R/readIMPACTIrrigInvCosts.R

In pik-piam/mrvalidation: madrat data preparation for validation purposes

#' @title       readIMPACTIrrigInvCosts
#' @description Average annual baseline water-related investment cost data (2016-2030)
#'              read from Rosegrant et al. (2017) "Quantitative Foresight Modeling to
#'              Inform the CGIAR Research Portfolio"
#'
#' @return magpie object containing average annual baseline water-related
#'         investment cost on IMPACT-region level
#'         (in billion 2000 USD per year)
#'
#' @author Felicitas Beier
#'
#' @seealso \code{\link{readSource}}
#' @examples
#' \dontrun{
#' a <- readSource("IMPACTIrrigInvCosts", convert = TRUE)
#' }
#'
#' @importFrom madrat toolCountry2isocode
#' @importFrom magclass new.magpie mbind getCells getNames getRegions

readIMPACTIrrigInvCosts <- function() {

  # time frame
  years <- seq(2016, 2030)

  # read in data
  a           <- read.csv("Rosegrant2017_TableK2.csv", header = FALSE, stringsAsFactors = FALSE)
  names(a)    <- paste(a[1, ], a[2, ], sep = ".")
  names(a)[1] <- "Region"
  a           <- a[-c(1, 2), ]

  # select relevant scenarios and transform to magpie object
  IPSL <- data.frame(Region = a$Region, BAU_IPSL = as.numeric(a$IPSL.Baseline_Expansion),
                     stringsAsFactors = FALSE)
  IPSL <- as.magpie(IPSL, spatial = 1, tidy = T)

  HGEM <- data.frame(Region = a$Region, BAU_HGEM = as.numeric(a$HGEM.Baseline_Expansion),
                     stringsAsFactors = FALSE)
  HGEM <- as.magpie(HGEM, spatial = 1, tidy = T)

  NoCC <- data.frame(Region = a$Region, BAU_NoCC = as.numeric(a$NoCC.Baseline_Expansion),
                     stringsAsFactors = FALSE)
  NoCC <- as.magpie(NoCC, spatial = 1, tidy = T)

  tmp  <- mbind(IPSL, HGEM, NoCC)
  x    <- new.magpie(cells_and_regions = getRegions(tmp),
                     years = years,
                     names = getNames(tmp))
  x[, years, ] <- tmp

  names(dimnames(x))[3] <- "scenario"

  return(x)
}