diff --git a/.lintr b/.lintr
index a645424..1ff6b85 100644
--- a/.lintr
+++ b/.lintr
@@ -1,12 +1,7 @@
-linters: linters_with_defaults(
-    cyclocomp_linter = NULL,
-    line_length_linter = NULL,
-    indentation_linter = NULL,
-    object_usage_linter = NULL,
-    trailing_whitespace_linter = NULL,
-    object_length_linter = NULL,
-    assignment_linter = NULL,
-    commas_linter = NULL,
-    trailing_blank_lines_linter = NULL,
-    commented_code_linter = NULL
+linters: all_linters(
+    object_usage_linter = NULL
+  )
+exclusions: list(
+    "inst/extdata/translation/",
+    "tests/testthat.R"
   )
diff --git a/DESCRIPTION b/DESCRIPTION
index 772492d..be12d31 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: workflow.pacta.dashboard
 Title: Run PACTA dashboard JSON generation
-Version: 0.0.0.9009
+Version: 0.0.0.9010
 Authors@R:
     c(person(given = "Alex",
              family = "Axthelm",
@@ -30,15 +30,19 @@ Imports:
     glue,
     jsonlite,
     logger,
-    magrittr,
     pacta.portfolio.utils,
     pacta.workflow.utils,
     readr,
     rlang,
-    stringr,
     tidyr,
     workflow.pacta
 Remotes:
     RMI-PACTA/pacta.portfolio.utils,
     RMI-PACTA/pacta.workflow.utils,
     RMI-PACTA/workflow.pacta
+Suggests: 
+    testthat (>= 3.0.0),
+    tibble
+Config/testthat/edition: 3
+Depends: 
+    R (>= 4.1.0)
diff --git a/NAMESPACE b/NAMESPACE
index b047321..de8e384 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -8,45 +8,11 @@ export(prep_key_bars_portfolio)
 export(prep_techmix_sector)
 export(prepare_schema_files)
 export(run_dashboard_workflow)
-importFrom(dplyr,across)
-importFrom(dplyr,all_of)
-importFrom(dplyr,arrange)
-importFrom(dplyr,bind_rows)
-importFrom(dplyr,case_when)
-importFrom(dplyr,desc)
-importFrom(dplyr,distinct)
-importFrom(dplyr,everything)
-importFrom(dplyr,filter)
-importFrom(dplyr,first)
-importFrom(dplyr,group_by)
-importFrom(dplyr,if_else)
-importFrom(dplyr,inner_join)
-importFrom(dplyr,join_by)
-importFrom(dplyr,lag)
-importFrom(dplyr,last)
-importFrom(dplyr,left_join)
-importFrom(dplyr,mutate)
-importFrom(dplyr,mutate_at)
-importFrom(dplyr,n)
-importFrom(dplyr,pull)
-importFrom(dplyr,reframe)
-importFrom(dplyr,rename)
-importFrom(dplyr,row_number)
-importFrom(dplyr,select)
-importFrom(dplyr,slice)
-importFrom(dplyr,summarise)
-importFrom(dplyr,tibble)
-importFrom(dplyr,transmute)
-importFrom(dplyr,ungroup)
 importFrom(logger,log_debug)
 importFrom(logger,log_error)
 importFrom(logger,log_info)
 importFrom(logger,log_trace)
 importFrom(logger,log_warn)
-importFrom(magrittr,"%>%")
 importFrom(rlang,":=")
 importFrom(rlang,.data)
 importFrom(rlang,.env)
-importFrom(tidyr,pivot_longer)
-importFrom(tidyr,pivot_wider)
-importFrom(tidyr,unite)
diff --git a/R/build_dashboard.R b/R/build_dashboard.R
index b9cf777..288f499 100644
--- a/R/build_dashboard.R
+++ b/R/build_dashboard.R
@@ -19,6 +19,7 @@ build_dashboard <- function(
     dashboard_skeleton_files_dir = dashboard_skeleton_files_dir,
     dashboard_output_dir = dashboard_output_dir
   )
+  stopifnot(dashboard_copy_success)
 
   dashboard_output_data_dir <- file.path(dashboard_output_dir, "data")
   if (dashboard_data_dir != dashboard_output_data_dir) {
@@ -31,6 +32,7 @@ build_dashboard <- function(
       to = dashboard_output_data_dir,
       recursive = TRUE
     )
+    stopifnot(data_copy_success)
   }
 
   out <- list(
diff --git a/R/choose_dictionary_language.R b/R/choose_dictionary_language.R
index a5ee7c8..e476877 100644
--- a/R/choose_dictionary_language.R
+++ b/R/choose_dictionary_language.R
@@ -1,12 +1,14 @@
-choose_dictionary_language <-
-  function(data, language) {
-    language <- tolower(language)
+choose_dictionary_language <- function(
+  data,
+  language
+) {
+  language <- tolower(language)
 
-    data %>%
-      transmute(
-        .data$id_data,
-        .data$id_column,
-        translate_key = .data$key,
-        translate_value = .data[[language]]
-      )
-  }
+  data |>
+    dplyr::transmute(
+      .data[["id_data"]],
+      .data[["id_column"]],
+      translate_key = .data[["key"]],
+      translate_value = .data[[language]]
+    )
+}
diff --git a/R/filter_scenarios_per_sector.R b/R/filter_scenarios_per_sector.R
index 2608c0c..229844d 100644
--- a/R/filter_scenarios_per_sector.R
+++ b/R/filter_scenarios_per_sector.R
@@ -1,12 +1,14 @@
-filter_scenarios_per_sector <-
-  function(data, select_scenario_other, select_scenario) {
-    special_sectors <- c("Aviation")
-    rest_of_sectors <- setdiff(unique(data$ald_sector), special_sectors)
-
-    data %>%
-      filter(
-        scenarios_found_in_sectors(.data, select_scenario_other, c("Aviation")) |
-          scenarios_found_in_sectors(.data, select_scenario, rest_of_sectors)
-      )
-  }
+filter_scenarios_per_sector <- function(
+  data,
+  select_scenario_other,
+  select_scenario
+) {
+  special_sectors <- "Aviation"
+  rest_of_sectors <- setdiff(unique(data[["ald_sector"]]), special_sectors)
 
+  data |>
+    dplyr::filter(
+      scenarios_found_in_sectors(.data, select_scenario_other, "Aviation") |
+        scenarios_found_in_sectors(.data, select_scenario, rest_of_sectors)
+    )
+}
diff --git a/R/get_scenario.R b/R/get_scenario.R
index be60d10..7fa5c81 100644
--- a/R/get_scenario.R
+++ b/R/get_scenario.R
@@ -1,4 +1,37 @@
-get_scenario <- function(scenario_parameter) {
-  scenario <- unlist(stringr::str_split(scenario_parameter,"_", n = 2))[2]
-  scenario
+get_scenario <- function(
+  scenario_parameter
+) {
+  scenario_name <- split_scenario_parameter(
+    scenario_parameter = scenario_parameter
+  )[["scenario_name"]]
+  return(scenario_name)
+}
+
+get_scenario_source <- function(
+  scenario_parameter
+) {
+  scenario_source <- split_scenario_parameter(
+    scenario_parameter = scenario_parameter
+  )[["scenario_source"]]
+  return(scenario_source)
+}
+
+split_scenario_parameter <- function(
+  scenario_parameter
+) {
+  split_parameter <- unlist(
+    strsplit(
+      x = scenario_parameter,
+      split = "_",
+      fixed = TRUE
+    )
+  )
+  scenario_source <- split_parameter[[1L]]
+  scenario_name <- paste(split_parameter[-1L], collapse = "_")
+  return(
+    list(
+      scenario_source = scenario_source,
+      scenario_name = scenario_name
+    )
+  )
 }
diff --git a/R/get_scenario_source.R b/R/get_scenario_source.R
deleted file mode 100644
index e153edd..0000000
--- a/R/get_scenario_source.R
+++ /dev/null
@@ -1,4 +0,0 @@
-get_scenario_source <- function(scenario_parameter) {
-  source <- unlist(stringr::str_split(scenario_parameter,"_", n = 2))[1]
-  source
-}
diff --git a/R/prep_audit_table.R b/R/prep_audit_table.R
index 4bc2a18..c2aa4f9 100644
--- a/R/prep_audit_table.R
+++ b/R/prep_audit_table.R
@@ -1,127 +1,167 @@
-prep_audit_table <-
-  function(audit_file, investor_name, portfolio_name, currency_exchange_value) {
-    sort_order <- c(
-      "Bonds",
-      "Equity",
-      "Other",
-      "Unclassified"
-    )
+prep_audit_table <- function(
+  audit_file,
+  investor_name,
+  portfolio_name,
+  currency_exchange_value
+) {
 
-    audit_table_init <-
-      audit_file %>%
-      filter(
-        .data$investor_name == .env$investor_name,
-        .data$portfolio_name == .env$portfolio_name
-      ) %>%
-      mutate(asset_type = if_else(
-        .data$valid_input, .data$asset_type, "Unknown"
-      )) %>%
-      mutate(is_included = if_else(
-        .data$asset_type %in% c("Others", "Funds"), FALSE, .data$valid_input
-      )) %>%
-      mutate(included = if_else(.data$is_included, "Yes", "No")) %>%
-      mutate(asset_type_analysis = case_when(
-        .data$asset_type %in% c("Bonds", "Equity") ~ .data$asset_type,
-        .data$asset_type == "Others" ~ "Other",
-        (.data$asset_type == "Funds") & (.data$direct_holding) ~ "Other",
+  audit_table_init <- audit_file |>
+    dplyr::filter(
+      .data[["investor_name"]] == .env[["investor_name"]],
+      .data[["portfolio_name"]] == .env[["portfolio_name"]]
+    ) |>
+    dplyr::mutate(
+      asset_type = dplyr::if_else(
+        .data[["valid_input"]], .data[["asset_type"]], "Unknown"
+      )
+    ) |>
+    dplyr::mutate(
+      is_included = dplyr::if_else(
+        .data[["asset_type"]] %in% c("Others", "Funds"),
+        FALSE,
+        .data[["valid_input"]]
+      )
+    ) |>
+    dplyr::mutate(
+      included = dplyr::if_else(
+        .data[["is_included"]],
+        "Yes",
+        "No"
+      )
+    ) |>
+    dplyr::mutate(
+      asset_type_analysis = dplyr::case_when(
+        .data[["asset_type"]] %in% c("Bonds", "Equity") ~ .data[["asset_type"]],
+        .data[["asset_type"]] == "Others" ~ "Other",
+        (
+          (.data[["asset_type"]] == "Funds") & (.data[["direct_holding"]])
+        ) ~ "Other",
         TRUE ~ "Unclassified"
-      )) %>%
-      mutate(
-        asset_type_analysis =
-          factor(.data$asset_type_analysis, levels = .env$sort_order)
-      ) %>%
-      mutate(value_usd = if_else(.data$value_usd < 0, 0, .data$value_usd)) %>%
-      mutate(value_usd = .data$value_usd / .env$currency_exchange_value)
-
-    included_table_totals <-
-      audit_table_init %>%
-      group_by(.data$asset_type_analysis, .data$included) %>%
-      summarise(
-        total_value_invested = sum(.data$value_usd, na.rm = TRUE),
-        .groups = "drop"
-      ) %>%
-      mutate(
-        percentage_value_invested =
-          .data$total_value_invested / sum(.data$total_value_invested)
       )
-
-    included_table_value_breakdown <-
-      audit_table_init %>%
-      mutate(investment_means = case_when(
-        (.data$asset_type == "Funds") & (.data$direct_holding) ~ "Unidentified Funds",
-        .data$direct_holding ~ "Direct",
-        !.data$direct_holding ~ "Via a Fund"
-      )) %>%
-      group_by(.data$asset_type_analysis, .data$investment_means) %>%
-      summarise(
-        value_invested = sum(.data$value_usd, na.rm = TRUE),
-        .groups = "drop"
+    ) |>
+    dplyr::mutate(
+      asset_type_analysis = factor(
+        .data[["asset_type_analysis"]],
+        levels = c(
+          "Bonds",
+          "Equity",
+          "Other",
+          "Unclassified"
+        )
       )
+    ) |>
+    dplyr::mutate(value_usd = pmax(.data[["value_usd"]], 0L)) |>
+    dplyr::mutate(
+      value_usd = .data[["value_usd"]] / .env[["currency_exchange_value"]]
+    )
 
-    fields_totals <-
-      c(
-        "asset_type_analysis",
-        "included",
-        "total_value_invested",
-        "percentage_value_invested"
+  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),
+      .groups = "drop"
+    ) |>
+    dplyr::mutate(
+      percentage_value_invested = (
+        .data[["total_value_invested"]] / sum(.data[["total_value_invested"]])
       )
+    )
 
-    included_table_per_asset <-
-      included_table_totals %>%
-      left_join(included_table_value_breakdown, by = "asset_type_analysis") %>%
-      remove_duplicate_entries_totals(fields_totals) %>%
-      select(
-        "asset_type_analysis",
-        "total_value_invested",
-        "percentage_value_invested",
-        "included",
-        "value_invested",
-        "investment_means"
+  included_table_value_breakdown <- audit_table_init |>
+    dplyr::mutate(
+      investment_means = dplyr::case_when(
+        (
+          (.data[["asset_type"]] == "Funds") & (.data[["direct_holding"]])
+        ) ~ "Unidentified Funds",
+        .data[["direct_holding"]] ~ "Direct",
+        !.data[["direct_holding"]] ~ "Via a Fund"
       )
+    ) |>
+    dplyr::group_by(
+      .data[["asset_type_analysis"]],
+      .data[["investment_means"]]
+    ) |>
+    dplyr::summarise(
+      value_invested = sum(.data[["value_usd"]], na.rm = TRUE),
+      .groups = "drop"
+    )
 
-    sum_table <-
-      included_table_per_asset %>%
-      summarise(
-        asset_type_analysis = "Total",
-        total_value_invested = sum(.data$total_value_invested, na.rm = TRUE),
-        percentage_value_invested = sum(.data$percentage_value_invested, na.rm = TRUE),
-        included = NA,
-        value_invested = sum(.data$value_invested, na.rm = TRUE),
-        investment_means = NA
-      )
+  fields_totals <- c(
+    "asset_type_analysis",
+    "included",
+    "total_value_invested",
+    "percentage_value_invested"
+  )
 
-    bind_rows(included_table_per_asset, sum_table)
-  }
+  included_table_per_asset <- included_table_totals |>
+    dplyr::left_join(
+      included_table_value_breakdown,
+      by = dplyr::join_by("asset_type_analysis")
+    ) |>
+    remove_dupe_entries_totals(fields_totals) |>
+    dplyr::select(
+      "asset_type_analysis",
+      "total_value_invested",
+      "percentage_value_invested",
+      "included",
+      "value_invested",
+      "investment_means"
+    )
+
+  sum_table <- included_table_per_asset |>
+    dplyr::summarise(
+      asset_type_analysis = "Total",
+      total_value_invested = sum(.data[["total_value_invested"]], na.rm = TRUE),
+      percentage_value_invested = sum(
+        .data[["percentage_value_invested"]],
+        na.rm = TRUE
+      ),
+      included = NA,
+      value_invested = sum(.data[["value_invested"]], na.rm = TRUE),
+      investment_means = NA
+    )
 
+  dplyr::bind_rows(included_table_per_asset, sum_table)
+}
 
-equal_adjacent_fields_totals <-
-  function(table, fields_totals, idx) {
-    are_equal <- TRUE
-    for (field in fields_totals) {
-      are_equal <- are_equal &&
-        (pull(slice(table, idx - 1), field) == pull(slice(table, idx), field))
-    }
-    are_equal
+
+equal_adjacent_fields_totals <- function(
+  table,
+  fields_totals,
+  idx
+) {
+  are_equal <- TRUE
+  for (field in fields_totals) {
+    are_equal <- are_equal &&
+      (
+        dplyr::pull(dplyr::slice(table, idx - 1L), field) ==
+          dplyr::pull(dplyr::slice(table, idx), field)
+      )
   }
+  are_equal
+}
 
 
-remove_duplicate_entries_totals <-
-  function(table, fields_totals) {
-    for (asset in unique(table$asset_type_analysis)) {
-      idx_asset <-
-        table %>%
-        mutate(is_chosen_asset = .data$asset_type_analysis == .env$asset) %>%
-        pull(.data$is_chosen_asset) %>%
-        which()
+remove_dupe_entries_totals <- function(
+  table,
+  fields_totals
+) {
+  for (asset in unique(table[["asset_type_analysis"]])) {
+    idx_asset <- table |>
+      dplyr::mutate(
+        is_chosen_asset = .data[["asset_type_analysis"]] == .env[["asset"]]
+      ) |>
+      dplyr::pull(.data[["is_chosen_asset"]]) |>
+      which()
 
