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| # Set-up | ||
| options(error = traceback) | ||
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| # Positional args | ||
| args <- commandArgs(TRUE) | ||
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| # Print args | ||
| print(args) | ||
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| # Single run args | ||
| lad <- args[1] | ||
| date <- as.integer(args[2]) | ||
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| # File paths | ||
| folderInOT <- args[3] | ||
| spenserInput <- args[4] | ||
| folderOut <- args[5] | ||
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| ## L326-L341: | ||
| ## - https://github.com/alan-turing-institute/uatk-spc/blob/31dd8b05e2a67fb73447d13581f6107d39a56820/scripts/data_prep/raw_to_prepared_Income.R#L326-L341 | ||
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| # Loop over all counties | ||
| i <- countyList[1] | ||
| temp <- addToData(i, lookUp, coefFFT, coefFPT, coefMFT, coefMPT) | ||
| write.table(temp, paste("output/tus_hse_", i, ".csv", sep = ",")) | ||
| checkRes <- data.frame( | ||
| MSOA11CD = temp$MSOA11CD, sex = temp$sex, age = temp$age, soc2010 = temp$soc2010, | ||
| pwkstat = temp$pwkstat, incomeH = temp$incomeH, incomeY = temp$incomeY | ||
| ) | ||
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| for (i in countyList[2:length(countyList)]) { | ||
| temp <- read.csv(paste("output/tus_hse_", i, ".csv", sep = ""), sep = " ") | ||
| checkRes2 <- data.frame( | ||
| MSOA11CD = temp$MSOA11CD, sex = temp$sex, age = temp$age, soc2010 = temp$soc2010, | ||
| pwkstat = temp$pwkstat, incomeH = temp$incomeH, incomeY = temp$incomeY | ||
| ) | ||
| checkRes <- rbind(checkRes, checkRes2) | ||
| } | ||
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| checkRes$pwkstat <- as.numeric(substr(checkRes$pwkstat, 1, 2)) | ||
| checkResF <- checkRes[checkRes$pwkstat == 1 | checkRes$pwkstat == 2, ] | ||
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| ## L583-L726: | ||
| ## - https://github.com/alan-turing-institute/uatk-spc/blob/31dd8b05e2a67fb73447d13581f6107d39a56820/scripts/data_prep/raw_to_prepared_Income.R#L583-L726 | ||
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| ################################### | ||
| ####### (4.) AGE RESCALING ######## | ||
| ################################### | ||
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| # /!\ /!\ /!\ /!\ /!\ /!\ /!\ /!\ /!\ /!\ /!\ /!\ /!\ /!\ /!\ /!\ /!\ /!\ /!\ /!\ /!\ /!\ /!\ /!\ /!\ /!\ /!\ /!\ /!\ /!\ /!\ /!\ | ||
| # /!\ /!\ /!\ The following is for reference only, it requires legacy data. Use content of SAVE_SPC_required_data.zip /!\ /!\ /!\ | ||
| # /!\ /!\ /!\ /!\ /!\ /!\ /!\ /!\ /!\ /!\ /!\ /!\ /!\ /!\ /!\ /!\ /!\ /!\ /!\ /!\ /!\ /!\ /!\ /!\ /!\ /!\ /!\ /!\ /!\ /!\ /!\ /!\ | ||
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| # print("Producing age rescaling coefficients") | ||
| print("Skipping age rescaling") | ||
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| # !!! ---> Ages above 67 are treated as 67 due to lack of data | ||
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| # Read raw data from ONS | ||
| download.file("https://www.ons.gov.uk/file?uri=%2femploymentandlabourmarket%2fpeopleinwork%2fearningsandworkinghours%2fdatasets%2fagegroupashetable6%2f2020revised/table62020revised.zip", | ||
| destfile = paste(folderIn, "incomeDataAge.zip", sep = "") | ||
| ) | ||
| unzip(paste(folderIn, "incomeDataAge.zip", sep = ""), exdir = paste(folderIn, "incomeDataAge", sep = "")) | ||
