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Commit bdd60802 authored by Simon, Wolfram's avatar Simon, Wolfram
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Update Crop_cifos_DataPreparation_DB.R - With JD's version -- Updated processing sheet creation

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R_to_SQL_database/Crop_cifos_DataPreparation_DB.R
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Loading packages --------------------------------------------------------
# Loading packages --------------------------------------------------------
# Loading the packages # Loading the packages
# .libPaths("C:/Wolfram_Admin/R-4.1.2/R-4.1.2/library") # .libPaths("C:/Wolfram_Admin/R-4.1.2/R-4.1.2/library")
# install.packages("easypackages", dependencies = T) # install.packages("easypackages", dependencies = T)
# library("easypackages") # library("easypackages")
# Loading packages # Loading packages
easypackages::packages("odbc","DBI", "RSQLite", "dbplyr", "readxl", "fuzzyjoin", easypackages::packages("odbc","DBI", "RSQLite", "dbplyr", "readxl", "fuzzyjoin",
"sqldf", "downloader", "tidyverse", "janitor", "FAOSTAT", "sqldf", "downloader", "tidyverse", "janitor", "FAOSTAT",
"validate") "validate")
# https://www.youtube.com/watch?v=CydajdNRJOU ----------------------------- # https://www.youtube.com/watch?v=CydajdNRJOU -----------------------------
...@@ -103,18 +102,18 @@ easypackages::packages("odbc","DBI", "RSQLite", "dbplyr", "readxl", "fuzzyjoin", ...@@ -103,18 +102,18 @@ easypackages::packages("odbc","DBI", "RSQLite", "dbplyr", "readxl", "fuzzyjoin",
# Connection to a structured EER diagramm # Connection to a structured EER diagramm
con = dbConnect(odbc::odbc(), con = dbConnect(odbc::odbc(),
.connection_string = "Driver={MySQL ODBC 8.0 Unicode Driver};", .connection_string = "Driver={MySQL ODBC 8.0 Unicode Driver};",
Server = "localhost", Database = "cifos_crop", UID = "root", PWD = "154236w.S", Server = "localhost", Database = "cifos_crop", UID = "root", PWD = "154236w.S",
Port = 3306) Port = 3306)
# Connection to a messy schema (no eer diagram) # Connection to a messy schema (no eer diagram)
con_crop_map = dbConnect(odbc::odbc(), con_crop_map = dbConnect(odbc::odbc(),
.connection_string = "Driver={MySQL ODBC 8.0 Unicode Driver};", .connection_string = "Driver={MySQL ODBC 8.0 Unicode Driver};",
Server = "localhost", Server = "localhost",
Database = "crop_mapping", Database = "crop_mapping",
UID = "root", UID = "root",
PWD = "154236w.S", PWD = "154236w.S",
Port = 3306) Port = 3306)
# https://db.rstudio.com/getting-started/database-queries # https://db.rstudio.com/getting-started/database-queries
# Loading the data sets from Database ------------------------------------- # Loading the data sets from Database -------------------------------------
...@@ -132,23 +131,23 @@ mapping_grasscrops = read_csv("Input_data/mapping_grasscrops_new.csv") %>% ...@@ -132,23 +131,23 @@ mapping_grasscrops = read_csv("Input_data/mapping_grasscrops_new.csv") %>%
dat_crop = as_tibble(crop) %>% dat_crop = as_tibble(crop) %>%
dplyr::mutate(old_cifos_crop = crop_cifos) %>% dplyr::mutate(old_cifos_crop = crop_cifos) %>%
dplyr::mutate(crop_cifos = ifelse(!is.na(crop_name_monfreda), dplyr::mutate(crop_cifos = ifelse(!is.na(crop_name_monfreda),
crop_name_monfreda, crop_name_monfreda,
crop_long_spam)) %>% crop_long_spam)) %>%
dplyr::filter(!grepl("grass", crop_cifos)) %>% dplyr::filter(!grepl("grass", crop_cifos)) %>%
bind_rows(.,mapping_grasscrops) %>% bind_rows(.,mapping_grasscrops) %>%
mutate(crop_cifos = tolower(crop_cifos), mutate(crop_cifos = tolower(crop_cifos),
crop_cifos = gsub("\\,", "", crop_cifos), crop_cifos = gsub("\\,", "", crop_cifos),
crop_cifos = gsub(" ", "_", crop_cifos)) %>% crop_cifos = gsub(" ", "_", crop_cifos)) %>%
dplyr::mutate(crop_cifos = dplyr::recode(crop_cifos, dplyr::mutate(crop_cifos = dplyr::recode(crop_cifos,
"tobacco_unmanufactured" = "tobacco", "tobacco_unmanufactured" = "tobacco",
"groundnuts_with_shell" = "groundnuts", "groundnuts_with_shell" = "groundnuts",
# "dark_green_vegetables " = "green_vegetables", # "dark_green_vegetables " = "green_vegetables",
"other_roots_and_tubers" = "other_tubers", "other_roots_and_tubers" = "other_tubers",
# grepl("red_and_orange",crop_cifos) = "red_vegetables", # grepl("red_and_orange",crop_cifos) = "red_vegetables",
# "other_vegetables " = "other_vegetables", # "other_vegetables " = "other_vegetables",
"sunflower_seed" = "sunflower", "sunflower_seed" = "sunflower",
"other_cereal_crops" = "other_cereals", "other_cereal_crops" = "other_cereals",
"sesame_seed" = "sesame")) "sesame_seed" = "sesame"))
# write_csv(dat_crop, "Input_data/dat_crop_with_grass.csv") # write_csv(dat_crop, "Input_data/dat_crop_with_grass.csv")
#ISSUE: The vegetable values are behaving weird. Had to remove two spaces at the end manually in CSV. #ISSUE: The vegetable values are behaving weird. Had to remove two spaces at the end manually in CSV.
