modules/offals_fats_skins/main.R
In SWS-Methodology/faoswsProduction: Package to perform the imputation of area harvested, production and yield for the FAO production domain
## load the libraries
library(faosws)
library(data.table)
library(faoswsUtil)
library(sendmailR)
library(dplyr)
library(faoswsFlag)
## set up for the test environment and parameters
R_SWS_SHARE_PATH = Sys.getenv("R_SWS_SHARE_PATH")
if(CheckDebug()){
message("Not on server, so setting up environment...")
library(faoswsModules)
SETT <- ReadSettings("sws.yml")
R_SWS_SHARE_PATH <- SETT[["share"]]
## Get SWS Parameters
SetClientFiles(dir = SETT[["certdir"]])
GetTestEnvironment(
baseUrl = SETT[["server"]],
token = SETT[["token"]]
)
}
sessionKey = swsContext.datasets[[1]]
geoKeys = GetCodeList(domain = "agriculture", dataset = "aproduction",
dimension = "geographicAreaM49")[type == "country", code]
sessionCountries =
getQueryKey("geographicAreaM49", sessionKey)
geoM49 = swsContext.computationParams$geographicAreaM49
COUNTRY <- switch(geoM49,
"session" = sessionCountries,
"all" = geoKeys)
START_YEAR <- swsContext.computationParams$start_year
END_YEAR <- swsContext.computationParams$end_year
############################################################################################################
#conversion factors
#cattle
conversion_cattles <- ReadDatatable("conversion_factors_cattles")[,-c("geographic_area","comments"),with = FALSE]
conversion_cattles[,live_animal := "02111"]
#pigs
conversion_pigs <- ReadDatatable("conversion_factors_pigs")[,-c("geographic_area","comments"),with = FALSE]
conversion_pigs[,live_animal := "02140"]
#buffalos
conversion_buffalos <- ReadDatatable("conversion_factors_buffalos")[,-c("geographic_area","comments"),with = FALSE]
conversion_buffalos[,live_animal := "02112"]
#goats
conversion_goats <- ReadDatatable("conversion_factors_goats")[,-c("geographic_area","comments"),with = FALSE]
conversion_goats[,live_animal := "02123"]
#horses
conversion_horses <- ReadDatatable("conversion_factors_horses")[,-c("geographic_area","comments"),with = FALSE]
conversion_horses[,live_animal := "02131"]
#camels
conversion_camels <- ReadDatatable("conversion_factors_camels")[,-c("geographic_area","comments"),with = FALSE]
conversion_camels[,live_animal := "02121.01"]
#sheep
conversion_sheep <- ReadDatatable("conversion_factors_sheep")[,-c("geographic_area","comments"),with = FALSE]
conversion_sheep[,live_animal := "02122"]
#rabbits
conversion_rabbits <- ReadDatatable("conversion_factors_rabbits")[,-c("geographic_area","comments"),with = FALSE]
conversion_rabbits[,live_animal := "02191"]
#bind all conversion factors
conversion_factors <- rbind(conversion_cattles,conversion_pigs,conversion_buffalos,conversion_sheep,conversion_goats,conversion_horses,conversion_camels,conversion_rabbits
,fill= T)
conversion_factors[, geographic_area_m49 := as.character(geographic_area_m49)]
conversion_factors <- conversion_factors[!is.na(geographic_area_m49)]
#meat table
meat_table <- ReadDatatable("meat")[,-c("live_animal", "meat"),with = F]
#offals and fats
offals_fats <- ReadDatatable("offals_fats")[,-c("meat"),with =FALSE]
offals_fats <-offals_fats[cpc_meat != ""]
setnames(offals_fats, "cpc","cpc_offals_fats")
#items to pull
items <- unique(c(meat_table$cpc_meat,offals_fats$cpc_offals_fats,offals_fats$cpc_meat))
#pull production data
prodKey <- DatasetKey(domain = "agriculture", dataset = "aproduction",
dimensions = list(
geographicAreaM49 = Dimension(name = "geographicAreaM49", keys = COUNTRY),
measuredElement = Dimension(name = "measuredElement", keys = c("5510","5320")),
measuredItemCPC = Dimension(name = "measuredItemCPC", keys = items),
timePointYears = Dimension(name = "timePointYears", keys = as.character(c(START_YEAR:END_YEAR))))
)
prodData <- GetData(prodKey)
prodFlags <- copy(prodData)
##########################Filling up missing conversion factors
