Merge pull request #405 from whitehacker/master

Adding value to where in toolGetMapping

.buildlibrary

@@ -1,4 +1,4 @@
-ValidationKey: '32027560'
+ValidationKey: '32076470'
 AcceptedWarnings:
 - 'Warning: package ''.*'' was built under R version'
 - 'Warning: namespace ''.*'' is not available and has been replaced'

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: 'mrremind: MadRat REMIND Input Data Package'
-version: 0.164.0
-date-released: '2023-06-21'
+version: 0.164.2
+date-released: '2023-06-27'
 abstract: The mrremind packages contains data preprocessing for the REMIND model.
 authors:
 - family-names: Baumstark

DESCRIPTION

@@ -1,8 +1,8 @@
 Type: Package
 Package: mrremind
 Title: MadRat REMIND Input Data Package
-Version: 0.164.0
-Date: 2023-06-21
+Version: 0.164.2
+Date: 2023-06-27
 Authors@R: c(
     person("Lavinia", "Baumstark", , "[email protected]", role = c("aut", "cre")),
     person("Renato", "Rodrigues", role = "aut"),

R/calcAGEB.R

@@ -15,7 +15,7 @@
 calcAGEB <- function(subtype = "balances") {
   ageb <- readSource("AGEB", subtype = subtype)
 
-  mapping <- toolGetMapping("Mapping_AGEB_REMIND.csv", type = "reportingVariables") %>%
+  mapping <- toolGetMapping("Mapping_AGEB_REMIND.csv", type = "reportingVariables", where = "mappingfolder") %>%
     mutate(!!sym("conversion") := as.numeric(!!sym("Factor")) * !!sym("Weight")) %>%
     select("variable" = "AGEB_variable", "REMIND_variable", "conversion", "unit" = "Unit_AGEB", "Unit_REMIND") %>%
     filter(!!sym("REMIND_variable") != "")

R/calcBP.R

@@ -14,7 +14,7 @@
 
 calcBP <- function() {
   .readFactors <- function() {
-    factors <- toolGetMapping("BP_Renewable_Efficiency_Factors.csv", type = "sectoral")
+    factors <- toolGetMapping("BP_Renewable_Efficiency_Factors.csv", type = "sectoral", where = "mappingfolder")
     colnames(factors) <- c("year", "factor")
     factors <- rbind(
       factors,

R/calcCapTarget.R

@@ -99,7 +99,8 @@ calcCapTarget <- function(sources) {
     # hydrogen capacity targets from national/EU hydrogen strategies
 
     # Region targets
-    reg.map <- toolGetMapping("regionmappingH12.csv", type = "regional") # get H12 regionmapping
+    reg.map <- toolGetMapping("regionmappingH12.csv", type = "regional", 
+                              where = "mappingfolder") # get H12 regionmapping
     H2Target.reg <- new.magpie(unique(reg.map$RegionCode), getYears(x), "elh2", fill = 0)
     # Electrolyzer capacity target from the EU Hydrogen Strategy
     # https://ec.europa.eu/energy/sites/ener/files/hydrogen_strategy.pdf

R/calcCapacityFactor.R

@@ -39,7 +39,7 @@ calcCapacityFactor <- function(){
   # Read generation data from Energy Balances
   coalgen_c <- calcOutput("IO",subtype="output",aggregate = F)[,,"pecoal.seel"]
   coalgen_c <- dimSums(coalgen_c,dim=3)
-  map <- toolGetMapping(getConfig("regionmapping"),type="regional")
+  map <- toolGetMapping(getConfig("regionmapping"),type="regional", where = "mappingfolder")
   coalgen_R <- toolAggregate(coalgen_c,map,weight=NULL)
   getNames(coalgen_c) <- "pc"
   getNames(coalgen_R) <- "pc"

