From 4d644d6dfb6dd4a333a9464420a76c34c04ee406 Mon Sep 17 00:00:00 2001
From: Kalash Singhal <125359076+kalashsinghal@users.noreply.github.com>
Date: Mon, 28 Oct 2024 00:49:17 +0530
Subject: [PATCH] Add `tiltTransitionRisk` code (#24)

* Refactor

* refactor

* refactor

* refactor

* refactor

* refactor
---
 DESCRIPTION                                   |  18 +-
 NAMESPACE                                     |  64 +-
 R/add_thresholds_transition_risk.R            |   8 +-
 R/add_transition_risk_NA_share.R              |  98 +++
 ...ransition_risk_category_at_company_level.R |  53 ++
 ...ansition_risk_category_at_product_level.R} |   4 +-
 R/best_case_worst_case_avg_profile_ranking.R  |  56 ++
 ...st_case_worst_case_avg_reduction_targets.R |  59 ++
 ..._case_worst_case_transition_risk_profile.R |  30 +
 ...transition_risk_profile_at_company_level.R |  65 ++
 ...ent_of_variation_transition_risk_profile.R | 133 ++++
 R/col-transition_risk_profile.R               |   8 -
 R/cols-best_case_worst_case.R                 |  35 +
 R/example_data.R                              | 140 ++++
 R/exclude_cols_then_pivot_wider.R             |  91 +++
 R/pivot_wider_transition_risk_profile.R       | 204 +++++
 R/prepare_webtool_output.R                    |  43 +
 R/relocate_transition_risk_profile_cols.R     | 108 +++
 R/round_off_to_4_decimal_places.R             |  16 +
 R/score_transition_risk.R                     | 124 +++
 R/score_transition_risk_and_polish.R          | 222 ++++++
 R/tiltTransitionRisk-options.R                |  62 ++
 R/tiltTransitionRisk-package.R                |  54 ++
 R/transition_risk_profile.R                   | 185 +++++
 R/utils-score_transition_risk.R               | 127 +++
 R/utils.R                                     |  12 +
 man/add_benchmark_tr_score.Rd                 |   4 +-
 ...nsition_risk_category_at_product_level.Rd} |  10 +-
 man/add_transition_risk_score.Rd              |   4 +-
 ...case_worst_case_transition_risk_profile.Rd |  18 +
 ...mple_emissions_profile_at_product_level.Rd |  22 +
 man/exclude_cols_then_pivot_wider.Rd          |  85 ++
 man/pivot_wider_transition_risk_profile.Rd    |  77 ++
 man/score_transition_risk.Rd                  |  73 ++
 man/score_transition_risk_and_polish.Rd       |  82 ++
 man/tiltIndicatorAfter_options.Rd             |  59 ++
 man/tiltTransitionRisk-package.Rd             |  14 +-
 man/transition_risk_profile.Rd                |  99 +++
 .../_snaps/exclude_cols_then_pivot_wider.md   |   4 +
 ...ansition_risk_category_at_product_level.R} |   8 +-
 ...avg_profile_ranking_best_case_worst_case.R |  94 +++
 ...avg_transition_risk_best_case_worst_case.R |  97 +++
 ...st_case_worst_case_avg_reduction_targets.R | 107 +++
 ..._case_worst_case_transition_risk_profile.R | 138 ++++
 .../testthat/test-coefficient_of_variation.R  | 107 +++
 ...-create_risk_categories_at_company_level.R |  31 +
 .../test-exclude_cols_then_pivot_wider.R      |  60 ++
 tests/testthat/test-score_transition_risk.R   | 239 ++++++
 .../test-score_transition_risk_and_polish.R   | 112 +++
 tests/testthat/test-transition_risk_profile.R | 746 ++++++++++++++++++
 50 files changed, 4274 insertions(+), 35 deletions(-)
 create mode 100644 R/add_transition_risk_NA_share.R
 create mode 100644 R/add_transition_risk_category_at_company_level.R
 rename R/{add_transition_risk_category.R => add_transition_risk_category_at_product_level.R} (87%)
 create mode 100644 R/best_case_worst_case_avg_profile_ranking.R
 create mode 100644 R/best_case_worst_case_avg_reduction_targets.R
 create mode 100644 R/best_case_worst_case_transition_risk_profile.R
 create mode 100644 R/best_case_worst_case_transition_risk_profile_at_company_level.R
 create mode 100644 R/coefficient_of_variation_transition_risk_profile.R
 create mode 100644 R/cols-best_case_worst_case.R
 create mode 100644 R/example_data.R
 create mode 100644 R/exclude_cols_then_pivot_wider.R
 create mode 100644 R/pivot_wider_transition_risk_profile.R
 create mode 100644 R/prepare_webtool_output.R
 create mode 100644 R/relocate_transition_risk_profile_cols.R
 create mode 100644 R/round_off_to_4_decimal_places.R
 create mode 100644 R/score_transition_risk.R
 create mode 100644 R/score_transition_risk_and_polish.R
 create mode 100644 R/tiltTransitionRisk-options.R
 create mode 100644 R/transition_risk_profile.R
 create mode 100644 R/utils-score_transition_risk.R
 create mode 100644 R/utils.R
 rename man/{add_transition_risk_category.Rd => add_transition_risk_category_at_product_level.Rd} (68%)
 create mode 100644 man/best_case_worst_case_transition_risk_profile.Rd
 create mode 100644 man/example_emissions_profile_at_product_level.Rd
 create mode 100644 man/exclude_cols_then_pivot_wider.Rd
 create mode 100644 man/pivot_wider_transition_risk_profile.Rd
 create mode 100644 man/score_transition_risk.Rd
 create mode 100644 man/score_transition_risk_and_polish.Rd
 create mode 100644 man/tiltIndicatorAfter_options.Rd
 create mode 100644 man/transition_risk_profile.Rd
 create mode 100644 tests/testthat/_snaps/exclude_cols_then_pivot_wider.md
 rename tests/testthat/{test-add_transition_risk_category.R => test-add_transition_risk_category_at_product_level.R} (82%)
 create mode 100644 tests/testthat/test-avg_profile_ranking_best_case_worst_case.R
 create mode 100644 tests/testthat/test-avg_transition_risk_best_case_worst_case.R
 create mode 100644 tests/testthat/test-best_case_worst_case_avg_reduction_targets.R
 create mode 100644 tests/testthat/test-best_case_worst_case_transition_risk_profile.R
 create mode 100644 tests/testthat/test-coefficient_of_variation.R
 create mode 100644 tests/testthat/test-create_risk_categories_at_company_level.R
 create mode 100644 tests/testthat/test-exclude_cols_then_pivot_wider.R
 create mode 100644 tests/testthat/test-score_transition_risk.R
 create mode 100644 tests/testthat/test-score_transition_risk_and_polish.R
 create mode 100644 tests/testthat/test-transition_risk_profile.R

diff --git a/DESCRIPTION b/DESCRIPTION
index f264ab6..a702f7b 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -24,16 +24,22 @@ Imports:
     rlang,
     stats,
     tibble,
+    tidyr,
+    tidyselect,
     tiltIndicator (>= 0.0.0.9223),
+    tiltIndicatorAfter (>= 0.0.0.9061),
+    tiltPolish (>= 0.0.0.9006),
     tiltToyData (>= 0.0.0.9204),
-    utils
-Suggests: 
-    testthat (>= 3.0.0),
+    utils,
     withr
-Remotes: 
+Suggests: 
+    testthat (>= 3.0.0)
+Remotes:  
     2DegreesInvesting/tiltIndicator,
-    2degreesinvesting/tiltToyData
+    2degreesinvesting/tiltToyData,
+    2degreesinvesting/tiltIndicatorAfter,
+    2degreesinvesting/tiltPolish
 Config/testthat/edition: 3
 Encoding: UTF-8
 Roxygen: list(markdown = TRUE)
-RoxygenNote: 7.3.1
+RoxygenNote: 7.3.2
diff --git a/NAMESPACE b/NAMESPACE
index 8a7097c..f6f7e5d 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -2,26 +2,88 @@
 