conversion_factors[, world_average_carcass := round(mean(carcass_weight_of_live_weight_perc, na.rm = TRUE),1), by = c("live_animal")]
conversion_factors[, world_average_offals := round(mean(edible_offals_content_of_live_weight_perc, na.rm = TRUE),1), by = c("live_animal")]
conversion_factors[, world_average_fats := round(mean(slaughter_fats_content_of_live_weight_perc, na.rm = TRUE),1), by = c("live_animal")]
conversion_factors[, world_average_skin := round(mean(hides_and_skins_content_of_live_weight_perc, na.rm = TRUE),1), by = c("live_animal")]
conversion_factors <- subset(conversion_factors, geographic_area_m49 %in% COUNTRY)
conversion_factors[world_average_carcass == "NaN", world_average_carcass := NA]
conversion_factors[world_average_offals == "NaN",world_average_offals := NA]
conversion_factors[world_average_fats == "NaN",world_average_fats := NA]
conversion_factors[world_average_skin == "NaN",world_average_skin := NA]
production_data <- copy(prodData)
production_data <- subset(production_data, measuredElement == "5510")
#remove offals, fats and skin cpcs
production_data <- subset(production_data, !(measuredItemCPC %in% unique(offals_fats$cpc_offals_fats)))
production_data[, c("flagObservationStatus","flagMethod"):= NULL]
production_data[,average_production := mean(Value, na.rm = TRUE), by = c("geographicAreaM49","measuredItemCPC")]
#remove NA average (this means for those CPC there have not been production in the past)
production_data <- production_data[!is.na(average_production)]
#meat cpc where production existed in the past
meatCPC <- data.table(meatCPC = unique(production_data$measuredItemCPC))
meatCPC <- merge(meatCPC, meat_table, by.x = c("meatCPC"),by.y = c("cpc_meat"),all.x = TRUE)
#live animals where their meat exists in the country
liveAnimals <- unique(meatCPC$cpc_live_animal)
#create an indicator variable for production existance
conversion_factors[, prodcution_indicator := ifelse(live_animal %in% liveAnimals,1,0)]
conversion_factors[, carcass_weight_of_live_weight_perc := ifelse(is.na(carcass_weight_of_live_weight_perc) & prodcution_indicator == 1,
world_average_carcass, carcass_weight_of_live_weight_perc)]
conversion_factors[, edible_offals_content_of_live_weight_perc := ifelse(is.na(edible_offals_content_of_live_weight_perc) & prodcution_indicator == 1,
world_average_offals, edible_offals_content_of_live_weight_perc)]
conversion_factors[, slaughter_fats_content_of_live_weight_perc := ifelse(is.na(slaughter_fats_content_of_live_weight_perc) & prodcution_indicator == 1,
world_average_fats, slaughter_fats_content_of_live_weight_perc)]
conversion_factors[, hides_and_skins_content_of_live_weight_perc := ifelse(is.na(hides_and_skins_content_of_live_weight_perc) & prodcution_indicator == 1,
world_average_skin, hides_and_skins_content_of_live_weight_perc)]
conversion_factors <- conversion_factors[,-c ("world_average_carcass","world_average_offals","world_average_fats","world_average_skin","prodcution_indicator"),with = FALSE]
#####################################################
timeseriesProd <-as.data.table(expand.grid(geographicAreaM49 = unique(prodData$geographicAreaM49),
measuredElement = unique(prodData$measuredElement), measuredItemCPC = unique(prodData$measuredItemCPC),
timePointYears = unique(prodData$timePointYears)))
timeseriesProd <- merge(timeseriesProd, prodData, by =c("geographicAreaM49","measuredElement", "measuredItemCPC","timePointYears"), all.x = TRUE)
prodData <- copy(timeseriesProd)
conv_factor <- copy(conversion_factors)
conv_factor[, offal_factor:= `edible_offals_content_of_live_weight_perc`/`carcass_weight_of_live_weight_perc`, by =c("geographic_area_m49","live_animal")]
conv_factor[, fat_factor:= `slaughter_fats_content_of_live_weight_perc`/`carcass_weight_of_live_weight_perc`,by =c("geographic_area_m49","live_animal")]
conv_factor[, skin_factor:= `hides_and_skins_content_of_live_weight_perc`/`carcass_weight_of_live_weight_perc`,by =c("geographic_area_m49","live_animal")]