R/calcCapital.R

@@ -73,7 +73,7 @@ calcCapital <- function(subtype = "Capital") {
       for(r in getRegions(cap_intensity_future)) {
       if(cap_intensity_future[r,t,"gdp_SSP2"]==0) {
         # get current mapping
-        map <- toolGetMapping(type = "regional", name = getConfig("regionmapping"))
+        map <- toolGetMapping(type = "regional", name = getConfig("regionmapping"), where = "mappingfolder")
         # get list of countries that belong to the same region as r
         regi   <- map$RegionCode[map$CountryCode==r]
         c_regi <- map$CountryCode[map$RegionCode==regi]

R/calcCementShare.R

@@ -34,7 +34,7 @@ calcCementShare <- function() {
 
   list(x = bind_rows(
     left_join(
-      toolGetMapping('regionmappingOECD.csv', 'regional') %>%
+      toolGetMapping('regionmappingOECD.csv', 'regional', where = "mappingfolder") %>%
         as_tibble() %>%
         select(iso3c = 'CountryCode', region = 'RegionCode'),
 

R/calcChemicalFeedstocksShare.R

@@ -23,12 +23,11 @@ calcChemicalFeedstocksShare <- function()
 {
   region_mapping <- toolGetMapping(
     name = 'regionmapping_21_EU11.csv',
-    type = 'regional') %>%
+    type = 'regional', where = "mappingfolder") %>%
     as_tibble() %>%
     select(region = 'RegionCode', iso3c = 'CountryCode')
 
-  OECD_iso3c <- toolGetMapping(name = 'regionmappingOECD.csv',
-                               type = 'regional') %>%
+  OECD_iso3c <- toolGetMapping(name = 'regionmappingOECD.csv', type = 'regional', where = "mappingfolder") %>%
     as_tibble() %>% 
     select(iso3c = 'CountryCode', region = 'RegionCode') %>% 
     filter('OECD' == .data$region) %>% 

R/calcCoolingSharesBase.R

@@ -68,7 +68,7 @@ calcCoolingSharesBase <- function() {
   # read in mapping to REMIND technologies
   map_table <- read_excel(toolGetMapping(type = "sectoral",
                                          name = "TechnologyMappingDavies2REMIND.xlsx",
-                                         returnPathOnly = TRUE))
+                                         returnPathOnly = TRUE, where = "mappingfolder"))
   map <- list()
   map$davies <- paste(map_table$'Davies Source/Technology',map_table$'Davies Cooling',sep=".")
   map$remind <- paste(map_table$'REMIND Technology',map_table$'REMIND Cooling',sep=".")

R/calcCoolingSharesFuture.R

@@ -65,7 +65,7 @@ calcCoolingSharesFuture <- function() {
 
   # read in mapping to REMIND technologies
   map_table <- read_excel(toolGetMapping(type = "sectoral", name = "TechnologyMappingDavies2REMIND.xlsx",
-                                         returnPathOnly = TRUE))
+                                         returnPathOnly = TRUE, where = "mappingfolder"))
   map <- list()
   map$davies <- paste(map_table$'Davies Source/Technology',map_table$'Davies Cooling',sep=".")
   map$remind <- paste(map_table$'REMIND Technology',map_table$'REMIND Cooling',sep=".")

R/calcEEAGHGProjections.R

@@ -13,7 +13,7 @@
 #' @export
 
 calcEEAGHGProjections <- function() {
-  mapping <- toolGetMapping(type = "sectoral", name = "mappingEEAGHGProjections.csv")
+  mapping <- toolGetMapping(type = "sectoral", name = "mappingEEAGHGProjections.csv", where = "mappingfolder")
   eea <- readSource("EEA_EuropeanEnvironmentAgency", subtype = "projections")
 
   projectionsOverview <- as.data.frame(eea) %>%
@@ -40,7 +40,8 @@ calcEEAGHGProjections <- function() {
     )
 
   eeaDetail <- readSource("EEA_EuropeanEnvironmentAgency", subtype = "projections-detailed")
-  mappingDetailed <- toolGetMapping(type = "sectoral", name = "mappingEEAGHGProjectionsDetailed.csv")
+  mappingDetailed <- toolGetMapping(type = "sectoral", name = "mappingEEAGHGProjectionsDetailed.csv", 
+                    where = "mappingfolder")
 
   projectionsDetail <- as.data.frame(eeaDetail) %>%
     filter(!is.na(!!sym("Value"))) %>%

