deleted weired headers

R_to_SQL_database/Cifos_Model_Data_EU.R

-Loading packages --------------------------------------------------------
+
+# Loading packages --------------------------------------------------------
 # Loading the packages
 # .libPaths("C:/Wolfram_Admin/R-4.1.2/R-4.1.2/library")
 # install.packages("easypackages", dependencies = T)
 # library("easypackages")
 # Loading packages 
 easypackages::packages("odbc","DBI", "RSQLite", "dbplyr", "readxl", "fuzzyjoin",
-<<<<<<< HEAD
-                         "sqldf", "downloader", "tidyverse", "janitor", "FAOSTAT",
-=======
                        "sqldf", "downloader", "tidyverse", "janitor", "FAOSTAT",
->>>>>>> bdd608029dd5899a8a1a0b93359f2d957d331ba1
                        "validate")
 
 # https://www.youtube.com/watch?v=CydajdNRJOU -----------------------------
@@ -247,7 +244,7 @@ dat_crop_final = read_csv("Input_data/dat_crop_with_grass_final.csv") %>%
   # Adding the grass to the old crop name columns so it will not result in NA later. 
   dplyr::mutate(old_cifos_crop = case_when(is.na(old_cifos_crop) ~ crop_cifos, 
                                            TRUE ~ old_cifos_crop))
-  
+
 # Fuzzy join of processing sheet and the crop map
 # crop_map_cifos_procraw = 
 dat_proc = Processing_sheet %>%
@@ -508,11 +505,7 @@ proc_new = tibble("crop_cifos" = NA, "proc_raw" = NA, "proc_in" = NA, "proc_out"
   add_row(crop_cifos = "cassava" , proc_raw = "cassava" , proc_in = "cassava_tapiocain", proc_out = "cassava_tapioca", value = 0.2, dietary_products = "Cassava and products") %>%
   add_row(crop_cifos = "cassava" , proc_raw = "cassava" , proc_in = "cassava_dryin", proc_out = "cassava_dry", value = 0.35, dietary_products = "Cassava and products") %>%
   add_row(crop_cifos = "cassava" , proc_raw = "cassava" , proc_in = "cassava_starchin", proc_out = "cassava_starch", value = 0.25, dietary_products = "Cassava and products") %>%
-<<<<<<< HEAD
-
-=======
   
->>>>>>> bdd608029dd5899a8a1a0b93359f2d957d331ba1
   # Pearl millet -- Source: from Technical Conversion Factors for Agricultural Commodities (http://countrystat.org/resources/documents/tcf.pdf); adjusted commodity tree; fractions from "cereals/millet"
   # levels: only one level
   add_row(crop_cifos = "pearl_millet" , proc_raw = "pearl_millet" , proc_in = "pearl_millet_flourin", proc_out = "pearl_millet_flour", value = 0.86, dietary_products = "Millet and products") %>% 
@@ -870,21 +863,6 @@ dat_proc_asf =
   clean_names() %>% rename(proc_raw = pro_raw) %>% 
   slice(269:363) %>% 
   dplyr::mutate(dietary_products= case_when(proc_raw == "Milk" ~ "Milk - Excluding Butter", 
-<<<<<<< HEAD
-                                     TRUE ~ dietary_products), 
-         dietary_products= case_when(proc_out == "Butter" ~ "Butter, Ghee", 
-                                     TRUE ~ dietary_products), 
-         dietary_products= case_when(dietary_products == "Fish (Calculated CiFoS)" ~ "Fish, Seafood",
-                                     TRUE ~ dietary_products),
-         proc_out = case_when(proc_out == "Butter_Milk" ~ "Butter_milk",
-                              TRUE ~ proc_out)) #this was written with a uppercase here and a lower case in the food losses sheet
-
-  
-
-Processing_sheet_final = bind_rows(dat_proc_new %>% dplyr::select(-crop_cifos),dat_proc_asf)
-write_csv(Processing_sheet_final, "Input_data/processing_sheet.csv")
-    
-=======
                                             TRUE ~ dietary_products), 
                 dietary_products= case_when(proc_out == "Butter" ~ "Butter, Ghee", 
                                             TRUE ~ dietary_products), 
@@ -898,7 +876,6 @@ write_csv(Processing_sheet_final, "Input_data/processing_sheet.csv")
 Processing_sheet_final = bind_rows(dat_proc_new %>% dplyr::select(-crop_cifos),dat_proc_asf)
 write_csv(Processing_sheet_final, "Input_data/processing_sheet.csv")
 
->>>>>>> bdd608029dd5899a8a1a0b93359f2d957d331ba1
 
 # Human nutrition sheet ---------------------------------------------------
 #  Human nutrtion sheet
@@ -1007,11 +984,7 @@ hum_nutr_all = bind_rows(food_old_match, food_psf_new, food_asf)
 
 # check = full_join(hum_nutr_all, proc_new, by = c("product"="proc_out"))
 write_csv(hum_nutr_all, "Input_data/hum_nutr_all.csv")
-<<<<<<< HEAD
-  
-=======
 
->>>>>>> bdd608029dd5899a8a1a0b93359f2d957d331ba1
 
 # Animal nutrition new ----------------------------------------------------
 # Cifos sheet 
@@ -2159,11 +2132,7 @@ crop_other_new =
   distinct_at(vars(crop_cifos),.keep_all = T) %>% drop_na(crop_cifos)
 
 write_csv(crop_other_new, "Input_Data/crop_other_new.csv")
-<<<<<<< HEAD
-  
-=======
 
->>>>>>> bdd608029dd5899a8a1a0b93359f2d957d331ba1
 # cropnutr = read_excel('C:/Wolfram_Admin/GAMS/EU_model_frmSep21/cifos-model_eu/European_CiFoS_model_data.xlsx',sheet="CropNutr")
 # write_csv(cropnutr,"Input_data/cropnutr_old")
 cropnutr = read_csv("Input_data/cropnutr_old")
@@ -2174,13 +2143,8 @@ cropnutr_new =
   full_join(crop_map %>% dplyr::select(old_cifos_crop, crop_cifos), by = c("crop_cifos_old"="old_cifos_crop")) %>% 
   relocate(crop_cifos, .before = crop_cifos_old) %>% 
   dplyr::select(-crop_cifos_old)
-<<<<<<< HEAD
-  
-  write_csv(cropnutr_new, "Input_data/cropnutr_new.csv")
-=======
 
 write_csv(cropnutr_new, "Input_data/cropnutr_new.csv")
->>>>>>> bdd608029dd5899a8a1a0b93359f2d957d331ba1
 
 # CropFert_New NOT DONE! ------------------------------------------------------------
 # CropFert_New = read_excel("C:/Wolfram_Admin/GAMS/EU_model_frmSep21/cifos-model_eu/European_CiFoS_model_data.xlsx", sheet = "CropFert_New")
@@ -2311,7 +2275,6 @@ write_csv(animal_map, "Input_Data/diets_animal_crop_datamap.csv")
 # # Animals
 # #Animal_yield sheet 
 # 1. Yields (column D row 2)
-<<<<<<< HEAD
 
 
 
@@ -2323,5 +2286,3 @@ write_csv(animal_map, "Input_Data/diets_animal_crop_datamap.csv")
 
 
 
-=======
->>>>>>> bdd608029dd5899a8a1a0b93359f2d957d331ba1