refactor

Author: kalashsinghal

NAMESPACE

@@ -26,6 +26,7 @@ importFrom(dplyr,inner_join)
 importFrom(dplyr,left_join)
 importFrom(dplyr,mutate)
 importFrom(dplyr,n)
+importFrom(dplyr,pick)
 importFrom(dplyr,pull)
 importFrom(dplyr,select)
 importFrom(dplyr,summarise)
@@ -88,6 +89,7 @@ importFrom(tibble,tribble)
 importFrom(tidyr,drop_na)
 importFrom(tidyr,pivot_wider)
 importFrom(tidyselect,all_of)
+importFrom(tidyselect,everything)
 importFrom(tidyselect,matches)
 importFrom(tiltIndicator,example_data_factory)
 importFrom(tiltIndicatorAfter,profile_emissions)

R/prepare_geo_data.R

@@ -14,11 +14,8 @@ prepare_geo_data <- function(data,
                              scenario = scenarios(),
                              year = years(),
                              risk_category = risk_category()) {
-  grouping_emission <- arg_match(grouping_emission)
   risk_category <- arg_match(risk_category)
   country_code <- arg_match(country_code)
-  scenario <- arg_match(scenario)
-  year <- year
 
   crucial <- c(
     aka("risk_category"),
@@ -32,10 +29,11 @@ prepare_geo_data <- function(data,
 
   shp_0 <- get_eurostat_geospatial(
     resolution = 10,
-    nuts_level = 3,
-    year = 2016,
+    nuts_level = "all",
+    year = 2021,
     crs = 3035
-  )
+  ) |>
+    filter(!(geo %in% c("FRY10", "FRY20", "FRY30", "FRY40", "FRY50")))
 
   # filter for the specified country
   shp_1 <- shp_0 |>
@@ -48,13 +46,27 @@ prepare_geo_data <- function(data,
   shp_1 <- shp_1 |>
     inner_join(nuts_all, by = "geo")
 
+  filtered_geo_data <- data
+
+  # Apply filtering only if the corresponding argument is not NULL
+  if (!is.null(grouping_emission)) {
+    filtered_geo_data <- filtered_geo_data |>
+      filter(.data$grouping_emission == .env$grouping_emission)
+  }
+
+  if (!is.null(scenario)) {
+    filtered_geo_data <- filtered_geo_data |>
+      filter(.data$scenario == .env$scenario)
+  }
+
+  if (!is.null(year)) {
+    filtered_geo_data <- filtered_geo_data |>
+      filter(.data$year == .env$year)
+  }
+
   # merge shapefile with financial data
-  geo <- data |>
-    filter(
-      .data$grouping_emission == .env$grouping_emission,
-      .data$scenario == .env$scenario,
-      .data$year == .env$year
-    ) |>
+  geo <- filtered_geo_data |>
+    distinct() |>
     left_join(shp_1, by = "postcode") |>
     st_as_sf()
 
@@ -77,15 +89,15 @@ prepare_geo_data <- function(data,
 #' library(tibble)
 #'
 #' # Create a sample geo_data tibble
-#' geo <- tibble(
+#' geo_example <- tibble(
 #'   postcode = c("1", "2", "3"),
 #'   company_name = c("A", "B", "C"),
 #'   emission_profile = factor(c("low", "medium", "high"),
 #'     levels = c("low", "medium", "high")
 #'   )
 #' )
 #'
-#' aggregate_geo(geo, mode = "emissions_profile_worst_case", risk_category = "emission_category")
+#' aggregate_geo(geo_example, mode = "emissions_profile_worst_case", risk_category = "emission_category")
 aggregate_geo <- function(geo, mode, risk_category) {
   aggregated_data <- geo |>
     group_by(.data$postcode, .data[[risk_category]]) |>
@@ -105,9 +117,9 @@ aggregate_geo <- function(geo, mode, risk_category) {
     summarise(
       total_mode = add(.data$total_mode),
       geometry = first(.data$geometry),
-      low = add(.data$low),
-      medium = add(.data$medium),
-      high = add(.data$high)
+      low = if ("low" %in% names(pick(everything()))) add(.data$low) else 0.0,
+      medium = if ("medium" %in% names(pick(everything()))) add(.data$medium) else 0.0,
+      high = if ("high" %in% names(pick(everything()))) add(.data$high) else 0.0
     ) |>
     mutate(color = pmap(list(.data$high, .data$medium, .data$low), custom_gradient_color))
   aggregated_data

R/tiltPlot-package.R

@@ -13,6 +13,7 @@
 #' @importFrom dplyr left_join
 #' @importFrom dplyr mutate
 #' @importFrom dplyr n
+#' @importFrom dplyr pick
 #' @importFrom dplyr pull
 #' @importFrom dplyr select
 #' @importFrom dplyr summarise
@@ -75,6 +76,7 @@
 #' @importFrom tidyr drop_na
 #' @importFrom tidyr pivot_wider
 #' @importFrom tidyselect all_of
+#' @importFrom tidyselect everything
 #' @importFrom tidyselect matches
 #' @importFrom tiltIndicator example_data_factory
 #' @importFrom tiltIndicatorAfter profile_emissions

R/utils.R

@@ -215,7 +215,8 @@ modes <- function() {
 scenarios <- function() {
   c(
     "1.5C RPS",
-    "NZ 2050"
+    "NZ 2050",
+    NULL
   )
 }
 
@@ -237,7 +238,8 @@ scenarios_financial <- function() {
 years <- function() {
   c(
     2030,
-    2050
+    2050,
+    NULL
   )
 }
 
@@ -266,6 +268,7 @@ grouping_emission <- function() {
     "tilt_sector",
     "unit",
     "unit_isic_4digit",
-    "unit_tilt_sector"
+    "unit_tilt_sector",
+    NULL
   )
 }