Bump version with lucode2 and fix linter warnings

.buildlibrary

@@ -1,4 +1,4 @@
-ValidationKey: '2742180'
+ValidationKey: '2955900'
 AutocreateReadme: yes
 AcceptedWarnings:
 - 'Warning: package ''.*'' was built under R version'

.github/workflows/check.yaml

@@ -23,6 +23,7 @@ jobs:
       - uses: r-lib/actions/setup-r-dependencies@v2
         with:
           extra-packages: |
+            gamstransfer=?ignore
             any::lucode2
             any::covr
             any::madrat
@@ -56,6 +57,8 @@ jobs:
 
       - name: Test coverage
         shell: Rscript {0}
-        run: covr::codecov(quiet = FALSE)
+        run: |
+          nonDummyTests <- setdiff(list.files("./tests/testthat/"), c("test-dummy.R", "_snaps"))
+          if(length(nonDummyTests) > 0) covr::codecov(quiet = FALSE)
         env:
           NOT_CRAN: "true"

.pre-commit-config.yaml

@@ -3,7 +3,7 @@
 exclude: '^tests/testthat/_snaps/.*$'
 repos:
 -   repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.4.0
+    rev: v4.5.0
     hooks:
     -   id: check-case-conflict
     -   id: check-json
@@ -15,7 +15,7 @@ repos:
     -   id: mixed-line-ending
 
 -   repo: https://github.com/lorenzwalthert/precommit
-    rev: v0.3.2.9019
+    rev: v0.3.2.9025
     hooks:
     -   id: parsable-R
     -   id: deps-in-desc

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: 1.4.0
-date-released: '2023-08-18'
+version: 1.5.0
+date-released: '2023-12-15'
 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: 1.4.0
+Version: 1.5.0
 Authors@R: c(person(given = "Johannes", 
                     family = "Koch", 
                     email = "[email protected]", 
@@ -48,6 +48,6 @@ Suggests:
 Encoding: UTF-8
 Roxygen: list(markdown = TRUE)
 RoxygenNote: 7.2.3
-Date: 2023-08-18
+Date: 2023-12-15
 Config/testthat/edition: 3
 VignetteBuilder: knitr

R/calcGDPFuture.R

@@ -76,7 +76,7 @@ calcInternalGDPFutureSSPs <- function(unit) {
       GDPuc::convertGDP("constant 2005 Int$PPP", unit, replace_NAs = c("linear", "no_conversion"))
 
     y2 <- getYears(data2005PPP)[getYears(data2005PPP, as.integer = TRUE) > c15 &
-                                 getYears(data2005PPP, as.integer = TRUE) < 2100]
+                                  getYears(data2005PPP, as.integer = TRUE) < 2100]
     dataFarFut <- data2005PPP[, y2, ] * NA
 
     # Convert to 2017 Int$PPP using the 2017 value of base 2005 GDP deflator
@@ -86,22 +86,22 @@ calcInternalGDPFutureSSPs <- function(unit) {
 
     data2017PPP <- mbind(dataNearFut, dataFarFut, data2100)
 
-    q <- data2005PPP / data2017PPP
+    ratio <- data2005PPP / data2017PPP
     # For interpolation to work, the last and first values have to be non-NA/non-NaN
-    q[, 2100, ][is.na(q[, 2100, ])] <- 0
+    ratio[, 2100, ][is.na(ratio[, 2100, ])] <- 0
     # The first 2 years of the SSP data set are incomplete. For countries that only lack data in these first 2 years,
     # set NaN to 0.
-    q[, 2000, ][is.nan(q[, 2000, ]) & !is.nan(q[, 2010, ])] <- 0
+    ratio[, 2000, ][is.nan(ratio[, 2000, ]) & !is.nan(ratio[, 2010, ])] <- 0
 
-    q <- as.data.frame(q, rev = 2) %>%
+    ratio <- as.data.frame(ratio, rev = 2) %>%
       dplyr::rename("value" = ".value") %>%
       dplyr::arrange(.data$year) %>%
       dplyr::group_by(.data$iso3c, .data$variable) %>%
       dplyr::mutate(value = zoo::na.approx(.data$value)) %>%
       dplyr::ungroup() %>%
       as.magpie(tidy = TRUE)
 
-    data2017PPP <- data2005PPP / q
+    data2017PPP <- data2005PPP / ratio
     data2017PPP[is.na(data2017PPP)] <- data2005PPP[is.na(data2017PPP)]
     # Above should probably be "<- 0"
     ##################
@@ -110,7 +110,7 @@ calcInternalGDPFutureSSPs <- function(unit) {
 