-      if (length(idx_asset) >= 2) {
-        for (i in length(idx_asset):2) {
-          idx <- idx_asset[i]
-          if (equal_adjacent_fields_totals(table, fields_totals, idx)) {
-            table[idx, fields_totals] <- NA
-          }
+    if (length(idx_asset) >= 2L) {
+      for (i in length(idx_asset):2L) {
+        idx <- idx_asset[i]
+        if (equal_adjacent_fields_totals(table, fields_totals, idx)) {
+          table[idx, fields_totals] <- NA
         }
       }
     }
-    table
   }
+  table
+}
diff --git a/R/prep_company_bubble.R b/R/prep_company_bubble.R
index bf9a0db..8fb3fdc 100644
--- a/R/prep_company_bubble.R
+++ b/R/prep_company_bubble.R
@@ -15,73 +15,136 @@
 #' @return (data.frame) suitible for serialization to JSON (using
 #' `jsonlite::toJSON`/`jsonlite::write_json`)
 #' @export
-prep_company_bubble <-
-  function(equity_results_company,
-           bonds_results_company,
-           portfolio_name,
-           start_year,
-           green_techs) {
-    
-    equity_data <-
-      equity_results_company %>%
-      filter(.data$portfolio_name == .env$portfolio_name) %>%
-      filter(.data$ald_sector %in% c("Power", "Automotive")) %>%
-      filter(.data$equity_market == "GlobalMarket") %>%
-      filter(.data$scenario_geography == "Global") %>%
-      filter(.data$year %in% c(.env$start_year, .env$start_year + 5)) %>%
-      mutate(
-        plan_buildout = last(.data$plan_tech_prod, order_by = .data$year) - first(.data$plan_tech_prod, order_by = .data$year),
-        scen_buildout = last(.data$scen_tech_prod, order_by = .data$year) - first(.data$scen_tech_prod, order_by = .data$year),
-        .by = c("company_name", "technology", "scenario_source", "scenario", "allocation")
-      ) %>%
-      filter(.data$year == .env$start_year) %>%
-      mutate(green = .data$technology %in% .env$green_techs) %>%
-      reframe(
-        plan_tech_share = sum(.data$plan_tech_share, na.rm = TRUE),
-        plan_buildout = sum(.data$plan_buildout, na.rm = TRUE),
-        scen_buildout = sum(.data$scen_buildout, na.rm = TRUE),
-        plan_carsten = sum(.data$plan_carsten, na.rm = TRUE),
-        port_weight = unique(.data$port_weight),
-        .by = c("company_name", "allocation", "scenario_source",
-                "scenario", "ald_sector", "green", "year")
-      ) %>%
-      mutate(y = .data$plan_buildout / .data$scen_buildout) %>%
-      filter(.data$green) %>%
-      select(-"plan_buildout", -"scen_buildout", -"green") %>%
-      filter(!is.na(.data$plan_tech_share)) %>%
-      mutate(y = pmax(.data$y, 0, na.rm = TRUE)) %>%
-      mutate(asset_class = "Listed Equity")
-    
-    bonds_data <-
-      bonds_results_company %>%
-      filter(.data$portfolio_name == .env$portfolio_name) %>%
-      filter(.data$ald_sector %in% c("Power", "Automotive")) %>%
-      filter(.data$equity_market == "GlobalMarket") %>%
-      filter(.data$scenario_geography == "Global") %>%
-      filter(.data$year %in% c(.env$start_year, .env$start_year + 5)) %>%
-      mutate(
-        plan_buildout = last(.data$plan_tech_prod, order_by = .data$year) - first(.data$plan_tech_prod, order_by = .data$year),
-        scen_buildout = last(.data$scen_tech_prod, order_by = .data$year) - first(.data$scen_tech_prod, order_by = .data$year),
-        .by = c("company_name", "technology", "scenario_source", "scenario", "allocation")
-      ) %>%
-      filter(.data$year == .env$start_year) %>%
-      mutate(green = .data$technology %in% .env$green_techs) %>%
-      reframe(
-        plan_tech_share = sum(.data$plan_tech_share, na.rm = TRUE),
-        plan_buildout = sum(.data$plan_buildout, na.rm = TRUE),
-        scen_buildout = sum(.data$scen_buildout, na.rm = TRUE),
-        plan_carsten = sum(.data$plan_carsten, na.rm = TRUE),
-        port_weight = unique(.data$port_weight),
-        .by = c("company_name", "allocation", "scenario_source",
-                "scenario", "ald_sector", "green", "year")
-      ) %>%
-      mutate(y = .data$plan_buildout / .data$scen_buildout) %>%
-      filter(.data$green) %>%
-      select(-"plan_buildout", -"scen_buildout", -"green") %>%
-      filter(!is.na(.data$plan_tech_share)) %>%
-      mutate(y = pmax(.data$y, 0, na.rm = TRUE)) %>%
-      mutate(asset_class = "Corporate Bonds")
-    
-    bind_rows(equity_data, bonds_data)
-  }
+prep_company_bubble <- function(
+  equity_results_company,
+  bonds_results_company,
+  portfolio_name,
+  start_year,
+  green_techs
+) {
+  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") |>
+    dplyr::filter(.data[["scenario_geography"]] == "Global") |>
+    dplyr::filter(
+      .data[["year"]] %in% c(
+        .env[["start_year"]],
+        .env[["start_year"]] + 5L
+      )
+    ) |>
+    dplyr::mutate(
+      plan_buildout = (
+        dplyr::last(
+          .data[["plan_tech_prod"]],
+          order_by = .data[["year"]]
+        ) - dplyr::first(
+          .data[["plan_tech_prod"]],
+          order_by = .data[["year"]]
+        )
+      ),
+      scen_buildout = (
+        dplyr::last(
+          .data[["scen_tech_prod"]],
+          order_by = .data[["year"]]
+        ) - dplyr::first(
+          .data[["scen_tech_prod"]],
+          order_by = .data[["year"]]
+        )
+      ),
+      .by = c(
+        "company_name",
+        "technology",
+        "scenario_source",
+        "scenario",
+        "allocation"
+      )
+    ) |>
+    dplyr::filter(.data[["year"]] == .env[["start_year"]]) |>
+    dplyr::mutate(green = .data[["technology"]] %in% .env[["green_techs"]]) |>
+    dplyr::reframe(
+      plan_tech_share = sum(.data[["plan_tech_share"]], na.rm = TRUE),
+      plan_buildout = sum(.data[["plan_buildout"]], na.rm = TRUE),
+      scen_buildout = sum(.data[["scen_buildout"]], na.rm = TRUE),
+      plan_carsten = sum(.data[["plan_carsten"]], na.rm = TRUE),
+      port_weight = unique(.data[["port_weight"]]),
+      .by = c(
+        "company_name",
+        "allocation",
+        "scenario_source",
+        "scenario",
+        "ald_sector",
+        "green",
+        "year"
+      )
+    ) |>
+    dplyr::mutate(y = .data[["plan_buildout"]] / .data[["scen_buildout"]]) |>
+    dplyr::filter(.data[["green"]]) |>
+    dplyr::select(-"plan_buildout", -"scen_buildout", -"green") |>
+    dplyr::filter(!is.na(.data[["plan_tech_share"]])) |>
+    dplyr::mutate(y = pmax(.data[["y"]], 0L, na.rm = TRUE)) |>
+    dplyr::mutate(asset_class = "Listed Equity")
 
+  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") |>
+    dplyr::filter(.data[["scenario_geography"]] == "Global") |>
+    dplyr::filter(
+      .data[["year"]] %in% c(
+        .env[["start_year"]],
+        .env[["start_year"]] + 5L
+      )
+    ) |>
+    dplyr::mutate(
+      plan_buildout = dplyr::last(
+        .data[["plan_tech_prod"]],
+        order_by = .data[["year"]]
+      ) - dplyr::first(
+        .data[["plan_tech_prod"]],
+        order_by = .data[["year"]]
+      ),
+      scen_buildout = (
+        dplyr::last(
+          .data[["scen_tech_prod"]],
+          order_by = .data[["year"]]
+        ) - dplyr::first(
+          .data[["scen_tech_prod"]],
+          order_by = .data[["year"]]
+        )
+      ),
+      .by = c(
+        "company_name",
+        "technology",
+        "scenario_source",
+        "scenario",
+        "allocation"
+      )
+    ) |>
+    dplyr::filter(.data[["year"]] == .env[["start_year"]]) |>
+    dplyr::mutate(green = .data[["technology"]] %in% .env[["green_techs"]]) |>
+    dplyr::reframe(
+      plan_tech_share = sum(.data[["plan_tech_share"]], na.rm = TRUE),
+      plan_buildout = sum(.data[["plan_buildout"]], na.rm = TRUE),
+      scen_buildout = sum(.data[["scen_buildout"]], na.rm = TRUE),
+      plan_carsten = sum(.data[["plan_carsten"]], na.rm = TRUE),
+      port_weight = unique(.data[["port_weight"]]),
+      .by = c(
+        "company_name",
+        "allocation",
+        "scenario_source",
+        "scenario",
+        "ald_sector",
+        "green",
+        "year"
+      )
+    ) |>
+    dplyr::mutate(y = .data[["plan_buildout"]] / .data[["scen_buildout"]]) |>
+    dplyr::filter(.data[["green"]]) |>
+    dplyr::select(-"plan_buildout", -"scen_buildout", -"green") |>
+    dplyr::filter(!is.na(.data[["plan_tech_share"]])) |>
+    dplyr::mutate(y = pmax(.data[["y"]], 0L, na.rm = TRUE)) |>
+    dplyr::mutate(asset_class = "Corporate Bonds")
+
+  dplyr::bind_rows(equity_data, bonds_data)
+}
diff --git a/R/prep_emissions_pie.R b/R/prep_emissions_pie.R
index 52b9c2b..043cd59 100644
--- a/R/prep_emissions_pie.R
+++ b/R/prep_emissions_pie.R
@@ -1,15 +1,29 @@
-prep_emissions_pie <-
-  function(data, asset_type, investor_name, portfolio_name, pacta_sectors) {
-    data %>%
-      ungroup() %>%
-      filter(.data$investor_name == .env$investor_name &
-               .data$portfolio_name == .env$portfolio_name) %>%
-      filter(.data$asset_type %in% c("Bonds", "Equity")) %>%
-      select("asset_type", "sector", "weighted_sector_emissions") %>%
-      mutate(exploded = .data$sector %in% .env$pacta_sectors) %>%
-      arrange(.data$asset_type, desc(.data$exploded), .data$sector) %>%
-      rename(key = .data$sector, value = .data$weighted_sector_emissions) %>%
-      filter(!is.na(.data$key)) %>%
-      filter(.data$asset_type == .env$asset_type) %>%
-      select(-"asset_type")
-  }
+prep_emissions_pie <- function(
+  data,
+  asset_type,
+  investor_name,
+  portfolio_name,
+  pacta_sectors
+) {
+  data |>
+    dplyr::ungroup() |>
+    dplyr::filter(
+      .data[["investor_name"]] == .env[["investor_name"]],
+      .data[["portfolio_name"]] == .env[["portfolio_name"]]
+    ) |>
+    dplyr::filter(.data[["asset_type"]] %in% c("Bonds", "Equity")) |>
+    dplyr::select("asset_type", "sector", "weighted_sector_emissions") |>
+    dplyr::mutate(exploded = .data[["sector"]] %in% .env[["pacta_sectors"]]) |>
+    dplyr::arrange(
+      .data[["asset_type"]],
+      dplyr::desc(.data[["exploded"]]),
+      .data[["sector"]]
+    ) |>
+    dplyr::rename(
+      key = .data[["sector"]],
+      value = .data[["weighted_sector_emissions"]]
+    ) |>
+    dplyr::filter(!is.na(.data[["key"]])) |>
+    dplyr::filter(.data[["asset_type"]] == .env[["asset_type"]]) |>
+    dplyr::select(-"asset_type")
+}
diff --git a/R/prep_emissions_trajectory.R b/R/prep_emissions_trajectory.R
index 713052c..8f5681a 100644
--- a/R/prep_emissions_trajectory.R
+++ b/R/prep_emissions_trajectory.R
@@ -1,53 +1,68 @@
-prep_emissions_trajectory <-
-  function(equity_results_portfolio,
-           bonds_results_portfolio,
-           portfolio_name,
-           pacta_sectors,
-           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"
-      )
+prep_emissions_trajectory <- function(
+  equity_results_portfolio,
+  bonds_results_portfolio,
+  portfolio_name,
+  pacta_sectors,
+  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"
+  )
 
