Style (manual): don't end lines in assignment operator

R/prep_audit_table.R

@@ -5,8 +5,7 @@ prep_audit_table <- function(
   currency_exchange_value
 ) {
 
-  audit_table_init <-
-    audit_file |>
+  audit_table_init <- audit_file |>
     dplyr::filter(
       .data[["investor_name"]] == .env[["investor_name"]],
       .data[["portfolio_name"]] == .env[["portfolio_name"]]
@@ -56,8 +55,7 @@ prep_audit_table <- function(
       value_usd = .data[["value_usd"]] / .env[["currency_exchange_value"]]
     )
 
-  included_table_totals <-
-    audit_table_init |>
+  included_table_totals <- audit_table_init |>
     dplyr::group_by(.data[["asset_type_analysis"]], .data[["included"]]) |>
     dplyr::summarise(
       total_value_invested = sum(.data[["value_usd"]], na.rm = TRUE),
@@ -69,8 +67,7 @@ prep_audit_table <- function(
       )
     )
 
-  included_table_value_breakdown <-
-    audit_table_init |>
+  included_table_value_breakdown <- audit_table_init |>
     dplyr::mutate(
       investment_means = dplyr::case_when(
         (
@@ -89,16 +86,14 @@ prep_audit_table <- function(
       .groups = "drop"
     )
 
-  fields_totals <-
-    c(
-      "asset_type_analysis",
-      "included",
-      "total_value_invested",
-      "percentage_value_invested"
-    )
+  fields_totals <- c(
+    "asset_type_analysis",
+    "included",
+    "total_value_invested",
+    "percentage_value_invested"
+  )
 
-  included_table_per_asset <-
-    included_table_totals |>
+  included_table_per_asset <- included_table_totals |>
     dplyr::left_join(
       included_table_value_breakdown,
       by = dplyr::join_by("asset_type_analysis")
@@ -113,8 +108,7 @@ prep_audit_table <- function(
       "investment_means"
     )
 
-  sum_table <-
-    included_table_per_asset |>
+  sum_table <- included_table_per_asset |>
     dplyr::summarise(
       asset_type_analysis = "Total",
       total_value_invested = sum(.data[["total_value_invested"]], na.rm = TRUE),
@@ -153,8 +147,7 @@ remove_dupe_entries_totals <- function(
   fields_totals
 ) {
   for (asset in unique(table[["asset_type_analysis"]])) {
-    idx_asset <-
-      table |>
+    idx_asset <- table |>
       dplyr::mutate(
         is_chosen_asset = .data[["asset_type_analysis"]] == .env[["asset"]]
       ) |>

R/prep_company_bubble.R

@@ -9,8 +9,7 @@ prep_company_bubble <- function(
   green_techs
 ) {
 
-  equity_data <-
-    equity_results_company |>
+  equity_data <- equity_results_company |>
     dplyr::filter(.data[["portfolio_name"]] == .env[["portfolio_name"]]) |>
     dplyr::filter(.data[["ald_sector"]] %in% c("Power", "Automotive")) |>
     dplyr::filter(.data[["equity_market"]] == "GlobalMarket") |>
@@ -73,8 +72,7 @@ prep_company_bubble <- function(
     dplyr::mutate(y = pmax(.data[["y"]], 0L, na.rm = TRUE)) |>
     dplyr::mutate(asset_class = "Listed Equity")
 
-  bonds_data <-
-    bonds_results_company |>
+  bonds_data <- bonds_results_company |>
     dplyr::filter(.data[["portfolio_name"]] == .env[["portfolio_name"]]) |>
     dplyr::filter(.data[["ald_sector"]] %in% c("Power", "Automotive")) |>
     dplyr::filter(.data[["equity_market"]] == "GlobalMarket") |>

