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Refactor and change default scenarios #89

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Refactor and change default scenarios #89

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1 change: 0 additions & 1 deletion DESCRIPTION
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Expand Up @@ -21,7 +21,6 @@ Depends:
Imports:
countrycode,
dplyr,
lifecycle,
GDPuc (>= 1.0.0),
glue,
magrittr,
Expand Down
1 change: 0 additions & 1 deletion NAMESPACE
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Expand Up @@ -6,6 +6,5 @@ exportPattern("^((calc(GDP|GDPpc|Population|Labour|Urban)(Past|Future|$))|read|d
import(madrat)
import(magclass)
importFrom(glue,glue)
importFrom(lifecycle,deprecate_soft)
importFrom(magrittr,"%>%")
importFrom(rlang,.data)
188 changes: 81 additions & 107 deletions R/calcDriver.R
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Expand Up @@ -39,7 +39,7 @@
#' @param naming A string giving the naming scheme of the data dimension. Can be either:
#' \itemize{
#' \item "indicator_scenario" (default): Returns names of the type "gdp_SSP2", or "pop_SSP2".
#' \item "indicator.scenario": Returns names of the type "gdp.SSP2", or "pop.SSP2".
#' \item "indicator.scenario": (deprecated) Returns names of the type "gdp.SSP2", or "pop.SSP2".
#' \item "scenario": Returns names of the type "SSP2".
#' }
#' Set naming to "scenario" when you want to operate on SSP2 gdp and population data for instance, and not have to
Expand All @@ -60,12 +60,6 @@ calcDriver <- function(driver,
# Load ... arguments into function environment
list2env(list(...), environment())

# Temporary warning left as information. Remove in next release.
if ("FiveYearSteps" %in% ls()) {
warning("FiveYearSteps is deprecated and will throw an error in the next release.")
rm("FiveYearSteps")
}

# If the pastData, futureData and harmonization arguments are not in ..., then query them using "scenario" and
# load them into the function environment.
if (!any(c("pastData", "futureData", "harmonization") %in% ls())) {
Expand All @@ -83,16 +77,17 @@ calcDriver <- function(driver,
toolReduce()
}

#' ScenarioConstructor
#' Scenario construction
#'
#' @details # Combining data sources with "-"
#' Data sources can be combined with "-" and passed to both the pastData and futureData arguments, i.e. "WDI-MI". This
#' signifies that WDI data will be taken first, but missing data will be then be filled in with data from MI.
#'
#' @param harmonization A string designating the harmonization function.
#' @param pastData A string passed to the calc'Driver'Past function, e.g. [calcGDPPast()] or [calcPopulationPast()].
#' @param futureData A string passed to the calc'Driver'Future function, e.g. [calcGDPFuture()] or
#' [calcPopulationFuture()].
#' @param pastData A string designating the source for the past data, passed to the calc'Driver'Past function, e.g.
#' [calcGDPPast()] or [calcPopulationPast()].
#' @param futureData A string designating the source for the future data, passed to the calc'Driver'Future function,
#' e.g. [calcGDPFuture()] or [calcPopulationFuture()].
#' @param ... Arguments passed on to the 'driver'Past, 'driver'Future and driver'Harmonization' functions.
#' @inheritParams calcDriver
#' @inherit calcHarmonizedData seealso
Expand All @@ -118,10 +113,31 @@ calcScenarioConstructor <- function(driver,
supplementary = TRUE,
...)

harmonizedData$x <- toolFinishingTouches(x = harmonizedData$x, extension2150 = extension2150, naming = naming)
harmonizedData$x <- toolInterpolateAndExtrapolate(harmonizedData$x)
if (extension2150 != "none") harmonizedData <- toolExtend2150(harmonizedData, extension2150)

weight <- NULL
description <- harmonizedData$description
# If required, add indicators (drivers) to names, or as additional dimension
if (naming != "scenario") {
indicator <- switch(
driver,
"GDP" = "gdp",
"GDPpc" = "gdppc",
"Population" = "pop",
"Urban" = "urb",
# Label labour scenarios with "pop". Currently required for REMIND to work.
"Labour" = "pop"
)
if (naming == "indicator_scenario") {
getNames(harmonizedData$x) <- paste0(indicator, "_", getNames(harmonizedData$x))
}
if (naming == "indicator.scenario") {
warning("The naming option = 'indicator.scenario' is deprecated and will be removed in the next release.")
getNames(harmonizedData$x) <- paste0(indicator, ".", getNames(harmonizedData$x))
getSets(harmonizedData$x) <- c(getSets(harmonizedData$x)[1:2], "indicator", "scenario")
}
}