...@@ -164,9 +163,9 @@ dat_crop_final = read_csv("Input_data/dat_crop_with_grass_final.csv") ...@@ -164,9 +163,9 @@ dat_crop_final = read_csv("Input_data/dat_crop_with_grass_final.csv")
# crop_cifos = case_when(crop_cifos != is.na(crop_cifos) ~ crop_long_spam)) %>% # crop_cifos = case_when(crop_cifos != is.na(crop_cifos) ~ crop_long_spam)) %>%
# dplyr::mutate(crop_cifos_2 = case_when(crop_cifos = is.na(crop_cifos) ~ crop_name_monfreda), # dplyr::mutate(crop_cifos_2 = case_when(crop_cifos = is.na(crop_cifos) ~ crop_name_monfreda),
# TRUE ~ crop_cifos) # TRUE ~ crop_cifos)
# class(data_map) # class(data_map)
# #
...@@ -244,7 +243,7 @@ dat_crop_final = read_csv("Input_data/dat_crop_with_grass_final.csv") %>% ...@@ -244,7 +243,7 @@ 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. # 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, dplyr::mutate(old_cifos_crop = case_when(is.na(old_cifos_crop) ~ crop_cifos,
TRUE ~ old_cifos_crop)) TRUE ~ old_cifos_crop))
# Fuzzy join of processing sheet and the crop map # Fuzzy join of processing sheet and the crop map
# crop_map_cifos_procraw = # crop_map_cifos_procraw =
dat_proc = Processing_sheet %>% dat_proc = Processing_sheet %>%
...@@ -261,12 +260,12 @@ dat_proc = Processing_sheet %>% ...@@ -261,12 +260,12 @@ dat_proc = Processing_sheet %>%
str_detect(crop_cifos, "grass") ~ crop_cifos, str_detect(crop_cifos, "grass") ~ crop_cifos,
crop_cifos == "tobacco" ~ crop_cifos, crop_cifos == "tobacco" ~ crop_cifos,
crop_cifos == "seed_cotton" ~ crop_cifos, crop_cifos == "seed_cotton" ~ crop_cifos,
TRUE ~ .)), TRUE ~ .)),
across(c(value), ~ case_when(str_detect(crop_cifos, "for") ~ 1, across(c(value), ~ case_when(str_detect(crop_cifos, "for") ~ 1,
str_detect(crop_cifos, "grass") ~ 1, str_detect(crop_cifos, "grass") ~ 1,
crop_cifos == "tobacco" ~ 1, crop_cifos == "tobacco" ~ 1,
crop_cifos == "seed_cotton" ~ 1, crop_cifos == "seed_cotton" ~ 1,
TRUE ~ .))) TRUE ~ .)))
# removing disaggregated group elemetns that are not needed - # removing disaggregated group elemetns that are not needed -
# Aim: To have only one surrogate crop per crop_group (temperate_fruits = Apples) # Aim: To have only one surrogate crop per crop_group (temperate_fruits = Apples)
...@@ -293,7 +292,7 @@ dat_2nd_level2 = anti_join(Processing_sheet,dat_2nd_level, by = "ProRaw") %>% ...@@ -293,7 +292,7 @@ dat_2nd_level2 = anti_join(Processing_sheet,dat_2nd_level, by = "ProRaw") %>%
"barley", "barley")) %>% "barley", "barley")) %>%
relocate(crop_cifos, .before = 1) %>% relocate(crop_cifos, .before = 1) %>%
clean_names() clean_names()
dat_group = sqldf('SELECT * FROM dat_proc WHERE dat_group = sqldf('SELECT * FROM dat_proc WHERE
...@@ -325,7 +324,7 @@ dat_group = sqldf('SELECT * FROM dat_proc WHERE ...@@ -325,7 +324,7 @@ dat_group = sqldf('SELECT * FROM dat_proc WHERE
dat_proc_2 = dat_proc %>% dat_proc_2 = dat_proc %>%
anti_join(., dat_group, by = "crop_cifos") %>% #then I delete all the rows with unwanted duplicates anti_join(., dat_group, by = "crop_cifos") %>% #then I delete all the rows with unwanted duplicates
bind_rows(dat_group)#then I add the nicely filtered rows and get a clean df bind_rows(dat_group)#then I add the nicely filtered rows and get a clean df
# Adding missing dietary products # Adding missing dietary products
# Cifos dietary products - here i filter for strings in the FAO database # Cifos dietary products - here i filter for strings in the FAO database
...@@ -382,7 +381,7 @@ dat_dietprod_comp = dat_proc_2 %>% ...@@ -382,7 +381,7 @@ dat_dietprod_comp = dat_proc_2 %>%
crop_cifos == "cocoa_beans" ~ "Cocoa Beans and products", # newly added to cifos; crop_cifos == "cocoa_beans" ~ "Cocoa Beans and products", # newly added to cifos;
crop_cifos == "other_tubers" ~ "Vegetables, Other", crop_cifos == "other_tubers" ~ "Vegetables, Other",
TRUE ~ dietary_products)) TRUE ~ dietary_products))
# Removing Nas in the columns procin and procout. Rough assumption: all prcessing fractions = 1. Names from proc_raw # Removing Nas in the columns procin and procout. Rough assumption: all prcessing fractions = 1. Names from proc_raw
vec_proc_raw = dat_dietprod_comp %>% dplyr::filter(is.na(proc_in)) %>% pull(proc_raw) vec_proc_raw = dat_dietprod_comp %>% dplyr::filter(is.na(proc_in)) %>% pull(proc_raw)
...@@ -400,14 +399,14 @@ dat_complete = dat_dietprod_comp %>% ...@@ -400,14 +399,14 @@ dat_complete = dat_dietprod_comp %>%
bind_rows(., dat_comp) %>% bind_rows(., dat_comp) %>%
distinct_all() %>% # tobacco was triplicated distinct_all() %>% # tobacco was triplicated
bind_rows(dat_2nd_level2) bind_rows(dat_2nd_level2)
# write_csv(dat_complete, "Input_data/processing_sheet_2.csv") # write_csv(dat_complete, "Input_data/processing_sheet_2.csv")
# dat_complete = read_csv("Input_data/processing_sheet_2.csv") # dat_complete = read_csv("Input_data/processing_sheet_2.csv")
# Isolate new dietary products # Isolate new dietary products
new_dietprods = dat_complete %>% new_dietprods = dat_complete %>%
anti_join(., dat_proc_2, by="dietary_products") anti_join(., dat_proc_2, by="dietary_products")
# Distinct new dietary products # Distinct new dietary products
new_dietprods_dist = dat_complete %>% new_dietprods_dist = dat_complete %>%
anti_join(., dat_proc_2, by="dietary_products") %>% anti_join(., dat_proc_2, by="dietary_products") %>%
...@@ -439,7 +438,7 @@ new_procin = dat_complete %>% ...@@ -439,7 +438,7 @@ new_procin = dat_complete %>%