 export(add_benchmark_tr_score)
 export(add_thresholds_transition_risk)
-export(add_transition_risk_category)
+export(add_transition_risk_category_at_product_level)
 export(add_transition_risk_score)
+export(best_case_worst_case_transition_risk_profile)
+export(example_emissions_profile_at_product_level)
+export(example_sector_profile_at_product_level)
+export(exclude_cols_then_pivot_wider)
+export(pivot_wider_transition_risk_profile)
+export(score_transition_risk)
+export(score_transition_risk_and_polish)
 export(toy_all_activities_scenario_sectors)
+export(transition_risk_profile)
 importFrom(dplyr,.data)
+importFrom(dplyr,across)
 importFrom(dplyr,all_of)
+importFrom(dplyr,arrange)
+importFrom(dplyr,bind_rows)
+importFrom(dplyr,case_when)
+importFrom(dplyr,coalesce)
 importFrom(dplyr,distinct)
 importFrom(dplyr,filter)
 importFrom(dplyr,full_join)
+importFrom(dplyr,left_join)
 importFrom(dplyr,mutate)
+importFrom(dplyr,n_distinct)
+importFrom(dplyr,relocate)
+importFrom(dplyr,rename)
+importFrom(dplyr,rename_with)
 importFrom(dplyr,select)
+importFrom(dplyr,summarise)
+importFrom(dplyr,where)
 importFrom(glue,glue)
 importFrom(purrr,walk)
 importFrom(readr,read_csv)
+importFrom(rlang,":=")
+importFrom(rlang,.data)
 importFrom(rlang,abort)
+importFrom(rlang,as_name)
+importFrom(rlang,ensym)
 importFrom(stats,quantile)
+importFrom(tibble,tibble)
 importFrom(tibble,tribble)
+importFrom(tidyr,pivot_wider)
+importFrom(tidyselect,all_of)
+importFrom(tidyselect,any_of)
+importFrom(tidyselect,matches)
 importFrom(tiltIndicator,categorize_risk)
+importFrom(tiltIndicator,epa_at_company_level)
 importFrom(tiltIndicator,epa_compute_profile_ranking)
+importFrom(tiltIndicator,example_data_factory)
+importFrom(tiltIndicator,insert_row_with_na_in_risk_category)
+importFrom(tiltIndicator,nest_levels)
 importFrom(tiltIndicator,spa_compute_profile_ranking)
+importFrom(tiltIndicator,tilt_profile)
+importFrom(tiltIndicator,unnest_company)
+importFrom(tiltIndicator,unnest_product)
+importFrom(tiltIndicatorAfter,best_case_worst_case_emission_profile)
+importFrom(tiltIndicatorAfter,best_case_worst_case_impl)
+importFrom(tiltIndicatorAfter,best_case_worst_case_sector_profile)
+importFrom(tiltIndicatorAfter,polish_best_case_worst_case)
+importFrom(tiltIndicatorAfter,profile_emissions)
+importFrom(tiltIndicatorAfter,profile_sector)
+importFrom(tiltIndicatorAfter,rename_with_prefix)
+importFrom(tiltPolish,rename_transition_risk_profile_cols_company)
+importFrom(tiltPolish,rename_transition_risk_profile_cols_product)
+importFrom(tiltPolish,rename_webtool_cols_at_company_level_wide)
+importFrom(tiltPolish,select_webtool_cols_at_company_level_wide)
+importFrom(tiltPolish,select_webtool_cols_at_product_level)
+importFrom(tiltToyData,toy_ecoinvent_activities)
+importFrom(tiltToyData,toy_ecoinvent_europages)
+importFrom(tiltToyData,toy_ecoinvent_inputs)
+importFrom(tiltToyData,toy_emissions_profile_any_companies)
+importFrom(tiltToyData,toy_emissions_profile_products)
 importFrom(tiltToyData,toy_emissions_profile_products_ecoinvent)
+importFrom(tiltToyData,toy_emissions_profile_upstream_products)
+importFrom(tiltToyData,toy_emissions_profile_upstream_products_ecoinvent)
+importFrom(tiltToyData,toy_europages_companies)
+importFrom(tiltToyData,toy_isic_name)
 importFrom(tiltToyData,toy_sector_profile_any_scenarios)
 importFrom(tiltToyData,toy_sector_profile_companies)
+importFrom(tiltToyData,toy_sector_profile_upstream_companies)
+importFrom(tiltToyData,toy_sector_profile_upstream_products)
+importFrom(utils,hasName)
 importFrom(utils,write.csv)
+importFrom(withr,local_options)
+importFrom(withr,local_seed)
diff --git a/R/add_thresholds_transition_risk.R b/R/add_thresholds_transition_risk.R
index b6a03f5..8bd221c 100644
--- a/R/add_thresholds_transition_risk.R
+++ b/R/add_thresholds_transition_risk.R
@@ -63,8 +63,8 @@ add_thresholds_transition_risk <- function(co2,
 #' Calulate `transition_risk_score` column
 #'
 #' @param data Dataframe.
-#' @param profile_ranking Dataframe column.
-#' @param reduction_targets Dataframe column.
+#' @param col_ranking Dataframe column.
+#' @param col_target Dataframe column.
 #' @keywords internal
 #' @export
 add_transition_risk_score <- function(data,
@@ -83,8 +83,8 @@ add_transition_risk_score <- function(data,
 #' Calulate `benchmark_tr_score` column
 #'
 #' @param data Dataframe.
-#' @param profile_ranking Dataframe column.
-#' @param reduction_targets Dataframe column.
+#' @param col_ranking Dataframe column.
+#' @param col_target Dataframe column.
 #' @keywords internal
 #' @export
 add_benchmark_tr_score <- function(data,
diff --git a/R/add_transition_risk_NA_share.R b/R/add_transition_risk_NA_share.R
new file mode 100644
index 0000000..27a82cc
--- /dev/null
+++ b/R/add_transition_risk_NA_share.R
@@ -0,0 +1,98 @@
+add_transition_risk_NA_share <- function(data) {
+  product <- data |>
+    unnest_product() |>
+    add_transition_risk_NA_share_at_product_level()
+
+  company <- data |>
+    unnest_company() |>
+    select_and_join_transition_risk_NA_share_at_company_level(product)
+
+  tilt_profile(nest_levels(product, company))
+}
+
+add_transition_risk_NA_share_at_product_level <- function(data) {
+  data |>
+    fill_benchmark_tr_score() |>
+    transition_risk_NA_amount_all() |>
+    transition_risk_NA_amount_benchmarks() |>
+    transition_risk_NA_total() |>
+    transition_risk_NA_share() |>
+    polish_transition_risk_NA_share()
+}
+
+select_and_join_transition_risk_NA_share_at_company_level <- function(data, product) {
+  join_table <- product |>
+    select(all_of(c(
+      "companies_id",
+      "benchmark_tr_score",
+      "transition_risk_NA_share"
+    ))) |>
+    distinct()
+
+  data |>
+    left_join(
+      join_table,
+      by = c("companies_id",
+        "benchmark_tr_score_avg" = "benchmark_tr_score"
+      )
+    )
+}
+
+fill_benchmark_tr_score <- function(data) {
+  mutate(data, benchmark_tr_score = ifelse(
+    is.na(.data[[col_transition_risk_grouped_by()]]),
+    paste(.data[[col_scenario()]],
+      .data[[col_year()]],
+      .data[[col_emission_grouped_by()]],
+      sep = "_"
+    ),
+    .data[[col_transition_risk_grouped_by()]]
+  ))
+}
+
+transition_risk_NA_amount_all <- function(data) {
+  mutate(data,
+    transition_risk_NA_amount_all = n_distinct(
+      .data[[col_europages_product()]][is.na(.data$matched_activity_name) | is.na(.data$reduction_targets)]
+    ),
+    .by = col_companies_id()
+  )
+}
+
+transition_risk_NA_amount_benchmarks <- function(data) {
+  mutate(data,
+    transition_risk_NA_amount_benchmarks = n_distinct(
+      .data[[col_europages_product()]][is.na(.data$transition_risk_score)]
+    ),
+    .by = all_of(c(col_companies_id(), col_transition_risk_grouped_by()))
+  )
+}
+
+transition_risk_NA_total <- function(data) {
+  mutate(data,
+    transition_risk_NA_total = ifelse(
+      is.na(.data$matched_activity_name) | is.na(.data$reduction_targets),
+      .data$transition_risk_NA_amount_all,
+      .data$transition_risk_NA_amount_all + .data$transition_risk_NA_amount_benchmarks
+    ),
+    .by = all_of(c(col_companies_id(), col_transition_risk_grouped_by()))
+  )
+}
+
+transition_risk_NA_share <- function(data) {
+  mutate(data,
+    transition_risk_NA_share = ifelse(
+      .data$amount_of_distinct_products == 0,
+      NA,
+      .data$transition_risk_NA_total / .data$amount_of_distinct_products
+    ),
+    .by = all_of(c(col_companies_id(), col_transition_risk_grouped_by()))
+  )
+}
+
+polish_transition_risk_NA_share <- function(data) {
+  select(data, -all_of(c(
+    "transition_risk_NA_amount_all",
+    "transition_risk_NA_amount_benchmarks"
+  )))
+}
diff --git a/R/add_transition_risk_category_at_company_level.R b/R/add_transition_risk_category_at_company_level.R
new file mode 100644
index 0000000..ca1a47e
--- /dev/null
+++ b/R/add_transition_risk_category_at_company_level.R
@@ -0,0 +1,53 @@
+add_transition_risk_category_at_company_level <- function(data) {
+  product <- data |>
+    unnest_product()
+
+  risk_categories <- product |>
+    create_risk_categories_at_company_level()
+
+  company <- data |>
+    unnest_company() |>
+    join_risk_categories_at_company_level(risk_categories)
+
+  tilt_profile(nest_levels(product, company))
+}
+
+create_risk_categories_at_company_level <- function(data) {
+  data |>
+    adapt_tr_product_cols_to_tiltIndicator_cols() |>
+    epa_at_company_level() |>
+    insert_row_with_na_in_risk_category() |>
+    adapt_tiltIndicator_cols_to_tr_company_cols()
+}
+
+join_risk_categories_at_company_level <- function(data, risk_categories) {
+  data |>
+    create_transition_risk_category_col_at_company_level() |>
+    left_join(risk_categories, by = c(
+      "companies_id",
+      "benchmark_tr_score_avg",
+      "transition_risk_category"
+    ))
+}
+
+adapt_tr_product_cols_to_tiltIndicator_cols <- function(data) {
+  rename(data,
+    grouped_by = "benchmark_tr_score",
+    risk_category = "transition_risk_category"
+  )
+}
+
+adapt_tiltIndicator_cols_to_tr_company_cols <- function(data) {
+  rename(data,
+    benchmark_tr_score_avg = "grouped_by",
+    transition_risk_category = "risk_category",
+    transition_risk_category_share = "value"
+  )
+}
+
+create_transition_risk_category_col_at_company_level <- function(data) {
+  mutate(data, transition_risk_category = coalesce(
+    .data$emission_profile,
+    .data$sector_profile
+  ))
+}
diff --git a/R/add_transition_risk_category.R b/R/add_transition_risk_category_at_product_level.R
similarity index 87%
rename from R/add_transition_risk_category.R
rename to R/add_transition_risk_category_at_product_level.R
index 9e35d21..4e43bf5 100644
--- a/R/add_transition_risk_category.R
+++ b/R/add_transition_risk_category_at_product_level.R
@@ -23,9 +23,9 @@
 #'   scenarios
 #' )
 #'
-#' output <- add_transition_risk_category(transition_risk_thresholds)
+#' output <- add_transition_risk_category_at_product_level(transition_risk_thresholds)
 #' output
-add_transition_risk_category <- function(data) {
+add_transition_risk_category_at_product_level <- function(data) {
   check_crucial_cols(data, c(
     col_transition_risk_score(), col_tr_low_threshold(),
     col_tr_high_threshold()
diff --git a/R/best_case_worst_case_avg_profile_ranking.R b/R/best_case_worst_case_avg_profile_ranking.R
new file mode 100644
index 0000000..2d1899f
--- /dev/null
+++ b/R/best_case_worst_case_avg_profile_ranking.R
@@ -0,0 +1,56 @@
+best_case_worst_case_avg_profile_ranking <- function(data) {
+  product <- data |>
+    unnest_product()
+
+  avg_best_case_worst_case_at_product_level <- product |>
+    prepare_for_join_at_company_level_profile_ranking()
+
+  avg_best_case <- prepare_avg_best_case_join_table_profile_ranking(
+    avg_best_case_worst_case_at_product_level
+  )
+  avg_worst_case <- prepare_avg_worst_case_join_table_profile_ranking(
+    avg_best_case_worst_case_at_product_level
+  )
+
+  company <- data |>
+    unnest_company() |>
+    left_join(avg_best_case, by = c(
+      col_companies_id(),
+      col_emission_grouped_by()
+    )) |>
+    left_join(avg_worst_case, by = c(
+      col_companies_id(),
+      col_emission_grouped_by()
+    ))
+
+  tilt_profile(nest_levels(product, company))
+}
+
+prepare_for_join_at_company_level_profile_ranking <- function(data) {
+  data |>
+    select(all_of(c(
+      col_companies_id(),
+      col_emission_grouped_by(),
+      "emissions_profile_best_case",
+      "emissions_profile_worst_case"
+    ))) |>
+    distinct() |>
+    rename("avg_profile_ranking_best_case" = "emissions_profile_best_case",
+           "avg_profile_ranking_worst_case" = "emissions_profile_worst_case")
+}
+
+prepare_avg_worst_case_join_table_profile_ranking <- function(data) {
+  data |>
+    prepare_avg_best_case_join_table(
+      "avg_profile_ranking_best_case",
+      "avg_profile_ranking_worst_case"
+    )
+}
+
+prepare_avg_best_case_join_table_profile_ranking <- function(data) {
+  data |>
+    prepare_avg_best_case_join_table(
+      "avg_profile_ranking_worst_case",
+      "avg_profile_ranking_best_case"
+    )
+}
diff --git a/R/best_case_worst_case_avg_reduction_targets.R b/R/best_case_worst_case_avg_reduction_targets.R
new file mode 100644
index 0000000..04cd1f5
--- /dev/null
+++ b/R/best_case_worst_case_avg_reduction_targets.R
@@ -0,0 +1,59 @@
+best_case_worst_case_avg_reduction_targets <- function(data) {
+  product <- data |>
+    unnest_product()
+
+  avg_best_case_worst_case_at_product_level <- product |>
+    prepare_for_join_at_company_level_reduction_targets()
+
+  avg_best_case <- prepare_avg_best_case_join_table_reduction_targets(
+    avg_best_case_worst_case_at_product_level
+  )
+  avg_worst_case <- prepare_avg_worst_case_join_table_reduction_targets(
+    avg_best_case_worst_case_at_product_level
+  )
+
+  company <- data |>
+    unnest_company() |>
+    left_join(avg_best_case, by = c(
+      col_companies_id(),
+      "scenario",
+      "year"
+    )) |>
+    left_join(avg_worst_case, by = c(
+      col_companies_id(),
+      "scenario",
+      "year"
+    ))
+
+  tilt_profile(nest_levels(product, company))
+}
+
+prepare_for_join_at_company_level_reduction_targets <- function(data) {
+  data |>
+    select(all_of(c(
+      col_companies_id(),
+      col_scenario(),
+      col_year(),
+      "sector_profile_best_case",
+      "sector_profile_worst_case"
+    ))) |>
+    distinct() |>
+    rename("avg_reduction_targets_best_case" = "sector_profile_best_case",
+           "avg_reduction_targets_worst_case" = "sector_profile_worst_case")
+}
+
+prepare_avg_worst_case_join_table_reduction_targets <- function(data) {
+  data |>
+    prepare_avg_best_case_join_table(
+      "avg_reduction_targets_best_case",
+      "avg_reduction_targets_worst_case"
+    )
+}
+
+prepare_avg_best_case_join_table_reduction_targets <- function(data) {
+  data |>
+    prepare_avg_best_case_join_table(
+      "avg_reduction_targets_worst_case",
+      "avg_reduction_targets_best_case"
+    )
+}
diff --git a/R/best_case_worst_case_transition_risk_profile.R b/R/best_case_worst_case_transition_risk_profile.R
new file mode 100644
index 0000000..631b745
--- /dev/null
+++ b/R/best_case_worst_case_transition_risk_profile.R
@@ -0,0 +1,30 @@
+#' Calculates best case and worst case for tranistion risk profile at product level
+#'
+#' @param data Dataframe. Transition risk profile product level output
+#'
+#' @return A dataframe
+#' @export
+#' @keywords internal
+best_case_worst_case_transition_risk_profile <- function(data) {
+  crucial_cols <- c(
+    col_companies_id(), col_europages_product(),
+    col_transition_risk_grouped_by(), col_transition_risk_category()
+  )
+  check_crucial_cols(data, crucial_cols)
+
+  best_case_worst_case_impl(data,
+    col_group_by = col_transition_risk_grouped_by(),
+    col_risk = col_transition_risk_category(),
+    col_rank = "transition_risk_score"
+
+  )
+}
+
+polish_best_case_worst_case_transition_risk_profile <- function(data) {
+  data |>
+    rename_with_prefix("transition_risk_profile_", match = c(
+      "^best_case$",
+      "^worst_case$",
+      "^equal_weight$"
+    ))
+}
diff --git a/R/best_case_worst_case_transition_risk_profile_at_company_level.R b/R/best_case_worst_case_transition_risk_profile_at_company_level.R
new file mode 100644
index 0000000..902c955
--- /dev/null
+++ b/R/best_case_worst_case_transition_risk_profile_at_company_level.R
@@ -0,0 +1,65 @@
+best_case_worst_case_transition_risk_profile_at_company_level <- function(data) {
+  product <- data |>
+    unnest_product()
+
+  avg_best_case_worst_case_at_product_level <- product |>
+    prepare_for_join_at_company_level_transition_risk()
+
+  avg_best_case <- prepare_avg_best_case_join_table_transition_risk(
+    avg_best_case_worst_case_at_product_level
+  )
+  avg_worst_case <- prepare_avg_worst_case_join_table_transition_risk(
+    avg_best_case_worst_case_at_product_level
+  )
+
+  company <- data |>
+    unnest_company() |>
+    left_join(avg_best_case, by = c(col_companies_id(),
+      "benchmark_tr_score_avg" = col_transition_risk_grouped_by()
+    )) |>
+    left_join(avg_worst_case, by = c(col_companies_id(),
+      "benchmark_tr_score_avg" = col_transition_risk_grouped_by()
+    )) |>
+    polish_transition_risk_best_case_worst_case_at_company_level()
+
+  tilt_profile(nest_levels(product, company))
+}
+
+prepare_for_join_at_company_level_transition_risk <- function(data) {
+  data |>
+    select(all_of(c(
+      col_companies_id(),
+      col_transition_risk_grouped_by(),
+      "transition_risk_profile_best_case",
+      "transition_risk_profile_worst_case"
+    ))) |>
+    distinct() |>
+    rename("avg_transition_risk_best_case" = "transition_risk_profile_best_case",
+           "avg_transition_risk_worst_case" = "transition_risk_profile_worst_case")
+}
+
+prepare_avg_worst_case_join_table_transition_risk <- function(data) {
+  data |>
+    prepare_avg_best_case_join_table(
+      "avg_transition_risk_best_case",
+      "avg_transition_risk_worst_case"
+    )
+}
+
+prepare_avg_best_case_join_table_transition_risk <- function(data) {
+  data |>
+    prepare_avg_best_case_join_table(
+      "avg_transition_risk_worst_case",
+      "avg_transition_risk_best_case"
+    )
+}
+
+polish_transition_risk_best_case_worst_case_at_company_level <- function(data) {
+  rename(data, avg_transition_risk_equal_weight = "transition_risk_score_avg")
+}
+
+prepare_avg_best_case_join_table <- function(data, case1_col, case2_col) {
+  data |>
+    select(-all_of(c(case1_col))) |>
+    filter(!is.na(.data[[case2_col]]))
+}
diff --git a/R/coefficient_of_variation_transition_risk_profile.R b/R/coefficient_of_variation_transition_risk_profile.R
new file mode 100644
index 0000000..26dfa4d
--- /dev/null
+++ b/R/coefficient_of_variation_transition_risk_profile.R
@@ -0,0 +1,133 @@
+coefficient_of_variation_transition_risk_profile <- function(data) {
+  product <- data |>
+    unnest_product()
+
+  company <- data |>
+    unnest_company() |>
+    add_coefficient_of_variation_transition_risk() |>
+    polish_coefficient_of_variation_transition_risk() |>
+    add_coefficient_of_variation_emission_rank() |>
+    polish_coefficient_of_variation_emission_rank() |>
+    add_coefficient_of_variation_sector_target() |>
+    polish_coefficient_of_variation_sector_target()
+
+  tilt_profile(nest_levels(product, company))
+}
+
+add_coefficient_of_variation_transition_risk <- function(data) {
+  data |>
+    add_mean_cov(
+      "mean_transition_risk",
+      "avg_transition_risk_equal_weight",
+      "avg_transition_risk_best_case",
+      "avg_transition_risk_worst_case"
+    ) |>
+    add_standard_deviation_cov(
+      "standard_deviation_transition_risk",
+      "mean_transition_risk",
+      "avg_transition_risk_equal_weight",
+      "avg_transition_risk_best_case",
+      "avg_transition_risk_worst_case"
+    ) |>
+    add_coefficient_of_variation(
+      "cov_transition_risk",
+      "standard_deviation_transition_risk",
+      "mean_transition_risk"
+    )
+}
+
+polish_coefficient_of_variation_transition_risk <- function(data) {
+  data |>
+    select(-c("mean_transition_risk", "standard_deviation_transition_risk"))
+}
+
+add_coefficient_of_variation_emission_rank <- function(data) {
+  data |>
+    add_mean_cov(
+      "mean_emission_rank",
+      "profile_ranking_avg",
+      "avg_profile_ranking_best_case",
+      "avg_profile_ranking_worst_case"
+    ) |>
+    add_standard_deviation_cov(
+      "standard_deviation_emission_rank",
+      "mean_emission_rank",
+      "profile_ranking_avg",
+      "avg_profile_ranking_best_case",
+      "avg_profile_ranking_worst_case"
+    ) |>
+    add_coefficient_of_variation(
+      "cov_emission_rank",
+      "standard_deviation_emission_rank",
+      "mean_emission_rank"
+    )
+}
+
+polish_coefficient_of_variation_emission_rank <- function(data) {
+  data |>
+    select(-c("mean_emission_rank", "standard_deviation_emission_rank"))
+}
+
+add_coefficient_of_variation_sector_target <- function(data) {
+  data |>
+    add_mean_cov(
+      "mean_sector_target",
+      "reduction_targets_avg",
+      "avg_reduction_targets_best_case",
+      "avg_reduction_targets_worst_case"
+    ) |>
+    add_standard_deviation_cov(
+      "standard_deviation_sector_target",
+      "mean_sector_target",
+      "reduction_targets_avg",
+      "avg_reduction_targets_best_case",
+      "avg_reduction_targets_worst_case"
+    ) |>
+    add_coefficient_of_variation(
+      "cov_sector_target",
+      "standard_deviation_sector_target",
+      "mean_sector_target"
+    )
+}
+
+polish_coefficient_of_variation_sector_target <- function(data) {
+  data |>
+    select(-c("mean_sector_target", "standard_deviation_sector_target"))
+}
+
+add_coefficient_of_variation <- function(data, cov_col, sd_col, mean_col) {
+  mutate(
+    data,
+    {{ cov_col }} := case_when(
+      is.na(.data[[mean_col]]) ~ NA_real_,
+      .data[[mean_col]] == 0.0 ~ NA_real_,
+      TRUE ~ (.data[[sd_col]] / .data[[mean_col]]) * 100
+    )
+  )
+}
+
+add_mean_cov <- function(data, mean_col, col1, col2, col3) {
+  mutate(
+    data,
+    {{ mean_col }} := ifelse(is.na(.data[[col1]]),
+      NA_real_,
+      (.data[[col1]] + .data[[col2]] + .data[[col3]]) / 3
+    )
+  )
+}
+
+# Denominator is `n` and not `n-1` because we are not calculating the standard
+# deviation of a sample from whole population.
+add_standard_deviation_cov <- function(data, sd_col, mean_col, col1, col2, col3) {
+  mutate(
+    data,
+    {{ sd_col }} := ifelse(is.na(.data[[mean_col]]),
+      NA_real_,
+      round(sqrt(
+        ((.data[[col1]] - .data[[mean_col]])^2 +
+          (.data[[col2]] - .data[[mean_col]])^2 +
+          (.data[[col3]] - .data[[mean_col]])^2) / 3
+      ), 8)
+    )
+  )
+}
diff --git a/R/col-transition_risk_profile.R b/R/col-transition_risk_profile.R
index c3a9b28..9239a1f 100644
--- a/R/col-transition_risk_profile.R
+++ b/R/col-transition_risk_profile.R
@@ -18,14 +18,6 @@ col_subsector <- function() {
   "subsector"
 }
 
