R/convertGini.R

In pik-piam/moinput: Model Operations Input Data Library

#' Convert Gini
#' 
#' Converts Gini data from readGini() to ISO country level. 
#' Countries missing in the original data set will have their Gini set to zero (a very small number for numerical reasons to be precise).
#' The original data range is 2011-2100 in one-year steps, here we extend it to 2000-2150 in 5-year steps. 
#' Values before (after) the original range are held fixed at 2011 (2100) levels.
#' Gini values for the SDP scenario are taken from the SSP1 scenario
#' 
#' 
#' @param x MAgPIE object containing Gini data with World Bank codes, 2011-2100, in percent (range 0-100)
#' @return MAgPIE object of the Gini data in ISO countries, range 0-1
#' @author Bjoern Soergel
#' @seealso \code{\link{readSource}} \code{\link{readGini}}
#' @examples
#' 
#' \dontrun{ a <- convertGini(x)
#' }
#' @importFrom countrycode countrycode
 

convertGini <- function(x){
  
  # add SDP scenario, with same data as SSP1
  xSDP <- x[,,"SSP1"]
  getNames(xSDP) <- "SDP"
  x <- mbind(x,xSDP)
  
  # conversion to iso3c codes, match Somalia by hand because countrycode fails
  getRegions(x) <- countrycode(getRegions(x), origin = 'wb', destination = 'iso3c' , custom_match = c('SOM' = 'SOM'))
  getSets(x)[1] <- 'iso3c'
  
  # fill missing countries with a small number (not zero because this might cause numerical problems in Gini aggregation later)
  x <- toolCountryFill(x, fill = 1e-8)
  
  # fill years outside original range
  # NB: time_interpolate breaks set names, so restore them explicitly
  xsets <- getSets(x)
  x <- time_interpolate(x,interpolated_year = c(seq(2000,2010,by=5),seq(2105,2150,by=5)), integrate_interpolated_years = TRUE, extrapolation_type = 'constant')
  x <- x[,seq(2000,2150,by=5),]
  getSets(x) <- xsets
  
  #convert to 0..1 range
  x <- x/100

  return(x)
}