incorporated rest of PR suggestions

.buildlibrary

@@ -1,4 +1,4 @@
-ValidationKey: '1236714'
+ValidationKey: '1397480'
 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: 0.6.2
-date-released: '2024-08-12'
+version: 0.7.0
+date-released: '2024-08-29'
 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: 0.6.2
-Date: 2024-08-12
+Version: 0.7.0
+Date: 2024-08-29
 Authors@R: c(
     person("Robin", "Hasse", , "[email protected]", role = c("aut", "cre"),
            comment = c(ORCID = "0000-0003-1818-3186")),

NAMESPACE

@@ -10,6 +10,7 @@ export(calcFEUE)
 export(calcFEUEefficiencies)
 export(calcFEbyEUEC)
 export(calcFEdemandBuildings)
+export(calcFloorspacePast)
 export(calcHeatingCapacity)
 export(calcHeatingSystem)
 export(calcHouseholdSize)

R/calcFloorspacePast.R

@@ -14,12 +14,12 @@
 #' @author Robin Hasse, Antoine Levesque, Hagen Tockhorn
 #'
 #' @importFrom madrat readSource calcOutput toolCountryFill
-#' @importFrom quitte as.quitte calc_addVariable factor.data.frame
+#' @importFrom quitte as.quitte calc_addVariable factor.data.frame interpolate_missing_periods
 #' @importFrom dplyr filter mutate select anti_join group_by left_join %>%
-#' ungroup .data %>%
+#' ungroup .data %>% group_modify
 #' @importFrom rlang .data
 #' @importFrom magclass mbind as.magpie collapseDim mselect
-#' @importFrom tidyr spread
+#' @importFrom tidyr spread replace_na
 #'
 #' @export
 
@@ -37,25 +37,12 @@ calcFloorspacePast <- function() {
   }
 
 
-  #' Predict missing historic floorspace data
-  #'
-  #' A linear regression is performed to establish a general relationship between
-  #' floorspace per capita and gdp per capita and population density.
-  #' Predicted values are corrected w.r.t. to historical data where available,
-  #' otherwise the prediction is chosen.
-  #'
-  #' @param df data.frame containing floorspace per capita
-  #' @param gdppop data.frame containing gdp per capita
-  #' @param dens data.frame containing population density
-  #' @param endOfHistory upper temporal boundary of historic data
-  #' @param periodBegin lower temporal boundary of historic data
-  #'
-  #' @importFrom quitte factor.data.frame interpolate_missing_periods
-  #' @importFrom tidyr replace_na
-  #' @importFrom dplyr group_modify
-  #'
-  #' @returns floorspace per capita with filled missing entries
-
+  # Predict missing historic floorspace data
+  #
+  # A linear regression is performed to establish a general relationship between
+  # floorspace per capita and gdp per capita and population density.
+  # Predicted values are corrected w.r.t. to historical data where available,
+  # otherwise the prediction is chosen.
   makeFloorspaceProjection <- function(df, gdppop, dens, endOfHistory, periodBegin) {
 
     # Clean data

R/calcPFUDB.R

@@ -42,26 +42,6 @@ calcPFUDB <- function() {
     }
   }
 
-  # convert enduse "appliances" to "refrigerators" with respective scaling factor
-  addThermal <- function(df, mapping, fridgeShare) {
-    df <- df %>%
-      filter(.data[["enduse"]] != "lighting") %>%
-      left_join(mapping %>%
-                  select(-"RegionCodeEUR", -"RegionCodeEUR_ETP", -"X") %>%
-                  rename(region = "CountryCode") %>%
-                  left_join(fridgeShare, by = "RegionCode") %>%
-                  select(-"RegionCode"),
-                by = "region") %>%
-      mutate(value = ifelse(.data[["enduse"]] != "appliances",
-                            .data[["value"]],
-                            .data[["value"]] * .data[["share"]]),
-             enduse = ifelse(.data[["enduse"]] == "appliances",
-                             "refrigerators",
-                             as.character(.data[["enduse"]]))) %>%
-      select(-"share")
-    return(df)
-  }
-
 
 
   # PARAMETERS -----------------------------------------------------------------
@@ -175,7 +155,7 @@ calcPFUDB <- function() {
     mutate(value = ifelse(is.na(.data[["value.x"]]),
                           .data[["value.y"]],
                           .data[["value.x"]])) %>%
-    addThermal(regmappingEDGE, fridgeShare) %>%
+    toolAddThermal(regmappingEDGE, fridgeShare) %>%
     select("region", "period", "carrier", "enduse", "value")
 
 

R/calcShareETP.R

@@ -179,6 +179,8 @@ calcShareETP <- function(subtype = c("enduse", "carrier"), feOnly = FALSE) {
         group_by(across(all_of(c("region", "period")))) %>%
         mutate(value = proportions(.data[["value"]])) %>%
         ungroup()
+    } else {
+      etpFilter <- shareCorr
     }
   }
 
@@ -188,7 +190,7 @@ calcShareETP <- function(subtype = c("enduse", "carrier"), feOnly = FALSE) {
 
   # return only FE data
   if (isTRUE(feOnly)) {
-    feData <- shareCorr %>%
+    feData <- etpFilter %>%
       as.quitte() %>%
       mutate(value = .data[["value"]] * PJ2EJ,
              unit = "EJ") %>%

R/calcShareOdyssee.R

@@ -109,13 +109,17 @@ calcShareOdyssee <- function(subtype = c("enduse", "carrier", "enduse_carrier"),
     meanApplightShares <- odyssee %>%
       filter(.data[["enduse"]] %in% c("lighting", "appliances"),
              !is.na(.data[["value"]])) %>%
-      group_by(across(all_of(c("region", "period", "sector", "carrier")))) %>%
+      group_by(across(all_of(c("period", "sector", "carrier", "enduse")))) %>%
+      mutate(value = sum(.data[["value"]], na.rm = TRUE)) %>%
+      ungroup() %>%
+      group_by(across(all_of(c("period", "sector", "carrier")))) %>%
       mutate(value = 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")))
+      select(all_of(c("period", "carrier", "sector", "enduse", "share"))) %>%
+      unique()
 
     # split existing aggregated data into "appliances" and "lighting"
     applightData <- odysseeData %>%