Increment version number

.buildlibrary

@@ -1,4 +1,4 @@
-ValidationKey: '1459854'
+ValidationKey: '1480814'
 AutocreateReadme: yes
 AcceptedWarnings:
 - 'Warning: package ''.*'' was built under R version'

.github/workflows/check.yaml

@@ -23,14 +23,14 @@ jobs:
       - uses: r-lib/actions/setup-r-dependencies@v2
         with:
           extra-packages: |
-            any::lucode2
-            any::covr
-            any::madrat
-            any::magclass
-            any::citation
-            any::gms
-            any::goxygen
-            any::GDPuc
+            lucode2
+            covr
+            madrat
+            magclass
+            citation
+            gms
+            goxygen
+            GDPuc
           # piam packages also available on CRAN (madrat, magclass, citation,
           # gms, goxygen, GDPuc) will usually have an outdated binary version
           # available; by using extra-packages we get the newest 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: 'mredgebuildings: Prepare data to be used by the EDGE-Buildings model'
-version: 0.7.3
-date-released: '2024-10-02'
+version: 0.7.4
+date-released: '2024-10-15'
 abstract: Prepare data to be used by the EDGE-Buildings model.
 authors:
 - family-names: Hasse

DESCRIPTION

@@ -1,8 +1,8 @@
 Type: Package
 Package: mredgebuildings
 Title: Prepare data to be used by the EDGE-Buildings model
-Version: 0.7.3
-Date: 2024-10-02
+Version: 0.7.4
+Date: 2024-10-15
 Authors@R: c(
     person("Robin", "Hasse", , "robin.hasse@pik-potsdam.de", role = c("aut", "cre"),
            comment = c(ORCID = "0000-0003-1818-3186")),

NAMESPACE

@@ -53,8 +53,8 @@ export(convertHDDCDD)
 export(convertHDDCDDtemp)
 export(convertOdyssee)
 export(convertPFUDB)
-export(convertTCEP)
 export(convertSPIUG)
+export(convertTCEP)
 export(convertUNHouseholds)
 export(convertUniclima)
 export(fullEDGEBUILDINGS)
@@ -68,8 +68,8 @@ export(readDeetman2020)
 export(readECEMF)
 export(readEEAfloorspace)
 export(readEHI)
-export(readETSAP)
 export(readEHPA)
+export(readETSAP)
 export(readEUBuildingsDB)
 export(readEUBuildingsObservatory)
 export(readEnergiforsk2016)
@@ -82,8 +82,8 @@ export(readHDDCDDtemp)
 export(readHotmaps)
 export(readOdyssee)
 export(readPFUDB)
-export(readTCEP)
 export(readSPIUG)
+export(readTCEP)
 export(readUNHouseholds)
 export(readUniclima)
 export(toolCountryFillAvg)

R/calcHeatingSystemReplacement.R

@@ -34,8 +34,9 @@ calcHeatingSystemReplacement <- function() {
 
   # replace boiler to boiler replacement by more detailed breakdown
   replaceMatrix <- mbind(
-    (replaceMatrix[["allHeating"]])[, , "Boiler.Boiler", invert = TRUE],
-     replaceMatrix[["boilerToBoiler"]])
+    replaceMatrix[["allHeating"]][, , "Boiler.Boiler", invert = TRUE],
+    replaceMatrix[["boilerToBoiler"]]
+  )
 
 
 
@@ -51,7 +52,7 @@ calcHeatingSystemReplacement <- function() {
     toolAggregate(map, from = "technology", to = "technologyBRICK", dim = 3.2, partrel = TRUE)
 
   # remove zero that the aggregation introduced
-  replaceMatrix <- replaceMatrix[,, replaceMatrix > 0]
+  replaceMatrix <- replaceMatrix[, , replaceMatrix > 0]
 
   # disaggregate flows between HP and boilers to boiler technologies
   replaceMatrix <- do.call(mbind, lapply(getNames(replaceMatrix), function(n) {
@@ -73,7 +74,8 @@ calcHeatingSystemReplacement <- function() {
                   weight = replaceMatrix %>%
                     mselect(flows[dimDisagg]) %>%
                     dimSums(dimKeep))
-  })) %>% mbind(replaceMatrix[, , grep("_", getNames(replaceMatrix), value = TRUE), invert = TRUE])
+  })) %>%
+    mbind(replaceMatrix[, , grep("_", getNames(replaceMatrix), value = TRUE), invert = TRUE])
 
