inserted roxygen doc + built README

DESCRIPTION

@@ -16,6 +16,7 @@ Imports:
     eurostat,
     ggalluvial,
     ggplot2,
+    ggpubr,
     giscoR,
     glue,
     grDevices,

NAMESPACE

@@ -55,6 +55,7 @@ importFrom(ggplot2,unit)
 importFrom(ggplot2,xlim)
 importFrom(ggplot2,ylab)
 importFrom(ggplot2,ylim)
+importFrom(ggpubr,ggarrange)
 importFrom(giscoR,gisco_get_nuts)
 importFrom(glue,glue)
 importFrom(grDevices,rgb)

R/scatter_plot_financial.R

@@ -28,20 +28,19 @@ scatter_plot_financial <- function(data,
                                    ),
                                    scenario = c("IPR", "WEO"),
                                    year = c(2030, 2050)) {
-  #FIXME: .env$ instead of _arg seems to cause a bug too ?
+  #FIXME: .env$ instead of _arg seems to cause a bug only for benchmarks.
   benchmarks_arg <- arg_match(benchmarks, multiple = TRUE)
-  scenario_arg <- arg_match(scenario)
-  year_arg <- year
+  scenario <- arg_match(scenario)
+  year <- year
   mode <- mode |>
     arg_match() |>
     switch_mode()
 
   data |>
     check_scatter_plot_financial() |>
-    prepare_scatter_plot_financial(benchmarks_arg, scenario_arg, year_arg) |>
+    prepare_scatter_plot_financial(benchmarks_arg, scenario, year) |>
     calculate_scatter_plot_financial(mode) |>
-    plot_scatter_financial_implementation()
-
+    plot_scatter_financial()
 }
 
 #' Check scatter plot with financial data
@@ -77,7 +76,7 @@ check_scatter_plot_financial <- function(data) {
   data
 }
 
-#' Process data
+#' Prepare data
 #'
 #' @param data A data frame.
 #' @param benchmarks A character vector.
@@ -86,12 +85,12 @@ check_scatter_plot_financial <- function(data) {
 #'
 #' @return A data frame.
 #' @noRd
-prepare_scatter_plot_financial <- function(data, benchmarks_arg, scenario_arg, year_arg) {
+prepare_scatter_plot_financial <- function(data, benchmarks, scenario, year) {
   data <- data |>
     filter(
-      .data$benchmark %in% benchmarks_arg,
-      .data$scenario == scenario_arg,
-      .data$year == year_arg
+      .data$benchmark %in% .env$benchmarks,
+      .data$scenario == .env$scenario,
+      .data$year == .env$year
     )
 
   data
@@ -119,8 +118,14 @@ calculate_rank <- function(data, mode, col) {
   list(rank,data)
 }
 
+#' Implement Rank
+#'
+#' @param data A data frame.
+#' @param mode A string.
+#'
+#' @return A data frame.
+#' @noRd
 calculate_scatter_plot_financial <- function(data, mode) {
-
   data <- calculate_rank(data, mode, "profile_ranking")[[2]]
 
   data$emission_profile_average <- calculate_rank(data, mode, "profile_ranking")[[1]]
@@ -129,8 +134,13 @@ calculate_scatter_plot_financial <- function(data, mode) {
   data
 }
 
-plot_scatter_financial_implementation <- function(data) {
-
+#' Plot the scatter financial plot
+#'
+#' @param data A data frame.
+#'
+#' @return A [ggplot] object.
+#' @noRd
+plot_scatter_financial <- function(data) {
   emission_rank_legend <- label_emission_rank() |> format_label()
   transition_risk_legend <- label_transition_risk() |> format_label()
 
@@ -150,9 +160,8 @@ plot_scatter_financial_implementation <- function(data) {
     ylab(transition_risk_legend) +
     theme_tiltplot()
 
-  plot <- ggarrange(emission_rank_plot, transition_score_plot, ncol = 2, nrow = 1)
+  plot <- ggarrange(emission_rank_plot, transition_score_plot)
   plot
-
 }
 
 

R/tiltPlot-package.R

@@ -50,6 +50,7 @@
 #' @importFrom ggplot2 xlim
 #' @importFrom ggplot2 ylab
 #' @importFrom ggplot2 ylim
+#' @importFrom ggpubr ggarrange
 #' @importFrom giscoR gisco_get_nuts
 #' @importFrom glue glue
 #' @importFrom grDevices rgb

R/utils.R

@@ -78,7 +78,6 @@ switch_mode <- function(mode) {
 #' names_matching(data, "ab")
 #' @noRd
 names_matching <- function(data, pattern) {
-  #names(select(data, matches(paste0("\\b", pattern, "\\b"))))
   names(select(data, matches(pattern)))
 }
 

README.Rmd

@@ -132,7 +132,7 @@ fin <- financial
 scenario <- "WEO"
 year <- 2030
 benchmarks = c("all", "unit")
-mode = "equal_weight"
+mode = "best_case"
 
 scatter_plot_financial(fin,
   benchmarks = benchmarks,

README.md

@@ -192,17 +192,14 @@ bar_plot_emission_profile(no_fin, benchmarks) +
 fin <- financial
 scenario <- "WEO"
 year <- 2030
+benchmarks = c("all", "unit")
+mode = "best_case"
 
 scatter_plot_financial(fin,
-  benchmarks = c("all", "unit"),
-  mode = "worst_case",
+  benchmarks = benchmarks,
+  mode = mode,
   scenario = scenario,
-  year = year
-) +
-  labs(title = paste(
-    "Scatter plot for financial data. Scenario", scenario,
-    "and year", year
-  ))
+  year = year)
 ```
 
 <img src="man/figures/README-unnamed-chunk-13-1.png" width="100%" />
@@ -220,6 +217,7 @@ map_region_risk(no_fin, "DE", benchmark = "tilt_sector", mode = "worst_case") +
   that are found in one region.
   © EuroGeographics for the administrative boundaries ")
 #> Extracting data using giscoR package, please report issues on https://github.com/rOpenGov/giscoR/issues
+#> Cache management as per giscoR. see 'giscoR::gisco_get_nuts()'
 ```
 
 <img src="man/figures/README-unnamed-chunk-14-1.png" width="100%" />