R/prep_emissions_trajectory.R

@@ -6,16 +6,15 @@ prep_emissions_trajectory <- function(
   year_span,
   start_year
 ) {
-  emissions_units <-
-    c(
-      Automotive = "tons of CO\U00002082 per km per cars produced",
-      Aviation = "tons of CO\U00002082 per passenger km per active planes",
-      Cement = "tons of CO\U00002082 per tons of cement",
-      Coal = "tons of CO\U00002082 per tons of coal",
-      `Oil&Gas` = "tons of CO\U00002082 per GJ",
-      Power = "tons of CO\U00002082 per MWh",
-      Steel = "tons of CO\U00002082 per tons of steel"
-    )
+  emissions_units <- c(
+    Automotive = "tons of CO\U00002082 per km per cars produced",
+    Aviation = "tons of CO\U00002082 per passenger km per active planes",
+    Cement = "tons of CO\U00002082 per tons of cement",
+    Coal = "tons of CO\U00002082 per tons of coal",
+    `Oil&Gas` = "tons of CO\U00002082 per GJ",
+    Power = "tons of CO\U00002082 per MWh",
+    Steel = "tons of CO\U00002082 per tons of steel"
+  )
 
   list(
     `Listed Equity` = equity_results_portfolio,

R/prep_key_bars_company.R

@@ -11,8 +11,7 @@ prep_key_bars_company <- function(
   all_tech_levels
 ) {
 
-  equity_data_company <-
-    equity_results_company |>
+  equity_data_company <- equity_results_company |>
     dplyr::filter(.data[["portfolio_name"]] == .env[["portfolio_name"]]) |>
     dplyr::filter(.data[["year"]] %in% c(.env[["start_year"]] + 5L)) |>
     dplyr::filter(.data[["equity_market"]] %in% c("Global", "GlobalMarket")) |>
@@ -40,8 +39,7 @@ prep_key_bars_company <- function(
     dplyr::filter(!is.null(.data[["port_weight"]])) |>
     dplyr::filter(!is.null(.data[["plan_tech_share"]]))
 
-  bonds_data_company <-
-    bonds_results_company |>
+  bonds_data_company <- bonds_results_company |>
     dplyr::filter(.data[["portfolio_name"]] == .env[["portfolio_name"]]) |>
     dplyr::filter(.data[["year"]] %in% c(.env[["start_year"]] + 5L)) |>
     dplyr::filter(.data[["equity_market"]] %in% c("Global", "GlobalMarket")) |>

R/prep_key_bars_portfolio.R

@@ -10,8 +10,7 @@ prep_key_bars_portfolio <- function(
   pacta_sectors_not_analysed,
   all_tech_levels
 ) {
-  equity_data_portfolio <-
-    equity_results_portfolio |>
+  equity_data_portfolio <- equity_results_portfolio |>
     dplyr::filter(.data[["portfolio_name"]] == .env[["portfolio_name"]]) |>
     dplyr::filter(.data[["equity_market"]] %in% c("Global", "GlobalMarket")) |>
     dplyr::filter(.data[["year"]] %in% c(.env[["start_year"]] + 5L)) |>
@@ -63,8 +62,7 @@ prep_key_bars_portfolio <- function(
     dplyr::mutate(asset_class = "Listed Equity") |>
     dplyr::mutate_at("id", as.character) # convert the col type to character to prevent errors in case empty df is bound by rows # nolint
 
-  bonds_data_portfolio <-
-    bonds_results_portfolio |>
+  bonds_data_portfolio <- bonds_results_portfolio |>
     dplyr::filter(.data[["portfolio_name"]] == .env[["portfolio_name"]]) |>
     dplyr::filter(.data[["equity_market"]] %in% c("Global", "GlobalMarket")) |>
     dplyr::filter(.data[["year"]] %in% c(.env[["start_year"]] + 5L)) |>

R/prep_techexposure.R

@@ -15,8 +15,7 @@ prep_techexposure <- function(
   equity_market_levels,
   all_tech_levels
 ) {
-  portfolio <-
-    list(
+  portfolio <- list(
       `Listed Equity` = equity_results_portfolio,
       `Corporate Bonds` = bonds_results_portfolio
     ) |>
@@ -27,25 +26,21 @@ prep_techexposure <- function(
     ) |>
     dplyr::filter(!is.na(.data[["ald_sector"]]))
 
