modules/Indigenous Meat/main.R
In SWS-Methodology/faoswsProduction: Package to perform the imputation of area harvested, production and yield for the FAO production domain
# Create Indigenous Meat Production
##'
##' **Author: Giulia Piva**
##'
##' **Description:**
##'
##' This module is designed to harvest the data from Trade and Production domain and create Indigenous Meat Production as: (SLaughtered - Imported)*carcass weight
##'
## load the library
library(faosws)
library(data.table)
library(faoswsUtil)
library(sendmailR)
library(tidyr)
library(faoswsFlag)
library(faoswsProcessing)
library(faoswsEnsure)
library(magrittr)
library(dplyr)
library(sendmailR)
## 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"]]
)
}
startYear = as.numeric(swsContext.computationParams$startYear)
endYear = as.numeric(swsContext.computationParams$endYear)
geoM49 = swsContext.computationParams$geom49
stopifnot(startYear <= endYear)
yearVals = startYear:endYear
##' Get data configuration and session
sessionKey = swsContext.datasets[[1]]
sessionCountries =
getQueryKey("geographicAreaM49", sessionKey)
geoKeys = GetCodeList(domain = "agriculture", dataset = "aproduction",
dimension = "geographicAreaM49")[type == "country", code]
##' Select the countries based on the user input parameter
selectedGEOCode =
switch(geoM49,
"session" = sessionCountries,
"all" = geoKeys)
################################################
##### Harvest from Agricultural Production #####
################################################
message("Pulling data from Agricultural Production")
geoDim = Dimension(name = "geographicAreaM49", keys = selectedGEOCode)
eleDim <- Dimension(name = "measuredElement", keys = c("5315", "5316","5417", "5424","54170","54240", "55100","5510"))
itemKeys = GetCodeList(domain = "agriculture", dataset = "aproduction", "measuredItemCPC")
itemKeys = itemKeys[, code]
itemDim <- Dimension(name = "measuredItemCPC", keys = itemKeys)
timeDim = Dimension(name = "timePointYears", keys = as.character(yearVals))
agKey = DatasetKey(domain = "agriculture", dataset = "aproduction",
dimensions = list(
geographicAreaM49 = geoDim,
measuredElement = eleDim,
measuredItemCPC = itemDim,
timePointYears = timeDim)
)
agData = GetData(agKey)
################################################
##### Harvest from Trade #####
################################################
message("Pulling data from Trade")
geoDim = Dimension(name = "geographicAreaM49", keys = selectedGEOCode)
Trade_eleDim <- Dimension(name = "measuredElementTrade", keys = c("5608", "5609", "5908", "5909"))
Tradekey = DatasetKey(domain = "trade", dataset = "total_trade_cpc_m49", dimensions = list(
geographicAreaM49 = geoDim,
measuredElementTrade = Trade_eleDim,
measuredItemCPC = itemDim,
timePointYears = timeDim
))
TradeData = GetData(Tradekey)
# Separate Meat data and Live Animals data
ProductionData<-agData[measuredElement %in% c("5315", "5316")]
MeatData <- agData[measuredElement %in% c("5417", "5424","54170","54240", "55100","5510")]
# give labels
ProductionData <-nameData("agriculture", "aproduction", ProductionData)
TradeData<- nameData("trade", "total_trade_cpc_m49", TradeData)
#Merge trade and Live animals data
#prepare trade table for merging
colnames(TradeData)<-c( "geographicAreaM49","geographicAreaM49_description", "measuredElement","measuredElement_description","measuredItemCPC",
"measuredItemCPC_description","timePointYears", "timePointYears_description","Value","flagObservationStatus","flagMethod")
#merge
IndigenTable<-rbind(ProductionData,TradeData)
#create timeseries for indigenous slaughtered animals
IndigenTable<-rbind(IndigenTable, data.table(measuredElement="53200"),fill=T)
