Merge pull request #89 from RMI-PACTA/88-rm-alignment-by-exposure-plot

rm plot that was extracted to package

R/plots.R

@@ -357,109 +357,6 @@ validate_input_data_generate_individual_outputs <- function(data,
   invisible()
 }
 
-# scatter plot net alignment by financial exposure----
-prep_scatter_alignment_exposure <- function(data,
-                                            year,
-                                            region,
-                                            scenario,
-                                            category,
-                                            exclude_group_ids = "benchmark") {
-  data <- data %>%
-    dplyr::filter(
-      !grepl(paste0(.env$exclude_group_ids, collapse = "|"), .data$group_id)
-    ) %>%
-    dplyr::filter(
-      .data$year == .env$year,
-      .data$region == .env$region,
-      .data$scenario == .env$scenario
-    ) %>%
-    dplyr::select(
-      dplyr::all_of(
-        c(
-          .env$category,
-          "scenario",
-          "region",
-          "sector",
-          "year",
-          "exposure_weighted_net_alignment",
-          "sum_loan_size_outstanding"
-        )
-      )
-    )
-
-  data
-}
-
-plot_scatter_alignment_exposure <- function(data,
-                                            floor_outliers,
-                                            cap_outliers,
-                                            category,
-                                            currency) {
-
-  if (!is.null(floor_outliers)) {
-    data <- data %>%
-      mutate(
-        exposure_weighted_net_alignment = if_else(
-          .data$exposure_weighted_net_alignment <= .env$floor_outliers,
-          .env$floor_outliers,
-          .data$exposure_weighted_net_alignment
-        )
-      )
-  }
-
-  if (!is.null(cap_outliers)) {
-    data <- data %>%
-      mutate(
-        exposure_weighted_net_alignment = if_else(
-          .data$exposure_weighted_net_alignment >= .env$cap_outliers,
-          .env$cap_outliers,
-          .data$exposure_weighted_net_alignment
-        )
-      )
-  }
-
-  title <- glue::glue("Net Aggregate Alignment By Financial Exposure And Sector")
-  subtitle <- ""
-  if (any(!is.null(floor_outliers), !is.null(cap_outliers))) {
-    subtitle <- glue::glue(
-      "{subtitle}Outliers are displayed on the lower and upper boundaries: {floor_outliers} and {cap_outliers}.",
-      .trim = FALSE
-    )
-  }
-
-  plot <- data %>%
-    dplyr::mutate(sector = tools::toTitleCase(.data$sector)) %>%
-    ggplot2::ggplot(
-      ggplot2::aes(
-        x = sum_loan_size_outstanding,
-        y = exposure_weighted_net_alignment,
-        color = !!rlang::sym(category)
-      )
-    ) +
-    ggplot2::geom_point() +
-    ggplot2::geom_hline(yintercept = 0) +
-    ggplot2::ylim(-1, 1) +
-    ggplot2::scale_x_continuous(labels = scales::comma) +
-    ggplot2::facet_wrap(
-      ~ sector
-    ) +
-    ggplot2::labs(
-      title = title,
-      subtitle = subtitle,
-      color = r2dii.plot::to_title(category)
-    ) +
-    ggplot2::xlab(glue::glue("Financial Exposure (in {currency})")) +
-    ggplot2::ylab("Net Aggregate Alignment") +
-    r2dii.plot::scale_colour_r2dii() +
-    ggplot2::theme_bw() +
-    ggplot2::theme(
-      axis.text.x = ggplot2::element_text(angle = 90, vjust = 0.5, hjust=1),
-      panel.grid = ggplot2::element_blank()
-    )
-
-  plot
-}
-
 # plot match success rate----
 plot_match_success_rate <- function(data,
                                     aggregate,