diff --git a/R_to_SQL_database/Cifos_Model_Data_EU.R b/R_to_SQL_database/Cifos_Model_Data_EU.R
index 01c9f9267dada7f6522cd6d1dcd72d284d3171e4..a9e61b2293d01bb3e3352a4f46fc18c7685d16f0 100644
--- a/R_to_SQL_database/Cifos_Model_Data_EU.R
+++ b/R_to_SQL_database/Cifos_Model_Data_EU.R
@@ -906,18 +906,21 @@ dat_map_processing = dat_proc_asf %>% dplyr::select(proc_out) %>%
# Comparing different processing sheets ----------------------------------
-dat_proc_rec = read_excel("D:/GAMS/EU_model_Feb2022/European_CiFoS_model_data.xlsx", sheet = "Processing_Fraction" )
+# dat_proc_rec = read_excel("D:/GAMS/EU_model_Feb2022/European_CiFoS_model_data.xlsx", sheet = "Processing_Fraction" )
-dat_proc_comp = Processing_sheet_final %>% left_join(dat_proc_rec %>% select(proc_out, Utilization), by="proc_out") %>%
- mutate(Utilization = case_when(proc_out == "barleyfor" ~ "FeedOnly",
- proc_out == "wheatfor" ~ "FeedOnly",
- TRUE ~ Utilization)) %>%
- dplyr::select(-dietary_products) %>% clean_names()
-
-write_csv(dat_proc_comp, "Input_data/processing_sheet_17_02_22.csv")
-
+# dat_proc_comp = Processing_sheet_final %>% left_join(dat_proc_rec %>% select(proc_out, Utilization), by="proc_out") %>%
+# mutate(Utilization = case_when(proc_out == "barleyfor" ~ "FeedOnly",
+# proc_out == "wheatfor" ~ "FeedOnly",
+# TRUE ~ Utilization)) %>%
+# dplyr::select(-dietary_products) %>% clean_names()
+#
+# write_csv(dat_proc_comp, "Input_data/processing_sheet_17_02_22.csv")
+# Removing duplicates 22.2.22 ----------------------------------------------------
+dat_proc_dup = read_excel("D:/GAMS/EU_model_Feb2022/European_CiFoS_model_data.xlsx", sheet = "Processing_Fraction" )
+dat_proc_dup %>% distinct() %>% write_csv("Input_data/processing_sheet_no_dup.csv")
+
# Human nutrition sheet ---------------------------------------------------
# Human nutrtion sheet
dat_humnutr = read_excel("Input_data/Copy of European_CiFoS_model_data_ANITANov21.xlsx", sheet = "Human_Nutr")
@@ -1108,6 +1111,26 @@ hum_nutr_all_FG = hum_nutr_all %>% dplyr::select(-FoodGroup) %>%
# check = full_join(hum_nutr_all, proc_new, by = c("product"="proc_out"))
write_csv(hum_nutr_all_FG, "Input_data/hum_nutr_all.csv")
+
+# Comparing versions - Demi NAs filling --------------------------------------
+hum_food_22.2.22 = read_xlsx('Input_data/Human_Food_Demi_22.2.22.xlsx', sheet = "Sheet1")
+hum_food_latest = read_excel("D:/GAMS/EU_model_Feb2022/European_CiFoS_model_data.xlsx", sheet = "Human_Food_Nutr" )
+
+# Consistency checks - Are the same products in both of the sheets
+# - Number of rows are identical 139
+
+# # both sheets have the same products. Sheet can be directly replaced by demis sheet!
+hum_food_22.2.22$Product[!hum_food_22.2.22$Product %in% hum_food_latest$Product]
+hum_food_latest$Product[!hum_food_latest$Product %in% hum_food_22.2.22$Product]
+
+# Minor transformation and N calclation
+hum_food_updated = hum_food_22.2.22 %>% dplyr::select(-USDA_Code) %>%
+ type.convert() %>%
+ dplyr::mutate(N = case_when(is.na(N) ~ Protein/6.25,
+ TRUE ~ N))
+
+write_csv(hum_food_updated, "Input_data/Human_food_update_22_2_22.csv")
+
# Animal nutrition new ----------------------------------------------------
# Cifos sheet
dat_animnutr = readxl::read_excel("Input_data/Copy of European_CiFoS_model_data_ANITANov21.xlsx", sheet = "Animal_Nutr")
@@ -1296,7 +1319,6 @@ FunAnimDatExtract = function(dat, cvb_code, cvb_name, product){
# Calculations
#[F.H11] GE (kJ/kg) = 24.14 x CP + 36.57 x CFAT + 20.92 x CF + 16.99 x NFE - 0.63 x SUG*
dplyr::mutate(
-
ge = case_when(
sug > 80 ~ round((cp*24.14+cfat*36.57+cf*13.81+nfe*16.99-0.63*sug)/1000,1),
sug <= 80 ~ round((cp*24.14+cfat*36.57+cf*13.81+nfe*16.99-0.63)/1000,1)),
@@ -2002,6 +2024,17 @@ ani_nutr_v7.2.22=animal_nutrition_all_old %>% dplyr::select(c(product)) %>%
write_csv(animal_nutrition_all, "Input_data/animal_nutrition_all.csv")
read_csv("Input_data/animal_nutrition_all.csv")
}
+
+
+# Update - 22.2.22 -------------------------------------------------------
+dat_aninutr_recent = read_excel("D:/GAMS/EU_model_Feb2022/European_CiFoS_model_data.xlsx", sheet = "Animal_Feed_Nutr" )
+
+dat_aninutr_updated = dat_aninutr_recent %>% dplyr::select(-Origin) %>%
+ distinct(Product, .keep_all = T)
+
+write_csv(dat_aninutr_updated, "Input_data/dat_ani_nutr_update_22_2_22.csv")
+
+
# Loss fraction sheet ----------------------------------------------------
dat_lossfrac = read_excel("Input_data/Copy of European_CiFoS_model_data_ANITANov21.xlsx", sheet = "Loss_Fraction_New")
dat_lossfrac_join= dat_lossfrac %>% dplyr::select(c(1:10)) %>% janitor::row_to_names(1)%>% select(-ProdCat ,-Fam_SPAM) %>%
@@ -2614,6 +2647,15 @@ CropFert =
write_csv(CropFert, "Input_data/cropfert_inter.csv")
+# Fert_Nutr ---------------------------------------------------------------
+# Sheet contains the products (procraw and procout) with NPK values on DM basis
+proc_recent = read_excel("D:/GAMS/EU_model_Feb2022/European_CiFoS_model_data.xlsx", sheet = "Processing" )
+
+dat_aninutr_updated = dat_aninutr_recent %>% dplyr::select(-Origin) %>%
+ distinct(Product, .keep_all = T)
+
+write_csv(dat_aninutr_updated, "Input_data/dat_ani_nutr_update_22_2_22.csv")
+
# Data maps ---------------------------------------------------------------
# Anitas excel -- differs a bit to mine
data_map = read_excel("Input_data/Copy of European_CiFoS_model_data_ANITANov21.xlsx", sheet = "Data_Map")