R/dataManagement.IMPACT.R

In GeraldCNelson/nutmod: Calculates nutrient information worldwide

#' @author Gerald C. Nelson, \email{nelson.gerald.c@@gmail.com}
#' @keywords utilities, IMPACT data, gdx
#' @title Import IMPACT data from a gdx file
#' @name dataPrep.IMPACT.R
#' @include nutrientModFunctions.R
source("R/nutrientModFunctions.R")


#Copyright (C) 2015 - 2018 Gerald C. Nelson, except where noted

#   This program is free software: you can redistribute it and/or modify it
#   under the terms of the GNU General Public License as published by the Free
#   Software Foundation, either version 3 of the License, or (at your option)
#   any later version.
#
#   This program is distributed in the hope that it will be useful, but
#   WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY
#   or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License
#   for more details at http://www.gnu.org/licenses/.

#' \description{
#' This script reads in the IMPACT rds files prepared in dataPrep.IMPACT.R and
#' creates the IMPACTfood data table for use in nutrientCalcs.R. It reads in dt.FoodAvail,
#' adds the fish and alcohol data, and pcGDPX0, PCX0 PWX0, and CSV and writes out dt.IMPACTfood.
#' The FoodAvailability variable in the data table dt.FoodAvail is in kgs/person/year.
#' }

sourceFile <- "dataManagement.IMPACT.R"
description <- "This script reads in the IMPACT rds files prepared in dataPrep.IMPACT.R and creates the IMPACTfood data table for use in nutrientCalcs.R. It reads in dt.FoodAvail, adds the fish and alcohol data, and pcGDPX0, PCX0 PWX0, and CSV and writes out dt.IMPACTfood. The FoodAvailability variable in the data table dt.FoodAvail is in kgs/person/year."
createScriptMetaData()

#' create data table with just food items, relevant years and scenarios
IMPACTfoodCommodList <- keyVariable("IMPACTfoodCommodList")
scenarioListIMPACT <- keyVariable("scenarioListIMPACT")
keepYearList  <- keyVariable("keepYearList")
dt.regions.all <- getNewestVersion("dt.regions.all", fileloc("uData"))

createFood <- function(fileShortName, fileloc) {
  dt <- getNewestVersion(fileShortName, fileloc)
  dt <- dt[IMPACT_code %in% IMPACTfoodCommodList &
             year %in% keepYearList]
  return(dt)
}

#' combine all relevant data tables for analysis
combineIMPACTData <- function() {
#  dt.FoodAvail <- createFood("dt.FoodAvailability", fileloc("iData")) # created in dataPrep.IMPACT

  dt.FoodAvail <- readRDS(paste0(fileloc("iData"), "dt.FoodAvailability"))
  dt.FoodAvail <- dt.FoodAvail[IMPACT_code %in% IMPACTfoodCommodList &
                                 year %in% keepYearList]
  
  # data.table::setkey(dt.FoodAvail, "scenario", "year", "region_code.IMPACT159", "IMPACT_code")
  
  # add fish and alcoholic beverages
  dt.fishnAlcScenarios <- createFood("dt.fishnAlcScenarios", fileloc("mData")) # added May 14, 2018 to parallel dt.FoodAvail. Moved to mData because there are gdx specific files Oct 13, 2018
  dt.fishnAlcScenarios[, scenario := gsub("-REF", "", scenario)]
  dt.fishnAlcScenarios[, scenario := gsub("-", "_", scenario)]
  
  gdxChoice <- "SSPs"
  
  #' add fish and alcohol via dt.fishnAlcScenarios to dt.FoodAvail, which has all the other commodities
  dtList <- list(dt.FoodAvail, dt.fishnAlcScenarios)
  dt.FoodAvail <- data.table::rbindlist(dtList, use.names = TRUE)
  #' now add the other variables
#  dt.PWX0.food <- createFood("dt.PWX0", fileloc("iData"))
  dt.PWX0.food <- readRDS(paste0(fileloc("iData"), "dt.PWX0"))
  dt.PWX0.food <- dt.PWX0.food[IMPACT_code %in% IMPACTfoodCommodList &
             year %in% keepYearList]
  #dt.PCX0.food <- createFood("dt.PCX0", fileloc("iData"))
  dt.PCX0.food <- readRDS(paste0(fileloc("iData"), "dt.PCX0"))
  dt.PCX0.food <- dt.PCX0.food[IMPACT_code %in% IMPACTfoodCommodList &
                                 year %in% keepYearList]
  