   # If unit was in $MER
   if (constructUnit != unit) {
-     data <- GDPuc::convertGDP(data, constructUnit, unit, replace_NAs = c("linear", "no_conversion"))
+    data <- GDPuc::convertGDP(data, constructUnit, unit, replace_NAs = c("linear", "no_conversion"))
   }
 
   list(x = data, weight = NULL, unit = glue("mil. {unit}"), description = "SSP projections")
@@ -128,7 +128,7 @@ calcInternalGDPFutureSDPs <- function(unit) {
 
 calcInternalGDPFutureSSP2EU <- function(unit) {
   dataSSP2EU <- readSource("ARIADNE", "gdp_corona") %>%
-      GDPuc::convertGDP("constant 2005 Int$PPP", unit, replace_NAs = c("linear", "no_conversion"))
+    GDPuc::convertGDP("constant 2005 Int$PPP", unit, replace_NAs = c("linear", "no_conversion"))
   dataSSP <- calcOutput("InternalGDPFutureSSPs", unit = unit, aggregate = FALSE)
 
   # Get EU-27 countries

R/calcGDPHarmonized.R

@@ -78,9 +78,9 @@ toolGDPHarmonizeSSP2EU <- function(past, future, unit) {
   combined <- combined[, getYears(combined)[getYears(combined, as.integer = TRUE) <= 2100], ]
 
   list(x = combined,
-      description = glue("equal to SSP2 in all countries except for EU countries. \\
-                         For EU countries use {past$description} until 2021, \\
-                         growth rates from Eurostat until 2024, \\
-                         growth rates from {future$description} until 2070, \\
-                         and converge to 2150 (bezier-extended) SSP2 values thereafter."))
+       description = glue("equal to SSP2 in all countries except for EU countries. \\
+                          For EU countries use {past$description} until 2021, \\
+                          growth rates from Eurostat until 2024, \\
+                          growth rates from {future$description} until 2070, \\
+                          and converge to 2150 (bezier-extended) SSP2 values thereafter."))
 }

R/calcPopulationFuture.R

@@ -32,7 +32,8 @@ calcInternalPopulationFuture <- function(PopulationFuture) { # nolint
     "UN_PopDiv" = calcOutput("InternalPopulationFutureUN_PopDiv", aggregate = FALSE, supplementary = TRUE),
     "MI"        = calcOutput("InternalPopMI", aggregate = FALSE, supplementary = TRUE),
     "SSPsOld"   = calcOutput("InternalPopulationFutureSSPsOld", aggregate = FALSE, supplementary = TRUE),
-    stop("Bad input for PopulationFuture. Invalid 'PopulationFuture' argument."))
+    stop("Bad input for PopulationFuture. Invalid 'PopulationFuture' argument.")
+  )
 
   data$x <- toolFinishingTouches(data$x)
   data

R/readIMF.R

@@ -34,9 +34,9 @@ readIMF <- function(subtype = "current_account", subset = "WEOOct2022all.xls") {
     dplyr::mutate(value = gsub(",", "", .data$value),
                   dplyr::across(.cols = c("year", "value"),
                                 ~ suppressWarnings(as.double(.x))),
-                                # The warnings that are being suppressed above, come from
-                                # character strings that can't be converted to numeric, and
-                                # are thus returned as NA.
+                  # The warnings that are being suppressed above, come from
+                  # character strings that can't be converted to numeric, and
+                  # are thus returned as NA.
                   value = tidyr::replace_na(.data$value, 0)) %>%
     tidyr::pivot_wider(names_from = "Subject Descriptor")
 

R/readUN_PopDiv.R

@@ -29,14 +29,14 @@ readUN_PopDiv <- function(subtype = "estimates") { # nolint
 #' @rdname readUN_PopDiv
 #' @order 3
 #' @param x MAgPIE object returned from readUN_PopDiv
-convertUN_PopDiv <- function(x) {
+convertUN_PopDiv <- function(x) { # nolint
   toolGeneralConvert(x, no_remove_warning = "XKX")
 }
 
