Merge pull request #68 from johanneskoch94/main

.buildlibrary

@@ -1,4 +1,4 @@
-ValidationKey: '2158950'
+ValidationKey: '2341080'
 AutocreateReadme: yes
 AcceptedWarnings:
 - 'Warning: package ''.*'' was built under R version'

CITATION.cff

@@ -2,8 +2,8 @@ cff-version: 1.2.0
 message: If you use this software, please cite it using the metadata from this file.
 type: software
 title: 'mrdrivers: Create GDP and Population Scenarios'
-version: 1.1.1
-date-released: '2023-04-03'
+version: 1.2.0
+date-released: '2023-06-01'
 abstract: Create GDP and population scenarios This package constructs the GDP and
   population scenarios used as drivers in both the REMIND and MAgPIE models.
 authors:

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: mrdrivers
 Type: Package
 Title: Create GDP and Population Scenarios
-Version: 1.1.1
+Version: 1.2.0
 Authors@R: c(person(given = "Johannes", 
                     family = "Koch", 
                     email = "[email protected]", 
@@ -49,6 +49,6 @@ Suggests:
 Encoding: UTF-8
 Roxygen: list(markdown = TRUE)
 RoxygenNote: 7.2.3
-Date: 2023-04-03
+Date: 2023-06-01
 Config/testthat/edition: 3
 VignetteBuilder: knitr

R/calcGDP.R

@@ -19,11 +19,11 @@
 #' @param unit A string specifying the unit of GDP. Can be either:
 #' \itemize{
 #'   \item "constant 2005 Int$PPP" (default): Scenarios are constructed in constant 2005 Int$PPP.
-#'   \item "constant 2005 US$MER": Scenarios are constructed in constant 2005 Int$PPP and converted before being
-#'                                 returned with [GDPuc::convertGDP()].
+#'   \item "constant 2005 US$MER": Scenarios are constructed in constant 2005 Int$PPP and then converted with
+#'   [GDPuc::convertGDP()].
 #'   \item "constant 2017 Int$PPP": Scenarios are constructed in constant 2017 Int$PPP.
-#'   \item "constant 2017 US$MER": Scenarios are constructed in constant 2017 Int$PPP and converted before being
-#'                                 returned with [GDPuc::convertGDP()].
+#'   \item "constant 2017 US$MER": Scenarios are constructed in constant 2017 Int$PPP and then converted with
+#'   [GDPuc::convertGDP()].
 #' }
 #' In all cases, GDP is returned in millions.
 #'
@@ -42,8 +42,6 @@
 #' }
 #'
 #' @examples \dontrun{
-#' library(mrdrivers)
-#'
 #' # Return default scenarios
 #' calcOutput("GDP")
 #'
@@ -76,7 +74,7 @@ calcGDP <- function(scenario = c("SSPs", "SDPs", "SSP2EU"),
                     supplementary = TRUE,
                     ...)
 
-  if (average2020 && grepl("SSPsOld", scenario)) {
+  if (average2020 && any(grepl("SSPsOld", scenario))) {
     warning("Average 2020 is not compatible with SSPsOld. Setting to FALSE.")
     average2020 <- FALSE
   }

R/calcGDPHarmonized.R

@@ -8,7 +8,8 @@ calcGDPHarmonized <- function(args) {
     "GDPpcWithPop"    = toolMultiplyGDPpcWithPop(args$scenario, args$unit),
     "calibSSP2EU"     = toolGDPHarmonizeSSP2EU(args$past, args$future, args$unit),
     "past_transition" = toolHarmonizePastTransition(args$past$x, args$future$x, yEnd = 2050, aslist = TRUE),
-    stop(glue("Bad input for calcGDPHarmonized. Argument harmonization = '{args$harmonization}' is invalid."))
+    stop(glue("Bad input for calcGDPHarmonized. Argument harmonization = '{args$harmonization}' is invalid. \\
+              Possible values are: 'GDPpcWithPop', 'calibsSSP2EU' or 'past_transition'."))
   )
   list(x = harmonizedData$x, weight = NULL, unit = glue("mil. {args$unit}"), description = harmonizedData$description)
 }

R/calcGDPpc.R

@@ -1,6 +1,5 @@
 #' @rdname calcGDP
 #' @examples \dontrun{
-#' library(mrdrivers)
 #' calcOutput("GDPpc")
 #' }
 #'
@@ -27,7 +26,7 @@ calcGDPpc <- function(scenario = c("SSPs", "SDPs", "SSP2EU"),
                       supplementary = TRUE,
                       ...)
 