#########################################################################################################################################
#offals imputation
offals_fat_factor <- conv_factor[,c("geographic_area_m49", "live_animal", "offal_factor", "fat_factor"),with=FALSE]
skin_factor <- conv_factor[,c("geographic_area_m49", "live_animal", "skin_factor"),with=FALSE]
#offals and meet
offals_fat_meat <- copy(offals_fats)
offals_fat_meat <- subset(offals_fat_meat, offals_fats %in% c("offals","fat"))
skin_meat <- copy(offals_fats)
skin_meat <- subset(skin_meat, !(offals_fats %in% c("offals","fat")))
#offals, meat and live
offals_fat_meat <- merge(offals_fat_meat, meat_table, by = c("cpc_meat"), all.x = TRUE)
skin_meat<- merge(skin_meat, meat_table, by = c("cpc_meat"), all.x = TRUE)
#merge offals_meat table with offals_factor table
xx<- copy(offals_fat_factor)
yy_offals <- merge(xx, offals_fat_meat[offals_fats == "offals"], by.x = "live_animal",by.y = "cpc_live_animal", all.x = TRUE)
yy_offals <- yy_offals[!is.na(offals_fats)]
yy_fat <- merge(xx, offals_fat_meat[offals_fats == "fat"], by.x = "live_animal",by.y = "cpc_live_animal", all.x = TRUE)
yy_fat <- yy_fat[!is.na(offals_fats)]
offals_fat_factor <- rbind(yy_offals, yy_fat)
# offals_fat_factor <- merge(offals_fat_factor, offals_fat_meat, by.x = "live_animal",by.y = "cpc_live_animal", all.x = TRUE)
skin_factor <- merge(skin_factor, skin_meat, by.x = "live_animal",by.y = "cpc_live_animal", all.x = TRUE)
production <- copy(prodData)
production_offals <- merge(production,offals_fat_factor[,-c("fat_factor"),with=F][offals_fats == "offals"], by.x = c("geographicAreaM49","measuredItemCPC"),
by.y = c("geographic_area_m49","cpc_meat"), all.x = TRUE)
production_fats <- merge(production,offals_fat_factor[,-c("offal_factor"),with=F][offals_fats == "fat"], by.x = c("geographicAreaM49","measuredItemCPC"),
by.y = c("geographic_area_m49","cpc_meat"), all.x = TRUE)
production_skin <- merge(production,skin_factor, by.x = c("geographicAreaM49","measuredItemCPC"),
by.y = c("geographic_area_m49","cpc_meat"), all.x = TRUE)
production_offals <- production_offals[!is.na(live_animal)]
production_fats <- production_fats[!is.na(live_animal)]
production_skin <- production_skin[!is.na(live_animal)]
#offals###############################################################################
production_offals[, offals_production := ifelse(measuredElement == "5510",offal_factor*Value[measuredElement == "5510"],NA),
, by= c("geographicAreaM49","live_animal","timePointYears")]
#1 metric ton = 10^6 grams
production_offals[, offals_yield := ifelse(measuredElement == "5510",offals_production[measuredElement == "5510"]*10^6/Value[measuredElement== "5320"],NA),
, by= c("geographicAreaM49","live_animal","timePointYears")]
prod_5320_offals <- copy(production_offals)
prod_5320_offals <- prod_5320_offals[,c("geographicAreaM49", "measuredElement", "cpc_offals_fats",
"timePointYears", "Value", "flagObservationStatus", "flagMethod"),with=FALSE]
prod_5320_offals <- subset(prod_5320_offals, measuredElement == "5320")
setnames(prod_5320_offals, "cpc_offals_fats","measuredItemCPC")
production_offals <-subset(production_offals, measuredElement == "5510")
production_offals <- production_offals[,c("geographicAreaM49", "timePointYears", "cpc_offals_fats", "offals_production","offals_yield"), with = FALSE]
######fats#################################################################################################################
production_fats[, fat_production := ifelse(measuredElement == "5510",fat_factor*Value[measuredElement == "5510"], NA
), by= c("geographicAreaM49","live_animal","timePointYears")]
#1 metric ton = 10^6 grams
production_fats[, fat_yield := ifelse(measuredElement == "5510",fat_production[measuredElement == "5510"]*10^6/Value[measuredElement== "5320"],
NA ), by= c("geographicAreaM49","live_animal","timePointYears")]
prod_5320_fats <- copy(production_fats)