timeSeriesData<- as.data.table(expand.grid(measuredItemCPC = unique(IndigenTable$measuredItemCPC),
geographicAreaM49 = unique(IndigenTable$geographicAreaM49),
measuredElement = unique(IndigenTable$measuredElement),
timePointYears = c(startYear:endYear)))
timeSeriesData[, names(timeSeriesData) := lapply(.SD, as.character), .SDcols = names(timeSeriesData)]
timeSeriesData<- na.omit(timeSeriesData, cols=c("geographicAreaM49", "measuredItemCPC"))
#fill timeseries with values and na
IndigenTable2 <- merge(timeSeriesData, IndigenTable, by=c("measuredElement","measuredItemCPC","timePointYears","geographicAreaM49"), all.x= TRUE)
##Attribute code for (1000 head) to small animal
IndigenTable2[ , measuredElement := ifelse(measuredItemCPC %in% c("02151", "02154", "02153", "02194", "02192.01","02191","02152")
& measuredElement=="53200", "53210", measuredElement)]
####create Indigenous animal number
IndigenTable2[ , Value := ifelse(measuredElement =="53200", sum(Value[measuredElement=="5315"], -Value[measuredElement=="5608"],Value[measuredElement=="5908"], na.rm=TRUE),
ifelse(measuredElement =="53210", sum(Value[measuredElement=="5316"], -Value[measuredElement=="5609"],Value[measuredElement=="5909"],na.rm=T),
Value)),
by = list(geographicAreaM49, measuredItemCPC, timePointYears)]
#correct too low (negative) values
IndigenTable2[ , Value := ifelse((measuredElement =="53200" |measuredElement=="53210") & Value <=0 , 1, Value)]
##correct too high values (where indigenous is higher than total slaughtered)
IndigenTable2[ , Diff:= ifelse(Value[get("measuredElement") %in% c("53200")] > Value[get("measuredElement") %in% c("5315")], TRUE,FALSE),
by = list(`geographicAreaM49`, `measuredItemCPC`, `timePointYears`)]
IndigenTable2[ , Diff2:= ifelse(Value[get("measuredElement") %in% c("53210")] > Value[get("measuredElement") %in% c("5316")], TRUE,FALSE),
by = list(`geographicAreaM49`, `measuredItemCPC`, `timePointYears`)]
IndigenTable2[ , Value:= ifelse(measuredElement=="53200" & Diff== TRUE, Value[measuredElement=="5315"],Value),
by = list(`geographicAreaM49`, `measuredItemCPC`, `timePointYears`)]
IndigenTable2[ , Value:= ifelse(measuredElement=="53210" & Diff2== TRUE, Value[measuredElement=="5316"],Value),
by = list(`geographicAreaM49`, `measuredItemCPC`, `timePointYears`)]
##Remove data where Slaughtered is NA or 0
IndigenTable2[ , Delete:= ifelse(measuredItemCPC %in% c("02132",
"02112",
"02121.01",
"02111",
"02123",
"02131",
"02133",
"02121.02",
"02192",
"02122",
"02140") & is.na(Value[measuredElement=="5315"]), TRUE, FALSE),
by= list(geographicAreaM49, measuredItemCPC, timePointYears)]
IndigenTable2[ , Delete2:= ifelse(measuredItemCPC %in% c("02151", "02154", "02153", "02194", "02192.01","02191","02152") &
is.na(Value[measuredElement=="5316"]) , TRUE, FALSE),
by= list(geographicAreaM49, measuredItemCPC, timePointYears)]
###>Subset Indigenous slughtered
Animal_toupload<-IndigenTable2[measuredElement %in% c("53200", "53210"), c(1,2,3,4,9,10,11,14,15), with = FALSE]
Animal_toupload_notNA<-Animal_toupload[Delete==FALSE&Delete2==FALSE]
Animals<-Animal_toupload_notNA[ , c(1,2,3,4,5,6,7), with=FALSE]
Animals[ , flagObservationStatus:= "I"]
Animals[ , flagMethod:= "i"]
############################
#Create Indigenous MEAT Production
###create two separate tables for big and small animals
tableBIG<-Animals[measuredItemCPC %in% c("02132",
"02112",
"02121.01",
"02111",
"02123",
"02131",
"02133",
"02121.02",
"02192",
"02122",
"02140")]
tableSMALL<- Animals[measuredItemCPC %in% c("02151", "02154", "02153", "02194", "02192.01","02191","02152")]
###Calculations for BIG animals
#match code for live animals with code for meat