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| ageMFT <- read_excel(paste(folderIn, "incomeDataAge/", "Age Group Table 6.5a Hourly pay - Gross 2020.xls", sep = ""), sheet = "Male Full-Time", skip = 4) | ||
| ageMFT <- ageMFT[c(1:8), c(1, 3:17)] | ||
| ageMPT <- read_excel(paste(folderIn, "incomeDataAge/", "Age Group Table 6.5a Hourly pay - Gross 2020.xls", sep = ""), sheet = "Male Part-Time", skip = 4) | ||
| ageMPT <- ageMPT[c(1:8), c(1, 3:17)] | ||
| ageFFT <- read_excel(paste(folderIn, "incomeDataAge/", "Age Group Table 6.5a Hourly pay - Gross 2020.xls", sep = ""), sheet = "Female Full-Time", skip = 4) | ||
| ageFFT <- ageFFT[c(1:8), c(1, 3:17)] | ||
| ageFPT <- read_excel(paste(folderIn, "incomeDataAge/", "Age Group Table 6.5a Hourly pay - Gross 2020.xls", sep = ""), sheet = "Female Part-Time", skip = 4) | ||
| ageFPT <- ageFPT[c(1:8), c(1, 3:17)] | ||
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| # Prepare data to read results of the previous modelling | ||
| checkResMFT <- checkResF[checkResF$sex == 1 & checkResF$pwkstat == 1, ] | ||
| checkResMPT <- checkResF[checkResF$sex == 1 & checkResF$pwkstat == 2, ] | ||
| checkResFFT <- checkResF[checkResF$sex == 0 & checkResF$pwkstat == 1, ] | ||
| checkResFPT <- checkResF[checkResF$sex == 0 & checkResF$pwkstat == 2, ] | ||
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| checkageMFT <- checkResMFT$age | ||
| checkageMPT <- checkResMPT$age | ||
| checkageFFT <- checkResFFT$age | ||
| checkageFPT <- checkResFPT$age | ||
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| checkincomeHMFT <- checkResMFT$incomeH | ||
| checkincomeHMPT <- checkResMPT$incomeH | ||
| checkincomeHFFT <- checkResFFT$incomeH | ||
| checkincomeHFPT <- checkResFPT$incomeH | ||
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| # Ready data to be able to model ONS data for any age | ||
| fitCol <- function(col, row, M, ord = 4) { | ||
| fit <- lm(M[, col] ~ poly(row, ord, raw = TRUE)) | ||
| return(as.numeric(c(fit$coefficients[1], fit$coefficients[2], fit$coefficients[3], fit$coefficients[4], fit$coefficients[5]))) | ||
| } | ||
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| outputRefAge <- function(ageData) { | ||
| ageData <- as.matrix(ageData[2:8, c(7:11, 3, 12:16)]) | ||
| ageData <- matrix(as.numeric(as.matrix(ageData)), ncol = ncol(ageData)) | ||
| coefAgeData <- sapply(1:ncol(ageData), function(x) { | ||
| fitCol(x, ageRef, ageData, 4) | ||
| }) | ||
| return(coefAgeData) | ||
| } | ||
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| coefAgeMFT <- outputRefAge(ageMFT) | ||
| coefAgeMPT <- outputRefAge(ageMPT) | ||
| coefAgeFFT <- outputRefAge(ageFFT) | ||
| coefAgeFPT <- outputRefAge(ageFPT) | ||
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| readCoefAgeData <- function(age, coefAgeData) { | ||
| refVal <- rep(NA, ncol(coefAgeData)) | ||
| for (i in 1:ncol(coefAgeData)) { | ||
| refVal[i] <- coefAgeData[1, i] + coefAgeData[2, i] * age + coefAgeData[3, i] * age^2 + coefAgeData[4, i] * age^3 + coefAgeData[5, i] * age^4 | ||
| } | ||
| return(refVal) | ||
| } | ||
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| # Build percentile shrinking / expansion reference table depending on age | ||
| makeAgeRow <- function(age, sex, fullTime) { | ||
| xAx <- c(10, 20, 25, 30, 40, 50, 60, 70, 75, 80, 90) | ||
| # fetch correct global distribution, distribution for specific age and previoulsy modelled distribution | ||
| if (sex == 1 & fullTime == T) { | ||