-  if (average2020 && grepl("SSPsOld", scenario)) {
+  if (average2020 && any(grepl("SSPsOld", scenario))) {
     warning("Average 2020 is not compatible with SSPsOld. Setting to FALSE.")
     average2020 <- FALSE
   }
@@ -53,6 +52,7 @@ calcGDPpc <- function(scenario = c("SSPs", "SDPs", "SSP2EU"),
                           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.")

R/calcLabour.R

@@ -1,4 +1,7 @@
 #' @rdname calcPopulation
+#' @examples \dontrun{
+#' calcOutput("Labour")
+#' }
 calcLabour <- function(scenario = c("SSPs", "SDPs", "SSP2EU"), ...) {
   toolCheckUserInput(driver = "Labour", args = c(list(...), as.list(environment())))
   calcOutput("Driver", driver = "Labour", scenario = scenario, aggregate = FALSE, supplementary = TRUE, ...)

R/calcPopulation.R

@@ -20,7 +20,6 @@
 #' @inherit calcGDP seealso
 #'
 #' @examples \dontrun{
-#' library(mrdrivers)
 #' # Return the default scenarios
 #' calcOutput("Population")
 #'

R/calcRatioPPP2MER.R

@@ -8,7 +8,6 @@
 #' @inherit madrat::calcOutput return
 #' @seealso [madrat::calcOutput()]
 #' @examples \dontrun{
-#' library(mrdrivers)
 #' calcOutput("RatioPPP2MER")
 #' }
 #'

R/calcUrban.R

@@ -2,7 +2,6 @@
 #' @param asShare If TRUE (default) urban population shares are returned. If FALSE, then urban population in millions is
 #' returned.
 #' @examples \dontrun{
-#' library(mrdrivers)
 #' calcOutput("Urban")
 #' }
 #'

R/readPopulationTWN.R

@@ -11,20 +11,20 @@
 #'
 #' @seealso [madrat::readSource()]
 #'
-#' @examples \dontrun{ 
-#' library(mrdrivers)
-#' readSource("PopulationTWN", subtype = "medium")}
+#' @examples \dontrun{
+#' readSource("PopulationTWN", subtype = "medium")
+#' }
 #' @keywords internal
 readPopulationTWN <- function(subtype) {
   files <- c(medium = "A1. Population Projections - Medium Variant.xlsx",
              high = "A2. Population Projections  - High Variant.xlsx",
              low = "A3. Population Projections  - Low Variant.xlsx")
-  
+
   file <- toolSubtypeSelect(subtype, files)
-  
+
   twn <- as.data.frame(suppressMessages(readxl::read_excel(file, sheet = "M3", skip = 1)))
   twn <- twn[!is.na(twn[[2]]),]
-    
+
   names(twn)[3]  <- paste(names(twn)[2], twn[1, 3],  sep="_")
   names(twn)[4]  <- paste(names(twn)[2], twn[1, 4],  sep="_")
   names(twn)[2]  <- paste(names(twn)[2], twn[1, 2],  sep="_")
@@ -34,14 +34,14 @@ readPopulationTWN <- function(subtype) {
   names(twn)[9]  <- paste(names(twn)[8], twn[1, 9],  sep="_")
   names(twn)[10] <- paste(names(twn)[8], twn[1, 10], sep="_")
   names(twn)[8]  <- paste(names(twn)[8], twn[1, 8],  sep="_")
-  
+
   twn <- twn[-1, ]
   twn <- tidyr::pivot_longer(twn, -"Year", names_to = "variable")
   twn$value <- as.numeric(twn$value)
   twn$variable <- gsub(" +", "_", twn$variable)
   twn$variable <- gsub("_years", " years", twn$variable, fixed = TRUE)
-  
-    
+
+
   x <- as.magpie(twn)
   x
 }

R/toolCheckUserInput.R

@@ -7,7 +7,8 @@ toolCheckUserInput <- function(driver, args) {
   overrideScen <- if (all(c("pastData", "futureData", "harmonization") %in% names(args))) TRUE else FALSE
 
   # Check population scenario availability for any GDPpc scenario
-  if (!overrideScen && driver == "GDPpc" && ! args$scenario %in% toolGetScenarioDefinition("Population")$scenario) {
+  if (!overrideScen && driver == "GDPpc" &&
+      !all(args$scenario %in% toolGetScenarioDefinition("Population")$scenario)) {
     stop("GDPpc scenarios require equivalent population scenarios to use as weight.")
   }
 
@@ -34,9 +35,10 @@ toolCheckUserInput <- function(driver, args) {
   }
 
   # Check parallel map-reduce compatibility
-  if (any(purrr::map_lgl(args, ~ !is.null(.x) &&
-                                 length(.x) != 1 &&
-                                 length(.x) != max(purrr::map_dbl(args, length))))) {
+  if (any(purrr::map_lgl(args,
+                         ~ !is.null(.x) &&
+                         length(.x) != 1 &&
+                         length(.x) != max(purrr::map_dbl(args, length))))) {
     stop(glue("Arguments to calc{driver} need to be either length 1 or equal to the length of the longest argument."))
   }
 }