R/calcEconometricEmiParameter.R

@@ -24,7 +24,7 @@ calcEconometricEmiParameter <- function() {
   pop   <- calcOutput("Population", aggregate = FALSE)[, 2005, "pop_SSP2"] / 1000
 
   p3Country <- dimSums(edgar[, , c("2A1", "2A2")], dim = 3) / pop
-  mapping    <- toolGetMapping(type = "regional", name = "regionmappingEconometricEmi.csv")
+  mapping    <- toolGetMapping(type = "regional", name = "regionmappingEconometricEmi.csv", where = "mappingfolder")
   p3         <- toolAggregate(p3Country[unique(mapping$RepCountry), , ], mapping, weight = NULL)
   p3[is.na(p3)] <- 0
   getNames(p3) <- "co2cement_process.p3"

R/calcEffortSharingRefEmi.R

@@ -28,7 +28,7 @@ calcEffortSharingRefEmi <- function(subtype){
 
   } else if (subtype=="REMIND_GHG") {
     e_ES <- readSource("Eurostat_EffortSharing",subtype="emissions") 
-    EU11map <- toolGetMapping("regionmapping_21_EU11.csv", type = "regional")
+    EU11map <- toolGetMapping("regionmapping_21_EU11.csv", type = "regional", where = "mappingfolder")
     e_REMIND <- as.magpie(data.frame(
        period=rep(2005,9),
        regionscode=c("DEU","ECE","ECS","ENC","ESC","ESW","EWN","FRA","UKI"),

R/calcEmber.R

@@ -41,7 +41,7 @@ calcEmber <- function(subtype = "all") {
   }
 
   # load and apply mapping to chosen variables
-  map <- toolGetMapping("Mapping_Ember.csv", type = "reportingVariables") %>%
+  map <- toolGetMapping("Mapping_Ember.csv", type = "reportingVariables", where = "mappingfolder") %>%
     filter(!is.na(!!sym("REMIND_Variable")), !!sym("REMIND_Variable") != "")  # remove incomplete mapping rows
 
   # combine variable and unit in mapping

R/calcEmissionFactors.R

@@ -110,21 +110,22 @@ calcEmissionFactors <- function(subtype = "emission_factors", sectoral_resolutio
 
   # read in sectoral mapping (ECLIPSE (IMAGE) <> REMIND)
   map_sectors_ECLIPSE2Agg    <- read.csv(toolGetMapping(type = "sectoral", name = "mappingECLIPSEtoAggREMINDsectors.csv",
-                                                        returnPathOnly = TRUE),
+                                                        returnPathOnly = TRUE, where = "mappingfolder"),
                                          stringsAsFactors = TRUE)
   map_sectors_Agg2REMIND     <- read.csv(toolGetMapping(type = "sectoral", name = "mappingAggREMINDtoREMINDsectors.csv",
-                                                        returnPathOnly = TRUE),
+                                                        returnPathOnly = TRUE, where = "mappingfolder"),
                                          stringsAsFactors = TRUE)
   map_sectors_ECLIPSE2REMIND <- read.csv(toolGetMapping(type = "sectoral", name = "mappingECLIPSEtoREMINDsectors.csv",
-                                                        returnPathOnly = TRUE),
+                                                        returnPathOnly = TRUE, where = "mappingfolder"),
                                          stringsAsFactors = TRUE)
   # map_sectors <- map_sectors[which(!is.na(map_sectors$EDGE)),] # Remove transport sector (which is not represented in EDGE)
 
   # read in regional map (select ISO and GAINS codes only). This is required for the construction of the SSPs
   map_REMINDregions  <- read.csv2(toolGetMapping(type = "regional", name = "regionmappingREMIND.csv",
-                                                 returnPathOnly = TRUE),
+                                                 returnPathOnly = TRUE, where = "mappingfolder"),
                                   stringsAsFactors = TRUE)
-  map_regions  <- read.csv2(toolGetMapping(type = "regional", name = "regionmappingGAINS.csv", returnPathOnly = TRUE),
+  map_regions  <- read.csv2(toolGetMapping(type = "regional", name = "regionmappingGAINS.csv", returnPathOnly = TRUE, 
+                where = "mappingfolder"),
                             stringsAsFactors = TRUE)[, c(2, 3)]
   map_regions  <- map_regions %>%
     filter(.data$CountryCode != "ANT") %>% # Remove Netherland Antilles (not in REMIND regional mapping)