-col_year <- function() {
-  "year"
-}
-
-col_scenario <- function() {
-  "scenario"
-}
-
 col_targets <- function() {
   "reductions"
 }
diff --git a/R/cols-best_case_worst_case.R b/R/cols-best_case_worst_case.R
new file mode 100644
index 0000000..328bb56
--- /dev/null
+++ b/R/cols-best_case_worst_case.R
@@ -0,0 +1,35 @@
+col_companies_id <- function() {
+  "companies_id"
+}
+
+col_europages_product <- function() {
+  "ep_product"
+}
+
+col_emission_grouped_by <- function() {
+  "benchmark"
+}
+
+col_emission_profile <- function() {
+  "emission_profile"
+}
+
+col_transition_risk_grouped_by <- function() {
+  "benchmark_tr_score"
+}
+
+col_transition_risk_category <- function() {
+  "transition_risk_category"
+}
+
+col_scenario <- function() {
+  "scenario"
+}
+
+col_year <- function() {
+  "year"
+}
+
+col_sector_profile <- function() {
+  "sector_profile"
+}
diff --git a/R/example_data.R b/R/example_data.R
new file mode 100644
index 0000000..b9e7aba
--- /dev/null
+++ b/R/example_data.R
@@ -0,0 +1,140 @@
+#' Example input datasets for Transition Risk Score
+#'
+#' @return A dataframe.
+#' @export
+#' @keywords internal
+#'
+#' @examples
+#' example_emissions_profile_at_product_level()
+#' example_sector_profile_at_product_level()
+example_emissions_profile_at_product_level <- function() {
+  local_options(readr.show_col_types = FALSE)
+  toy_emissions_profile_products_ecoinvent <-
+    read_csv(toy_emissions_profile_products_ecoinvent())
+  toy_emissions_profile_any_companies <-
+    read_csv(toy_emissions_profile_any_companies())
+  toy_europages_companies <- read_csv(toy_europages_companies())
+  toy_ecoinvent_activities <- read_csv(toy_ecoinvent_activities())
+  toy_ecoinvent_europages <- read_csv(toy_ecoinvent_europages())
+  toy_ecoinvent_inputs <- read_csv(toy_ecoinvent_inputs())
+  toy_isic_name <- read_csv(toy_isic_name())
+
+  emissions_profile_at_product_level <- profile_emissions(
+    companies = toy_emissions_profile_any_companies,
+    co2 = toy_emissions_profile_products_ecoinvent,
+    europages_companies = toy_europages_companies,
+    ecoinvent_activities = toy_ecoinvent_activities,
+    ecoinvent_europages = toy_ecoinvent_europages,
+    isic = toy_isic_name
+  ) |>
+    unnest_product()
+  emissions_profile_at_product_level
+}
+
+#' @export
+#' @rdname example_emissions_profile_at_product_level
+example_sector_profile_at_product_level <- function() {
+  local_options(readr.show_col_types = FALSE)
+  toy_sector_profile_any_scenarios <-
+    read_csv(toy_sector_profile_any_scenarios())
+  toy_sector_profile_companies <-
+    read_csv(toy_sector_profile_companies())
+  toy_europages_companies <- read_csv(toy_europages_companies())
+  toy_ecoinvent_activities <- read_csv(toy_ecoinvent_activities())
+  toy_ecoinvent_europages <- read_csv(toy_ecoinvent_europages())
+  toy_ecoinvent_inputs <- read_csv(toy_ecoinvent_inputs())
+  toy_isic_name <- read_csv(toy_isic_name())
+
+  sector_profile_at_product_level <- profile_sector(
+    companies = toy_sector_profile_companies,
+    scenarios = toy_sector_profile_any_scenarios,
+    europages_companies = toy_europages_companies,
+    ecoinvent_activities = toy_ecoinvent_activities,
+    ecoinvent_europages = toy_ecoinvent_europages,
+    isic = toy_isic_name
+  ) |>
+    unnest_product()
+  sector_profile_at_product_level
+}
+
+
+example_best_case_worst_case_transition_risk_profile_product_level <- example_data_factory(
+  # styler: off
+  tribble(
+    ~companies_id, ~ep_product, ~benchmark_tr_score, ~transition_risk_category, ~transition_risk_score,
+            "any",       "one", "1.5C RPS_2030_all",                     "low",                    1.0,
+            "any",       "two", "1.5C RPS_2030_all",                  "medium",                    2.0,
+            "any",     "three", "1.5C RPS_2030_all",                    "high",                    3.0,
+  )
+  # styler: on
+)
+
+example_best_case_worst_case_transition_risk_profile_company_level <- example_data_factory(
+  # styler: off
+  tribble(
+    ~companies_id, ~benchmark_tr_score_avg, ~transition_risk_score_avg,
+            "any",     "1.5C RPS_2030_all",                        5.0,
+            "any",    "1.5C RPS_2030_unit",                        6.0,
+            "any",     "1.5C RPS_2050_all",                        7.0
+  )
+  # styler: on
+)
+
+
+example_best_case_worst_case_profile_ranking_product_level <- example_data_factory(
+  # styler: off
+  tribble(
+    ~companies_id, ~ep_product, ~benchmark, ~emission_profile, ~profile_ranking,
+            "any",       "one",      "all",             "low",              1.0,
+            "any",       "two",      "all",          "medium",              2.0,
+            "any",     "three",      "all",            "high",              3.0
+  )
+  # styler: on
+)
+
+example_best_case_worst_case_profile_ranking_company_level <- example_data_factory(
+  # styler: off
+  tribble(
+    ~companies_id,    ~benchmark, ~profile_ranking_avg,
+            "any",         "all",                  5.0,
+            "any",        "unit",                  6.0,
+            "any", "tilt_sector",                  7.0
+  )
+  # styler: on
+)
+
+example_best_case_worst_case_reduction_targets_product_level <- example_data_factory(
+  # styler: off
+  tribble(
+    ~companies_id, ~ep_product,  ~scenario_year, ~sector_profile, ~reduction_targets,
+            "any",       "one", "1.5C RPS_2030",           "low",                1.0,
+            "any",       "two", "1.5C RPS_2030",        "medium",                2.0,
+            "any",     "three", "1.5C RPS_2030",          "high",                3.0
+  )
+  # styler: on
+)
+
+example_best_case_worst_case_reduction_targets_company_level <- example_data_factory(
+  # styler: off
+  tribble(
+    ~companies_id,  ~scenario, ~year, ~reduction_targets,
+            "any", "1.5C RPS",  2030,                1.0,
+            "any", "1.5C RPS",  2050,                2.0,
+            "any",  "NZ 2050",  2030,                3.0
+  )
+  # styler: on
+)
+
+example_risk_categories_at_product_level <- example_data_factory(
+  # styler: off
+  tribble(
+    ~companies_id, ~ep_product, ~benchmark_tr_score, ~transition_risk_category,
+            "any",         "a",               "all",                     "low",
+            "any",         "a",               "all",                  "medium",
+            "any",         "a",               "all",                    "high",
+            "any",         "b",              "unit",                     "low",
+            "any",         "b",              "unit",                  "medium",
+            "any",         "b",              "unit",                    "high",
+  )
+  # styler: on
+)
diff --git a/R/exclude_cols_then_pivot_wider.R b/R/exclude_cols_then_pivot_wider.R
new file mode 100644
index 0000000..75ba582
--- /dev/null
+++ b/R/exclude_cols_then_pivot_wider.R
@@ -0,0 +1,91 @@
+#' Excluding irrelevant columns and duplicates, then pivot from long to wide
+#'
+#' @param data A data frame to pivot.
+#' @param ... Arguments passed to [tidyr::pivot_wider()].
+#' @param exclude_cols A character vector giving regular expressions matching
+#'   column names to exclude. If lengh > 1, the union is taken.
+#' @param avoid_list_cols Logical. Avoid all list-columns, duplicates, and the
+#'   associated warning?
+#'
+#' @return A data frame giving the result you get from [tidyr::pivot_wider()] if
+#'   `data` lacks the excluded columns and the resulting duplicates.
+#' @export
+#' @keywords internal
+#'
+#' @examples
+#' library(tidyr, warn.conflicts = FALSE)
+#' library(dplyr, warn.conflicts = FALSE)
+#'
+#' # styler: off
+#' data <- tribble(
+#'   ~to_exclude,  ~id, ~name,  ~value,
+#'             1, "id",   "a",       1,
+#'             2, "id",   "a",       1,
+#'             1, "id",   "b",       2,
+#'             2, "id",   "b",       2,
+#' )
+#' # styler: on
+#'
+#' # `exclude_cols_then_pivot_wider()` excludes columns and duplicates
+#' data |> exclude_cols_then_pivot_wider(exclude_cols = "exclude")
+#'
+#' # Why is this useful?
+#' # `pivot_wider()` defaults to using all columns
+#' data |> pivot_wider()
+#'
+#' # You may specify relevant columns but the result may still have duplicates
+#' data |>
+#'   pivot_wider(id_cols = id, names_from = "name", values_from = "value") |>
+#'   unnest(c(a, b))
+#'
+#' # styler: off
+#' data <- tribble(
+#'    ~id, ~name, ~value, ~to_exclude, ~yields_duplicates,
+#'   "id",   "a",      1,           1,                  1,
+#'   "id",   "a",      1,           2,                  2
+#' )
+#' # styler: on
+#'
+#' # `data` may have columns that yield duplicates and thus list-columns
+#' with_list_cols <- exclude_cols_then_pivot_wider(
+#'   data,
+#'   exclude_cols = "to_exclude",
+#'   id_cols = "id"
+#' )
+#' # You can handle it after the fact
+#' with_list_cols |>
+#'   tidyr::unnest(everything()) |>
+#'   distinct()
+#'
+#' # But also you can avoid it with `avoid_list_cols = TRUE`
+#' exclude_cols_then_pivot_wider(
+#'   data,
+#'   exclude_cols = "to_exclude",
+#'   id_cols = "id",
+#'   avoid_list_cols = TRUE
+#' )
+exclude_cols_then_pivot_wider <- function(data,
+                                          ...,
+                                          exclude_cols = NULL,
+                                          avoid_list_cols = FALSE) {
+  pruned <- data |>
+    select(-matches(exclude_cols)) |>
+    distinct()
+
+  if (!avoid_list_cols) {
+    pruned |> pivot_wider(...)
+  } else {
+    check_values_fn(...)
+
+    pruned |>
+      pivot_wider(..., values_fn = list) |>
+      tidyr::unchop(tidyselect::everything()) |>
+      distinct()
+  }
+}
+
+check_values_fn <- function(...) {
+  if (hasName(list(...), "values_fn")) {
+    abort("`values_fn` should not be used when `avoid_list_cols = TRUE`.")
+  }
+}
diff --git a/R/pivot_wider_transition_risk_profile.R b/R/pivot_wider_transition_risk_profile.R
new file mode 100644
index 0000000..cc94074
--- /dev/null
+++ b/R/pivot_wider_transition_risk_profile.R
@@ -0,0 +1,204 @@
+#' Pivot company-level columns to wide format for indicator "transition risk profile"
+#'
+#' @param include_co2 Logical. Include `co2_*` columns ?
+#'
+#' @return A Dataframe
+#' @export
+#' @keywords internal
+#'
+#' @examples
+#' \dontrun{
+#' library(readr, warn.conflicts = FALSE)
+#' library(dplyr, warn.conflicts = FALSE)
+#' library(tiltToyData, warn.conflicts = FALSE)
+#' library(tiltIndicator)
+#' library(tiltIndicatorAfter)
+#'
+#' restore <- options(list(
+#'   readr.show_col_types = FALSE,
+#'   tiltIndicatorAfter.output_co2_footprint = TRUE
+#' ))
+#'
+#' toy_emissions_profile_products_ecoinvent <- read_csv(toy_emissions_profile_products_ecoinvent())
+#' toy_emissions_profile_any_companies <- read_csv(toy_emissions_profile_any_companies())
+#' toy_sector_profile_any_scenarios <- read_csv(toy_sector_profile_any_scenarios())
+#' toy_sector_profile_companies <- read_csv(toy_sector_profile_companies())
+#' toy_europages_companies <- read_csv(toy_europages_companies())
+#' toy_ecoinvent_activities <- read_csv(toy_ecoinvent_activities())
+#' toy_ecoinvent_europages <- read_csv(toy_ecoinvent_europages())
+#' toy_ecoinvent_inputs <- read_csv(toy_ecoinvent_inputs())
+#' toy_isic_name <- read_csv(toy_isic_name())
+#' toy_all_activities_scenario_sectors <- read_csv(toy_all_activities_scenario_sectors())
+#'
+#' toy_emissions_profile <- profile_emissions(
+#'   companies = toy_emissions_profile_any_companies,
+#'   co2 = toy_emissions_profile_products_ecoinvent,
+#'   europages_companies = toy_europages_companies,
+#'   ecoinvent_activities = toy_ecoinvent_activities,
+#'   ecoinvent_europages = toy_ecoinvent_europages,
+#'   isic = toy_isic_name
+#' )
+#'
+#' toy_sector_profile <- profile_sector(
+#'   companies = toy_sector_profile_companies,
+#'   scenarios = toy_sector_profile_any_scenarios,
+#'   europages_companies = toy_europages_companies,
+#'   ecoinvent_activities = toy_ecoinvent_activities,
+#'   ecoinvent_europages = toy_ecoinvent_europages,
+#'   isic = toy_isic_name
+#' )
+#'
+#' wide_format <- transition_risk_profile_impl(
+#'   emissions_profile,
+#'   sector_profile,
+#'   co2,
+#'   all_activities_scenario_sectors,
+#'   scenarios
+#' ) |>
+#'   add_transition_risk_category_at_company_level() |>
+#'   best_case_worst_case_transition_risk_profile_at_company_level() |>
+#'   pivot_wider_transition_risk_profile(include_co2 = TRUE) |>
+#'   unnest_company()
+#' wide_format
+#'
+#' # Cleanup
+#' options(restore)
+#' }
+pivot_wider_transition_risk_profile <- function(data, include_co2 = FALSE) {
+  product <- data |>
+    unnest_product()
+
+  company <- data |>
+    unnest_company()
+
+  emission_profile_company <- company |>
+    select_emissions_profile_pivot_cols(include_co2 = include_co2) |>
+    exclude_subset_cols_then_pivot_wider(
+      subset_cols = select_subset_emissions_profile_id_cols(
+        include_co2 = include_co2
+      ),
+      names_from = "emission_profile",
+      names_prefix = "emission_category_",
+      values_from = "emission_profile_share"
+    )
+
+  sector_profile_company <- company |>
+    select_sector_profile_pivot_cols() |>
+    exclude_subset_cols_then_pivot_wider(
+      subset_cols = select_subset_sector_profile_id_cols(),
+      names_from = "sector_profile",
+      names_prefix = "sector_category_",
+      values_from = "sector_profile_share"
+    )
+
+  transition_risk_profile_company <- company |>
+    select_transition_risk_profile_pivot_cols() |>
+    exclude_subset_cols_then_pivot_wider(
+      subset_cols = select_subset_transition_risk_profile_id_cols(),
+      names_from = "transition_risk_category",
+      names_prefix = "transition_risk_category_",
+      values_from = "transition_risk_category_share"
+    )
+
+  company <- full_join(emission_profile_company,
+    sector_profile_company,
+    by = c(
+      "companies_id",
+      "country",
+      "main_activity"
+    ),
+    relationship = "many-to-many"
+  ) |>
+    add_benchmark_tr_score_avg() |>
+    full_join(transition_risk_profile_company,
+      by = c(
+        "companies_id",
+        "country",
+        "main_activity",
+        "benchmark_tr_score_avg"
+      ),
+      relationship = "many-to-many"
+    ) |>
+    distinct()
+
+  tilt_profile(nest_levels(product, company))
+}
+
+exclude_subset_cols_then_pivot_wider <- function(data,
+                                                 subset_cols,
+                                                 names_from,
+                                                 names_prefix,
+                                                 values_from) {
+  data |>
+    exclude_cols_then_pivot_wider(
+      exclude_cols = "co2e",
+      avoid_list_cols = TRUE,
+      id_cols = subset_cols,
+      names_from = names_from,
+      names_prefix = names_prefix,
+      values_from = values_from
+    )
+}
+
+select_subset_emissions_profile_id_cols <- function(data, include_co2 = FALSE) {
+  c(
+    "companies_id",
+    "company_name",
+    "country",
+    "main_activity",
+    "benchmark",
+    "profile_ranking_avg",
+    "postcode",
+    "address",
+    if (include_co2) "co2_avg",
+    "min_headcount",
+    "max_headcount"
+  )
+}
+
+select_emissions_profile_pivot_cols <- function(data, include_co2 = FALSE) {
+  select(data, all_of(c(
+    select_subset_emissions_profile_id_cols(include_co2 = include_co2),
+    "emission_profile",
+    "emission_profile_share"
+  )))
+}
+
+select_subset_sector_profile_id_cols <- function(data) {
+  c(
+    "companies_id",
+    "country",
+    "main_activity",
+    "scenario",
+    "year",
+    "reduction_targets_avg"
+  )
+}
+
+select_sector_profile_pivot_cols <- function(data) {
+  select(data, all_of(c(
+    select_subset_sector_profile_id_cols(),
+    "sector_profile",
+    "sector_profile_share"
+  )))
+}
+
+select_subset_transition_risk_profile_id_cols <- function(data) {
+  c(
+    "companies_id",
+    "country",
+    "main_activity",
+    "benchmark_tr_score_avg",
+    "avg_transition_risk_equal_weight",
+    "avg_transition_risk_best_case",
+    "avg_transition_risk_worst_case"
+  )
+}
+
+select_transition_risk_profile_pivot_cols <- function(data) {
+  select(data, all_of(c(
+    select_subset_transition_risk_profile_id_cols(),
+    "transition_risk_category",
+    "transition_risk_category_share"
+  )))
+}
diff --git a/R/prepare_webtool_output.R b/R/prepare_webtool_output.R
new file mode 100644
index 0000000..929e858
--- /dev/null
+++ b/R/prepare_webtool_output.R
@@ -0,0 +1,43 @@
+prepare_webtool_output <- function(data,
+                                   for_webtool = FALSE,
+                                   include_co2 = FALSE) {
+  if (for_webtool) {
+    prepare_webtool_output_impl(data, include_co2 = include_co2)
+  }
+  else {
+    data
+  }
+}
+
+prepare_webtool_output_impl <- function(data, include_co2 = FALSE) {
+  product <- data |>
+    unnest_product() |>
+    select_webtool_cols_at_product_level()
+
+  company <- data |>
+    unnest_company() |>
+    select_webtool_cols_at_company_level_wide() |>
+    rename_webtool_cols_at_company_level_wide()
+
+  if (include_co2) {
+    product <- product |>
+      select(-all_of(c("co2_footprint")))
+
+    company <- company |>
+      select(-all_of(c("co2e_avg")))
+  }
+
+  tilt_profile(nest_levels(product, company))
+}
+
+polish_transition_risk_profile <- function(data) {
+  product <- data |>
+    unnest_product() |>
+    rename_transition_risk_profile_cols_product()
+
+  company <- data |>
+    unnest_company() |>
+    rename_transition_risk_profile_cols_company()
+
+  tilt_profile(nest_levels(product, company))
+}
diff --git a/R/relocate_transition_risk_profile_cols.R b/R/relocate_transition_risk_profile_cols.R
new file mode 100644
index 0000000..c989280
--- /dev/null
+++ b/R/relocate_transition_risk_profile_cols.R
@@ -0,0 +1,108 @@
+relocate_transition_risk_profile_cols <- function(
+    data,
+    include_co2 = FALSE) {
+  product <- data |>
+    unnest_product() |>
+    relocate_transition_risk_profile_cols_at_product_level(
+      include_co2 = include_co2
+    )
+
+  company <- data |>
+    unnest_company() |>
+    relocate_transition_risk_profile_cols_at_company_level(
+      include_co2 = include_co2
+    )
+
+  tilt_profile(nest_levels(product, company))
+}
+
+relocate_transition_risk_profile_cols_at_product_level <- function(
+    data,
+    include_co2 = FALSE) {
+  data |>
+    relocate(
+      "companies_id",
+      "company_name",
+      "country",
+      "postcode",
+      "address",
+      "main_activity",
+      "ep_product",
+      "matched_activity_name",
+      "matched_reference_product",
+      "matching_certainty",
+      if (include_co2) "co2_footprint",
+      "unit",
+      "isic_4digit",
+      "tilt_sector",
+      "tilt_subsector",
+      "benchmark",
+      "co2e_lower",
+      "co2e_upper",
+      "profile_ranking",
+      "emission_profile",
+      "emissions_profile_equal_weight",
+      "emissions_profile_best_case",
+      "emissions_profile_worst_case",
+      "scenario",
+      "year",
+      "reduction_targets",
+      "sector_profile",
+      "sector_profile_equal_weight",
+      "sector_profile_best_case",
+      "sector_profile_worst_case",
+      "benchmark_tr_score",
+      "transition_risk_score",
+      "transition_risk_low_threshold",
+      "transition_risk_high_threshold",
+      "transition_risk_category",
+      "transition_risk_profile_equal_weight",
+      "transition_risk_profile_best_case",
+      "transition_risk_profile_worst_case",
+      "amount_of_distinct_products",
+      "amount_of_distinct_products_matched",
+      "transition_risk_NA_total",
+      "transition_risk_NA_share",
+      "min_headcount",
+      "max_headcount"
+    )
+}
+
+relocate_transition_risk_profile_cols_at_company_level <- function(
+    data,
+    include_co2 = FALSE) {
+  data |>
+    relocate(
+      "companies_id",
+      "company_name",
+      "country",
+      if (include_co2) "co2_avg",
+      "benchmark",
+      "profile_ranking_avg",
+      "avg_profile_ranking_best_case",
+      "avg_profile_ranking_worst_case",
+      "emission_category_low",
+      "emission_category_medium",
+      "emission_category_high",
+      "emission_category_NA",
+      "scenario",
+      "year",
+      "reduction_targets_avg",
+      "avg_reduction_targets_best_case",
+      "avg_reduction_targets_worst_case",
+      "sector_category_low",
+      "sector_category_medium",
+      "sector_category_high",
+      "sector_category_NA",
+      "benchmark_tr_score_avg",
+      "avg_transition_risk_equal_weight",
+      "avg_transition_risk_best_case",
+      "avg_transition_risk_worst_case",
+      "transition_risk_NA_share",
+      "postcode",
+      "address",
+      "main_activity",
+      "min_headcount",
+      "max_headcount"
+    )
+}
diff --git a/R/round_off_to_4_decimal_places.R b/R/round_off_to_4_decimal_places.R
new file mode 100644
index 0000000..ed6b59c
--- /dev/null
+++ b/R/round_off_to_4_decimal_places.R
@@ -0,0 +1,16 @@
+round_off_to_4_decimal_places <- function(data) {
+  product <- data |>
+    unnest_product() |>
+    round_off_to_4_decimal_places_impl()
+
+  company <- data |>
+    unnest_company() |>
+    round_off_to_4_decimal_places_impl()
+
+  tilt_profile(nest_levels(product, company))
+}
+
+round_off_to_4_decimal_places_impl <- function(data) {
+  data |>
+    mutate(across(where(is.numeric), ~ round(.x, 4)))
+}
diff --git a/R/score_transition_risk.R b/R/score_transition_risk.R
new file mode 100644
index 0000000..b9d2767
--- /dev/null
+++ b/R/score_transition_risk.R
@@ -0,0 +1,124 @@
+#' Transition Risk Score
+#'
+#' Calulate Transition Risk Score at product level and company level
+#'
+#' @param emissions_profile_at_product_level Dataframe. Emissions profile product level output
+#' @param sector_profile_at_product_level Dataframe. Sector profile product level output
+#'
+#' @family top-level functions
+#'
+#' @return A dataframe
+#' @export
+#'
+#' @examples
+#' library(dplyr)
+#' library(readr, warn.conflicts = FALSE)
+#' library(tiltToyData)
+#' library(tiltIndicator)
+#' library(tiltIndicatorAfter)
+#'
+#' restore <- options(readr.show_col_types = FALSE)
+#'
+#' emissions_companies <- read_csv(toy_emissions_profile_any_companies())
+#' products <- read_csv(toy_emissions_profile_products_ecoinvent())
+#' europages_companies <- read_csv(toy_europages_companies())
+#' ecoinvent_activities <- read_csv(toy_ecoinvent_activities())
+#' ecoinvent_europages <- read_csv(toy_ecoinvent_europages())
+#' isic_name <- read_csv(toy_isic_name())
+#'
+#' emissions_profile_at_product_level <- profile_emissions(
+#'   companies = emissions_companies,
+#'   co2 = products,
+#'   europages_companies = europages_companies,
+#'   ecoinvent_activities = ecoinvent_activities,
+#'   ecoinvent_europages = ecoinvent_europages,
+#'   isic = isic_name
+#' ) |> unnest_product()
+#'
+#' sector_companies <- read_csv(toy_sector_profile_companies())
+#' scenarios <- read_csv(toy_sector_profile_any_scenarios())
+#'
+#' sector_profile_at_product_level <- profile_sector(
+#'   companies = sector_companies,
+#'   scenarios = scenarios,
+#'   europages_companies = europages_companies,
+#'   ecoinvent_activities = ecoinvent_activities,
+#'   ecoinvent_europages = ecoinvent_europages,
+#'   isic = isic_name
+#' ) |> unnest_product()
+#'
+#' result <- score_transition_risk(emissions_profile_at_product_level, sector_profile_at_product_level)
+#'
+#' result |> unnest_product()
+#'
+#' result |> unnest_company()
+#'
+#' # Cleanup
+#' options(restore)
+score_transition_risk <-
+  function(emissions_profile_at_product_level,
+           sector_profile_at_product_level) {
+    union_emissions_sector_rows <-
+      get_rows_union_for_common_cols(
+        emissions_profile_at_product_level,
+        sector_profile_at_product_level
+      )
+    trs_emissions <-
+      prepare_trs_emissions(emissions_profile_at_product_level)
+    trs_sector <-
+      prepare_trs_sector(sector_profile_at_product_level)
+
+    trs_product <-
+      full_join_emmissions_sector(trs_emissions, trs_sector) |>
+      add_transition_risk_score(
+        col_ranking = col_ranking(),
+        col_target = "reduction_targets"
+      ) |>
+      create_benchmarks_tr_score() |>
+      limit_transition_risk_score_between_0_and_1() |>
+      select(-all_of(c("scenario_year", "benchmark"))) |>
+      left_join(
+        union_emissions_sector_rows,
+        by = c("companies_id", "ep_product", "activity_uuid_product_uuid"),
+        relationship = "many-to-many"
+      ) |>
+      relocate(
+        relocate_trs_columns(product_level_trs_column()),
+        "profile_ranking",
+        "reduction_targets"
+      ) |>
+      distinct()
+
+    trs_company <- trs_product |>
+      select(common_columns_emissions_sector_at_company_level(), "benchmark_tr_score", product_level_trs_column()) |>
+      create_trs_average() |>
+      select(-product_level_trs_column()) |>
+      relocate(relocate_trs_columns(company_level_trs_avg_column())) |>
+      rename(benchmark_tr_score_avg = "benchmark_tr_score") |>
+      distinct()
+
+    nest_levels(trs_product, trs_company)
+  }
+
+create_benchmarks_tr_score <- function(data) {
+  mutate(
+    data,
+    benchmark_tr_score = ifelse(
+      is.na(.data$profile_ranking) | is.na(.data$reduction_targets),
+      NA,
+      paste(.data$scenario_year, .data$benchmark, sep = "_")
+    )
+  )
+}
+
+create_trs_average <- function(data) {
+  mutate(
+    data,
+    transition_risk_score_avg = mean(.data$transition_risk_score, na.rm = TRUE),
+    .by = c("companies_id", "benchmark_tr_score")
+  )
+}
+
+limit_transition_risk_score_between_0_and_1 <- function(data) {
+  mutate(data, transition_risk_score = pmin(pmax(data$transition_risk_score, 0), 1))
+}
diff --git a/R/score_transition_risk_and_polish.R b/R/score_transition_risk_and_polish.R
new file mode 100644
index 0000000..1b50c43
--- /dev/null
+++ b/R/score_transition_risk_and_polish.R
@@ -0,0 +1,222 @@
+#' Add transition risk score and polish the output for delivery
+#'
+#' @param emissions_profile Nested data frame. The output of
+#'   `profile_emissions()`.
+#' @param sector_profile Nested data frame. The output of `profile_sector()`.
+#' @param include_co2 Logical. Include `co2_*` columns ?
+#'
+#' @return A data frame with the column `companies_id`, and the nested
+#'   columns`product` and `company` holding the outputs at product and company
+#'   level.
+#' @export
+#' @keywords internal
+#'
+#' @examples
+#' library(readr, warn.conflicts = FALSE)
+#' library(dplyr, warn.conflicts = FALSE)
+#' library(tiltToyData, warn.conflicts = FALSE)
+#' library(tiltIndicator)
+#' library(tiltIndicatorAfter)
+#'
+#' set.seed(123)
+#' restore <- options(list(
+#'   readr.show_col_types = FALSE,
+#'   tiltIndicatorAfter.output_co2_footprint = TRUE
+#' ))
+#'
+#' toy_emissions_profile_products_ecoinvent <- read_csv(toy_emissions_profile_products_ecoinvent())
+#' toy_emissions_profile_any_companies <- read_csv(toy_emissions_profile_any_companies())
+#' toy_sector_profile_any_scenarios <- read_csv(toy_sector_profile_any_scenarios())
+#' toy_sector_profile_companies <- read_csv(toy_sector_profile_companies())
+#' toy_europages_companies <- read_csv(toy_europages_companies())
+#' toy_ecoinvent_activities <- read_csv(toy_ecoinvent_activities())
+#' toy_ecoinvent_europages <- read_csv(toy_ecoinvent_europages())
+#' toy_ecoinvent_inputs <- read_csv(toy_ecoinvent_inputs())
+#' toy_isic_name <- read_csv(toy_isic_name())
+#'
+#' emissions_profile <- profile_emissions(
+#'   companies = toy_emissions_profile_any_companies,
+#'   co2 = toy_emissions_profile_products_ecoinvent,
+#'   europages_companies = toy_europages_companies,
+#'   ecoinvent_activities = toy_ecoinvent_activities,
+#'   ecoinvent_europages = toy_ecoinvent_europages,
+#'   isic = toy_isic_name
+#' )
+#'
+#' sector_profile <- profile_sector(
+#'   companies = toy_sector_profile_companies,
+#'   scenarios = toy_sector_profile_any_scenarios,
+#'   europages_companies = toy_europages_companies,
+#'   ecoinvent_activities = toy_ecoinvent_activities,
+#'   ecoinvent_europages = toy_ecoinvent_europages,
+#'   isic = toy_isic_name
+#' )
+#'
+#' result <- score_transition_risk_and_polish(emissions_profile,
+#'   sector_profile,
+#'   include_co2 = TRUE
+#' )
+#'
+#' result |> unnest_product()
+#'
+#' result |> unnest_company()
+#'
+#' # Cleanup
+#' options(restore)
+score_transition_risk_and_polish <- function(emissions_profile,
+                                             sector_profile,
+                                             include_co2 = FALSE) {
+  transition_risk_score <- score_transition_risk(
+    unnest_product(emissions_profile),
+    unnest_product(sector_profile)
+  )
+
+  if (include_co2) {
+    hint <- "Do you need `options(tiltIndicatorAfter.output_co2_footprint = TRUE)`?"
+    unnest_product(emissions_profile) |> check_col("co2_footprint", hint)
+  }
+
+  select_emissions_profile_product <- unnest_product(emissions_profile) |>
+    select(
+      c(
+        "companies_id",
+        "country",
+        "main_activity",
+        "ep_product",
+        "postcode",
+        "address",
+        "activity_uuid_product_uuid",
+        "matched_activity_name",
+        "matched_reference_product",
+        "unit",
+        "co2e_lower",
+        "co2e_upper",
+        "emission_profile",
+        "benchmark",
+        "profile_ranking",
+        "tilt_sector",
+        "tilt_subsector",
+        "min_headcount",
+        "max_headcount",
+        "emissions_profile_best_case",
+        "emissions_profile_worst_case",
+        "isic_4digit",
+        "matching_certainty",
+        "company_name",
+        "emissions_profile_equal_weight",
+        if (include_co2) "co2_footprint"
+      )
+    )
+  select_sector_profile_product <- unnest_product(sector_profile) |>
+    select(
+      c(
+        "companies_id",
+        "ep_product",
+        "sector_profile",
+        "scenario",
+        "year",
+        "reduction_targets",
+        "tilt_sector",
+        "tilt_subsector",
+        "isic_4digit",
+        "sector_profile_best_case",
+        "sector_profile_worst_case",
+        "sector_profile_equal_weight"
+      )
+    )
+
+  select_transition_risk_score_product <- unnest_product(transition_risk_score) |>
+    select(c(
+      "companies_id",
+      "ep_product",
+      "benchmark_tr_score",
+      "transition_risk_score"
+    ))
+
+  out_product <- select_emissions_profile_product |>
+    left_join(
+      select_sector_profile_product,
+      relationship = "many-to-many",
+      by = c("companies_id", "ep_product"),
+      suffix = c(".emissions", ".sector")
+    ) |>
+    coalesce_common_col("tilt_sector", "emissions", "sector") |>
+    coalesce_common_col("tilt_subsector", "emissions", "sector") |>
+    coalesce_common_col("isic_4digit", "emissions", "sector") |>
+    mutate(benchmark_tr_score = ifelse(
+      is.na(.data$profile_ranking) | is.na(.data$reduction_targets),
+      NA_character_,
+      paste(.data$scenario, .data$year, .data$benchmark, sep = "_")
+    )) |>
+    left_join(
+      select_transition_risk_score_product,
+      by = c("companies_id", "ep_product", "benchmark_tr_score"),
+      relationship = "many-to-many"
+    ) |>
+    distinct()
+
+  select_emissions_profile_company <- unnest_company(emissions_profile) |>
+    select(
+      c(
+        "companies_id",
+        "company_name",
+        "country",
+        "main_activity",
+        "postcode",
+        "address",
+        "benchmark",
+        "min_headcount",
+        "max_headcount",
+        "emission_profile",
+        "emission_profile_share",
+        "profile_ranking_avg",
+        if (include_co2) "co2_avg"
+      )
+    )
+
+  select_sector_profile_company <- unnest_company(sector_profile) |>
+    select(c("companies_id", "sector_profile", "sector_profile_share", "scenario", "year", "reduction_targets_avg"))
+
+  select_transition_risk_score_company <- unnest_company(transition_risk_score) |>
+    select(
+      c(
+        "companies_id",
+        "benchmark_tr_score_avg",
+        "transition_risk_score_avg"
+      )
+    )
+
+  out_company <- select_emissions_profile_company |>
+    left_join(
+      select_sector_profile_company,
+      relationship = "many-to-many",
+      by = c("companies_id")
+    ) |>
+    add_benchmark_tr_score_avg() |>
+    left_join(
+      select_transition_risk_score_company,
+      by = c("companies_id", "benchmark_tr_score_avg")
+    ) |>
+    distinct()
+
+  nest_levels(out_product, out_company)
+}
+
+add_benchmark_tr_score_avg <- function(data) {
+  mutate(
+    data,
+    benchmark_tr_score_avg = ifelse(
+      is.na(.data$profile_ranking_avg) | is.na(.data$reduction_targets_avg),
+      NA_character_,
+      paste(.data$scenario, .data$year, .data$benchmark, sep = "_")
+    )
+  )
+}
+
+coalesce_common_col <- function(data, col, suffix1, suffix2) {
+  col_suffix1 = paste(col, suffix1, sep = ".")
+  col_suffix2 = paste(col, suffix2, sep = ".")
+  data |>
+    mutate({{ col }} := coalesce(.data[[col_suffix1]], .data[[col_suffix2]])) |>
+    select(-all_of(c(col_suffix1, col_suffix2)))
+}
diff --git a/R/tiltTransitionRisk-options.R b/R/tiltTransitionRisk-options.R
new file mode 100644
index 0000000..ba23938
--- /dev/null
+++ b/R/tiltTransitionRisk-options.R
@@ -0,0 +1,62 @@
+#' tiltTransitionRisk options
+#'
+#' @description
+#' These options are meant to be used mainly by developers or analysts while
+#' testing the code or creating data:
+#' * `tiltIndicatorAfter.output_co2_footprint`:
+#'     * At product level it outputs licensed column `co2_footprint`.
+#'     * At company level it outputs the column `co2_avg` (average `co2_footprint`
+#'     by `companies_id`).
+#' * `tiltIndicatorAfter.verbose`: Controls verbosity.
+#'
+#' @keywords internal
+#' @name tiltIndicatorAfter_options
+#'
+#' @examples
+#' library(readr, warn.conflicts = FALSE)
+#' library(dplyr, warn.conflicts = FALSE)
+#' library(withr)
+#' library(tiltToyData)
+#' library(tiltIndicator)
+#' library(tiltIndicatorAfter)
+#'
+#' set.seed(1)
+#'
+#' restore <- options(list(
+#'   readr.show_col_types = FALSE,
+#'   tiltIndicatorAfter.verbose = TRUE,
+#'   tiltIndicatorAfter.output_co2_footprint = TRUE
+#' ))
+#'
+#' companies <- read_csv(toy_emissions_profile_any_companies())
+#' products <- read_csv(toy_emissions_profile_products_ecoinvent())
+#' europages_companies <- read_csv(toy_europages_companies())
+#' ecoinvent_activities <- read_csv(toy_ecoinvent_activities())
+#' ecoinvent_europages <- read_csv(toy_ecoinvent_europages())
+#' isic_name <- read_csv(toy_isic_name())
+#'
+#' result <- profile_emissions(
+#'   companies,
+#'   products,
+#'   europages_companies = europages_companies,
+#'   ecoinvent_activities = ecoinvent_activities,
+#'   ecoinvent_europages = ecoinvent_europages,
+#'   isic = isic_name
+#' )
+#'
+#' result |>
+#'   unnest_product() |>
+#'   select(matches(c("co2")))
+#'
+#' result |>
+#'   unnest_company() |>
+#'   select(matches(c("co2")))
+NULL
+
+option_output_co2_footprint <- function() {
+  getOption("tiltIndicatorAfter.output_co2_footprint", default = FALSE)
+}
+
+option_verbose <- function() {
+  getOption("tiltIndicatorAfter.verbose", default = TRUE)
+}
diff --git a/R/tiltTransitionRisk-package.R b/R/tiltTransitionRisk-package.R
index 212b01e..5c16513 100644
--- a/R/tiltTransitionRisk-package.R
+++ b/R/tiltTransitionRisk-package.R
@@ -3,24 +3,78 @@
 
