modules/SingleSeries_corrector_outliers/Correct_single_triplet.R
In SWS-Methodology/faoswsProduction: Package to perform the imputation of area harvested, production and yield for the FAO production domain
# Correcting outliers
# Parametrize input var, output var and productivity var
#data1 <- copy(data)
correctSingleTriplet <- function(data = data,
triplet = livestock_triplet_lst_1,
partial = FALSE,
factor = 1,
last = FALSE,
type = "big"
) {
# data_triplet<-copy(data)
# tranform to na data falgged as missing
data[flagObservationStatus=="M",Value:=NA_real_]
#protect the productivity values coming from manual estimated input or output or the production in tonnes for eggs coming from 1000 value
data[flagObservationStatus %in% "T" & flagMethod %in% "i", Protected := TRUE]
data[flagObservationStatus %in% "E" & flagMethod %in% "i", Protected := TRUE]
if (imputation_selection == "LIVESTOCK") {
#filter only data with element in the current triplet
if(type == "big"){
data_triplet <- data[measuredElement %in% triplet & measuredItemCPC %in% big_list]
} else {
data_triplet <- data[measuredElement %in% triplet & measuredItemCPC %in% small_list]
}
# data_triplet<-imput_with_average(data_triplet,triplet$productivity)
#take the cpc list of items that have input
inteminput <- data_triplet[measuredElement %in% c(triplet$input, triplet$output),get("measuredItemCPC")]
data_triplet <- data_triplet[measuredItemCPC %in% unique(inteminput)]
#First condition: if meat round accept the new column Case created in the previous animal detection round
cols_initial_data_triplet <- names(data_triplet)
data_triplet <- dcast.data.table(data_triplet, geographicAreaM49 + measuredItemCPC + timePointYears ~ measuredElement, value.var = list('Value', 'Protected', 'EF'))
#take only single series
data_single <- data_triplet[is.na(get(paste0("Value_", triplet$input))) & !is.na(get(paste0("Value_", triplet$output)))
& timePointYears %in% as.character(startYear:endYear) |
!is.na(get(paste0("Value_", triplet$input))) & is.na(get(paste0("Value_", triplet$output)))
& timePointYears %in% as.character(startYear:endYear),
]
#combination_of_interest <- unique(data_single[, .(geographicAreaM49,measuredItemCPC)])
combination_of_interest <- unique(data_single[,c("geographicAreaM49","measuredItemCPC"), with = FALSE])
combination_of_interest[, Single := TRUE]
data_triplet <- merge(data_triplet, combination_of_interest, by = c("geographicAreaM49","measuredItemCPC"), all.x = TRUE)
data_triplet <- data_triplet[Single == TRUE,]
data_triplet[, Single := NULL]
Label_outlier(data_label = data_triplet, element = triplet$output, type = "output")
compute_movav(data = data_triplet, element = triplet$output, type = "output")
Label_outlier(data_label = data_triplet, element = triplet$input, type = "input")
compute_movav(data = data_triplet, element = triplet$input, type = "input")
Label_outlier(data_label = data_triplet, element = triplet$productivity, type = "productivity")
compute_movav(data = data_triplet, element = triplet$productivity, type = "productivity")
input <- paste0("Value_", triplet$input)
output <- paste0("Value_", triplet$output)
productivity <- paste0("Value_", triplet$productivity)
ef_input <- paste0("EF_", triplet$input)
ef_output <- paste0("EF_", triplet$output)
ef_productivity <- paste0("EF_", triplet$productivity)
data_triplet[get(ef_input) == TRUE, isOutlier_input:= TRUE]
data_triplet[get(ef_output) == TRUE,isOutlier_output:= TRUE]
