getRegions -> getItems & fixed various lintr warnings

.buildlibrary

@@ -1,4 +1,4 @@
-ValidationKey: '32636076'
+ValidationKey: '32656842'
 AcceptedWarnings:
 - 'Warning: package ''.*'' was built under R version'
 - 'Warning: namespace ''.*'' is not available and has been replaced'

.zenodo.json

@@ -1,6 +1,6 @@
 {
   "title": "mrcommons: MadRat commons Input Data Library",
-  "version": "1.7.13",
+  "version": "1.7.14",
   "description": "<p>Provides useful functions and a common structure to all the input data required to run models like MAgPIE\n    and REMIND of model input data.<\/p>",
   "creators": [
     {
@@ -85,7 +85,7 @@
       "name": "Soergel, Bjoern"
     },
     {
-      "name": "Führlich, Pascal"
+      "name": "Führlich, Pascal"
     },
     {
       "name": "Dietrich, Jan Philipp"

DESCRIPTION

@@ -1,8 +1,8 @@
 Package: mrcommons
 Type: Package
 Title: MadRat commons Input Data Library
-Version: 1.7.13
-Date: 2022-03-01
+Version: 1.7.14
+Date: 2022-03-02
 Authors@R: c(person("Benjamin Leon", "Bodirsky", email = "[email protected]", role = "aut"),
              person("Kristine", "Karstens", role = "aut"),
              person("Lavinia", "Baumstark", role = "aut"),

R/convertACCMIP.R

@@ -13,7 +13,7 @@
 #' }
 convertACCMIP <- function(x) {
   map <- toolGetMapping(name = "CountryToCellMapping.rds", where = "mrcommons")
-  getRegions(x) <- map$cell
+  getItems(x, dim = 1, raw = TRUE) <- map$cell
   y <- toolAggregate(x, map, from = 1, to = 3, dim = 1)
   y <- toolCountryFill(y, fill = NA)
   return(y)

R/convertCEDS.R

@@ -1,52 +1,52 @@
-convertCEDS <- function(x,subtype) {
-  
+convertCEDS <- function(x, subtype) {
+
   # fill all missing countries with 0
   x[is.na(x)] <- 0
 
-  x1 <- x["srb (kosovo)",,]
-  getRegions(x1) <- c("srb")
-  x["srb",,] <- x["srb",,] + x1
-  x          <- x[c("srb (kosovo)"),,,invert=TRUE]
-  
-  # Steve Smith 11.3.2016 on CEDS_Review_3-10-16.zip: there is a huge bug for fugitive emissions in zmb, and eth 
-  # I believe just past 2010. so do something to correct that (just keep those emissions constant from 2010 forward 
+  x1 <- x["srb (kosovo)", , ]
+  getItems(x1, dim = 1) <- "srb"
+  x["srb", , ] <- x["srb", , ] + x1
+  x          <- x[c("srb (kosovo)"), , , invert = TRUE]
+
+  # Steve Smith 11.3.2016 on CEDS_Review_3-10-16.zip: there is a huge bug for fugitive emissions in zmb, and eth
+  # I believe just past 2010. so do something to correct that (just keep those emissions constant from 2010 forward
   # for that sector in those two countries).
-
-  # checked: all zero expept for NMVOC. But for NMVOC emission after 2010 do not show significant deviations from before 2010
-
+
+  # checked: all zero expept for NMVOC. But for NMVOC emission after 2010 do not
+  # show significant deviations from before 2010
+
   # rename global to glo
-  getRegions(x) <- gsub("global","glo", getRegions(x))
-  getRegions(x) <- toupper(getRegions(x))
-
-  # delete ANT and SCG from raw data because their successors are already included in the data
-  #x <- x[c("ANT","SCG"),,invert=TRUE]
+  getItems(x, dim = 1) <- gsub("global", "glo", getItems(x, dim = 1))
+  getItems(x, dim = 1) <- toupper(getItems(x, dim = 1))
 