 ## usethis namespace: start
 #' @importFrom dplyr .data
+#' @importFrom dplyr across
 #' @importFrom dplyr all_of
+#' @importFrom dplyr arrange
+#' @importFrom dplyr bind_rows
+#' @importFrom dplyr case_when
+#' @importFrom dplyr coalesce
 #' @importFrom dplyr distinct
 #' @importFrom dplyr filter
 #' @importFrom dplyr full_join
+#' @importFrom dplyr left_join
 #' @importFrom dplyr mutate
+#' @importFrom dplyr n_distinct
+#' @importFrom dplyr relocate
+#' @importFrom dplyr rename
+#' @importFrom dplyr rename_with
 #' @importFrom dplyr select
+#' @importFrom dplyr summarise
+#' @importFrom dplyr where
 #' @importFrom glue glue
 #' @importFrom purrr walk
 #' @importFrom readr read_csv
+#' @importFrom rlang :=
+#' @importFrom rlang .data
 #' @importFrom rlang abort
+#' @importFrom rlang as_name
+#' @importFrom rlang ensym
 #' @importFrom stats quantile
+#' @importFrom tibble tibble
 #' @importFrom tibble tribble
+#' @importFrom tidyr pivot_wider
+#' @importFrom tidyselect all_of
+#' @importFrom tidyselect any_of
+#' @importFrom tidyselect matches
 #' @importFrom tiltIndicator categorize_risk
+#' @importFrom tiltIndicator epa_at_company_level
 #' @importFrom tiltIndicator epa_compute_profile_ranking
+#' @importFrom tiltIndicator example_data_factory
+#' @importFrom tiltIndicator insert_row_with_na_in_risk_category
+#' @importFrom tiltIndicator nest_levels
 #' @importFrom tiltIndicator spa_compute_profile_ranking
+#' @importFrom tiltIndicator tilt_profile
+#' @importFrom tiltIndicator unnest_company
+#' @importFrom tiltIndicator unnest_product
+#' @importFrom tiltIndicatorAfter best_case_worst_case_emission_profile
+#' @importFrom tiltIndicatorAfter best_case_worst_case_impl
+#' @importFrom tiltIndicatorAfter best_case_worst_case_sector_profile
+#' @importFrom tiltIndicatorAfter polish_best_case_worst_case
+#' @importFrom tiltIndicatorAfter profile_emissions
+#' @importFrom tiltIndicatorAfter profile_sector
+#' @importFrom tiltIndicatorAfter rename_with_prefix
+#' @importFrom tiltPolish rename_transition_risk_profile_cols_company
+#' @importFrom tiltPolish rename_transition_risk_profile_cols_product
+#' @importFrom tiltPolish rename_webtool_cols_at_company_level_wide
+#' @importFrom tiltPolish select_webtool_cols_at_company_level_wide
+#' @importFrom tiltPolish select_webtool_cols_at_product_level
+#' @importFrom tiltToyData toy_ecoinvent_activities
+#' @importFrom tiltToyData toy_ecoinvent_europages
+#' @importFrom tiltToyData toy_ecoinvent_inputs
+#' @importFrom tiltToyData toy_emissions_profile_any_companies
+#' @importFrom tiltToyData toy_emissions_profile_products
 #' @importFrom tiltToyData toy_emissions_profile_products_ecoinvent
+#' @importFrom tiltToyData toy_emissions_profile_upstream_products
+#' @importFrom tiltToyData toy_emissions_profile_upstream_products_ecoinvent
+#' @importFrom tiltToyData toy_europages_companies
+#' @importFrom tiltToyData toy_isic_name
 #' @importFrom tiltToyData toy_sector_profile_any_scenarios
 #' @importFrom tiltToyData toy_sector_profile_companies
+#' @importFrom tiltToyData toy_sector_profile_upstream_companies
+#' @importFrom tiltToyData toy_sector_profile_upstream_products
+#' @importFrom utils hasName
 #' @importFrom utils write.csv
+#' @importFrom withr local_options
+#' @importFrom withr local_seed
 ## usethis namespace: end
 NULL
diff --git a/R/transition_risk_profile.R b/R/transition_risk_profile.R
new file mode 100644
index 0000000..83a0520
--- /dev/null
+++ b/R/transition_risk_profile.R
@@ -0,0 +1,185 @@
+#' Calculate the indicator "transition risk profile"
+#'
+#' Adds the risk classification to calculated transition risk scores from
+#' emission profile and sector profile indicator.
+#'
+#' @param emissions_profile Nested data frame. The output of
+#'   `profile_emissions()`.
+#' @param sector_profile Nested data frame. The output of `profile_sector()`.
+#' @param co2 A dataframe
+#' @param all_activities_scenario_sectors A dataframe
+#' @param scenarios A dataframe
+#' @param for_webtool Logical. Is it output for webtool or not?
+#'
+#' @return A data frame with the column `companies_id`, and the nested
+#'   columns`product` and `company` holding the outputs at product and company
+#'   level.
+#' @export
+#'
+#' @family top-level functions
+#' @family profile functions
+#'
+#' @examples
+#' library(readr, warn.conflicts = FALSE)
+#' library(dplyr, warn.conflicts = FALSE)
+#' library(tiltToyData, warn.conflicts = FALSE)
+#' library(tiltIndicator)
+#' library(tiltIndicatorAfter)
+#'
+#' set.seed(123)
+#' restore <- options(list(
+#'   readr.show_col_types = FALSE,
+#'   tiltIndicatorAfter.output_co2_footprint = TRUE
+#' ))
+#'
+#' toy_emissions_profile_products_ecoinvent <- read_csv(toy_emissions_profile_products_ecoinvent())
+#' toy_emissions_profile_any_companies <- read_csv(toy_emissions_profile_any_companies())
+#' toy_sector_profile_any_scenarios <- read_csv(toy_sector_profile_any_scenarios())
+#' toy_sector_profile_companies <- read_csv(toy_sector_profile_companies())
+#' toy_europages_companies <- read_csv(toy_europages_companies())
+#' toy_ecoinvent_activities <- read_csv(toy_ecoinvent_activities())
+#' toy_ecoinvent_europages <- read_csv(toy_ecoinvent_europages())
+#' toy_ecoinvent_inputs <- read_csv(toy_ecoinvent_inputs())
+#' toy_isic_name <- read_csv(toy_isic_name())
+#' toy_all_activities_scenario_sectors <- read_csv(toy_all_activities_scenario_sectors())
+#'
+#' toy_emissions_profile <- profile_emissions(
+#'   companies = toy_emissions_profile_any_companies,
+#'   co2 = toy_emissions_profile_products_ecoinvent,
+#'   europages_companies = toy_europages_companies,
+#'   ecoinvent_activities = toy_ecoinvent_activities,
+#'   ecoinvent_europages = toy_ecoinvent_europages,
+#'   isic = toy_isic_name
+#' )
+#'
+#' toy_sector_profile <- profile_sector(
+#'   companies = toy_sector_profile_companies,
+#'   scenarios = toy_sector_profile_any_scenarios,
+#'   europages_companies = toy_europages_companies,
+#'   ecoinvent_activities = toy_ecoinvent_activities,
+#'   ecoinvent_europages = toy_ecoinvent_europages,
+#'   isic = toy_isic_name
+#' )
+#'
+#' output <- transition_risk_profile(
+#'   emissions_profile = toy_emissions_profile,
+#'   sector_profile = toy_sector_profile,
+#'   co2 = toy_emissions_profile_products_ecoinvent,
+#'   all_activities_scenario_sectors = toy_all_activities_scenario_sectors,
+#'   scenarios = toy_sector_profile_any_scenarios,
+#'   for_webtool = FALSE
+#' )
+#'
+#' output |> unnest_product()
+#'
+#' output |> unnest_company()
+transition_risk_profile <- function(emissions_profile,
+                                    sector_profile,
+                                    co2,
+                                    all_activities_scenario_sectors,
+                                    scenarios,
+                                    for_webtool = FALSE) {
+  transition_risk_profile_impl(
+    emissions_profile,
+    sector_profile,
+    co2,
+    all_activities_scenario_sectors,
+    scenarios
+  ) |>
+    add_transition_risk_category_at_company_level() |>
+    best_case_worst_case_transition_risk_profile_at_company_level() |>
+    pivot_wider_transition_risk_profile(
+      include_co2 = option_output_co2_footprint()
+    ) |>
+    best_case_worst_case_avg_profile_ranking() |>
+    best_case_worst_case_avg_reduction_targets() |>
+    add_transition_risk_NA_share() |>
+    relocate_transition_risk_profile_cols(
+      include_co2 = option_output_co2_footprint()
+    ) |>
+    round_off_to_4_decimal_places() |>
+    coefficient_of_variation_transition_risk_profile() |>
+    polish_transition_risk_profile() |>
+    prepare_webtool_output(
+      for_webtool = for_webtool,
+      include_co2 = option_output_co2_footprint()
+    ) |>
+    remove_case3_companies()
+}
+
+transition_risk_profile_impl <- function(emissions_profile,
+                                         sector_profile,
+                                         co2,
+                                         all_activities_scenario_sectors,
+                                         scenarios) {
+  transition_risk_scores <- score_transition_risk_and_polish(emissions_profile,
+    sector_profile,
+    include_co2 = option_output_co2_footprint()
+  )
+  transition_risk_thresholds <- add_thresholds_transition_risk(
+    co2,
+    all_activities_scenario_sectors,
+    scenarios
+  ) |>
+    select_crucial_threshold_cols()
+
+  product <- transition_risk_scores |>
+    unnest_product() |>
+    left_join(transition_risk_thresholds, by = c(
+      "benchmark_tr_score",
+      "activity_uuid_product_uuid"
+    )) |>
+    add_transition_risk_category_at_product_level() |>
+    best_case_worst_case_transition_risk_profile() |>
+    polish_best_case_worst_case() |>
+    polish_best_case_worst_case_transition_risk_profile()
+
+  company <- transition_risk_scores |>
+    unnest_company() |>
+    arrange_transition_risk_at_company_level()
+
+  tilt_profile(nest_levels(product, company))
+}
+
+arrange_transition_risk_at_company_level <- function(data) {
+  arrange(data, .data$companies_id, .data$benchmark_tr_score_avg)
+}
+
+select_crucial_threshold_cols <- function(data) {
+  select(data, all_of(c(
+    "activity_uuid_product_uuid", "benchmark_tr_score",
+    "transition_risk_low_threshold", "transition_risk_high_threshold"
+  )))
+}
+
+remove_case3_companies <- function(data) {
+  product <- data |>
+    unnest_product()
+
+  company <- data |>
+    unnest_company()
+
+  case_3_companies <- identify_case3_companies(product)
+
+  final_product <- product |>
+    filter(!(.data$companies_id %in% unique(case_3_companies$companies_id))) |>
+    distinct()
+
+  final_company <- company |>
+    filter(!(.data$companies_id %in% unique(case_3_companies$companies_id))) |>
+    distinct()
+
+  tilt_profile(nest_levels(final_product, final_company))
+}
+
+identify_case3_companies <- function(data) {
+  # To identify which companies belong to Case 3, please follow this link:
+  # https://github.com/2DegreesInvesting/TiltDevProjectMGMT/issues/169#issuecomment-2284344632
+  data |>
+    mutate(
+      check =
+        all(is.na(.data$sector_target) & is.na(.data$matched_activity_name)),
+      .by = col_companies_id()
+    ) |>
+    filter(.data$check)
+}
diff --git a/R/utils-score_transition_risk.R b/R/utils-score_transition_risk.R
new file mode 100644
index 0000000..c3789ea
--- /dev/null
+++ b/R/utils-score_transition_risk.R
@@ -0,0 +1,127 @@
+prepare_trs_emissions <- function(data) {
+  select(
+    data,
+    c(
+      "companies_id",
+      "benchmark",
+      "profile_ranking",
+      "ep_product",
+      "activity_uuid_product_uuid"
+    )
+  )
+}
+
+prepare_trs_sector <- function(data) {
+  data |>
+    select(
+      c(
+        "companies_id",
+        "scenario",
+        "year",
+        "reduction_targets",
+        "ep_product",
+        "activity_uuid_product_uuid"
+      )
+    ) |>
+    mutate(scenario_year = paste(.data$scenario, .data$year, sep = "_")) |>
+    select(-c("scenario", "year"))
+}
+
+full_join_emmissions_sector <- function(emissions, sector) {
+  full_join(
+    emissions,
+    sector,
+    by = c("companies_id", "ep_product", "activity_uuid_product_uuid"),
+    relationship = "many-to-many"
+  )
+}
+
+get_rows_union_for_common_cols <-
+  function(emissions_at_product_level,
+           sector_at_product_level) {
+    emission_common_columns <- emissions_at_product_level |>
+      select(common_columns_emissions_sector_at_product_level()) |>
+      distinct()
+
+    sector_common_columns <- sector_at_product_level |>
+      select(common_columns_emissions_sector_at_product_level()) |>
+      distinct()
+
+    distinct(bind_rows(emission_common_columns, sector_common_columns))
+  }
+
+relocate_trs_columns <- function(columns) {
+  c(
+    "companies_id",
+    "company_name",
+    "country",
+    "benchmark_tr_score",
+    columns
+  )
+}
+
+product_level_trs_column <- function() {
+  c("transition_risk_score")
+}
+
+company_level_trs_avg_column <- function() {
+  c("transition_risk_score_avg")
+}
+
+trs_company_columns <- function() {
+  c(
+    common_columns_emissions_sector_at_company_level(),
+    "benchmark_tr_score_avg"
+  )
+}
+
+trs_product_output_columns <- function() {
+  c(
+    common_columns_emissions_sector_at_product_level(),
+    product_level_trs_column(),
+    "profile_ranking",
+    "reduction_targets",
+    "benchmark_tr_score"
+  )
+}
+
+trs_company_output_columns <- function() {
+  c(trs_company_columns(), company_level_trs_avg_column())
+}
+
+common_columns_emissions_sector_at_product_level <- function() {
+  c(
+    "companies_id",
+    "company_name",
+    "country",
+    "ep_product",
+    "matched_activity_name",
+    "matched_reference_product",
+    "unit",
+    "multi_match",
+    "matching_certainty",
+    "matching_certainty_company_average",
+    "company_city",
+    "postcode",
+    "address",
+    "main_activity",
+    "activity_uuid_product_uuid",
+    "tilt_sector",
+    "tilt_subsector",
+    "isic_4digit",
+    "isic_4digit_name",
+    "ei_geography"
+  )
+}
+
+common_columns_emissions_sector_at_company_level <- function() {
+  c(
+    "companies_id",
+    "company_name",
+    "country",
+    "company_city",
+    "postcode",
+    "address",
+    "main_activity"
+  )
+}
diff --git a/R/utils.R b/R/utils.R
new file mode 100644
index 0000000..1a61724
--- /dev/null
+++ b/R/utils.R
@@ -0,0 +1,12 @@
+#' @examples
+#' this_data <- tibble(x = 1)
+#' this_data |> check_col("y", hint = "Did you forget something?")
+#' @noRd
+check_col <- function(data, col, hint = NULL) {
+  if (!hasName(data, col)) {
+    label <- deparse(substitute(data))
+    abort(c(glue("`{label}` must have the column `{col}`."), i = hint))
+  }
+
+  invisible(data)
+}
diff --git a/man/add_benchmark_tr_score.Rd b/man/add_benchmark_tr_score.Rd
index 77621bb..6125e35 100644
--- a/man/add_benchmark_tr_score.Rd
+++ b/man/add_benchmark_tr_score.Rd
@@ -13,9 +13,9 @@ add_benchmark_tr_score(
 \arguments{
 \item{data}{Dataframe.}
 