-    list(`Listed Equity` = equity_results_portfolio,
-           `Corporate Bonds` = bonds_results_portfolio) %>%
-      bind_rows(.id = "asset_class") %>%
-      filter(.data$portfolio_name == .env$portfolio_name) %>%
-      filter(.data$scenario_geography == "Global") %>%
-      select(
-        "asset_class",
-        "allocation",
-        "equity_market",
-        sector = "ald_sector",
-        "year",
-        plan = "plan_sec_emissions_factor",
-        scen = "scen_sec_emissions_factor",
-        "scenario",
-        "scenario_source"
-      ) %>%
-      distinct() %>%
-      filter(!is.nan(.data$plan)) %>%
-      pivot_longer(c("plan", "scen"), names_to = "plan") %>%
-      unite("name", "sector", "plan", remove = FALSE) %>%
-      mutate(disabled = !.data$sector %in% .env$pacta_sectors) %>%
-      mutate(unit = .env$emissions_units[.data$sector]) %>%
-      group_by(.data$asset_class) %>%
-      filter(!all(.data$disabled)) %>%
-      mutate(equity_market =  case_when(
-        .data$equity_market == "GlobalMarket" ~ "Global Market",
-        .data$equity_market == "DevelopedMarket" ~ "Developed Market",
-        .data$equity_market == "EmergingMarket" ~ "Emerging Market",
-        TRUE ~ .data$equity_market)
-      ) %>%
-      filter(.data$year <= .env$start_year + .env$year_span) %>%
-      arrange(.data$asset_class, factor(.data$equity_market, levels = c("Global Market", "Developed Market", "Emerging Market"))) %>%
-      ungroup()
-  }
+  list(
+    `Listed Equity` = equity_results_portfolio,
+    `Corporate Bonds` = bonds_results_portfolio
+  ) |>
+    dplyr::bind_rows(.id = "asset_class") |>
+    dplyr::filter(.data[["portfolio_name"]] == .env[["portfolio_name"]]) |>
+    dplyr::filter(.data[["scenario_geography"]] == "Global") |>
+    dplyr::select(
+      "asset_class",
+      "allocation",
+      "equity_market",
+      sector = "ald_sector",
+      "year",
+      plan = "plan_sec_emissions_factor",
+      scen = "scen_sec_emissions_factor",
+      "scenario",
+      "scenario_source"
+    ) |>
+    dplyr::distinct() |>
+    dplyr::filter(!is.nan(.data[["plan"]])) |>
+    tidyr::pivot_longer(c("plan", "scen"), names_to = "plan") |>
+    tidyr::unite("name", "sector", "plan", remove = FALSE) |>
+    dplyr::mutate(disabled = !.data[["sector"]] %in% .env[["pacta_sectors"]]) |>
+    dplyr::mutate(unit = .env[["emissions_units"]][.data[["sector"]]]) |>
+    dplyr::group_by(.data[["asset_class"]]) |>
+    dplyr::filter(!all(.data[["disabled"]])) |>
+    dplyr::mutate(
+      equity_market =  dplyr::case_when(
+        .data[["equity_market"]] == "GlobalMarket" ~ "Global Market",
+        .data[["equity_market"]] == "DevelopedMarket" ~ "Developed Market",
+        .data[["equity_market"]] == "EmergingMarket" ~ "Emerging Market",
+        TRUE ~ .data[["equity_market"]]
+      )
+    ) |>
+    dplyr::filter(
+      .data[["year"]] <= .env[["start_year"]] + .env[["year_span"]]
+    ) |>
+    dplyr::arrange(
+      .data[["asset_class"]],
+      factor(
+        .data[["equity_market"]],
+        levels = c(
+          "Global Market",
+          "Developed Market",
+          "Emerging Market"
+        )
+      )
+    ) |>
+    dplyr::ungroup()
+}
diff --git a/R/prep_exposure_pie.R b/R/prep_exposure_pie.R
index 8522127..624256d 100644
--- a/R/prep_exposure_pie.R
+++ b/R/prep_exposure_pie.R
@@ -12,36 +12,52 @@
 #' @return (data.frame) suitible for serialization to JSON (using
 #' `jsonlite::toJSON`/`jsonlite::write_json`)
 #' @export
-prep_exposure_pie <-
-  function(data,
-           asset_type,
-           investor_name,
-           portfolio_name,
-           pacta_sectors,
-           currency_exchange_value) {
-    data %>%
-      filter(.data$investor_name == .env$investor_name &
-        .data$portfolio_name == .env$portfolio_name) %>%
-      filter(.data$asset_type %in% c("Bonds", "Equity")) %>%
-      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
-          )
-      ) %>%
-      group_by(.data$asset_type, .data$sector) %>%
-      summarise(
-        value = sum(.data$value_usd, na.rm = TRUE) / .env$currency_exchange_value,
-        .groups = "drop"
-      ) %>%
-      mutate(exploded = .data$sector %in% .env$pacta_sectors) %>%
-      arrange(.data$asset_type, desc(.data$exploded), .data$sector) %>%
-      rename(key = .data$sector) %>%
-      filter(!is.na(.data$key)) %>%
-      ungroup() %>%
-      filter(.data$asset_type == .env$asset_type) %>%
-      select(-"asset_type")
-  }
+prep_exposure_pie <- function(
+  data,
+  asset_type,
+  investor_name,
+  portfolio_name,
+  pacta_sectors,
+  currency_exchange_value
+) {
+  data |>
+    dplyr::filter(
+      .data[["investor_name"]] == .env[["investor_name"]],
+      .data[["portfolio_name"]] == .env[["portfolio_name"]]
+    ) |>
+    dplyr::filter(.data[["asset_type"]] %in% c("Bonds", "Equity")) |>
+    dplyr::filter(.data[["valid_input"]]) |>
+    dplyr::mutate(
+      dplyr::across(c("bics_sector", "financial_sector"), as.character)
+    ) |>
+    dplyr::mutate(
+      sector = dplyr::if_else(
+        .data[["financial_sector"]] %in% .env[["pacta_sectors"]],
+        .data[["financial_sector"]],
+        "Other"
+      )
+    ) |>
+    dplyr::group_by(.data[["asset_type"]], .data[["sector"]]) |>
+    dplyr::summarise(
+      value = (
+        sum(
+          .data[["value_usd"]],
+          na.rm = TRUE
+        ) / (
+          .env[["currency_exchange_value"]]
+        )
+      ),
+      .groups = "drop"
+    ) |>
+    dplyr::mutate(exploded = .data[["sector"]] %in% .env[["pacta_sectors"]]) |>
+    dplyr::arrange(
+      .data[["asset_type"]],
+      dplyr::desc(.data[["exploded"]]),
+      .data[["sector"]]
+    ) |>
+    dplyr::rename(key = .data[["sector"]]) |>
+    dplyr::filter(!is.na(.data[["key"]])) |>
+    dplyr::ungroup() |>
+    dplyr::filter(.data[["asset_type"]] == .env[["asset_type"]]) |>
+    dplyr::select(-"asset_type")
+}
diff --git a/R/prep_exposure_stats.R b/R/prep_exposure_stats.R
index 5751e8a..d000cae 100644
--- a/R/prep_exposure_stats.R
+++ b/R/prep_exposure_stats.R
@@ -1,4 +1,4 @@
-#' Prepare exposure statistics for inclusion in the dashboard 
+#' Prepare exposure statistics for inclusion in the dashboard
 #'
 #' Prepare JSON data for the exposure statistics.
 #'
@@ -11,68 +11,110 @@
 #' @return (data.frame) suitible for serialization to JSON (using
 #' `jsonlite::toJSON`/`jsonlite::write_json`)
 #' @export
-prep_exposure_stats <- function(audit_file, investor_name, portfolio_name, pacta_sectors, currency_exchange_value) {
+prep_exposure_stats <- function(
+  audit_file,
+  investor_name,
+  portfolio_name,
+  pacta_sectors,
+  currency_exchange_value
+) {
   pacta_asset_classes <- c("Bonds", "Equity")
-  
+
   audit_table <- 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
+    audit_file,
+    investor_name = investor_name,
+    portfolio_name = portfolio_name,
+    currency_exchange_value = currency_exchange_value
+  )
+
+  exposure_stats <- audit_file |>
+    dplyr::filter(
+      .data[["investor_name"]] == .env[["investor_name"]],
+      .data[["portfolio_name"]] == .env[["portfolio_name"]]
+    ) |>
+    dplyr::filter(.data[["asset_type"]] %in% pacta_asset_classes) |>
+    dplyr::filter(.data[["valid_input"]]) |>
+    dplyr::mutate(
+      dplyr::across(c("bics_sector", "financial_sector"), as.character)
+    ) |>
+    dplyr::mutate(
+      sector = dplyr::if_else(
+        .data[["financial_sector"]] %in% .env[["pacta_sectors"]],
+        .data[["financial_sector"]],
+        "Other"
+      )
+    ) |>
+    dplyr::summarise(
+      value = (
+        sum(
+          .data[["value_usd"]],
+          na.rm = TRUE
+        ) / (
+          .env[["currency_exchange_value"]]
         )
-    ) %>%
-    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$asset_type))
-  
-  all_stats_with_zero_sector_exposure <- expand.grid(
+    ) |>
+    dplyr::mutate(
+      perc_asset_val_sector = (
+        .data[["value"]] / sum(.data[["value"]], na.rm = TRUE)
+      ),
+      .by = "asset_type"
+    ) |>
+    dplyr::inner_join(
+      audit_table,
+      by = dplyr::join_by("asset_type" == "asset_type_analysis")
+    ) |>
+    dplyr::select(
+      "asset_type",
+      "percentage_value_invested",
+      "sector",
+      "perc_asset_val_sector"
+    )
+
+  asset_classes_in_portfolio <- intersect(
+    pacta_asset_classes,
+    unique(exposure_stats[["asset_type"]])
+  )
+
+  all_stats_zero_sector_exposure <- expand.grid(
     asset_type = asset_classes_in_portfolio,
     sector = pacta_sectors,
-    val_sector = 0
-    ) %>% inner_join(
-      distinct(select(exposure_stats, c("asset_type", "percentage_value_invested"))),
-      by = join_by("asset_type")
-    )
-  
-  exposure_stats_all <- all_stats_with_zero_sector_exposure %>%
-    left_join(exposure_stats, by = join_by("asset_type", "sector", "percentage_value_invested")) %>%
-    mutate(
-      perc_asset_val_sector = if_else(
-        is.na(.data$perc_asset_val_sector),
-        .data$val_sector, 
-        .data$perc_asset_val_sector
+    val_sector = 0L
+  ) |> dplyr::inner_join(
+    dplyr::distinct(
+      dplyr::select(
+        exposure_stats,
+        c("asset_type", "percentage_value_invested")
+      )
+    ),
+    by = dplyr::join_by("asset_type")
+  )
+
+  exposure_stats_all <- all_stats_zero_sector_exposure |>
+    dplyr::left_join(
+      exposure_stats,
+      by = dplyr::join_by("asset_type", "sector", "percentage_value_invested")
+    ) |>
+    dplyr::mutate(
+      perc_asset_val_sector = dplyr::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"
+    ) |>
+    dplyr::mutate(
+      asset_type = dplyr::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")
-  
+    ) |>
+    dplyr::select(
+      "asset_type",
+      "percentage_value_invested",
+      "sector",
+      "perc_asset_val_sector"
+    )
+
   exposure_stats_all
 }
diff --git a/R/prep_key_bars_company.R b/R/prep_key_bars_company.R
index bb18d31..b85e143 100644
--- a/R/prep_key_bars_company.R
+++ b/R/prep_key_bars_company.R
@@ -17,62 +17,92 @@
 #' @return (data.frame) suitible for serialization to JSON (using
 #' `jsonlite::toJSON`/`jsonlite::write_json`)
 #' @export
-prep_key_bars_company <-
-  function(equity_results_company,
-           bonds_results_company,
-           portfolio_name,
-           start_year,
-           pacta_sectors_not_analysed,
-           all_tech_levels) {
-    
-    equity_data_company <-
-      equity_results_company %>%
-      filter(.data$portfolio_name == .env$portfolio_name) %>%
-      filter(.data$year %in% c(.env$start_year + 5)) %>%
-      filter(.data$equity_market %in% c("Global", "GlobalMarket")) %>%
-      filter(.data$scenario_geography == "Global") %>%
-      filter(.data$ald_sector %in% c("Power", "Automotive")) %>%
-      select(-"id") %>%
-      rename(id = "company_name") %>%
-      select("id", "ald_sector", "technology", "plan_tech_share", "port_weight",
-             "allocation", "scenario_source", "scenario", "year") %>%
-      arrange(desc(.data$port_weight)) %>%
-      mutate(asset_class = "Listed Equity") %>%
-      mutate_at("id", as.character) %>% # convert the col type to character to prevent errors in case empty df is binded by rows
-      group_by(.data$ald_sector, .data$technology) %>% # select at most 15 companies with the highest weigths per sector+technology
-      arrange(dplyr::desc(.data$port_weight), .by_group = TRUE) %>%
-      slice(1:15)  %>%
-      filter(!is.null(.data$port_weight)) %>%
-      filter(!is.null(.data$plan_tech_share))
-    
-    bonds_data_company <-
-      bonds_results_company %>%
-      filter(.data$portfolio_name == .env$portfolio_name) %>%
-      filter(.data$year %in% c(.env$start_year + 5)) %>%
-      filter(.data$equity_market %in% c("Global", "GlobalMarket")) %>%
-      filter(.data$scenario_geography == "Global") %>%
-      filter(.data$ald_sector %in% c("Power", "Automotive")) %>%
-      select(-"id") %>%
-      rename(id = "company_name") %>%
-      select("id", "ald_sector", "technology", "plan_tech_share", "port_weight",
-             "allocation", "scenario_source", "scenario", "year") %>%
-      group_by(.data$id, .data$ald_sector, .data$technology) %>%
-      mutate(port_weight = sum(.data$port_weight, na.rm = TRUE)) %>%
-      group_by(.data$id, .data$technology) %>%
-      filter(row_number() == 1) %>%
-      filter(!.data$ald_sector %in% .env$pacta_sectors_not_analysed | !grepl("Aligned", .data$id)) %>%
-      arrange(desc(.data$port_weight)) %>%
-      mutate(asset_class = "Corporate Bonds") %>%
-      mutate_at("id", as.character) %>% # convert the col type to character to prevent errors in case empty df is bound by rows
-      group_by(.data$ald_sector, .data$technology) %>% # select at most 15 companies with the highest weigths per sector+technology
-      arrange(.data$port_weight, .by_group = TRUE) %>%
-      slice(1:15) %>%
-      group_by(.data$ald_sector) %>%
-      arrange(factor(.data$technology, levels = .env$all_tech_levels)) %>%
-      arrange(dplyr::desc(.data$port_weight), .by_group = TRUE) %>%
-      filter(!is.null(.data$port_weight)) %>%
-      filter(!is.null(.data$plan_tech_share))
-    
-    bind_rows(equity_data_company, bonds_data_company)
-  }
+prep_key_bars_company <- function(
+  equity_results_company,
+  bonds_results_company,
+  portfolio_name,
+  start_year,
+  pacta_sectors_not_analysed,
+  all_tech_levels
+) {
 
+  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")) |>
+    dplyr::filter(.data[["scenario_geography"]] == "Global") |>
+    dplyr::filter(.data[["ald_sector"]] %in% c("Power", "Automotive")) |>
+    dplyr::select(-"id") |>
+    dplyr::rename(id = "company_name") |>
+    dplyr::select(
+      "id",
+      "ald_sector",
+      "technology",
+      "plan_tech_share",
+      "port_weight",
+      "allocation",
+      "scenario_source",
+      "scenario",
+      "year"
+    ) |>
+    dplyr::arrange(dplyr::desc(.data[["port_weight"]])) |>
+    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 binded by rows #nolint
+    dplyr::group_by(.data[["ald_sector"]], .data[["technology"]]) |> # select at most 15 companies with the highest weigths per sector+technology #nolint
+    dplyr::arrange(dplyr::desc(.data[["port_weight"]]), .by_group = TRUE) |>
+    dplyr::slice(1L:15L)  |>
+    dplyr::filter(!is.null(.data[["port_weight"]])) |>
+    dplyr::filter(!is.null(.data[["plan_tech_share"]]))
+
+  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")) |>
+    dplyr::filter(.data[["scenario_geography"]] == "Global") |>
+    dplyr::filter(.data[["ald_sector"]] %in% c("Power", "Automotive")) |>
+    dplyr::select(-"id") |>
+    dplyr::rename(id = "company_name") |>
+    dplyr::select(
+      "id",
+      "ald_sector",
+      "technology",
+      "plan_tech_share",
+      "port_weight",
+      "allocation",
+      "scenario_source",
+      "scenario",
+      "year"
+    ) |>
+    dplyr::group_by(
+      .data[["id"]], .data[["ald_sector"]], .data[["technology"]]
+    ) |>
+    dplyr::mutate(port_weight = sum(.data[["port_weight"]], na.rm = TRUE)) |>
+    dplyr::group_by(.data[["id"]], .data[["technology"]]) |>
+    dplyr::filter(dplyr::row_number() == 1L) |>
+    dplyr::filter(
+      !(.data[["ald_sector"]] %in% .env[["pacta_sectors_not_analysed"]]) |
+        !grepl(
+          pattern = "Aligned",
+          x = .data[["id"]],
+          fixed = TRUE
+        )
+    ) |>
+    dplyr::arrange(dplyr::desc(.data[["port_weight"]])) |>
+    dplyr::mutate(asset_class = "Corporate Bonds") |>
+    dplyr::mutate_at("id", as.character) |> # convert the col type to character to prevent errors in case empty df is bound by rows #nolint
+    dplyr::group_by(.data[["ald_sector"]], .data[["technology"]]) |> # select at most 15 companies with the highest weigths per sector+technology #nolint
+    dplyr::arrange(.data[["port_weight"]], .by_group = TRUE) |>
+    dplyr::slice(1L:15L) |>
+    dplyr::group_by(.data[["ald_sector"]]) |>
+    dplyr::arrange(
+      factor(
+        .data[["technology"]],
+        levels = .env[["all_tech_levels"]]
+      )
+    ) |>
+    dplyr::arrange(dplyr::desc(.data[["port_weight"]]), .by_group = TRUE) |>
+    dplyr::filter(!is.null(.data[["port_weight"]])) |>
+    dplyr::filter(!is.null(.data[["plan_tech_share"]]))
+
+  dplyr::bind_rows(equity_data_company, bonds_data_company)
+}
diff --git a/R/prep_key_bars_portfolio.R b/R/prep_key_bars_portfolio.R
index 5cabdf0..8eba515 100644
--- a/R/prep_key_bars_portfolio.R
+++ b/R/prep_key_bars_portfolio.R
@@ -1,7 +1,3 @@
-# prep_key_bars_portfolio ------------------------------------------------------
-# based on pacta.portfolio.report:::prep_key_bars_portfolio, but does not filter 
-# to allocation == "portfolio_weight" nor by scenario and scenario source
-
 #' Prepare data for portfolio-level technology exposoure plot
 #'
 #' Prepare JSON data for the portfolio-level technology exposoure plot.
@@ -21,51 +17,115 @@
 #' @return (data.frame) suitible for serialization to JSON (using
 #' `jsonlite::toJSON`/`jsonlite::write_json`)
 #' @export
-prep_key_bars_portfolio <-
-  function(equity_results_portfolio,
-           bonds_results_portfolio,
-           portfolio_name,
-           start_year,
-           pacta_sectors_not_analysed,
-           all_tech_levels) {
-    equity_data_portfolio <-
-      equity_results_portfolio %>%
-      filter(.data$portfolio_name == .env$portfolio_name) %>%
-      filter(.data$equity_market %in% c("Global", "GlobalMarket")) %>%
-      filter(.data$year %in% c(.env$start_year + 5)) %>%
-      filter(.data$ald_sector %in% c("Power", "Automotive")) %>%
-      filter(.data$scenario_geography == "Global") %>%
-      mutate(port_weight = 1) %>%
-      select("ald_sector", "technology", "plan_tech_share", "scen_tech_share",
-             "port_weight", "scenario", "scenario_source", "allocation", "year") %>%
-      pivot_longer(c("plan_tech_share", "scen_tech_share"), names_to = "plan") %>%
-      mutate(id = if_else(.data$plan == "plan_tech_share", "Portfolio", "Aligned* Portfolio")) %>%
-      rename(plan_tech_share = "value") %>%
-      select("id", "ald_sector", "technology", "plan_tech_share", "port_weight",
-             "scenario", "scenario_source", "allocation", "year") %>%
-      filter(!.data$ald_sector %in% .env$pacta_sectors_not_analysed | !grepl("Aligned", .data$id)) %>%
-      mutate(asset_class = "Listed Equity") %>%
-      mutate_at("id", as.character) # convert the col type to character to prevent errors in case empty df is bound by rows
-    
-    bonds_data_portfolio <-
-      bonds_results_portfolio %>%
-      filter(.data$portfolio_name == .env$portfolio_name) %>%
-      filter(.data$equity_market %in% c("Global", "GlobalMarket")) %>%
-      filter(.data$year %in% c(.env$start_year + 5)) %>%
-      filter(.data$ald_sector %in% c("Power", "Automotive")) %>%
-      filter(.data$scenario_geography == "Global") %>%
-      mutate(port_weight = 1) %>%
-      select("ald_sector", "technology", "plan_tech_share", "scen_tech_share",
-             "port_weight", "scenario", "scenario_source", "allocation", "year") %>%
-      pivot_longer(c("plan_tech_share", "scen_tech_share"), names_to = "plan") %>%
-      mutate(id = if_else(.data$plan == "plan_tech_share", "Portfolio", "Aligned* Portfolio")) %>%
-      rename(plan_tech_share = "value") %>%
-      select("id", "ald_sector", "technology", "plan_tech_share", "port_weight",
-             "scenario", "scenario_source", "allocation", "year") %>%
-      mutate(asset_class = "Corporate Bonds") %>%
-      mutate_at("id", as.character) %>%
-      arrange(factor(.data$technology, levels = .env$all_tech_levels))
-    
-    bind_rows(equity_data_portfolio, bonds_data_portfolio)
-  }
+prep_key_bars_portfolio <- function(
+  equity_results_portfolio,
+  bonds_results_portfolio,
+  portfolio_name,
+  start_year,
+  pacta_sectors_not_analysed,
+  all_tech_levels
+) {
+  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)) |>
+    dplyr::filter(.data[["ald_sector"]] %in% c("Power", "Automotive")) |>
+    dplyr::filter(.data[["scenario_geography"]] == "Global") |>
+    dplyr::mutate(port_weight = 1L) |>
+    dplyr::select(
+      "ald_sector",
+      "technology",
+      "plan_tech_share",
+      "scen_tech_share",
+      "port_weight",
+      "scenario",
+      "scenario_source",
+      "allocation",
+      "year"
+    ) |>
+    tidyr::pivot_longer(
+      c("plan_tech_share", "scen_tech_share"),
+      names_to = "plan"
+    ) |>
+    dplyr::mutate(
+      id = dplyr::if_else(
+        .data[["plan"]] == "plan_tech_share",
+        "Portfolio",
+        "Aligned* Portfolio"
+      )
+    ) |>
+    dplyr::rename(plan_tech_share = "value") |>
+    dplyr::select(
+      "id",
+      "ald_sector",
+      "technology",
+      "plan_tech_share",
+      "port_weight",
+      "scenario",
+      "scenario_source",
+      "allocation",
+      "year"
+    ) |>
+    dplyr::filter(
+      !(.data[["ald_sector"]] %in% .env[["pacta_sectors_not_analysed"]]) |
+        !grepl(
+          pattern = "Aligned",
+          x = .data[["id"]],
+          fixed = TRUE
+        )
+    ) |>
+    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 |>
+    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)) |>
+    dplyr::filter(.data[["ald_sector"]] %in% c("Power", "Automotive")) |>
+    dplyr::filter(.data[["scenario_geography"]] == "Global") |>
+    dplyr::mutate(port_weight = 1L) |>
+    dplyr::select(
+      "ald_sector",
+      "technology",
+      "plan_tech_share",
+      "scen_tech_share",
+      "port_weight",
+      "scenario",
+      "scenario_source",
+      "allocation",
+      "year"
+    ) |>
+    tidyr::pivot_longer(
+      c("plan_tech_share", "scen_tech_share"),
+      names_to = "plan"
+    ) |>
+    dplyr::mutate(
+      id = dplyr::if_else(
+        .data[["plan"]] == "plan_tech_share",
+        "Portfolio",
+        "Aligned* Portfolio"
+      )
+    ) |>
+    dplyr::rename(plan_tech_share = "value") |>
+    dplyr::select(
+      "id",
+      "ald_sector",
+      "technology",
+      "plan_tech_share",
+      "port_weight",
+      "scenario",
+      "scenario_source",
+      "allocation",
+      "year"
+    ) |>
+    dplyr::mutate(asset_class = "Corporate Bonds") |>
+    dplyr::mutate_at("id", as.character) |>
+    dplyr::arrange(
+      factor(
+        .data[["technology"]],
+        levels = .env[["all_tech_levels"]]
+      )
+    )
 