R/calcEmissionFactorsFeedstocks.R

@@ -48,7 +48,7 @@ calcEmissionFactorsFeedstocks <- function() {
   product_mapping <- toolGetMapping(
     name = "structuremappingIO_outputs_NECHEM.csv",
     type = "sectoral"
-  ) %>%
+  , where = "mappingfolder") %>%
     as_tibble() %>%
     right_join(
       tribble(

R/calcEmissions.R

@@ -44,7 +44,8 @@ calcEmissions <- function(datasource="CEDS16") {
       emi <- emi[,,"6B_Other-not-in-total",invert=TRUE]
 
       # aggregate and rename CEDS59 sectors to CEDS16 sectors
-      map_CEDS59_to_CEDS16  <- toolGetMapping(type = "sectoral", name = "mappingCEDS59toCEDS16.csv")
+      map_CEDS59_to_CEDS16  <- toolGetMapping(type = "sectoral", name = "mappingCEDS59toCEDS16.csv", 
+                              where = "mappingfolder")
       tmp <- toolAggregate(x=emi,weight = NULL, dim=3.1, rel = map_CEDS59_to_CEDS16, from="CEDS59",to="CEDS16")
 
       # rename emissions according to map (currently only relevant for VOC)
@@ -91,7 +92,8 @@ calcEmissions <- function(datasource="CEDS16") {
     emi <- emi[,,"6B_Other-not-in-total", invert=TRUE]
 
     # aggregate and rename CEDS59 sectors to REMIND variables
-    map_CEDS59_to_REMIND <- toolGetMapping(type = "sectoral", name = "mappingCEDS59toREMINDreporting.csv")
+    map_CEDS59_to_REMIND <- toolGetMapping(type = "sectoral", name = "mappingCEDS59toREMINDreporting.csv", 
+                            where = "mappingfolder")
     emi <- toolAggregate(x=emi,weight = NULL, dim=3.1, rel = map_CEDS59_to_REMIND, from="CEDS59",to="REMIND")
 
     # rename emissions according to map (currently only relevant for VOC)
@@ -170,7 +172,8 @@ calcEmissions <- function(datasource="CEDS16") {
       emi <- emi[,, "6B_Other-not-in-total", invert=TRUE]
 
       # aggregate and rename CEDS59 sectors to REMIND sectors
-      map_CEDS59_to_REMIND <- toolGetMapping(type = "sectoral", name = "mappingCEDS59toREMINDreporting_2021.csv")
+      map_CEDS59_to_REMIND <- toolGetMapping(type = "sectoral", name = "mappingCEDS59toREMINDreporting_2021.csv", 
+                              where = "mappingfolder")
       emi <- toolAggregate(x=emi,weight = NULL, dim=3.1, rel = map_CEDS59_to_REMIND, from="CEDS59",to="REMIND")
 
       # undo unnecessary conversion from convertCEDS2021.R
@@ -397,7 +400,7 @@ calcEmissions <- function(datasource="CEDS16") {
     emi[,, "n2o"] <- emi[,, "n2o"] * 1000
 
     # map sectors and pollutants to REMIND nomenclature
-    map_sec <- toolGetMapping("mappingEDGAR6toREMIND.csv", type = "sectoral")
+    map_sec <- toolGetMapping("mappingEDGAR6toREMIND.csv", type = "sectoral", where = "mappingfolder")
     map_pol <- c(n2o="N2O", ch4="CH4", co2_excl_short="CO2",
                  nh3="NH3", no2="NOX", bc="BC", co="CO", oc="OC", nmvoc="VOC", pm10="PM10", pm25="PM25", so2="Sulfur")
 