-  asset_classes <-
-    portfolio |>
+  asset_classes <- portfolio |>
     dplyr::pull("asset_class") |>
     unique()
 
-  equity_sectors <-
-    portfolio |>
+  equity_sectors <- portfolio |>
     dplyr::filter(.data[["asset_class"]] == "Listed Equity") |>
     dplyr::pull("ald_sector") |>
     unique()
 
-  bonds_sectors <-
-    portfolio |>
+  bonds_sectors <- portfolio |>
     dplyr::filter(.data[["asset_class"]] == "Corporate Bonds") |>
     dplyr::pull("ald_sector") |>
     unique()
 
-  indices <-
-    list(
+  indices <- list(
       `Listed Equity` = indices_eq_results_portfolio,
       `Corporate Bonds` = indices_cb_results_portfolio
     ) |>
@@ -61,8 +56,7 @@ prep_techexposure <- function(
       )
     )
 
-  peers <-
-    list(
+  peers <- list(
       `Listed Equity` = peers_equity_results_portfolio,
       `Corporate Bonds` = peers_bonds_results_portfolio
     ) |>

R/prep_techmix_sector.R

@@ -14,38 +14,33 @@ prep_techmix_sector <- function(
   all_tech_levels
 ) {
 
-  portfolio <-
-    list(
-      `Listed Equity` = equity_results_portfolio,
-      `Corporate Bonds` = bonds_results_portfolio
-    ) |>
+  portfolio <- list(
+    `Listed Equity` = equity_results_portfolio,
+    `Corporate Bonds` = bonds_results_portfolio
+  ) |>
     dplyr::bind_rows(.id = "asset_class") |>
     dplyr::filter(
       .data[["investor_name"]] == .env[["investor_name"]],
       .data[["portfolio_name"]] == .env[["portfolio_name"]]
     ) |>
     dplyr::filter(!is.na(.data[["ald_sector"]]))
 
-  asset_classes <-
-    portfolio |>
+  asset_classes <- portfolio |>
     dplyr::pull("asset_class") |>
     unique()
 
-  equity_sectors <-
-    portfolio |>
+  equity_sectors <- portfolio |>
     dplyr::filter(.data[["asset_class"]] == "Listed Equity") |>
     dplyr::filter(.data[["allocation"]] == "portfolio_weight") |>
     dplyr::pull("ald_sector") |>
     unique()
 
-  bonds_sectors <-
-    portfolio |>
+  bonds_sectors <- portfolio |>
     dplyr::filter(.data[["asset_class"]] == "Corporate Bonds") |>
     dplyr::pull("ald_sector") |>
     unique()
 
-  indices <-
-    list(
+  indices <- list(
       `Listed Equity` = indices_eq_results_portfolio,
       `Corporate Bonds` = indices_cb_results_portfolio
     ) |>
@@ -61,8 +56,7 @@ prep_techmix_sector <- function(
       )
     )
 
-  peers <-
-    list(
+  peers <- list(
       `Listed Equity` = peers_equity_results_portfolio,
       `Corporate Bonds` = peers_bonds_results_portfolio
     ) |>
@@ -79,8 +73,7 @@ prep_techmix_sector <- function(
     ) |>
     dplyr::filter(.data[["investor_name"]] == .env[["peer_group"]])
 
-  techexposure_data <-
-    dplyr::bind_rows(portfolio, peers, indices) |>
+  techexposure_data <- dplyr::bind_rows(portfolio, peers, indices) |>
     dplyr::filter(.data[["allocation"]] == "portfolio_weight") |>
     dplyr::filter(.data[["scenario_geography"]] == "Global") |>
     dplyr::filter(
@@ -91,8 +84,7 @@ prep_techmix_sector <- function(
     )
 
   if (nrow(techexposure_data) > 0L) {
-    techexposure_data <-
-      techexposure_data |>
+    techexposure_data <- techexposure_data |>
       dplyr::mutate(green = .data[["technology"]] %in% .env[["green_techs"]]) |>
       dplyr::group_by(
         .data[["asset_class"]],