# - If forage crops are represented as well as food crops they have to be merged to the food crop; # - If forage crops are represented as well as food crops they have to be merged to the food crop;
# That means they should get the same procin name but then get the different procout ("wheat_feed" or so) # That means they should get the same procin name but then get the different procout ("wheat_feed" or so)
print(new_procin, n=33) print(new_procin, n=33)
print(new_procin, n=33) print(new_procin, n=33)
# A tibble: 33 x 6 # A tibble: 33 x 6
...@@ -498,14 +497,14 @@ proc_new = tibble("crop_cifos" = NA, "proc_raw" = NA, "proc_in" = NA, "proc_out" ...@@ -498,14 +497,14 @@ proc_new = tibble("crop_cifos" = NA, "proc_raw" = NA, "proc_in" = NA, "proc_out"
add_row(crop_cifos = "coconuts" , proc_raw = "coconuts" , proc_in = "coconuts_copra", proc_out = "coconuts_oil_copra", value = 0.21*0.64, dietary_products = "Coconut Oil") %>% add_row(crop_cifos = "coconuts" , proc_raw = "coconuts" , proc_in = "coconuts_copra", proc_out = "coconuts_oil_copra", value = 0.21*0.64, dietary_products = "Coconut Oil") %>%
add_row(crop_cifos = "coconuts" , proc_raw = "coconuts" , proc_in = "coconuts_copra", proc_out = "coconuts_cake_copra", value = 0.21*0.36, dietary_products = "Coconuts - Incl Copra") %>% add_row(crop_cifos = "coconuts" , proc_raw = "coconuts" , proc_in = "coconuts_copra", proc_out = "coconuts_cake_copra", value = 0.21*0.36, dietary_products = "Coconuts - Incl Copra") %>%
# Cassava -- Source: from Technical Conversion Factors for Agricultural Commodities (http://countrystat.org/resources/documents/tcf.pdf); adjusted commodity tree. # Cassava -- Source: from Technical Conversion Factors for Agricultural Commodities (http://countrystat.org/resources/documents/tcf.pdf); adjusted commodity tree.
# Levels: only one level in tcf doc! # Levels: only one level in tcf doc!
add_row(crop_cifos = "cassava" , proc_raw = "cassava" , proc_in = "cassava_flourin", proc_out = "cassava_flour", value = 0.3, dietary_products = "Cassava and products") %>% add_row(crop_cifos = "cassava" , proc_raw = "cassava" , proc_in = "cassava_flourin", proc_out = "cassava_flour", value = 0.3, dietary_products = "Cassava and products") %>%
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_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_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") %>% add_row(crop_cifos = "cassava" , proc_raw = "cassava" , proc_in = "cassava_starchin", proc_out = "cassava_starch", value = 0.25, dietary_products = "Cassava and products") %>%
# Pearl millet -- Source: from Technical Conversion Factors for Agricultural Commodities (http://countrystat.org/resources/documents/tcf.pdf); adjusted commodity tree; fractions from "cereals/millet" # 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 # 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") %>% 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") %>%
...@@ -538,12 +537,12 @@ proc_new = tibble("crop_cifos" = NA, "proc_raw" = NA, "proc_in" = NA, "proc_out" ...@@ -538,12 +537,12 @@ proc_new = tibble("crop_cifos" = NA, "proc_raw" = NA, "proc_in" = NA, "proc_out"
add_row(crop_cifos = "sugar_cane" , proc_raw = "sugar_cane" , proc_in = "sugar_canein", proc_out = "sugarcane_molasse", value = 0.05, dietary_products = "Sugar cane") %>% add_row(crop_cifos = "sugar_cane" , proc_raw = "sugar_cane" , proc_in = "sugar_canein", proc_out = "sugarcane_molasse", value = 0.05, dietary_products = "Sugar cane") %>%
add_row(crop_cifos = "sugar_cane" , proc_raw = "sugar_cane" , proc_in = "sugar_canein", proc_out = "sugarcane_bagasse", value = 0.25, dietary_products = "Sugar cane") %>% add_row(crop_cifos = "sugar_cane" , proc_raw = "sugar_cane" , proc_in = "sugar_canein", proc_out = "sugarcane_bagasse", value = 0.25, dietary_products = "Sugar cane") %>%
#sweet_potatoes -- commodity tree #sweet_potatoes -- commodity tree
add_row(crop_cifos = "sweet_potatoes" , proc_raw = "sweet_potatoes" , proc_in = "sweet_potatoe_flourin", proc_out = "sweet_potatoe_flour", value = 0.25, dietary_products = "Sweet potatoes") %>% add_row(crop_cifos = "sweet_potatoes" , proc_raw = "sweet_potatoes" , proc_in = "sweet_potatoe_flourin", proc_out = "sweet_potatoe_flour", value = 0.25, dietary_products = "Sweet potatoes") %>%
add_row(crop_cifos = "sweet_potatoes" , proc_raw = "sweet_potatoes" , proc_in = "sweet_potatoe_starchin", proc_out = "sweet_potatoe_starch", value = 0.2, dietary_products = "Sweet potatoes") %>% add_row(crop_cifos = "sweet_potatoes" , proc_raw = "sweet_potatoes" , proc_in = "sweet_potatoe_starchin", proc_out = "sweet_potatoe_starch", value = 0.2, dietary_products = "Sweet potatoes") %>%
# tea -- # tea --
add_row(crop_cifos = "tea" , proc_raw = "tea" , proc_in = "teain", proc_out = "tea_leave", value = 1, dietary_products = "Tea (including mate)") %>% add_row(crop_cifos = "tea" , proc_raw = "tea" , proc_in = "teain", proc_out = "tea_leave", value = 1, dietary_products = "Tea (including mate)") %>%
# yams -- # yams --
...@@ -613,12 +612,12 @@ proc_new = tibble("crop_cifos" = NA, "proc_raw" = NA, "proc_in" = NA, "proc_out" ...@@ -613,12 +612,12 @@ proc_new = tibble("crop_cifos" = NA, "proc_raw" = NA, "proc_in" = NA, "proc_out"
add_row(crop_cifos = "groundnuts" , proc_raw = "groundnut_shelled" , proc_in = "groundnuts_oilin", proc_out = "groundnut_cake", value = 0.54, dietary_products = "Groundnut Oil") %>% add_row(crop_cifos = "groundnuts" , proc_raw = "groundnut_shelled" , proc_in = "groundnuts_oilin", proc_out = "groundnut_cake", value = 0.54, dietary_products = "Groundnut Oil") %>%
