R/readLassaletta2014.R

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

#' @title readLassaletta2014
#' @description reads nitrogen budgets for a country dataset from
#' Lassaletta, L., G. Billen, B. Grizzetti, J. Angalde, and J. Garnier. 2014.
#' 50 Year Trends in Nitrogen Use Efficiency of World Cropping Systems: The Relationship between Yield and Nitrogen
#' Input to Cropland.
#' Environmental Research Letters.
#'
#' @param subtype budget provides the nr cropland budgets, fert_to_cropland the sahre of inorganic fertilizers being
#'                applied to croplands
#' @return Magpie object with results on country level.
#' @author Benjamin Leon Bodirsky, Felicitas Beier
#' @seealso
#' [convertLassaletta2014()],
#' [readSource()]
#' @examples
#' \dontrun{
#' readSource("Lassaletta2014", subtype = "budget", convert = FALSE)
#' }
#' @importFrom readxl read_excel


readLassaletta2014 <- function(subtype = "budget") {
  if (subtype == "budget") {
    out <- NULL
    for (sheet_x in 3:8) {
      data        <- as.data.frame(read_excel("BUDGET_N_Countries_1961_2009_Paper_NUE_Lassaletta_etal_ERL_3.xlsx",
                                              sheet = sheet_x))
      names(data) <- gsub(" ", ".", names(data))
      tmp  <- strsplit(names(data), split = "\\.")
      year <- paste0("y", unlist(lapply(tmp, FUN = function(x) {
        x[length(x)]
      })))
      year <- year[2:length(year)]
      indicator <- strsplit(names(data)[2], split = "\\.")[[1]]
      indicator <- paste(indicator[1:(length(indicator) - 1)], collapse = "_")
      countries <- data[, 1]
      countries <- toolCountry2isocode(countries, mapping = c(
        "Belgium-Luxemburg" = "BEL",
        "Bolivia (Plurinational State of)" = "BOL",
        "Ethiopia PDR" = "ETH",
        "FSU" = "SUN",
        "Sudan (former)" = "SDN",
        "Venezuela (Bolivarian Republic of)" = "VEN",
        "Yugoslav SFR" = "YUG"
      ))
      rownames(data) <- countries
      data           <- data[, -1]
      colnames(data) <- year
      data           <- as.magpie(data)
      getNames(data) <- indicator
      out            <- mbind(out, data)
    }
    out <- out * setNames(out[, , "Surfaces_ha"], NULL)
    out <- setNames(out[, , c(
      "Prod_kgN/ha", "Fert_adjusted_grass_kgN/ha", "Manure_kgN/ha", "Fixing_kgN/ha", "Dep_arable_kgN"
    )], c(
      "harvest", "fertilizer", "manure_conf", "fixation_crops", "deposition"
    ))
    out <- out / 10^9
  } else if (subtype == "fert_to_cropland") {
    a <- read.csv(file = "erl502906suppdata1annex.csv", sep = ";", header = 1, row.names = 1)
    dimnames(a)[[2]] <- gsub(dimnames(a)[[2]], pattern = "X", replacement = "y")
    dimnames(a)[[1]] <- toolCountry2isocode(dimnames(a)[[1]], mapping = c(
      "Belgium-Luxemburg" = "BEL",
      "Bolivia (Plurinational State of)" = "BOL",
      "Ethiopia PDR" = "ETH",
      "FSU" = "SUN",
      "Sudan (former)" = "XFS",
      "Venezuela (Bolivarian Republic of)" = "VEN",
      "Yugoslav SFR" = "YUG",
      "dprepublic of korea" = "PRK"
    ))
    out <- as.magpie(a)
  } else {
    stop("unknown subtype")
  }
  out <- clean_magpie(out)
  return(out)
}