R/readNCDrisc.R

In pik-piam/mrcommons: MadRat commons Input Data Library

#' @title readNCDrisc
#' @description Reads in data from the NCD risc consortium
#' body height:
#' Collaboration (NCD-RisC), NCD Risk Factor. 2016. "A Century of Trends in Adult Human Height." ELife 5 (July):e13410.
#' https://doi.org/10.7554/eLife.13410.
#' @param subtype "height" for body height data
#' @author Benjamin Leon Bodirsky
#'
#' @return magpie object with the dataset downloaded. It contains missing values
#' and it is possible to replace them with the function convertNCDrisc
#'
#' @seealso
#' [convertNCDrisc()]


readNCDrisc <- function(subtype) {

  countryMapping <- c("China (Hong Kong SAR)" = "HKG", "dr congo" = "COD", "macedonia (tfyr)" = "MKD",
                      "micronesia (federated states of)" = "FSM")

  if (subtype == "height") {
    a <- read.csv("NCD_RisC_eLife_2016_height_age18_countries.csv")
    a <- a[, which(dimnames(a)[[2]] %in% c("ISO", "Sex", "Year.of.birth", "Mean.height..cm."))]
    dimnames(a)[[2]] <- c("region", "sex", "year", "height")
    a$sex <- gsub(pattern = "Men", replacement = "M", x = a$sex)
    a$sex <- gsub(pattern = "Women", replacement = "F", x = a$sex)
    out <- as.magpie(a)
  } else if (subtype == "BMI") {

    a <- read.csv("NCD_RisC_Lancet_2017_mean_BMI_female_age_specific_country_estimates.csv")
    a$Country <- toolCountry2isocode(a$Country, mapping = countryMapping)
    a <- a[, c("Country", "Year", "Age.group", "Mean.body.mass.index")]
    a$Year <- paste0("y", a$Year)
    dimnames(a)[[2]] <- c("iso", "year", "age", "BMI")
    bmiFemale <- add_dimension(collapseNames(as.magpie(a, spatial = 1, temporal = 2)),
                               dim = 3.2, add = "sex", nm = "F")

    a <- read.csv("NCD_RisC_Lancet_2017_mean_BMI_male_age_specific_country_estimates.csv")
    a$Country <- toolCountry2isocode(a$Country, mapping = countryMapping)
    a <- a[, c("Country", "Year", "Age.group", "Mean.body.mass.index")]
    a$Year <- paste0("y", a$Year)
    dimnames(a)[[2]] <- c("iso", "year", "age", "BMI")
    bmiMale <- add_dimension(collapseNames(as.magpie(a, spatial = 1, temporal = 2)),
                             dim = 3.2, add = "sex", nm = "M")

    a <- read.csv("underaged_NCD_RisC_Lancet_2017_mean_BMI_female_age_specific_country_estimates.csv")
    a$Country <- toolCountry2isocode(a$Country, mapping = countryMapping)
    a <- a[, c("Country", "Year", "Age.group", "Mean.body.mass.index")]
    a$Year <- paste0("y", a$Year)
    a$Age.group <- paste0("age", a$Age.group)
    dimnames(a)[[2]] <- c("iso", "year", "age", "BMI")
    bmiUnderagedFemale <- add_dimension(collapseNames(as.magpie(a, spatial = 1, temporal = 2)),
                                        dim = 3.2, add = "sex", nm = "F")

    a <- read.csv("underaged_NCD_RisC_Lancet_2017_mean_BMI_male_age_specific_country_estimates.csv")
    a$Country <- toolCountry2isocode(a$Country, mapping = countryMapping)
    a <- a[, c("Country", "Year", "Age.group", "Mean.body.mass.index")]
    a$Year <- paste0("y", a$Year)
    a$Age.group <- paste0("age", a$Age.group)
    dimnames(a)[[2]] <- c("iso", "year", "age", "BMI")
    bmiUnderagedMale <- add_dimension(collapseNames(as.magpie(a, spatial = 1, temporal = 2)),
                                      dim = 3.2, add = "sex", nm = "M")

    eins <- mbind(bmiFemale, bmiMale)
    zwei <- mbind(bmiUnderagedMale, bmiUnderagedFemale)
    out <- mbind(zwei, eins)

  } else if (subtype == "BMI_shr") {

    out <- NULL

    for (sex in c("M", "F")) {
      for (BMI in c("BMI_18-5", "BMI_18-5_20", "BMI_20_25", "BMI_25_30", "BMI_30_35", "BMI_35_40", "BMI_40")) {
        if (sex == "M") {
          long <- "male"
        } else {
          long <- "female"
        }
        a <- read.csv(paste0("NCD_RisC_Lancet_2017_prevalence_", BMI, "kgm2_", long,
                             "_age_specific_country_estimates.csv"))
        a$Country <- toolCountry2isocode(a$Country, mapping = countryMapping)
        a <- a[, c("Country", "Year", "Age.group", names(a)[3])]
        a$Year <- paste0("y", a$Year)
        dimnames(a)[[2]] <- c("iso", "year", "age", BMI)
        new <- add_dimension(add_dimension(collapseNames(as.magpie(a, spatial = 1, temporal = 2)),
                                           dim = 3.2, add = "sex", nm = sex), dim = 3.3, add = "BMI_shr",  nm = BMI)
        out <- mbind(out, new)
      }
    }
  } else if (subtype == "BMI_shr_underaged") {

    out <- NULL

    for (sex in c("M", "F")) {
      for (BMI in c("BMI_2sd", "BMI_1sd_2sd", "BMI_minus1sd_1sd", "BMI_minus1sd_minus2sd", "BMI_minus2sd")) {
        if (sex == "M") {
          long <- "male"
        } else {
          long <- "female"
        }
        a <- read.csv(paste0("underaged_NCD_RisC_Lancet_2017_prevalence_", BMI, "_", long,
                             "_age_specific_country_estimates.csv"))
        a$Country <- toolCountry2isocode(a$Country, mapping = countryMapping)
        a <- a[, c("Country", "Year", "Age.group", names(a)[3])]
        a$Year <- paste0("y", a$Year)
        a$Age.group <- paste0("age", a$Age.group)
        dimnames(a)[[2]] <- c("iso", "year", "age", BMI)
        new <- add_dimension(add_dimension(collapseNames(as.magpie(a, spatial = 1, temporal = 2)),
                                           dim = 3.2, add = "sex", nm = sex), dim = 3.3, add = "BMI_shr",  nm = BMI)
        out <- mbind(out, new)
      }
    }

  }
  return(out)
}