-\item{profile_ranking}{Dataframe column.}
+\item{col_ranking}{Dataframe column.}
 
-\item{reduction_targets}{Dataframe column.}
+\item{col_target}{Dataframe column.}
 }
 \description{
 Calulate \code{benchmark_tr_score} column
diff --git a/man/add_transition_risk_category.Rd b/man/add_transition_risk_category_at_product_level.Rd
similarity index 68%
rename from man/add_transition_risk_category.Rd
rename to man/add_transition_risk_category_at_product_level.Rd
index 48d7472..fd2e839 100644
--- a/man/add_transition_risk_category.Rd
+++ b/man/add_transition_risk_category_at_product_level.Rd
@@ -1,10 +1,10 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/add_transition_risk_category.R
-\name{add_transition_risk_category}
-\alias{add_transition_risk_category}
+% Please edit documentation in R/add_transition_risk_category_at_product_level.R
+\name{add_transition_risk_category_at_product_level}
+\alias{add_transition_risk_category_at_product_level}
 \title{Adds transition risk categories for transition risk scores}
 \usage{
-add_transition_risk_category(data)
+add_transition_risk_category_at_product_level(data)
 }
 \arguments{
 \item{data}{A dataframe}
@@ -31,7 +31,7 @@ transition_risk_thresholds <- add_thresholds_transition_risk(
   scenarios
 )
 
-output <- add_transition_risk_category(transition_risk_thresholds)
+output <- add_transition_risk_category_at_product_level(transition_risk_thresholds)
 output
 }
 \keyword{internal}
diff --git a/man/add_transition_risk_score.Rd b/man/add_transition_risk_score.Rd
index 0c6671f..fe6c0ce 100644
--- a/man/add_transition_risk_score.Rd
+++ b/man/add_transition_risk_score.Rd
@@ -13,9 +13,9 @@ add_transition_risk_score(
 \arguments{
 \item{data}{Dataframe.}
 
-\item{profile_ranking}{Dataframe column.}
+\item{col_ranking}{Dataframe column.}
 
-\item{reduction_targets}{Dataframe column.}
+\item{col_target}{Dataframe column.}
 }
 \description{
 Calulate \code{transition_risk_score} column
diff --git a/man/best_case_worst_case_transition_risk_profile.Rd b/man/best_case_worst_case_transition_risk_profile.Rd
new file mode 100644
index 0000000..371bac4
--- /dev/null
+++ b/man/best_case_worst_case_transition_risk_profile.Rd
@@ -0,0 +1,18 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/best_case_worst_case_transition_risk_profile.R
+\name{best_case_worst_case_transition_risk_profile}
+\alias{best_case_worst_case_transition_risk_profile}
+\title{Calculates best case and worst case for tranistion risk profile at product level}
+\usage{
+best_case_worst_case_transition_risk_profile(data)
+}
+\arguments{
+\item{data}{Dataframe. Transition risk profile product level output}
+}
+\value{
+A dataframe
+}
+\description{
+Calculates best case and worst case for tranistion risk profile at product level
+}
+\keyword{internal}
diff --git a/man/example_emissions_profile_at_product_level.Rd b/man/example_emissions_profile_at_product_level.Rd
new file mode 100644
index 0000000..475a211
--- /dev/null
+++ b/man/example_emissions_profile_at_product_level.Rd
@@ -0,0 +1,22 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/example_data.R
+\name{example_emissions_profile_at_product_level}
+\alias{example_emissions_profile_at_product_level}
+\alias{example_sector_profile_at_product_level}
+\title{Example input datasets for Transition Risk Score}
+\usage{
+example_emissions_profile_at_product_level()
+
+example_sector_profile_at_product_level()
+}
+\value{
+A dataframe.
+}
+\description{
+Example input datasets for Transition Risk Score
+}
+\examples{
+example_emissions_profile_at_product_level()
+example_sector_profile_at_product_level()
+}
+\keyword{internal}
diff --git a/man/exclude_cols_then_pivot_wider.Rd b/man/exclude_cols_then_pivot_wider.Rd
new file mode 100644
index 0000000..754dd1e
--- /dev/null
+++ b/man/exclude_cols_then_pivot_wider.Rd
@@ -0,0 +1,85 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/exclude_cols_then_pivot_wider.R
+\name{exclude_cols_then_pivot_wider}
+\alias{exclude_cols_then_pivot_wider}
+\title{Excluding irrelevant columns and duplicates, then pivot from long to wide}
+\usage{
+exclude_cols_then_pivot_wider(
+  data,
+  ...,
+  exclude_cols = NULL,
+  avoid_list_cols = FALSE
+)
+}
+\arguments{
+\item{data}{A data frame to pivot.}
+
+\item{...}{Arguments passed to \code{\link[tidyr:pivot_wider]{tidyr::pivot_wider()}}.}
+
+\item{exclude_cols}{A character vector giving regular expressions matching
+column names to exclude. If lengh > 1, the union is taken.}
+
+\item{avoid_list_cols}{Logical. Avoid all list-columns, duplicates, and the
+associated warning?}
+}
+\value{
+A data frame giving the result you get from \code{\link[tidyr:pivot_wider]{tidyr::pivot_wider()}} if
+\code{data} lacks the excluded columns and the resulting duplicates.
+}
+\description{
+Excluding irrelevant columns and duplicates, then pivot from long to wide
+}
+\examples{
+library(tidyr, warn.conflicts = FALSE)
+library(dplyr, warn.conflicts = FALSE)
+
+# styler: off
+data <- tribble(
+  ~to_exclude,  ~id, ~name,  ~value,
+            1, "id",   "a",       1,
+            2, "id",   "a",       1,
+            1, "id",   "b",       2,
+            2, "id",   "b",       2,
+)
+# styler: on
+
+# `exclude_cols_then_pivot_wider()` excludes columns and duplicates
+data |> exclude_cols_then_pivot_wider(exclude_cols = "exclude")
+
+# Why is this useful?
+# `pivot_wider()` defaults to using all columns
+data |> pivot_wider()
+
+# You may specify relevant columns but the result may still have duplicates
+data |>
+  pivot_wider(id_cols = id, names_from = "name", values_from = "value") |>
+  unnest(c(a, b))
+
+# styler: off
+data <- tribble(
+   ~id, ~name, ~value, ~to_exclude, ~yields_duplicates,
+  "id",   "a",      1,           1,                  1,
+  "id",   "a",      1,           2,                  2
+)
+# styler: on
+
+# `data` may have columns that yield duplicates and thus list-columns
+with_list_cols <- exclude_cols_then_pivot_wider(
+  data,
+  exclude_cols = "to_exclude",
+  id_cols = "id"
+)
+# You can handle it after the fact
+with_list_cols |>
+  tidyr::unnest(everything()) |>
+  distinct()
+
+# But also you can avoid it with `avoid_list_cols = TRUE`
+exclude_cols_then_pivot_wider(
+  data,
+  exclude_cols = "to_exclude",
+  id_cols = "id",
+  avoid_list_cols = TRUE
+)
+}
+\keyword{internal}
diff --git a/man/pivot_wider_transition_risk_profile.Rd b/man/pivot_wider_transition_risk_profile.Rd
new file mode 100644
index 0000000..f160b69
--- /dev/null
+++ b/man/pivot_wider_transition_risk_profile.Rd
@@ -0,0 +1,77 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/pivot_wider_transition_risk_profile.R
+\name{pivot_wider_transition_risk_profile}
+\alias{pivot_wider_transition_risk_profile}
+\title{Pivot company-level columns to wide format for indicator "transition risk profile"}
+\usage{
+pivot_wider_transition_risk_profile(data, include_co2 = FALSE)
+}
+\arguments{
+\item{include_co2}{Logical. Include \verb{co2_*} columns ?}
+}
+\value{
+A Dataframe
+}
+\description{
+Pivot company-level columns to wide format for indicator "transition risk profile"
+}
+\examples{
+\dontrun{
+library(readr, warn.conflicts = FALSE)
+library(dplyr, warn.conflicts = FALSE)
+library(tiltToyData, warn.conflicts = FALSE)
+library(tiltIndicator)
+library(tiltIndicatorAfter)
+
+restore <- options(list(
+  readr.show_col_types = FALSE,
+  tiltIndicatorAfter.output_co2_footprint = TRUE
+))
+
+toy_emissions_profile_products_ecoinvent <- read_csv(toy_emissions_profile_products_ecoinvent())
+toy_emissions_profile_any_companies <- read_csv(toy_emissions_profile_any_companies())
+toy_sector_profile_any_scenarios <- read_csv(toy_sector_profile_any_scenarios())
+toy_sector_profile_companies <- read_csv(toy_sector_profile_companies())
+toy_europages_companies <- read_csv(toy_europages_companies())
+toy_ecoinvent_activities <- read_csv(toy_ecoinvent_activities())
+toy_ecoinvent_europages <- read_csv(toy_ecoinvent_europages())
+toy_ecoinvent_inputs <- read_csv(toy_ecoinvent_inputs())
+toy_isic_name <- read_csv(toy_isic_name())
+toy_all_activities_scenario_sectors <- read_csv(toy_all_activities_scenario_sectors())
+
+toy_emissions_profile <- profile_emissions(
+  companies = toy_emissions_profile_any_companies,
+  co2 = toy_emissions_profile_products_ecoinvent,
+  europages_companies = toy_europages_companies,
+  ecoinvent_activities = toy_ecoinvent_activities,
+  ecoinvent_europages = toy_ecoinvent_europages,
+  isic = toy_isic_name
+)
+
+toy_sector_profile <- profile_sector(
+  companies = toy_sector_profile_companies,
+  scenarios = toy_sector_profile_any_scenarios,
+  europages_companies = toy_europages_companies,
+  ecoinvent_activities = toy_ecoinvent_activities,
+  ecoinvent_europages = toy_ecoinvent_europages,
+  isic = toy_isic_name
+)
+
+wide_format <- transition_risk_profile_impl(
+  emissions_profile,
+  sector_profile,
+  co2,
+  all_activities_scenario_sectors,
+  scenarios
+) |>
+  add_transition_risk_category_at_company_level() |>
+  best_case_worst_case_transition_risk_profile_at_company_level() |>
+  pivot_wider_transition_risk_profile(include_co2 = TRUE) |>
+  unnest_company()
+wide_format
+
+# Cleanup
+options(restore)
+}
+}
+\keyword{internal}
diff --git a/man/score_transition_risk.Rd b/man/score_transition_risk.Rd
new file mode 100644
index 0000000..a409946
--- /dev/null
+++ b/man/score_transition_risk.Rd
@@ -0,0 +1,73 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/score_transition_risk.R
+\name{score_transition_risk}
+\alias{score_transition_risk}
+\title{Transition Risk Score}
+\usage{
+score_transition_risk(
+  emissions_profile_at_product_level,
+  sector_profile_at_product_level
+)
+}
+\arguments{
+\item{emissions_profile_at_product_level}{Dataframe. Emissions profile product level output}
+
+\item{sector_profile_at_product_level}{Dataframe. Sector profile product level output}
+}
+\value{
+A dataframe
+}
+\description{
+Calulate Transition Risk Score at product level and company level
+}
+\examples{
+library(dplyr)
+library(readr, warn.conflicts = FALSE)
+library(tiltToyData)
+library(tiltIndicator)
+library(tiltIndicatorAfter)
+
+restore <- options(readr.show_col_types = FALSE)
+
+emissions_companies <- read_csv(toy_emissions_profile_any_companies())
+products <- read_csv(toy_emissions_profile_products_ecoinvent())
+europages_companies <- read_csv(toy_europages_companies())
+ecoinvent_activities <- read_csv(toy_ecoinvent_activities())
+ecoinvent_europages <- read_csv(toy_ecoinvent_europages())
+isic_name <- read_csv(toy_isic_name())
+
+emissions_profile_at_product_level <- profile_emissions(
+  companies = emissions_companies,
+  co2 = products,
+  europages_companies = europages_companies,
+  ecoinvent_activities = ecoinvent_activities,
+  ecoinvent_europages = ecoinvent_europages,
+  isic = isic_name
+) |> unnest_product()
+
+sector_companies <- read_csv(toy_sector_profile_companies())
+scenarios <- read_csv(toy_sector_profile_any_scenarios())
+
+sector_profile_at_product_level <- profile_sector(
+  companies = sector_companies,
+  scenarios = scenarios,
+  europages_companies = europages_companies,
+  ecoinvent_activities = ecoinvent_activities,
+  ecoinvent_europages = ecoinvent_europages,
+  isic = isic_name
+) |> unnest_product()
+
+result <- score_transition_risk(emissions_profile_at_product_level, sector_profile_at_product_level)
+
+result |> unnest_product()
+
+result |> unnest_company()
+
+# Cleanup
+options(restore)
+}
+\seealso{
+Other top-level functions: 
+\code{\link{transition_risk_profile}()}
+}
+\concept{top-level functions}
diff --git a/man/score_transition_risk_and_polish.Rd b/man/score_transition_risk_and_polish.Rd
new file mode 100644
index 0000000..cc0843c
--- /dev/null
+++ b/man/score_transition_risk_and_polish.Rd
@@ -0,0 +1,82 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/score_transition_risk_and_polish.R
+\name{score_transition_risk_and_polish}
+\alias{score_transition_risk_and_polish}
+\title{Add transition risk score and polish the output for delivery}
+\usage{
+score_transition_risk_and_polish(
+  emissions_profile,
+  sector_profile,
+  include_co2 = FALSE
+)
+}
+\arguments{
+\item{emissions_profile}{Nested data frame. The output of
+\code{profile_emissions()}.}
+
+\item{sector_profile}{Nested data frame. The output of \code{profile_sector()}.}
+
+\item{include_co2}{Logical. Include \verb{co2_*} columns ?}
+}
+\value{
+A data frame with the column \code{companies_id}, and the nested
+columns\code{product} and \code{company} holding the outputs at product and company
+level.
+}
+\description{
+Add transition risk score and polish the output for delivery
+}
+\examples{
+library(readr, warn.conflicts = FALSE)
+library(dplyr, warn.conflicts = FALSE)
+library(tiltToyData, warn.conflicts = FALSE)
+library(tiltIndicator)
+library(tiltIndicatorAfter)
+
+set.seed(123)
+restore <- options(list(
+  readr.show_col_types = FALSE,
+  tiltIndicatorAfter.output_co2_footprint = TRUE
+))
+
+toy_emissions_profile_products_ecoinvent <- read_csv(toy_emissions_profile_products_ecoinvent())
+toy_emissions_profile_any_companies <- read_csv(toy_emissions_profile_any_companies())
+toy_sector_profile_any_scenarios <- read_csv(toy_sector_profile_any_scenarios())
+toy_sector_profile_companies <- read_csv(toy_sector_profile_companies())
+toy_europages_companies <- read_csv(toy_europages_companies())
+toy_ecoinvent_activities <- read_csv(toy_ecoinvent_activities())
+toy_ecoinvent_europages <- read_csv(toy_ecoinvent_europages())
+toy_ecoinvent_inputs <- read_csv(toy_ecoinvent_inputs())
+toy_isic_name <- read_csv(toy_isic_name())
+
+emissions_profile <- profile_emissions(
+  companies = toy_emissions_profile_any_companies,
+  co2 = toy_emissions_profile_products_ecoinvent,
+  europages_companies = toy_europages_companies,
+  ecoinvent_activities = toy_ecoinvent_activities,
+  ecoinvent_europages = toy_ecoinvent_europages,
+  isic = toy_isic_name
+)
+
+sector_profile <- profile_sector(
+  companies = toy_sector_profile_companies,
+  scenarios = toy_sector_profile_any_scenarios,
+  europages_companies = toy_europages_companies,
+  ecoinvent_activities = toy_ecoinvent_activities,
+  ecoinvent_europages = toy_ecoinvent_europages,
+  isic = toy_isic_name
+)
+
+result <- score_transition_risk_and_polish(emissions_profile,
+  sector_profile,
+  include_co2 = TRUE
+)
+
+result |> unnest_product()
+
+result |> unnest_company()
+
+# Cleanup
+options(restore)
+}
+\keyword{internal}
diff --git a/man/tiltIndicatorAfter_options.Rd b/man/tiltIndicatorAfter_options.Rd
new file mode 100644
index 0000000..66f63c9
--- /dev/null
+++ b/man/tiltIndicatorAfter_options.Rd
@@ -0,0 +1,59 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/tiltTransitionRisk-options.R
+\name{tiltIndicatorAfter_options}
+\alias{tiltIndicatorAfter_options}
+\title{tiltTransitionRisk options}
+\description{
+These options are meant to be used mainly by developers or analysts while
+testing the code or creating data:
+\itemize{
+\item \code{tiltIndicatorAfter.output_co2_footprint}:
+\itemize{
+\item At product level it outputs licensed column \code{co2_footprint}.
+\item At company level it outputs the column \code{co2_avg} (average \code{co2_footprint}
+by \code{companies_id}).
+}
+\item \code{tiltIndicatorAfter.verbose}: Controls verbosity.
+}
+}
+\examples{
+library(readr, warn.conflicts = FALSE)
+library(dplyr, warn.conflicts = FALSE)
+library(withr)
+library(tiltToyData)
+library(tiltIndicator)
+library(tiltIndicatorAfter)
+
+set.seed(1)
+
+restore <- options(list(
+  readr.show_col_types = FALSE,
+  tiltIndicatorAfter.verbose = TRUE,
+  tiltIndicatorAfter.output_co2_footprint = TRUE
+))
+
+companies <- read_csv(toy_emissions_profile_any_companies())
+products <- read_csv(toy_emissions_profile_products_ecoinvent())
+europages_companies <- read_csv(toy_europages_companies())
+ecoinvent_activities <- read_csv(toy_ecoinvent_activities())
+ecoinvent_europages <- read_csv(toy_ecoinvent_europages())
+isic_name <- read_csv(toy_isic_name())
+
+result <- profile_emissions(
+  companies,
+  products,
+  europages_companies = europages_companies,
+  ecoinvent_activities = ecoinvent_activities,
+  ecoinvent_europages = ecoinvent_europages,
+  isic = isic_name
+)
+
+result |>
+  unnest_product() |>
+  select(matches(c("co2")))
+
+result |>
+  unnest_company() |>
+  select(matches(c("co2")))
+}
+\keyword{internal}
diff --git a/man/tiltTransitionRisk-package.Rd b/man/tiltTransitionRisk-package.Rd
index 45d055a..8472357 100644
--- a/man/tiltTransitionRisk-package.Rd
+++ b/man/tiltTransitionRisk-package.Rd
@@ -18,7 +18,19 @@ Useful links:
 