+  dplyr::bind_rows(equity_data_portfolio, bonds_data_portfolio)
+}
diff --git a/R/prep_techexposure.R b/R/prep_techexposure.R
index 37d2f34..43eaa0e 100644
--- a/R/prep_techexposure.R
+++ b/R/prep_techexposure.R
@@ -1,97 +1,156 @@
-prep_techexposure <-
-  function(
-    equity_results_portfolio,
-    bonds_results_portfolio,
-    investor_name,
-    portfolio_name,
-    indices_equity_results_portfolio,
-    indices_bonds_results_portfolio,
-    peers_equity_results_portfolio,
-    peers_bonds_results_portfolio,
-    peer_group,
-    select_scenario_other,
-    select_scenario,
-    start_year,
-    green_techs,
-    equity_market_levels,
-    all_tech_levels
-    ) {
-    portfolio <-
-      list(`Listed Equity` = equity_results_portfolio,
-           `Corporate Bonds` = bonds_results_portfolio) %>%
-      bind_rows(.id = "asset_class") %>%
-      filter(.data$investor_name == .env$investor_name,
-             .data$portfolio_name == .env$portfolio_name) %>%
-      filter(!is.na(.data$ald_sector))
+prep_techexposure <- function(
+  equity_results_portfolio,
+  bonds_results_portfolio,
+  investor_name,
+  portfolio_name,
+  indices_eq_results_portfolio,
+  indices_cb_results_portfolio,
+  peers_equity_results_portfolio,
+  peers_bonds_results_portfolio,
+  peer_group,
+  select_scenario_other,
+  select_scenario,
+  start_year,
+  green_techs,
+  equity_market_levels,
+  all_tech_levels
+) {
+  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 %>%
-      pull("asset_class") %>%
-      unique()
+  asset_classes <- portfolio |>
+    dplyr::pull("asset_class") |>
+    unique()
 
-    equity_sectors <-
-      portfolio %>%
-      filter(.data$asset_class == "Listed Equity") %>%
-      pull("ald_sector") %>%
-      unique()
+  equity_sectors <- portfolio |>
+    dplyr::filter(.data[["asset_class"]] == "Listed Equity") |>
+    dplyr::pull("ald_sector") |>
+    unique()
 
-    bonds_sectors <-
-      portfolio %>%
-      filter(.data$asset_class == "Corporate Bonds") %>%
-      pull("ald_sector") %>%
-      unique()
+  bonds_sectors <- portfolio |>
+    dplyr::filter(.data[["asset_class"]] == "Corporate Bonds") |>
+    dplyr::pull("ald_sector") |>
+    unique()
 
-    indices <-
-      list(`Listed Equity` = indices_equity_results_portfolio,
-           `Corporate Bonds` = indices_bonds_results_portfolio) %>%
-      bind_rows(.id = "asset_class") %>%
-      filter(.data$asset_class %in% .env$asset_classes) %>%
-      filter(.data$asset_class == "Listed Equity" & .data$ald_sector %in% .env$equity_sectors |
-               .data$asset_class == "Corporate Bonds" & .data$ald_sector %in% .env$bonds_sectors)
+  indices <- list(
+    `Listed Equity` = indices_eq_results_portfolio,
+    `Corporate Bonds` = indices_cb_results_portfolio
+  ) |>
+    dplyr::bind_rows(.id = "asset_class") |>
+    dplyr::filter(.data[["asset_class"]] %in% .env[["asset_classes"]]) |>
+    dplyr::filter(
+      (
+        .data[["asset_class"]] == "Listed Equity" &
+          .data[["ald_sector"]] %in% .env[["equity_sectors"]]
+      ) | (
+        .data[["asset_class"]] == "Corporate Bonds" &
+          .data[["ald_sector"]] %in% .env[["bonds_sectors"]]
+      )
+    )
 
-    peers <-
-      list(`Listed Equity` = peers_equity_results_portfolio,
-           `Corporate Bonds` = peers_bonds_results_portfolio) %>%
-      bind_rows(.id = "asset_class") %>%
-      filter(.data$asset_class %in% .env$asset_classes) %>%
-      filter(.data$asset_class == "Listed Equity" & .data$ald_sector %in% .env$equity_sectors |
-               .data$asset_class == "Corporate Bonds" & .data$ald_sector %in% .env$bonds_sectors) %>%
-      filter(.data$investor_name == .env$peer_group)
+  peers <- list(
+    `Listed Equity` = peers_equity_results_portfolio,
+    `Corporate Bonds` = peers_bonds_results_portfolio
+  ) |>
+    dplyr::bind_rows(.id = "asset_class") |>
+    dplyr::filter(.data[["asset_class"]] %in% .env[["asset_classes"]]) |>
+    dplyr::filter(
+      (
+        .data[["asset_class"]] == "Listed Equity" &
+          .data[["ald_sector"]] %in% .env[["equity_sectors"]]
+      ) | (
+        .data[["asset_class"]] == "Corporate Bonds" &
+          .data[["ald_sector"]] %in% .env[["bonds_sectors"]]
+      )
+    ) |>
+    dplyr::filter(.data[["investor_name"]] == .env[["peer_group"]])
 
-    bind_rows(portfolio, peers, indices) %>%
-      filter(.data$allocation == "portfolio_weight") %>%
-      filter_scenarios_per_sector(
-        select_scenario_other,
-        select_scenario
-      ) %>%
-      filter(.data$scenario_geography == "Global") %>%
-      filter(.data$year == .env$start_year) %>%
-      filter(.data$equity_market == "GlobalMarket") %>%
-      mutate(green = .data$technology %in% .env$green_techs) %>%
-      group_by(.data$asset_class, .data$equity_market, .data$portfolio_name, .data$ald_sector) %>%
-      arrange(.data$asset_class,  .data$portfolio_name,
-              factor(.data$technology, levels = all_tech_levels), desc(.data$green)) %>%
-      mutate(sector_sum = sum(.data$plan_carsten)) %>%
-      mutate(sector_prcnt = .data$plan_carsten / sum(.data$plan_carsten)) %>%
-      mutate(sector_cumprcnt = cumsum(.data$sector_prcnt)) %>%
-      mutate(sector_cumprcnt = lag(.data$sector_cumprcnt, default = 0)) %>%
-      mutate(cumsum = cumsum(.data$plan_carsten)) %>%
-      mutate(cumsum = lag(.data$cumsum, default = 0)) %>%
-      ungroup() %>%
-      group_by(.data$asset_class, .data$equity_market, .data$portfolio_name, .data$ald_sector, .data$green) %>%
-      mutate(green_sum = sum(.data$plan_carsten)) %>%
-      mutate(green_prcnt = sum(.data$plan_carsten) / .data$sector_sum) %>%
-      ungroup() %>%
-      mutate(this_portfolio = .data$portfolio_name == .env$portfolio_name) %>%
-      mutate(equity_market =  case_when(
-        .data$equity_market == "GlobalMarket" ~ "Global Market",
-        .data$equity_market == "DevelopedMarket" ~ "Developed Market",
-        .data$equity_market == "EmergingMarket" ~ "Emerging Market",
-        TRUE ~ .data$equity_market)
-      ) %>%
-      arrange(.data$asset_class, factor(.data$equity_market, levels = equity_market_levels), desc(.data$this_portfolio), .data$portfolio_name,
-              factor(.data$technology, levels = all_tech_levels), desc(.data$green)) %>%
-      select("asset_class", "equity_market", "portfolio_name", "this_portfolio", "ald_sector", "technology",
-             "plan_carsten", "sector_sum", "sector_prcnt", "cumsum", "sector_cumprcnt",
-             "green", "green_sum", "green_prcnt")
-  }
+  dplyr::bind_rows(portfolio, peers, indices) |>
+    dplyr::filter(.data[["allocation"]] == "portfolio_weight") |>
+    filter_scenarios_per_sector(
+      select_scenario_other,
+      select_scenario
+    ) |>
+    dplyr::filter(.data[["scenario_geography"]] == "Global") |>
+    dplyr::filter(.data[["year"]] == .env[["start_year"]]) |>
+    dplyr::filter(.data[["equity_market"]] == "GlobalMarket") |>
+    dplyr::mutate(green = .data[["technology"]] %in% .env[["green_techs"]]) |>
+    dplyr::group_by(
+      .data[["asset_class"]],
+      .data[["equity_market"]],
+      .data[["portfolio_name"]],
+      .data[["ald_sector"]]
+    ) |>
+    dplyr::arrange(
+      .data[["asset_class"]],
+      .data[["portfolio_name"]],
+      factor(.data[["technology"]], levels = all_tech_levels),
+      dplyr::desc(.data[["green"]])
+    ) |>
+    dplyr::mutate(sector_sum = sum(.data[["plan_carsten"]])) |>
+    dplyr::mutate(
+      sector_prcnt = .data[["plan_carsten"]] / sum(.data[["plan_carsten"]])
+    ) |>
+    dplyr::mutate(sector_cumprcnt = cumsum(.data[["sector_prcnt"]])) |>
+    dplyr::mutate(
+      sector_cumprcnt = dplyr::lag(.data[["sector_cumprcnt"]], default = 0L)
+    ) |>
+    dplyr::mutate(cumsum = cumsum(.data[["plan_carsten"]])) |>
+    dplyr::mutate(cumsum = dplyr::lag(.data[["cumsum"]], default = 0L)) |>
+    dplyr::ungroup() |>
+    dplyr::group_by(
+      .data[["asset_class"]],
+      .data[["equity_market"]],
+      .data[["portfolio_name"]],
+      .data[["ald_sector"]],
+      .data[["green"]]
+    ) |>
+    dplyr::mutate(green_sum = sum(.data[["plan_carsten"]])) |>
+    dplyr::mutate(
+      green_prcnt = sum(.data[["plan_carsten"]]) / .data[["sector_sum"]]
+    ) |>
+    dplyr::ungroup() |>
+    dplyr::mutate(
+      this_portfolio = .data[["portfolio_name"]] == .env[["portfolio_name"]]
+    ) |>
+    dplyr::mutate(
+      equity_market =  dplyr::case_when(
+        .data[["equity_market"]] == "GlobalMarket" ~ "Global Market",
+        .data[["equity_market"]] == "DevelopedMarket" ~ "Developed Market",
+        .data[["equity_market"]] == "EmergingMarket" ~ "Emerging Market",
+        TRUE ~ .data[["equity_market"]]
+      )
+    ) |>
+    dplyr::arrange(
+      .data[["asset_class"]],
+      factor(.data[["equity_market"]], levels = equity_market_levels),
+      dplyr::desc(.data[["this_portfolio"]]),
+      .data[["portfolio_name"]],
+      factor(.data[["technology"]], levels = all_tech_levels),
+      dplyr::desc(.data[["green"]])
+    ) |>
+    dplyr::select(
+      "asset_class",
+      "equity_market",
+      "portfolio_name",
+      "this_portfolio",
+      "ald_sector",
+      "technology",
+      "plan_carsten",
+      "sector_sum",
+      "sector_prcnt",
+      "cumsum",
+      "sector_cumprcnt",
+      "green",
+      "green_sum",
+      "green_prcnt"
+    )
+}
diff --git a/R/prep_techmix_sector.R b/R/prep_techmix_sector.R
index ac46992..6b8eb4e 100644
--- a/R/prep_techmix_sector.R
+++ b/R/prep_techmix_sector.R
@@ -1,4 +1,4 @@
-#' Prepare techmix data for plot 
+#' Prepare techmix data for plot
 #'
 #' Prepare JSON data for the technology mix plot.
 #'
@@ -6,9 +6,9 @@
 #' `workflow.pacta` for listed equity
 #' @param bonds_results_portfolio (data.frame) Portfolio-level results from
 #' `workflow.pacta` for corporate bonds
-#' @param indices_equity_results_portfolio (data.frame) Benchmark results from
+#' @param indices_eq_results_portfolio (data.frame) Benchmark results from
 #' `workflow.pacta` for listed equity
-#' @param indices_bonds_results_portfolio (data.frame) Benchmark results from
+#' @param indices_cb_results_portfolio (data.frame) Benchmark results from
 #' `workflow.pacta` for corporate bonds
 #' @param peers_equity_results_portfolio (data.frame) Peer group results from
 #' `workflow.pacta` for listed equity
@@ -25,134 +25,245 @@
 #' @return (data.frame) suitible for serialization to JSON (using
 #' `jsonlite::toJSON`/`jsonlite::write_json`)
 #' @export
-prep_techmix_sector <-
-  function(equity_results_portfolio,
-           bonds_results_portfolio,
-           indices_equity_results_portfolio,
-           indices_bonds_results_portfolio,
-           peers_equity_results_portfolio,
-           peers_bonds_results_portfolio,
-           investor_name,
-           portfolio_name,
-           start_year,
-           year_span,
-           peer_group,
-           green_techs,
-           all_tech_levels
-           ) {
+prep_techmix_sector <- function(
+  equity_results_portfolio,
+  bonds_results_portfolio,
+  indices_eq_results_portfolio,
+  indices_cb_results_portfolio,
+  peers_equity_results_portfolio,
+  peers_bonds_results_portfolio,
+  investor_name,
+  portfolio_name,
+  start_year,
+  year_span,
+  peer_group,
+  green_techs,
+  all_tech_levels
+) {
 
-  portfolio <-
-      list(`Listed Equity` = equity_results_portfolio,
-           `Corporate Bonds` = bonds_results_portfolio) %>%
-      bind_rows(.id = "asset_class") %>%
-      filter(.data$investor_name == .env$investor_name,
-             .data$portfolio_name == .env$portfolio_name) %>%
-      filter(!is.na(.data$ald_sector))
+  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 %>%
-      pull("asset_class") %>%
-      unique()
+  asset_classes <- portfolio |>
+    dplyr::pull("asset_class") |>
+    unique()
 
-equity_sectors <-
-      portfolio %>%
-      filter(.data$asset_class == "Listed Equity") %>%
-      filter(.data$allocation == "portfolio_weight") %>%
-      pull("ald_sector") %>%
-      unique()
+  equity_sectors <- portfolio |>
+    dplyr::filter(.data[["asset_class"]] == "Listed Equity") |>
+    dplyr::filter(.data[["allocation"]] == "portfolio_weight") |>
+    dplyr::pull("ald_sector") |>
+    unique()
 
-bonds_sectors <-
-      portfolio %>%
-      filter(.data$asset_class == "Corporate Bonds") %>%
-      pull("ald_sector") %>%
-      unique()
+  bonds_sectors <- portfolio |>
+    dplyr::filter(.data[["asset_class"]] == "Corporate Bonds") |>
+    dplyr::pull("ald_sector") |>
+    unique()
 
-indices <-
-      list(`Listed Equity` = indices_equity_results_portfolio,
-           `Corporate Bonds` = indices_bonds_results_portfolio) %>%
-      bind_rows(.id = "asset_class") %>%
-      filter(.data$asset_class %in% .env$asset_classes) %>%
-      filter(.data$asset_class == "Listed Equity" & .data$ald_sector %in% .env$equity_sectors |
-               .data$asset_class == "Corporate Bonds" & .data$ald_sector %in% .env$bonds_sectors)
-
-peers <-
-      list(`Listed Equity` = peers_equity_results_portfolio,
-           `Corporate Bonds` = peers_bonds_results_portfolio) %>%
-      bind_rows(.id = "asset_class") %>%
-      filter(.data$asset_class %in% .env$asset_classes) %>%
-      filter(.data$asset_class == "Listed Equity" & .data$ald_sector %in% .env$equity_sectors |
-               .data$asset_class == "Corporate Bonds" & .data$ald_sector %in% .env$bonds_sectors) %>%
-      filter(.data$investor_name == .env$peer_group)
+  indices <- list(
+    `Listed Equity` = indices_eq_results_portfolio,
+    `Corporate Bonds` = indices_cb_results_portfolio
+  ) |>
+    dplyr::bind_rows(.id = "asset_class") |>
+    dplyr::filter(.data[["asset_class"]] %in% .env[["asset_classes"]]) |>
+    dplyr::filter(
+      (
+        .data[["asset_class"]] == "Listed Equity" &
+          .data[["ald_sector"]] %in% .env[["equity_sectors"]]
+      ) | (
+        .data[["asset_class"]] == "Corporate Bonds" &
+          .data[["ald_sector"]] %in% .env[["bonds_sectors"]]
+      )
+    )
 
-techexposure_data <-
-      bind_rows(portfolio, peers, indices) %>%
-      filter(.data$allocation == "portfolio_weight") %>%
-      filter(.data$scenario_geography == "Global") %>%
-      filter(.data$year %in% c(.env$start_year, .env$start_year + .env$year_span))
+  peers <- list(
+    `Listed Equity` = peers_equity_results_portfolio,
+    `Corporate Bonds` = peers_bonds_results_portfolio
+  ) |>
+    dplyr::bind_rows(.id = "asset_class") |>
+    dplyr::filter(.data[["asset_class"]] %in% .env[["asset_classes"]]) |>
+    dplyr::filter(
+      (
+        .data[["asset_class"]] == "Listed Equity" &
+          .data[["ald_sector"]] %in% .env[["equity_sectors"]]
+      ) | (
+        .data[["asset_class"]] == "Corporate Bonds" &
+          .data[["ald_sector"]] %in% .env[["bonds_sectors"]]
+      )
+    ) |>
+    dplyr::filter(.data[["investor_name"]] == .env[["peer_group"]])
 
