Adding keywords internal

Author: AliceBeckmann

R/GRAFS_N_Inputs_git.R

@@ -48,6 +48,7 @@ create_n_inputs_grafs_spain <- function() {
 
 # N Inputs --------------------------------------------------------------------
 # load data -------------------------------------------------------------------
+# @keywords internal
 .load_inputs_n_inputs <- function() {
   result <-
     list(
@@ -65,6 +66,7 @@ create_n_inputs_grafs_spain <- function() {
 }
 
 # Assign some special items to Boxes ------------------------------------------
+# @keywords internal
 .assign_items <- function() {
   list(
     semi_natural_agroecosystems = c(
@@ -80,6 +82,7 @@ create_n_inputs_grafs_spain <- function() {
 }
 
 # Calculate n Inputs ----------------------------------------------------------
+# @keywords internal
 .calculate_n_inputs <- function(n_balance_ygpit_all, codes_coefs) {
   categories <- .assign_items()
   firewood_biomass <- categories$Firewood_biomass
@@ -143,6 +146,7 @@ create_n_inputs_grafs_spain <- function() {
 }
 
 # Combine all Inputs ----------------------------------------------------------
+# @keywords internal
 .summarise_inputs <- function(n_inputs_prepared) {
   n_inputs <- dplyr::full_join(
     n_inputs_prepared$n_inputs_summary,
@@ -168,6 +172,7 @@ create_n_inputs_grafs_spain <- function() {
 # Summarize and calculate new columns: Prod_MgN
 # Spread the Destiny column to separate columns for Food,
 # Feed, Other_uses, Export
+# @keywords internal
 .summarise_production <- function(grafs_prod_destiny, n_inputs_sum) {
   grafs_prod_destiny_summary <- grafs_prod_destiny |>
     tidyr::pivot_wider(
@@ -200,6 +205,7 @@ create_n_inputs_grafs_spain <- function() {
 }
 
 # NUE for Cropland and Semi-natural agroecosystems ----------------------------
+# @keywords internal
 .calculate_nue <- function(n_inputs_combined) {
   nue <- n_inputs_combined |>
     dplyr::mutate(

R/GRAFS_Prod_Destiny_git.R

@@ -138,6 +138,7 @@
 
 # Production of Cropland, Livestock, and Semi natural agroecosystems ---------
 # Merge items with biomasses
+# @keywords internal
 .merge_items_biomass <- function(crop_area_npp_ygpit_all, npp_ygpit_csv,
                                  codes_coefs) {
   crop_area_npp_merged <- crop_area_npp_ygpit_all |>
@@ -161,6 +162,7 @@
 }
 
 # Crops Production and Residues ----------------------------------------------
+# @keywords internal
 .summarise_crops_residues <- function(crop_area_npp_ygpitr_no_fallow) {
   crop_area_npp_prod_residue <- crop_area_npp_ygpitr_no_fallow |>
     dplyr::select(
@@ -194,6 +196,7 @@
 
 
 # Combining crops, residues, feed (grass, fallow) production -----------------
+# @keywords internal
 .aggregate_grazed_cropland <- function(npp_ygpit_merged,
                                        crop_area_npp_prod_residue) {
   grazed_data <- npp_ygpit_merged |>
@@ -256,7 +259,7 @@
 #'
 #' @return A tibble filtered and transformed with selected columns for
 #' semi-natural agroecosystems.
-#'
+#' @keywords internal
 .aggregate_seminatural_system <- function(npp_ygpit_merged) {
   semi_natural_agroecosystems <- npp_ygpit_merged |>
     dplyr::ungroup() |>
@@ -272,7 +275,7 @@
 
 #' Livestock Production -------------------------------------------------------
 #' @param livestock_prod_ygps A data frame including livestock production data.
-#'
+#' @keywords internal
 .prepare_livestock_production <- function(livestock_prod_ygps) {
   livestock <- livestock_prod_ygps |>
     dplyr::select(
@@ -290,6 +293,7 @@
 }
 