tableBIG[ , measuredItemCPC:= ifelse(measuredItemCPC=="02111", "21111.01", ifelse(measuredItemCPC=="02153", "21123",
ifelse(measuredItemCPC=="02112", "21112",
ifelse(measuredItemCPC=="02154", "21122",
ifelse(measuredItemCPC=="02140", "21113.01",
ifelse(measuredItemCPC=="02152", "21124",
ifelse(measuredItemCPC=="02122", "21115",
ifelse(measuredItemCPC=="02121.01", "21117.01",
ifelse(measuredItemCPC=="02123", "21116",
ifelse(measuredItemCPC=="02191", "21114",
ifelse(measuredItemCPC=="02131", "21118.01",
ifelse(measuredItemCPC=="02132", "21118.02",
ifelse(measuredItemCPC=="02194", "21170.01",
ifelse(measuredItemCPC=="02121.02", "21117.02",
ifelse(measuredItemCPC=="02151", "21121",
ifelse(measuredItemCPC=="02133", "21118.03",NA))))))))))))))))]
##Merge useful data
MeatDataBIG<-MeatData[measuredItemCPC %in% c(unique(tableBIG$measuredItemCPC))]
tableBIG<- rbind(tableBIG, MeatDataBIG)
#Clean dataset
towipe <-
tableBIG[measuredElement=="55100" | measuredElement=="54240" | measuredElement=="54170"]
towipe[, `:=`(Value = NA_real_, flagObservationStatus = NA_character_, flagMethod = NA_character_)]
tokeep <-
tableBIG[!towipe, on = c('geographicAreaM49', 'measuredElement', 'measuredItemCPC', 'timePointYears')]
if (nrow(towipe) > 0) {
tableBIG <- rbind(tokeep,towipe)
}
#create timeseries for indigenous meat for big animals
timeSeriesData<- as.data.table(expand.grid(measuredItemCPC = unique(tableBIG$measuredItemCPC),
geographicAreaM49 = unique(tableBIG$geographicAreaM49),
measuredElement = unique(tableBIG$measuredElement),
timePointYears = c(startYear:endYear)))
timeSeriesData[, names(timeSeriesData) := lapply(.SD, as.character), .SDcols = names(timeSeriesData)]
timeSeriesData<- na.omit(timeSeriesData, cols=c("geographicAreaM49", "measuredItemCPC"))
#fill timeseries with values and na
tableBIG <- merge(timeSeriesData, tableBIG, by=c("measuredElement","measuredItemCPC","timePointYears","geographicAreaM49"), all.x= TRUE)
##assign carcass weight value to indigenous carcass weight
tableBIG[ , Value:= ifelse( measuredElement=="54170" & !is.na(Value[measuredElement=="5417"]), Value[measuredElement=="5417"], Value), by= list(geographicAreaM49, measuredItemCPC, timePointYears)]
#calculate prod ind in tonnes for big animals (head)
tableBIG[ , Value:= ifelse (measuredElement=="55100",(Value[get("measuredElement") %in% c("53200")])*(Value[get("measuredElement") %in% c("54170")])/1000, Value),
by = list(geographicAreaM49, measuredItemCPC, timePointYears)]
#####Calculations for SMALL animals
#match code for live animals with code for meat
tableSMALL[ , measuredItemCPC:= ifelse(measuredItemCPC=="02111", "21111.01", ifelse(measuredItemCPC=="02153", "21123",
ifelse(measuredItemCPC=="02112", "21112",
ifelse(measuredItemCPC=="02154", "21122",
ifelse(measuredItemCPC=="02140", "21113.01",
ifelse(measuredItemCPC=="02152", "21124",
ifelse(measuredItemCPC=="02122", "21115",
ifelse(measuredItemCPC=="02121.01", "21117.01",
ifelse(measuredItemCPC=="02123", "21116",
ifelse(measuredItemCPC=="02191", "21114",
ifelse(measuredItemCPC=="02131", "21118.01",
ifelse(measuredItemCPC=="02132", "21118.02",
ifelse(measuredItemCPC=="02194", "21170.01",
ifelse(measuredItemCPC=="02121.02", "21117.02",
ifelse(measuredItemCPC=="02151", "21121",
ifelse(measuredItemCPC=="02133", "21118.03",NA))))))))))))))))]
##Merge useful data
MeatDataSMALL<-MeatData[measuredItemCPC %in% c(unique(tableSMALL$measuredItemCPC))]
tableSMALL<- rbind(tableSMALL, MeatDataSMALL)
#Clean dataset
towipe <-
tableSMALL[measuredElement=="55100" | measuredElement=="54240" | measuredElement=="54170"]
towipe[, `:=`(Value = NA_real_, flagObservationStatus = NA_character_, flagMethod = NA_character_)]