-if (nrow(techexposure_data) > 0) {
-  techexposure_data <-
-        techexposure_data %>%
-        mutate(green = .data$technology %in% .env$green_techs) %>%
-        group_by(.data$asset_class, .data$equity_market, .data$portfolio_name,
-                 .data$ald_sector, .data$scenario, .data$year) %>%
-        mutate(plan_alloc_wt_sec_prod = sum(.data$plan_alloc_wt_tech_prod),
-               scen_alloc_wt_sec_prod = sum(.data$scen_alloc_wt_tech_prod)) %>%
-        mutate(production_plan = if_else(.data$plan_alloc_wt_tech_prod > 0, .data$plan_alloc_wt_tech_prod / .data$plan_alloc_wt_sec_prod, 0),
-               scenario_plan = if_else(.data$scen_alloc_wt_tech_prod > 0, .data$scen_alloc_wt_tech_prod / .data$scen_alloc_wt_sec_prod, 0)) %>%
-        group_by(.data$asset_class, .data$equity_market, .data$portfolio_name,
-                 .data$ald_sector, .data$scenario, .data$year, .data$green) %>%
-        mutate(green_sum_prod = sum(.data$production_plan),
-              green_sum_scenario = sum(.data$scenario_plan)) %>%
-        ungroup() %>%
-        select("asset_class", "investor_name", "portfolio_name", "scenario_source",
-               "scenario", "allocation", "equity_market", "year", "ald_sector",
-               "technology", "production_plan", "scenario_plan", "green",
-               "green_sum_prod", "green_sum_scenario") %>%
-        pivot_longer(
-          cols = -c("asset_class", "investor_name", "portfolio_name", "scenario_source",
-                    "scenario", "allocation", "equity_market", "year", "ald_sector",
-                    "technology", "green","green_sum_prod", "green_sum_scenario"),
-          names_to = "val_type", values_to = "value") %>%
-        mutate(
-          green_sum = if_else(.data$val_type == "production_plan", .data$green_sum_prod, .data$green_sum_scenario)
-        ) %>%
-        select(-c("green_sum_prod", "green_sum_scenario")) %>%
-        ungroup() %>%
-        mutate(this_portfolio = .data$portfolio_name == .env$portfolio_name,
-               val_type = if_else(.data$this_portfolio == TRUE, paste0(.data$val_type, "_portfolio"), paste0(.data$val_type, "_benchmark"))) %>%
-        mutate(equity_market =  case_when(
-          .data$equity_market == "GlobalMarket" ~ "Global Market",
-          .data$equity_market == "DevelopedMarket" ~ "Developed Market",
-          .data$equity_market == "EmergingMarket" ~ "Emerging Market",
-          TRUE ~ .data$equity_market)
-        ) %>%
-    # no need for showing scenario mix for the benchmark
-    filter(
-      .data$val_type != "scenario_plan_benchmark"
-    ) %>%
-    mutate(val_type =  case_when(
-          .data$val_type == "production_plan_portfolio" ~ "Portfolio",
-          .data$val_type == "scenario_plan_portfolio" ~ "Scenario",
-          .data$val_type == "production_plan_benchmark" ~ "Benchmark",
-          TRUE ~ .data$val_type)
-        ) %>%
-    arrange(
-          .data$asset_class,
-          factor(.data$equity_market, levels = c("Global Market", "Developed Market", "Emerging Market")),
-          desc(.data$this_portfolio),
-          factor(.data$val_type, levels = c("Portfolio", "Scenario", "Benchmark")),
-          .data$portfolio_name,
-          factor(.data$technology, levels = .env$all_tech_levels)
-        ) %>%
-    select("asset_class", "equity_market", "portfolio_name", "scenario", "scenario_source",
-               "this_portfolio", "val_type", "ald_sector", "technology", "value",
-               "green", "green_sum", "year") %>%
-    filter(
-      !(.data$year == .env$start_year & .data$val_type == "Scenario")
+  techexposure_data <- dplyr::bind_rows(portfolio, peers, indices) |>
+    dplyr::filter(.data[["allocation"]] == "portfolio_weight") |>
+    dplyr::filter(.data[["scenario_geography"]] == "Global") |>
+    dplyr::filter(
+      .data[["year"]] %in% c(
+        .env[["start_year"]],
+        .env[["start_year"]] + .env[["year_span"]]
+      )
     )
-}
 
-techexposure_data
+  if (nrow(techexposure_data) > 0L) {
+    techexposure_data <- techexposure_data |>
+      dplyr::mutate(green = .data[["technology"]] %in% .env[["green_techs"]]) |>
+      dplyr::group_by(
+        .data[["asset_class"]],
+        .data[["equity_market"]],
+        .data[["portfolio_name"]],
+        .data[["ald_sector"]],
+        .data[["scenario"]],
+        .data[["year"]]
+      ) |>
+      dplyr::mutate(
+        plan_alloc_wt_sec_prod = sum(.data[["plan_alloc_wt_tech_prod"]]),
+        scen_alloc_wt_sec_prod = sum(.data[["scen_alloc_wt_tech_prod"]])
+      ) |>
+      dplyr::mutate(
+        production_plan = dplyr::if_else(
+          .data[["plan_alloc_wt_tech_prod"]] > 0L,
+          (
+            .data[["plan_alloc_wt_tech_prod"]] /
+              .data[["plan_alloc_wt_sec_prod"]]
+          ),
+          0L
+        ),
+        scenario_plan = dplyr::if_else(
+          .data[["scen_alloc_wt_tech_prod"]] > 0L,
+          (
+            .data[["scen_alloc_wt_tech_prod"]] /
+              .data[["scen_alloc_wt_sec_prod"]]
+          ),
+          0L
+        )
+      ) |>
+      dplyr::group_by(
+        .data[["asset_class"]],
+        .data[["equity_market"]],
+        .data[["portfolio_name"]],
+        .data[["ald_sector"]],
+        .data[["scenario"]],
+        .data[["year"]],
+        .data[["green"]]
+      ) |>
+      dplyr::mutate(
+        green_sum_prod = sum(.data[["production_plan"]]),
+        green_sum_scenario = sum(.data[["scenario_plan"]])
+      ) |>
+      dplyr::ungroup() |>
+      dplyr::select(
+        "asset_class",
+        "investor_name",
+        "portfolio_name",
+        "scenario_source",
+        "scenario",
+        "allocation",
+        "equity_market",
+        "year",
+        "ald_sector",
+        "technology",
+        "production_plan",
+        "scenario_plan",
+        "green",
+        "green_sum_prod",
+        "green_sum_scenario"
+      ) |>
+      tidyr::pivot_longer(
+        cols = -c( #nolint
+          "asset_class",
+          "investor_name",
+          "portfolio_name",
+          "scenario_source",
+          "scenario",
+          "allocation",
+          "equity_market",
+          "year",
+          "ald_sector",
+          "technology",
+          "green",
+          "green_sum_prod",
+          "green_sum_scenario"
+        ),
+        names_to = "val_type", values_to = "value") |>
+      dplyr::mutate(
+        green_sum = dplyr::if_else(
+          .data[["val_type"]] == "production_plan",
+          .data[["green_sum_prod"]],
+          .data[["green_sum_scenario"]]
+        )
+      ) |>
+      dplyr::select(-c("green_sum_prod", "green_sum_scenario")) |>
+      dplyr::ungroup() |>
+      dplyr::mutate(
+        this_portfolio = .data[["portfolio_name"]] == .env[["portfolio_name"]],
+        val_type = dplyr::if_else(
+          .data[["this_portfolio"]],
+          paste0(.data[["val_type"]], "_portfolio"),
+          paste0(.data[["val_type"]], "_benchmark")
+        )
+      ) |>
+      dplyr::mutate(
+        equity_market =  dplyr::case_when(
+          .data[["equity_market"]] == "GlobalMarket" ~ "Global Market",
+          .data[["equity_market"]] == "DevelopedMarket" ~ "Developed Market",
+          .data[["equity_market"]] == "EmergingMarket" ~ "Emerging Market",
+          TRUE ~ .data[["equity_market"]]
+        )
+      ) |>
+      # no need for showing scenario mix for the benchmark
+      dplyr::filter(
+        .data[["val_type"]] != "scenario_plan_benchmark"
+      ) |>
+      dplyr::mutate(
+        val_type =  dplyr::case_when(
+          .data[["val_type"]] == "production_plan_portfolio" ~ "Portfolio",
+          .data[["val_type"]] == "scenario_plan_portfolio" ~ "Scenario",
+          .data[["val_type"]] == "production_plan_benchmark" ~ "Benchmark",
+          TRUE ~ .data[["val_type"]]
+        )
+      ) |>
+      dplyr::arrange(
+        .data[["asset_class"]],
+        factor(
+          .data[["equity_market"]],
+          levels = c("Global Market", "Developed Market", "Emerging Market")
+        ),
+        dplyr::desc(.data[["this_portfolio"]]),
+        factor(
+          .data[["val_type"]],
+          levels = c("Portfolio", "Scenario", "Benchmark")
+        ),
+        .data[["portfolio_name"]],
+        factor(.data[["technology"]], levels = .env[["all_tech_levels"]])
+      ) |>
+      dplyr::select(
+        "asset_class",
+        "equity_market",
+        "portfolio_name",
+        "scenario",
+        "scenario_source",
+        "this_portfolio",
+        "val_type",
+        "ald_sector",
+        "technology",
+        "value",
+        "green",
+        "green_sum",
+        "year"
+      ) |>
+      dplyr::filter(
+        !(
+          .data[["year"]] == .env[["start_year"]] &
+            .data[["val_type"]] == "Scenario"
+        )
+      )
   }
 
+  techexposure_data
+}
diff --git a/R/prep_trajectory_alignment.R b/R/prep_trajectory_alignment.R
index 3074e24..02c149e 100644
--- a/R/prep_trajectory_alignment.R
+++ b/R/prep_trajectory_alignment.R
@@ -1,146 +1,236 @@
-prep_trajectory_alignment <-
-  function(equity_results_portfolio,
-           bonds_results_portfolio,
-           peers_equity_results_portfolio,
-           peers_bonds_results_portfolio,
-           indices_equity_results_portfolio,
-           indices_bonds_results_portfolio,
-           investor_name,
-           portfolio_name,
-           tech_roadmap_sectors,
-           peer_group,
-           start_year,
-           year_span,
-           scen_geo_levels,
-           all_tech_levels) {
+prep_trajectory_alignment <- function(
+  equity_results_portfolio,
+  bonds_results_portfolio,
+  peers_equity_results_portfolio,
+  peers_bonds_results_portfolio,
+  indices_eq_results_portfolio,
+  indices_cb_results_portfolio,
+  investor_name,
+  portfolio_name,
+  tech_roadmap_sectors,
+  peer_group,
+  start_year,
+  year_span,
+  scen_geo_levels,
+  all_tech_levels
+) {
 
-    portfolio <-
-      list(`Listed Equity` = equity_results_portfolio,
-           `Corporate Bonds` = bonds_results_portfolio) %>%
-      bind_rows(.id = "asset_class") %>%
-      filter(.data$investor_name == .env$investor_name,
-             .data$portfolio_name == .env$portfolio_name) %>%
-      filter(.data$ald_sector %in% .env$tech_roadmap_sectors) %>%
-      filter(.data$scenario_geography != "GlobalAggregate") %>%
-      group_by(.data$asset_class,
-               .data$allocation,
-               .data$equity_market,
-               .data$technology,
-               .data$scenario) %>%
-      filter(n() > 1) %>%
-      ungroup()
+  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(.data[["ald_sector"]] %in% .env[["tech_roadmap_sectors"]]) |>
+    dplyr::filter(.data[["scenario_geography"]] != "GlobalAggregate") |>
+    dplyr::group_by(
+      .data[["asset_class"]],
+      .data[["allocation"]],
+      .data[["equity_market"]],
+      .data[["technology"]],
+      .data[["scenario"]]
+    ) |>
+    dplyr::filter(dplyr::n() > 1L) |>
+    dplyr::ungroup()
 
-    asset_classes <-
-      portfolio %>%
-      pull("asset_class") %>%
-      unique()
+  asset_classes <- portfolio |>
+    dplyr::pull("asset_class") |>
+    unique()
 
-    equity_markets <-
-      portfolio %>%
-      filter(.data$asset_class == "Listed Equity") %>%
-      pull("equity_market") %>%
-      unique()
+  equity_markets <- portfolio |>
+    dplyr::filter(.data[["asset_class"]] == "Listed Equity") |>
+    dplyr::pull("equity_market") |>
+    unique()
 
-    bonds_markets <-
-      portfolio %>%
-      filter(.data$asset_class == "Corporate Bonds") %>%
-      pull("equity_market") %>%
-      unique()
+  bonds_markets <- portfolio |>
+    dplyr::filter(.data[["asset_class"]] == "Corporate Bonds") |>
+    dplyr::pull("equity_market") |>
+    unique()
 
-    equity_techs <-
-      portfolio %>%
-      filter(.data$asset_class == "Listed Equity") %>%
-      pull("technology") %>%
-      unique()
+  equity_techs <- portfolio |>
+    dplyr::filter(.data[["asset_class"]] == "Listed Equity") |>
+    dplyr::pull("technology") |>
+    unique()
 
-    equity_scenario_geography <-
-      portfolio %>%
-      filter(.data$asset_class == "Listed Equity") %>%
-      pull("scenario_geography") %>%
-      unique()
+  equity_scenario_geography <- portfolio |>
+    dplyr::filter(.data[["asset_class"]] == "Listed Equity") |>
+    dplyr::pull("scenario_geography") |>
+    unique()
 
-    bonds_scenario_geography <-
-      portfolio %>%
-      filter(.data$asset_class == "Corporate Bonds") %>%
-      pull("scenario_geography") %>%
-      unique()
+  bonds_scenario_geography <- portfolio |>
+    dplyr::filter(.data[["asset_class"]] == "Corporate Bonds") |>
+    dplyr::pull("scenario_geography") |>
+    unique()
 
-    bonds_techs <-
-      portfolio %>%
-      filter(.data$asset_class == "Corporate Bonds") %>%
-      pull("technology") %>%
-      unique()
+  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) %>%
-      bind_rows(.id = "asset_class") %>%
-      filter(.data$ald_sector %in% .env$tech_roadmap_sectors) %>%
-      filter(.data$scenario_geography != "GlobalAggregate") %>%
-      filter(.data$asset_class %in% .env$asset_classes) %>%
-      filter(.data$asset_class == "Listed Equity" & .data$equity_market %in% .env$equity_markets |
-               .data$asset_class == "Corporate Bonds" & .data$equity_market %in% .env$bonds_markets) %>%
-      filter(.data$asset_class == "Listed Equity" & .data$technology %in% .env$equity_techs |
-               .data$asset_class == "Corporate Bonds" & .data$technology %in% .env$bonds_techs) %>%
-      filter(.data$asset_class == "Listed Equity" & .data$scenario_geography %in% .env$equity_scenario_geography |
-               .data$asset_class == "Corporate Bonds" & .data$scenario_geography %in% .env$bonds_scenario_geography) %>%
-      filter(.data$investor_name == .env$peer_group)
+  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") |>
+    dplyr::filter(.data[["asset_class"]] %in% .env[["asset_classes"]]) |>
+    dplyr::filter(
+      (
+        .data[["asset_class"]] == "Listed Equity" &
+          .data[["equity_market"]] %in% .env[["equity_markets"]]
+      ) | (
+        .data[["asset_class"]] == "Corporate Bonds" &
+          .data[["equity_market"]] %in% .env[["bonds_markets"]]
+      )
+    ) |>
+    dplyr::filter(
+      (
+        .data[["asset_class"]] == "Listed Equity" &
+          .data[["technology"]] %in% .env[["equity_techs"]]
+      ) | (
+        .data[["asset_class"]] == "Corporate Bonds" &
+          .data[["technology"]] %in% .env[["bonds_techs"]]
+      )
+    ) |>
+    dplyr::filter(
+      (
+        .data[["asset_class"]] == "Listed Equity" &
+          .data[["scenario_geography"]] %in% .env[["equity_scenario_geography"]]
+      ) | (
+        .data[["asset_class"]] == "Corporate Bonds" &
+          .data[["scenario_geography"]] %in% .env[["bonds_scenario_geography"]]
+      )
+    ) |>
+    dplyr::filter(.data[["investor_name"]] == .env[["peer_group"]])
 
-    indices <-
-      list(`Listed Equity` = indices_equity_results_portfolio,
-           `Corporate Bonds` = indices_bonds_results_portfolio) %>%
-      bind_rows(.id = "asset_class") %>%
-      filter(.data$ald_sector %in% .env$tech_roadmap_sectors) %>%
-      filter(.data$scenario_geography != "GlobalAggregate") %>%
-      filter(.data$asset_class %in% .env$asset_classes) %>%
-      filter(.data$asset_class == "Listed Equity" & .data$equity_market %in% .env$equity_markets |
-               .data$asset_class == "Corporate Bonds" & .data$equity_market %in% .env$bonds_markets) %>%
-      filter(.data$asset_class == "Listed Equity" & .data$technology %in% .env$equity_techs |
-               .data$asset_class == "Corporate Bonds" & .data$technology %in% .env$bonds_techs) %>%
-      filter(.data$asset_class == "Listed Equity" & .data$scenario_geography %in% .env$equity_scenario_geography |
-               .data$asset_class == "Corporate Bonds" & .data$scenario_geography %in% .env$bonds_scenario_geography)
+  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") |>
+    dplyr::filter(.data[["asset_class"]] %in% .env[["asset_classes"]]) |>
+    dplyr::filter(
+      (
+        .data[["asset_class"]] == "Listed Equity" &
+          .data[["equity_market"]] %in% .env[["equity_markets"]]
+      ) | (
+        .data[["asset_class"]] == "Corporate Bonds" &
+          .data[["equity_market"]] %in% .env[["bonds_markets"]]
+      )
+    ) |>
+    dplyr::filter(
+      (
+        .data[["asset_class"]] == "Listed Equity" &
+          .data[["technology"]] %in% .env[["equity_techs"]]
+      ) | (
+        .data[["asset_class"]] == "Corporate Bonds" &
+          .data[["technology"]] %in% .env[["bonds_techs"]]
+      )
+    ) |>
+    dplyr::filter(
+      (
+        .data[["asset_class"]] == "Listed Equity" &
+          .data[["scenario_geography"]] %in% .env[["equity_scenario_geography"]]
+      ) | (
+        .data[["asset_class"]] == "Corporate Bonds" &
+          .data[["scenario_geography"]] %in% .env[["bonds_scenario_geography"]]
+      )
+    )
 
-    benchmark_data <- bind_rows(peers, indices)
+  benchmark_data <- dplyr::bind_rows(peers, indices)
 
-    cols_with_supporting_info <- c("benchmark", "portfolio_name", "asset_class", "equity_market",
-             "scenario_source", "scenario_geography", "allocation",
-             "ald_sector", "technology", "year", "unit")
+  cols_with_supporting_info <- c(
+    "benchmark",
+    "portfolio_name",
+    "asset_class",
+    "equity_market",
+    "scenario_source",
+    "scenario_geography",
+    "allocation",
+    "ald_sector",
+    "technology",
+    "year",
+    "unit"
+  )
 
