Add population downscaling functionality to import_inputs

Have not yet updated documentation. If new sysdata is configured and transferred over from the new branch of the same name on FrEDI_Data, this current iteration should work for "state", "regional", and "conus" values of the new popArea input assuming column names and values are as expected (see comments in code here). To get "national" functionality working, the sysdata will also need to include a df with cols "year" and "nation_to_conus" (ratio of conus/full US) saved at rDataList[["stateData"]][["data"]][["df_conusRatios"]] (or can update this in the code here if we want to save this elsewhere/use a different name).

FrEDI/R/import_inputs.R

@@ -88,7 +88,8 @@ import_inputs <- function(
   slrfile  = NULL,
   popfile  = NULL,
   gdpfile  = NULL,
-  temptype = "conus" ### "global", or "conus" (default)
+  temptype = "conus", ### "global", or "conus" (default)
+  popArea  = "state"  ### "national", "conus", "regional", or "state" (default)
 ){
   ###### Messaging ######
   namesInputs  <- c("tempfile", "slrfile", "popfile", "gdpfile")
@@ -113,6 +114,8 @@ import_inputs <- function(
   temptype_default <- "conus"
   temptype         <- (temptype |> is.null()) |> ifelse(temptype_default, temptype)
   conus            <- ((temptype |> tolower()) == "conus"); #conus |> print()
+
+  popArea <- tolower(popArea)
 
   ###### Initialize Inputs List ######
   ### Get input scenario info: co_inputScenarioInfo
@@ -143,12 +146,19 @@ import_inputs <- function(
     ###### Column Info ######
     region_i    <- inputInfo_i$region |> unique()
     valueCol_i  <- inputInfo_i$valueCol |> unique()
-    valueCol_i  <- (input_i == "pop") |> ifelse("state_pop", valueCol_i)
+    #valueCol_i  <- (input_i == "pop") |> ifelse("state_pop", valueCol_i)
     ### Initialize column names
     numCols_i   <- colNames_i <- c("year") |> c(valueCol_i)
     # cols0       <- c("region")
-    statecols0  <- c("region", "state", "postal")
-    if(input_i == "pop") {colNames_i <- statecols0 |> c(colNames_i)}
+    #statecols0  <- c("region", "state", "postal")
+    if(popArea == "regional"){
+      popcols0 <- c("region")
+    }
+    if(popArea == "state"){
+      popcols0 <- c("state", "postal")
+    }
+    if(input_i == "pop" & (popArea == "regional"|popArea == "state")) {colNames_i <- popcols0 |> c(colNames_i)}
+
     # if(byState){
     #   cols0      <- statecols0
     #   colNames_i <- numCols_i <- c("year", "state_pop")
@@ -171,7 +181,7 @@ import_inputs <- function(
 
     ###### Format Data Frame ######
     if(!isNullFile_i){
-      msg1 |> paste0("User supplied ", msgName_i, " input...") |> message()
+      msg1 |> paste0("User supplied ", msgName_i, " inputs...") |> message()
       msg2 |> paste0("Importing data from ", inputFile_i, "...") |> message()
       ### Try to import the file and initialize the list value
       fileInput_i   <- inputFile_i |> fun_tryInput(silent=T)
@@ -247,6 +257,51 @@ import_inputs <- function(
         #   ### Return list and not an inputs list if an error occurred
         #   return(returnList)
         # } ### End if flagged
+
+        ##### Calculate State Populations #####
+        if(input_i == "pop" & popArea != "state"){
+          ### Message user
+          msg3 |> paste0("User specified `popArea = '", popArea, "'`...") |> message()
+          msg3 |> paste0("Converting ", popArea, " populations to state populations") |> message()
+
+          ### Convert populations
+          df_popRatios    <- rDataList[["stateData"]][["data"]][["df_popRatios"]]
+          state_ratios    <- df_popRatios |> select(state, postal, region, year, region_to_state) |> distinct()
+          reg_ratios      <- df_popRatios |> select(region, year, conus_to_region) |> distinct()
+          conus_ratios    <- rDataList[["stateData"]][["data"]][["df_conusRatios"]] ## set to appropriate name
+
+          if(popArea == "national"){# input file should have cols year, pop
+            df_input_i <- df_input_i |>
+              group_by(year) |>
+              summarize(pop = sum(pop)) |>
+              left_join(conus_ratios, by = "year") |>
+              left_join(reg_ratios, by = "year") |>
+              left_join(state_ratios, by = c("year", "region")) |>
+              mutate(state_pop = pop * nation_to_conus * conus_to_region * region_to_state)
+          }
+          if(popArea == "conus"){# input file should have cols year, pop
+            df_input_i <- df_input_i |>
+              group_by(year) |>
+              summarize(pop = sum(pop)) |>
+              left_join(reg_ratios, by = c("year")) |>
+              left_join(state_ratios, by = c("year", "region")) |>
+              mutate(state_pop = pop * conus_to_region * region_to_state)
+          }
+          if(popArea == "regional"){# input file should have cols region, year, pop, region must be in dot form (e.g. Southern.Plains)
+            df_input_i <- df_input_i |>
+              group_by(year, region) |>
+              summarize(pop = sum(pop)) |>
+              left_join(state_ratios, by = c("year", "region")) |>
+              mutate(state_pop = pop * region_to_state)
+            print(head(df_input_i))
+          }
+          df_input_i <- df_input_i |>
+            select(state, postal, year, state_pop) # do we also need region here?
+        } else if(input_i == "pop" & popArea == "state"){
+          df_input_i <- df_input_i |>
+            rename(state_pop = pop) |>
+            select(state, postal, year, state_pop) # need region?
+        }
 
         ###### Update Results List Element ######
         ### Add results to the file