prod_5320_fats <- prod_5320_fats[,c("geographicAreaM49", "measuredElement", "cpc_offals_fats",
"timePointYears", "Value", "flagObservationStatus", "flagMethod"),with=FALSE]
prod_5320_fats <- subset(prod_5320_fats, measuredElement == "5320")
setnames(prod_5320_fats, "cpc_offals_fats","measuredItemCPC")
production_fats <-subset(production_fats, measuredElement == "5510")
production_fats <- production_fats[,c("geographicAreaM49", "timePointYears", "cpc_offals_fats", "fat_production","fat_yield"), with = FALSE]
###skin######################################################################################################################
production_skin[, skin_production := ifelse(measuredElement == "5510",skin_factor*Value[measuredElement == "5510"], NA
), by= c("geographicAreaM49","live_animal","timePointYears")]
#1 metric ton = 10^6 grams
production_skin[, skin_yield := ifelse(measuredElement == "5510",skin_production[measuredElement == "5510"]*10^6/Value[measuredElement== "5320"],
NA ), by= c("geographicAreaM49","live_animal","timePointYears")]
prod_5320_skin <- copy(production_skin)
prod_5320_skin <- prod_5320_skin[,c("geographicAreaM49", "measuredElement", "cpc_offals_fats",
"timePointYears", "Value", "flagObservationStatus", "flagMethod"),with=FALSE]
prod_5320_skin <- subset(prod_5320_skin, measuredElement == "5320")
setnames(prod_5320_skin, "cpc_offals_fats","measuredItemCPC")
production_skin <-subset(production_skin, measuredElement == "5510")
production_skin <- production_skin[,c("geographicAreaM49", "timePointYears", "cpc_offals_fats", "skin_production","skin_yield"), with = FALSE]
############################Melting data sets of fats and offals
production_offals <- melt.data.table(production_offals, id=c("geographicAreaM49","cpc_offals_fats","timePointYears"), na.rm = TRUE)
production_fats <- melt.data.table(production_fats, id=c("geographicAreaM49","cpc_offals_fats","timePointYears"), na.rm = TRUE)
production_skin <- melt.data.table(production_skin, id=c("geographicAreaM49","cpc_offals_fats","timePointYears"), na.rm = TRUE)
##rbind estimated data
prod_fat_offals_skin <- rbind(production_offals,production_fats,production_skin)
prod_fat_offals_skin[, variable := ifelse(variable %in% c( "offals_production", "fat_production", "skin_production"), "5510", "5424")]
prod_fat_offals_skin[, `:=` (flagObservationStatus = "I", flagMethod = "i")]
setnames(prod_fat_offals_skin, c("cpc_offals_fats", "variable", "value"),c("measuredItemCPC","measuredElement","Value"))
#rbind the element 5320
prod_fat_offals_skin <- rbind(prod_fat_offals_skin, prod_5320_offals,prod_5320_fats,prod_5320_skin)
prod_fat_offals_skin <- prod_fat_offals_skin[!is.na(Value)]
###porduction data in SWS
setnames(prodFlags, c("Value","flagObservationStatus","flagMethod"),c("ValueSWS", "FlagSWS","MethodSWS"))
prod_fat_offals_skin <- merge(prod_fat_offals_skin, prodFlags, by=c("geographicAreaM49","measuredElement","measuredItemCPC", "timePointYears"), all.x = TRUE)
#replace official vlaues, flag and method. retain (M,-) combinations
prod_fat_offals_skin[, Value := ifelse(measuredElement == "5510" & FlagSWS == "M" & !is.na(FlagSWS) & !is.na(MethodSWS) & MethodSWS == "-", ValueSWS, Value)]
prod_fat_offals_skin[, flagObservationStatus := ifelse(measuredElement == "5510" & FlagSWS == "M" & !is.na(FlagSWS) & !is.na(MethodSWS) & MethodSWS == "-", FlagSWS, flagObservationStatus)]
prod_fat_offals_skin[, flagMethod := ifelse(measuredElement == "5510" & FlagSWS == "M" & !is.na(FlagSWS) & !is.na(MethodSWS) & MethodSWS == "-", MethodSWS, flagMethod)]
prod_fat_offals_skin[,c("ValueSWS","FlagSWS","MethodSWS") := NULL]
prod_fat_offals_skin <- prod_fat_offals_skin[!is.na(Value)]
#remove rows with official flag
SaveData(domain = "agriculture",
dataset = "aproduction",
data = prod_fat_offals_skin,waitTimeout = 1800)
paste0("The plugin ran successfully ! ")
SWS-Methodology/faoswsProduction documentation built on March 21, 2023, 8:27 p.m.