-    list(portfolio = portfolio,
-         benchmark = benchmark_data) %>%
-      bind_rows(.id = "benchmark") %>%
-      mutate(benchmark = .data$benchmark == "benchmark") %>%
-      mutate(unit = case_when(
-        .data$ald_sector == "Power" ~ "MW",
-        .data$ald_sector == "Oil&Gas" ~ "GJ/a",
-        .data$ald_sector == "Coal" ~ "t/a",
-        .data$ald_sector == "Automotive" ~ "number of cars",
-        .data$ald_sector == "Aviation" ~ "number of planes",
-        .data$ald_sector == "Cement" ~ "t/a",
-        .data$ald_sector == "Steel" ~ "t/a"
-      )) %>%
-      select(all_of(cols_with_supporting_info), "scenario",
-             production = "plan_alloc_wt_tech_prod", "scen_alloc_wt_tech_prod") %>%
-      pivot_wider(names_from = "scenario", values_from = "scen_alloc_wt_tech_prod") %>%
-      pivot_longer(cols = -cols_with_supporting_info, names_to = "scenario", values_to = "value",
-                   values_drop_na = TRUE) %>%
-      mutate(value = if_else(.data$year > min(.data$year + 5) & .data$value == 0, NA_real_, .data$value)) %>%
-      filter(!is.na(.data$value)) %>%
-      filter(.data$scenario == "production" | !.data$benchmark) %>%
-      mutate(equity_market =  case_when(
-        .data$equity_market == "GlobalMarket" ~ "Global Market",
-        .data$equity_market == "DevelopedMarket" ~ "Developed Market",
-        .data$equity_market == "EmergingMarket" ~ "Emerging Market",
-        TRUE ~ .data$equity_market)
-      ) %>%
-      mutate(allocation = case_when(
-        .data$allocation == "portfolio_weight" ~ "Portfolio Weight",
-        .data$allocation == "ownership_weight" ~ "Ownership Weight",
-      )) %>%
-      filter(.data$year <= .env$start_year + .env$year_span) %>%
-      arrange(.data$asset_class,
-              factor(.data$equity_market, levels = c("Global Market", "Developed Market", "Emerging Market")),
-              factor(.data$scenario_source, levels = c("WEO2021", "GECO2021", "ETP2020", "IPR2021", "ISF2021")),
-              factor(.data$scenario_geography, levels = .env$scen_geo_levels),
-              factor(.data$technology, levels = .env$all_tech_levels))
-  }
+  list(
+    portfolio = portfolio,
+    benchmark = benchmark_data
+  ) |>
+    dplyr::bind_rows(.id = "benchmark") |>
+    dplyr::mutate(benchmark = .data[["benchmark"]] == "benchmark") |>
+    dplyr::mutate(
+      unit = dplyr::case_when(
+        .data[["ald_sector"]] == "Power" ~ "MW",
+        .data[["ald_sector"]] == "Oil&Gas" ~ "GJ/a",
+        .data[["ald_sector"]] == "Coal" ~ "t/a",
+        .data[["ald_sector"]] == "Automotive" ~ "number of cars",
+        .data[["ald_sector"]] == "Aviation" ~ "number of planes",
+        .data[["ald_sector"]] == "Cement" ~ "t/a",
+        .data[["ald_sector"]] == "Steel" ~ "t/a"
+      )
+    ) |>
+    dplyr::select(
+      dplyr::all_of(cols_with_supporting_info),
+      "scenario",
+      production = "plan_alloc_wt_tech_prod",
+      "scen_alloc_wt_tech_prod"
+    ) |>
+    tidyr::pivot_wider(
+      names_from = "scenario",
+      values_from = "scen_alloc_wt_tech_prod"
+    ) |>
+    tidyr::pivot_longer(
+      cols = -cols_with_supporting_info,
+      names_to = "scenario",
+      values_to = "value",
+      values_drop_na = TRUE
+    ) |>
+    dplyr::mutate(
+      value = dplyr::if_else(
+        .data[["year"]] > min(.data[["year"]] + 5L) & .data[["value"]] == 0L,
+        NA_real_,
+        .data[["value"]]
+      )
+    ) |>
+    dplyr::filter(!is.na(.data[["value"]])) |>
+    dplyr::filter(
+      .data[["scenario"]] == "production" | !.data[["benchmark"]]
+    ) |>
+    dplyr::mutate(
+      equity_market =  dplyr::case_when(
+        .data[["equity_market"]] == "GlobalMarket" ~ "Global Market",
+        .data[["equity_market"]] == "DevelopedMarket" ~ "Developed Market",
+        .data[["equity_market"]] == "EmergingMarket" ~ "Emerging Market",
+        TRUE ~ .data[["equity_market"]]
+      )
+    ) |>
+    dplyr::mutate(
+      allocation = dplyr::case_when(
+        .data[["allocation"]] == "portfolio_weight" ~ "Portfolio Weight",
+        .data[["allocation"]] == "ownership_weight" ~ "Ownership Weight"
+      )
+    ) |>
+    dplyr::filter(
+      .data[["year"]] <= .env[["start_year"]] + .env[["year_span"]]
+    ) |>
+    dplyr::arrange(
+      .data[["asset_class"]],
+      factor(
+        .data[["equity_market"]],
+        levels = c("Global Market", "Developed Market", "Emerging Market")
+      ),
+      factor(
+        .data[["scenario_source"]],
+        levels = c("WEO2021", "GECO2021", "ETP2020", "IPR2021", "ISF2021")
+      ),
+      factor(.data[["scenario_geography"]], levels = .env[["scen_geo_levels"]]),
+      factor(.data[["technology"]], levels = .env[["all_tech_levels"]])
+    )
+}
diff --git a/R/prepare_pacta_dashboard_data.R b/R/prepare_pacta_dashboard_data.R
index ee408d8..95f46f8 100644
--- a/R/prepare_pacta_dashboard_data.R
+++ b/R/prepare_pacta_dashboard_data.R
@@ -5,361 +5,452 @@ prepare_pacta_dashboard_data <- function(
   benchmarks_dir = Sys.getenv("BENCHMARKS_DIR")
 ) {
 
-log_info("Preparing data for the PACTA dashboard.")
+  log_info("Preparing data for the PACTA dashboard.")
 
-# portfolio/user parameters ----------------------------------------------------
-log_debug("Reading portfolio/user parameters.")
+  # portfolio/user parameters
+  log_debug("Reading parameters.")
 
-investor_name <- params[["user"]][["name"]]
-portfolio_name <- params[["portfolio"]][["name"]]
-peer_group <- params[["user"]][["peerGroup"]]
-language_select <- params[["user"]][["languageSelect"]]
+  investor_name <- params[["user"]][["name"]]
+  portfolio_name <- params[["portfolio"]][["name"]]
+  peer_group <- params[["user"]][["peerGroup"]]
+  language_select <- params[["user"]][["languageSelect"]]
 
-currency_exchange_value <- params[["user"]][["currencyExchangeValue"]]
-display_currency <- params[["user"]][["displayCurrency"]]
+  currency_exchange_value <- params[["user"]][["currencyExchangeValue"]]
+  display_currency <- params[["user"]][["displayCurrency"]]
 
-select_scenario_other <- params[["reporting"]][["scenarioOther"]]
-select_scenario <- params[["reporting"]][["selectScenario"]]
+  select_scenario_other <- params[["reporting"]][["scenarioOther"]]
+  select_scenario <- params[["reporting"]][["selectScenario"]]
 
-green_techs <- params[["reporting"]][["greenTechs"]]
-tech_roadmap_sectors <- params[["reporting"]][["techRoadmapSectors"]]
-pacta_sectors_not_analysed <- params[["reporting"]][["pactaSectorsNotAnalysed"]]
+  green_techs <- params[["reporting"]][["greenTechs"]]
+  tech_roadmap_sectors <- params[["reporting"]][["techRoadmapSectors"]]
+  pacta_sectors_not_analysed <- params[["reporting"]][["pactaSectorsNotAnalysed"]] #nolint
 
-power_tech_levels = c("RenewablesCap", "HydroCap", "NuclearCap", "GasCap", "OilCap", "CoalCap")
-oil_gas_levels = c("Oil", "Gas")
-coal_levels = c("Coal")
-auto_levels = c("Electric", "Electric_HDV", "FuelCell","FuelCell_HDV", "Hybrid","Hybrid_HDV", "ICE", "ICE_HDV")
-cement_levels = c("Integrated facility", "Grinding")
-steel_levels = c("Electric Arc Furnace", "Open Hearth Furnace", "Basic Oxygen Furnace")
-aviation_levels = c("Freight", "Passenger", "Mix", "Other")
-all_tech_levels = c(power_tech_levels, auto_levels, oil_gas_levels, coal_levels, cement_levels, steel_levels, aviation_levels)
+  power_tech_levels <- c(
+    "RenewablesCap",
+    "HydroCap",
+    "NuclearCap",
+    "GasCap",
+    "OilCap",
+    "CoalCap"
+  )
+  oil_gas_levels <- c(
+    "Oil",
+    "Gas"
+  )
+  coal_levels <- "Coal"
+  auto_levels <- c(
+    "Electric",
+    "Electric_HDV",
+    "FuelCell",
+    "FuelCell_HDV",
+    "Hybrid",
+    "Hybrid_HDV",
+    "ICE",
+    "ICE_HDV"
+  )
+  cement_levels <- c("Integrated facility", "Grinding")
+  steel_levels <- c(
+    "Electric Arc Furnace",
+    "Open Hearth Furnace",
+    "Basic Oxygen Furnace"
+  )
+  aviation_levels <- c(
+    "Freight",
+    "Passenger",
+    "Mix",
+    "Other"
+  )
+  all_tech_levels <- c(
+    power_tech_levels,
+    auto_levels,
+    oil_gas_levels,
+    coal_levels,
+    cement_levels,
+    steel_levels,
+    aviation_levels
+  )
 
 
-# config parameters from manifest ----------------------------------------------
-log_debug("Reading config parameters from analysis outputs manifest.")
+  # config parameters from manifest
+  log_debug("Reading config parameters from analysis outputs manifest.")
 
-manifest <- jsonlite::read_json(path = file.path(analysis_output_dir, "manifest.json"))
+  manifest <- jsonlite::read_json(
+    path = file.path(analysis_output_dir, "manifest.json")
+  )
 
-start_year <- manifest$params$analysis$startYear
-year_span <- manifest$params$analysis$timeHorizon
-pacta_sectors <- unlist(manifest$params$analysis$sectorList)
-equity_market_levels <- unlist(manifest$params$analysis$equityMarketList)
-scen_geo_levels <- unlist(manifest$params$analysis$scenarioGeographiesList)
+  start_year <- manifest[["params"]][["analysis"]][["startYear"]]
+  year_span <- manifest[["params"]][["analysis"]][["timeHorizon"]]
+  pacta_sectors <- unlist(manifest[["params"]][["analysis"]][["sectorList"]])
+  equity_market_levels <- unlist(manifest[["params"]][["analysis"]][["equityMarketList"]]) #nolint
+  scen_geo_levels <- unlist(manifest[["params"]][["analysis"]][["scenarioGeographiesList"]]) #nolint
 
 
-# load results from input directory --------------------------------------------
-log_debug("Loading results from input directory.")
+  # load results from input directory
+  log_debug("Loading results from input directory.")
 
-audit_file <- readRDS(file.path(analysis_output_dir, "audit_file.rds"))
-emissions <- readRDS(file.path(analysis_output_dir, "emissions.rds"))
-equity_results_portfolio <- readRDS(file.path(analysis_output_dir, "Equity_results_portfolio.rds"))
-bonds_results_portfolio <- readRDS(file.path(analysis_output_dir, "Bonds_results_portfolio.rds"))
-equity_results_company <- readRDS(file.path(analysis_output_dir, "Equity_results_company.rds"))
-bonds_results_company <- readRDS(file.path(analysis_output_dir, "Bonds_results_company.rds"))
+  audit_file <- readRDS(file.path(analysis_output_dir, "audit_file.rds"))
+  emissions <- readRDS(file.path(analysis_output_dir, "emissions.rds"))
+  equity_results_portfolio <- readRDS(
+    file.path(analysis_output_dir, "Equity_results_portfolio.rds")
+  )
+  bonds_results_portfolio <- readRDS(
+    file.path(analysis_output_dir, "Bonds_results_portfolio.rds")
+  )
+  equity_results_company <- readRDS(
+    file.path(analysis_output_dir, "Equity_results_company.rds")
+  )
+  bonds_results_company <- readRDS(
+    file.path(analysis_output_dir, "Bonds_results_company.rds")
+  )
 
 
-# data from PACTA inputs used to generate the results --------------------------
-log_debug("Loading benchmark results.")
+  # data from PACTA inputs used to generate the results
+  log_debug("Loading benchmark results.")
 
-indices_bonds_results_portfolio <- readRDS(file.path(benchmarks_dir, "Indices_bonds_results_portfolio.rds"))
-indices_equity_results_portfolio <- readRDS(file.path(benchmarks_dir, "Indices_equity_results_portfolio.rds"))
+  indices_cb_results_portfolio <- readRDS(
+    file.path(benchmarks_dir, "Indices_bonds_results_portfolio.rds")
+  )
+  indices_eq_results_portfolio <- readRDS(
+    file.path(benchmarks_dir, "Indices_equity_results_portfolio.rds")
+  )
 
-log_debug("Loading peer results.")
-peers_bonds_results_portfolio <- pacta.portfolio.utils::empty_portfolio_results()
-peers_equity_results_portfolio <- pacta.portfolio.utils::empty_portfolio_results()
+  log_debug("Loading peer results.")
+  peers_bonds_results_portfolio <- pacta.portfolio.utils::empty_portfolio_results() #nolint
+  peers_equity_results_portfolio <- pacta.portfolio.utils::empty_portfolio_results() #nolint
 
 
-# translations -----------------------------------------------------------------
-log_debug("Loading translations.")
+  # translations
+  log_debug("Loading translations.")
 
-dataframe_translations <- readr::read_csv(
-  system.file("extdata/translation/dataframe_labels.csv", package = "workflow.pacta.dashboard"),
-  col_types = readr::cols()
-)
+  dataframe_translations <- readr::read_csv(
+    system.file(
+      "extdata", "translation", "dataframe_labels.csv",
+      package = "workflow.pacta.dashboard"
+    ),
+    col_types = readr::cols()
+  )
 
-header_dictionary <- readr::read_csv(
-  system.file("extdata/translation/dataframe_headers.csv", package = "workflow.pacta.dashboard"),
-  col_types = readr::cols()
-)
+  header_dictionary <- readr::read_csv(
+    system.file(
+      "extdata", "translation", "dataframe_headers.csv",
+      package = "workflow.pacta.dashboard"
+    ),
+    col_types = readr::cols()
+  )
 
-js_translations <- jsonlite::fromJSON(
-  txt = system.file("extdata/translation/js_labels.json", package = "workflow.pacta.dashboard")
-)
+  js_translations <- jsonlite::fromJSON(
+    txt = system.file(
+      "extdata", "translation", "js_labels.json",
+      package = "workflow.pacta.dashboard"
+    )
+  )
 
-sector_order <- readr::read_csv(
-  system.file("extdata/sector_order/sector_order.csv", package = "workflow.pacta.dashboard"),
-  col_types = readr::cols()
-)
+  sector_order <- readr::read_csv(
+    system.file(
+      "extdata", "sector_order", "sector_order.csv",
+      package = "workflow.pacta.dashboard"
+    ),
+    col_types = readr::cols()
+  )
 
-dictionary <-
-  choose_dictionary_language(
+  dictionary <- choose_dictionary_language(
     data = dataframe_translations,
     language = language_select
   )
 
-header_dictionary <- replace_contents(header_dictionary, display_currency)
+  header_dictionary <- replace_contents(header_dictionary, display_currency)
 
 
-# add investor_name and portfolio_name to results data frames because ----------
-# pacta.portfolio.report functions expect that ---------------------------------
-log_debug("Adding investor_name and portfolio_name to results data frames.")
+  # add investor_name and portfolio_name to results data frames because
+  # pacta.portfolio.report functions expect that
+  log_debug("Adding investor_name and portfolio_name to results data frames.")
 
