R/zchunk_L100.GDP_hist.R

In bpbond/gcamdata: Build the GCAM Input Datasets

# Copyright 2019 Battelle Memorial Institute; see the LICENSE file.

#' module_socioeconomics_L100.GDP_hist
#'
#' Prepare GDP database for later use: filter missing values and convert units to 1990 USD.
#'
#' @param command API command to execute
#' @param ... other optional parameters, depending on command
#' @return Depends on \code{command}: either a vector of required inputs,
#' a vector of output names, or (if \code{command} is "MAKE") all
#' the generated outputs: \code{L100.gdp_mil90usd_ctry_Yh}. The corresponding file in the
#' original data system was \code{L100.GDP_hist.R} (socioeconomics level1).
#' @details At present the GDP database used requires no downscaling and all
#' major countries are included, so really no processing steps are needed.
#' All that happens in this file right now is filtering out \code{NA} values
#' and converting the units to GCAM's GDP unit (million 1990 USD).
#' @importFrom assertthat assert_that
#' @importFrom tibble tibble
#' @importFrom dplyr filter mutate select
#' @author BBL February 2017
module_socioeconomics_L100.GDP_hist <- function(command, ...) {
  if(command == driver.DECLARE_INPUTS) {
    return(c(FILE = "socioeconomics/USDA_GDP_MER",
             FILE = "socioeconomics/WB_ExtraCountries_GDP_MER"))
  } else if(command == driver.DECLARE_OUTPUTS) {
    return(c("L100.gdp_mil90usd_ctry_Yh"))
  } else if(command == driver.MAKE) {

    Country <- year <- value <- iso <- NULL # silence package checks.

    all_data <- list(...)[[1]]

    # Load required inputs
    usda_gdp_mer <- get_data(all_data, "socioeconomics/USDA_GDP_MER")
    WB_ExtraCountries_GDP_MER <- get_data(all_data, "socioeconomics/WB_ExtraCountries_GDP_MER")
    assert_that(tibble::is_tibble(usda_gdp_mer))
    assert_that(tibble::is_tibble(WB_ExtraCountries_GDP_MER))

    # bind qatar's GDP data to the USDA's nation-level data
    usda_gdp_mer <- bind_rows(usda_gdp_mer, WB_ExtraCountries_GDP_MER) %>%
      arrange(Country)

    # Convert to long form, filter to historical years, convert units
    usda_gdp_mer %>%
      select(-Country) %>%
      gather_years %>%
      filter(!is.na(value), !is.na(iso)) %>%
      mutate(value = value * CONV_BIL_MIL * gdp_deflator(1990, base_year = 2010),
             year = as.integer(year)) %>%
      add_title("Historical GDP downscaled to country (iso)") %>%
      add_comments("Units converted to constant 1990 USD") %>%
      add_precursors("socioeconomics/USDA_GDP_MER",
                     "socioeconomics/WB_ExtraCountries_GDP_MER") %>%
      add_units("Million 1990 USD") %>%
      add_legacy_name("L100.gdp_mil90usd_ctry_Yh") ->
      L100.gdp_mil90usd_ctry_Yh

    return_data(L100.gdp_mil90usd_ctry_Yh)
  } else {
    stop("Unknown command")
  }
}