fix lintr warnings and other format problems

.buildlibrary

@@ -1,4 +1,4 @@
-ValidationKey: '38506518'
+ValidationKey: '38526511'
 AcceptedWarnings:
 - 'Warning: package ''.*'' was built under R version'
 - 'Warning: namespace ''.*'' is not available and has been replaced'

CITATION.cff

@@ -2,7 +2,7 @@ cff-version: 1.2.0
 message: If you use this software, please cite it using the metadata from this file.
 type: software
 title: 'mrremind: MadRat REMIND Input Data Package'
-version: 0.192.6
+version: 0.192.7
 date-released: '2024-09-27'
 abstract: The mrremind packages contains data preprocessing for the REMIND model.
 authors:

DESCRIPTION

@@ -1,7 +1,7 @@
 Type: Package
 Package: mrremind
 Title: MadRat REMIND Input Data Package
-Version: 0.192.6
+Version: 0.192.7
 Date: 2024-09-27
 Authors@R: c(
     person("Lavinia", "Baumstark", , "[email protected]", role = c("aut", "cre")),

NAMESPACE

@@ -48,7 +48,7 @@ export(convertUNFCCC)
 export(convertUNIDO)
 export(convertUSGS)
 export(filter_historical_mif)
-export(fullThresholds)
+export(fullTHRESHOLDS)
 export(readADVANCE_WP2)
 export(readAGEB)
 export(readAR6GWP)

R/fullThresholds.R → R/fullTHRESHOLDS.R

@@ -8,7 +8,8 @@
 #'        validationConfig or "full" to export all pipeline data
 #'
 #' @export
-fullThresholds <- function(type = "config") {
+fullTHRESHOLDS <- function(type = "config") {
+
   # get region mappings for aggregation ----
   # Determines all regions data should be aggregated to by examining the columns
   # of the `regionmapping` and `extramappings` currently configured.
@@ -53,8 +54,8 @@ fullThresholds <- function(type = "config") {
                         warnNA = FALSE, try = FALSE, years = years)
 
   wind <- calcOutput("ProjectPipelines", subtype = "wind",
-                      aggregate = columnsForAggregation, round = 3,
-                      warnNA = FALSE, try = FALSE, years = years)
+                     aggregate = columnsForAggregation, round = 3,
+                     warnNA = FALSE, try = FALSE, years = years)
 
   solar <- calcOutput("ProjectPipelines", subtype = "solar",
                       aggregate = columnsForAggregation, round = 3,
@@ -137,13 +138,13 @@ fullThresholds <- function(type = "config") {
     out <- out[, , c("min_", "max_"), pmatch = TRUE] %>%
       as.quitte() %>%
       pivot_wider(names_from = "status") %>%
-      select(-scenario)
+      select(-"scenario")
     # exclude rows without any threshold
     out[!(is.na(out$min_red)
           & is.na(out$min_yel)
           & is.na(out$max_yel)
           & is.na(out$max_red)), ] %>%
-      write.csv(file = outfile, row.names = FALSE, quote = FALSE)
+      utils::write.csv(file = outfile, row.names = FALSE, quote = FALSE)
 
   } else {
     warning("`type` must be either `full` or `config`")

R/readGlobalEnergyMonitor.R

@@ -13,7 +13,7 @@ readGlobalEnergyMonitor <- function() {
   # GEM GIPT 2024
   # file available after filling out questionnaire:
   # https://globalenergymonitor.org/projects/global-integrated-power-tracker/download-data/
-  d <- read_excel("Global-Integrated-Power-June-2024.xlsx",
+  d <- readxl::read_excel("Global-Integrated-Power-June-2024.xlsx",
                   sheet = "Power facilities",
                   trim_ws = TRUE,
                   col_types = "text") %>%
@@ -32,11 +32,13 @@ readGlobalEnergyMonitor <- function() {
     filter(status %in% c("announced", "pre-construction", "construction", "operating")) %>%
     # ASSUMPTION: rows with empty start year are ignored
     # only look at pipeline until 2030
-    filter(!is.na(start), start < 2031) %>%
+    filter(!is.na(.data$start), .data$start < 2031) %>%
     # Oil/Gas needs to be separated by technology or fuel (seems complicated?)
     # remove for now
-    filter(variable != "oil/gas") %>%
-    mutate(start = as.numeric(start), end = as.numeric(end), value = as.numeric(value))
+    filter(.data$variable != "oil/gas") %>%
+    mutate(start = as.numeric(.data$start),
+           end = as.numeric(.data$end),
+           value = as.numeric(.data$value))
 
   # no end year defined:
   # use average lifetime of technology (from generisdata_tech)
@@ -51,10 +53,10 @@ readGlobalEnergyMonitor <- function() {
   # find all of these cases and put them in 2 rows, one per country
   d <- rbind(
     d[is.na(d$c2), ],
-    d[!is.na(d$c2), ] %>% mutate(region = c1, value = as.numeric(v1)),
-    d[!is.na(d$c2), ] %>% mutate(region = c2, value = as.numeric(v2))
+    d[!is.na(d$c2), ] %>% mutate(region = .data$c1, value = as.numeric(.data$v1)),
+    d[!is.na(d$c2), ] %>% mutate(region = .data$c2, value = as.numeric(.data$v2))
     ) %>%
-    select(-c1, -c2, -v1, -v2)
+    select(-"c1", -"c2", -"v1", -"v2")
 
