import and adapt the run_pacta script

plots.R

@@ -119,3 +119,364 @@ plot_match_success_rate <- function(data,
   }
   plot
 }
+
+
+generate_individual_outputs <- function(data,
+                                        matched_prioritized,
+                                        output_directory,
+                                        target_type = c("tms", "sda"),
+                                        group_id,
+                                        scenario_source,
+                                        scenario,
+                                        region = "global",
+                                        sector,
+                                        start_year,
+                                        time_horizon) {
+
+  # match input values
+  target_type <- match.arg(target_type)
+
+  target_scenario <- paste0("target_", scenario)
+
+  # validate input values
+  validate_input_args_generate_individual_outputs(
+    output_directory = output_directory,
+    group_id = group_id,
+    scenario_source = scenario_source,
+    target_scenario = target_scenario,
+    region = region,
+    sector = sector,
+    start_year = start_year,
+    time_horizon = time_horizon
+  )
+
+  # validate input data
+  validate_input_data_generate_individual_outputs(
+    data = data,
+    matched_prioritized = matched_prioritized,
+    target_type = target_type
+  )
+
+  # create sub directory for the selected institute
+  dir.create(file.path(output_directory, group_id), showWarnings = FALSE)
+
+  data <- data %>%
+    dplyr::filter(
+      group_id == .env$group_id,
+      scenario_source == .env$scenario_source,
+      region == .env$region,
+      sector %in% .env$sector
+    )
+
+  matched_prioritized <- matched_prioritized %>%
+    dplyr::filter(
+      group_id == .env$group_id,
+      sector %in% .env$sector
+    )
+
+  if (target_type == "tms") {
+    # plot tech mix for given sector
+    data_techmix <- data %>%
+      dplyr::filter(
+        .data$metric %in% c("projected", "corporate_economy", .env$target_scenario),
+        dplyr::between(.data$year, .env$start_year, .env$start_year + .env$time_horizon)
+      ) %>%
+      dplyr::mutate(
+        label = case_when(
+          .data$metric == "projected" ~ "Portfolio",
+          .data$metric == "corporate_economy" ~ "Corporate Economy",
+          .data$metric == .env$target_scenario ~ glue::glue("{r2dii.plot::to_title(toupper(.env$scenario))} Scenario")
+        )
+      ) %>%
+      r2dii.plot::prep_techmix(
+        span_5yr = TRUE
+      )
+
+    plot_techmix <- data_techmix %>%
+      r2dii.plot::plot_techmix()
+
+    # colors in tech mix plot set to make technologies more distinguishable
+    if (sector == "automotive") {
+      plot_techmix <- plot_techmix +
+        ggplot2::scale_fill_manual(
+          values = c("#4a5e54", "#d0d7e1", "#1b324f", "#00c082"),
+          labels = c("ICE", "Hybrid", "Fuelcell","Electric")
+        )
+    } else if (sector == "power") {
+      plot_techmix <- plot_techmix +
+        ggplot2::scale_fill_manual(
+          values = c("#4a5e54", "#d0d7e1", "#a63d57", "#f2e06e", "#1b324f", "#00c082"),
+          labels = paste(c("Coal", "Oil", "Gas", "Nuclear", "Hydro", "Renewables"), "Cap.")
