Continue the restructure

Remove GCAM info in primary eq.

R/xfaostat_L101_RawDataPreProc4_FBSH_CB.R → R/xfaostat_L101_RawDataPreProc4_FBSH_CBH.R

@@ -1,8 +1,8 @@
 # Copyright 2019 Battelle Memorial Institute; see the LICENSE file.
 
-#' module_xfaostat_L101_RawDataPreProc4_FBSH_CB
+#' module_xfaostat_L101_RawDataPreProc4_FBSH_CBH
 #'
-#' Preprocess raw faostat data part 4 FBSH and CB
+#' Preprocess raw faostat data part 4 FBSH and CBH
 #'
 #' @param command API command to execute
 #' @param ... other optional parameters, depending on command
@@ -15,7 +15,7 @@
 #' @importFrom tibble tibble
 #' @importFrom tidyr complete drop_na gather nesting spread replace_na
 #' @author XZ 2023
-module_xfaostat_L101_RawDataPreProc4_FBSH_CB <- function(command, ...) {
+module_xfaostat_L101_RawDataPreProc4_FBSH_CBH <- function(command, ...) {
 
   MODULE_INPUTS <-
     c(FAOSTAT_FILE = file.path(DIR_RAW_DATA_FAOSTAT, "FoodBalanceSheetsHistoric_E_All_Data_Normalized"),
@@ -106,7 +106,7 @@ module_xfaostat_L101_RawDataPreProc4_FBSH_CB <- function(command, ...) {
              unit = "1000 tonnes") -> CBH1 # convert to Kton
 
 
-    ## *FBSH_CB merge the two----
+    ## *FBSH_CBH merge the two----
     # load processed data
 
     FBSH1 %>% distinct(item_code) %>%

R/xfaostat_L105_DataConnectionToSUA.R

@@ -64,7 +64,7 @@ module_xfaostat_L105_DataConnectionToSUA <- function(command, ...) {
 
     FBSH_CBH_wide %>% gather_years() %>%
       filter(year >= min(FAOSTAT_Hist_Year_FBS)) %>%
-      FAOSTAT_AREA_RM_NONEXIST() -> FBSH_CB
+      FAOSTAT_AREA_RM_NONEXIST() -> FBSH_CBH
 
     TCL_wide %>% gather_years() %>%
       filter(year >= min(FAOSTAT_Hist_Year_FBS)) %>%
@@ -160,7 +160,7 @@ module_xfaostat_L105_DataConnectionToSUA <- function(command, ...) {
     # Merge Sudan regions to be consistent with data
     # Mainly for storage data concerns
     # And only keep data > min(FAOSTAT_Hist_Year_FBS)
-    for (.DF in c("SCL", "TCL_TM", "TCL_gross", "FBSH_CB", "FBS", "QCL_PROD")) {
+    for (.DF in c("SCL", "TCL_TM", "TCL_gross", "FBSH_CBH", "FBS", "QCL_PROD")) {
       get(.DF) %>% filter(year >= min(FAOSTAT_Hist_Year_FBS)) %>%
         # merge Sudan and South Sudan
         FAO_AREA_DISAGGREGATE_HIST_DISSOLUTION_ALL(SUDAN2012_MERGE = T) %>%
@@ -645,7 +645,7 @@ module_xfaostat_L105_DataConnectionToSUA <- function(command, ...) {
 
     assert_FBS_balance(.DF = Bal_new_all)
 
-    rm(TCL_gross, TCL_TM, SCL, FBS, FBSH_CB, FAO_items)
+    rm(TCL_gross, TCL_TM, SCL, FBS, FBSH_CBH, FAO_items)
     rm(list = ls(pattern = "Bal_new_tier*"))
 
 

R/xfaostat_L106_FoodMacroNutrient.R

@@ -22,7 +22,7 @@ module_xfaostat_L106_FoodMacroNutrient <- function(command, ...) {
       "FBS_wide",
       "OA",
       FILE = file.path(DIR_RAW_DATA_FAOSTAT, "FAO_an_items_cal_SUA"),
-      FILE = file.path(DIR_RAW_DATA_FAOSTAT, "MAPPING_FAO_FBS_SUA"))
+      FILE = file.path(DIR_RAW_DATA_FAOSTAT, "Mapping_FAO_FBS_SUA"))
 
   MODULE_OUTPUTS <-
     c("SUA_food_macronutrient_rate")
@@ -39,7 +39,7 @@ module_xfaostat_L106_FoodMacroNutrient <- function(command, ...) {
       value_world <- value_reg <- Diff <- p_Diff <- quantile <- `Food supply quantity (kg/capita/yr)` <-
       `Protein supply quantity (g/capita/day)` <- `Fat supply quantity (g/capita/day)` <-
       FAO_an_items_cal_SUA <- Mcal_t <- fat_Perc  <- protein_Perc <- FAO_FBS_code <-
-      SCL_item_code <- CPC_code <- MAPPING_FAO_FBS_SUA <- unit <- element <-
+      SCL_item_code <- CPC_code <- Mapping_FAO_FBS_SUA <- unit <- element <-
       area_code <- item_code <- element_code <- OA <- FBS_wide <- SCL_wide <-
       `Food supply (kcal/capita/day)` <- NULL
 
@@ -125,7 +125,7 @@ module_xfaostat_L106_FoodMacroNutrient <- function(command, ...) {
     #*******************************************
 
     SCL %>% filter(element_code %in% c(261, 271, 281, 5141)) %>% #All 3 cal protein fats and food in ton
-      right_join(MAPPING_FAO_FBS_SUA %>%
+      right_join(Mapping_FAO_FBS_SUA %>%
                    filter(!is.na(CPC_code)) %>%
                    select(item_code = SCL_item_code, FAO_FBS_code, FBS_label),
                  by = "item_code") %>%
@@ -197,7 +197,7 @@ module_xfaostat_L106_FoodMacroNutrient <- function(command, ...) {
     #*******************************************
 
     # Adding the 12 fish item from FBS
-    MAPPING_FAO_FBS_SUA %>% filter(is.na(CPC_code)) %>%
+    Mapping_FAO_FBS_SUA %>% filter(is.na(CPC_code)) %>%
       select(item = FBS_label, item_code = FAO_FBS_code) -> Fish_item
 
     FBS %>% right_join(Fish_item, by = c("item_code", "item")) -> FBS_fish
@@ -245,7 +245,7 @@ module_xfaostat_L106_FoodMacroNutrient <- function(command, ...) {
 
     rm(SUA_food_macronutrient, SUA_food_yearmean,
        SUA_food_yearmean_fill, SUA_food_yearareamean,
-       OA, POP, SCL, FBS, MAPPING_FAO_FBS_SUA,
+       OA, POP, SCL, FBS, Mapping_FAO_FBS_SUA,
        SUA_food_macronutrient_rate_nofish, FAO_an_items_cal_SUA)
     rm(Fish_item)
     rm(checkarea, checkitem, checkelement)
@@ -260,7 +260,7 @@ module_xfaostat_L106_FoodMacroNutrient <- function(command, ...) {
                      "FBS_wide",
                      "OA",
                      file.path(DIR_RAW_DATA_FAOSTAT, "FAO_an_items_cal_SUA"),
-                     file.path(DIR_RAW_DATA_FAOSTAT, "MAPPING_FAO_FBS_SUA")) ->
+                     file.path(DIR_RAW_DATA_FAOSTAT, "Mapping_FAO_FBS_SUA")) ->
       SUA_food_macronutrient_rate
 
     # P.S. ----