data_triplet[get(ef_productivity) == TRUE,isOutlier_productivity:= TRUE]
}else if(imputation_selection == "NOT_LIVESTOCK"){
if(partial == FALSE){
data_triplet <- data[measuredElement %in% triplet$input & measuredItemCPC %in% c("02910","02920","02960.01","21119.90","21170.02","02941")]
} else {
data_triplet <- data[measuredElement %in% triplet$input & measuredItemCPC %in% "02196"]
}
# data_triplet<-imput_with_average(data_triplet,triplet$productivity)
#take the cpc list of items that have input
inteminput <- data_triplet[measuredElement %in% triplet$input,get("measuredItemCPC")]
data_triplet <- data_triplet[measuredItemCPC %in% unique(inteminput)]
#First condition: if meat round accept the new column Case created in the previous animal detection round
cols_initial_data_triplet <- names(data_triplet)
data_triplet <- dcast.data.table(data_triplet, geographicAreaM49 + measuredItemCPC + timePointYears ~ measuredElement, value.var = list('Value', 'Protected', 'EF'))
Label_outlier(data_label = data_triplet, element = triplet$input, type = "input")
compute_movav(data = data_triplet, element = triplet$input, type = "input")
input <- paste0("Value_", triplet$input)
ef_input <- paste0("EF_", triplet$input)
data_triplet[get(ef_input) == TRUE, isOutlier_input:= TRUE]
}
if (imputation_selection == "LIVESTOCK") {
# remove both zero cases
data_triplet[, check := FALSE]
data_triplet[get(paste0("Value_", triplet$input)) == 0 & get(paste0("Value_", triplet$output)) == 0, check := TRUE]
data_triplet <- data_triplet[check == FALSE,]
##' divide series with input from the one with output
data_triplet_input <- data_triplet[get(paste0("Value_", triplet$input)) != 0,]
data_triplet_output <- data_triplet[get(paste0("Value_", triplet$output)) != 0,]
#check all imputed saries to correct tails
if(triplet$input == "5111"){
data_triplet_input[, tailCheck := ifelse(sum(Protected_5111[timePointYears %in% as.character(startYear:endYear)]) == 0 , TRUE, FALSE),
by = c("measuredItemCPC", "geographicAreaM49")]
data_triplet_output[, tailCheck := ifelse(sum(Protected_5315[timePointYears %in% as.character(startYear:endYear)]) == 0 , TRUE, FALSE),
by = c("measuredItemCPC", "geographicAreaM49")]
}else if(triplet$input == "5112"){
data_triplet_input[, tailCheck := ifelse(sum(Protected_5112[timePointYears %in% as.character(startYear:endYear)]) == 0 , TRUE, FALSE),
by = c("measuredItemCPC", "geographicAreaM49")]
data_triplet_output[, tailCheck := ifelse(sum(Protected_5316[timePointYears %in% as.character(startYear:endYear)]) == 0 , TRUE, FALSE),
by = c("measuredItemCPC", "geographicAreaM49")]
}else if(triplet$input == "5320"){
data_triplet_input[, tailCheck := ifelse(sum(Protected_5320[timePointYears %in% as.character(startYear:endYear)]) == 0 , TRUE, FALSE),
by = c("measuredItemCPC", "geographicAreaM49")]
data_triplet_output[, tailCheck := ifelse(sum(Protected_5510[timePointYears %in% as.character(startYear:endYear)]) == 0 , TRUE, FALSE),
by = c("measuredItemCPC", "geographicAreaM49")]
}else if(triplet$input == "5321"){
data_triplet_input[, tailCheck := ifelse(sum(Protected_5321[timePointYears %in% as.character(startYear:endYear)]) == 0 , TRUE, FALSE),
by = c("measuredItemCPC", "geographicAreaM49")]
data_triplet_output[, tailCheck := ifelse(sum(Protected_5510[timePointYears %in% as.character(startYear:endYear)]) == 0 , TRUE, FALSE),
by = c("measuredItemCPC", "geographicAreaM49")]
}
data_triplet_input[tailCheck == TRUE,
tailOutlier_input := ifelse(abs((get(paste0("Value_", triplet$input)) / movav_input)-1) < 0.05,
