Merge pull request #17 from RMI-PACTA/implement-prep-stats

Add data prep function for exposure statistics

main.R

@@ -136,6 +136,72 @@ if (nrow(techexposure_data) > 0) {
 techexposure_data
   }
 
+prep_exposure_stats <- function(audit_file, investor_name, portfolio_name, pacta_sectors) {
+  pacta_asset_classes <- c("Bonds", "Equity")
+
+  audit_table <- pacta.portfolio.report:::prep_audit_table(
+      audit_file,
+      investor_name = investor_name,
+      portfolio_name = portfolio_name,
+      currency_exchange_value = currency_exchange_value
+    )
+
+  exposure_stats <- audit_file %>%
+    filter(
+      .data$investor_name == .env$investor_name &
+      .data$portfolio_name == .env$portfolio_name) %>%
+    filter(.data$asset_type %in% pacta_asset_classes) %>%
+    filter(.data$valid_input == TRUE) %>%
+    mutate(across(c("bics_sector", "financial_sector"), as.character)) %>%
+    mutate(
+      sector =
+        if_else(!.data$financial_sector %in% .env$pacta_sectors,
+          "Other",
+          .data$financial_sector
+        )
+    ) %>%
+    summarise(
+      value = sum(.data$value_usd, na.rm = TRUE) / .env$currency_exchange_value,
+      .by = c("asset_type", "sector")
+    ) %>%
+    mutate(
+      perc_asset_val_sector = .data$value / sum(.data$value, na.rm = TRUE),
+      .by = c("asset_type")
+    ) %>%
+    inner_join(audit_table, by = join_by(asset_type == asset_type_analysis)) %>%
+    select("asset_type", "percentage_value_invested", "sector", "perc_asset_val_sector")
+
+  asset_classes_in_portfolio <- intersect(pacta_asset_classes, unique(exposure_stats$asset_type))
+
+  all_stats_with_zero_sector_exposure <- expand.grid(
+    asset_type = asset_classes_in_portfolio,
+    sector = pacta_sectors,
+    val_sector = 0
+    ) %>% inner_join(
+      distinct(select(exposure_stats, c("asset_type", "percentage_value_invested"))),
+      by = join_by(asset_type)
+    )
+
+  exposure_stats_all <- all_stats_with_zero_sector_exposure %>%
+    left_join(exposure_stats, by = join_by(asset_type, sector, percentage_value_invested)) %>%
+    mutate(
+      perc_asset_val_sector = if_else(
+        is.na(.data$perc_asset_val_sector),
+        .data$val_sector, 
+        .data$perc_asset_val_sector
+      )
+    ) %>%
+    mutate(
+      asset_type = case_when(
+        .data$asset_type == "Bonds" ~ "Corporate Bonds",
+        .data$asset_type == "Equity" ~ "Listed Equity"
+      )
+    ) %>%
+    select("asset_type", "percentage_value_invested", "sector", "perc_asset_val_sector")
+
+  exposure_stats_all
+}
+
 
 # input and output directories -------------------------------------------------
 
@@ -434,3 +500,13 @@ pacta.portfolio.report:::prep_emissions_trajectory(
   ) %>%
   pacta.portfolio.report:::translate_df_contents("data_emissions", dictionary) %>%
   jsonlite::write_json(path = file.path(output_dir, "data_emissions.json"))
+
+# data_exposure_stats.json
+
+prep_exposure_stats(
+  audit_file = audit_file,
+  investor_name = investor_name,
+  portfolio_name = portfolio_name,
+  pacta_sectors = pacta_sectors
+  ) %>%
+  jsonlite::write_json(path = file.path(output_dir, "data_exposure_stats.json"))