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## load the libraries
library(faosws)
library(data.table)
library(faoswsUtil)
library(sendmailR)
library(dplyr)
library(faoswsFlag)
## set up for the test environment and parameters
R_SWS_SHARE_PATH = Sys.getenv("R_SWS_SHARE_PATH")
if(CheckDebug()){
message("Not on server, so setting up environment...")
library(faoswsModules)
SETT <- ReadSettings("sws.yml")
R_SWS_SHARE_PATH <- SETT[["share"]]
## Get SWS Parameters
SetClientFiles(dir = SETT[["certdir"]])
GetTestEnvironment(
baseUrl = SETT[["server"]],
token = SETT[["token"]]
)
}
sessionKey = swsContext.datasets[[1]]
geoKeys = GetCodeList(domain = "agriculture", dataset = "aproduction",
dimension = "geographicAreaM49")[type == "country", code]
sessionCountries =
getQueryKey("geographicAreaM49", sessionKey)
geoM49 = swsContext.computationParams$geographicAreaM49
COUNTRY <- switch(geoM49,
"session" = sessionCountries,
"all" = geoKeys)
START_YEAR <- swsContext.computationParams$start_year
END_YEAR <- swsContext.computationParams$end_year
############################################################################################################
#conversion factors
#cattle
conversion_cattles <- ReadDatatable("conversion_factors_cattles")[,-c("geographic_area","comments"),with = FALSE]
conversion_cattles[,live_animal := "02111"]
#pigs
conversion_pigs <- ReadDatatable("conversion_factors_pigs")[,-c("geographic_area","comments"),with = FALSE]
conversion_pigs[,live_animal := "02140"]
#buffalos
conversion_buffalos <- ReadDatatable("conversion_factors_buffalos")[,-c("geographic_area","comments"),with = FALSE]
conversion_buffalos[,live_animal := "02112"]
#goats
conversion_goats <- ReadDatatable("conversion_factors_goats")[,-c("geographic_area","comments"),with = FALSE]
conversion_goats[,live_animal := "02123"]
#horses
conversion_horses <- ReadDatatable("conversion_factors_horses")[,-c("geographic_area","comments"),with = FALSE]
conversion_horses[,live_animal := "02131"]
#camels
conversion_camels <- ReadDatatable("conversion_factors_camels")[,-c("geographic_area","comments"),with = FALSE]
conversion_camels[,live_animal := "02121.01"]
#sheep
conversion_sheep <- ReadDatatable("conversion_factors_sheep")[,-c("geographic_area","comments"),with = FALSE]
conversion_sheep[,live_animal := "02122"]
#rabbits
conversion_rabbits <- ReadDatatable("conversion_factors_rabbits")[,-c("geographic_area","comments"),with = FALSE]
conversion_rabbits[,live_animal := "02191"]
#bind all conversion factors
conversion_factors <- rbind(conversion_cattles,conversion_pigs,conversion_buffalos,conversion_sheep,conversion_goats,conversion_horses,conversion_camels,conversion_rabbits
,fill= T)
conversion_factors[, geographic_area_m49 := as.character(geographic_area_m49)]
conversion_factors <- conversion_factors[!is.na(geographic_area_m49)]
#meat table
meat_table <- ReadDatatable("meat")[,-c("live_animal", "meat"),with = F]
#offals and fats
offals_fats <- ReadDatatable("offals_fats")[,-c("meat"),with =FALSE]
offals_fats <-offals_fats[cpc_meat != ""]
setnames(offals_fats, "cpc","cpc_offals_fats")
#items to pull
items <- unique(c(meat_table$cpc_meat,offals_fats$cpc_offals_fats,offals_fats$cpc_meat))
#pull production data
prodKey <- DatasetKey(domain = "agriculture", dataset = "aproduction",
dimensions = list(
geographicAreaM49 = Dimension(name = "geographicAreaM49", keys = COUNTRY),
measuredElement = Dimension(name = "measuredElement", keys = c("5510","5320")),
measuredItemCPC = Dimension(name = "measuredItemCPC", keys = items),
timePointYears = Dimension(name = "timePointYears", keys = as.character(c(START_YEAR:END_YEAR))))
)
prodData <- GetData(prodKey)
prodFlags <- copy(prodData)
##########################Filling up missing conversion factors
conversion_factors[, world_average_carcass := round(mean(carcass_weight_of_live_weight_perc, na.rm = TRUE),1), by = c("live_animal")]
