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Simon, Wolfram
CiFoS_Crops
Commits
c8e21941
Commit
c8e21941
authored
Feb 23, 2022
by
Simon, Wolfram
Browse files
worked on procssing, human nutrition sheet.
parent
69cadd1e
Changes
1
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Inline
Side-by-side
R_to_SQL_database/Cifos_Model_Data_EU.R
View file @
c8e21941
...
...
@@ -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"
)
...
...
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