 }
 \author{
-\strong{Maintainer}: Mauro Lepore \email{maurolepore@gmail.com} (\href{https://orcid.org/0000-0002-1986-7988}{ORCID})
+\strong{Maintainer}: Kalash Singhal \email{kalash@2degrees-investing.org}
+
+Authors:
+\itemize{
+  \item Mauro Lepore \email{maurolepore@gmail.com} (\href{https://orcid.org/0000-0002-1986-7988}{ORCID})
+  \item Anne Schoenauer \email{anne.schoenauer@2degrees-investing.org} (\href{https://orcid.org/0000-0002-4576-8799}{ORCID})
+  \item Tilman Trompke \email{tilman@2degrees-investing.org} (\href{https://orcid.org/0009-0003-3351-5131}{ORCID})
+}
+
+Other contributors:
+\itemize{
+  \item 2 Degrees Investing Initiative \email{contact@2degrees-investing.org} [copyright holder, funder]
+}
 
 }
 \keyword{internal}
diff --git a/man/transition_risk_profile.Rd b/man/transition_risk_profile.Rd
new file mode 100644
index 0000000..aad59f5
--- /dev/null
+++ b/man/transition_risk_profile.Rd
@@ -0,0 +1,99 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/transition_risk_profile.R
+\name{transition_risk_profile}
+\alias{transition_risk_profile}
+\title{Calculate the indicator "transition risk profile"}
+\usage{
+transition_risk_profile(
+  emissions_profile,
+  sector_profile,
+  co2,
+  all_activities_scenario_sectors,
+  scenarios,
+  for_webtool = FALSE
+)
+}
+\arguments{
+\item{emissions_profile}{Nested data frame. The output of
+\code{profile_emissions()}.}
+
+\item{sector_profile}{Nested data frame. The output of \code{profile_sector()}.}
+
+\item{co2}{A dataframe}
+
+\item{all_activities_scenario_sectors}{A dataframe}
+
+\item{scenarios}{A dataframe}
+
+\item{for_webtool}{Logical. Is it output for webtool or not?}
+}
+\value{
+A data frame with the column \code{companies_id}, and the nested
+columns\code{product} and \code{company} holding the outputs at product and company
+level.
+}
+\description{
+Adds the risk classification to calculated transition risk scores from
+emission profile and sector profile indicator.
+}
+\examples{
+library(readr, warn.conflicts = FALSE)
+library(dplyr, warn.conflicts = FALSE)
+library(tiltToyData, warn.conflicts = FALSE)
+library(tiltIndicator)
+library(tiltIndicatorAfter)
+
+set.seed(123)
+restore <- options(list(
+  readr.show_col_types = FALSE,
+  tiltIndicatorAfter.output_co2_footprint = TRUE
+))
+
+toy_emissions_profile_products_ecoinvent <- read_csv(toy_emissions_profile_products_ecoinvent())
+toy_emissions_profile_any_companies <- read_csv(toy_emissions_profile_any_companies())
+toy_sector_profile_any_scenarios <- read_csv(toy_sector_profile_any_scenarios())
+toy_sector_profile_companies <- read_csv(toy_sector_profile_companies())
+toy_europages_companies <- read_csv(toy_europages_companies())
+toy_ecoinvent_activities <- read_csv(toy_ecoinvent_activities())
+toy_ecoinvent_europages <- read_csv(toy_ecoinvent_europages())
+toy_ecoinvent_inputs <- read_csv(toy_ecoinvent_inputs())
+toy_isic_name <- read_csv(toy_isic_name())
+toy_all_activities_scenario_sectors <- read_csv(toy_all_activities_scenario_sectors())
+
+toy_emissions_profile <- profile_emissions(
+  companies = toy_emissions_profile_any_companies,
+  co2 = toy_emissions_profile_products_ecoinvent,
+  europages_companies = toy_europages_companies,
+  ecoinvent_activities = toy_ecoinvent_activities,
+  ecoinvent_europages = toy_ecoinvent_europages,
+  isic = toy_isic_name
+)
+
+toy_sector_profile <- profile_sector(
+  companies = toy_sector_profile_companies,
+  scenarios = toy_sector_profile_any_scenarios,
+  europages_companies = toy_europages_companies,
+  ecoinvent_activities = toy_ecoinvent_activities,
+  ecoinvent_europages = toy_ecoinvent_europages,
+  isic = toy_isic_name
+)
+
+output <- transition_risk_profile(
+  emissions_profile = toy_emissions_profile,
+  sector_profile = toy_sector_profile,
+  co2 = toy_emissions_profile_products_ecoinvent,
+  all_activities_scenario_sectors = toy_all_activities_scenario_sectors,
+  scenarios = toy_sector_profile_any_scenarios,
+  for_webtool = FALSE
+)
+
+output |> unnest_product()
+
+output |> unnest_company()
+}
+\seealso{
+Other top-level functions: 
+\code{\link{score_transition_risk}()}
+}
+\concept{profile functions}
+\concept{top-level functions}
diff --git a/tests/testthat/_snaps/exclude_cols_then_pivot_wider.md b/tests/testthat/_snaps/exclude_cols_then_pivot_wider.md
new file mode 100644
index 0000000..504d67b
--- /dev/null
+++ b/tests/testthat/_snaps/exclude_cols_then_pivot_wider.md
@@ -0,0 +1,4 @@
+# with `avoid_list_cols` AND `values_fn` errors gracefully
+
+    `values_fn` should not be used when `avoid_list_cols = TRUE`.
+
diff --git a/tests/testthat/test-add_transition_risk_category.R b/tests/testthat/test-add_transition_risk_category_at_product_level.R
similarity index 82%
rename from tests/testthat/test-add_transition_risk_category.R
rename to tests/testthat/test-add_transition_risk_category_at_product_level.R
index 541c715..59f0694 100644
--- a/tests/testthat/test-add_transition_risk_category.R
+++ b/tests/testthat/test-add_transition_risk_category_at_product_level.R
@@ -11,15 +11,15 @@ test_that("if input data lacks crucial columns, errors gracefully", {
 
   crucial <- col_transition_risk_score()
   bad <- select(input_data, -all_of(crucial))
-  expect_error(add_transition_risk_category(bad), crucial)
+  expect_error(add_transition_risk_category_at_product_level(bad), crucial)
 
   crucial <- col_tr_low_threshold()
   bad <- select(input_data, -all_of(crucial))
-  expect_error(add_transition_risk_category(bad), crucial)
+  expect_error(add_transition_risk_category_at_product_level(bad), crucial)
 
   crucial <- col_tr_high_threshold()
   bad <- select(input_data, -all_of(crucial))
-  expect_error(add_transition_risk_category(bad), crucial)
+  expect_error(add_transition_risk_category_at_product_level(bad), crucial)
 })
 
 test_that("if `transition_risk_category` column has only NAs, then class of the column is `character`", {
@@ -34,7 +34,7 @@ test_that("if `transition_risk_category` column has only NAs, then class of the
     all_activities_scenario_sectors,
     scenarios
   ) |>
-    add_transition_risk_category()
+    add_transition_risk_category_at_product_level()
 
