Merge pull request #37 from robinhasse/complete_tgz

Complete tgz with all input data for EDGE-B

.buildlibrary

@@ -1,4 +1,4 @@
-ValidationKey: '1418367'
+ValidationKey: '1438488'
 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: 'mredgebuildings: Prepare data to be used by the EDGE-Buildings model'
-version: 0.7.1
-date-released: '2024-09-11'
+version: 0.7.2
+date-released: '2024-09-13'
 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: 0.7.1
-Date: 2024-09-11
+Version: 0.7.2
+Date: 2024-09-13
 Authors@R: c(
     person("Robin", "Hasse", , "robin.hasse@pik-potsdam.de", role = c("aut", "cre"),
            comment = c(ORCID = "0000-0003-1818-3186")),

NAMESPACE

@@ -15,7 +15,6 @@ export(calcHeatingCapacity)
 export(calcHeatingSystem)
 export(calcHouseholdSize)
 export(calcIEAPFU)
-export(calcIEAfloorspace)
 export(calcLifetimeParams)
 export(calcMatchingReference)
 export(calcPFUDB)
@@ -26,9 +25,10 @@ export(calcShareOdyssee)
 export(calcShareTCEP)
 export(calcSharesBuildingDemand)
 export(calcSurface)
+export(calcTCEP)
 export(calcUEdemand)
 export(calcUValue)
-export(calcUvalues)
+export(calcUValueSource)
 export(convertCensusHub)
 export(convertDaioglou)
 export(convertDeetman2020)
@@ -40,6 +40,7 @@ export(convertEuropeanCommissionRenovation)
 export(convertEurostatBuildings)
 export(convertGDL)
 export(convertHDDCDD)
+export(convertHDDCDDtemp)
 export(convertOdyssee)
 export(convertPFUDB)
 export(convertTCEP)
@@ -60,8 +61,8 @@ export(readEurObservER)
 export(readEuropeanCommissionRenovation)
 export(readEurostatBuildings)
 export(readGDL)
+export(readHDDCDDtemp)
 export(readHotmaps)
-export(readIEAfloorspace)
 export(readOdyssee)
 export(readPFUDB)
 export(readTCEP)
@@ -148,7 +149,6 @@ importFrom(quitte,removeColNa)
 importFrom(quitte,replace_column)
 importFrom(quitte,revalue.levels)
 importFrom(raster,writeRaster)
-importFrom(readxl,read_excel)
 importFrom(readxl,read_xlsx)
 importFrom(rlang,.data)
 importFrom(rlang,syms)

R/calcEfficiencyRegression.R

@@ -60,7 +60,7 @@ calcEfficiencyRegression <- function() {
     as.quitte()
 
   # Get Mapping (ISO<->PFU)
-  regionmapping <- toolGetMapping("pfu_regionmapping.csv", type = "regional", where = "mredegebuildings")
+  regionmapping <- toolGetMapping("pfu_regionmapping.csv", type = "regional", where = "mredgebuildings")
 
   # Get Population Data
   pop <- calcOutput("PopulationPast", aggregate = FALSE) %>%
@@ -169,7 +169,7 @@ calcEfficiencyRegression <- function() {
 
 
   return(list(x = parsFull,
-              weight = NA,
+              isocountries = FALSE,
               description = "Regression Parameter for FE-UE-Efficiency Projection",
               unit = ""))
 }

R/calcFloorspacePast.R

@@ -92,7 +92,7 @@ calcFloorspacePast <- function() {
       mutate(pred = .data[["pred"]] * replace_na(.data[["factor"]], 1)) %>%
 
       # fill missing values w/ predictions
-      mutate(value = .data[["pred"]] * replace_na(.data[["value"]], 1)) %>%
+      mutate(value = ifelse(is.na(.data[["value"]]), .data[["pred"]], .data[["value"]])) %>%
 
       # select columns
       select("region", "period", "variable", "unit", "demographic", "value")
@@ -128,12 +128,11 @@ calcFloorspacePast <- function() {
            unit = "million m2")
 
   # IEA data: take only India
-  ind <- readSource("IEAfloorspace", convert = FALSE) %>%
+  ind <- readSource("TCEP", subtype = "floorspace", convert = FALSE) %>%
     as.quitte() %>%
     filter(.data[["region"]] == "India",
            .data[["period"]] %in% c(2000, 2011),
-           .data[["subsector"]] == "Residential") %>%
-    select(-"subsector") %>%
+           .data[["variable"]] == "Residential") %>%
     mutate(variable = "floor space",
            value = .data[["value"]] * 1000, # billion m2 -> million m2
            unit = "million m2",