# If required, get population as weight
if (popAsWeight) {
weight <- calcOutput("Population",
scenario = scenario,
Expand All @@ -139,24 +155,19 @@ calcScenarioConstructor <- function(driver,
weight$x <- weight$x[, sort(getYears(weight$x)), ]
weight$x <- toolInterpolateAndExtrapolate(weight$x, extrapolate = FALSE)

description <- glue("{description} Associated {weight$description}")
}

if (extension2150 == "bezier") {
description <- glue("{description} Extended from 2100 to 2150 using bezier curves, resulting in a smooth \\
flattening of the scenario (the slope in 2150 is equal to half of that in 2100).")
} else if (extension2150 == "constant") {
description <- glue("{description} Extended from 2100 to 2150 using the constant 2100 value.")
harmonizedData$description <- glue("{harmonizedData$description} Associated {weight$description}")
} else {
weight <- NULL
}

list(x = harmonizedData$x,
weight = weight$x,
unit = harmonizedData$unit,
description = glue("{driver} {scenario} scenarios: {description}"))
description = glue("{driver} {scenario} scenarios: {harmonizedData$description}"))
}


#' Get Harmonized Data
#' Get harmonized data
#'
#' @param ... Arguments passed on to harmonization functions
#' @inheritParams calcScenarioConstructor
Expand All @@ -174,51 +185,48 @@ calcHarmonizedData <- function(driver, scenario, pastData, futureData, harmoniza
# population scenarios.
past <- if (pastData != "-") {
calcOutput("PastData", driver = driver, pastData = pastData, aggregate = FALSE, supplementary = TRUE, ...)
} else NULL
} else {
NULL
}
future <- if (futureData != "-") {
calcOutput("FutureData", driver = driver, futureData = futureData, aggregate = FALSE, supplementary = TRUE, ...)
} else NULL
} else {
NULL
}

switch(
# Load ... arguments into function environment
list2env(list(...), environment())

# Combine "past" and "future" time series.
harmonizedData <- switch(
harmonization,
"pastAndLevel" = toolHarmonizePast(past, future, method = "level"),
"pastAndGrowth" = toolHarmonizePast(past, future, method = "growth"),
"pastAndTransition" = toolHarmonizePast(past, future, method = "transition", yEnd = 2100),
"PopSSPs" = toolHarmonizeWithPEAPandFuture(past, future),
"PopISIMIP" = toolHarmonizePast(past, future, method = "transition", yEnd = 2030),
"GDPpcSSPs" = toolHarmonizeGDPpcSSPs(past, future, unit, yEnd = 2100),
"GDPpcSDPs" = toolBuildGDPpcSDPs(unit),
"GDPpcADBs" = toolHarmonizeGDPpcADBs(past, future),
"GDPoverPop" = toolDivideGDPbyPop(scenario, unit),
"GDPpcWithPop" = toolMultiplyGDPpcWithPop(scenario, unit),
"LabourADBs" = toolHarmonizeLabourADBs(),
stop(glue("Bad input for calcHarmonizedData Argument harmonization = '{harmonization}' is invalid."))
)

unit <- switch(
driver,
"Population" = calcOutput("PopulationHarmonized",
harmonization = harmonization,
past = past,
future = future,
aggregate = FALSE,
supplementary = TRUE,
...),
"GDP" = calcOutput("GDPHarmonized",
harmonization = harmonization,
past = past,
future = future,
scenario = scenario,
aggregate = FALSE,
supplementary = TRUE,
...),
"GDPpc" = calcOutput("GDPpcHarmonized",harmonization = harmonization,
past = past,
future = future,
scenario = scenario,
aggregate = FALSE,
supplementary = TRUE,
...),
"Labour" = calcOutput("LabourHarmonized",harmonization = harmonization,
past = past,
future = future,
aggregate = FALSE,
supplementary = TRUE,
...),
"Urban" = calcOutput("UrbanHarmonized",harmonization = harmonization,
past = past,
future = future,
aggregate = FALSE,
supplementary = TRUE,
...)
"GDP" = glue("mil. {unit}"),
"GDPpc" = unit,
"Population" = "million",
"Urban" = "share of population",
"Labour" = "million"
)

list(x = harmonizedData$x, weight = NULL, unit = unit, description = harmonizedData$description)
}