   expected_class <- "character"
   expect_equal(class(output$transition_risk_category), expected_class)
diff --git a/tests/testthat/test-avg_profile_ranking_best_case_worst_case.R b/tests/testthat/test-avg_profile_ranking_best_case_worst_case.R
new file mode 100644
index 0000000..68a211b
--- /dev/null
+++ b/tests/testthat/test-avg_profile_ranking_best_case_worst_case.R
@@ -0,0 +1,94 @@
+test_that("NA best case and worst case for a `benchmark` at produuct level gives NA to `avg_profile_ranking_best_case` and `avg_profile_ranking_worst_case` at company level", {
+  example_data_product <- example_best_case_worst_case_profile_ranking_product_level(
+    benchmark = c("all", "all", "unit"),
+    profile_ranking = c(1.0, 2.0, NA)
+  )
+
+  example_data_company <- example_best_case_worst_case_profile_ranking_company_level()
+  input <- tilt_profile(nest_levels(example_data_product, example_data_company))
+  out <- input |>
+    best_case_worst_case_emission_profile() |>
+    best_case_worst_case_avg_profile_ranking()
+
+  case_product <- out |> unnest_product()
+  case_company <- out |> unnest_company()
+
+  case_product_benchmark <- filter(case_product, benchmark == "unit")
+  # `emissions_profile_best_case` is NA at product level for benchmark `unit`
+  expect_true(is.na(case_product_benchmark$emissions_profile_best_case))
+  # `emissions_profile_worst_case` is NA at product level for benchmark `unit`
+  expect_true(is.na(case_product_benchmark$emissions_profile_worst_case))
+
+  case_company_benchmark <- filter(case_company, benchmark == "unit")
+  # `avg_profile_ranking_best_case` is NA at company level for benchmark `unit`
+  expect_true(is.na(case_company_benchmark$avg_profile_ranking_best_case))
+  # `avg_profile_ranking_worst_case` is NA at company level for benchmark `unit`
+  expect_true(is.na(case_company_benchmark$avg_profile_ranking_worst_case))
+})
+
+test_that("same best case and worst case for two products at produuct level gives only one row of that value to `avg_profile_ranking_best_case` and `avg_profile_ranking_worst_case` at company level", {
+  example_data_product <- example_best_case_worst_case_profile_ranking_product_level(
+    benchmark = c("all", "all", "unit"),
+    profile_ranking = c(1.0, 1.0, NA)
+  )
+
+  example_data_company <- example_best_case_worst_case_profile_ranking_company_level()
+  input <- tilt_profile(nest_levels(example_data_product, example_data_company))
+  out <- input |>
+    best_case_worst_case_emission_profile() |>
+    best_case_worst_case_avg_profile_ranking()
+
+  case_product <- out |> unnest_product()
+  case_company <- out |> unnest_company()
+
+  # `emissions_profile_best_case` has same value for products `one` and `two` for benchmark `all`
+  case_product_benchmark <- distinct(filter(case_product, benchmark == "all"))
+  expected_same_value <- 1
+  expect_equal(filter(case_product_benchmark, ep_product == "one")$emissions_profile_best_case, expected_same_value)
+  expect_equal(filter(case_product_benchmark, ep_product == "two")$emissions_profile_best_case, expected_same_value)
+  # `emissions_profile_worst_case` has same value for products `one` and `two` for benchmark `all`
+  expect_equal(filter(case_product_benchmark, ep_product == "one")$emissions_profile_worst_case, expected_same_value)
+  expect_equal(filter(case_product_benchmark, ep_product == "two")$emissions_profile_worst_case, expected_same_value)
+
+  # `avg_profile_ranking_best_case` at company level is assigned one value for two similar `emissions_profile_best_case` of different products
+  expect_true(nrow(filter(case_company, benchmark == "all")) == 1)
+})
+
+test_that("if input to `best_case_worst_case_avg_profile_ranking` lacks crucial columns, errors gracefully", {
+  example_data_product <- example_best_case_worst_case_profile_ranking_product_level()
+  example_data_company <- example_best_case_worst_case_profile_ranking_company_level()
+  case_example_data_product <- tilt_profile(nest_levels(example_data_product, example_data_company)) |>
+    best_case_worst_case_emission_profile() |>
+    unnest_product()
+
+  crucial <- col_companies_id()
+  bad <- select(example_data_product, -all_of(crucial))
+  expect_error(
+    tilt_profile(nest_levels(bad, example_data_company)),
+    crucial
+  )
+
+  crucial <- col_emission_grouped_by()
+  bad <- select(case_example_data_product, -all_of(crucial))
+  bad_product <- tilt_profile(nest_levels(bad, example_data_company))
+  expect_error(
+    best_case_worst_case_avg_profile_ranking(bad_product),
+    crucial
+  )
+
+  crucial <- "emissions_profile_best_case"
+  bad <- select(case_example_data_product, -all_of(crucial))
+  bad_product <- tilt_profile(nest_levels(bad, example_data_company))
+  expect_error(
+    best_case_worst_case_avg_profile_ranking(bad_product),
+    crucial
+  )
+
+  crucial <- "emissions_profile_worst_case"
+  bad <- select(case_example_data_product, -all_of(crucial))
+  bad_product <- tilt_profile(nest_levels(bad, example_data_company))
+  expect_error(
+    best_case_worst_case_avg_profile_ranking(bad_product),
+    crucial
+  )
+})
diff --git a/tests/testthat/test-avg_transition_risk_best_case_worst_case.R b/tests/testthat/test-avg_transition_risk_best_case_worst_case.R
new file mode 100644
index 0000000..f315de4
--- /dev/null
+++ b/tests/testthat/test-avg_transition_risk_best_case_worst_case.R
@@ -0,0 +1,97 @@
+test_that("NA best case and worst case for a `benchmark_tr_score` at produuct level gives NA to `avg_transition_risk_best_case` and `avg_transition_risk_worst_case` at company level", {
+  example_data_product <- example_best_case_worst_case_transition_risk_profile_product_level(
+    benchmark_tr_score = c("1.5C RPS_2030_all", "1.5C RPS_2030_all", "1.5C RPS_2050_all"),
+    transition_risk_score = c(1.0, 2.0, NA)
+  ) |>
+    best_case_worst_case_transition_risk_profile() |>
+    polish_best_case_worst_case() |>
+    polish_best_case_worst_case_transition_risk_profile()
+
+  example_data_company <- example_best_case_worst_case_transition_risk_profile_company_level()
+  input <- tilt_profile(nest_levels(example_data_product, example_data_company))
+  out <- best_case_worst_case_transition_risk_profile_at_company_level(input)
+
+  case_product <- out |> unnest_product()
+  case_company <- out |> unnest_company()
+
+  case_product_benchmark <- filter(case_product, benchmark_tr_score == "1.5C RPS_2050_all")
+  # `transition_risk_profile_best_case` is NA at product level for benchmark_tr_score `1.5C RPS_2050_all`
+  expect_true(is.na(case_product_benchmark$transition_risk_profile_best_case))
+  # `transition_risk_profile_worst_case` is NA at product level for benchmark_tr_score `1.5C RPS_2050_all`
+  expect_true(is.na(case_product_benchmark$transition_risk_profile_worst_case))
+
+  case_company_benchmark <- filter(case_company, benchmark_tr_score_avg == "1.5C RPS_2050_all")
+  # `avg_transition_risk_best_case` is NA at company level for benchmark_tr_score `1.5C RPS_2050_all`
+  expect_true(is.na(case_company_benchmark$avg_transition_risk_best_case))
+  # `avg_transition_risk_worst_case` is NA at company level for benchmark_tr_score `1.5C RPS_2050_all`
+  expect_true(is.na(case_company_benchmark$avg_transition_risk_worst_case))
+})
+
+test_that("same best case and worst case for two products at produuct level gives only one row of that value to `avg_transition_risk_best_case` and `avg_transition_risk_worst_case` at company level", {
+  example_data_product <- example_best_case_worst_case_transition_risk_profile_product_level(
+    benchmark_tr_score = c("1.5C RPS_2030_all", "1.5C RPS_2030_all", "1.5C RPS_2050_all"),
+    transition_risk_score = c(1.0, 1.0, NA)
+  ) |>
+    best_case_worst_case_transition_risk_profile() |>
+    polish_best_case_worst_case() |>
+    polish_best_case_worst_case_transition_risk_profile()
+
+  example_data_company <- example_best_case_worst_case_transition_risk_profile_company_level()
+  input <- tilt_profile(nest_levels(example_data_product, example_data_company))
+  out <- best_case_worst_case_transition_risk_profile_at_company_level(input)
+
+  case_product <- out |> unnest_product()
+  case_company <- out |> unnest_company()
+
+  # `transition_risk_profile_best_case` has same value for products `one` and `two` for benchmark_tr_score `1.5C RPS_2030_all`
+  case_product_benchmark <- distinct(filter(case_product, benchmark_tr_score == "1.5C RPS_2030_all"))
+  expected_same_value <- 1
+  expect_equal(filter(case_product_benchmark, ep_product == "one")$transition_risk_profile_best_case, expected_same_value)
+  expect_equal(filter(case_product_benchmark, ep_product == "two")$transition_risk_profile_best_case, expected_same_value)
+  # `transition_risk_profile_worst_case` has same value for products `one` and `two` for benchmark_tr_score `1.5C RPS_2030_all`
+  expect_equal(filter(case_product_benchmark, ep_product == "one")$transition_risk_profile_worst_case, expected_same_value)
+  expect_equal(filter(case_product_benchmark, ep_product == "two")$transition_risk_profile_worst_case, expected_same_value)
+
+  # `avg_transition_risk_best_case` at company level is assigned one value for two similar `transition_risk_profile_best_case` of different products
+  expect_true(nrow(filter(case_company, benchmark_tr_score_avg == "1.5C RPS_2030_all")) == 1)
+})
+
+test_that("if input to `best_case_worst_case_transition_risk_profile_at_company_level` lacks crucial columns, errors gracefully", {
+  example_data_product <- example_best_case_worst_case_transition_risk_profile_product_level() |>
+    best_case_worst_case_transition_risk_profile() |>
+    polish_best_case_worst_case() |>
+    polish_best_case_worst_case_transition_risk_profile()
+
+  example_data_company <- example_best_case_worst_case_transition_risk_profile_company_level()
+
+  crucial <- col_companies_id()
+  bad <- select(example_data_product, -all_of(crucial))
+  expect_error(
+    tilt_profile(nest_levels(bad, example_data_company)),
+    crucial
+  )
+
+  crucial <- col_transition_risk_grouped_by()
+  bad <- select(example_data_product, -all_of(crucial))
+  bad_product <- tilt_profile(nest_levels(bad, example_data_company))
+  expect_error(
+    best_case_worst_case_transition_risk_profile_at_company_level(bad_product),
+    crucial
+  )
+
+  crucial <- "transition_risk_profile_best_case"
+  bad <- select(example_data_product, -all_of(crucial))
+  bad_product <- tilt_profile(nest_levels(bad, example_data_company))
+  expect_error(
+    best_case_worst_case_transition_risk_profile_at_company_level(bad_product),
+    crucial
+  )
+
+  crucial <- "transition_risk_profile_worst_case"
+  bad <- select(example_data_product, -all_of(crucial))
+  bad_product <- tilt_profile(nest_levels(bad, example_data_company))
+  expect_error(
+    best_case_worst_case_transition_risk_profile_at_company_level(bad_product),
+    crucial
+  )
+})
diff --git a/tests/testthat/test-best_case_worst_case_avg_reduction_targets.R b/tests/testthat/test-best_case_worst_case_avg_reduction_targets.R
new file mode 100644
index 0000000..3f69061
--- /dev/null
+++ b/tests/testthat/test-best_case_worst_case_avg_reduction_targets.R
@@ -0,0 +1,107 @@
+test_that("NA best case and worst case for a `scenario-year` at produuct level gives NA to `avg_reduction_targets_best_case` and `avg_reduction_targets_worst_case` at company level", {
+  example_data_product <- example_best_case_worst_case_reduction_targets_product_level(
+    scenario = c("1.5C RPS", "1.5C RPS", "1.5C RPS"),
+    year = c(2030, 2030, 2050),
+    reduction_targets = c(1.0, 2.0, NA)
+  )
+
+  example_data_company <- example_best_case_worst_case_reduction_targets_company_level()
+  input <- tilt_profile(nest_levels(example_data_product, example_data_company))
+  out <- input |>
+    best_case_worst_case_sector_profile() |>
+    best_case_worst_case_avg_reduction_targets()
+
+  case_product <- out |> unnest_product()
+  case_company <- out |> unnest_company()
+
+  case_product_benchmark <- filter(case_product, scenario == "1.5C RPS", year == 2050)
+  # `sector_profile_best_case` is NA at product level for scenario `1.5C RPS` and year 2050
+  expect_true(is.na(case_product_benchmark$sector_profile_best_case))
+  # `sector_profile_worst_case` is NA at product level for scenario `1.5C RPS` and year 2050`
+  expect_true(is.na(case_product_benchmark$sector_profile_worst_case))
+
+  case_company_benchmark <- filter(case_company, scenario == "1.5C RPS", year == 2050)
+  # `avg_reduction_targets_best_case` is NA at company level for scenario `1.5C RPS` and year 2050
+  expect_true(is.na(case_company_benchmark$avg_reduction_targets_best_case))
+  # `avg_reduction_targets_worst_case` is NA at company level for scenario `1.5C RPS` and year 2050
+  expect_true(is.na(case_company_benchmark$avg_reduction_targets_worst_case))
+})
+
+test_that("same best case and worst case for two products at product level gives only one row of that value to `avg_reduction_targets_best_case` and `avg_reduction_targets_worst_case` at company level", {
+  example_data_product <- example_best_case_worst_case_reduction_targets_product_level(
+    scenario = c("1.5C RPS", "1.5C RPS", "1.5C RPS"),
+    year = c(2030, 2030, 2050),
+    reduction_targets = c(1.0, 1.0, NA)
+  )
+
+  example_data_company <- example_best_case_worst_case_reduction_targets_company_level()
+  input <- tilt_profile(nest_levels(example_data_product, example_data_company))
+  out <- input |>
+    best_case_worst_case_sector_profile() |>
+    best_case_worst_case_avg_reduction_targets()
+
+  case_product <- out |> unnest_product()
+  case_company <- out |> unnest_company()
+
+  # `sector_profile_best_case` has same value for products `one` and `two` for scenario `1.5C RPS` and year 2030
+  case_product_benchmark <- distinct(filter(case_product, scenario == "1.5C RPS", year == 2030))
+  expected_same_value <- 1
+  expect_equal(filter(case_product_benchmark, ep_product == "one")$sector_profile_best_case, expected_same_value)
+  expect_equal(filter(case_product_benchmark, ep_product == "two")$sector_profile_best_case, expected_same_value)
+  # `sector_profile_worst_case` has same value for products `one` and `two` for scenario `1.5C RPS` and year 2030
+  expect_equal(filter(case_product_benchmark, ep_product == "one")$sector_profile_worst_case, expected_same_value)
+  expect_equal(filter(case_product_benchmark, ep_product == "two")$sector_profile_worst_case, expected_same_value)
+
+  # `avg_reduction_targets_best_case` at company level is assigned one value for two similar `sector_profile_best_case` of different products
+  expect_true(nrow(filter(case_company, scenario == "1.5C RPS", year == 2030)) == 1)
+})
+
+test_that("if input to `best_case_worst_case_avg_profile_ranking` lacks crucial columns, errors gracefully", {
+  example_data_product <- example_best_case_worst_case_reduction_targets_product_level(
+    scenario = c("1.5C RPS", "1.5C RPS", "1.5C RPS"),
+    year = c(2030, 2030, 2050)
+  )
+  example_data_company <- example_best_case_worst_case_reduction_targets_company_level()
+  case_example_data_product <- tilt_profile(nest_levels(example_data_product, example_data_company)) |>
+    best_case_worst_case_sector_profile() |>
+    unnest_product()
+
+  crucial <- col_companies_id()
+  bad <- select(example_data_product, -all_of(crucial))
+  expect_error(
+    tilt_profile(nest_levels(bad, example_data_company)),
+    crucial
+  )
+
+  crucial <- col_scenario()
+  bad <- select(case_example_data_product, -all_of(crucial))
+  bad_product <- tilt_profile(nest_levels(bad, example_data_company))
+  expect_error(
+    best_case_worst_case_avg_reduction_targets(bad_product),
+    crucial
+  )
+
+  crucial <- col_year()
+  bad <- select(case_example_data_product, -all_of(crucial))
+  bad_product <- tilt_profile(nest_levels(bad, example_data_company))
+  expect_error(
+    best_case_worst_case_avg_reduction_targets(bad_product),
+    crucial
+  )
+
+  crucial <- "sector_profile_best_case"
+  bad <- select(case_example_data_product, -all_of(crucial))
+  bad_product <- tilt_profile(nest_levels(bad, example_data_company))
+  expect_error(
+    best_case_worst_case_avg_reduction_targets(bad_product),
+    crucial
+  )
+
+  crucial <- "sector_profile_worst_case"
+  bad <- select(case_example_data_product, -all_of(crucial))
+  bad_product <- tilt_profile(nest_levels(bad, example_data_company))
+  expect_error(
+    best_case_worst_case_avg_reduction_targets(bad_product),
+    crucial
+  )
+})
diff --git a/tests/testthat/test-best_case_worst_case_transition_risk_profile.R b/tests/testthat/test-best_case_worst_case_transition_risk_profile.R
new file mode 100644
index 0000000..78697c1
--- /dev/null
+++ b/tests/testthat/test-best_case_worst_case_transition_risk_profile.R
@@ -0,0 +1,138 @@
+test_that("Three `ep_products` with the same `benchmark_tr_score` but with different `transition_risk_score` will have `best_case` only for the lowest-scored product and have `worst_case` only for the highest-scored product", {
+  example_data <- example_best_case_worst_case_transition_risk_profile_product_level()
+  out <- best_case_worst_case_impl(example_data,
+    col_group_by = col_transition_risk_grouped_by(),
+    col_risk = col_transition_risk_category(),
+    col_rank = "transition_risk_score"
+  )
+
+  only_one_best_case <- 1
+  expect_equal(nrow(filter(out, best_case == 1.0)), only_one_best_case)
+
+  only_one_worst_case <- 1
+  expect_equal(nrow(filter(out, worst_case == 3.0)), only_one_worst_case)
+
+  # Expected best case for lowest-scored product
+  expected_best_case <- 1
+  expect_equal(filter(out, transition_risk_score == 1.0)$best_case, expected_best_case)
+
+  # Expected worst case for highest-scored product
+  expected_worst_case <- 3
+  expect_equal(filter(out, transition_risk_score == 3.0)$worst_case, expected_worst_case)
+})
+
+test_that("`NA` in `transition_risk_score` for a single product gives `NA` in `best_case` and `worst_case` for that product", {
+  example_data <- example_best_case_worst_case_transition_risk_profile_product_level(
+    transition_risk_score = c(1.0, 2.0, NA_real_)
+  )
+  out <- best_case_worst_case_impl(example_data,
+    col_group_by = col_transition_risk_grouped_by(),
+    col_risk = col_transition_risk_category(),
+    col_rank = "transition_risk_score"
+  )
+
+  # Expected best case for NA in `transition_risk_score`
+  expected_best_case <- NA_real_
+  expect_equal(filter(out, is.na(transition_risk_score))$best_case, expected_best_case)
+
+  # Expected worst case for NA in `transition_risk_score`
+  expected_worst_case <- NA_real_
+  expect_equal(filter(out, is.na(transition_risk_score))$worst_case, expected_worst_case)
+})
+
+test_that("`NA` in `transition_risk_score` for all products gives `NA` in `best_case` and `worst_case` for all products", {
+  example_data <- example_best_case_worst_case_transition_risk_profile_product_level(
+    transition_risk_score = c(NA_real_, NA_real_, NA_real_)
+  )
+  out <- best_case_worst_case_impl(example_data,
+    col_group_by = col_transition_risk_grouped_by(),
+    col_risk = col_transition_risk_category(),
+    col_rank = "transition_risk_score"
+  )
+
+  # Expected best case for NA in `transition_risk_score`
+  expected_best_case <- NA_real_
+  expect_equal(unique(filter(out, is.na(transition_risk_score))$best_case), expected_best_case)
+
+  # Expected worst case for NA in `transition_risk_score`
+  expected_worst_case <- NA_real_
+  expect_equal(unique(filter(out, is.na(transition_risk_score))$worst_case), expected_worst_case)
+})
+
+test_that("gives `NA` in `equal_weight` if a company has missing `ep_product`", {
+  example_data <- example_best_case_worst_case_transition_risk_profile_product_level(
+    transition_risk_category = NA_character_,
+    ep_product = NA_character_
+  )
+  out <- best_case_worst_case_impl(example_data,
+    col_group_by = col_transition_risk_grouped_by(),
+    col_risk = col_transition_risk_category(),
+    col_rank = "transition_risk_score"
+  )
+
+  expect_true(all(is.na(out$equal_weight)))
+})
+
+test_that("if input to `best_case_worst_case_impl` lacks crucial columns, errors gracefully", {
+  example_data <- example_best_case_worst_case_transition_risk_profile_product_level()
+
+  crucial <- col_companies_id()
+  bad <- select(example_data, -all_of(crucial))
+  expect_error(best_case_worst_case_impl(bad,
+    col_group_by = col_transition_risk_grouped_by(),
+    col_risk = col_transition_risk_category(),
+    col_rank = "transition_risk_score"
+  ), crucial)
+
+  crucial <- col_europages_product()
+  bad <- select(example_data, -all_of(crucial))
+  expect_error(best_case_worst_case_impl(bad,
+    col_group_by = col_transition_risk_grouped_by(),
+    col_risk = col_transition_risk_category(),
+    col_rank = "transition_risk_score"
+  ), crucial)
+
+  crucial <- col_transition_risk_grouped_by()
+  bad <- select(example_data, -all_of(crucial))
+  expect_error(best_case_worst_case_impl(bad,
+    col_group_by = col_transition_risk_grouped_by(),
+    col_risk = col_transition_risk_category(),
+    col_rank = "transition_risk_score"
+  ), crucial)
+
+  crucial <- col_transition_risk_category()
+  bad <- select(example_data, -all_of(crucial))
+  expect_error(best_case_worst_case_impl(bad,
+    col_group_by = col_transition_risk_grouped_by(),
+    col_risk = col_transition_risk_category(),
+    col_rank = "transition_risk_score"
+  ), crucial)
+})
+
+test_that("`equal_weight` does not count unmatched `ep_product` after grouping by `companies_id` and `benchmark_tr_score`", {
+  example_data <- example_best_case_worst_case_transition_risk_profile_product_level(
+    companies_id = c("any", "any", "any", "any", "any"),
+    ep_product = c("one", "two", "three", "four", "five"),
+    benchmark_tr_score = c("1.5C RPS_2030_all", "1.5C RPS_2030_all", "1.5C RPS_2030_all", "1.5C RPS_2050_all", NA_character_),
+    transition_risk_category = c("low", "medium", NA_character_, "low", NA_character_),
+    transition_risk_score = c(1.0, 2.0, 3.0, 4.0, 5.0)
+  )
+
+  out <- best_case_worst_case_impl(example_data,
+    col_group_by = col_transition_risk_grouped_by(),
+    col_risk = col_transition_risk_category(),
+    col_rank = "transition_risk_score"
+  )
+
+  out_1.5C_RPS_2030_all <- filter(out, benchmark_tr_score == "1.5C RPS_2030_all")
+  expected_equal_weight_1.5C_RPS_2030_all <- 0.5
+  expect_equal(unique(out_1.5C_RPS_2030_all$equal_weight), expected_equal_weight_1.5C_RPS_2030_all)
+
+  out_1.5C_RPS_2050_all <- filter(out, benchmark_tr_score == "1.5C RPS_2050_all")
+  expected_equal_weight_1.5C_RPS_2050_all <- 1.0
+  expect_equal(unique(out_1.5C_RPS_2050_all$equal_weight), expected_equal_weight_1.5C_RPS_2050_all)
+
+  out_NA <- filter(out, is.na(benchmark_tr_score))
+  expected_equal_weight_NA <- NA_real_
+  expect_equal(unique(out_NA$equal_weight), expected_equal_weight_NA)
+})
diff --git a/tests/testthat/test-coefficient_of_variation.R b/tests/testthat/test-coefficient_of_variation.R
new file mode 100644
index 0000000..471dc1c
--- /dev/null
+++ b/tests/testthat/test-coefficient_of_variation.R
@@ -0,0 +1,107 @@
+test_that("`add_coefficient_of_variation_transition_risk()` outputs correct `cov_transition_risk`", {
+  transition_risk_input <- tibble(
+    avg_transition_risk_equal_weight = c(1, 2, 5, NA_real_),
+    avg_transition_risk_best_case = c(1, 3, 7, NA_real_),
+    avg_transition_risk_worst_case = c(1, 4, 9, NA_real_)