FALSE, TRUE)]
data_triplet_input<-
data_triplet_input[
order(geographicAreaM49,measuredItemCPC, timePointYears),
tailMovag_input := roll_meanr(get(paste0("Value_", triplet$input)), 5),
by = c("geographicAreaM49","measuredItemCPC")
]
cols_triplet_input <- names(data_triplet_input)
if("tailOutlier_input" %!in% cols_triplet_input){
data_triplet_input[,tailOutlier_input := FALSE]
}
#correct input as outlier with tails
data_triplet_input[tailCheck == TRUE & tailOutlier_input == TRUE,
c(input):= tailMovag_input]
#correct input as outliers not tails cases
data_triplet_input[tailCheck == FALSE & isOutlier_input == TRUE,
c(input):= movav_input]
# doing the same for output
data_triplet_output[tailCheck == TRUE,
tailOutlier_output := ifelse(abs((get(paste0("Value_", triplet$output)) / movav_output)-1) < 0.05,
FALSE, TRUE)]
data_triplet_output <-
data_triplet_output[
order(geographicAreaM49,measuredItemCPC, timePointYears),
tailMovag_output := roll_meanr(get(paste0("Value_", triplet$output)), 5),
by = c("geographicAreaM49","measuredItemCPC")
]
cols_triplet_output <- names(data_triplet_output)
if("tailOutlier_output" %!in% cols_triplet_output){
data_triplet_output[,tailOutlier_output := FALSE]
}
#correct input as outlier with tails
data_triplet_output[tailCheck == TRUE & tailOutlier_output == TRUE,
c(output):= tailMovag_output]
#correct input as outliers not tails cases
data_triplet_output[tailCheck == FALSE & isOutlier_output == TRUE,
c(output):= movav_output]
data_triplet_input[, c("check","tailCheck","tailOutlier_input","tailMovag_input") := NULL]
data_triplet_output[, c("check","tailCheck","tailOutlier_output","tailMovag_output") := NULL]
data_triplet <- rbind(data_triplet_input, data_triplet_output)
} else if( imputation_selection == "NOT_LIVESTOCK"){
if(triplet$input == "5510"){
data_triplet[, tailCheck := ifelse(sum(Protected_5510[timePointYears %in% as.character(startYear:endYear)]) == 0 , TRUE, FALSE),
by = c("measuredItemCPC", "geographicAreaM49")]
}else if(triplet$input == "5114"){
data_triplet[, tailCheck := ifelse(sum(Protected_5114[timePointYears %in% as.character(startYear:endYear)]) == 0 , TRUE, FALSE),
by = c("measuredItemCPC", "geographicAreaM49")]
}
data_triplet[tailCheck == TRUE,
tailOutlier_input := ifelse(abs((get(paste0("Value_", triplet$input)) / movav_input)-1) < 0.05,
FALSE, TRUE)]
data_triplet<-
data_triplet[
order(geographicAreaM49,measuredItemCPC, timePointYears),
tailMovag_input := roll_meanr(get(paste0("Value_", triplet$input)), 5),
by = c("geographicAreaM49","measuredItemCPC")
]
cols_triplet <- names(data_triplet)
if("tailOutlier_input" %!in% cols_triplet){
data_triplet[,tailOutlier_input := FALSE]
}
#correct input as outlier with tails
data_triplet[tailCheck == TRUE & tailOutlier_input == TRUE,
c(input):= tailMovag_input]
#correct input as outliers not tails cases
data_triplet[tailCheck == FALSE & isOutlier_input == TRUE,
c(input):= movav_input]
data_triplet[, c("tailCheck","tailOutlier_input","tailMovag_input") := NULL]
}
# Putting the data in the initial format
#in outlier data also cases are saved in livestock correction round
cols_data_triplet <- names(data_triplet)
if(imputation_selection == "LIVESTOCK"){
outlierdata <- data_triplet[, list(geographicAreaM49, timePointYears, measuredItemCPC,isOutlier_productivity)]
}else{
outlierdata <- data_triplet[, list(geographicAreaM49, timePointYears, measuredItemCPC)]
}
id.vars = c("geographicAreaM49", "timePointYears", "measuredItemCPC")
