readline(prompt = "update? ('y' or 'n'): ")
 
     if (overwrite == "y") {
-      write.csv(layers_eez_base_updated, here("metadata_documentation/layers_eez_base.csv"),
+      write.csv(layers_eez_base_updated, here::here("metadata_documentation/layers_eez_base.csv"),
                 row.names = FALSE)
       message("\nfile has been updated\n")
 

R/split_regions.R

@@ -50,7 +50,7 @@ split_regions <- function(m, country_column = "country", value_column = "value",
 
   # Rename data frame columns
   m <- m %>%
-    rename(!!paste0(country_column) := country, !!paste0(value_column) := value)
+    dplyr::rename(!!paste0(country_column) := country, !!paste0(value_column) := value)
 
   # Load population weighting data
   population <- split_pops
@@ -69,34 +69,34 @@ split_regions <- function(m, country_column = "country", value_column = "value",
 
       # Calculate area weights for the regions
       area_weights <- population %>%
-        filter(country %in% regions) %>%
-        mutate(weight = ifelse(duplicate, 1, population / pop_sum)) %>% # Conditionally set weight
-        select(country, weight) %>%
-        mutate(id = row_number()) 
+        dplyr::filter(country %in% regions) %>%
+        dplyr::mutate(weight = ifelse(duplicate, 1, population / pop_sum)) %>% # Conditionally set weight
+        dplyr::select(country, weight) %>%
+        dplyr::mutate(id = row_number()) 
 
       # Split the data into different regions
       m_new <- m %>%
-        filter(country == country_name) %>%
-        uncount(length(regions), .id = "id") %>%
-        left_join(area_weights, by = c("id" = "id")) %>%
-        mutate(value = value * weight) %>%
-        mutate(country = country.y) %>%
-        select(-country.x, -country.y, -id, -weight)
+        dplyr::filter(country == country_name) %>%
+        tidyr::uncount(length(regions), .id = "id") %>%
+        dplyr::left_join(area_weights, by = c("id" = "id")) %>%
+        dplyr::mutate(value = value * weight) %>%
+        dplyr::mutate(country = country.y) %>%
+        dplyr::select(-country.x, -country.y, -id, -weight)
 
       # Update m to remove the original country and add broken down rows
       m <- m %>%
-        filter(!(country %in% country_name)) %>%
+        dplyr::filter(!(country %in% country_name)) %>%
         rbind(m_new)
 
       # Sum duplicate rows if duplicate is False
       if (!duplicate) {
         grouping_vars <- setdiff(names(m), "value")
 
         m <- m %>%
-          group_by(across(all_of(grouping_vars))) %>%
-          summarize(value = case_when(all(is.na(value)) ~ NA,
+          dplyr::group_by(across(all_of(grouping_vars))) %>%
+          dplyr::summarize(value = case_when(all(is.na(value)) ~ NA,
                                       TRUE ~ sum(value, na.rm = TRUE))) %>%
-          ungroup()
+          dplyr::ungroup()
 
         # Remove duplicates if duplicate is True
       } else {
@@ -112,7 +112,7 @@ split_regions <- function(m, country_column = "country", value_column = "value",
 
   # Rename columns to match data frame input
   m <- m %>%
-    rename(!!country_column := country, !!value_column := value)
+    dplyr::rename(!!country_column := country, !!value_column := value)
 
   # Return the new data frame
   return(m)