@@ -464,7 +467,7 @@ calcEmissions <- function(datasource="CEDS16") {
     getNames(em_limits, dim=2)[which(getNames(em_limits, dim=2) == "NOX")] <- "NOx"
 
     map <- read.csv2(toolGetMapping(type = "sectoral", name = "mappingLIMITSsectorstoREMINDsectors.csv",
-                                    returnPathOnly = TRUE),
+                                    returnPathOnly = TRUE, where = "mappingfolder"),
                      stringsAsFactors=TRUE)
 
     em_limits[is.na(em_limits)] <- 0.0
@@ -496,7 +499,7 @@ calcEmissions <- function(datasource="CEDS16") {
     #                                returnPathOnly = TRUE),
     #                 stringsAsFactors=TRUE)
     map <- read.csv2(toolGetMapping(type = "sectoral", name = "mappingECLIPSEsectorstoREMINDsectors.csv",
-                                    returnPathOnly = TRUE),
+                                    returnPathOnly = TRUE, where = "mappingfolder"),
                      stringsAsFactors=TRUE)
 
     em_eclipse[is.na(em_eclipse)] <- 0.0

R/calcEmploymentfactors.R

@@ -8,8 +8,8 @@
 
 calcEmploymentfactors <- function(improvements,multiplier){
   if (improvements=="None"||improvements=="CEEW"||improvements=="Dias"||improvements=="Rutovitz_aus"||improvements=="Solar_found"||improvements=="All"){
-    mapping <- toolGetMapping(type = "regional",name = "regionalmappingWEO2014.csv")
-    mapping_remind <- toolGetMapping(type = "regional",name = "regionmappingH12.csv")
+    mapping <- toolGetMapping(type = "regional",name = "regionalmappingWEO2014.csv", where = "mappingfolder")
+    mapping_remind <- toolGetMapping(type = "regional",name = "regionmappingH12.csv", where = "mappingfolder")
     colnames(mapping) <- c("region","country")
     mapping$country <- toolCountry2isocode(mapping$country)  
 

R/calcEuropeanEnergyDatasheets.R

@@ -27,7 +27,8 @@ calcEuropeanEnergyDatasheets <- function(subtype) {
         "year" = "Year", "value" = "Value"
       )
 
-    mapping <- toolGetMapping("Mapping_EuropeanEnergyDatasheets.csv", type = "reportingVariables") %>%
+    mapping <- toolGetMapping("Mapping_EuropeanEnergyDatasheets.csv", type = "reportingVariables", 
+                              where = "mappingfolder") %>%
       filter(!is.na(!!sym("REMIND")), !!sym("REMIND") != "") %>%
       mutate(!!sym("Conversion") := as.numeric(!!sym("Conversion"))) %>%
       select("variable" = "EED", "REMIND", "Conversion")

R/calcFE.R

@@ -16,7 +16,8 @@ calcFE <- function(source = "IEA", scenario_proj = "SSP2") {
     # remove period where only 0s appear
     data <- data[, 2021, , invert = T]
 
-    mapping <- toolGetMapping(type = "sectoral", name = "structuremappingIO_reporting.csv", returnPathOnly = TRUE)
+    mapping <- toolGetMapping(type = "sectoral", name = "structuremappingIO_reporting.csv", 
+                              returnPathOnly = TRUE, where = "mappingfolder")
     target <- c("output")
     map <- read.csv2(mapping, stringsAsFactors = FALSE, na.strings = "")
     # delete NAs rows

R/calcFEdemand.R

@@ -72,7 +72,8 @@ calcFEdemand <- function(subtype = "FE", use_ODYM_RECC = FALSE) {
       trp_nodes <- c("ueelTt", "ueLDVt", "ueHDVt")
 
       ## we work in the REMIND H12 regions to avoid strange ISO country behavior when rescaling
-      mappingfile <- toolGetMapping(type = "regional", name = "regionmappingH12.csv", returnPathOnly = TRUE)
+      mappingfile <- toolGetMapping(type = "regional", name = "regionmappingH12.csv", 
+                                    returnPathOnly = TRUE, where = "mappingfolder")
       rmnd_reg <- toolAggregate(rmnditem, mappingfile, from="CountryCode", to="RegionCode")
 