+        )
+    }
+
+    # export tech mix
+    data_techmix %>%
+      readr::write_csv(
+        file = file.path(
+          output_directory,
+          group_id,
+          glue::glue("data_tech_mix_{sector}_{group_id}.csv")
+        ),
+        na = ""
+      )
+
+    ggplot2::ggsave(
+      filename = glue::glue("plot_tech_mix_{sector}_{group_id}.png"),
+      plot = plot_techmix,
+      device = "png",
+      path = file.path(output_directory, group_id)
+    )
+
+    # plot trajectory charts for all available techs in given sector
+    technologies_in_sector <- r2dii.data::increasing_or_decreasing %>%
+      dplyr::filter(.data$sector == .env$sector) %>%
+      dplyr::pull(.data$technology)
+
+    technologies_to_plot <- data %>%
+      dplyr::filter(
+        .data$metric == .env$target_scenario,
+        .data$technology %in% .env$technologies_in_sector
+      ) %>%
+      dplyr::distinct(.data$technology) %>%
+      dplyr::arrange(.data$technology) %>%
+      dplyr::pull()
+
+    for (i in 1:length(technologies_to_plot)) {
+      data_trajectory <- data %>%
+        dplyr::filter(
+          .data$technology == .env$technologies_to_plot[i],
+          dplyr::between(.data$year, .env$start_year, .env$start_year + .env$time_horizon)
+        ) %>%
+        r2dii.plot::prep_trajectory(
+          convert_label = r2dii.plot::recode_metric_trajectory,
+          span_5yr = TRUE,
+          value_col = "percentage_of_initial_production_by_scope"
+        )
+
+      if (sector == "power") {
+        y_lab_trajectory <- "Normalized Capacity"
+      } else {
+        y_lab_trajectory <- "Normalized Production"
+      }
+
+      plot_trajectory <- data_trajectory %>%
+        r2dii.plot::plot_trajectory(
+          center_y = TRUE,
+          perc_y_scale = TRUE
+        ) +
+        ggplot2::xlab("Year") +
+        ggplot2::ylab(y_lab_trajectory)
+
+      # export trajectory chart
+      data_trajectory %>%
+        readr::write_csv(
+          file = file.path(
+            output_directory,
+            group_id,
+            glue::glue("data_trajectory_{sector}_{technologies_to_plot[i]}_{group_id}.csv")
+          ),
+          na = ""
+        )
+
+      ggplot2::ggsave(
+        filename = glue::glue("plot_trajectory_{sector}_{technologies_to_plot[i]}_{group_id}.png"),
+        plot = plot_trajectory,
+        device = "png",
+        path = file.path(output_directory, group_id)
+      )
+    }
+  } else {
+    # plot convergence chart for given sector
+    adjusted_scenario <- paste0("adjusted_scenario_", scenario)
+
+    data_emission_intensity <- data %>%
+      dplyr::filter(
+        dplyr::between(
+          .data$year,
+          .env$start_year,
+          .env$start_year + .env$time_horizon)
+      ) %>%
+      dplyr::filter(
+        .data$emission_factor_metric %in% c(
+          "projected",
+          "corporate_economy",
+          .env$target_scenario,
+          .env$adjusted_scenario
+        )
+      ) %>%
+      dplyr::mutate(
+        emission_factor_metric = factor(
+          .data$emission_factor_metric,
+          levels = c(
+            "projected",
+            "corporate_economy",
+            .env$target_scenario,
+            .env$adjusted_scenario
+          )
+        )
+      ) %>%
+      r2dii.plot::prep_emission_intensity(
+        span_5yr = TRUE
+      )
+
+    colours_scale <- c(
+      "dark_blue",
+      "green",
+      "orange",
+      "grey"
+    )
+
+    if (sector == "cement") {
+      y_lab_emissions_intensity <- "Tons of CO2 per Ton of Cement Produced"
+    } else if (sector == "steel") {
+      y_lab_emissions_intensity <- "Tons of CO2 per Ton of Steel Produced"
+    } else if (sector == "aviation") {
+      y_lab_emissions_intensity <- "Tons of CO2 per Passenger Kilometer"
+    } else {
+      y_lab_emissions_intensity <- "Emission Factor Value"
+    }
+
+    plot_emission_intensity <- data_emission_intensity  %>%
+      r2dii.plot::plot_emission_intensity() +
+      r2dii.plot::scale_colour_r2dii(
+        colour_labels = colours_scale,
+        labels = c(
+          "Portfolio",
+          "Corporate Economy",
+          glue::glue("Target {r2dii.plot::to_title(toupper(scenario))}"),
+          glue::glue("Adjusted Scenario {r2dii.plot::to_title(toupper(scenario))}")
+        )
+      ) +
+      ggplot2::xlab("Year") +
+      ggplot2::ylab(y_lab_emissions_intensity)
+
+    # export convergence chart
+    data_emission_intensity %>%
+      readr::write_csv(
+        file = file.path(
+          output_directory,