| trueGlob <- as.numeric(ageMFT[1, c(7:11, 3, 12:16)]) | ||
| if (age > 66) { | ||
| temp <- checkincomeHMFT[checkageMFT > 66] | ||
| true <- readCoefAgeData(67, coefAgeMFT) | ||
| } else { | ||
| temp <- checkincomeHMFT[checkageMFT == age] | ||
| true <- readCoefAgeData(age, coefAgeMFT) | ||
| } | ||
| mod <- quantile(temp, c(.10, .20, .25, .30, .40, .50, .60, .70, .75, .80, .90), na.rm = T) | ||
| } else if (sex == 1 & fullTime == F) { | ||
| trueGlob <- as.numeric(ageMPT[1, c(7:11, 3, 12:16)]) | ||
| if (age > 66) { | ||
| temp <- checkincomeHMPT[checkageMPT > 66] | ||
| true <- readCoefAgeData(67, coefAgeMPT) | ||
| } else { | ||
| temp <- checkincomeHMPT[checkageMPT == age] | ||
| true <- readCoefAgeData(age, coefAgeMPT) | ||
| } | ||
| mod <- quantile(temp, c(.10, .20, .25, .30, .40, .50, .60, .70, .75, .80, .90), na.rm = T) | ||
| } else if (sex == 0 & fullTime == T) { | ||
| trueGlob <- as.numeric(ageFFT[1, c(7:11, 3, 12:16)]) | ||
| if (age > 66) { | ||
| temp <- checkincomeHFFT[checkageFFT > 66] | ||
| true <- readCoefAgeData(67, coefAgeFFT) | ||
| } else { | ||
| temp <- checkincomeHFFT[checkageFFT == age] | ||
| true <- readCoefAgeData(age, coefAgeFFT) | ||
| } | ||
| mod <- quantile(temp, c(.10, .20, .25, .30, .40, .50, .60, .70, .75, .80, .90), na.rm = T) | ||
| } else { | ||
| trueGlob <- as.numeric(ageFPT[1, c(7:11, 3, 12:16)]) | ||
| if (age > 66) { | ||
| temp <- checkincomeHFPT[checkageFPT > 66] | ||
| true <- readCoefAgeData(67, coefAgeFPT) | ||
| } else { | ||
| temp <- checkincomeHFPT[checkageFPT == age] | ||
| true <- readCoefAgeData(age, coefAgeFPT) | ||
| } | ||
| mod <- quantile(temp, c(.10, .20, .25, .30, .40, .50, .60, .70, .75, .80, .90), na.rm = T) | ||
| } | ||
| # deduce relevant fittings | ||
| fitTrueGlob <- lm(trueGlob ~ poly(xAx, 3, raw = TRUE)) | ||
| fitTrue <- lm(true ~ poly(xAx, 3, raw = TRUE)) | ||
| fitXAxTrueGlob <- lm(xAx ~ poly(trueGlob, 3, raw = TRUE)) | ||
| fitXAxMod <- lm(xAx ~ poly(mod, 3, raw = TRUE)) | ||
| # deduce new percentile value (see methods) | ||
| a <- fitted2(fitTrueGlob, 1) | ||
| b <- fitted2(fitXAxMod, a) | ||
| c <- fitted2(fitTrue, b) | ||
| newPerc <- min(max(1, as.numeric(round(fitted2(fitXAxTrueGlob, c)))), 100) | ||
| for (i in 2:100) { | ||
| a <- fitted2(fitTrueGlob, i) | ||
| b <- fitted2(fitXAxMod, a) | ||
| c <- fitted2(fitTrue, b) | ||
| newPerc <- c(newPerc, min(max(1, as.numeric(round(fitted2(fitXAxTrueGlob, c)))), 100)) | ||
| } | ||
| return(newPerc) | ||
| } | ||
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| ageRescaleMFT <- mcmapply(function(x) { | ||
| makeAgeRow(x, 1, T) | ||
| }, 16:86, mc.cores = detectCores()) | ||
| ageRescaleMPT <- mcmapply(function(x) { | ||
| makeAgeRow(x, 1, F) | ||
| }, 16:86, mc.cores = detectCores()) | ||
| ageRescaleFFT <- mcmapply(function(x) { | ||
| makeAgeRow(x, 0, T) | ||
| }, 16:86, mc.cores = detectCores()) | ||
| ageRescaleFPT <- mcmapply(function(x) { | ||
| makeAgeRow(x, 0, F) | ||
| }, 16:86, mc.cores = detectCores()) | ||
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| print("Writing modelled coefficients") | ||
| write.table(ageRescaleMFT, paste(folderOut, "ageRescaleMFT.csv", sep = ""), row.names = F, sep = ",") | ||
| write.table(ageRescaleMPT, paste(folderOut, "ageRescaleMPT.csv", sep = ""), row.names = F, sep = ",") | ||
| write.table(ageRescaleFFT, paste(folderOut, "ageRescaleFFT.csv", sep = ""), row.names = F, sep = ",") | ||
| write.table(ageRescaleFPT, paste(folderOut, "ageRescaleFPT.csv", sep = ""), row.names = F, sep = ",") |