add_row(crop_cifos = "groundnuts" , proc_raw = "groundnut_shelled" , proc_in = "groundnuts_butterin", proc_out = "groundnut_butter", value = 0.85, dietary_products = "Groundnuts (Shelled Eq)") %>% add_row(crop_cifos = "groundnuts" , proc_raw = "groundnut_shelled" , proc_in = "groundnuts_butterin", proc_out = "groundnut_butter", value = 0.85, dietary_products = "Groundnuts (Shelled Eq)") %>%
# Other cereals - commodity tree # Other cereals - commodity tree
add_row(crop_cifos = "other_cereals" , proc_raw = "other_cereals" , proc_in = "other_cereals_flourin", proc_out = "other_cereals_flour", value = 0.8, dietary_products = "Cereals, Other") %>% add_row(crop_cifos = "other_cereals" , proc_raw = "other_cereals" , proc_in = "other_cereals_flourin", proc_out = "other_cereals_flour", value = 0.8, dietary_products = "Cereals, Other") %>%
add_row(crop_cifos = "other_cereals" , proc_raw = "other_cereals" , proc_in = "other_cereals_flourin", proc_out = "other_cereals_bran", value = 0.2, dietary_products = "Cereals, Other") %>% add_row(crop_cifos = "other_cereals" , proc_raw = "other_cereals" , proc_in = "other_cereals_flourin", proc_out = "other_cereals_bran", value = 0.2, dietary_products = "Cereals, Other") %>%
add_row(crop_cifos = "other_cereals" , proc_raw = "other_cereals" , proc_in = "other_cereals_wholein", proc_out = "other_cereals_whole", value = 1, dietary_products = "Cereals, Other") %>% add_row(crop_cifos = "other_cereals" , proc_raw = "other_cereals" , proc_in = "other_cereals_wholein", proc_out = "other_cereals_whole", value = 1, dietary_products = "Cereals, Other") %>%
# other_oil_crops - commodity tree (tables) # other_oil_crops - commodity tree (tables)
# 1 level # 1 level
add_row(crop_cifos = "other_oil_crops" , proc_raw = "olives" , proc_in = "olive_tablein", proc_out = "olive_table", value = 1, dietary_products = "Olives (including preserved)") %>% add_row(crop_cifos = "other_oil_crops" , proc_raw = "olives" , proc_in = "olive_tablein", proc_out = "olive_table", value = 1, dietary_products = "Olives (including preserved)") %>%
...@@ -666,7 +665,7 @@ proc_new = tibble("crop_cifos" = NA, "proc_raw" = NA, "proc_in" = NA, "proc_out" ...@@ -666,7 +665,7 @@ proc_new = tibble("crop_cifos" = NA, "proc_raw" = NA, "proc_in" = NA, "proc_out"
## grass_rangeland - ## grass_rangeland -
add_row(crop_cifos = "grass_rangeland" , proc_raw = "grass_rangeland" , proc_in = "grass_rangeland_fresh", proc_out = "grass_rangeland_fresh", value = 0.30, dietary_products = "Forage and silage, grasses nes") %>% # Source: https://doi.org/10.1111/j.1469-8137.1994.tb04036.x - I take the lower bound add_row(crop_cifos = "grass_rangeland" , proc_raw = "grass_rangeland" , proc_in = "grass_rangeland_fresh", proc_out = "grass_rangeland_fresh", value = 0.30, dietary_products = "Forage and silage, grasses nes") %>% # Source: https://doi.org/10.1111/j.1469-8137.1994.tb04036.x - I take the lower bound
## alfalfafor ## alfalfafor
# grass_pasture - Source: Source: https://www.feedipedia.org/node/275 # grass_pasture - Source: Source: https://www.feedipedia.org/node/275
add_row(crop_cifos = "alfalfafor" , proc_raw = "alfalfafor" , proc_in = "alfalfafor_dry", proc_out = "alfalfafor_hay", value = 0.894, dietary_products = "Forage and silage, legumes") %>% #Alfalfa, hay, (feedepedia) add_row(crop_cifos = "alfalfafor" , proc_raw = "alfalfafor" , proc_in = "alfalfafor_dry", proc_out = "alfalfafor_hay", value = 0.894, dietary_products = "Forage and silage, legumes") %>% #Alfalfa, hay, (feedepedia)
...@@ -702,7 +701,7 @@ proc_new = tibble("crop_cifos" = NA, "proc_raw" = NA, "proc_in" = NA, "proc_out" ...@@ -702,7 +701,7 @@ proc_new = tibble("crop_cifos" = NA, "proc_raw" = NA, "proc_in" = NA, "proc_out"
# Levels: all pulses only one level # Levels: all pulses only one level
add_row(crop_cifos = "legumenesfor" , proc_raw = "legumenesfor_feed" , proc_in = "legumenesfor_feedin", proc_out = "legumenesfor_bean", value = 0.866 , dietary_products = "Forage and silage, legumes") %>% # Faba bean (Vicia faba), all cultivars add_row(crop_cifos = "legumenesfor" , proc_raw = "legumenesfor_feed" , proc_in = "legumenesfor_feedin", proc_out = "legumenesfor_bean", value = 0.866 , dietary_products = "Forage and silage, legumes") %>% # Faba bean (Vicia faba), all cultivars
add_row(crop_cifos = "legumenesfor" , proc_raw = "legumenesfor_feed" , proc_in = "legumenesfor_feedin", proc_out = "legumenesfor_straw", value = 0.194 , dietary_products = "Forage and silage, legumes") %>% # Faba bean (Vicia faba), aerial part, fresh add_row(crop_cifos = "legumenesfor" , proc_raw = "legumenesfor_feed" , proc_in = "legumenesfor_feedin", proc_out = "legumenesfor_straw", value = 0.194 , dietary_products = "Forage and silage, legumes") %>% # Faba bean (Vicia faba), aerial part, fresh
# maizefor - https://www.feedipedia.org/node/13883 AND commodity tree FAO # maizefor - https://www.feedipedia.org/node/13883 AND commodity tree FAO
add_row(crop_cifos = "maizefor" , proc_raw = "maize_whole_feed" , proc_in = "maize_silagein", proc_out = "maize_silage", value = 0.325, dietary_products = "Forage products") %>% # Reference: Maize silage, dry matter 30-35% from https://www.feedipedia.org/node/12871 add_row(crop_cifos = "maizefor" , proc_raw = "maize_whole_feed" , proc_in = "maize_silagein", proc_out = "maize_silage", value = 0.325, dietary_products = "Forage products") %>% # Reference: Maize silage, dry matter 30-35% from https://www.feedipedia.org/node/12871