conversion_factors[, world_average_offals := round(mean(edible_offals_content_of_live_weight_perc, na.rm = TRUE),1), by = c("live_animal")]
conversion_factors[, world_average_fats := round(mean(slaughter_fats_content_of_live_weight_perc, na.rm = TRUE),1), by = c("live_animal")]
conversion_factors[, world_average_skin := round(mean(hides_and_skins_content_of_live_weight_perc, na.rm = TRUE),1), by = c("live_animal")]
conversion_factors <- subset(conversion_factors, geographic_area_m49 %in% COUNTRY)
conversion_factors[world_average_carcass == "NaN", world_average_carcass := NA]
conversion_factors[world_average_offals == "NaN",world_average_offals := NA]
conversion_factors[world_average_fats == "NaN",world_average_fats := NA]
conversion_factors[world_average_skin == "NaN",world_average_skin := NA]
production_data <- copy(prodData)
production_data <- subset(production_data, measuredElement == "5510")
#remove offals, fats and skin cpcs
production_data <- subset(production_data, !(measuredItemCPC %in% unique(offals_fats$cpc_offals_fats)))
production_data[, c("flagObservationStatus","flagMethod"):= NULL]
production_data[,average_production := mean(Value, na.rm = TRUE), by = c("geographicAreaM49","measuredItemCPC")]
#remove NA average (this means for those CPC there have not been production in the past)
production_data <- production_data[!is.na(average_production)]
#meat cpc where production existed in the past
meatCPC <- data.table(meatCPC = unique(production_data$measuredItemCPC))
meatCPC <- merge(meatCPC, meat_table, by.x = c("meatCPC"),by.y = c("cpc_meat"),all.x = TRUE)
#live animals where their meat exists in the country
liveAnimals <- unique(meatCPC$cpc_live_animal)
#create an indicator variable for production existance
conversion_factors[, prodcution_indicator := ifelse(live_animal %in% liveAnimals,1,0)]
conversion_factors[, carcass_weight_of_live_weight_perc := ifelse(is.na(carcass_weight_of_live_weight_perc) & prodcution_indicator == 1,
world_average_carcass, carcass_weight_of_live_weight_perc)]
conversion_factors[, edible_offals_content_of_live_weight_perc := ifelse(is.na(edible_offals_content_of_live_weight_perc) & prodcution_indicator == 1,
world_average_offals, edible_offals_content_of_live_weight_perc)]
conversion_factors[, slaughter_fats_content_of_live_weight_perc := ifelse(is.na(slaughter_fats_content_of_live_weight_perc) & prodcution_indicator == 1,
world_average_fats, slaughter_fats_content_of_live_weight_perc)]
conversion_factors[, hides_and_skins_content_of_live_weight_perc := ifelse(is.na(hides_and_skins_content_of_live_weight_perc) & prodcution_indicator == 1,
world_average_skin, hides_and_skins_content_of_live_weight_perc)]
conversion_factors <- conversion_factors[,-c ("world_average_carcass","world_average_offals","world_average_fats","world_average_skin","prodcution_indicator"),with = FALSE]
#####################################################
timeseriesProd <-as.data.table(expand.grid(geographicAreaM49 = unique(prodData$geographicAreaM49),
measuredElement = unique(prodData$measuredElement), measuredItemCPC = unique(prodData$measuredItemCPC),
timePointYears = unique(prodData$timePointYears)))
timeseriesProd <- merge(timeseriesProd, prodData, by =c("geographicAreaM49","measuredElement", "measuredItemCPC","timePointYears"), all.x = TRUE)
prodData <- copy(timeseriesProd)
conv_factor <- copy(conversion_factors)
conv_factor[, offal_factor:= `edible_offals_content_of_live_weight_perc`/`carcass_weight_of_live_weight_perc`, by =c("geographic_area_m49","live_animal")]
conv_factor[, fat_factor:= `slaughter_fats_content_of_live_weight_perc`/`carcass_weight_of_live_weight_perc`,by =c("geographic_area_m49","live_animal")]
conv_factor[, skin_factor:= `hides_and_skins_content_of_live_weight_perc`/`carcass_weight_of_live_weight_perc`,by =c("geographic_area_m49","live_animal")]