   # use proper variable names
   d[d$variable == "coal", "variable"]       <- "Cap|Electricity|Coal"
@@ -77,7 +79,7 @@ readGlobalEnergyMonitor <- function() {
 
   # transform data from list of capacities with start and end date
   # to sum of active capacities in 2025 and 2030
-  d <- mutate(d, end = pmin(end, 2030))  # improves performance of next step
+  d <- mutate(d, end = pmin(.data$end, 2030))  # improves performance of next step
   tmp_list <- vector("list", nrow(d))
   for (i in seq_len(nrow(d))) {
     tmp_list[[i]] <- data.frame(
@@ -86,9 +88,9 @@ readGlobalEnergyMonitor <- function() {
     )
   }
   tmp <- do.call(rbind, tmp_list) %>%
-    group_by(region, variable, status, period) %>%
-    summarise(value = sum(value)) %>%
-    filter(period %in% c(2020, 2025, 2030))
+    group_by(.data$region, .data$variable, .data$status, .data$period) %>%
+    summarise(value = sum(.data$value)) %>%
+    filter(.data$period %in% c(2020, 2025, 2030))
 
   # convert to magclass object
   x <- as.magpie(tmp, spatial = "region")
@@ -103,9 +105,6 @@ readGlobalEnergyMonitor <- function() {
       add_dimension(dim = 3.1, add = "variable", nm = "Cap|Electricity|Wind")
   )
 
-  # collapse variable and status dimension into one
-  # getNames(x) <- gsub(pattern = "\\.", replacement = "|", x = getNames(x))
-
   # convert MW to GW
   x <- x / 1000
   x <- add_dimension(x, dim = 3.4, add = "unit", nm = "GW")

R/readIEA_CCUS.R

@@ -16,7 +16,7 @@ readIEA_CCUS <- function(subtype) {
   # project types filter applied to source
   projectTypes <- c("Full chain", "Storage", "T&S")
 
-  data <- read_excel("IEA CCUS Projects Database 2024.xlsx",
+  data <- readxl::read_excel("IEA CCUS Projects Database 2024.xlsx",
     sheet = "CCUS Projects Database"
   ) %>%
     select(
@@ -49,12 +49,12 @@ readIEA_CCUS <- function(subtype) {
 
   # remove mixed-country data and attach single country data weighted by GDP
   data <- data %>%
-    filter(!country %in% c("Japan-Malaysia", "Australia-Japan")) %>%
+    filter(!.data$country %in% c("Japan-Malaysia", "Australia-Japan")) %>%
     rbind(
-    mutate(JM, country = "Japan",     value = value*gdp_JPN/(gdp_JPN+gdp_MYS)),
-    mutate(JM, country = "Malaysia",  value = value*gdp_MYS/(gdp_JPN+gdp_MYS)),
-    mutate(AJ, country = "Australia", value = value*gdp_AUS/(gdp_JPN+gdp_AUS)),
-    mutate(AJ, country = "Japan",     value = value*gdp_JPN/(gdp_JPN+gdp_AUS))
+    mutate(JM, country = "Japan",     value = .data$value*gdp_JPN/(gdp_JPN + gdp_MYS)),
+    mutate(JM, country = "Malaysia",  value = .data$value*gdp_MYS/(gdp_JPN + gdp_MYS)),
+    mutate(AJ, country = "Australia", value = .data$value*gdp_AUS/(gdp_JPN + gdp_AUS)),
+    mutate(AJ, country = "Japan",     value = .data$value*gdp_JPN/(gdp_JPN + gdp_AUS))
     )
 
   if (subtype == "historical") {

R/readIEA_HSMR.R

@@ -9,20 +9,20 @@
 #' @export
 readIEA_HSMR <- function() {
 
-  x <- read_xlsx("IEA_Hydropower_Special_Market_Report.xlsx") %>%
+  x <- readxl::read_xlsx("IEA_Hydropower_Special_Market_Report.xlsx") %>%
     # get capacities in 2030 by adding 2020 values to additions
     # subtract pumped storage as it is not part of Cap Hydro in REMIND
-    mutate("2020_operational" = capacity_2020,
-           "2030_expected" = capacity_2020 + add_expected_2030 - of_that_pumped,
-           "2030_accelerated" = capacity_2020 + add_accelerated_2030 - of_that_pumped,
-           "2020_pumped" = cap_2020_pumped,
-           "2030_pumped" = cap_2020_pumped + of_that_pumped,
+    mutate("2020_operational" = .data$capacity_2020,
+           "2030_expected"    = .data$capacity_2020 + .data$add_expected_2030 - .data$of_that_pumped,
+           "2030_accelerated" = .data$capacity_2020 + .data$add_accelerated_2030 - .data$of_that_pumped,
+           "2020_pumped" = .data$cap_2020_pumped,
+           "2030_pumped" = .data$cap_2020_pumped + .data$of_that_pumped,
            variable = "Cap|Electricity|Hydro") %>%
     pivot_longer(cols = c("2020_operational", "2030_expected",
                           "2030_accelerated", "2020_pumped", "2030_pumped"),
                  names_to = "year_scen") %>%
-    select(country, variable, year_scen, unit, value) %>%
-    separate(year_scen, c("year", "status")) %>%
+    select("country", "variable", "year_scen", "unit", "value") %>%
+    separate(.data$year_scen, c("year", "status")) %>%
     as.magpie(spatial = "country")
 
   x[, 2020, "accelerated"] <- x[, 2020, "operational"]