#' Get Past Data Building Block
#' Get past data
#'
#' @inheritParams calcScenarioConstructor
#' @inherit madrat::calcOutput return
Expand All @@ -227,32 +235,15 @@ calcHarmonizedData <- function(driver, scenario, pastData, futureData, harmoniza
calcPastData <- function(driver, pastData, unit) {
switch(
driver,
"GDP" = calcOutput("GDPPast",
GDPPast = pastData,
unit = unit,
aggregate = FALSE,
supplementary = TRUE),
"GDPpc" = calcOutput("GDPpcPast",
GDPpcPast = pastData,
unit = unit,
aggregate = FALSE,
supplementary = TRUE),
"Population" = calcOutput("PopulationPast",
PopulationPast = pastData,
aggregate = FALSE,
supplementary = TRUE),
"Urban" = calcOutput("UrbanPast",
UrbanPast = pastData,
aggregate = FALSE,
supplementary = TRUE),
"Labour" = calcOutput("LabourPast",
LabourPast = pastData,
aggregate = FALSE,
supplementary = TRUE)
"GDP" = calcOutput("GDPPast", pastData = pastData, unit = unit, aggregate = FALSE, supplementary = TRUE),
"GDPpc" = calcOutput("GDPpcPast", pastData = pastData, unit = unit, aggregate = FALSE, supplementary = TRUE),
"Population" = calcOutput("PopulationPast", pastData = pastData, aggregate = FALSE, supplementary = TRUE),
"Urban" = calcOutput("UrbanPast", pastData = pastData, aggregate = FALSE, supplementary = TRUE),
"Labour" = calcOutput("LabourPast", pastData = pastData, aggregate = FALSE, supplementary = TRUE)
)
}

#' Get Future Data Building Block
#' Get future data
#'
#' @inheritParams calcScenarioConstructor
#' @inherit madrat::calcOutput return
Expand All @@ -261,27 +252,10 @@ calcPastData <- function(driver, pastData, unit) {
calcFutureData <- function(driver, futureData, unit) {
switch(
driver,
"Population" = calcOutput("PopulationFuture",
PopulationFuture = futureData,
aggregate = FALSE,
supplementary = TRUE),
"GDP" = calcOutput("GDPFuture",
GDPFuture = futureData,
unit = unit,
aggregate = FALSE,
supplementary = TRUE),
"GDPpc" = calcOutput("GDPpcFuture",
GDPpcFuture = futureData,
unit = unit,
aggregate = FALSE,
supplementary = TRUE),
"Labour" = calcOutput("LabourFuture",
LabourFuture = futureData,
aggregate = FALSE,
supplementary = TRUE),
"Urban" = calcOutput("UrbanFuture",
UrbanFuture = futureData,
aggregate = FALSE,
supplementary = TRUE)
"GDP" = calcOutput("GDPFuture", futureData = futureData, unit = unit, aggregate = FALSE, supplementary = TRUE),
"GDPpc" = calcOutput("GDPpcFuture", futureData = futureData, unit = unit, aggregate = FALSE, supplementary = TRUE),
"Population" = calcOutput("PopulationFuture", futureData = futureData, aggregate = FALSE, supplementary = TRUE),
"Labour" = calcOutput("LabourFuture", futureData = futureData, aggregate = FALSE, supplementary = TRUE),
"Urban" = calcOutput("UrbanFuture", futureData = futureData, aggregate = FALSE, supplementary = TRUE),
)
}
79 changes: 75 additions & 4 deletions R/calcGDP.R
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Expand Up @@ -9,7 +9,6 @@
#' \itemize{
#' \item the SSPs, i.e. SSP1-5
#' \item the SDPs, i.e. SDP, SDP_EI, SDP_RC, and SDP_MC
#' \item SSP2EU
#' }
#'
#' See the vignette: \code{vignette("scenarios")} for scenario options, definitions and references.
Expand Down Expand Up @@ -43,11 +42,11 @@
#' calcOutput("GDP")
#' calcOutput("GDPpc")
#'
#' # Return only the SSP2EU GDP scenario
#' calcOutput("GDP", scenario = "SSP2EU")
#' # Return only the SSP2 GDP scenario
#' calcOutput("GDP", scenario = "SSP2")
#' }
#'
calcGDP <- function(scenario = c("SSPs", "SDPs", "SSP2EU"),
calcGDP <- function(scenario = c("SSPs", "SDPs"),
unit = "constant 2017 Int$PPP",
average2020 = TRUE,
...) {
Expand Down Expand Up @@ -95,3 +94,75 @@ calcGDP <- function(scenario = c("SSPs", "SDPs", "SSP2EU"),