R/calcGDPPop.R

@@ -9,7 +9,8 @@ calcGDPPop <- function() {
 
   # LOAD DATA ------------------------------------------------------------------
 
-  gdp <- calcOutput("GDPPast", aggregate = FALSE, average2020 = FALSE) %>%
+  gdp <- calcOutput("GDP", aggregate = FALSE, average2020 = FALSE) %>%
+    mselect(variable = "gdp_SSP2") %>%
     as.quitte()
 
   pop <- calcOutput("PopulationPast", aggregate = FALSE) %>%
@@ -21,9 +22,9 @@ calcGDPPop <- function() {
   # Join and Calculate
   gdpPop <- gdp %>%
     select(-"unit", -"model", -"variable", -"scenario") %>%
-    left_join(pop %>%
-                select(-"unit", -"model", -"variable", -"scenario"),
-              by = c("region", "period")) %>%
+    inner_join(pop %>%
+                 select(-"unit", -"model", -"variable", -"scenario"),
+               by = c("region", "period")) %>%
     mutate(value = .data[["value.x"]] / .data[["value.y"]],
            variable = "gdppop in constant 2005 Int$PPP") %>%
     select(-"value.x", -"value.y")

R/calcHDDCDD.R

@@ -9,6 +9,8 @@
 #' @param multiscen boolean, does \code{mappingFile} cover more than one scenario?
 #' @param rasDir absolute path to directory for saving raster files
 #' @param cacheDir absolute path to directory for pre-calculated BAIT regression parameters
+#' @param fromSource logical, if TRUE, read the previously calculated data that
+#'   is temporarily saved as a source
 #'
 #' @return magpie object of heating and cooling degree days
 #'
@@ -26,7 +28,23 @@ calcHDDCDD <- function(mappingFile,
                        bait = FALSE,
                        multiscen = FALSE,
                        rasDir = NULL,
-                       cacheDir = NULL) {
+                       cacheDir = NULL,
+                       fromSource = FALSE) {
+
+  if (isTRUE(fromSource)) {
+    # Read previously calculated data from source
+    hddcdd <- readSource("HDDCDDtemp")
+
+    weight <- hddcdd
+    weight[] <- 1
+
+    return(list(x = hddcdd,
+                min = 0,
+                weight = weight,
+                unit = "K.d/yr",
+                description = "Heating and cooling degree days"))
+  }
+
 
   # initialize full calculation
   makeCalculations <- function(f, m, n, tLim, countries, pop, hddcddFactor,

R/calcShareOdyssee.R

@@ -21,7 +21,7 @@
 #' @importFrom madrat readSource toolCountryFill
 #' @importFrom quitte as.quitte revalue.levels
 #' @importFrom dplyr filter %>% mutate group_by across all_of left_join
-#' summarise .data syms
+#' summarise .data syms bind_rows pull
 #' @importFrom tidyr separate replace_na complete
 #' @importFrom utils tail
 #' @export
@@ -81,7 +81,7 @@ calcShareOdyssee <- function(subtype = c("enduse", "carrier", "enduse_carrier"),
     filter(.data[["variable"]] %in% paste0(vars, "_EJ"),
            !is.na(.data[["value"]])) %>%
     mutate(region = droplevels(.data[["region"]]),
-           variable = gsub("_.*$", "", .data[["variable"]])) %>%
+           variable = sub("_.*$", "", .data[["variable"]])) %>%
     separate("variable", c("carrier", "sector", "enduse"), c(3, 8)) %>%
     revalue.levels(carrier = carrierMap,
                    sector  = sectorMap,
@@ -110,42 +110,37 @@ calcShareOdyssee <- function(subtype = c("enduse", "carrier", "enduse_carrier"),
       filter(.data[["enduse"]] %in% c("lighting", "appliances"),
              !is.na(.data[["value"]])) %>%
       group_by(across(all_of(c("period", "sector", "carrier", "enduse")))) %>%
-      mutate(value = sum(.data[["value"]], na.rm = TRUE)) %>%
-      ungroup() %>%
+      summarise(value = sum(.data[["value"]], na.rm = TRUE), .groups = "drop") %>%
       group_by(across(all_of(c("period", "sector", "carrier")))) %>%
-      mutate(value = proportions(.data[["value"]])) %>%
+      mutate(share = proportions(.data[["value"]])) %>%
       ungroup() %>%
-      group_by(across(all_of(c("period", "sector", "enduse")))) %>%
-      mutate(share = mean(.data[["value"]])) %>%
-      ungroup() %>%
-      select(all_of(c("period", "carrier", "sector", "enduse", "share"))) %>%
-      unique()
+      select(-"value")
 
