R/readImageMacc.R

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

#' Read in ImageMacc Costcurves for different subtypes
#'
#' Read in ImageMacc Costcurves for different subtypes, which are based on a Paper
#' fro Lucas et al 2007 (http://linkinghub.elsevier.com/retrieve/pii/S1462901106001316)
#' Rows are removed, the dataframe is reshaped and numbers are replaced by descriptions.
#'
#'
#' @param subtype data subtype.
#'                Options are: "CH4_Energy_Industry", "CH4_Landuse",
#'                             "N2O_Energy_Industry", "N2O_Landuse",
#'                             "HFC_tot", "SF6_tot", "PFC_tot" or "baseline_sources"
#' @return magpie object of the ImageMacc data
#' @author Nele Steinmetz
#' @seealso [readSource()]
#' @examples
#' \dontrun{
#' a <- readSource("ImageMacc", "CH4_Energy_Industry")
#' a <- readSource("ImageMacc", "CH4_Landuse")
#' a <- readSource("ImageMacc", "N2O_Energy_Industry")
#' a <- readSource("ImageMacc", "N2O_Landuse")
#' a <- readSource("ImageMacc", "HFC_tot")
#' a <- readSource("ImageMacc", "SF6_tot")
#' a <- readSource("ImageMacc", "PFC_tot")
#' a <- readSource("ImageMacc", "baseline_sources")
#' }
#' @importFrom stats reshape

readImageMacc <- function(subtype) {

  files <- c(CH4_Energy_Industry = "Costcurves_CH4_Energy_Industry.csv",
              CH4_Landuse = "Costcurves_CH4_landuse.csv",
               N2O_Energy_Industry = "Costcurves_N2O_Energy_Industry.csv",
               N2O_Landuse = "Costcurves_N2O_landuse.csv",
               HFC_tot = "Costcurves_HFCtot.csv",
               SF6_tot = "Costcurves_SF6tot.csv",
               PFC_tot = "Costcurves_PFCtot.csv",
               baseline_sources = "Costcurves_baseline_sources.csv")

  file <- toolSubtypeSelect(subtype, files)

  data <- read.csv(file, sep = ";", stringsAsFactors = FALSE)

  if (subtype == "CH4_Energy_Industry") {
    # get first rows
    head <- data[2:4, 1]
    # remove rows of dataframe
    data <- data[-c(1:6), ]
    # define first row as header
    names(data) <- data[1, ]
    data <- data[-1, ]
    # tidy dataframe
    data <- reshape(data, idvar = c("t2", "Source", "Region"),
                     timevar = "class", v.names = "value", varying = list(4:ncol(data)),
                     direction = "long")
    data$value <- as.numeric(as.character(data$value))
     # replace numbers in Source
    head <- strsplit(head, ") ")
    head <- unlist(head)
    data$Source[data$Source == 1] <- "CH4 coal losses/leakages"
    data$Source[data$Source == 2] <- "CH4 oil losses/leakages"
    data$Source[data$Source == 3] <- "CH4 natural gas losses/leakages"
    data <- data[, c(1, 3, 2, 4, 5)]
    }

  if (subtype == "CH4_Landuse") {
    head <- data[2:6, 1]
    data <- data[-c(1:8), ]
    names(data) <- data[1, ]
    data <- data[-1, ]
    data <- reshape(data, idvar = c("t2", "Source", "Region"),
                    timevar = "class", v.names = "value", varying = list(4:ncol(data)),
                    direction = "long")
    data$value <- as.numeric(as.character(data$value))
    head <- strsplit(head, ") ")
    head <- unlist(head)
    data$Source[data$Source == 1] <- "CH4 Landfills"
    data$Source[data$Source == 2] <- "CH4 Domestic Sewage"
    data$Source[data$Source == 3] <- "CH4 Wetland rice"
    data$Source[data$Source == 4] <- "CH4 Animals"
    data$Source[data$Source == 5] <- "CH4 Animal waste"
    data <- data[, c(1, 3, 2, 4, 5)]
    }

  if (subtype == "N2O_Energy_Industry") {
    head <- data[2:4, 1]
    data <- data[-c(1:6), ]
    names(data) <- data[1, ]
    data <- data[-1, ]
    data <- reshape(data, idvar = c("t2", "Source", "Region"),
                    timevar = "class", v.names = "value", varying = list(4:ncol(data)),
                    direction = "long")
    data$value <- as.numeric(as.character(data$value))
    head <- strsplit(head, ") ")
    head <- unlist(head)
    data$Source[data$Source == 1] <- "N2O Transport"
    data$Source[data$Source == 2] <- "N2O Adipic acid production"
    data$Source[data$Source == 3] <- "N2O Nitric acid production"
    data <- data[, c(1, 3, 2, 4, 5)]
    }