-  # most shipping and aviation data is global only (except 1A3dii_Domestic-navigation regional). We want to distribute it evenly across all countries.
+  # most shipping and aviation data is global only (except 1A3dii_Domestic-navigation
+  # regional). We want to distribute it evenly across all countries.
   # Therefore, save global data because it will be removed by toolCountryfill
-  
+
   # 1A3dii_Domestic-navigation   regional (global value is zero )
   # 1A3di_International-shipping global   (no regional values exist)
   # 1A3ai_International-aviation global   (no regional values exist)
   # 1A3aii_Domestic-aviation     global   (no regional values exist)
 
-  var_glob <- c("1A3di_International-shipping",
+  varGlob <- c("1A3di_International-shipping",
                 "1A3ai_International-aviation",
                 "1A3aii_Domestic-aviation")
-  x_glo <- x["GLO",,var_glob] 
-
-  # remove global values. Note: the sector 2A1_Cement-production has a global sum that is indentical to the sum over regions
-  x <- x["GLO",,invert=TRUE]
+  xGLO <- x["GLO", , varGlob]
+
+  # remove global values. Note: the sector 2A1_Cement-production has a global
+  # sum that is indentical to the sum over regions
+  x <- x["GLO", , invert = TRUE]
   # fills missing ISO countires and remove unknown ISO countires
-  x <- toolCountryFill(x,fill=0)
+  x <- toolCountryFill(x, fill = 0)
+
+  # Create weight 1 for xGLO
+  w <- new.magpie(getItems(x, dim = 1), getItems(x, dim = 2), getItems(xGLO, dim = 3), fill = 1)
 
-  # Create weight 1 for x_glo
-  w <- new.magpie(getRegions(x),getYears(x),getNames(x_glo),fill=1)
-
   # Create mapping of each country to GLO
-  mapping <- matrix(c(getRegions(x),rep("GLO",length(getRegions(x)))),length(getRegions(x))) 
-  
+  mapping <- data.frame(from = getItems(x, dim = 1), to = "GLO")
+
   # Spread global shipping and aviation data evenly across countries and save it to regions of x
-  x[,,var_glob] <- toolAggregate(x_glo,mapping,weight=w)
-  
+  x[, , varGlob] <- toolAggregate(xGLO, mapping, weight = w)
+
   return(x)
-}  
+}

R/convertCEDS2021.R

@@ -1,73 +1,75 @@
 #' @title convertCEDS2021
-#' 
+#'
 #' @description converts emission data from the CEDS database
 #' @param x magpie object from source function
 #' @return MAgPIE object
 #' @author Benjamin Leon Bodirsky, David Klein
 