 # dt.pcGDPX0 <- getNewestVersion("dt.pcGDPX0", fileloc("iData"))
  dt.pcGDPX0 <- readRDS(paste0(fileloc("iData"), "dt.pcGDPX0"))
  dt.pcGDPX0 <- dt.pcGDPX0[year %in% keepYearList & scenario %in% scenarioListIMPACT,]
  
  data.table::setkeyv(dt.PCX0.food, c("scenario", "region_code.IMPACT159", "IMPACT_code", "year"))
  data.table::setkeyv(dt.FoodAvail, c("scenario", "region_code.IMPACT159", "IMPACT_code", "year"))
  data.table::setkeyv(dt.pcGDPX0,   c("scenario", "region_code.IMPACT159",                "year"))
  data.table::setkeyv(dt.PWX0.food, c("scenario",                          "IMPACT_code", "year"))
  dtlist <- list(dt.FoodAvail, dt.pcGDPX0, dt.PCX0.food, dt.PWX0.food)
  dt.IMPACTfood <- suppressMessages(plyr::join_all(dtlist))
  # an alternative to above that might not work. Might work but can't figure it out now.
  # dtlist <- c("dt.FoodAvail", "dt.pcGDPX0", "dt.PCX0.food", "dt.PWX0.food")
  # testList <- mget(dtlist)
  # mergedDT <- Reduce(function(...) merge(...), testList)
  # set CSEs, PCX, and PWX that are NA to 0
  #  data.table::set(dt.IMPACTfood, which(is.na(dt.IMPACTfood[["CSE"]])), "CSE", 0)
  # needed because fish and alcohol don't have prices
  data.table::set(dt.IMPACTfood, which(is.na(dt.IMPACTfood[["PCX0"]])), "PCX0", 0)
  data.table::set(dt.IMPACTfood, which(is.na(dt.IMPACTfood[["PWX0"]])), "PWX0", 0)
  dt.IMPACTfood <- dt.IMPACTfood[IMPACT_code %in% keyVariable("IMPACTfoodCommodList")]
  # dt.IMPACTfood <- dt.IMPACTfood[!is.na(pcGDPX0),] commented out Nov 12, 2018 because not needed
  data.table::setorderv(dt.IMPACTfood, cols = c("scenario",  "region_code.IMPACT159", "IMPACT_code", "year"))
  data.table::setkeyv(dt.IMPACTfood, c("scenario",  "region_code.IMPACT159", "IMPACT_code"))
  dt.IMPACTfood <- dt.IMPACTfood[!region_code.IMPACT159 %in% keyVariable("dropListCty")]
  dt.IMPACTfood[, foodAvailpDay := FoodAvailability / keyVariable("DinY")]
  dt.IMPACTfood <- unique(dt.IMPACTfood)
  
  # at the moment dt.IMPACTfood for the SSPs gdx has a MIROC scenario because the scenario list includes it. I'm going to get rid of it for now. May 12, 2018
  dt.IMPACTfood <- dt.IMPACTfood[!scenario %in% "SSP2-MIROC-REF"]
  dt.IMPACTfood[, scenario := gsub("-", "_", scenario)]
  dt.IMPACTfood[, scenario := gsub("_REF", "", scenario)]
  inDT <- dt.IMPACTfood
  outName <- "dt.IMPACTfood"
  desc <- "Annual (FoodAvailability, kgs per person per year) and daily availability (foodAvailpDay, kgs per person per day) of each food item, per capita GDP and domestic and world prices."
  cleanup(inDT, outName, fileloc("iData"), desc = desc)
}

#region <- keyVariable("region")

combineIMPACTData()

finalizeScriptMetadata(metadataDT, sourceFile)