From 3cbf404db11505e57f75911b6480a40fbaec5ce7 Mon Sep 17 00:00:00 2001 From: Alex Axthelm Date: Wed, 18 Dec 2024 17:25:42 +0100 Subject: [PATCH] Style (manual): don't end lines in assignment operator --- R/prep_audit_table.R | 31 ++++++++------------- R/prep_company_bubble.R | 6 ++-- R/prep_emissions_trajectory.R | 19 ++++++------- R/prep_key_bars_company.R | 6 ++-- R/prep_key_bars_portfolio.R | 6 ++-- R/prep_techexposure.R | 18 ++++-------- R/prep_techmix_sector.R | 30 ++++++++------------ R/prep_trajectory_alignment.R | 48 +++++++++++++------------------- R/prepare_pacta_dashboard_data.R | 21 +++++--------- R/translate_column_contents.R | 3 +- R/translate_df_contents.R | 6 ++-- R/translate_df_headers.R | 6 ++-- 12 files changed, 75 insertions(+), 125 deletions(-) diff --git a/R/prep_audit_table.R b/R/prep_audit_table.R index 16dd386..c2aa4f9 100644 --- a/R/prep_audit_table.R +++ b/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"]] ) |> diff --git a/R/prep_company_bubble.R b/R/prep_company_bubble.R index 6550d30..dff7527 100644 --- a/R/prep_company_bubble.R +++ b/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") |> diff --git a/R/prep_emissions_trajectory.R b/R/prep_emissions_trajectory.R index d62821a..8f5681a 100644 --- a/R/prep_emissions_trajectory.R +++ b/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, diff --git a/R/prep_key_bars_company.R b/R/prep_key_bars_company.R index d242162..e2c3dc6 100644 --- a/R/prep_key_bars_company.R +++ b/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")) |> diff --git a/R/prep_key_bars_portfolio.R b/R/prep_key_bars_portfolio.R index 60f9c33..3e4760e 100644 --- a/R/prep_key_bars_portfolio.R +++ b/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)) |> diff --git a/R/prep_techexposure.R b/R/prep_techexposure.R index 3239ac7..e46eddd 100644 --- a/R/prep_techexposure.R +++ b/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 ) |> diff --git a/R/prep_techmix_sector.R b/R/prep_techmix_sector.R index 923b9fa..483a273 100644 --- a/R/prep_techmix_sector.R +++ b/R/prep_techmix_sector.R @@ -14,11 +14,10 @@ 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"]], @@ -26,26 +25,22 @@ prep_techmix_sector <- 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::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"]], diff --git a/R/prep_trajectory_alignment.R b/R/prep_trajectory_alignment.R index a3a9d3c..02c149e 100644 --- a/R/prep_trajectory_alignment.R +++ b/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") |> diff --git a/R/prepare_pacta_dashboard_data.R b/R/prepare_pacta_dashboard_data.R index 05685bc..950e163 100644 --- a/R/prepare_pacta_dashboard_data.R +++ b/R/prepare_pacta_dashboard_data.R @@ -153,8 +153,7 @@ prepare_pacta_dashboard_data <- function( col_types = readr::cols() ) - dictionary <- - choose_dictionary_language( + dictionary <- choose_dictionary_language( data = dataframe_translations, language = language_select ) @@ -166,43 +165,37 @@ prepare_pacta_dashboard_data <- function( # pacta.portfolio.report functions expect that log_debug("Adding investor_name and portfolio_name to results data frames.") - audit_file <- - audit_file |> + audit_file <- audit_file |> dplyr::mutate( investor_name = investor_name, portfolio_name = portfolio_name ) - emissions <- - emissions |> + emissions <- emissions |> dplyr::mutate( investor_name = investor_name, portfolio_name = portfolio_name ) - equity_results_portfolio <- - equity_results_portfolio |> + equity_results_portfolio <- equity_results_portfolio |> dplyr::mutate( investor_name = investor_name, portfolio_name = portfolio_name ) - bonds_results_portfolio <- - bonds_results_portfolio |> + bonds_results_portfolio <- bonds_results_portfolio |> dplyr::mutate( investor_name = investor_name, portfolio_name = portfolio_name ) - equity_results_company <- - equity_results_company |> + equity_results_company <- equity_results_company |> dplyr::mutate( investor_name = investor_name, portfolio_name = portfolio_name ) - bonds_results_company <- - bonds_results_company |> + bonds_results_company <- bonds_results_company |> dplyr::mutate( investor_name = investor_name, portfolio_name = portfolio_name diff --git a/R/translate_column_contents.R b/R/translate_column_contents.R index 2400139..0b12e0d 100644 --- a/R/translate_column_contents.R +++ b/R/translate_column_contents.R @@ -4,8 +4,7 @@ translate_column_contents <- function( column, inplace = FALSE ) { - dictionary_column <- - dictionary |> + dictionary_column <- dictionary |> dplyr::filter(.data[["id_column"]] == .env[["column"]]) |> dplyr::select(-"id_column") diff --git a/R/translate_df_contents.R b/R/translate_df_contents.R index 8ff4352..6b44de0 100644 --- a/R/translate_df_contents.R +++ b/R/translate_df_contents.R @@ -13,8 +13,7 @@ translate_df_contents <- function( ) } - dictionary_subset <- - dictionary |> + dictionary_subset <- dictionary |> dplyr::filter(.data[["id_data"]] == .env[["id_data"]]) |> dplyr::transmute( .data[["id_column"]], @@ -23,8 +22,7 @@ translate_df_contents <- function( ) for (column in unique(dictionary_subset[["id_column"]])) { - data <- - translate_column_contents( + data <- translate_column_contents( data = data, dictionary = dictionary_subset, column = column, diff --git a/R/translate_df_headers.R b/R/translate_df_headers.R index 334495a..36c1ba1 100644 --- a/R/translate_df_headers.R +++ b/R/translate_df_headers.R @@ -17,13 +17,11 @@ translate_df_headers <- function( column_tibble <- dplyr::tibble(column_name = names(data)) - dictionary_subset <- - dictionary |> + dictionary_subset <- dictionary |> dplyr::filter(.data[["id_data"]] == .env[["id_data"]]) |> dplyr::transmute(.data[["id_column"]], .data[[!!language]]) - translated_headers <- - dictionary_subset |> + translated_headers <- dictionary_subset |> dplyr::left_join(column_tibble, by = c(id_column = "column_name")) names(data) <- translated_headers[[language]]