 # Combine Cropland, Semi_natural_agroecosystems and Livestock ----------------
+# @keywords internal
 .combine_production_boxes <- function(crops_residues_grazed,
                                       semi_natural_agroecosystems, livestock) {
   grafs_prod_combined <- dplyr::bind_rows(
@@ -313,6 +317,7 @@
 }
 
 # Seed production per province, based on the national seed share per Area ----
+# @keywords internal
 .remove_seeds_from_system <- function(crop_area_npp_ygpit_all,
                                       pie_full_destinies_fm,
                                       grafs_prod_combined) {
@@ -367,6 +372,7 @@
 # Structuring dataset (GrazedWeeds und Used_Residues in ProductionFM) --------
 # Rename Prod_Residue_Product_Mg to Production_FM and replace Production_FM
 # with GrazedWeeds_MgDM (for Fallow)
+# @keywords internal
 .adding_grass_wood <- function(grafs_prod_combined_no_seeds, biomass_coefs) {
   grafs_prod_structured <- grafs_prod_combined_no_seeds |>
     dplyr::rename(Production_FM = Prod_Residue_Product_Mg) |>
@@ -462,6 +468,7 @@
 
 # Processed Items ------------------------------------------------------------
 # Summarise processed items by Year, Province, Biomass, Item, and ProcessedItem
+# @keywords internal
 .prepare_processed_data <- function(processed_prov_fixed) {
   processed_data <- processed_prov_fixed |>
     dplyr::group_by(Year, Province_name, Name_biomass, Item, ProcessedItem) |>
@@ -480,6 +487,7 @@
 }
 
 # Match structure of grafs_prod_combined_no_seeds ----------------------------
+# @keywords internal
 .prepare_prod_data <- function(grafs_prod_added_grass_wood,
                                processed_data, codes_coefs_items_full) {
   added_grass_wood_prepared <- grafs_prod_added_grass_wood |>
@@ -509,6 +517,7 @@
 # Convert Fresh Matter (FM) to Dry Matter (DM) and finally to Nitrogen (N) ---
 # Define a list of special items that require using the primary biomass name
 # for selecting conversion coefficients
+# @keywords internal
 .convert_fm_dm_n <- function(added_grass_wood_merged,
                              biomass_coefs) {
   special_items <- c(
@@ -572,6 +581,7 @@
 # Province_name and Item
 # Comment!!! Feed from all animals are summed together, also from pets.
 # Do they have to be assigned to humans?
+# @keywords internal
 .adding_feed <- function(feed_intake) {
   feed_intake <- feed_intake |>
     dplyr::select(Year, Province_name, Item, FM_Mg) |>
@@ -584,6 +594,7 @@
 # Popoulation: use column Pop_Mpeop_yg. Calculate the share of population ----
 # (population in each province divided through whole population in
 # Spain to get the share)
+# @keywords internal
 .calculate_population_share <- function(population_share) {
   population_share <- population_share |>
     dplyr::select(Year, Province_name, Pop_Mpeop_yg) |>
@@ -600,6 +611,7 @@
 
 # Food -----------------------------------------------------------------------
 # Sum all Elements for Food and multiply with population share
+# @keywords internal
 .adding_food <- function(pie_full_destinies_fm, population_share) {
   total_food <- pie_full_destinies_fm |>
     dplyr::filter(Destiny == "Food", Element == "Domestic_supply") |>
@@ -623,6 +635,7 @@
 
 # Other_uses -----------------------------------------------------------------
 # Sum all Elements for Other_uses and multiply with population share
+# @keywords internal
 .adding_other_uses <- function(pie_full_destinies_fm, population_share) {
   other_uses_with_share <- pie_full_destinies_fm |>
     dplyr::filter(Destiny == "Other_uses", Element == "Domestic_supply") |>
@@ -639,6 +652,7 @@
 }
 
 # Putting all together -------------------------------------------------------
+# @keywords internal
 .combine_destinies <- function(grafs_prod_item, feed_intake, food_with_share,
                                other_uses_with_share) {
   grafs_prod_item_combined <- grafs_prod_item |>
@@ -658,6 +672,7 @@
 }
 