data_triplet <- data_triplet[, c(id.vars,grep("^Value",names(data_triplet), value = TRUE)), with=FALSE]
data_triplet <- melt(data_triplet, id.vars=c("geographicAreaM49","timePointYears",
"measuredItemCPC"), grep("^Value",names(data_triplet), value = TRUE),
variable.name = "measuredElement", value.name = "value_new")
data_triplet[, measuredElement:= substr(measuredElement, start = 7, stop = 10)]
data_triplet[value_new ==Inf, value_new:= NA_real_]
data <- merge(
data,
data_triplet,
by = c("geographicAreaM49", "timePointYears", "measuredElement", "measuredItemCPC"),
all.x = TRUE
)
data <- merge(
data,
outlierdata,
by = c("geographicAreaM49", "timePointYears", "measuredItemCPC"),
all.x = TRUE
)
data[, difference:= value_new - Value]
data[flagObservationStatus %in% c("M"), Value:= 0]
# data[,check:=ifelse(Protected==FALSE & !is.na(value_new) &
# round(value_new,6)!=round(Value,6) &
# timePointYears %in% yearVals,TRUE,FALSE)]
data[is.na(Protected), Protected:= FALSE]
if(imputation_selection == "LIVESTOCK"){
data[is.na(isOutlier_productivity), isOutlier_productivity:= FALSE]
#(round(value_new,2) != round(Value,2))
data[(measuredElement %!in% triplet$productivity) & (Protected == FALSE) & (!is.na(value_new)) & round(value_new) != round(Value) &
(timePointYears %in% yearVals) ,`:=`(Value = value_new,
flagObservationStatus = "E",
flagMethod = "e")]
data[, c('value_new', 'difference', 'isOutlier_productivity'):= NULL]
}else{
#(round(value_new,2) != round(Value,2))
data[(Protected == FALSE) & (!is.na(value_new)) & round(value_new) != round(Value) &
(timePointYears %in% yearVals) ,`:=`(Value = value_new,
flagObservationStatus = "E",
flagMethod = "e")]
data[, c('value_new', 'difference'):= NULL]
}
return(data)
}
SWS-Methodology/faoswsProduction documentation built on March 21, 2023, 8:27 p.m.
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# Correcting outliers
# Parametrize input var, output var and productivity var
#data1 <- copy(data)
correctSingleTriplet <- function(data = data,
triplet = livestock_triplet_lst_1,
partial = FALSE,
factor = 1,
last = FALSE,
type = "big"
) {
# data_triplet<-copy(data)
# tranform to na data falgged as missing
data[flagObservationStatus=="M",Value:=NA_real_]
#protect the productivity values coming from manual estimated input or output or the production in tonnes for eggs coming from 1000 value
data[flagObservationStatus %in% "T" & flagMethod %in% "i", Protected := TRUE]
data[flagObservationStatus %in% "E" & flagMethod %in% "i", Protected := TRUE]
if (imputation_selection == "LIVESTOCK") {
#filter only data with element in the current triplet
if(type == "big"){
data_triplet <- data[measuredElement %in% triplet & measuredItemCPC %in% big_list]
} else {
data_triplet <- data[measuredElement %in% triplet & measuredItemCPC %in% small_list]
}
# data_triplet<-imput_with_average(data_triplet,triplet$productivity)
#take the cpc list of items that have input
inteminput <- data_triplet[measuredElement %in% c(triplet$input, triplet$output),get("measuredItemCPC")]
data_triplet <- data_triplet[measuredItemCPC %in% unique(inteminput)]
#First condition: if meat round accept the new column Case created in the previous animal detection round
cols_initial_data_triplet <- names(data_triplet)
data_triplet <- dcast.data.table(data_triplet, geographicAreaM49 + measuredItemCPC + timePointYears ~ measuredElement, value.var = list('Value', 'Protected', 'EF'))
#take only single series