add_row(crop_cifos = "maizefor" , proc_raw = "maize_whole_feed" , proc_in = "maize_oilin", proc_out = "maize_oil", value = 0.45*0.06, dietary_products = "Forage products") %>% # Reference: Commodidty tree fao same as with humans add_row(crop_cifos = "maizefor" , proc_raw = "maize_whole_feed" , proc_in = "maize_oilin", proc_out = "maize_oil", value = 0.45*0.06, dietary_products = "Forage products") %>% # Reference: Commodidty tree fao same as with humans
...@@ -769,7 +768,7 @@ proc_new = tibble("crop_cifos" = NA, "proc_raw" = NA, "proc_in" = NA, "proc_out" ...@@ -769,7 +768,7 @@ proc_new = tibble("crop_cifos" = NA, "proc_raw" = NA, "proc_in" = NA, "proc_out"
add_row(crop_cifos = "potatoes" , proc_raw = "potatoes" , proc_in = "potatoes_starch", proc_out = "potatoes_starch", value = 0.19, dietary_products = "Potatoes and products") %>% add_row(crop_cifos = "potatoes" , proc_raw = "potatoes" , proc_in = "potatoes_starch", proc_out = "potatoes_starch", value = 0.19, dietary_products = "Potatoes and products") %>%
add_row(crop_cifos = "potatoes" , proc_raw = "potatoes" , proc_in = "potatoes_starch", proc_out = "tuber_peel", value = 0.02, dietary_products = "Potatoes and products") %>% add_row(crop_cifos = "potatoes" , proc_raw = "potatoes" , proc_in = "potatoes_starch", proc_out = "tuber_peel", value = 0.02, dietary_products = "Potatoes and products") %>%
# Soybeans - tcf table if not otherwise stated # Soybeans - tcf table if not otherwise stated
add_row(crop_cifos = "soybeans" , proc_raw = "soybeans" , proc_in = "soyabean_for_oil", proc_out = "soyabean_oil", value = 0.19, dietary_products = "Soyabean Oil") %>% add_row(crop_cifos = "soybeans" , proc_raw = "soybeans" , proc_in = "soyabean_for_oil", proc_out = "soyabean_oil", value = 0.19, dietary_products = "Soyabean Oil") %>%
add_row(crop_cifos = "soybeans" , proc_raw = "soybeans" , proc_in = "soyabean_for_oil", proc_out = "soyabean_cake", value = 0.71, dietary_products = "Soyabeans") %>% add_row(crop_cifos = "soybeans" , proc_raw = "soybeans" , proc_in = "soyabean_for_oil", proc_out = "soyabean_cake", value = 0.71, dietary_products = "Soyabeans") %>%
add_row(crop_cifos = "soybeans" , proc_raw = "soybeans" , proc_in = "soyabean_for_oil", proc_out = "soyabean_hulls", value = 0.07, dietary_products = "Soyabeans") %>% add_row(crop_cifos = "soybeans" , proc_raw = "soybeans" , proc_in = "soyabean_for_oil", proc_out = "soyabean_hulls", value = 0.07, dietary_products = "Soyabeans") %>%
...@@ -784,11 +783,11 @@ proc_new = tibble("crop_cifos" = NA, "proc_raw" = NA, "proc_in" = NA, "proc_out" ...@@ -784,11 +783,11 @@ proc_new = tibble("crop_cifos" = NA, "proc_raw" = NA, "proc_in" = NA, "proc_out"
# sunflower - tcf fao # sunflower - tcf fao
add_row(crop_cifos = "sunflower" , proc_raw = "sunflower_seed" , proc_in = "sunflower_seed", proc_out = "sunflower_oil", value = 0.41, dietary_products = "Soyabean Oil") %>% add_row(crop_cifos = "sunflower" , proc_raw = "sunflower_seed" , proc_in = "sunflower_seed", proc_out = "sunflower_oil", value = 0.41, dietary_products = "Soyabean Oil") %>%
add_row(crop_cifos = "sunflower" , proc_raw = "sunflower_seed" , proc_in = "soyabean_for_oil", proc_out = "sunflower_cake", value = 0.47, dietary_products = "Soyabeans") %>% add_row(crop_cifos = "sunflower" , proc_raw = "sunflower_seed" , proc_in = "sunflower_for_oil", proc_out = "sunflower_cake", value = 0.47, dietary_products = "Soyabeans") %>%
add_row(crop_cifos = "sunflower" , proc_raw = "sunflower_seed" , proc_in = "soyabean_for_oil", proc_out = "sunflower_hulls", value = 0.07, dietary_products = "Soyabeans") %>% #ref from ollie (old proccessing sheet) add_row(crop_cifos = "sunflower" , proc_raw = "sunflower_seed" , proc_in = "sunflower_for_oil", proc_out = "sunflower_hulls", value = 0.07, dietary_products = "Soyabeans") %>% #ref from ollie (old proccessing sheet)
# Paddy Rice - tcf fao # Paddy Rice - tcf fao
# husked rice # husked rice
add_row(crop_cifos = "rice" , proc_raw = "rice" , proc_in = "rice_husk", proc_out = "rice_husked", value = 0.77, dietary_products = "Rice (Milled Equivalent)") %>% add_row(crop_cifos = "rice" , proc_raw = "rice" , proc_in = "rice_husk", proc_out = "rice_husked", value = 0.77, dietary_products = "Rice (Milled Equivalent)") %>%
add_row(crop_cifos = "rice" , proc_raw = "rice" , proc_in = "rice_husk", proc_out = "rice_hulls", value = 0.25, dietary_products = "Rice (Milled Equivalent)") %>% add_row(crop_cifos = "rice" , proc_raw = "rice" , proc_in = "rice_husk", proc_out = "rice_hulls", value = 0.25, dietary_products = "Rice (Milled Equivalent)") %>%
# 2nd stage # 2nd stage
...@@ -803,15 +802,15 @@ proc_new = tibble("crop_cifos" = NA, "proc_raw" = NA, "proc_in" = NA, "proc_out" ...@@ -803,15 +802,15 @@ proc_new = tibble("crop_cifos" = NA, "proc_raw" = NA, "proc_in" = NA, "proc_out"
# 2nd stage for bran oil/cake # 2nd stage for bran oil/cake
add_row(crop_cifos = "rice" , proc_raw = "bran_paddy_milled" , proc_in = "rice_bran_oil", proc_out = "rice_bran_oil", value = 0.80, dietary_products = "Ricebran Oil") %>% add_row(crop_cifos = "rice" , proc_raw = "bran_paddy_milled" , proc_in = "rice_bran_oil", proc_out = "rice_bran_oil", value = 0.80, dietary_products = "Ricebran Oil") %>%
add_row(crop_cifos = "rice" , proc_raw = "bran_paddy_milled" , proc_in = "rice_bran_oil", proc_out = "rice_bran_cake", value = 0.14, dietary_products = "Ricebran Oil") %>% add_row(crop_cifos = "rice" , proc_raw = "bran_paddy_milled" , proc_in = "rice_bran_oil", proc_out = "rice_bran_cake", value = 0.14, dietary_products = "Ricebran Oil") %>%