-audit_file <-
-  audit_file %>%
-  mutate(
-    investor_name = investor_name,
-    portfolio_name = portfolio_name
-  )
+  audit_file <- audit_file |>
+    dplyr::mutate(
+      investor_name = investor_name,
+      portfolio_name = portfolio_name
+    )
 
-emissions <-
-  emissions %>%
-  mutate(
-    investor_name = investor_name,
-    portfolio_name = portfolio_name
-  )
+  emissions <- emissions |>
+    dplyr::mutate(
+      investor_name = investor_name,
+      portfolio_name = portfolio_name
+    )
 
-equity_results_portfolio <-
-  equity_results_portfolio %>%
-  mutate(
-    investor_name = investor_name,
-    portfolio_name = portfolio_name
-  )
+  equity_results_portfolio <- equity_results_portfolio |>
+    dplyr::mutate(
+      investor_name = investor_name,
+      portfolio_name = portfolio_name
+    )
 
-bonds_results_portfolio <-
-  bonds_results_portfolio %>%
-  mutate(
-    investor_name = investor_name,
-    portfolio_name = portfolio_name
-  )
+  bonds_results_portfolio <- bonds_results_portfolio |>
+    dplyr::mutate(
+      investor_name = investor_name,
+      portfolio_name = portfolio_name
+    )
 
-equity_results_company <-
-  equity_results_company %>%
-  mutate(
-    investor_name = investor_name,
-    portfolio_name = portfolio_name
-  )
+  equity_results_company <- equity_results_company |>
+    dplyr::mutate(
+      investor_name = investor_name,
+      portfolio_name = portfolio_name
+    )
+
+  bonds_results_company <- bonds_results_company |>
+    dplyr::mutate(
+      investor_name = investor_name,
+      portfolio_name = portfolio_name
+    )
+
+
+  # data_included_table.json
+
+  log_info("Preparing data_included_table.json.")
+  audit_file |>
+    prep_audit_table(
+      investor_name = investor_name,
+      portfolio_name = portfolio_name,
+      currency_exchange_value = currency_exchange_value
+    ) |>
+    translate_df_contents("data_included_table", dictionary, inplace = TRUE) |>
+    translate_df_headers(
+      "data_included_table",
+      language_select,
+      header_dictionary
+    ) |>
+    jsonlite::write_json(
+      path = file.path(dashboard_data_dir, "data_included_table.json")
+    )
+
+
+  # data_value_pie_bonds.json
+
+  log_info("Preparing data_value_pie_bonds.json.")
+  audit_file |>
+    prep_exposure_pie(
+      asset_type = "Bonds",
+      investor_name = investor_name,
+      portfolio_name = portfolio_name,
+      pacta_sectors = pacta_sectors,
+      currency_exchange_value = currency_exchange_value
+    ) |>
+    translate_df_contents("data_value_pie_bonds", dictionary) |>
+    jsonlite::write_json(
+      path = file.path(dashboard_data_dir, "data_value_pie_bonds.json")
+    )
+
+
+  # data_emissions_equity.json
 
-bonds_results_company <-
-  bonds_results_company %>%
-  mutate(
+  log_info("Preparing data_emissions_equity.json.")
+  emissions |>
+    prep_emissions_pie(
+      asset_type = "Equity",
+      investor_name = investor_name,
+      portfolio_name = portfolio_name,
+      pacta_sectors = pacta_sectors
+    ) |>
+    translate_df_contents("data_emissions_pie_equity", dictionary) |>
+    jsonlite::write_json(
+      path = file.path(dashboard_data_dir, "data_emissions_pie_equity.json")
+    )
+
+
+  # data_emissions_bonds.json
+
+  log_info("Preparing data_emissions_bonds.json.")
+  emissions |>
+    prep_emissions_pie(
+      asset_type = "Bonds",
+      investor_name = investor_name,
+      portfolio_name = portfolio_name,
+      pacta_sectors = pacta_sectors
+    ) |>
+    translate_df_contents("data_emissions_pie_bonds", dictionary) |>
+    jsonlite::write_json(
+      path = file.path(dashboard_data_dir, "data_emissions_pie_bonds.json")
+    )
+
+
+  # data_value_pie_equity.json
+
+  log_info("Preparing data_value_pie_equity.json.")
+  audit_file |>
+    prep_exposure_pie(
+      asset_type = "Equity",
+      investor_name = investor_name,
+      portfolio_name = portfolio_name,
+      pacta_sectors = pacta_sectors,
+      currency_exchange_value = currency_exchange_value
+    ) |>
+    translate_df_contents("data_value_pie_equity", dictionary) |>
+    jsonlite::write_json(
+      path = file.path(dashboard_data_dir, "data_value_pie_equity.json")
+    )
+
+
+  # data_techmix.json
+
+  log_info("Preparing data_techmix.json.")
+  prep_techexposure(
+    equity_results_portfolio = equity_results_portfolio,
+    bonds_results_portfolio = bonds_results_portfolio,
     investor_name = investor_name,
-    portfolio_name = portfolio_name
-  )
+    portfolio_name = portfolio_name,
+    indices_eq_results_portfolio = indices_eq_results_portfolio,
+    indices_cb_results_portfolio = indices_cb_results_portfolio,
+    peers_equity_results_portfolio = peers_equity_results_portfolio,
+    peers_bonds_results_portfolio = peers_bonds_results_portfolio,
+    peer_group = peer_group,
+    select_scenario_other = select_scenario_other,
+    select_scenario = select_scenario,
+    start_year = start_year,
+    green_techs = green_techs,
+    equity_market_levels = equity_market_levels,
+    all_tech_levels = all_tech_levels
+  ) |>
+    translate_df_contents("techexposure_data", dictionary) |>
+    jsonlite::write_json(
+      path = file.path(dashboard_data_dir, "data_techexposure.json")
+    )
 
 
-# data_included_table.json -----------------------------------------------------
+  # data_techmix_sector.json
+
+  log_info("Preparing data_techmix_sector.json.")
+  prep_techmix_sector(
+    equity_results_portfolio,
+    bonds_results_portfolio,
+    indices_eq_results_portfolio,
+    indices_cb_results_portfolio,
+    peers_equity_results_portfolio,
+    peers_bonds_results_portfolio,
+    investor_name,
+    portfolio_name,
+    start_year,
+    year_span,
+    peer_group,
+    green_techs,
+    all_tech_levels
+  ) |>
+    jsonlite::write_json(
+      path = file.path(dashboard_data_dir, "data_techmix_sector.json")
+    )
+
+  # data_trajectory_alignment.json
 
-log_info("Preparing data_included_table.json.")
-audit_file %>%
-  prep_audit_table(
+  log_info("Preparing data_trajectory_alignment.json.")
+  prep_trajectory_alignment(
+    equity_results_portfolio = equity_results_portfolio,
+    bonds_results_portfolio = bonds_results_portfolio,
+    peers_equity_results_portfolio = peers_equity_results_portfolio,
+    peers_bonds_results_portfolio = peers_bonds_results_portfolio,
+    indices_eq_results_portfolio = indices_eq_results_portfolio,
+    indices_cb_results_portfolio = indices_cb_results_portfolio,
     investor_name = investor_name,
     portfolio_name = portfolio_name,
-    currency_exchange_value = currency_exchange_value
-  ) %>%
-  translate_df_contents("data_included_table", dictionary, inplace = TRUE) %>%
-  translate_df_headers("data_included_table", language_select, header_dictionary) %>%
-  jsonlite::write_json(path = file.path(dashboard_data_dir, "data_included_table.json"))
+    tech_roadmap_sectors = tech_roadmap_sectors,
+    peer_group = peer_group,
+    start_year = start_year,
+    year_span = year_span,
+    scen_geo_levels = scen_geo_levels,
+    all_tech_levels = all_tech_levels
+  ) |>
+    translate_df_contents("data_trajectory_alignment", dictionary) |>
+    jsonlite::write_json(
+      path = file.path(dashboard_data_dir, "data_trajectory_alignment.json")
+    )
+
 
+  # data_emissions.json
 
-# data_value_pie_bonds.json ----------------------------------------------------
+  log_info("Preparing data_emissions.json.")
+  prep_emissions_trajectory(
+    equity_results_portfolio = equity_results_portfolio,
+    bonds_results_portfolio = bonds_results_portfolio,
+    portfolio_name = portfolio_name,
+    pacta_sectors = pacta_sectors,
+    year_span = year_span,
+    start_year = start_year
+  ) |>
+    translate_df_contents("data_emissions", dictionary) |>
+    jsonlite::write_json(
+      path = file.path(dashboard_data_dir, "data_emissions.json")
+    )
 
-log_info("Preparing data_value_pie_bonds.json.")
-audit_file %>%
-  prep_exposure_pie(
-    asset_type = "Bonds",
+  # data_exposure_stats.json
+
+  log_info("Preparing data_exposure_stats.json.")
+  prep_exposure_stats(
+    audit_file = audit_file,
     investor_name = investor_name,
     portfolio_name = portfolio_name,
     pacta_sectors = pacta_sectors,
     currency_exchange_value = currency_exchange_value
-  ) %>%
-  translate_df_contents("data_value_pie_bonds", dictionary) %>%
-  jsonlite::write_json(path = file.path(dashboard_data_dir, "data_value_pie_bonds.json"))
+  ) |>
+    jsonlite::write_json(
+      path = file.path(dashboard_data_dir, "data_exposure_stats.json")
+    )
 
 
-# data_emissions_equity.json ---------------------------------------------------
+  # data_company_bubble.json
 
-log_info("Preparing data_emissions_equity.json.")
-emissions %>%
-  prep_emissions_pie(
-    asset_type = "Equity",
-    investor_name = investor_name,
+  log_info("Preparing data_company_bubble.json.")
+  prep_company_bubble(
+    equity_results_company = equity_results_company,
+    bonds_results_company = bonds_results_company,
     portfolio_name = portfolio_name,
-    pacta_sectors = pacta_sectors
-  ) %>%
-  translate_df_contents("data_emissions_pie_equity", dictionary) %>%
-  jsonlite::write_json(path = file.path(dashboard_data_dir, "data_emissions_pie_equity.json"))
+    start_year = start_year,
+    green_techs = green_techs
+  ) |>
+    translate_df_contents("data_company_bubble", dictionary) |>
+    jsonlite::write_json(
+      path = file.path(dashboard_data_dir, "data_company_bubble.json")
+    )
 
 
-# data_emissions_bonds.json ----------------------------------------------------
+  # data_techexposure_company_companies.json
 
-log_info("Preparing data_emissions_bonds.json.")
-emissions %>%
-  prep_emissions_pie(
-    asset_type = "Bonds",
-    investor_name = investor_name,
+  log_info("Preparing data_techexposure_company_companies.json.")
+  prep_key_bars_company(
+    equity_results_company = equity_results_company,
+    bonds_results_company = bonds_results_company,
     portfolio_name = portfolio_name,
-    pacta_sectors = pacta_sectors
-  ) %>%
-  translate_df_contents("data_emissions_pie_bonds", dictionary) %>%
-  jsonlite::write_json(path = file.path(dashboard_data_dir, "data_emissions_pie_bonds.json"))
+    start_year = start_year,
+    pacta_sectors_not_analysed = pacta_sectors_not_analysed,
+    all_tech_levels = all_tech_levels
+  ) |>
+    translate_df_contents("data_key_bars_company", dictionary) |>
+    jsonlite::write_json(
+      path = file.path(
+        dashboard_data_dir,
+        "data_techexposure_company_companies.json"
+      )
+    )
 
 
-# data_value_pie_equity.json ---------------------------------------------------
+  # data_techexposure_company_portfolio.json
 
-log_info("Preparing data_value_pie_equity.json.")
-audit_file %>%
-  prep_exposure_pie(
-    asset_type = "Equity",
-    investor_name = investor_name,
+  log_info("Preparing data_techexposure_company_portfolio.json.")
+  prep_key_bars_portfolio(
+    equity_results_portfolio = equity_results_portfolio,
+    bonds_results_portfolio = bonds_results_portfolio,
     portfolio_name = portfolio_name,
-    pacta_sectors = pacta_sectors,
-    currency_exchange_value = currency_exchange_value
-  ) %>%
-  translate_df_contents("data_value_pie_equity", dictionary) %>%
-  jsonlite::write_json(path = file.path(dashboard_data_dir, "data_value_pie_equity.json"))
-
-
-# data_techmix.json ------------------------------------------------------------
-
-log_info("Preparing data_techmix.json.")
-prep_techexposure(
-  equity_results_portfolio = equity_results_portfolio,
-  bonds_results_portfolio = bonds_results_portfolio,
-  investor_name = investor_name,
-  portfolio_name = portfolio_name,
-  indices_equity_results_portfolio = indices_equity_results_portfolio,
-  indices_bonds_results_portfolio = indices_bonds_results_portfolio,
-  peers_equity_results_portfolio = peers_equity_results_portfolio,
-  peers_bonds_results_portfolio = peers_bonds_results_portfolio,
-  peer_group = peer_group,
-  select_scenario_other = select_scenario_other,
-  select_scenario = select_scenario,
-  start_year = start_year,
-  green_techs = green_techs,
-  equity_market_levels = equity_market_levels,
-  all_tech_levels = all_tech_levels
-  ) %>%
-  translate_df_contents("techexposure_data", dictionary) %>%
-  jsonlite::write_json(path = file.path(dashboard_data_dir, "data_techexposure.json"))
-
-
-# data_techmix_sector.json -----------------------------------------------------
-
-log_info("Preparing data_techmix_sector.json.")
-prep_techmix_sector(
-  equity_results_portfolio,
-  bonds_results_portfolio,
-  indices_equity_results_portfolio,
-  indices_bonds_results_portfolio,
-  peers_equity_results_portfolio,
-  peers_bonds_results_portfolio,
-  investor_name,
-  portfolio_name,
-  start_year,
-  year_span,
-  peer_group,
-  green_techs,
-  all_tech_levels
-  ) %>%
-  jsonlite::write_json(path = file.path(dashboard_data_dir, "data_techmix_sector.json"))
-
-# data_trajectory_alignment.json -----------------------------------------------
-
-log_info("Preparing data_trajectory_alignment.json.")
-prep_trajectory_alignment(
-  equity_results_portfolio = equity_results_portfolio,
-  bonds_results_portfolio = bonds_results_portfolio,
-  peers_equity_results_portfolio = peers_equity_results_portfolio,
-  peers_bonds_results_portfolio = peers_bonds_results_portfolio,
-  indices_equity_results_portfolio = indices_equity_results_portfolio,
-  indices_bonds_results_portfolio = indices_bonds_results_portfolio,
-  investor_name = investor_name,
-  portfolio_name = portfolio_name,
-  tech_roadmap_sectors = tech_roadmap_sectors,
-  peer_group = peer_group,
-  start_year = start_year,
-  year_span = year_span,
-  scen_geo_levels = scen_geo_levels,
-  all_tech_levels = all_tech_levels
-  ) %>%
-  translate_df_contents("data_trajectory_alignment", dictionary) %>%
-  jsonlite::write_json(path = file.path(dashboard_data_dir, "data_trajectory_alignment.json"))
-
-
-# data_emissions.json ----------------------------------------------------------
-
-log_info("Preparing data_emissions.json.")
-prep_emissions_trajectory(
-  equity_results_portfolio = equity_results_portfolio,
-  bonds_results_portfolio = bonds_results_portfolio,
-  portfolio_name = portfolio_name,
-  pacta_sectors = pacta_sectors,
-  year_span = year_span,
-  start_year = start_year
-  ) %>%
-  translate_df_contents("data_emissions", dictionary) %>%
-  jsonlite::write_json(path = file.path(dashboard_data_dir, "data_emissions.json"))
-
-# data_exposure_stats.json
-
-log_info("Preparing data_exposure_stats.json.")
-prep_exposure_stats(
-  audit_file = audit_file,
-  investor_name = investor_name,
-  portfolio_name = portfolio_name,
-  pacta_sectors = pacta_sectors,
-  currency_exchange_value = currency_exchange_value
-  ) %>%
-  jsonlite::write_json(path = file.path(dashboard_data_dir, "data_exposure_stats.json"))
-
-
-# data_company_bubble.json -----------------------------------------------------
-
-log_info("Preparing data_company_bubble.json.")
-prep_company_bubble(
-  equity_results_company = equity_results_company,
-  bonds_results_company = bonds_results_company,
-  portfolio_name = portfolio_name,
-  start_year = start_year,
-  green_techs = green_techs
-  ) %>%
-  translate_df_contents("data_company_bubble", dictionary) %>% 
-  jsonlite::write_json(path = file.path(dashboard_data_dir, "data_company_bubble.json"))
-
-
-# data_techexposure_company_companies.json -------------------------------------
-
-log_info("Preparing data_techexposure_company_companies.json.")
-prep_key_bars_company(
-  equity_results_company = equity_results_company,
-  bonds_results_company = bonds_results_company,
-  portfolio_name = portfolio_name,
-  start_year = start_year,
-  pacta_sectors_not_analysed = pacta_sectors_not_analysed,
-  all_tech_levels = all_tech_levels
-  ) %>%
-  translate_df_contents("data_key_bars_company", dictionary) %>%
-  jsonlite::write_json(path = file.path(dashboard_data_dir, "data_techexposure_company_companies.json"))
-
-
-# data_techexposure_company_portfolio.json -------------------------------------
-
-log_info("Preparing data_techexposure_company_portfolio.json.")
-prep_key_bars_portfolio(
-  equity_results_portfolio = equity_results_portfolio,
-  bonds_results_portfolio = bonds_results_portfolio,
-  portfolio_name = portfolio_name,
-  start_year = start_year,
-  pacta_sectors_not_analysed = pacta_sectors_not_analysed,
-  all_tech_levels = all_tech_levels
-  ) %>% 
-  translate_df_contents("data_key_bars_portfolio", dictionary) %>%
-  jsonlite::write_json(path = file.path(dashboard_data_dir, "data_techexposure_company_portfolio.json"))
-
-
-# put JSON and CSV outputs into a zip archive ----------------------------------
-
-zip_outputs(dashboard_data_dir)
+    start_year = start_year,
+    pacta_sectors_not_analysed = pacta_sectors_not_analysed,
+    all_tech_levels = all_tech_levels
+  ) |>
+    translate_df_contents("data_key_bars_portfolio", dictionary) |>
+    jsonlite::write_json(
+      path = file.path(
+        dashboard_data_dir,
+        "data_techexposure_company_portfolio.json"
+      )
+    )
+
+
+  # put JSON and CSV outputs into a zip archive
+
+  zip_outputs(dashboard_data_dir)
 