data_single <- data_triplet[is.na(get(paste0("Value_", triplet$input))) & !is.na(get(paste0("Value_", triplet$output)))
& timePointYears %in% as.character(startYear:endYear) |
!is.na(get(paste0("Value_", triplet$input))) & is.na(get(paste0("Value_", triplet$output)))
& timePointYears %in% as.character(startYear:endYear),
]
#combination_of_interest <- unique(data_single[, .(geographicAreaM49,measuredItemCPC)])
combination_of_interest <- unique(data_single[,c("geographicAreaM49","measuredItemCPC"), with = FALSE])
combination_of_interest[, Single := TRUE]
data_triplet <- merge(data_triplet, combination_of_interest, by = c("geographicAreaM49","measuredItemCPC"), all.x = TRUE)
data_triplet <- data_triplet[Single == TRUE,]
data_triplet[, Single := NULL]
Label_outlier(data_label = data_triplet, element = triplet$output, type = "output")
compute_movav(data = data_triplet, element = triplet$output, type = "output")
Label_outlier(data_label = data_triplet, element = triplet$input, type = "input")
compute_movav(data = data_triplet, element = triplet$input, type = "input")
Label_outlier(data_label = data_triplet, element = triplet$productivity, type = "productivity")
compute_movav(data = data_triplet, element = triplet$productivity, type = "productivity")
input <- paste0("Value_", triplet$input)
output <- paste0("Value_", triplet$output)
productivity <- paste0("Value_", triplet$productivity)
ef_input <- paste0("EF_", triplet$input)
ef_output <- paste0("EF_", triplet$output)
ef_productivity <- paste0("EF_", triplet$productivity)
data_triplet[get(ef_input) == TRUE, isOutlier_input:= TRUE]
data_triplet[get(ef_output) == TRUE,isOutlier_output:= TRUE]
data_triplet[get(ef_productivity) == TRUE,isOutlier_productivity:= TRUE]
}else if(imputation_selection == "NOT_LIVESTOCK"){
if(partial == FALSE){
data_triplet <- data[measuredElement %in% triplet$input & measuredItemCPC %in% c("02910","02920","02960.01","21119.90","21170.02","02941")]
} else {
data_triplet <- data[measuredElement %in% triplet$input & measuredItemCPC %in% "02196"]
}
# data_triplet<-imput_with_average(data_triplet,triplet$productivity)
#take the cpc list of items that have input
inteminput <- data_triplet[measuredElement %in% triplet$input,get("measuredItemCPC")]
data_triplet <- data_triplet[measuredItemCPC %in% unique(inteminput)]
#First condition: if meat round accept the new column Case created in the previous animal detection round
cols_initial_data_triplet <- names(data_triplet)
data_triplet <- dcast.data.table(data_triplet, geographicAreaM49 + measuredItemCPC + timePointYears ~ measuredElement, value.var = list('Value', 'Protected', 'EF'))
Label_outlier(data_label = data_triplet, element = triplet$input, type = "input")
compute_movav(data = data_triplet, element = triplet$input, type = "input")
input <- paste0("Value_", triplet$input)
ef_input <- paste0("EF_", triplet$input)
data_triplet[get(ef_input) == TRUE, isOutlier_input:= TRUE]
}
if (imputation_selection == "LIVESTOCK") {
# remove both zero cases
data_triplet[, check := FALSE]
data_triplet[get(paste0("Value_", triplet$input)) == 0 & get(paste0("Value_", triplet$output)) == 0, check := TRUE]
data_triplet <- data_triplet[check == FALSE,]
##' divide series with input from the one with output
data_triplet_input <- data_triplet[get(paste0("Value_", triplet$input)) != 0,]
data_triplet_output <- data_triplet[get(paste0("Value_", triplet$output)) != 0,]
#check all imputed saries to correct tails
if(triplet$input == "5111"){