       ## to data.table (we use gdp_SSP2 as a starting point)
@@ -259,7 +260,8 @@ calcFEdemand <- function(subtype = "FE", use_ODYM_RECC = FALSE) {
       # - cumulate the reduction factor over the time horizon
 
       SSA_countries <- read_delim(
-        file = toolGetMapping(type = 'regional', name = 'regionmappingH12.csv', returnPathOnly = TRUE),
+        file = toolGetMapping(type = 'regional', name = 'regionmappingH12.csv', 
+        returnPathOnly = TRUE, where = "mappingfolder"),
         delim = ';',
         col_names = c('country', 'iso3c', 'region'),
         col_types = 'ccc',
@@ -417,7 +419,7 @@ calcFEdemand <- function(subtype = "FE", use_ODYM_RECC = FALSE) {
         character.data.frame()
 
       regionmapping <- toolGetMapping(type = 'regional',
-                                      name = 'regionmappingH12.csv') %>%
+                                      name = 'regionmappingH12.csv', where = "mappingfolder") %>%
         select(country = 'X', iso3c = 'CountryCode', region = 'RegionCode')
 
       historic_trend <- c(2004, 2020)
@@ -506,7 +508,7 @@ calcFEdemand <- function(subtype = "FE", use_ODYM_RECC = FALSE) {
     }
 
     if (subtype %in% c("FE", "FE_buildings", "UE_buildings", "FE_for_Eff", "UE_for_Eff", "ES")) {
-      mapping = toolGetMapping(type = "sectoral", name = "structuremappingIO_outputs.csv")
+      mapping = toolGetMapping(type = "sectoral", name = "structuremappingIO_outputs.csv", where = "mappingfolder")
 
       REMIND_dimensions = "REMINDitems_out"
       sets_names = getSets(data)
@@ -521,7 +523,8 @@ calcFEdemand <- function(subtype = "FE", use_ODYM_RECC = FALSE) {
 
     } else if (subtype %in% c("EsUeFe_in","EsUeFe_out")){
 
-        mapping_path <- toolGetMapping(type = "sectoral", name = "structuremappingIO_EsUeFe.csv", returnPathOnly = TRUE)
+        mapping_path <- toolGetMapping(type = "sectoral", name = "structuremappingIO_EsUeFe.csv", 
+                                    returnPathOnly = TRUE, where = "mappingfolder")
         mapping = read.csv2(mapping_path, stringsAsFactors = F)
     }
     #----- PROCESS DATA ------------------
@@ -880,7 +883,7 @@ calcFEdemand <- function(subtype = "FE", use_ODYM_RECC = FALSE) {
         ) %>%
         ungroup()
 
-      region_mapping_21 <- toolGetMapping('regionmapping_21_EU11.csv', 'regional') %>%
+      region_mapping_21 <- toolGetMapping('regionmapping_21_EU11.csv', 'regional', where = "mappingfolder") %>%
         as_tibble() %>%
         select(iso3c = 'CountryCode', region = 'RegionCode')
 
@@ -1454,7 +1457,7 @@ calcFEdemand <- function(subtype = "FE", use_ODYM_RECC = FALSE) {
               mutate(year = as.integer(as.character(.data$year))) %>%
               inner_join(
                 toolGetMapping(name = 'regionmappingOECD.csv',
-                               type = 'regional') %>%
+                               type = 'regional', where = "mappingfolder") %>%
                   as_tibble() %>%
                   select(iso3c = 'CountryCode', region = 'RegionCode'),
 
@@ -1496,7 +1499,7 @@ calcFEdemand <- function(subtype = "FE", use_ODYM_RECC = FALSE) {
           expand.values = TRUE) %>%
         inner_join(
           toolGetMapping(name = 'regionmappingOECD.csv',
-                         type = 'regional') %>%
+                         type = 'regional', where = "mappingfolder") %>%
             as_tibble() %>%
             select(iso3c = 'CountryCode', region = 'RegionCode'),