# coffee - feedepedia -> https://www.feedipedia.org/node/549 # coffee - feedepedia -> https://www.feedipedia.org/node/549
add_row(crop_cifos = "arabica_coffee" , proc_raw = "coffee_green" , proc_in = "coffee_green", proc_out = "coffee_roasted", value = 0.8, dietary_products = "Coffee and products") %>% add_row(crop_cifos = "arabica_coffee" , proc_raw = "coffee_green" , proc_in = "coffee_green", proc_out = "coffee_roasted", value = 0.8, dietary_products = "Coffee and products") %>%
add_row(crop_cifos = "arabica_coffee" , proc_raw = "coffee_green" , proc_in = "coffee_green", proc_out = "coffee_hulls", value = 0.2, dietary_products = "Cocoa Beans and products") %>%#fraction comes from the cocoa husk. Could be improbved add_row(crop_cifos = "arabica_coffee" , proc_raw = "coffee_green" , proc_in = "coffee_green", proc_out = "coffee_hulls", value = 0.2, dietary_products = "Cocoa Beans and products") %>%#fraction comes from the cocoa husk. Could be improbved
# Sesame # Sesame
add_row(crop_cifos = "sesame" , proc_raw = "sesame" , proc_in = "sesame_seed", proc_out = "sesame_oil", value = 0.42, dietary_products = "Sesameseed Oil") %>% #ref from old processing sheet add_row(crop_cifos = "sesame" , proc_raw = "sesame" , proc_in = "sesame_seed", proc_out = "sesame_oil", value = 0.42, dietary_products = "Sesameseed Oil") %>% #ref from old processing sheet
add_row(crop_cifos = "sesame" , proc_raw = "sesame" , proc_in = "sesame_seed", proc_out = "sesame_cake", value = 0.57, dietary_products = "Sesame seed") %>% add_row(crop_cifos = "sesame" , proc_raw = "sesame" , proc_in = "sesame_seed", proc_out = "sesame_cake", value = 0.57, dietary_products = "Sesame seed") %>%
# Banana # Banana
add_row(crop_cifos = "bananas" , proc_raw = "bananas" , proc_in = "bananas", proc_out = "bananas", value = 1, dietary_products = "Bananas") %>% #ref from old processing sheet add_row(crop_cifos = "bananas" , proc_raw = "bananas" , proc_in = "bananas", proc_out = "bananas", value = 1, dietary_products = "Bananas") %>% #ref from old processing sheet
...@@ -824,7 +823,7 @@ proc_new = tibble("crop_cifos" = NA, "proc_raw" = NA, "proc_in" = NA, "proc_out" ...@@ -824,7 +823,7 @@ proc_new = tibble("crop_cifos" = NA, "proc_raw" = NA, "proc_in" = NA, "proc_out"
# Treenuts # Treenuts
add_row(crop_cifos = "treenuts" , proc_raw = "walnuts" , proc_in = "walnuts", proc_out = "walnuts", value = 0.04, dietary_products = "Nuts and products") %>% add_row(crop_cifos = "treenuts" , proc_raw = "walnuts" , proc_in = "walnuts", proc_out = "walnuts", value = 0.04, dietary_products = "Nuts and products") %>%
add_row(crop_cifos = "treenuts" , proc_raw = "walnuts" , proc_in = "walnuts", proc_out = "nut_shell", value = 0.04, dietary_products = "Nuts and products") add_row(crop_cifos = "treenuts" , proc_raw = "walnuts" , proc_in = "walnuts", proc_out = "nut_shell", value = 0.04, dietary_products = "Nuts and products")
# Pulling crops that are updated # Pulling crops that are updated
vec_proc_new = proc_new %>% distinct_at(vars(crop_cifos)) %>% pull(crop_cifos) vec_proc_new = proc_new %>% distinct_at(vars(crop_cifos)) %>% pull(crop_cifos)
...@@ -860,22 +859,22 @@ dat_proc_new %>% write_csv("Input_data/dat_proc_new.csv") ...@@ -860,22 +859,22 @@ dat_proc_new %>% write_csv("Input_data/dat_proc_new.csv")
dat_proc_asf = dat_proc_asf =
Processing_sheet %>% dplyr::select(-c(6,7)) %>% Processing_sheet %>% dplyr::select(-c(6,7)) %>%
clean_names() %>% rename(proc_raw = pro_raw) %>% clean_names() %>% rename(proc_raw = pro_raw) %>%
slice(269:363) %>% slice(269:363) %>%
dplyr::mutate(dietary_products= case_when(proc_raw == "Milk" ~ "Milk - Excluding Butter", dplyr::mutate(dietary_products= case_when(proc_raw == "Milk" ~ "Milk - Excluding Butter",
TRUE ~ dietary_products), TRUE ~ dietary_products),
dietary_products= case_when(proc_out == "Butter" ~ "Butter, Ghee", dietary_products= case_when(proc_out == "Butter" ~ "Butter, Ghee",
TRUE ~ dietary_products), TRUE ~ dietary_products),
dietary_products= case_when(dietary_products == "Fish (Calculated CiFoS)" ~ "Fish, Seafood", dietary_products= case_when(dietary_products == "Fish (Calculated CiFoS)" ~ "Fish, Seafood",
TRUE ~ dietary_products), TRUE ~ dietary_products),
proc_out = case_when(proc_out == "Butter_Milk" ~ "Butter_milk", 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 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) 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") write_csv(Processing_sheet_final, "Input_data/processing_sheet.csv")
# Human nutrition sheet --------------------------------------------------- # Human nutrition sheet ---------------------------------------------------
# Human nutrtion sheet # Human nutrtion sheet
...@@ -984,7 +983,7 @@ hum_nutr_all = bind_rows(food_old_match, food_psf_new, food_asf) ...@@ -984,7 +983,7 @@ 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")) # check = full_join(hum_nutr_all, proc_new, by = c("product"="proc_out"))
write_csv(hum_nutr_all, "Input_data/hum_nutr_all.csv") write_csv(hum_nutr_all, "Input_data/hum_nutr_all.csv")
# Animal nutrition new ---------------------------------------------------- # Animal nutrition new ----------------------------------------------------
# Cifos sheet # Cifos sheet
...@@ -1939,9 +1938,6 @@ loss_frac_all$Product[!loss_frac_all$Product %in% rbind(Processing_sheet_final$p ...@@ -1939,9 +1938,6 @@ loss_frac_all$Product[!loss_frac_all$Product %in% rbind(Processing_sheet_final$p