R/readIEA_PRIS.R

@@ -10,19 +10,19 @@
 readIEA_PRIS <- function() {
   # only information given is what is currently operating and what is under
   # construction (without start date)
-  x <- read_xlsx("nuclear_capacities_20240716.xlsx") %>%
-    mutate("operational" = operational,
-           "construction" = construction,
+  x <- readxl::read_xlsx("nuclear_capacities_20240716.xlsx") %>%
+    mutate("operational"  = .data$operational,
+           "construction" = .data$construction,
            variable = "Cap|Electricity|Nuclear") %>%
     mutate(year = "y2020") %>%  # technically, its 2024
     pivot_longer(cols = c("operational", "construction"),
                  names_to = "status") %>%
-    select(country, year, variable, status, unit, value) %>%
+    select("country", "year", "variable", "status", "unit", "value") %>%
     as.magpie(spatial = "country", temporal = "year") %>%
     add_dimension(dim = 3.1, add = "model", nm = "IEA_PRIS")
 
   # move "construction" values to year 2030
-  x <- add_columns(x, addnm = c("y2030"), dim = 2, fill= NA)
+  x <- add_columns(x, addnm = c("y2030"), dim = 2, fill = NA)
 
   x[, 2030, "construction"] <- x[, 2020, "construction"]
   x[, 2020, "construction"] <- 0

README.md

@@ -1,6 +1,6 @@
 # MadRat REMIND Input Data Package
 
-R package **mrremind**, version **0.192.6**
+R package **mrremind**, version **0.192.7**
 
 [![CRAN status](https://www.r-pkg.org/badges/version/mrremind)](https://cran.r-project.org/package=mrremind)  [![R build status](https://github.com/pik-piam/mrremind/workflows/check/badge.svg)](https://github.com/pik-piam/mrremind/actions) [![codecov](https://codecov.io/gh/pik-piam/mrremind/branch/master/graph/badge.svg)](https://app.codecov.io/gh/pik-piam/mrremind) [![r-universe](https://pik-piam.r-universe.dev/badges/mrremind)](https://pik-piam.r-universe.dev/builds)
 
@@ -39,7 +39,7 @@ In case of questions / problems please contact Lavinia Baumstark <lavinia@pik-po
 
 To cite package **mrremind** in publications use:
 
-Baumstark L, Rodrigues R, Levesque A, Oeser J, Bertram C, Mouratiadou I, Malik A, Schreyer F, Soergel B, Rottoli M, Mishra A, Dirnaichner A, Pehl M, Giannousakis A, Klein D, Strefler J, Feldhaus L, Brecha R, Rauner S, Dietrich J, Bi S, Benke F, Weigmann P, Richters O, Hasse R, Fuchs S, Mandaroux R, Koch J (2024). _mrremind: MadRat REMIND Input Data Package_. R package version 0.192.6, <https://github.com/pik-piam/mrremind>.
+Baumstark L, Rodrigues R, Levesque A, Oeser J, Bertram C, Mouratiadou I, Malik A, Schreyer F, Soergel B, Rottoli M, Mishra A, Dirnaichner A, Pehl M, Giannousakis A, Klein D, Strefler J, Feldhaus L, Brecha R, Rauner S, Dietrich J, Bi S, Benke F, Weigmann P, Richters O, Hasse R, Fuchs S, Mandaroux R, Koch J (2024). _mrremind: MadRat REMIND Input Data Package_. R package version 0.192.7, <https://github.com/pik-piam/mrremind>.
 
 A BibTeX entry for LaTeX users is
 
@@ -48,7 +48,7 @@ A BibTeX entry for LaTeX users is
   title = {mrremind: MadRat REMIND Input Data Package},
   author = {Lavinia Baumstark and Renato Rodrigues and Antoine Levesque and Julian Oeser and Christoph Bertram and Ioanna Mouratiadou and Aman Malik and Felix Schreyer and Bjoern Soergel and Marianna Rottoli and Abhijeet Mishra and Alois Dirnaichner and Michaja Pehl and Anastasis Giannousakis and David Klein and Jessica Strefler and Lukas Feldhaus and Regina Brecha and Sebastian Rauner and Jan Philipp Dietrich and Stephen Bi and Falk Benke and Pascal Weigmann and Oliver Richters and Robin Hasse and Sophie Fuchs and Rahel Mandaroux and Johannes Koch},
   year = {2024},
-  note = {R package version 0.192.6},
+  note = {R package version 0.192.7},
   url = {https://github.com/pik-piam/mrremind},
 }
 ```