tokeep <-
tableSMALL[!towipe, on = c('geographicAreaM49', 'measuredElement', 'measuredItemCPC', 'timePointYears')]
if (nrow(towipe) > 0) {
tableSMALL <- rbind(tokeep,towipe)
}
#create timeseries for indigenous meat for small animals
timeSeriesData<- as.data.table(expand.grid(measuredItemCPC = unique(tableSMALL$measuredItemCPC),
geographicAreaM49 = unique(tableSMALL$geographicAreaM49),
measuredElement = unique(tableSMALL$measuredElement),
timePointYears = c(startYear:endYear)))
timeSeriesData[, names(timeSeriesData) := lapply(.SD, as.character), .SDcols = names(timeSeriesData)]
timeSeriesData<- na.omit(timeSeriesData, cols=c("geographicAreaM49", "measuredItemCPC"))
#fill timeseries with values and na
tableSMALL <- merge(timeSeriesData, tableSMALL, by=c("measuredElement","measuredItemCPC","timePointYears","geographicAreaM49"), all.x= TRUE)
#assign carcass weight (yield) to Indigenous carcass weight
tableSMALL[ , Value:= ifelse( measuredElement=="54240"& !is.na(Value[measuredElement=="5424"]), Value[measuredElement=="5424"], Value), by= list(geographicAreaM49, measuredItemCPC, timePointYears)]
#calculate prod ind in tonnes for small animals (1000 head)
tableSMALL[ , Value:= ifelse (measuredElement=="55100",(Value[get("measuredElement") %in% c("53210")])*(Value[get("measuredElement") %in% c("54240")])/1000, Value),
by = list(geographicAreaM49, measuredItemCPC, timePointYears)]
#####Merge data for small and big animals
meat_total<- rbind(tableBIG,tableSMALL)
meat_toupload<- meat_total
meat_toupload[ , flagObservationStatus:=ifelse(measuredElement=="55100"& !is.na(Value), "I", flagObservationStatus)]
meat_toupload[ , flagMethod:=ifelse(measuredElement=="55100" & !is.na(Value), "i", flagMethod)]
meat_toupload[ , flagObservationStatus:=ifelse(measuredElement=="54170" & !is.na(Value) , flagObservationStatus[measuredElement=="5417"], flagObservationStatus),
by = list(geographicAreaM49, measuredItemCPC, timePointYears)]
meat_toupload[ , flagMethod:=ifelse(measuredElement=="54170" & !is.na(Value), "c", flagMethod) ]
meat_toupload[ , flagObservationStatus:=ifelse(measuredElement=="54240" & !is.na(Value), flagObservationStatus[measuredElement=="5424"], flagObservationStatus),
by = list(geographicAreaM49, measuredItemCPC, timePointYears)]
meat_toupload[ , flagMethod:=ifelse(measuredElement=="54240" & !is.na(Value), "c", flagMethod) ]
##transform indigenous meat from 0 to blank in 2014-2016 (if they are base year, value cannot be 0) (NOT EXISTING CASES)
# # meat_toupload_FINAL<- meat_toupload[ , Value:= ifelse(measuredElement=="55100" & timePointYears>=2014 & timePointYears<=2016 & Value[measuredElement=="55100"]==0, NA, Value),
# by = list(geographicAreaM49, measuredItemCPC, timePointYears) ]
meat_toupload_FINAL<-meat_toupload[ measuredElement=="53200" |measuredElement=="53210" |measuredElement=="54170" |measuredElement=="54240" |measuredElement=="55100"]
###SAVE in session
##check to perform before saving. all should be character but value
setDT(meat_toupload_FINAL)[, ("timePointYears") := lapply(.SD, as.character), .SDcols = "timePointYears"]
setDT(meat_toupload_FINAL)[, ("measuredElement") := lapply(.SD, as.character), .SDcols = "measuredElement"]
setDT(meat_toupload_FINAL)[, ("measuredItemCPC") := lapply(.SD, as.character), .SDcols = "measuredItemCPC"]
setDT(meat_toupload_FINAL)[, ("geographicAreaM49") := lapply(.SD, as.character), .SDcols = "geographicAreaM49"]
setDT(meat_toupload_FINAL)[, ("Value") := lapply(.SD, as.numeric), .SDcols = "Value"]
meat_toupload_FINAL[flagObservationStatus %in% "M" & flagMethod %in% "c", flagMethod := "u"]
SaveData(domain = "agriculture", dataset = "aproduction", data= meat_toupload_FINAL)