# NONE --> GOOOD! # NONE --> GOOOD!
# Fert_suitability sheet -------------------------------------------------- # Fert_suitability sheet --------------------------------------------------
dat_fertsuitability = read_excel("Input_data/Copy of European_CiFoS_model_data_ANITANov21.xlsx", sheet = "Fert_Suitability") %>% dat_fertsuitability = read_excel("Input_data/Copy of European_CiFoS_model_data_ANITANov21.xlsx", sheet = "Fert_Suitability") %>%
dplyr::rename(scenario=1,crop_cifos = 2) dplyr::rename(scenario=1,crop_cifos = 2)
...@@ -1951,18 +1947,18 @@ table_fert_suit = dat_fertsuitability %>% distinct_at(vars(scenario), .keep_all ...@@ -1951,18 +1947,18 @@ table_fert_suit = dat_fertsuitability %>% distinct_at(vars(scenario), .keep_all
# List of all crops possible # List of all crops possible
crop_list = proc_new %>% distinct_at(vars(crop_cifos)) %>% pull() crop_list = proc_new %>% distinct_at(vars(crop_cifos)) %>% pull()
scen_baseline = tibble(scenario = "Baseline", scen_baseline = tibble(scenario = "Baseline",
crop_cifos = crop_list) crop_cifos = crop_list)
scen_circular = tibble(scenario = "Circular", scen_circular = tibble(scenario = "Circular",
crop_cifos = crop_list) crop_cifos = crop_list)
fert_suit = bind_rows(scen_baseline, scen_circular) %>% fert_suit = bind_rows(scen_baseline, scen_circular) %>%
left_join(table_fert_suit, by= "scenario") left_join(table_fert_suit, by= "scenario")
write_csv(fert_suit, "Input_data/fert_suitability.csv") write_csv(fert_suit, "Input_data/fert_suitability.csv")
# Import sheet ----------------------------------------------------------- # Import sheet -----------------------------------------------------------
dat_ImportExport = read_excel("Input_data/Copy of European_CiFoS_model_data_ANITANov21.xlsx", sheet = "Import_Export") %>% dat_ImportExport = read_excel("Input_data/Copy of European_CiFoS_model_data_ANITANov21.xlsx", sheet = "Import_Export") %>%
dplyr::select(c(1:3)) dplyr::select(c(1:3))
...@@ -2002,7 +1998,7 @@ Baseline_import_export = FoodBalanceSheetFAO %>% as_tibble() %>% ...@@ -2002,7 +1998,7 @@ Baseline_import_export = FoodBalanceSheetFAO %>% as_tibble() %>%
dietary_products=item) %>% dietary_products=item) %>%
replace(is.na(.), 0) %>% #all the NA values (crops that are not grown or imported to the EU) are 0. replace(is.na(.), 0) %>% #all the NA values (crops that are not grown or imported to the EU) are 0.
dplyr::filter(dietary_products %in% (diet_prod_dist %>% pull(dietary_products))) %>% dplyr::filter(dietary_products %in% (diet_prod_dist %>% pull(dietary_products))) %>%
replace(is.na(.), 0) # The forage, fibre and tabacco crops that have no match in column item are set to 0 (no import export assumed) replace(is.na(.), 0) # The forage, fibre and tabacco crops that have no match in column item are set to 0 (no import export assumed)
write_csv(Baseline_import_export, "Input_data/Baseline_import_export.csv") write_csv(Baseline_import_export, "Input_data/Baseline_import_export.csv")
...@@ -2065,11 +2061,11 @@ Baseline_animal_number = ...@@ -2065,11 +2061,11 @@ Baseline_animal_number =
area_code %in% country_eu, #filtering for EU+UKD countries area_code %in% country_eu, #filtering for EU+UKD countries
item %in% c('Beef and Buffalo Meat','Chickens', 'Meat, Poultry',"Pigs", 'Milk, whole fresh cow'), item %in% c('Beef and Buffalo Meat','Chickens', 'Meat, Poultry',"Pigs", 'Milk, whole fresh cow'),
element %in% c("producing_animals_slaughtered", "stocks","milk_animals")) #%>% element %in% c("producing_animals_slaughtered", "stocks","milk_animals")) #%>%
dplyr::group_by(area_code, item, element) %>% dplyr::group_by(area_code, item, element) %>%
dplyr::summarise(value = mean(value, na.rm=T)) #%>% dplyr::summarise(value = mean(value, na.rm=T)) #%>%
pivot_wider(values_from = value, pivot_wider(values_from = value,
names_from = element) #%>% names_from = element) #%>%
rename(crops=item) %>% rename(crops=item) %>%
replace(is.na(.), 0) %>% #all the NA values (crops that are not grown or imported to the EU) are 0. replace(is.na(.), 0) %>% #all the NA values (crops that are not grown or imported to the EU) are 0.