#########################################################################################################################################
#offals imputation
offals_fat_factor <- conv_factor[,c("geographic_area_m49", "live_animal", "offal_factor", "fat_factor"),with=FALSE]
skin_factor <- conv_factor[,c("geographic_area_m49", "live_animal", "skin_factor"),with=FALSE]
#offals and meet
offals_fat_meat <- copy(offals_fats)
offals_fat_meat <- subset(offals_fat_meat, offals_fats %in% c("offals","fat"))
skin_meat <- copy(offals_fats)
skin_meat <- subset(skin_meat, !(offals_fats %in% c("offals","fat")))
#offals, meat and live
offals_fat_meat <- merge(offals_fat_meat, meat_table, by = c("cpc_meat"), all.x = TRUE)
skin_meat<- merge(skin_meat, meat_table, by = c("cpc_meat"), all.x = TRUE)
#merge offals_meat table with offals_factor table
xx<- copy(offals_fat_factor)
yy_offals <- merge(xx, offals_fat_meat[offals_fats == "offals"], by.x = "live_animal",by.y = "cpc_live_animal", all.x = TRUE)
yy_offals <- yy_offals[!is.na(offals_fats)]
yy_fat <- merge(xx, offals_fat_meat[offals_fats == "fat"], by.x = "live_animal",by.y = "cpc_live_animal", all.x = TRUE)
yy_fat <- yy_fat[!is.na(offals_fats)]
offals_fat_factor <- rbind(yy_offals, yy_fat)
# offals_fat_factor <- merge(offals_fat_factor, offals_fat_meat, by.x = "live_animal",by.y = "cpc_live_animal", all.x = TRUE)
skin_factor <- merge(skin_factor, skin_meat, by.x = "live_animal",by.y = "cpc_live_animal", all.x = TRUE)
production <- copy(prodData)
production_offals <- merge(production,offals_fat_factor[,-c("fat_factor"),with=F][offals_fats == "offals"], by.x = c("geographicAreaM49","measuredItemCPC"),
by.y = c("geographic_area_m49","cpc_meat"), all.x = TRUE)
production_fats <- merge(production,offals_fat_factor[,-c("offal_factor"),with=F][offals_fats == "fat"], by.x = c("geographicAreaM49","measuredItemCPC"),
by.y = c("geographic_area_m49","cpc_meat"), all.x = TRUE)
production_skin <- merge(production,skin_factor, by.x = c("geographicAreaM49","measuredItemCPC"),
by.y = c("geographic_area_m49","cpc_meat"), all.x = TRUE)
production_offals <- production_offals[!is.na(live_animal)]
production_fats <- production_fats[!is.na(live_animal)]
production_skin <- production_skin[!is.na(live_animal)]
#offals###############################################################################
production_offals[, offals_production := ifelse(measuredElement == "5510",offal_factor*Value[measuredElement == "5510"],NA),
, by= c("geographicAreaM49","live_animal","timePointYears")]
#1 metric ton = 10^6 grams
production_offals[, offals_yield := ifelse(measuredElement == "5510",offals_production[measuredElement == "5510"]*10^6/Value[measuredElement== "5320"],NA),
, by= c("geographicAreaM49","live_animal","timePointYears")]
prod_5320_offals <- copy(production_offals)
prod_5320_offals <- prod_5320_offals[,c("geographicAreaM49", "measuredElement", "cpc_offals_fats",
"timePointYears", "Value", "flagObservationStatus", "flagMethod"),with=FALSE]
prod_5320_offals <- subset(prod_5320_offals, measuredElement == "5320")
setnames(prod_5320_offals, "cpc_offals_fats","measuredItemCPC")
production_offals <-subset(production_offals, measuredElement == "5510")
production_offals <- production_offals[,c("geographicAreaM49", "timePointYears", "cpc_offals_fats", "offals_production","offals_yield"), with = FALSE]
######fats#################################################################################################################
production_fats[, fat_production := ifelse(measuredElement == "5510",fat_factor*Value[measuredElement == "5510"], NA
), by= c("geographicAreaM49","live_animal","timePointYears")]
#1 metric ton = 10^6 grams
production_fats[, fat_yield := ifelse(measuredElement == "5510",fat_production[measuredElement == "5510"]*10^6/Value[measuredElement== "5320"],
NA ), by= c("geographicAreaM49","live_animal","timePointYears")]
prod_5320_fats <- copy(production_fats)