R/prep_trajectory_alignment.R

@@ -15,11 +15,10 @@ prep_trajectory_alignment <- function(
   all_tech_levels
 ) {
 
-  portfolio <-
-    list(
-      `Listed Equity` = equity_results_portfolio,
-      `Corporate Bonds` = bonds_results_portfolio
-    ) |>
+  portfolio <- list(
+    `Listed Equity` = equity_results_portfolio,
+    `Corporate Bonds` = bonds_results_portfolio
+  ) |>
     dplyr::bind_rows(.id = "asset_class") |>
     dplyr::filter(
       .data[["investor_name"]] == .env[["investor_name"]],
@@ -37,52 +36,44 @@ prep_trajectory_alignment <- function(
     dplyr::filter(dplyr::n() > 1L) |>
     dplyr::ungroup()
 
-  asset_classes <-
-    portfolio |>
+  asset_classes <- portfolio |>
     dplyr::pull("asset_class") |>
     unique()
 
-  equity_markets <-
-    portfolio |>
+  equity_markets <- portfolio |>
     dplyr::filter(.data[["asset_class"]] == "Listed Equity") |>
     dplyr::pull("equity_market") |>
     unique()
 
-  bonds_markets <-
-    portfolio |>
+  bonds_markets <- portfolio |>
     dplyr::filter(.data[["asset_class"]] == "Corporate Bonds") |>
     dplyr::pull("equity_market") |>
     unique()
 
-  equity_techs <-
-    portfolio |>
+  equity_techs <- portfolio |>
     dplyr::filter(.data[["asset_class"]] == "Listed Equity") |>
     dplyr::pull("technology") |>
     unique()
 
-  equity_scenario_geography <-
-    portfolio |>
+  equity_scenario_geography <- portfolio |>
     dplyr::filter(.data[["asset_class"]] == "Listed Equity") |>
     dplyr::pull("scenario_geography") |>
     unique()
 
-  bonds_scenario_geography <-
-    portfolio |>
+  bonds_scenario_geography <- portfolio |>
     dplyr::filter(.data[["asset_class"]] == "Corporate Bonds") |>
     dplyr::pull("scenario_geography") |>
     unique()
 
-  bonds_techs <-
-    portfolio |>
+  bonds_techs <- portfolio |>
     dplyr::filter(.data[["asset_class"]] == "Corporate Bonds") |>
     dplyr::pull("technology") |>
     unique()
 
-  peers <-
-    list(
-      `Listed Equity` = peers_equity_results_portfolio,
-      `Corporate Bonds` = peers_bonds_results_portfolio
-    ) |>
+  peers <- list(
+    `Listed Equity` = peers_equity_results_portfolio,
+    `Corporate Bonds` = peers_bonds_results_portfolio
+  ) |>
     dplyr::bind_rows(.id = "asset_class") |>
     dplyr::filter(.data[["ald_sector"]] %in% .env[["tech_roadmap_sectors"]]) |>
     dplyr::filter(.data[["scenario_geography"]] != "GlobalAggregate") |>
@@ -116,11 +107,10 @@ prep_trajectory_alignment <- function(
     ) |>
     dplyr::filter(.data[["investor_name"]] == .env[["peer_group"]])
 
-  indices <-
-    list(
-      `Listed Equity` = indices_eq_results_portfolio,
-      `Corporate Bonds` = indices_cb_results_portfolio
-    ) |>
+  indices <- list(
+    `Listed Equity` = indices_eq_results_portfolio,
+    `Corporate Bonds` = indices_cb_results_portfolio
+  ) |>
     dplyr::bind_rows(.id = "asset_class") |>
     dplyr::filter(.data[["ald_sector"]] %in% .env[["tech_roadmap_sectors"]]) |>
     dplyr::filter(.data[["scenario_geography"]] != "GlobalAggregate") |>