list(x = gdp$x, weight = gdp$weight, unit = glue("mil. {unit}"), description = gdp$description)
}


#' @rdname calcGDP
#' @examples \dontrun{
#' calcOutput("GDPpc")
#' }
#'
calcGDPpc <- function(scenario = c("SSPs", "SDPs"),
unit = "constant 2017 Int$PPP",
average2020 = TRUE,
...) {
# Check user input
toolCheckUserInput(driver = "GDPpc", args = c(list(...), as.list(environment())))

# GDPpc scenarios are constructed in 2017 Int$PPP, and converted, if necessary, at the end.
gdppc <- calcOutput("Driver",
driver = "GDPpc",
scenario = scenario,
unit = "constant 2017 Int$PPP",
popAsWeight = TRUE,
aggregate = FALSE,
supplementary = TRUE,
...)

if (average2020) {
# For REMIND, the consensus is to average the 2020 value so as to dampen the effect of the COVID shock. (The
# reasoning being that REMIND uses 5-year time steps, and that the year-in-itself should represent the 2,5 years
# before and after.)
# The dampening is supposed to take place on GDP. So for GDP per capita in 2020 to be consistent with the dampened
# GDP, it has to calculated from GDP and population. (In other words we can't just use the same formula as for GDP,
# since it would lead to inconsistency at the end.) This is a bit hacky...
gdp2020 <- calcOutput("GDP",
scenario = scenario,
unit = "constant 2017 Int$PPP",
average2020 = TRUE,
naming = "scenario",
extension2150 = "none",
aggregate = FALSE,
years = 2020)
pop2020 <- calcOutput("Population",
scenario = scenario,
naming = "scenario",
extension2150 = "none",
aggregate = FALSE,
years = 2020)
gdppc2020 <- gdp2020 / pop2020
gdppc2020[is.nan(gdppc2020)] <- 0
getNames(gdppc2020) <- getNames(gdppc$x)
gdppc$x[, 2020, ] <- gdppc2020
gdppc$description <- paste(gdppc$description, "|| 2020 value averaged over 2018-2022 time period.")
# Return only 5 year time steps, since the yearly data around 2020 is not connected to the 2020 value anymore.
years5ts <- getYears(gdppc$x, as.integer = TRUE)[getYears(gdppc$x, as.integer = TRUE) %% 5 == 0 &
getYears(gdppc$x, as.integer = TRUE) != 1960]
gdppc$x <- gdppc$x[, years5ts, ]
gdppc$weight <- gdppc$weight[, years5ts, ]
gdppc$description <- paste(gdppc$description, "5 year time steps only.")
message("The 2020 value is an an avergae over the 2018-2022 time period!!")
}

# Convert to US$MER if required
if (grepl("US\\$MER", unit)) {
# Convert by interpolating and extrapolating missing conversion factors when possible.
gdppc$x <- GDPuc::convertGDP(gdppc$x,
unit_in = "constant 2017 Int$PPP",
unit_out = "constant 2017 US$MER",
replace_NAs = c("linear", "no_conversion"))
}
# Temporary shifting to 2005 prices, using only the US deflator
if (grepl("2005", unit)) gdppc$x <- gdppc$x * 0.8121123

list(x = gdppc$x, weight = gdppc$weight, unit = unit, description = gdppc$description)
}
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