print("Plug in completed")
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# Create Indigenous Meat Production
##'
##' **Author: Giulia Piva**
##'
##' **Description:**
##'
##' This module is designed to harvest the data from Trade and Production domain and create Indigenous Meat Production as: (SLaughtered - Imported)*carcass weight
##'
## load the library
library(faosws)
library(data.table)
library(faoswsUtil)
library(sendmailR)
library(tidyr)
library(faoswsFlag)
library(faoswsProcessing)
library(faoswsEnsure)
library(magrittr)
library(dplyr)
library(sendmailR)
## 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"]]
)
}
startYear = as.numeric(swsContext.computationParams$startYear)
endYear = as.numeric(swsContext.computationParams$endYear)
geoM49 = swsContext.computationParams$geom49
stopifnot(startYear <= endYear)
yearVals = startYear:endYear
##' Get data configuration and session
sessionKey = swsContext.datasets[[1]]
sessionCountries =
getQueryKey("geographicAreaM49", sessionKey)
geoKeys = GetCodeList(domain = "agriculture", dataset = "aproduction",
dimension = "geographicAreaM49")[type == "country", code]
##' Select the countries based on the user input parameter
selectedGEOCode =
switch(geoM49,
"session" = sessionCountries,
"all" = geoKeys)
################################################
##### Harvest from Agricultural Production #####
################################################
message("Pulling data from Agricultural Production")
geoDim = Dimension(name = "geographicAreaM49", keys = selectedGEOCode)
eleDim <- Dimension(name = "measuredElement", keys = c("5315", "5316","5417", "5424","54170","54240", "55100","5510"))
itemKeys = GetCodeList(domain = "agriculture", dataset = "aproduction", "measuredItemCPC")
itemKeys = itemKeys[, code]
itemDim <- Dimension(name = "measuredItemCPC", keys = itemKeys)
timeDim = Dimension(name = "timePointYears", keys = as.character(yearVals))
agKey = DatasetKey(domain = "agriculture", dataset = "aproduction",
dimensions = list(
geographicAreaM49 = geoDim,
measuredElement = eleDim,
measuredItemCPC = itemDim,
timePointYears = timeDim)
)
agData = GetData(agKey)
################################################
##### Harvest from Trade #####
################################################
message("Pulling data from Trade")
geoDim = Dimension(name = "geographicAreaM49", keys = selectedGEOCode)
Trade_eleDim <- Dimension(name = "measuredElementTrade", keys = c("5608", "5609", "5908", "5909"))
Tradekey = DatasetKey(domain = "trade", dataset = "total_trade_cpc_m49", dimensions = list(
geographicAreaM49 = geoDim,
measuredElementTrade = Trade_eleDim,
measuredItemCPC = itemDim,
timePointYears = timeDim
))
TradeData = GetData(Tradekey)
# Separate Meat data and Live Animals data
ProductionData<-agData[measuredElement %in% c("5315", "5316")]
MeatData <- agData[measuredElement %in% c("5417", "5424","54170","54240", "55100","5510")]
# give labels
ProductionData <-nameData("agriculture", "aproduction", ProductionData)
TradeData<- nameData("trade", "total_trade_cpc_m49", TradeData)
#Merge trade and Live animals data
#prepare trade table for merging
colnames(TradeData)<-c( "geographicAreaM49","geographicAreaM49_description", "measuredElement","measuredElement_description","measuredItemCPC",
"measuredItemCPC_description","timePointYears", "timePointYears_description","Value","flagObservationStatus","flagMethod")
#merge
IndigenTable<-rbind(ProductionData,TradeData)
#create timeseries for indigenous slaughtered animals
IndigenTable<-rbind(IndigenTable, data.table(measuredElement="53200"),fill=T)
timeSeriesData<- as.data.table(expand.grid(measuredItemCPC = unique(IndigenTable$measuredItemCPC),