 # Converting Item and Name_biomass again and converting FM to DM, and DM to N
+# @keywords internal
 .convert_to_items_n <- function(grafs_prod_item_combined,
                                 codes_coefs_items_full, biomass_coefs) {
   grafs_prod_item_n <- grafs_prod_item_combined |>
@@ -692,6 +707,7 @@
 }
 
 # Calculating Consumption and Trade ------------------------------------------
+# @keywords internal
 .calculate_trade <- function(grafs_prod_item_n) {
   grafs_prod_item_trade <- grafs_prod_item_n |>
     dplyr::group_by(Year, Province_name, Item, Name_biomass, Box) |>
@@ -711,6 +727,7 @@
 }
 
 # Adding missing Boxes for Imports -------------------------------------------
+# @keywords internal
 .finalize_prod_destiny <- function(grafs_prod_item_trade,
                                    codes_coefs_items_full) {
   grafs_prod_destiny <- grafs_prod_item_trade |>

R/Typologies_Josette.R

@@ -81,7 +81,7 @@ create_typologies_of_josette <- function(
 #' Load input datasets ---------------------------------------------------------
 #' @param shapefile_path The local path where the input data are located.
 #' @param inputs_dir Path to the input data directory.
-#'
+#' @keywords internal
 .load_inputs_josette <- function(inputs_dir, shapefile_path) {
   layer_name <- tools::file_path_sans_ext(basename(shapefile_path))
 
@@ -109,7 +109,7 @@ create_typologies_of_josette <- function(
 }
 
 # Calculate food consumption and total production
-#
+# @keywords internal
 .calculate_consumption_prod <- function(grafs_prod_destiny_git) {
   # Food consumption
   food_consumption <- grafs_prod_destiny_git |>
@@ -143,6 +143,7 @@ create_typologies_of_josette <- function(
   )
 }
 
+# @keywords internal
 .calculate_crop_prod_feed <- function(grafs_prod_destiny_git) {
   # Cropland  production
   cropland_prod <- grafs_prod_destiny_git |>
@@ -194,7 +195,7 @@ create_typologies_of_josette <- function(
 #'
 #' @return A list containing LU per province and year, Livestock Density,
 #' share of imported feed
-#'
+#' @keywords internal
 .calculate_imported_feed <- function(
     livestock_df, codes_coefs_df, npp_df, feed_df, destiny_df) {
   lu_coefs <- .prepare_lu_coefs(codes_coefs_df)
@@ -225,7 +226,7 @@ create_typologies_of_josette <- function(
 }
 
 # Decision: >50% animal feed from Semi-natural agroecosystems
-#
+# @keywords internal
 .calculate_natural_feed_share <- function(destiny_df) {
   seminatural_feed <- destiny_df |>
     dplyr::filter(Destiny == "Feed", Box == "Semi_natural_agroecosystems") |>
@@ -251,7 +252,7 @@ create_typologies_of_josette <- function(
 
 # Decision: >25% animal feed from local crop ---------------------------------
 # Calculate domestic feed supply per province
-#
+# @keywords internal
 .calculate_feed_domestic_share <- function(feed_df, lu_df) {
   # Filter relevant feed data: Production, Exports and
   # Imports destined for feed use
@@ -301,7 +302,7 @@ create_typologies_of_josette <- function(
 
 
 # Decision: >25% cropland N input from manure
-#
+# @keywords internal
 .calculate_manure_share <- function(n_input_df) {
   cropland_n_inputs <- n_input_df |>
     dplyr::filter(Box == "Cropland") |>
@@ -322,7 +323,7 @@ create_typologies_of_josette <- function(
 }
 
 # Assign Typologies
-#
+# @keywords internal
 .assign_typologies <- function(df) {
   df |>
     dplyr::mutate(
@@ -342,7 +343,7 @@ create_typologies_of_josette <- function(
 }
 
 # Create map
-#
+# @keywords internal
 .create_typologies_map_josette <- function(
     typologies_df, shapefile_path, map_year) {
   layer_name <- tools::file_path_sans_ext(basename(shapefile_path))