 convertCEDS2021 <- function(x) {
-  
+
   # fill all missing countries with 0
   x[is.na(x)] <- 0
-  
+
   # change unit to Mt
-  x=x/1000
-
-  x1 <- x["srb (kosovo)",,]
-  getRegions(x1) <- c("srb")
-  x["srb",,] <- x["srb",,] + x1
-  x          <- x[c("srb (kosovo)"),,,invert=TRUE]
-  
-  getRegions(x) <- gsub("global","glo", getRegions(x))
-  getRegions(x) <- toupper(getRegions(x))
-  
-  map2 <- c(BC_ktC="bc_c",
-            CO_ktCO="co",
-            CH4_ktCH4="ch4",
-            N2O_ktN2O="n2o_n",
-            NH3_ktNH3="nh3_n",
-            NOx_ktNO2="no2_n",
-            NMVOC_ktNMVOC="nmvoc",
-            OC_ktC="oc_c",
-            SO2_ktSO2="so2",
-            CO2_ktCO2="co2_c"
+  x <- x / 1000
+
+  x1 <- x["srb (kosovo)", , ]
+  getItems(x1, dim = 1) <- "srb"
+  x["srb", , ] <- x["srb", , ] + x1
+  x          <- x[c("srb (kosovo)"), , , invert = TRUE]
+
+  getItems(x, dim = 1) <- gsub("global", "glo", getItems(x, dim = 1))
+  getItems(x, dim = 1) <- toupper(getItems(x, dim = 1))
+
+  map2 <- c(BC_ktC = "bc_c",
+            CO_ktCO = "co",
+            CH4_ktCH4 = "ch4",
+            N2O_ktN2O = "n2o_n",
+            NH3_ktNH3 = "nh3_n",
+            NOx_ktNO2 = "no2_n",
+            NMVOC_ktNMVOC = "nmvoc",
+            OC_ktC = "oc_c",
+            SO2_ktSO2 = "so2",
+            CO2_ktCO2 = "co2_c"
             )
-  getNames(x,dim=2) <- map2[getNames(x,dim=2)]
-
-  x[,,"n2o_n"]=x[,,"n2o_n"]/44*28
-  x[,,"nh3_n"]=x[,,"nh3_n"]/17*14
-  x[,,"no2_n"]=x[,,"no2_n"]/46*14
-  x[,,"co2_c"]=x[,,"co2_c"]/44*12
-
-
-
-
-  # most shipping and aviation data is global only (except 1A3dii_Domestic-navigation regional). We want to distribute it evenly across all countries.
+  getNames(x, dim = 2) <- map2[getNames(x, dim = 2)]
+
+  x[, , "n2o_n"] <- x[, , "n2o_n"] / 44 * 28
+  x[, , "nh3_n"] <- x[, , "nh3_n"] / 17 * 14
+  x[, , "no2_n"] <- x[, , "no2_n"] / 46 * 14
+  x[, , "co2_c"] <- x[, , "co2_c"] / 44 * 12
+
+
+
+
+  # most shipping and aviation data is global only (except 1A3dii_Domestic-navigation
+  # regional). We want to distribute it evenly across all countries.
   # Therefore, save global data because it will be removed by toolCountryfill
-  
+
   # 1A3dii_Domestic-navigation   regional (global value is zero )
   # 1A3di_International-shipping global   (no regional values exist)
   # 1A3ai_International-aviation global   (no regional values exist)
   # 1A3aii_Domestic-aviation     global   (no regional values exist)
-  
-  var_glob <- c("1A3di_International-shipping",
+
+  varGlob <- c("1A3di_International-shipping",
                 "1A3ai_International-aviation",
                 "1A3aii_Domestic-aviation")
-  x_glo <- x["GLO",,var_glob] 
-
-  # remove global values. Note: the sector 2A1_Cement-production has a global sum that is indentical to the sum over regions
-  x <- x["GLO",,invert=TRUE]
+  xGLO <- x["GLO", , varGlob]
+
+  # remove global values. Note: the sector 2A1_Cement-production has a global
+  # sum that is indentical to the sum over regions
+  x <- x["GLO", , invert = TRUE]
   # fills missing ISO countires and remove unknown ISO countires
-  x <- toolCountryFill(x,fill=0)
-  
-  # Create weight 1 for x_glo
-  w <- new.magpie(getRegions(x),getYears(x),getNames(x_glo),fill=1)
-  
+  x <- toolCountryFill(x, fill = 0)
+
+  # Create weight 1 for xGLO
+  w <- new.magpie(getItems(x, dim = 1), getItems(x, dim = 2), getItems(xGLO, dim = 3), fill = 1)
+
   # Create mapping of each country to GLO
-  mapping <- matrix(c(getRegions(x),rep("GLO",length(getRegions(x)))),length(getRegions(x))) 
-  
+  mapping <- data.frame(from = getItems(x, dim = 1), to = "GLO")
+
   # Spread global shipping and aviation data evenly across countries and save it to regions of x
-  x[,,var_glob] <- toolAggregate(x_glo,mapping,weight=w)
-  
+  x[, , varGlob] <- toolAggregate(xGLO, mapping, weight = w)
+
   return(x)
-}  
+}