     # split existing aggregated data into "appliances" and "lighting"
     applightData <- odysseeData %>%
       filter(.data[["variable"]] %in% paste0(vars, "_EJ"),
              !is.na(.data[["value"]])) %>%
       mutate(region = droplevels(.data[["region"]]),
-             variable = gsub("_.*$", "", .data[["variable"]])) %>%
+             variable = sub("_.*$", "", .data[["variable"]])) %>%
       separate("variable", c("carrier", "sector", "enduse"), c(3, 8)) %>%
       revalue.levels(carrier = carrierMap,
                      sector  = sectorMap,
                      enduse  = enduseMap) %>%
       interpolate_missing_periods(expand.values = TRUE) %>%
-      pivot_wider(names_from = "enduse", values_from = "value") %>%
-      left_join(meanApplightShares,
-                by = c("period", "carrier", "sector"),
-                relationship = "many-to-many") %>%
-      mutate(value = .data[["appliances_light"]] * .data[["share"]]) %>%
-      select(all_of(c("region", "period", "carrier", "enduse", "sector", "value"))) %>%
-      unique()
+      filter(.data[["enduse"]] == "appliances_light") %>%
+      select(-"enduse") %>%
+      inner_join(meanApplightShares,
+                 by = c("period", "carrier", "sector"),
+                 relationship = "many-to-many") %>%
+      mutate(value = .data[["value"]] * .data[["share"]]) %>%
+      select("region", "period", "carrier", "enduse", "sector", "value")
 
     # replace missing values
-    odyssee <- rbind(odyssee,
-                     applightData %>%
-                       anti_join(odyssee,
-                                 by = c("region", "period", "carrier", "enduse", "sector")) %>%
-                       select(-"variable"))
+    odyssee <- applightData %>%
+      anti_join(odyssee,
+                by = c("region", "period", "carrier", "enduse", "sector")) %>%
+      bind_rows(odyssee)
+
   }
 
   if (feOnly) {
@@ -194,7 +189,7 @@ calcShareOdyssee <- function(subtype = c("enduse", "carrier", "enduse_carrier"),
 
 
 
-    # OUTPUT ---------------------------------------------------------------------
+    # OUTPUT -------------------------------------------------------------------
 
     # Convert to magpie object
     share <- share %>%

R/calcTCEP.R

@@ -0,0 +1,15 @@
+#' IEA's Tracking clean energy progress 2014
+#'
+#' @param subtype character, type of data
+#' @author Robin Hasse
+#'
+#' @importFrom madrat readSource
+#' @export
+
+calcTCEP <- function(subtype) {
+  return(list(x = readSource("TCEP", subtype, convert = FALSE),
+              min = 0,
+              isocountries = FALSE,
+              unit = "m2/cap",
+              description = "Floor space per capita"))
+}

R/calcUValueSource.R

@@ -0,0 +1,24 @@
+#' Passes U-values read from source
+#'
+#' @author Falk Benke, Robin Hasse
+#'
+#' @param subtype source of U-values
+#' @export
+
+calcUValueSource <- function(subtype = c("EUBuildingsObservatory", "ETSAP")) {
+
+  subtype <- match.arg(subtype)
+
+  x <- if (subtype == "EUBuildingsObservatory") {
+    readSource("EUBuildingsObservatory", convert = FALSE)
+  } else {
+    readSource("ETSAP", convert = FALSE)
+  }
+
+  return(list(x = x,
+              min = 0,
+              isocountries = FALSE,
+              unit = "W/m2K",
+              description = paste("U-values as reported by", subtype)
+  ))
+}

R/convertHDDCDDtemp.R

@@ -0,0 +1,22 @@
+#' Convert temporary HDD and CDD data
+#'
+#' The temporary data already is at EDGE-B regional resolution. It is uniformly
+#' disaggregated to country level here to comply with the madrat framework
+#' before it is aggregated to the identical resolution in fullEDGEBUILDINGS.
+#'
+#' @author Robin Hasse
+#'
+#' @param x MAgPIE object with HDD and CDD data at EDGE-B resolution
+#' @returns HDD CDD data at country resolution
+#'
+#' @importFrom madrat toolGetMapping toolAggregate
+#' @export
+
+convertHDDCDDtemp <- function(x) {
+  mapping <- toolGetMapping(name = "regionmappingEDGE.csv",
+                            type = "regional",
+                            where = "mredgebuildings")
+
+  toolAggregate(x, mapping,
+                from = "RegionCodeEUR_ETP", to = "CountryCode")
+}