   # normalise back
   replaceMatrix <- proportions(replaceMatrix) %>%
@@ -86,6 +88,5 @@ calcHeatingSystemReplacement <- function() {
               min = 0,
               max = 1,
               description = "Share of respondents that replaced a heating system",
-              unit = "1"
-              ))
+              unit = "1"))
 }

R/calcHeatingSystemSales.R

@@ -41,7 +41,6 @@ calcHeatingSystemSales <- function() {
     mselect(corrected = TRUE) %>%
     .removeNADims() %>%
     .mapToBRICK(mapEHPA, from = "hpType")
-  # salesHpEurobserver <- readSource("EurObservER", subtype = "HPbarometer")
 
 
 
@@ -50,12 +49,12 @@ calcHeatingSystemSales <- function() {
   ## many European countries ====
 
   # common regions
-  reg <- Reduce(intersect,lapply(list(salesTotal, salesEff, salesHp),
-                                 getItems, dim = 1))
+  reg <- Reduce(intersect, lapply(list(salesTotal, salesEff, salesHp),
+                                  getItems, dim = 1))
 
   # all time steps
-  t <- Reduce(union,lapply(list(salesTotal, salesEff, salesHp),
-                           getItems, dim = 2))
+  t <- Reduce(union, lapply(list(salesTotal, salesEff, salesHp),
+                            getItems, dim = 2))
 
   # extrapolate to all time steps
   salesTotal <- .time_interpolate(salesTotal, t, 0.2)
@@ -68,7 +67,6 @@ calcHeatingSystemSales <- function() {
   salesMissing <- salesTotal[reg, , ] - dimSums(sales)
   salesMissing[salesMissing < 0] <- 0
   incompleteFactor <- 1 + salesMissing / dimSums(sales[, , incompleteHs])
-  incompleteFactorGlo <- 1 + dimSums(salesMissing, 1) / dimSums(sales[, , incompleteHs], c(1, 3))
   sales[, , incompleteHs] <- sales[, , incompleteHs] * incompleteFactor
 
   # add zeros for missing technologies
@@ -81,8 +79,8 @@ calcHeatingSystemSales <- function() {
 
   ### AUT ####
   mapBMK <- toolGetMapping("technologyMapping_BMK.csv",
-                            type = "sectoral",
-                            where = "mredgebuildings")
+                           type = "sectoral",
+                           where = "mredgebuildings")
   bmk <- readSource("BMK")["AUT", t] %>%
     .mapToBRICK(mapBMK, from = "technology")
   hs <- setdiff(getItems(bmk, "hs"), "sobo_biom")
@@ -102,8 +100,8 @@ calcHeatingSystemSales <- function() {
 
   ### ESP ####
   mapFEGECA <- toolGetMapping("technologyMapping_FEGECA.csv",
-                          type = "sectoral",
-                          where = "mredgebuildings") %>%
+                              type = "sectoral",
+                              where = "mredgebuildings") %>%
     filter(.data[["considerSales"]])
   # scale sum of gabo and libo to match total of boilers and add gas heaters
   fegeca <- readSource("FEGECA")["ESP", , ] %>%
@@ -171,7 +169,6 @@ calcHeatingSystemSales <- function() {
     dimSums(c("variable", "typ", "loc", "vin")) %>%
     .removeNADims("region") %>%
     .time_interpolate(t, 0.3)
-  pop <- calcOutput("PopulationPast", aggregate = FALSE)
 
   # based on dwelling numbers for all regions that have stock data
   salesExtrStock <- .predictSales(sales, stock)
@@ -183,7 +180,7 @@ calcHeatingSystemSales <- function() {
 
 
   salesExtr <- salesExtrStock
-  salesExtr[getItems(sales, 1), ,] <- sales
+  salesExtr[getItems(sales, 1), , ] <- sales
 
 
   ## District heating ====
@@ -255,16 +252,17 @@ calcHeatingSystemSales <- function() {
 
 
 .toDf <- function(x) {
-  x %>% as.quitte() %>%
+  x %>%
+    as.quitte() %>%
     removeColNa()
 }
 
 
-.time_interpolate <- function(dataset,
-                              interpolated_year,
+.time_interpolate <- function(dataset, # nolint: object_name_linter.
+                              interpolated_year, # nolint: object_name_linter.
                               wLinear = 1,
                               nonNegative = TRUE,
-                              integrate_interpolated_years = FALSE) {
+                              integrate_interpolated_years = FALSE) { # nolint: object_name_linter.
   out <- wLinear  * time_interpolate(dataset,
                                      interpolated_year,
                                      integrate_interpolated_years,