geographicAreaM49 = unique(IndigenTable$geographicAreaM49),
measuredElement = unique(IndigenTable$measuredElement),
timePointYears = c(startYear:endYear)))
timeSeriesData[, names(timeSeriesData) := lapply(.SD, as.character), .SDcols = names(timeSeriesData)]
timeSeriesData<- na.omit(timeSeriesData, cols=c("geographicAreaM49", "measuredItemCPC"))
#fill timeseries with values and na
IndigenTable2 <- merge(timeSeriesData, IndigenTable, by=c("measuredElement","measuredItemCPC","timePointYears","geographicAreaM49"), all.x= TRUE)
##Attribute code for (1000 head) to small animal
IndigenTable2[ , measuredElement := ifelse(measuredItemCPC %in% c("02151", "02154", "02153", "02194", "02192.01","02191","02152")
& measuredElement=="53200", "53210", measuredElement)]
####create Indigenous animal number
IndigenTable2[ , Value := ifelse(measuredElement =="53200", sum(Value[measuredElement=="5315"], -Value[measuredElement=="5608"],Value[measuredElement=="5908"], na.rm=TRUE),
ifelse(measuredElement =="53210", sum(Value[measuredElement=="5316"], -Value[measuredElement=="5609"],Value[measuredElement=="5909"],na.rm=T),
Value)),
by = list(geographicAreaM49, measuredItemCPC, timePointYears)]
#correct too low (negative) values
IndigenTable2[ , Value := ifelse((measuredElement =="53200" |measuredElement=="53210") & Value <=0 , 1, Value)]
##correct too high values (where indigenous is higher than total slaughtered)
IndigenTable2[ , Diff:= ifelse(Value[get("measuredElement") %in% c("53200")] > Value[get("measuredElement") %in% c("5315")], TRUE,FALSE),
by = list(`geographicAreaM49`, `measuredItemCPC`, `timePointYears`)]
IndigenTable2[ , Diff2:= ifelse(Value[get("measuredElement") %in% c("53210")] > Value[get("measuredElement") %in% c("5316")], TRUE,FALSE),
by = list(`geographicAreaM49`, `measuredItemCPC`, `timePointYears`)]
IndigenTable2[ , Value:= ifelse(measuredElement=="53200" & Diff== TRUE, Value[measuredElement=="5315"],Value),
by = list(`geographicAreaM49`, `measuredItemCPC`, `timePointYears`)]
IndigenTable2[ , Value:= ifelse(measuredElement=="53210" & Diff2== TRUE, Value[measuredElement=="5316"],Value),
by = list(`geographicAreaM49`, `measuredItemCPC`, `timePointYears`)]
##Remove data where Slaughtered is NA or 0
IndigenTable2[ , Delete:= ifelse(measuredItemCPC %in% c("02132",
"02112",
"02121.01",
"02111",
"02123",
"02131",
"02133",
"02121.02",
"02192",
"02122",
"02140") & is.na(Value[measuredElement=="5315"]), TRUE, FALSE),
by= list(geographicAreaM49, measuredItemCPC, timePointYears)]
IndigenTable2[ , Delete2:= ifelse(measuredItemCPC %in% c("02151", "02154", "02153", "02194", "02192.01","02191","02152") &
is.na(Value[measuredElement=="5316"]) , TRUE, FALSE),
by= list(geographicAreaM49, measuredItemCPC, timePointYears)]
###>Subset Indigenous slughtered
Animal_toupload<-IndigenTable2[measuredElement %in% c("53200", "53210"), c(1,2,3,4,9,10,11,14,15), with = FALSE]
Animal_toupload_notNA<-Animal_toupload[Delete==FALSE&Delete2==FALSE]
Animals<-Animal_toupload_notNA[ , c(1,2,3,4,5,6,7), with=FALSE]
Animals[ , flagObservationStatus:= "I"]
Animals[ , flagMethod:= "i"]
############################
#Create Indigenous MEAT Production
###create two separate tables for big and small animals
tableBIG<-Animals[measuredItemCPC %in% c("02132",
"02112",
"02121.01",
"02111",
"02123",
"02131",
"02133",
"02121.02",
"02192",
"02122",
"02140")]
tableSMALL<- Animals[measuredItemCPC %in% c("02151", "02154", "02153", "02194", "02192.01","02191","02152")]
###Calculations for BIG animals
#match code for live animals with code for meat
tableBIG[ , measuredItemCPC:= ifelse(measuredItemCPC=="02111", "21111.01", ifelse(measuredItemCPC=="02153", "21123",