data_triplet_input[, tailCheck := ifelse(sum(Protected_5111[timePointYears %in% as.character(startYear:endYear)]) == 0 , TRUE, FALSE),
by = c("measuredItemCPC", "geographicAreaM49")]
data_triplet_output[, tailCheck := ifelse(sum(Protected_5315[timePointYears %in% as.character(startYear:endYear)]) == 0 , TRUE, FALSE),
by = c("measuredItemCPC", "geographicAreaM49")]
}else if(triplet$input == "5112"){
data_triplet_input[, tailCheck := ifelse(sum(Protected_5112[timePointYears %in% as.character(startYear:endYear)]) == 0 , TRUE, FALSE),
by = c("measuredItemCPC", "geographicAreaM49")]
data_triplet_output[, tailCheck := ifelse(sum(Protected_5316[timePointYears %in% as.character(startYear:endYear)]) == 0 , TRUE, FALSE),
by = c("measuredItemCPC", "geographicAreaM49")]
}else if(triplet$input == "5320"){
data_triplet_input[, tailCheck := ifelse(sum(Protected_5320[timePointYears %in% as.character(startYear:endYear)]) == 0 , TRUE, FALSE),
by = c("measuredItemCPC", "geographicAreaM49")]
data_triplet_output[, tailCheck := ifelse(sum(Protected_5510[timePointYears %in% as.character(startYear:endYear)]) == 0 , TRUE, FALSE),
by = c("measuredItemCPC", "geographicAreaM49")]
}else if(triplet$input == "5321"){
data_triplet_input[, tailCheck := ifelse(sum(Protected_5321[timePointYears %in% as.character(startYear:endYear)]) == 0 , TRUE, FALSE),
by = c("measuredItemCPC", "geographicAreaM49")]
data_triplet_output[, tailCheck := ifelse(sum(Protected_5510[timePointYears %in% as.character(startYear:endYear)]) == 0 , TRUE, FALSE),
by = c("measuredItemCPC", "geographicAreaM49")]
}
data_triplet_input[tailCheck == TRUE,
tailOutlier_input := ifelse(abs((get(paste0("Value_", triplet$input)) / movav_input)-1) < 0.05,
FALSE, TRUE)]
data_triplet_input<-
data_triplet_input[
order(geographicAreaM49,measuredItemCPC, timePointYears),
tailMovag_input := roll_meanr(get(paste0("Value_", triplet$input)), 5),
by = c("geographicAreaM49","measuredItemCPC")
]
cols_triplet_input <- names(data_triplet_input)
if("tailOutlier_input" %!in% cols_triplet_input){
data_triplet_input[,tailOutlier_input := FALSE]
}
#correct input as outlier with tails
data_triplet_input[tailCheck == TRUE & tailOutlier_input == TRUE,
c(input):= tailMovag_input]
#correct input as outliers not tails cases
data_triplet_input[tailCheck == FALSE & isOutlier_input == TRUE,
c(input):= movav_input]
# doing the same for output
data_triplet_output[tailCheck == TRUE,
tailOutlier_output := ifelse(abs((get(paste0("Value_", triplet$output)) / movav_output)-1) < 0.05,
FALSE, TRUE)]
data_triplet_output <-
data_triplet_output[
order(geographicAreaM49,measuredItemCPC, timePointYears),
tailMovag_output := roll_meanr(get(paste0("Value_", triplet$output)), 5),
by = c("geographicAreaM49","measuredItemCPC")
]
cols_triplet_output <- names(data_triplet_output)
if("tailOutlier_output" %!in% cols_triplet_output){
data_triplet_output[,tailOutlier_output := FALSE]
}
#correct input as outlier with tails
data_triplet_output[tailCheck == TRUE & tailOutlier_output == TRUE,
c(output):= tailMovag_output]
#correct input as outliers not tails cases
data_triplet_output[tailCheck == FALSE & isOutlier_output == TRUE,
c(output):= movav_output]
data_triplet_input[, c("check","tailCheck","tailOutlier_input","tailMovag_input") := NULL]
data_triplet_output[, c("check","tailCheck","tailOutlier_output","tailMovag_output") := NULL]
data_triplet <- rbind(data_triplet_input, data_triplet_output)
} else if( imputation_selection == "NOT_LIVESTOCK"){