 #' @rdname readUN_PopDiv
 #' @order 1
 downloadUN_PopDiv <- function() { # nolint
-  url <- "https://population.un.org/wpp/Download/Files/1_Indicators%20(Standard)/EXCEL_FILES/2_Population/WPP2022_POP_F01_1_POPULATION_SINGLE_AGE_BOTH_SEXES.xlsx"
+  url <- "https://population.un.org/wpp/Download/Files/1_Indicators%20(Standard)/EXCEL_FILES/2_Population/WPP2022_POP_F01_1_POPULATION_SINGLE_AGE_BOTH_SEXES.xlsx" # nolint
   utils::download.file(url, basename(url), quiet = TRUE)
 
   # Compose meta data

R/toolExtend2150.R

@@ -36,18 +36,18 @@ bezierExtension <- function(data, timeExtend) {
 
   # If Bezier extension would lead to negative GDP, set bezier coordinates equal to start point
   # (comes down to a constant extension instead)
-  for (i in 1:nr) for (j in 1:nd) if (bc[i, 2100, j] == 0 || bc[i, 2150, j] < 0 ) bc[i, , j] <- bc[i, 2100, j]
+  for (i in 1:nr) for (j in 1:nd) if (bc[i, 2100, j] == 0 || bc[i, 2150, j] < 0) bc[i, , j] <- bc[i, 2100, j]
 
   x <- rep(c(2100, 2110, 2140, 2150), nr * nd)
-  y <- purrr::reduce(purrr::map(1:nd, ~purrr::reduce(purrr::map(1:nr, function(y) bc[y, ,.x]), c)), c)
+  y <- purrr::reduce(purrr::map(1:nd, ~purrr::reduce(purrr::map(1:nr, function(y) bc[y, , .x]), c)), c)
   z <- purrr::reduce(purrr::map(1:(nr * nd), ~rep(.x, 4)), c)
 
   # grid::bezierGrob returns the point in a weird graphical unit, and "only" returns 48 points, but is super fast.
   bezierPoints <- grid::bezierGrob(x, y, id = z) %>% grid::bezierPoints()
   cfx <- x[1] / as.numeric(bezierPoints[[1]]$x[[1]])
   cfy <- y[1] / as.numeric(bezierPoints[[1]]$y[[1]])
   id <- paste(purrr::reduce(purrr::map(getNames(data), ~rep(.x, nr)), c),
-              rep(getRegions(data), nd),
+              rep(getItems(data, 1), nd),
               sep = "-")
 
   extension <- purrr::map2(bezierPoints, id,

R/toolGetEUcountries.R

@@ -7,7 +7,7 @@ toolGetEUcountries <- function(onlyWithARIADNEgdpData = FALSE) {
     dplyr::pull(.data$CountryCode)
 
   if (onlyWithARIADNEgdpData) {
-     x <- x[x %in% where(readSource("ARIADNE", "gdp_corona") != 0)$true$regions]
+    x <- x[x %in% where(readSource("ARIADNE", "gdp_corona") != 0)$true$regions]
   }
   x
 }

R/toolReduce.R

@@ -2,17 +2,19 @@ toolReduce <- function(x, mbindOrFillWith = "mbind") {
   # Combine list elements using mbind and glue.
   if (mbindOrFillWith == "mbind") {
     x %>%
-       purrr::reduce(~ list(x = mbind(.x$x, .y$x),
-                            weight = mbind(.x$weight, .y$weight),
-                            unit = glue("{.x$unit} || {.y$unit}"),
-                            description = glue("{.x$description} || {.y$description}")))
+      purrr::reduce(~ list(x = mbind(.x$x, .y$x),
+                           weight = mbind(.x$weight, .y$weight),
+                           unit = glue("{.x$unit} || {.y$unit}"),
+                           description = glue("{.x$description} || {.y$description}")))
   } else if (mbindOrFillWith == "fillWith") {
 
     if (length(x) > 1) {
       sep <- if (length(x) == 2) " (completed with" else c(" (completed with", c(rep(",", length(x) - 3), " and"))
       closer <- c(rep("", length(sep) - 1), ")")
       helper <- purrr::map2(sep, closer, c)
-    } else helper <- NULL
+    } else {
+      helper <- NULL
+    }
 
     purrr::reduce2(x, helper,
                    ~ list(x = toolFillWith(.x$x, .y$x),

README.md

@@ -1,6 +1,6 @@
 # Create GDP and Population Scenarios
 
-R package **mrdrivers**, version **1.4.0**
+R package **mrdrivers**, version **1.5.0**
 
 [![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 1.4.0, <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 1.5.0, <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 1.4.0},
+  note = {R package version 1.5.0},
   url = {https://pik-piam.github.io/mrdrivers},
   url = {https://github.com/pik-piam/mrdrivers},
 }