ifelse(measuredItemCPC=="02112", "21112",
ifelse(measuredItemCPC=="02154", "21122",
ifelse(measuredItemCPC=="02140", "21113.01",
ifelse(measuredItemCPC=="02152", "21124",
ifelse(measuredItemCPC=="02122", "21115",
ifelse(measuredItemCPC=="02121.01", "21117.01",
ifelse(measuredItemCPC=="02123", "21116",
ifelse(measuredItemCPC=="02191", "21114",
ifelse(measuredItemCPC=="02131", "21118.01",
ifelse(measuredItemCPC=="02132", "21118.02",
ifelse(measuredItemCPC=="02194", "21170.01",
ifelse(measuredItemCPC=="02121.02", "21117.02",
ifelse(measuredItemCPC=="02151", "21121",
ifelse(measuredItemCPC=="02133", "21118.03",NA))))))))))))))))]
##Merge useful data
MeatDataBIG<-MeatData[measuredItemCPC %in% c(unique(tableBIG$measuredItemCPC))]
tableBIG<- rbind(tableBIG, MeatDataBIG)
#Clean dataset
towipe <-
tableBIG[measuredElement=="55100" | measuredElement=="54240" | measuredElement=="54170"]
towipe[, `:=`(Value = NA_real_, flagObservationStatus = NA_character_, flagMethod = NA_character_)]
tokeep <-
tableBIG[!towipe, on = c('geographicAreaM49', 'measuredElement', 'measuredItemCPC', 'timePointYears')]
if (nrow(towipe) > 0) {
tableBIG <- rbind(tokeep,towipe)
}
#create timeseries for indigenous meat for big animals
timeSeriesData<- as.data.table(expand.grid(measuredItemCPC = unique(tableBIG$measuredItemCPC),
geographicAreaM49 = unique(tableBIG$geographicAreaM49),
measuredElement = unique(tableBIG$measuredElement),
timePointYears = c(startYear:endYear)))
timeSeriesData[, names(timeSeriesData) := lapply(.SD, as.character), .SDcols = names(timeSeriesData)]
timeSeriesData<- na.omit(timeSeriesData, cols=c("geographicAreaM49", "measuredItemCPC"))
#fill timeseries with values and na
tableBIG <- merge(timeSeriesData, tableBIG, by=c("measuredElement","measuredItemCPC","timePointYears","geographicAreaM49"), all.x= TRUE)
##assign carcass weight value to indigenous carcass weight
tableBIG[ , Value:= ifelse( measuredElement=="54170" & !is.na(Value[measuredElement=="5417"]), Value[measuredElement=="5417"], Value), by= list(geographicAreaM49, measuredItemCPC, timePointYears)]
#calculate prod ind in tonnes for big animals (head)
tableBIG[ , Value:= ifelse (measuredElement=="55100",(Value[get("measuredElement") %in% c("53200")])*(Value[get("measuredElement") %in% c("54170")])/1000, Value),
by = list(geographicAreaM49, measuredItemCPC, timePointYears)]
#####Calculations for SMALL animals
#match code for live animals with code for meat
tableSMALL[ , measuredItemCPC:= ifelse(measuredItemCPC=="02111", "21111.01", ifelse(measuredItemCPC=="02153", "21123",
ifelse(measuredItemCPC=="02112", "21112",
ifelse(measuredItemCPC=="02154", "21122",
ifelse(measuredItemCPC=="02140", "21113.01",
ifelse(measuredItemCPC=="02152", "21124",
ifelse(measuredItemCPC=="02122", "21115",
ifelse(measuredItemCPC=="02121.01", "21117.01",
ifelse(measuredItemCPC=="02123", "21116",
ifelse(measuredItemCPC=="02191", "21114",
ifelse(measuredItemCPC=="02131", "21118.01",
ifelse(measuredItemCPC=="02132", "21118.02",
ifelse(measuredItemCPC=="02194", "21170.01",
ifelse(measuredItemCPC=="02121.02", "21117.02",
ifelse(measuredItemCPC=="02151", "21121",
ifelse(measuredItemCPC=="02133", "21118.03",NA))))))))))))))))]
##Merge useful data
MeatDataSMALL<-MeatData[measuredItemCPC %in% c(unique(tableSMALL$measuredItemCPC))]
tableSMALL<- rbind(tableSMALL, MeatDataSMALL)
#Clean dataset
towipe <-
tableSMALL[measuredElement=="55100" | measuredElement=="54240" | measuredElement=="54170"]
towipe[, `:=`(Value = NA_real_, flagObservationStatus = NA_character_, flagMethod = NA_character_)]
tokeep <-
tableSMALL[!towipe, on = c('geographicAreaM49', 'measuredElement', 'measuredItemCPC', 'timePointYears')]