prod_5320_fats <- prod_5320_fats[,c("geographicAreaM49", "measuredElement", "cpc_offals_fats",
"timePointYears", "Value", "flagObservationStatus", "flagMethod"),with=FALSE]
prod_5320_fats <- subset(prod_5320_fats, measuredElement == "5320")
setnames(prod_5320_fats, "cpc_offals_fats","measuredItemCPC")
production_fats <-subset(production_fats, measuredElement == "5510")
production_fats <- production_fats[,c("geographicAreaM49", "timePointYears", "cpc_offals_fats", "fat_production","fat_yield"), with = FALSE]
###skin######################################################################################################################
production_skin[, skin_production := ifelse(measuredElement == "5510",skin_factor*Value[measuredElement == "5510"], NA
), by= c("geographicAreaM49","live_animal","timePointYears")]
#1 metric ton = 10^6 grams
production_skin[, skin_yield := ifelse(measuredElement == "5510",skin_production[measuredElement == "5510"]*10^6/Value[measuredElement== "5320"],
NA ), by= c("geographicAreaM49","live_animal","timePointYears")]
prod_5320_skin <- copy(production_skin)
prod_5320_skin <- prod_5320_skin[,c("geographicAreaM49", "measuredElement", "cpc_offals_fats",
"timePointYears", "Value", "flagObservationStatus", "flagMethod"),with=FALSE]
prod_5320_skin <- subset(prod_5320_skin, measuredElement == "5320")
setnames(prod_5320_skin, "cpc_offals_fats","measuredItemCPC")
production_skin <-subset(production_skin, measuredElement == "5510")
production_skin <- production_skin[,c("geographicAreaM49", "timePointYears", "cpc_offals_fats", "skin_production","skin_yield"), with = FALSE]
############################Melting data sets of fats and offals
production_offals <- melt.data.table(production_offals, id=c("geographicAreaM49","cpc_offals_fats","timePointYears"), na.rm = TRUE)
production_fats <- melt.data.table(production_fats, id=c("geographicAreaM49","cpc_offals_fats","timePointYears"), na.rm = TRUE)
production_skin <- melt.data.table(production_skin, id=c("geographicAreaM49","cpc_offals_fats","timePointYears"), na.rm = TRUE)
##rbind estimated data
prod_fat_offals_skin <- rbind(production_offals,production_fats,production_skin)
prod_fat_offals_skin[, variable := ifelse(variable %in% c( "offals_production", "fat_production", "skin_production"), "5510", "5424")]
prod_fat_offals_skin[, `:=` (flagObservationStatus = "I", flagMethod = "i")]
setnames(prod_fat_offals_skin, c("cpc_offals_fats", "variable", "value"),c("measuredItemCPC","measuredElement","Value"))
#rbind the element 5320
prod_fat_offals_skin <- rbind(prod_fat_offals_skin, prod_5320_offals,prod_5320_fats,prod_5320_skin)
prod_fat_offals_skin <- prod_fat_offals_skin[!is.na(Value)]
###porduction data in SWS
setnames(prodFlags, c("Value","flagObservationStatus","flagMethod"),c("ValueSWS", "FlagSWS","MethodSWS"))
prod_fat_offals_skin <- merge(prod_fat_offals_skin, prodFlags, by=c("geographicAreaM49","measuredElement","measuredItemCPC", "timePointYears"), all.x = TRUE)
#replace official vlaues, flag and method. retain (M,-) combinations
prod_fat_offals_skin[, Value := ifelse(measuredElement == "5510" & FlagSWS == "M" & !is.na(FlagSWS) & !is.na(MethodSWS) & MethodSWS == "-", ValueSWS, Value)]
prod_fat_offals_skin[, flagObservationStatus := ifelse(measuredElement == "5510" & FlagSWS == "M" & !is.na(FlagSWS) & !is.na(MethodSWS) & MethodSWS == "-", FlagSWS, flagObservationStatus)]
prod_fat_offals_skin[, flagMethod := ifelse(measuredElement == "5510" & FlagSWS == "M" & !is.na(FlagSWS) & !is.na(MethodSWS) & MethodSWS == "-", MethodSWS, flagMethod)]
prod_fat_offals_skin[,c("ValueSWS","FlagSWS","MethodSWS") := NULL]
prod_fat_offals_skin <- prod_fat_offals_skin[!is.na(Value)]
#remove rows with official flag
SaveData(domain = "agriculture",
dataset = "aproduction",
data = prod_fat_offals_skin,waitTimeout = 1800)
paste0("The plugin ran successfully ! ")
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