 }
 
@@ -367,23 +458,23 @@ zip_outputs(dashboard_data_dir)
 zip_outputs <- function(dashboard_data_dir) {
   log_debug("Preparing outputs zip archive.")
   json_filenames <- list.files(dashboard_data_dir, pattern = "[.]json$")
-  
+
   zip_temp <- file.path(tempdir(), "zip_temp")
   dir.create(zip_temp, showWarnings = FALSE)
-  
+
   for (json_filename in json_filenames) {
     log_trace(paste("Adding", json_filename, "to zip archive."))
     file.copy(
       from = file.path(dashboard_data_dir, json_filename),
       to = file.path(zip_temp, json_filename)
     )
-    
+
     csv_filename <- sub("[.]json$", ".csv", json_filename)
-    
+
     df <- jsonlite::read_json(
       path = file.path(dashboard_data_dir, json_filename),
       simplifyVector = TRUE
-    ) 
+    )
 
     if (inherits(df, "data.frame")) {
       log_trace(paste("Adding", csv_filename, "to zip archive."))
@@ -395,7 +486,7 @@ zip_outputs <- function(dashboard_data_dir) {
       )
     }
   }
-  
+
   log_debug("Creating zip archive.")
   utils::zip(
     zipfile = file.path(dashboard_data_dir, "archive.zip"),
diff --git a/R/prepare_schema_files.R b/R/prepare_schema_files.R
index 40d9749..319422d 100644
--- a/R/prepare_schema_files.R
+++ b/R/prepare_schema_files.R
@@ -8,7 +8,9 @@
 #' @return directory
 #'
 #' @export
-prepare_schema_files <- function(directory) {
+prepare_schema_files <- function(
+  directory
+) {
   log_debug("Preparing schema files.")
   log_debug("Schema tempdir: ", directory)
   dashboard_schema_files <- list.files(
diff --git a/R/replace_contents.R b/R/replace_contents.R
index 07e01c4..e2a29f8 100644
--- a/R/replace_contents.R
+++ b/R/replace_contents.R
@@ -1,5 +1,17 @@
-replace_contents <- function(data, display_currency) {
-  mutate(data, across(.cols = everything(), .fns = ~ gsub("_CUR_", display_currency, .x)))
+replace_contents <- function(
+  data,
+  display_currency
+) {
+  dplyr::mutate(
+    data,
+    dplyr::across(
+      .cols = dplyr::everything(),
+      .fns = ~ gsub(
+        pattern = "_CUR_",
+        replacement = display_currency,
+        x = .x,
+        fixed = TRUE
+      )
+    )
+  )
 }
-
-
diff --git a/R/run_dashboard_workflow.R b/R/run_dashboard_workflow.R
index 0a71c2f..c2af79b 100644
--- a/R/run_dashboard_workflow.R
+++ b/R/run_dashboard_workflow.R
@@ -81,4 +81,3 @@ run_dashboard_workflow <- function(
   }
   return(invisible(TRUE))
 }
-
diff --git a/R/scenarios_found_in_sectors.R b/R/scenarios_found_in_sectors.R
index c675ad1..447e5fb 100644
--- a/R/scenarios_found_in_sectors.R
+++ b/R/scenarios_found_in_sectors.R
@@ -1,7 +1,10 @@
-scenarios_found_in_sectors <- function(data, select_scenario_param, sectors) {
-  out <- (data$ald_sector %in% sectors) &
-    (data$scenario == get_scenario(select_scenario_param)) &
-    (data$scenario_source == get_scenario_source(select_scenario_param))
+scenarios_found_in_sectors <- function(
+  data,
+  select_scenario_param,
+  sectors
+) {
+  out <- (data[["ald_sector"]] %in% sectors) &
+    (data[["scenario"]] == get_scenario(select_scenario_param)) &
+    (data[["scenario_source"]] == get_scenario_source(select_scenario_param))
   out
 }
-
diff --git a/R/translate_column_contents.R b/R/translate_column_contents.R
index 4a3ec71..0b12e0d 100644
--- a/R/translate_column_contents.R
+++ b/R/translate_column_contents.R
@@ -1,27 +1,30 @@
-translate_column_contents <-
-  function(data, dictionary, column, inplace = FALSE) {
-    dictionary_column <-
-      dictionary %>%
-      filter(.data$id_column == .env$column) %>%
-      select(-"id_column")
+translate_column_contents <- function(
+  data,
+  dictionary,
+  column,
+  inplace = FALSE
+) {
+  dictionary_column <- dictionary |>
+    dplyr::filter(.data[["id_column"]] == .env[["column"]]) |>
+    dplyr::select(-"id_column")
 
-    if (inplace) {
-      new_column <- column
-    } else {
-      new_column <- glue::glue(column, "_translation")
-    }
-
-    data %>%
-      left_join(
-        dictionary_column,
-        by = rlang::set_names("translate_key", column)
-      ) %>%
-      mutate(
-        !!new_column := if_else(
-          is.na(.data$translate_value),
-          .data[[!!column]],
-          .data$translate_value
-        )
-      ) %>%
-      select(-"translate_value")
+  if (inplace) {
+    new_column <- column
+  } else {
+    new_column <- glue::glue(column, "_translation")
   }
+
+  data |>
+    dplyr::left_join(
+      dictionary_column,
+      by = rlang::set_names("translate_key", column)
+    ) |>
+    dplyr::mutate(
+      !!new_column := dplyr::if_else(
+        is.na(.data[["translate_value"]]),
+        .data[[!!column]],
+        .data[["translate_value"]]
+      )
+    ) |>
+    dplyr::select(-"translate_value")
+}
diff --git a/R/translate_df_contents.R b/R/translate_df_contents.R
index 9a6d33b..b497921 100644
--- a/R/translate_df_contents.R
+++ b/R/translate_df_contents.R
@@ -1,30 +1,34 @@
-translate_df_contents <-
-  function(data, id_data, dictionary, inplace = FALSE) {
-    if (!(id_data %in% dictionary$id_data)) {
-      rlang::abort(
-        class = "dataset not in dictionary",
-        glue::glue("the dataset {id_data} is not defined in translation dictionary.")
-      )
-    }
-
-    dictionary_subset <-
-      dictionary %>%
-      filter(.data$id_data == .env$id_data) %>%
-      transmute(
-        .data$id_column,
-        .data$translate_key,
-        .data$translate_value
+translate_df_contents <- function(
+  data,
+  id_data,
+  dictionary,
+  inplace = FALSE
+) {
+  if (!(id_data %in% dictionary[["id_data"]])) {
+    rlang::abort(
+      class = "dataset not in dictionary",
+      glue::glue(
+        "the dataset {id_data} is not defined in translation dictionary."
       )
+    )
+  }
 
-    for (column in unique(dictionary_subset$id_column)) {
-      data <-
-        translate_column_contents(
-          data = data,
-          dictionary = dictionary_subset,
-          column = column,
-          inplace = inplace
-        )
-    }
+  dictionary_subset <- dictionary |>
+    dplyr::filter(.data[["id_data"]] == .env[["id_data"]]) |>
+    dplyr::transmute(
+      .data[["id_column"]],
+      .data[["translate_key"]],
+      .data[["translate_value"]]
+    )
 
-    data
+  for (column in unique(dictionary_subset[["id_column"]])) {
+    data <- translate_column_contents(
+      data = data,
+      dictionary = dictionary_subset,
+      column = column,
+      inplace = inplace
+    )
   }
+
+  data
+}
diff --git a/R/translate_df_headers.R b/R/translate_df_headers.R
index 7907e85..36c1ba1 100644
--- a/R/translate_df_headers.R
+++ b/R/translate_df_headers.R
@@ -1,26 +1,30 @@
-translate_df_headers <-
-  function(data, id_data, language_select, dictionary) {
-    language <- tolower(language_select)
+translate_df_headers <- function(
+  data,
+  id_data,
+  language_select,
+  dictionary
+) {
+  language <- tolower(language_select)
 
-    if (!(id_data %in% dictionary$id_data)) {
-      rlang::abort(
-        class = "dataset not in dictionary",
-        glue::glue("the dataset {id_data} is not defined in translation dictionary.")
+  if (!(id_data %in% dictionary[["id_data"]])) {
+    rlang::abort(
+      class = "dataset not in dictionary",
+      glue::glue(
+        "the dataset {id_data} is not defined in translation dictionary."
       )
-    }
+    )
+  }
 
-    column_tibble <- tibble(column_name = names(data))
+  column_tibble <- dplyr::tibble(column_name = names(data))
 
-    dictionary_subset <-
-      dictionary %>%
-      filter(.data$id_data == .env$id_data) %>%
-      transmute(.data$id_column, .data[[!!language]])
+  dictionary_subset <- dictionary |>
+    dplyr::filter(.data[["id_data"]] == .env[["id_data"]]) |>
+    dplyr::transmute(.data[["id_column"]], .data[[!!language]])
 
-    translated_headers <-
-      dictionary_subset %>%
-      left_join(column_tibble, by = c(id_column = "column_name"))
+  translated_headers <- dictionary_subset |>
+    dplyr::left_join(column_tibble, by = c(id_column = "column_name"))
 
-    names(data) <- translated_headers[[language]]
+  names(data) <- translated_headers[[language]]
 
-    data
-  }
+  data
+}
diff --git a/R/workflow.pacta.dashboard-package.R b/R/workflow.pacta.dashboard-package.R
index 4fd1011..032eea1 100644
--- a/R/workflow.pacta.dashboard-package.R
+++ b/R/workflow.pacta.dashboard-package.R
@@ -2,47 +2,13 @@
 "_PACKAGE"
 
 ## usethis namespace: start
-#' @importFrom dplyr across
-#' @importFrom dplyr all_of
-#' @importFrom dplyr arrange
-#' @importFrom dplyr bind_rows
-#' @importFrom dplyr case_when
-#' @importFrom dplyr desc
-#' @importFrom dplyr distinct
-#' @importFrom dplyr everything
-#' @importFrom dplyr filter
-#' @importFrom dplyr first
-#' @importFrom dplyr group_by
-#' @importFrom dplyr if_else
-#' @importFrom dplyr inner_join
-#' @importFrom dplyr join_by
-#' @importFrom dplyr lag
-#' @importFrom dplyr last
-#' @importFrom dplyr left_join
-#' @importFrom dplyr mutate
-#' @importFrom dplyr mutate_at
-#' @importFrom dplyr n
-#' @importFrom dplyr pull
-#' @importFrom dplyr reframe
-#' @importFrom dplyr rename
-#' @importFrom dplyr row_number
-#' @importFrom dplyr select
-#' @importFrom dplyr slice
-#' @importFrom dplyr summarise
-#' @importFrom dplyr tibble
-#' @importFrom dplyr transmute
-#' @importFrom dplyr ungroup
 #' @importFrom logger log_debug
 #' @importFrom logger log_error
 #' @importFrom logger log_info
 #' @importFrom logger log_trace
 #' @importFrom logger log_warn
-#' @importFrom magrittr %>%
 #' @importFrom rlang :=
 #' @importFrom rlang .data
 #' @importFrom rlang .env
-#' @importFrom tidyr pivot_longer
-#' @importFrom tidyr pivot_wider
-#' @importFrom tidyr unite
 ## usethis namespace: end
 NULL
diff --git a/inst/extdata/scripts/prepare_dashboard_data.R b/inst/extdata/scripts/prepare_dashboard_data.R
index ceb6413..22946d7 100644
--- a/inst/extdata/scripts/prepare_dashboard_data.R
+++ b/inst/extdata/scripts/prepare_dashboard_data.R
@@ -1,7 +1,7 @@
 logger::log_threshold(Sys.getenv("LOG_LEVEL", "INFO"))
 
 schema_tempdir <- tempdir()
-workflow.pacta.dashboard:::prepare_schema_files(
+workflow.pacta.dashboard::prepare_schema_files(
   directory = schema_tempdir
 )
 
@@ -12,18 +12,18 @@ params <- pacta.workflow.utils::parse_raw_params(
     system.file(
       "extdata", "parameters",
       package = "workflow.pacta.dashboard"
-      ),
+    ),
     system.file(
       "extdata", "parameters",
       package = "workflow.pacta"
-      )
+    )
   ),
   schema_file = file.path(schema_tempdir, "reportingParameters.json"),
   raw_schema_file = file.path(schema_tempdir, "rawParameters.json"),
   force_array = c("portfolio", "files")
 )
 
-manifest_info <- workflow.pacta.dashboard:::run_dashboard_workflow(
+manifest_info <- workflow.pacta.dashboard::run_dashboard_workflow(
   params = params,
   raw_params = raw_params
 )
diff --git a/man/prep_techmix_sector.Rd b/man/prep_techmix_sector.Rd
index f959926..d625fe6 100644
--- a/man/prep_techmix_sector.Rd
+++ b/man/prep_techmix_sector.Rd
@@ -7,8 +7,8 @@
 prep_techmix_sector(
   equity_results_portfolio,
   bonds_results_portfolio,
-  indices_equity_results_portfolio,
-  indices_bonds_results_portfolio,
+  indices_eq_results_portfolio,
+  indices_cb_results_portfolio,
   peers_equity_results_portfolio,
   peers_bonds_results_portfolio,
   investor_name,
@@ -27,10 +27,10 @@ prep_techmix_sector(
 \item{bonds_results_portfolio}{(data.frame) Portfolio-level results from
 \code{workflow.pacta} for corporate bonds}
 
-\item{indices_equity_results_portfolio}{(data.frame) Benchmark results from
+\item{indices_eq_results_portfolio}{(data.frame) Benchmark results from
 \code{workflow.pacta} for listed equity}
 
-\item{indices_bonds_results_portfolio}{(data.frame) Benchmark results from
+\item{indices_cb_results_portfolio}{(data.frame) Benchmark results from
 \code{workflow.pacta} for corporate bonds}
 
 \item{peers_equity_results_portfolio}{(data.frame) Peer group results from
diff --git a/tests/testthat.R b/tests/testthat.R
new file mode 100644
index 0000000..320e18b
--- /dev/null
+++ b/tests/testthat.R
@@ -0,0 +1,12 @@
+# This file is part of the standard setup for testthat.
+# It is recommended that you do not modify it.
+#
+# Where should you do additional test configuration?
+# Learn more about the roles of various files in:
+# * https://r-pkgs.org/testing-design.html#sec-tests-files-overview
+# * https://testthat.r-lib.org/articles/special-files.html
+
+library(testthat)
+library(workflow.pacta.dashboard)
+
+test_check("workflow.pacta.dashboard")
diff --git a/tests/testthat/test-get_scenario.R b/tests/testthat/test-get_scenario.R
new file mode 100644
index 0000000..1edd921
--- /dev/null
+++ b/tests/testthat/test-get_scenario.R
@@ -0,0 +1,44 @@
+test_that("get_scenario splits on underscore", {
+  scenario_string <- "SOURCE_SCENARIO"
+  results <- get_scenario(scenario_string)
+  expect_identical(
+    object = results,
+    expected = "SCENARIO"
+  )
+})
+
+test_that("get_scenario respects case of original string", {
+  scenario_string <- "source_scenario"
+  results <- get_scenario(scenario_string)
+  expect_identical(
+    object = results,
+    expected = "scenario"
+  )
+})
+
+test_that("get_scenario respects mixed case of original string", {
+  scenario_string <- "sOuRcE_ScEnArIo"
+  results <- get_scenario(scenario_string)
+  expect_identical(
+    object = results,
+    expected = "ScEnArIo"
+  )
+})
+
+test_that("get_scenario extracts scenario name string with 1 underscore", {
+  scenario_string <- "GECO2022_1.5C"
+  results <- get_scenario(scenario_string)
+  expect_identical(
+    object = results,
+    expected = "1.5C"
+  )
+})
+
+test_that("get_scenario extracts scenario name string with 2 underscores", {
+  scenario_string <- "WEO2022_NZE_2050"
+  results <- get_scenario(scenario_string)
+  expect_identical(
+    object = results,
+    expected = "NZE_2050"
+  )
+})
diff --git a/tests/testthat/test-get_scenario_source.R b/tests/testthat/test-get_scenario_source.R
new file mode 100644
index 0000000..6f630d3
--- /dev/null
+++ b/tests/testthat/test-get_scenario_source.R
@@ -0,0 +1,44 @@
+test_that("get_scenario_source splits on underscore", {
+  scenario_string <- "SOURCE_SCENARIO"
+  results <- get_scenario_source(scenario_string)
+  expect_identical(
+    object = results,
+    expected = "SOURCE"
+  )
+})
+
+test_that("get_scenario_source respects case of original string", {
+  scenario_string <- "source_scenario"
+  results <- get_scenario_source(scenario_string)
+  expect_identical(
+    object = results,
+    expected = "source"
+  )
+})
+
+test_that("get_scenario_source respects mixed case of original string", {
+  scenario_string <- "sOuRcE_ScEnArIo"
+  results <- get_scenario_source(scenario_string)
+  expect_identical(
+    object = results,
+    expected = "sOuRcE"
+  )
+})
+
+test_that("get_scenario_source extracts source name with 1 underscore", {
+  scenario_string <- "GECO2022_1.5C"
+  results <- get_scenario_source(scenario_string)
+  expect_identical(
+    object = results,
+    expected = "GECO2022"
+  )
+})
+
+test_that("get_scenario_source extracts source name with 2 underscores", {
+  scenario_string <- "WEO2022_NZE_2050"
+  results <- get_scenario_source(scenario_string)
+  expect_identical(
+    object = results,
+    expected = "WEO2022"
+  )
+})
diff --git a/tests/testthat/test-replace_contents.R b/tests/testthat/test-replace_contents.R
new file mode 100644
index 0000000..f494e94
--- /dev/null
+++ b/tests/testthat/test-replace_contents.R
@@ -0,0 +1,76 @@
+test_that("replace_contents replaces currency _CUR_ in column values", {
+  test_data <- data.frame(
+    id = LETTERS[1L:5L],
+    currency = "_CUR_",
+    stringsAsFactors = FALSE
+  )
+  result <- replace_contents(test_data, "USD")
+  expect_identical(
+    object = result,
+    expected = data.frame(
+      id = LETTERS[1L:5L],
+      currency = "USD",
+      stringsAsFactors = FALSE
+    )
+  )
+})
+
+test_that("replace_contents replaces currency _CUR_ as substrings", {
+  test_data <- data.frame(
+    id = LETTERS[1L:5L],
+    currency = "Foo_CUR_ Bar",
+    stringsAsFactors = FALSE
+  )
+  result <- replace_contents(test_data, "USD")
+  expect_identical(
+    object = result,
+    expected = data.frame(
+      id = LETTERS[1L:5L],
+      currency = "FooUSD Bar",
+      stringsAsFactors = FALSE
+    )
+  )
+})
+
+test_that("replace_contents operates on tibbles", {
+  test_data <- tibble::tibble(
+    id = LETTERS[1L:5L],
+    currency = "_CUR_"
+  )
+  result <- replace_contents(test_data, "USD")
+  expect_identical(
+    object = result,
+    expected = tibble::tibble(
+      id = LETTERS[1L:5L],
+      currency = "USD"
+    )
+  )
+})
+
+test_that("replace_contents casts all columns as character", {
+  test_data <- data.frame(
+    id = LETTERS[1L:5L],
+    value = 1L:5L,
+    currency = "_CUR_",
+    stringsAsFactors = FALSE
+  )
+  result <- replace_contents(test_data, "USD")
+  expect_true(
+    all(
+      vapply(
+        X = result,
+        FUN = is.character,
+        FUN.VALUE = logical(1L)
+      )
+    )
+  )
+  expect_identical(
+    object = result,
+    expected = data.frame(
+      id = LETTERS[1L:5L],
+      value = as.character(1L:5L),
+      currency = "USD",
+      stringsAsFactors = FALSE
+    )
+  )
+})