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# group by different NUTS with YEAR, CROP/ANIM, VARIABLE,ORGANIC
fadn.filter <- function(data, group.by, type ) {
# filtered <- data %>%
# group_by({{group.by}},YEAR,{{type}},VARIABLE,ORGANIC) %>%
# summarise(sum_Value = sum(value2), .groups ="drop") %>%
# as.data.table() %>% rename(REGION = {{group.by}})
if (group.by == "EU"){
filtered <- data %>% filter(COUNTRY %in% EU_list) %>%
group_by(YEAR,.data[[type]],ORGANIC,VARIABLE) %>%
summarise(sum_Value = sum(value2), .groups ="drop") %>%
as.data.table() %>%
mutate(REGION = group.by,
REG_TYPE = group.by)
} else{
filtered <- data %>%
group_by(.data[[group.by]],
YEAR,
.data[[type]],
VARIABLE,
ORGANIC) %>%
summarise(sum_Value = sum(value2), .groups ="drop") %>%
as.data.table() %>%
rename(REGION = .data[[group.by]]) %>%
mutate(REG_TYPE = group.by)
}
return(filtered)
}
convert.load.str.crops <- function(countries ) {
before2013.json = paste0(getwd(), "/corrected.json.full/corrected.2013_before.json")
after2014.json = paste0(getwd(), "/corrected.json.full/corrected.2014_after.json")
if ( "all" %in% countries) {
beforeyears = "before2013"
afteryears = "after2014"
# all countries and years 719.24s
# convert raw data to structured data ---
# before 2013 and 2013
convert.to.fadn.str.rds(countries,
beforeyears,
raw_str_map.file = before2013.json,
str.name = "crops",
force_external_raw_str_map = T)# 413.25 for all countries
# after 2014 and 2014
convert.to.fadn.str.rds(countries,
afteryears,
raw_str_map.file = after2014.json,
str.name = "crops",
force_external_raw_str_map = T)# 305.99 for all countries
# load
after2014 <- readRDS(paste0(rds.dir, "/crops/fadn.str.after2014.all.rds"))
before2013 <- readRDS(paste0(rds.dir, "/crops/fadn.str.before2013.all.rds"))
fadn.str.crops <- bind_rows(before2013$crops,after2014$crops)
}
else{
beforeyears = c(2004:2013)
afteryears = c(2014:2018)
# before 2014
# only DEU 84s
# BEL and DEU 107.26s
for (country in countries ){
sapply(seq_along(beforeyears), function(i)
convert.to.fadn.str.rds(country,
beforeyears[i],
raw_str_map.file = after2014.json,
str.name = "crops",
force_external_raw_str_map = T) )
# after 2013
sapply(seq_along(afteryears), function(i)
convert.to.fadn.str.rds(country,
afteryears[i],
raw_str_map.file = after2014.json,
str.name = "crops",
force_external_raw_str_map = T) )
}
# load crops str data
# load all data 22.49s
fadn.str.crops <- load.fadn.str.rds("crops",countries,"all") # 2.82s
}
return(fadn.str.crops)
}
# get animals data
get.ifm_cap.animals = function(column="AN", years.eff=2010:2013, data.cur=TABLE_J.all) {
#Livestock, number of animals
cols = c("ID","YEAR","ANIM",column)
tmp1 = data.table::dcast(data.cur[YEAR%in%years.eff,..cols],ID+YEAR~ANIM,value.var=column,fill=0)
tmp1 = merge(tmp1,BOV1_PERC,all.x=T,by="ID")[is.na(LBOV0.perc),LBOV0.perc:=0]
# setnames(tmp1,"ID","FD")
#check no columns are missing. If yes, create one
cols.used = c("LBOVFAT","LBOV0","LHEIFBRE","LHEIFFAT","LBOV1_2F","LCOWBUFDAIR","LCOWOTH","LEWEBRE","LGOATBRE","LSHEPOTH","LGOATOTH","LSOWBRE", "LPIGFAT","LPIGOTH","LPLTRBROYL","LPLTROTH","LHENSLAY","LEQD","LBOV1_2M","LBOV2","LRABBRE")
for(col.used in cols.used) {
if(!col.used%in%names(tmp1)){
warning(paste0("nCreating column ",col.used))
tmp1[,(col.used):=0]
}
}
#If LBOV0+LBOVFAT are not present and LBOV1 is present, calculate the share
tmp1[LBOV1>0 & LBOVFAT==0 & LBOV0==0,":="(LBOV0=LBOV0.perc*LBOV1,LBOVFAT=(1-LBOV0.perc)*LBOV1)]
tmp2 = tmp1[,.(
FD,
YEAR,
variable=column,
CAMF = pmax(0.5*LBOVFAT),
CAFF = pmax(0.5*LBOVFAT),
CAMR = pmax(0,LBOV0-LHEIFBRE),
CAFR = pmin(LHEIFBRE,LBOV0),
HEIF = LHEIFFAT+pmax(0,LBOV1_2F-LHEIFBRE),
BULF = LBOV1_2M+LBOV2,
HEIR = LHEIFBRE+pmin(LBOV1_2F,LHEIFBRE),
DCOW = LCOWBUFDAIR+LCOWCUL+LCOWDAIR+LBUFDAIRPRS,
SCOW = LCOWOTH,
SHGM = LEWEBRE+ LGOATBRE,
SHGF = LSHEPOTH + LGOATOTH,
SOWS = LSOWBRE ,
PIGF = LPIGFAT + LPIGOTH,
POUF = LPLTRBROYL + LPLTROTH,
HENS = LHENSLAY ,
OANI = LRABBRE+LEQD #LANIMOTH is not there
)]
return(
melt(tmp2,id.vars = c("ID","YEAR","variable"),variable.name = "ANIM")[]
)
}