  if (subtype == "N2O_Landuse") {
    head <- data[2:4, 6]
    data <- data[-c(1:6), ]
    names(data) <- data[1, ]
    data <- data[-1, ]
    data <- reshape(data, idvar = c("t2", "Source", "Region"),
                    timevar = "class", v.names = "value", varying = list(4:ncol(data)),
                    direction = "long")
    data$value <- as.numeric(as.character(data$value))
    head <- strsplit(head, ") ")
    head <- unlist(head)
    data$Source[data$Source == 1] <- "N2O Fertilizer"
    data$Source[data$Source == 2] <- "N2O Animal waste"
    data$Source[data$Source == 3] <- "N2O Domestic sewage"
    data <- data[, c(1, 3, 2, 4, 5)]
    }

  if (subtype == "HFC_tot") {
    data <- data[-c(1:4), ]
    names(data) <- data[1, ]
    data <- data[-1, ]
    data <- reshape(data, idvar = c("t2", "Region"),
                    timevar = "class", v.names = "value", varying = list(3:ncol(data)),
                    direction = "long")
    data$value <- as.numeric(as.character(data$value))
    }

  if (subtype == "SF6_tot") {
    data <- data[-c(1:4), ]
    names(data) <- data[1, ]
    data <- data[-1, ]
    data <- reshape(data, idvar = c("t2", "Region"),
                    timevar = "class", v.names = "value", varying = list(3:ncol(data)),
                    direction = "long")
    data$value <- as.numeric(as.character(data$value))
    }

  if (subtype == "PFC_tot") {
    data <- data[-c(1:4), ]
    names(data) <- data[1, ]
    data <- data[-1, ]
    data <- reshape(data, idvar = c("t2", "Region"),
                    timevar = "class", v.names = "value", varying = list(3:ncol(data)),
                    direction = "long")
    data$value <- as.numeric(as.character(data$value))
   } else if (subtype == "baseline_sources") {
    names(data) <- data[1, ]
    data <- data[-1, ]
    data <- data[-c(5:11)]
    data$Emission <- as.numeric(as.character(data$Emission))
    data <- reshape(data, idvar = c("time", "SRC", "Region"),
                    timevar = "Emission", varying = list(4:ncol(data)),
                    direction = "long")
    data$SRC[data$SRC == 6] <- "CH4 coal losses/leakages"
    data$SRC[data$SRC == 7] <- "CH4 oil losses/leakages"
    data$SRC[data$SRC == 8] <- "CH4 natural gas losses/leakages"
    data$SRC[data$SRC == 9] <- "CH4 Landfills"
    data$SRC[data$SRC == 10] <- "CH4 Domestic Sewage"
    data$SRC[data$SRC == 11] <- "CH4 Wetland rice"
    data$SRC[data$SRC == 12] <- "CH4 Animals"
    data$SRC[data$SRC == 13] <- "CH4 Animal waste"
    data$SRC[data$SRC == 14] <- "N2O Transport"
    data$SRC[data$SRC == 15] <- "N2O Adipic acid production"
    data$SRC[data$SRC == 16] <- "N2O Nitric acid production"
    data$SRC[data$SRC == 17] <- "N2O Fertilizer"
    data$SRC[data$SRC == 18] <- "N2O Animal waste"
    data$SRC[data$SRC == 19] <- "N2O Domestic sewage"
    data$SRC[data$SRC == 20] <- "HFC"
    data$SRC[data$SRC == 21] <- "PFC"
    data$SRC[data$SRC == 22] <- "SF6"
    data <- data[data$SRC != "23", ]
    data <- data[, c(1, 3, 2, 4)]
  }

  x <- as.magpie(data, temporal = 1, spatial = 2, tidy = TRUE)
  regions <- c("1" = "CAN", "2" = "USA", "3" = "CAM", "4" = "SAM", "5" = "NAF",
             "6" = "WAF", "7" = "EAF", "8" = "SAF", "9" = "WEU", "10" = "CEU",
             "11" = "FSU", "12" = "MID", "13" = "SAS", "14" = "EAS", "15" = "SEA",
             "16" = "OCE", "17" = "JAP")

  row.names(x) <- regions

  return(x)
}