formatting

R/readNewClimate.R

@@ -1,7 +1,7 @@
 #' Reads NPI policy database with technology capacity target from the Policy data base (v4 August 2024)
 #' by PBL that translate the high impact policies of https://climatepolicydatabase.org/.
 #' @description Reads excel sheet with NPi (National Policies Implemented)
-#' data on different policy targets (capacity) with different variations: 
+#' data on different policy targets (capacity) with different variations:
 #' unconditional for minimal and conditionnal for maximum targets.
 #' NPI targets only include targets that are based on implemented policy instruments.
 #' @details Country name is ISO coded. Capacity/Additional Capacity targets are in GW.
@@ -11,10 +11,9 @@
 #' @importFrom readxl read_xlsx
 #' @importFrom dplyr select
 
-
 readNewClimate <- function(subtype) {
-
-
+  
+  
   if (grepl("2025", subtype, fixed = TRUE)) { # keep structure to compare when new versions (NPi target updates)
     NPIfile <- "NPi_2025-02-03.xlsx"
   } else {
@@ -23,17 +22,15 @@ readNewClimate <- function(subtype) {
       warning("\nNo data for year in ", subtype, " available. Choose default: ", NPIfile)
     }
   }
-
+  
   if (grepl("Capacity", subtype, fixed = TRUE)) {
     # Capacity/Additional Capacity targets are in GW.
     NPI <- read_excel(NPIfile, sheet = "Capacity_target_PBL_2025",
                       col_types = c("text", "skip", "numeric", "text", "text", "numeric",
                                     "numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
                                     "numeric", "numeric", "numeric", "numeric", "numeric", "skip", "skip", "skip"))
     x <- as.magpie(NPI, spatial = 1, temporal = 2, datacol = 3)
-  }
-
-  if (grepl("Emissions", subtype, fixed = TRUE)) {
+  } else if (grepl("Emissions", subtype, fixed = TRUE)) {
     input <- read_xlsx(NPIfile, sheet = "EmissionTargets", skip = 3, na = c("?", ""))
     # select the relevant columns to work upon
     input2 <- select(input, 2, 7:14)
@@ -54,29 +51,29 @@ readNewClimate <- function(subtype) {
               paste(unique(input2$Type)[!unique(input2$Type) %in% allowedType], collapse = ", "),
               ". Please use: ", paste(allowedType, collapse = " or "), ".")
     }
-
+    
     # drop extra columns
     input2[is.na(input2$Unconditional), ]$Unconditional <- input2[is.na(input2$Unconditional), ]$Uncond2
     input2[is.na(input2$Conditional), ]$Conditional <- input2[is.na(input2$Conditional), ]$Cond2
     input2$Uncond2 <- NULL
     input2$Cond2 <- NULL
-
+    
     # if conditional is empty, fill with unconditional
     input2[is.na(input2$Conditional), ]$Conditional <- input2[is.na(input2$Conditional), ]$Unconditional
-
+    
     # in case a country has two or more types of targets for same year, use GHG-Absolute targets
     # note: the only remaining country in 2021 is MGD Madagascar based on its 2016 submission
     input2 <- input2[!(input2$ISO_Code %in% input2[duplicated(input2[c(1, 4)]), ]$ISO_Code &
-    input2$Target_Year %in% input2[duplicated(input2[c(1, 4)]), ]$Target_Year &
-    input2$Type != "GHG-Absolute"), ]
-
+                         input2$Target_Year %in% input2[duplicated(input2[c(1, 4)]), ]$Target_Year &
+                         input2$Type != "GHG-Absolute"), ]
+    
     # check whether conditional is more stringent than unconditional
     condTrumpsUncond <- (input2$Conditional <= input2$Unconditional) | is.na(input2$Unconditional)
     if (any(!condTrumpsUncond)) {
       warning("readNewClimate with subtype=", subtype, ": unconditional target more stringent than conditional in: ",
               paste(input2$ISO_Code[!condTrumpsUncond], collapse = ", "))
     }
-
+    
     # warning if emission changes have no reference year or BAU reference
     ref4change <- input2$ISO_Code[input2$Reference_Year %in% c("no", NA) &
                                     input2$Type %in% c("GHG", "CO2/GDP", "GHG/GDP", "GHG-Absolute")]
@@ -85,11 +82,13 @@ readNewClimate <- function(subtype) {
               paste(ref4change, collapse = ", "))
     }
     # as magclass can only cover numerical values well, transform: in column 2 BAU into -1, and Type into number based on allowedType
-    input2[2] <- as.numeric(unlist(rapply(input2[2], function(x)
-                                            ifelse(x == "BAU", -1, ifelse(x == "no", -2, x)), how = "replace")))
+    input2[2] <- as.numeric(unlist(rapply(input2[2], function(x) 
+      ifelse(x == "BAU", -1, ifelse(x == "no", -2, x)), how = "replace")))
     input2[5] <- as.numeric(unlist(rapply(input2[5], function(x) match(x, allowedType), how = "replace")))
     # sort with c(1,4,2,3,5,6,7) to get region into first and years into second column
     x <- as.magpie(input2[c(1, 4, 2, 3, 5, 6, 7)], spatial = 1, temporal = 2, datacol = 3)
-  }
+  } else {
+    stop("Incorrect subtype, please use Capacity_YYYY_cond or Emissions_YYYY_cond (or uncond).")
+  } 
   return(x)
 }