if (nrow(towipe) > 0) {
tableSMALL <- rbind(tokeep,towipe)
}
#create timeseries for indigenous meat for small animals
timeSeriesData<- as.data.table(expand.grid(measuredItemCPC = unique(tableSMALL$measuredItemCPC),
geographicAreaM49 = unique(tableSMALL$geographicAreaM49),
measuredElement = unique(tableSMALL$measuredElement),
timePointYears = c(startYear:endYear)))
timeSeriesData[, names(timeSeriesData) := lapply(.SD, as.character), .SDcols = names(timeSeriesData)]
timeSeriesData<- na.omit(timeSeriesData, cols=c("geographicAreaM49", "measuredItemCPC"))
#fill timeseries with values and na
tableSMALL <- merge(timeSeriesData, tableSMALL, by=c("measuredElement","measuredItemCPC","timePointYears","geographicAreaM49"), all.x= TRUE)
#assign carcass weight (yield) to Indigenous carcass weight
tableSMALL[ , Value:= ifelse( measuredElement=="54240"& !is.na(Value[measuredElement=="5424"]), Value[measuredElement=="5424"], Value), by= list(geographicAreaM49, measuredItemCPC, timePointYears)]
#calculate prod ind in tonnes for small animals (1000 head)
tableSMALL[ , Value:= ifelse (measuredElement=="55100",(Value[get("measuredElement") %in% c("53210")])*(Value[get("measuredElement") %in% c("54240")])/1000, Value),
by = list(geographicAreaM49, measuredItemCPC, timePointYears)]
#####Merge data for small and big animals
meat_total<- rbind(tableBIG,tableSMALL)
meat_toupload<- meat_total
meat_toupload[ , flagObservationStatus:=ifelse(measuredElement=="55100"& !is.na(Value), "I", flagObservationStatus)]
meat_toupload[ , flagMethod:=ifelse(measuredElement=="55100" & !is.na(Value), "i", flagMethod)]
meat_toupload[ , flagObservationStatus:=ifelse(measuredElement=="54170" & !is.na(Value) , flagObservationStatus[measuredElement=="5417"], flagObservationStatus),
by = list(geographicAreaM49, measuredItemCPC, timePointYears)]
meat_toupload[ , flagMethod:=ifelse(measuredElement=="54170" & !is.na(Value), "c", flagMethod) ]
meat_toupload[ , flagObservationStatus:=ifelse(measuredElement=="54240" & !is.na(Value), flagObservationStatus[measuredElement=="5424"], flagObservationStatus),
by = list(geographicAreaM49, measuredItemCPC, timePointYears)]
meat_toupload[ , flagMethod:=ifelse(measuredElement=="54240" & !is.na(Value), "c", flagMethod) ]
##transform indigenous meat from 0 to blank in 2014-2016 (if they are base year, value cannot be 0) (NOT EXISTING CASES)
# # meat_toupload_FINAL<- meat_toupload[ , Value:= ifelse(measuredElement=="55100" & timePointYears>=2014 & timePointYears<=2016 & Value[measuredElement=="55100"]==0, NA, Value),
# by = list(geographicAreaM49, measuredItemCPC, timePointYears) ]
meat_toupload_FINAL<-meat_toupload[ measuredElement=="53200" |measuredElement=="53210" |measuredElement=="54170" |measuredElement=="54240" |measuredElement=="55100"]
###SAVE in session
##check to perform before saving. all should be character but value
setDT(meat_toupload_FINAL)[, ("timePointYears") := lapply(.SD, as.character), .SDcols = "timePointYears"]
setDT(meat_toupload_FINAL)[, ("measuredElement") := lapply(.SD, as.character), .SDcols = "measuredElement"]
setDT(meat_toupload_FINAL)[, ("measuredItemCPC") := lapply(.SD, as.character), .SDcols = "measuredItemCPC"]
setDT(meat_toupload_FINAL)[, ("geographicAreaM49") := lapply(.SD, as.character), .SDcols = "geographicAreaM49"]
setDT(meat_toupload_FINAL)[, ("Value") := lapply(.SD, as.numeric), .SDcols = "Value"]
meat_toupload_FINAL[flagObservationStatus %in% "M" & flagMethod %in% "c", flagMethod := "u"]
SaveData(domain = "agriculture", dataset = "aproduction", data= meat_toupload_FINAL)
print("Plug in completed")
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