R/calcUValueSource.R

@@ -19,6 +19,5 @@ calcUValueSource <- function(subtype = c("EUBuildingsObservatory", "ETSAP")) {
               min = 0,
               isocountries = FALSE,
               unit = "W/m2K",
-              description = paste("U-values as reported by", subtype)
-  ))
+              description = paste("U-values as reported by", subtype)))
 }

R/convertBMK.R

@@ -20,7 +20,7 @@ convertBMK <- function(x) {
 
   # translate technologies to English
   mapBMK <- toolGetMapping("technologyMapping_BMK.csv",
-                              type = "sectoral", where = "mredgebuildings")
+                           type = "sectoral", where = "mredgebuildings")
   mapBMK <- stats::setNames(mapBMK[["technology"]],
                             mapBMK[["technologyAUT"]])
   getItems(x, 3) <- unname(mapBMK[getItems(x, 3)])

R/convertEHI.R

@@ -19,7 +19,7 @@ convertEHI <- function(x) {
   }))
 
   # split aggregated regions
-  weight <- feBuildings <- calcOutput("FEdemandBuildings", aggregate = FALSE) %>%
+  weight <- calcOutput("FEdemandBuildings", aggregate = FALSE) %>%
     dimSums("typ") %>%
     time_interpolate(getItems(x, 2), extrapolation_type = "constant")
 

R/convertEUBuildingsDB.R

@@ -42,8 +42,8 @@ convertEUBuildingsDB <- function(x, subtype) {
 
   # manually drop erroneous data points
   if (category == "BuildingStockCharacteristics") {
-      data[, 2017, c("Total floor area of single family dwellings_m2",
-                     "Total floor area of multi family dwellings_m2")] <- NA
+    data[, 2017, c("Total floor area of single family dwellings_m2",
+                   "Total floor area of multi family dwellings_m2")] <- NA
   }
 
   # fill missing regions with NA
@@ -66,11 +66,12 @@ convertEUBuildingsDB <- function(x, subtype) {
       # the shares perfectly add up to 1, so 'more than 5' means '5 or more'
       getNames(data) <- gsub(
         "^(Number|Share) of dwelling(s|) |occupied by | persons.*|_1$", "",
-         gsub("with single-person households", "1",
-              gsub("more than ", ">=", getNames(data))))
+        gsub("with single-person households", "1",
+             gsub("more than ", ">=", getNames(data)))
+      )
       getSets(data)[3] <- "householdSize"
     },
-      stop("'", subtype, "' is an invalid subtype.")
+    stop("'", subtype, "' is an invalid subtype.")
   )
 
   return(data)

R/convertEurObservER.R

@@ -27,7 +27,8 @@ convertEurObservER <- function(x, subtype) {
     # Check that there are only unique values in disregarded columns
     nUnique <- lapply(
       apply(select(ts, -any_of(c("period", "reportperiod", "value"))), 2, unique),
-      length)
+      length
+    )
     if (any(nUnique > 1)) {
       stop(paste(names(nUnique)[which(nUnique > 1)], collapse = ", "),
            "have more than one unique value. Group by more dimensions.")

R/convertFEGECA.R

@@ -21,7 +21,7 @@ convertFEGECA <- function(x) {
 
   # translate technologies to English
   mapFEGECA <- toolGetMapping("technologyMapping_FEGECA.csv",
-                                   type = "sectoral", where = "mredgebuildings")
+                              type = "sectoral", where = "mredgebuildings")
   mapFEGECA <- stats::setNames(mapFEGECA[["variable"]],
                                mapFEGECA[["variableESP"]])
   getItems(x, 3.1) <- unname(mapFEGECA[getItems(x, 3.1)])

R/readCensusHub.R

@@ -25,8 +25,7 @@
 readCensusHub <- function(subtype) {
 
   # pick file
-  files <- list(
-    typeVintage = "csvoutput_HC53_2022_04_08_17_01.csv")
+  files <- list(typeVintage = "csvoutput_HC53_2022_04_08_17_01.csv")
 
   data <- toolSubtypeSelect(subtype, files) %>%
     read.csv() %>%

R/readEHPA.R

@@ -34,7 +34,7 @@ readEHPA <- function(subtype = "hpSales") {
     },
     hpMarketShare = {
 
-      file = "EHPA_market_report_2023_Final full report_Table3.5-1.txt"
+      file <- "EHPA_market_report_2023_Final full report_Table3.5-1.txt"
       data <- read.csv(file, sep = " ", comment.char = "#") %>%
         rename(region = "X") %>%
         pivot_longer(matches("X\\d{4}"), names_to = "period") %>%