R/readExpertGuess.R

In pik-piam/mrremind: MadRat REMIND Input Data Package

#' Read ExpertGuess
#'
#' Read-in data that are based on expert guess
#'
#' @md
#' @param subtype Type of data that should be read.  One of
#'   - `Chinese_Steel_Production`: "Smooth" production estimates by Robert
#'     Pietzcker (2022).
#'   - `industry_max_secondary_steel_share`: Maximum share of secondary steel
#'     production in total steel production and years between which a linear
#'     convergence from historic to target shares is to be applied.
#'   - `cement_production_convergence_parameters`: convergence year and level
#'     (relative to global average) to which per-capita cement demand converges
#'   - Any of the others nobody cared to document.
#' @return magpie object of the data
#' @author Lavinia Baumstark
#' @seealso \code{\link{readSource}}
#' @examples
#'
#' \dontrun{ a <- readSource(type="ExpertGuess",subtype="ies")
#' }
#'
#' @importFrom dplyr bind_rows filter pull select
#' @importFrom magrittr %>%
#' @importFrom quitte madrat_mule
#' @importFrom readr read_csv
#' @importFrom tidyr expand_grid

readExpertGuess <- function(subtype) {
  path <- '~/PIK/swap/inputdata/sources/ExpertGuess/'
  path <- './'

  if (subtype == "ies") {
    a <- read.csv("ies.csv", sep = ";")
  } else if (subtype == "prtp") {
    a <- read.csv("prtp.csv", sep = ";")
  } else if (subtype == "CCSbounds") {
    a <- read.csv("CCSbounds.csv", sep = ";")
  } else if (subtype == "co2prices") {
    a <- read.csv("co2prices-2023-06.csv", sep = ";")
  } else if (subtype == "costsTradePeFinancial") {
    a <- read.csv("pm_costsTradePeFinancial.csv",
                  sep = ";",
                  skip = 2)
  }

  if (subtype == "ies" |
      subtype == "prtp" |
      subtype == "CCSbounds" | subtype == "co2prices") {
    a$RegionCode <- NULL
    a$Country    <- NULL
    out <- as.magpie(a)
  } else if (subtype == "costsTradePeFinancial") {
    out <- as.magpie(a,
                     spatial = 1,
                     temporal = 0,
                     datacol = 3)
    out <- collapseNames(out)
    #    getSets(out) <- c("region","year","type","pe")
  }

  if (subtype == "ies" | subtype == "prtp") {
    getYears(out) <- "2005"
  }

  if ('Chinese_Steel_Production' == subtype) {
    out <- read_csv(file = file.path(path, 'Chinese_Steel_Production.csv'),
                    comment = '#',
                    show_col_types = FALSE) %>%
      madrat_mule()
  } else if ('industry_max_secondary_steel_share' == subtype) {
    out <- read_csv(
      file = file.path(path, 'industry_max_secondary_steel_share.csv'),
      comment = '#',
      show_col_types = FALSE
    ) %>%
      madrat_mule()
  } else if ('cement_production_convergence_parameters' == subtype) {
    out <- read_csv(
      file = file.path(path, 'cement_production_convergence_parameters.csv'),
      col_types = 'cdi',
      comment = '#')

    out <- bind_rows(
      out %>%
        filter(!is.na(.data$region)),

      out %>%
        head(n = 1) %>%
        filter(is.na(.data$region)) %>%
        select(-'region') %>%
        expand_grid(region = toolGetMapping(name = 'regionmapping_21_EU11.csv',
                                            type = 'regional', where = "mappingfolder") %>%
                      pull('RegionCode') %>%
                      unique() %>%
                      sort() %>%
                      setdiff(out$region))
    ) %>%
      madrat_mule()
  }

  return(out)
}