if(triplet$input == "5510"){
data_triplet[, tailCheck := ifelse(sum(Protected_5510[timePointYears %in% as.character(startYear:endYear)]) == 0 , TRUE, FALSE),
by = c("measuredItemCPC", "geographicAreaM49")]
}else if(triplet$input == "5114"){
data_triplet[, tailCheck := ifelse(sum(Protected_5114[timePointYears %in% as.character(startYear:endYear)]) == 0 , TRUE, FALSE),
by = c("measuredItemCPC", "geographicAreaM49")]
}
data_triplet[tailCheck == TRUE,
tailOutlier_input := ifelse(abs((get(paste0("Value_", triplet$input)) / movav_input)-1) < 0.05,
FALSE, TRUE)]
data_triplet<-
data_triplet[
order(geographicAreaM49,measuredItemCPC, timePointYears),
tailMovag_input := roll_meanr(get(paste0("Value_", triplet$input)), 5),
by = c("geographicAreaM49","measuredItemCPC")
]
cols_triplet <- names(data_triplet)
if("tailOutlier_input" %!in% cols_triplet){
data_triplet[,tailOutlier_input := FALSE]
}
#correct input as outlier with tails
data_triplet[tailCheck == TRUE & tailOutlier_input == TRUE,
c(input):= tailMovag_input]
#correct input as outliers not tails cases
data_triplet[tailCheck == FALSE & isOutlier_input == TRUE,
c(input):= movav_input]
data_triplet[, c("tailCheck","tailOutlier_input","tailMovag_input") := NULL]
}
# Putting the data in the initial format
#in outlier data also cases are saved in livestock correction round
cols_data_triplet <- names(data_triplet)
if(imputation_selection == "LIVESTOCK"){
outlierdata <- data_triplet[, list(geographicAreaM49, timePointYears, measuredItemCPC,isOutlier_productivity)]
}else{
outlierdata <- data_triplet[, list(geographicAreaM49, timePointYears, measuredItemCPC)]
}
id.vars = c("geographicAreaM49", "timePointYears", "measuredItemCPC")
data_triplet <- data_triplet[, c(id.vars,grep("^Value",names(data_triplet), value = TRUE)), with=FALSE]
data_triplet <- melt(data_triplet, id.vars=c("geographicAreaM49","timePointYears",
"measuredItemCPC"), grep("^Value",names(data_triplet), value = TRUE),
variable.name = "measuredElement", value.name = "value_new")
data_triplet[, measuredElement:= substr(measuredElement, start = 7, stop = 10)]
data_triplet[value_new ==Inf, value_new:= NA_real_]
data <- merge(
data,
data_triplet,
by = c("geographicAreaM49", "timePointYears", "measuredElement", "measuredItemCPC"),
all.x = TRUE
)
data <- merge(
data,
outlierdata,
by = c("geographicAreaM49", "timePointYears", "measuredItemCPC"),
all.x = TRUE
)
data[, difference:= value_new - Value]
data[flagObservationStatus %in% c("M"), Value:= 0]
# data[,check:=ifelse(Protected==FALSE & !is.na(value_new) &
# round(value_new,6)!=round(Value,6) &
# timePointYears %in% yearVals,TRUE,FALSE)]
data[is.na(Protected), Protected:= FALSE]
if(imputation_selection == "LIVESTOCK"){
data[is.na(isOutlier_productivity), isOutlier_productivity:= FALSE]
#(round(value_new,2) != round(Value,2))
data[(measuredElement %!in% triplet$productivity) & (Protected == FALSE) & (!is.na(value_new)) & round(value_new) != round(Value) &
(timePointYears %in% yearVals) ,`:=`(Value = value_new,
flagObservationStatus = "E",
flagMethod = "e")]
data[, c('value_new', 'difference', 'isOutlier_productivity'):= NULL]
}else{
#(round(value_new,2) != round(Value,2))
data[(Protected == FALSE) & (!is.na(value_new)) & round(value_new) != round(Value) &
(timePointYears %in% yearVals) ,`:=`(Value = value_new,
flagObservationStatus = "E",
flagMethod = "e")]
data[, c('value_new', 'difference'):= NULL]
}
return(data)
}
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