+          group_id,
+          glue::glue("data_emission_intensity_{sector}_{group_id}.csv")
+        ),
+        na = ""
+      )
+
+    ggplot2::ggsave(
+      filename = glue::glue("plot_emission_intensity_{sector}_{group_id}.png"),
+      plot = plot_emission_intensity,
+      device = "png",
+      path = file.path(output_directory, group_id)
+    )
+  }
+  companies_included <- matched_prioritized %>%
+    dplyr::select(
+      "group_id", "name_abcd", "sector_abcd", "loan_size_outstanding",
+      "loan_size_outstanding_currency", "loan_size_credit_limit",
+      "loan_size_credit_limit_currency"
+    )
+
+  companies_included %>%
+    readr::write_csv(
+      file = file.path(
+        output_directory,
+        group_id,
+        glue::glue("companies_included_{sector}_{group_id}.csv")
+      ),
+      na = ""
+    )
+
+}
+
+
+validate_input_args_generate_individual_outputs <- function(output_directory,
+                                                            group_id,
+                                                            scenario_source,
+                                                            target_scenario,
+                                                            region,
+                                                            sector,
+                                                            start_year,
+                                                            time_horizon) {
+  if (!length(output_directory) == 1) {
+    stop("Argument output_directory must be of length 1. Please check your input.")
+  }
+  if (!inherits(output_directory, "character")) {
+    stop("Argument output_directory must be of class character. Please check your input.")
+  }
+  if (!length(group_id) == 1) {
+    stop("Argument group_id must be of length 1. Please check your input.")
+  }
+  if (!length(scenario_source) == 1) {
+    stop("Argument scenario_source must be of length 1. Please check your input.")
+  }
+  if (!inherits(scenario_source, "character")) {
+    stop("Argument scenario_source must be of class character. Please check your input.")
+  }
+  if (!length(target_scenario) == 1) {
+    stop("Argument target_scenario must be of length 1. Please check your input.")
+  }
+  if (!inherits(target_scenario, "character")) {
+    stop("Argument target_scenario must be of class character. Please check your input.")
+  }
+  if (!length(region) == 1) {
+    stop("Argument region must be of length 1. Please check your input.")
+  }
+  if (!inherits(region, "character")) {
+    stop("Argument region must be of class character. Please check your input.")
+  }
+  if (!length(sector) == 1) {
+    stop("Argument sector must be of length 1. Please check your input.")
+  }
+  if (!inherits(sector, "character")) {
+    stop("Argument sector must be of class character. Please check your input.")
+  }
+  if (!length(start_year) == 1) {
+    stop("Argument start_year must be of length 1. Please check your input.")
+  }
+  if (!inherits(start_year, "integer")) {
+    stop("Argument start_year must be of class integer. Please check your input.")
+  }
+  if (!length(time_horizon) == 1) {
+    stop("Argument time_horizon must be of length 1. Please check your input.")
+  }
+  if (!inherits(time_horizon, "integer")) {
+    stop("Argument time_horizon must be of class integer. Please check your input.")
+  }
+
+  invisible()
+}
+
+
+validate_input_data_generate_individual_outputs <- function(data,
+                                                            matched_prioritized,
+                                                            target_type) {
+  if (target_type == "sda") {
+    validate_data_has_expected_cols(
+      data = data,
+      expected_columns = c(
+        "sector", "year", "region", "scenario_source", "emission_factor_metric",
+        "emission_factor_value", "group_id"
+      )
+    )
+  } else if (target_type == "tms") {
+    validate_data_has_expected_cols(
+      data = data,
+      expected_columns = c(
+        "sector", "technology", "year", "region", "scenario_source", "metric",
+        "production", "technology_share", "scope",
+        "percentage_of_initial_production_by_scope", "group_id"
+      )
+    )
+  }
+
+  validate_data_has_expected_cols(
+    data = matched_prioritized,
+    expected_columns = c(
+      "group_id", "name_abcd", "sector", "sector_abcd", "loan_size_outstanding",
+      "loan_size_outstanding_currency", "loan_size_credit_limit",
+      "loan_size_credit_limit_currency"
+    )
+  )
+
+  invisible()
+}