R/toolGetScenarioDefinition.R

@@ -71,13 +71,17 @@ toolGetScenarioDefinition <- function(driver = NULL, scen = NULL, aslist = FALSE
   # End of scenario-design section
 
   s <- scenarios
+  if (exists("mrdrivers_scenarios")) {
+    message("Also considering user defined scenarios in mrdrivers_scenarios.")
+    s <- dplyr::bind_rows(s, get("mrdrivers_scenarios"))
+  }
 
   if (!is.null(driver)) {
     availableDrivers <- dplyr::pull(scenarios, driver) %>% unique()
     if (!all(driver %in% availableDrivers)) {
       stop(glue::glue("Unknown driver. Available drivers are: {paste(availableDrivers, collapse = ', ')}"))
     }
-    s <- dplyr::filter(scenarios, driver %in% !!driver)
+    s <- dplyr::filter(s, driver %in% !!driver)
   }
 
   if (!is.null(scen)) {

README.md

@@ -1,6 +1,6 @@
 # Create GDP and Population Scenarios
 
-R package **mrdrivers**, version **1.1.1**
+R package **mrdrivers**, version **1.2.0**
 
 [![CRAN status](https://www.r-pkg.org/badges/version/mrdrivers)](https://cran.r-project.org/package=mrdrivers)  [![R build status](https://pik-piam.github.io/mrdrivers/workflows/check/badge.svg)](https://pik-piam.github.io/mrdrivers/actions) [![codecov](https://codecov.io/gh/mrdrivers/branch/master/graph/badge.svg)](https://app.codecov.io/gh/mrdrivers) [![r-universe](https://pik-piam.r-universe.dev/badges/mrdrivers)](https://pik-piam.r-universe.dev/builds)
 
@@ -103,7 +103,7 @@ In case of questions / problems please contact Johannes Koch <jokoch@pik-potsdam
 
 To cite package **mrdrivers** in publications use:
 
-Koch J, Soergel B, Leip D, Benke F, Dietrich J (2023). _mrdrivers: Create GDP and Population Scenarios_. R package version 1.1.1, <URL: https://pik-piam.github.io/mrdrivershttps://github.com/pik-piam/mrdrivers>.
+Koch J, Soergel B, Leip D, Benke F, Dietrich J (2023). _mrdrivers: Create GDP and Population Scenarios_. R package version 1.2.0, <https://pik-piam.github.io/mrdrivershttps://github.com/pik-piam/mrdrivers>.
 
 A BibTeX entry for LaTeX users is
 
@@ -112,7 +112,7 @@ A BibTeX entry for LaTeX users is
   title = {mrdrivers: Create GDP and Population Scenarios},
   author = {Johannes Koch and Bjoern Soergel and Deborra Leip and Falk Benke and Jan Philipp Dietrich},
   year = {2023},
-  note = {R package version 1.1.1},
+  note = {R package version 1.2.0},
   url = {https://pik-piam.github.io/mrdrivers},
   url = {https://github.com/pik-piam/mrdrivers},
 }

vignettes/scenarios.Rmd

@@ -29,7 +29,21 @@ library(magrittr)
 toolGetScenarioDefinition() %>% print(n = 200)
 ```
 
-So for example, there are currently 6 GDPpc scenarios available: the SSPs, the SDPs, SSP2EU, noCovid, longCovid and shortCovid scenarios.
+So for example, there are currently 7 GDPpc scenarios available: the SSPs, the SDPs, SSP2EU, noCovid, longCovid, shortCovid and SSPsOld scenarios.
+
+# User Defined Scenarios
+
+The user can create custom scenarios by creating a tibble called "mrdivers_scenarios" in the global environment, and filling it with the desired scenario definitions. The structure of the "mrdivers_scenarios" object should be identical to that of the return object of `toolGetScenarioDefinition()`, and the scenario building blocks have to be available. For example, say the user wanted to create SSP scenarios, but without using the Missing Islands data-set. The following command executed in the global environment would make the "nomi" (no-missing islands) scenario available.
+
+```{r}
+mrdrivers_scenarios <- tibble::tribble(
+  ~driver,      ~scenario,      ~pastData,                    ~futureData,            ~harmonization,
+  "GDPpc",      "nomi",         "WDI",                        "SSPsOld",              "calibSSPs",
+  "Population", "nomi",         "WDI",                        "SSPs",                 "withPEAPandFuture",
+  "GDP",        "nomi",         "-",                          "-",                    "GDPpcWithPop"
+)
+```
+
 
 # Default Scenarios