dplyr::filter(crops %in% dat_crop_fao_cifos_vec) %>% dplyr::filter(crops %in% dat_crop_fao_cifos_vec) %>%
replace(is.na(.), 0) %>% #The forage, fibre and replace(is.na(.), 0) %>% #The forage, fibre and
...@@ -2130,12 +2126,12 @@ crop_other_new = ...@@ -2130,12 +2126,12 @@ crop_other_new =
dplyr::mutate(crop_cifos = case_when(crop_cifos_old == "Grass_Managed_HQ" ~ "grass_arable", dplyr::mutate(crop_cifos = case_when(crop_cifos_old == "Grass_Managed_HQ" ~ "grass_arable",
crop_cifos_old == "Grass_Managed_MQ" ~ "grass_pasture", crop_cifos_old == "Grass_Managed_MQ" ~ "grass_pasture",
crop_cifos_old == "Grass_Natural_MQ" ~ "grass_rangeland", crop_cifos_old == "Grass_Natural_MQ" ~ "grass_rangeland",
TRUE ~ crop_cifos)) %>% TRUE ~ crop_cifos)) %>%
dplyr::select(-crop_cifos_old, -code_spam2010) %>% dplyr::select(-crop_cifos_old, -code_spam2010) %>%
distinct_at(vars(crop_cifos),.keep_all = T) %>% drop_na(crop_cifos) distinct_at(vars(crop_cifos),.keep_all = T) %>% drop_na(crop_cifos)
write_csv(crop_other_new, "Input_Data/crop_other_new.csv") write_csv(crop_other_new, "Input_Data/crop_other_new.csv")
# cropnutr = read_excel('C:/Wolfram_Admin/GAMS/EU_model_frmSep21/cifos-model_eu/European_CiFoS_model_data.xlsx',sheet="CropNutr") # 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") # write_csv(cropnutr,"Input_data/cropnutr_old")
cropnutr = read_csv("Input_data/cropnutr_old") cropnutr = read_csv("Input_data/cropnutr_old")
...@@ -2146,14 +2142,14 @@ cropnutr_new = ...@@ -2146,14 +2142,14 @@ cropnutr_new =
full_join(crop_map %>% dplyr::select(old_cifos_crop, crop_cifos), by = c("crop_cifos_old"="old_cifos_crop")) %>% 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) %>% relocate(crop_cifos, .before = crop_cifos_old) %>%
dplyr::select(-crop_cifos_old) dplyr::select(-crop_cifos_old)
write_csv(cropnutr_new, "Input_data/cropnutr_new.csv") write_csv(cropnutr_new, "Input_data/cropnutr_new.csv")
# CropFert_New NOT DONE! ------------------------------------------------------------ # 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") # CropFert_New = read_excel("C:/Wolfram_Admin/GAMS/EU_model_frmSep21/cifos-model_eu/European_CiFoS_model_data.xlsx", sheet = "CropFert_New")
# CropFert_New %>% write_csv("Input_data/CropFert_New.csv") # CropFert_New %>% write_csv("Input_data/CropFert_New.csv")
CropFert_New = read_csv("Input_data/CropFert_New.csv") CropFert_New = read_csv("Input_data/CropFert_New.csv")
CropFert = CropFert =
CropFert_New %>% CropFert_New %>%
dplyr::rename(crop_cifos_old = `...1`) %>% dplyr::rename(crop_cifos_old = `...1`) %>%
...@@ -2174,7 +2170,7 @@ proc_sheet_crops = bind_rows(dat_proc_new ,dat_proc_asf) #%>% write_csv("Input_ ...@@ -2174,7 +2170,7 @@ proc_sheet_crops = bind_rows(dat_proc_new ,dat_proc_asf) #%>% write_csv("Input_
# Crops # Crops
crop_datmap = dat_proc_new %>% select(crop_cifos) %>% distinct_all() crop_datmap = dat_proc_new %>% select(crop_cifos) %>% distinct_all()
write_csv(crop_datmap, "Input_data/crop_datmap.csv") write_csv(crop_datmap, "Input_data/crop_datmap.csv")
# ProcoutH and dietary products map # ProcoutH and dietary products map
ProcOutH_dietProduct = Processing_sheet_final %>% distinct_at(vars(proc_out), .keep_all = T) %>% ProcOutH_dietProduct = Processing_sheet_final %>% distinct_at(vars(proc_out), .keep_all = T) %>%
...@@ -2224,15 +2220,15 @@ Crop_Map2 =read_csv("Input_data/crop_map_mine.csv") ...@@ -2224,15 +2220,15 @@ Crop_Map2 =read_csv("Input_data/crop_map_mine.csv")
diets_animal_crop_datamap = diets_animal_crop_datamap =
Crop_Map2 %>% slice(121:210) %>% Crop_Map2 %>% slice(121:210) %>%
dplyr::select(c(1:2)) %>% dplyr::rename(crop_cifos = `CROPS FAO...1`, dplyr::select(c(1:2)) %>% dplyr::rename(crop_cifos = `CROPS FAO...1`,
proc_out = `Co-Product...2`) %>% proc_out = `Co-Product...2`) %>%
filter(proc_out != 'Grass_Natural_LQ') %>% filter(proc_out != 'Grass_Natural_LQ') %>%
full_join(dat_proc_asf %>% dplyr::select(proc_out, dietary_products)) %>% full_join(dat_proc_asf %>% dplyr::select(proc_out, dietary_products)) %>%
distinct_all() %>% distinct_all() %>%
bind_rows(.,dat_proc_new %>% select(c(crop_cifos,4,6))) bind_rows(.,dat_proc_new %>% select(c(crop_cifos,4,6)))
write_csv(animal_map, "Input_Data/diets_animal_crop_datamap.csv") write_csv(animal_map, "Input_Data/diets_animal_crop_datamap.csv")
# crop_animal_co_prod_dietary # crop_animal_co_prod_dietary
...@@ -2278,14 +2274,3 @@ write_csv(animal_map, "Input_Data/diets_animal_crop_datamap.csv") ...@@ -2278,14 +2274,3 @@ write_csv(animal_map, "Input_Data/diets_animal_crop_datamap.csv")
# # Animals # # Animals
# #Animal_yield sheet # #Animal_yield sheet
# 1. Yields (column D row 2) # 1. Yields (column D row 2)
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