+  )
+
+  out <- add_coefficient_of_variation_transition_risk(transition_risk_input)
+
+  check_no_dispersion <- filter(out, avg_transition_risk_equal_weight == 1.0)
+  expected_coefficient_of_variation <- 0
+  expect_true(check_no_dispersion$cov_transition_risk ==
+    expected_coefficient_of_variation)
+
+  check_na <- filter(out, is.na(avg_transition_risk_equal_weight))
+  expect_true(is.na(check_na$cov_transition_risk))
+})
+
+test_that("`add_coefficient_of_variation_emission_rank()` outputs correct `cov_emission_rank`", {
+  emission_rank_input <- tibble(
+    profile_ranking_avg = c(1, 2, 5, NA_real_),
+    avg_profile_ranking_best_case = c(1, 3, 7, NA_real_),
+    avg_profile_ranking_worst_case = c(1, 4, 9, NA_real_)
+  )
+
+  out <- add_coefficient_of_variation_emission_rank(emission_rank_input)
+
+  check_no_dispersion <- filter(out, profile_ranking_avg == 1.0)
+  expected_coefficient_of_variation <- 0
+  expect_true(check_no_dispersion$cov_emission_rank ==
+                expected_coefficient_of_variation)
+
+  check_na <- filter(out, is.na(profile_ranking_avg))
+  expect_true(is.na(check_na$cov_emission_rank))
+})
+
+test_that("`add_coefficient_of_variation_sector_target()` outputs correct `cov_sector_target`", {
+  sector_target_input <- tibble(
+    reduction_targets_avg = c(1, 2, 5, NA_real_),
+    avg_reduction_targets_best_case = c(1, 3, 7, NA_real_),
+    avg_reduction_targets_worst_case = c(1, 4, 9, NA_real_)
+  )
+
+  out <- add_coefficient_of_variation_sector_target(sector_target_input)
+
+  check_no_dispersion <- filter(out, reduction_targets_avg == 1.0)
+  expected_coefficient_of_variation <- 0
+  expect_true(check_no_dispersion$cov_sector_target ==
+                expected_coefficient_of_variation)
+
+  check_na <- filter(out, is.na(reduction_targets_avg))
+  expect_true(is.na(check_na$cov_sector_target))
+})
+
+test_that("`add_coefficient_of_variation_sector_target()` outputs NA `cov_sector_target` for zero mean", {
+  sector_target_input <- tibble(
+    reduction_targets_avg = -1,
+    avg_reduction_targets_best_case = 1,
+    avg_reduction_targets_worst_case = 0
+  )
+  out <- add_coefficient_of_variation_sector_target(sector_target_input)
+
+  expect_true(is.na(out$cov_sector_target))
+})
+
+test_that("`add_coefficient_of_variation_emission_rank()` outputs NA `cov_emission_rank` for zero mean", {
+  emission_rank_input <- tibble(
+    profile_ranking_avg = -1,
+    avg_profile_ranking_best_case = 1,
+    avg_profile_ranking_worst_case = 0
+  )
+  out <- add_coefficient_of_variation_emission_rank(emission_rank_input)
+
+  expect_true(is.na(out$cov_emission_rank))
+})
+
+test_that("`add_coefficient_of_variation_transition_risk()` outputs NA `cov_transition_risk` for zero mean", {
+  transition_risk_input <- tibble(
+    avg_transition_risk_equal_weight = -1,
+    avg_transition_risk_best_case = 1,
+    avg_transition_risk_worst_case = 0
+  )
+  out <- add_coefficient_of_variation_transition_risk(transition_risk_input)
+
+  expect_true(is.na(out$cov_transition_risk))
+})
+
+test_that("round off to 4 decimal places is required to give correct `cov_transition_risk`", {
+  transition_risk_input <- tibble(
+    avg_transition_risk_equal_weight = c(1.23456789, NA_real_),
+    avg_transition_risk_best_case = c(1.234567, NA_real_),
+    avg_transition_risk_worst_case = c(1.234567, NA_real_)
+  )
+
+  with_round_off <- round_off_to_4_decimal_places_impl(transition_risk_input) |>
+    add_coefficient_of_variation_transition_risk()
+  without_round_off <- add_coefficient_of_variation_transition_risk(transition_risk_input)
+
+  zero_cov <- filter(with_round_off, !is.na(avg_transition_risk_equal_weight))
+  non_zero_cov <- filter(without_round_off, !is.na(avg_transition_risk_equal_weight))
+
+  expected_coefficient_of_variation <- 0.0
+  expect_true(zero_cov$cov_transition_risk ==
+                expected_coefficient_of_variation)
+  expect_false(non_zero_cov$cov_transition_risk ==
+                expected_coefficient_of_variation)
+})
diff --git a/tests/testthat/test-create_risk_categories_at_company_level.R b/tests/testthat/test-create_risk_categories_at_company_level.R
new file mode 100644
index 0000000..42b2491
--- /dev/null
+++ b/tests/testthat/test-create_risk_categories_at_company_level.R
@@ -0,0 +1,31 @@
+test_that("outputs expected columns at company level", {
+  example_data <- example_risk_categories_at_product_level()
+  out <- create_risk_categories_at_company_level(example_data)
+
+  expected <- c(
+    "companies_id", "benchmark_tr_score_avg",
+    "transition_risk_category", "transition_risk_category_share"
+  )
+  expect_equal(names(out)[seq_along(expected)], expected)
+})
+
+test_that("`transition_risk_category_share` sums up to 1 per `benchmark_tr_score_avg`", {
+  example_data <- example_risk_categories_at_product_level()
+  out <- create_risk_categories_at_company_level(example_data)
+
+  sum <- unique(summarise(out,
+    sum = sum(transition_risk_category_share),
+    .by = benchmark_tr_score_avg
+  )$sum)
+  expect_equal(sum, 1)
+})
+
+test_that("outputs NA `transition_risk_category_share` for NA in `benchmark_tr_score` and `transition_risk_category`", {
+  example_data <- example_risk_categories_at_product_level(
+    benchmark_tr_score = NA_character_,
+    transition_risk_category = NA_character_
+  )
+  out <- create_risk_categories_at_company_level(example_data)
+
+  expect_true(is.na(out$transition_risk_category_share))
+})
diff --git a/tests/testthat/test-exclude_cols_then_pivot_wider.R b/tests/testthat/test-exclude_cols_then_pivot_wider.R
new file mode 100644
index 0000000..f49daf6
--- /dev/null
+++ b/tests/testthat/test-exclude_cols_then_pivot_wider.R
@@ -0,0 +1,60 @@
+test_that("excludes columns matching a pattern and leaves no duplicates", {
+  # styler: off
+  data <- tribble(
+    ~to_exclude,  ~id, ~name,  ~value,
+              1, "id",   "a",       1,
+              2, "id",   "a",       1,
+              1, "id",   "b",       2,
+              2, "id",   "b",       2,
+  )
+  # styler: on
+
+  out <- exclude_cols_then_pivot_wider(data, exclude_cols = "exclude")
+  expect_equal(nrow(out), 1L)
+  expect_false(hasName(out, "to_exclude"))
+})
+
+test_that("can avoid list-columns, the warning, and duplicates", {
+  # styler: off
+  data <- tribble(
+    ~to_exclude,  ~id, ~name,  ~value,
+              1, "id",   "a",       1,
+              2, "id",   "a",       1,
+  ) |>
+    mutate(another_col_that_yields_duplicates = to_exclude)
+  # styler: on
+
+
+  expect_no_warning({
+    out <- exclude_cols_then_pivot_wider(
+      data,
+      exclude_cols = "to_exclude",
+      id_cols = "id",
+      avoid_list_cols = TRUE
+    )
+  })
+
+  expect_type(out$a, "double")
+  expect_false(any(duplicated(out)))
+})
+
+test_that("with `avoid_list_cols` AND `values_fn` errors gracefully", {
+  # styler: off
+  data <- tribble(
+    ~to_exclude,  ~id, ~name,  ~value,
+              1, "id",   "a",       1,
+              2, "id",   "a",       1,
+  ) |>
+    mutate(another_col_that_yields_duplicates = to_exclude)
+  # styler: on
+
+  expect_snapshot_error(
+    exclude_cols_then_pivot_wider(
+      data,
+      exclude_cols = "to_exclude",
+      id_cols = "id",
+      avoid_list_cols = TRUE,
+      values_fn = list
+    )
+  )
+})
diff --git a/tests/testthat/test-score_transition_risk.R b/tests/testthat/test-score_transition_risk.R
new file mode 100644
index 0000000..0f448bf
--- /dev/null
+++ b/tests/testthat/test-score_transition_risk.R
@@ -0,0 +1,239 @@
+test_that("outputs expected columns at product level", {
+  emissions_profile_at_product_level <-
+    example_emissions_profile_at_product_level() |>
+    filter(companies_id == "antimonarchy_canine")
+  sector_profile_at_product_level <-
+    example_sector_profile_at_product_level() |>
+    filter(companies_id == "antimonarchy_canine")
+
+  out <-
+    score_transition_risk(
+      emissions_profile_at_product_level,
+      sector_profile_at_product_level
+    ) |>
+    unnest_product()
+
+  expect_equal(sort(names(out)), sort(trs_product_output_columns()))
+})
+
+test_that("outputs expected columns at company level", {
+  emissions_profile_at_product_level <-
+    example_emissions_profile_at_product_level() |>
+    filter(companies_id == "antimonarchy_canine")
+  sector_profile_at_product_level <-
+    example_sector_profile_at_product_level() |>
+    filter(companies_id == "antimonarchy_canine")
+
+  out <-
+    score_transition_risk(
+      emissions_profile_at_product_level,
+      sector_profile_at_product_level
+    ) |>
+    unnest_company()
+
+  expect_equal(sort(names(out)), sort(trs_company_output_columns()))
+})
+
+test_that("calculates `transition_risk_score` and `benchmark_tr_score` correctly", {
+  emissions_profile_at_product_level <-
+    example_emissions_profile_at_product_level() |>
+    filter(
+      companies_id %in% c("antimonarchy_canine"),
+      benchmark == "all"
+    )
+  sector_profile_at_product_level <-
+    example_sector_profile_at_product_level() |>
+    filter(
+      companies_id %in% c("antimonarchy_canine"),
+      scenario == "1.5C RPS",
+      year == "2030"
+    )
+
+  out <-
+    unnest_product(
+      score_transition_risk(
+        emissions_profile_at_product_level,
+        sector_profile_at_product_level
+      )
+    )
+
+  expect_equal(out$benchmark_tr_score, "1.5C RPS_2030_all")
+  expect_equal(out$transition_risk_score, 0.59)
+})
+
+test_that("calculates `transition_risk_score_avg` correctly", {
+  emissions_profile_at_product_level <-
+    example_emissions_profile_at_product_level() |>
+    filter(
+      companies_id %in% c("nonphilosophical_llama"),
+      benchmark == "all"
+    )
+  sector_profile_at_product_level <-
+    example_sector_profile_at_product_level() |>
+    filter(
+      companies_id %in% c("nonphilosophical_llama"),
+      scenario == "1.5C RPS",
+      year == "2030"
+    )
+
+  out <-
+    unnest_company(
+      score_transition_risk(
+        emissions_profile_at_product_level,
+        sector_profile_at_product_level
+      )
+    )
+
+  expect_equal(round(out$transition_risk_score_avg, 4), 0.2117)
+})
+
+test_that(
+  "calculates `transition_risk_score_avg` correctly for unmatched `ep_product`
+          of a company",
+  {
+    emissions_profile_at_product_level <-
+      example_emissions_profile_at_product_level() |>
+      filter(
+        companies_id %in% c("nonphilosophical_llama"),
+        ep_product == "surface finishing, galvanic"
+      )
+    sector_profile_at_product_level <-
+      example_sector_profile_at_product_level() |>
+      filter(
+        companies_id %in% c("nonphilosophical_llama"),
+        ep_product == "surface engineering"
+      )
+
+    out <-
+      unnest_company(
+        score_transition_risk(
+          emissions_profile_at_product_level,
+          sector_profile_at_product_level
+        )
+      )
+
+    # Both the ep_products are present only in one dataframe which will lead to
+    # unmatched results and thereafter Null in `transition_risk_score_avg` column
+    expect_equal(out$transition_risk_score_avg, NaN)
+  }
+)
+
+
+test_that(
+  "`transition_risk_score` and `benchmark_tr_score` has NA due to
+          NA in either column `profile_ranking` or `reduction_targets`",
+  {
+    emissions_profile_at_product_level <-
+      example_emissions_profile_at_product_level() |>
+      filter(companies_id %in% c("antimonarchy_canine", "nonphilosophical_llama"))
+    sector_profile_at_product_level <-
+      example_sector_profile_at_product_level() |>
+      filter(companies_id %in% c("celestial_lovebird", "nonphilosophical_llama"))
+
+    out <-
+      score_transition_risk(
+        emissions_profile_at_product_level,
+        sector_profile_at_product_level
+      ) |>
+      unnest_product()
+
+    tr_score_na <- out |>
+      filter(is.na(transition_risk_score))
+
+    benchmark_tr_score_na <- out |>
+      filter(is.na(benchmark_tr_score))
+
+    ranking_reduction_na <- out |>
+      filter(is.na(profile_ranking) | is.na(reduction_targets))
+
+    expect_equal(tr_score_na, ranking_reduction_na)
+    expect_equal(benchmark_tr_score_na, ranking_reduction_na)
+  }
+)
+
+test_that(
+  "product level and company level outputs contain non-null info of all
+  matched and unmatched companies after joining dataframes",
+  {
+    # uncommon companies in either dataframe will give unmatched results
+    emissions_profile_at_product_level <-
+      example_emissions_profile_at_product_level() |>
+      filter(companies_id %in% c("antimonarchy_canine", "nonphilosophical_llama"))
+    sector_profile_at_product_level <-
+      example_sector_profile_at_product_level() |>
+      filter(companies_id %in% c("celestial_lovebird", "nonphilosophical_llama"))
+
+    trs_product <-
+      score_transition_risk(
+        emissions_profile_at_product_level,
+        sector_profile_at_product_level
+      ) |>
+      unnest_product()
+
+    trs_company <-
+      score_transition_risk(
+        emissions_profile_at_product_level,
+        sector_profile_at_product_level
+      ) |>
+      unnest_company()
+
+    # Select common columns of both matched and unmatched companies (except columns
+    # computed by `score_transition_risk` function)
+    common_cols_product <- trs_product |>
+      select(common_columns_emissions_sector_at_product_level())
+    common_cols_company <- trs_company |>
+      select(common_columns_emissions_sector_at_company_level())
+
+    # These checks ensures that there is not even a single NA in common columns
+    # of matched and unmatched companies at both product and company level
+    expect_false(any(is.na(common_cols_product)))
+    expect_false(any(is.na(common_cols_company)))
+  }
+)
+
+test_that("characterize commit 663d12", {
+  emissions_profile_at_product_level <-
+    example_emissions_profile_at_product_level() |>
+    filter(companies_id == "antimonarchy_canine")
+  sector_profile_at_product_level <-
+    example_sector_profile_at_product_level() |>
+    filter(companies_id == "antimonarchy_canine")
+
+  out <-
+    score_transition_risk(
+      emissions_profile_at_product_level,
+      sector_profile_at_product_level
+    )
+
+  out |>
+    unnest_product() |>
+    hasName("benchmark_tr_score") |>
+    expect_true()
+
+  out |>
+    unnest_company() |>
+    hasName("benchmark_tr_score_avg") |>
+    expect_true()
+})
+
+test_that("limits `transition_risk_score` between 0 and 1", {
+  emissions_profile_at_product_level <-
+    example_emissions_profile_at_product_level() |>
+    filter(companies_id == "antimonarchy_canine")
+  sector_profile_at_product_level <-
+    example_sector_profile_at_product_level() |>
+    filter(companies_id == "antimonarchy_canine") |>
+    mutate(reduction_targets = c(-100, -100, 100, 100))
+
+  out <-
+    score_transition_risk(
+      emissions_profile_at_product_level,
+      sector_profile_at_product_level
+    ) |>
+    unnest_product()
+
+  # Due to large positive and negative `reduction_targets` values,
+  # `transition_risk_score` should not be more than 1 and less than 0.
+  expected_values <- c(0, 1)
+  expect_equal(unique(out$transition_risk_score), expected_values)
+})
diff --git a/tests/testthat/test-score_transition_risk_and_polish.R b/tests/testthat/test-score_transition_risk_and_polish.R
new file mode 100644
index 0000000..29b1e92
--- /dev/null
+++ b/tests/testthat/test-score_transition_risk_and_polish.R
@@ -0,0 +1,112 @@
+test_that("outputs results both at product and company level", {
+  withr::local_options(list(tiltIndicatorAfter.output_co2_footprint = TRUE))
+
+  toy_emissions_profile_products_ecoinvent <- read_csv(toy_emissions_profile_products_ecoinvent())
+  toy_emissions_profile_any_companies <- read_csv(toy_emissions_profile_any_companies())
+  toy_sector_profile_any_scenarios <- read_csv(toy_sector_profile_any_scenarios())
+  toy_sector_profile_companies <- read_csv(toy_sector_profile_companies())
+  toy_europages_companies <- read_csv(toy_europages_companies())
+  toy_ecoinvent_activities <- read_csv(toy_ecoinvent_activities())
+  toy_ecoinvent_europages <- read_csv(toy_ecoinvent_europages())
+  toy_ecoinvent_inputs <- read_csv(toy_ecoinvent_inputs())
+  toy_isic_name <- read_csv(toy_isic_name())
+
+  emissions_profile <- profile_emissions(
+    companies = toy_emissions_profile_any_companies,
+    co2 = toy_emissions_profile_products_ecoinvent,
+    europages_companies = toy_europages_companies,
+    ecoinvent_activities = toy_ecoinvent_activities,
+    ecoinvent_europages = toy_ecoinvent_europages,
+    isic = toy_isic_name
+  )
+
+  sector_profile <- profile_sector(
+    companies = toy_sector_profile_companies,
+    scenarios = toy_sector_profile_any_scenarios,
+    europages_companies = toy_europages_companies,
+    ecoinvent_activities = toy_ecoinvent_activities,
+    ecoinvent_europages = toy_ecoinvent_europages,
+    isic = toy_isic_name
+  )
+
+  out <- score_transition_risk_and_polish(emissions_profile, sector_profile)
+  expect_named(out, c("companies_id", "product", "company"))
+})
+
+test_that("with `*.output_co2_footprint` unset, `include_co2 = TRUE` yields an error", {
+  unset <- NULL
+  withr::local_options(list(tiltIndicatorAfter.output_co2_footprint = unset))
+
+  toy_emissions_profile_products_ecoinvent <- read_csv(toy_emissions_profile_products_ecoinvent())
+  toy_emissions_profile_any_companies <- read_csv(toy_emissions_profile_any_companies())
+  toy_sector_profile_any_scenarios <- read_csv(toy_sector_profile_any_scenarios())
+  toy_sector_profile_companies <- read_csv(toy_sector_profile_companies())
+  toy_europages_companies <- read_csv(toy_europages_companies())
+  toy_ecoinvent_activities <- read_csv(toy_ecoinvent_activities())
+  toy_ecoinvent_europages <- read_csv(toy_ecoinvent_europages())
+  toy_ecoinvent_inputs <- read_csv(toy_ecoinvent_inputs())
+  toy_isic_name <- read_csv(toy_isic_name())
+
+  emissions_profile <- profile_emissions(
+    companies = toy_emissions_profile_any_companies,
+    co2 = toy_emissions_profile_products_ecoinvent,
+    europages_companies = toy_europages_companies,
+    ecoinvent_activities = toy_ecoinvent_activities,
+    ecoinvent_europages = toy_ecoinvent_europages,
+    isic = toy_isic_name
+  )
+
+  sector_profile <- profile_sector(
+    companies = toy_sector_profile_companies,
+    scenarios = toy_sector_profile_any_scenarios,
+    europages_companies = toy_europages_companies,
+    ecoinvent_activities = toy_ecoinvent_activities,
+    ecoinvent_europages = toy_ecoinvent_europages,
+    isic = toy_isic_name
+  )
+
+  expect_error(
+    score_transition_risk_and_polish(emissions_profile, sector_profile, include_co2 = TRUE),
+    "tiltIndicatorAfter.output_co2_footprint"
+  )
+})
+
+test_that("with `*.output_co2_footprint` unset, `include_co2 = FALSE` yields no error", {
+  unset <- NULL
+  withr::local_options(list(tiltIndicatorAfter.output_co2_footprint = unset))
+
+  toy_emissions_profile_products_ecoinvent <- read_csv(toy_emissions_profile_products_ecoinvent())
+  toy_emissions_profile_any_companies <- read_csv(toy_emissions_profile_any_companies())
+  toy_sector_profile_any_scenarios <- read_csv(toy_sector_profile_any_scenarios())
+  toy_sector_profile_companies <- read_csv(toy_sector_profile_companies())
+  toy_europages_companies <- read_csv(toy_europages_companies())
+  toy_ecoinvent_activities <- read_csv(toy_ecoinvent_activities())
+  toy_ecoinvent_europages <- read_csv(toy_ecoinvent_europages())
+  toy_ecoinvent_inputs <- read_csv(toy_ecoinvent_inputs())
+  toy_isic_name <- read_csv(toy_isic_name())
+
+  emissions_profile <- profile_emissions(
+    companies = toy_emissions_profile_any_companies,
+    co2 = toy_emissions_profile_products_ecoinvent,
+    europages_companies = toy_europages_companies,
+    ecoinvent_activities = toy_ecoinvent_activities,
+    ecoinvent_europages = toy_ecoinvent_europages,
+    isic = toy_isic_name
+  )
+
+  sector_profile <- profile_sector(
+    companies = toy_sector_profile_companies,
+    scenarios = toy_sector_profile_any_scenarios,
+    europages_companies = toy_europages_companies,
+    ecoinvent_activities = toy_ecoinvent_activities,
+    ecoinvent_europages = toy_ecoinvent_europages,
+    isic = toy_isic_name
+  )
+
+  expect_no_error(
+    score_transition_risk_and_polish(
+      emissions_profile,
+      sector_profile
+    )
+  )
+})
diff --git a/tests/testthat/test-transition_risk_profile.R b/tests/testthat/test-transition_risk_profile.R
new file mode 100644
index 0000000..851cb89
--- /dev/null
+++ b/tests/testthat/test-transition_risk_profile.R
@@ -0,0 +1,746 @@
+test_that("yields a 'tilt_profile'", {
+  restore <- options(list(tiltIndicatorAfter.output_co2_footprint = TRUE))
+  toy_emissions_profile_products_ecoinvent <- read_csv(toy_emissions_profile_products_ecoinvent()) |>
+    filter(activity_uuid_product_uuid != "76269c17-78d6-420b-991a-aa38c51b45b7")
+  toy_emissions_profile_any_companies <- read_csv(toy_emissions_profile_any_companies())
+  toy_sector_profile_any_scenarios <- read_csv(toy_sector_profile_any_scenarios())
+  toy_sector_profile_companies <- read_csv(toy_sector_profile_companies()) |>
+    filter(activity_uuid_product_uuid == "76269c17-78d6-420b-991a-aa38c51b45b7")
+  toy_europages_companies <- read_csv(toy_europages_companies())
+  toy_ecoinvent_activities <- read_csv(toy_ecoinvent_activities())
+  toy_ecoinvent_europages <- read_csv(toy_ecoinvent_europages())
+  toy_ecoinvent_inputs <- read_csv(toy_ecoinvent_inputs())
+  toy_isic_name <- read_csv(toy_isic_name())
+  toy_all_activities_scenario_sectors <- read_csv(toy_all_activities_scenario_sectors()) |>
+    filter(activity_uuid_product_uuid == "76269c17-78d6-420b-991a-aa38c51b45b7")
+
+  toy_emissions_profile <- profile_emissions(
+    companies = toy_emissions_profile_any_companies,
+    co2 = toy_emissions_profile_products_ecoinvent,
+    europages_companies = toy_europages_companies,
+    ecoinvent_activities = toy_ecoinvent_activities,
+    ecoinvent_europages = toy_ecoinvent_europages,
+    isic = toy_isic_name
+  )
+  toy_sector_profile <- profile_sector(
+    companies = toy_sector_profile_companies,
+    scenarios = toy_sector_profile_any_scenarios,
+    europages_companies = toy_europages_companies,
+    ecoinvent_activities = toy_ecoinvent_activities,
+    ecoinvent_europages = toy_ecoinvent_europages,
+    isic = toy_isic_name
+  )
+
+  output <- transition_risk_profile(
+    emissions_profile = toy_emissions_profile,
+    sector_profile = toy_sector_profile,
+    co2 = toy_emissions_profile_products_ecoinvent,
+    all_activities_scenario_sectors = toy_all_activities_scenario_sectors,
+    scenarios = toy_sector_profile_any_scenarios
+  )
+
+  expect_s3_class(output, "tilt_profile")
+})
+
+
+test_that("outputs `NA` transition risk category for `NA` transition risk score at product level", {
+  restore <- options(list(tiltIndicatorAfter.output_co2_footprint = TRUE))
+  toy_emissions_profile_products_ecoinvent <- read_csv(toy_emissions_profile_products_ecoinvent()) |>
+    filter(activity_uuid_product_uuid != "76269c17-78d6-420b-991a-aa38c51b45b7")
+  toy_emissions_profile_any_companies <- read_csv(toy_emissions_profile_any_companies())
+  toy_sector_profile_any_scenarios <- read_csv(toy_sector_profile_any_scenarios())
+  toy_sector_profile_companies <- read_csv(toy_sector_profile_companies()) |>
+    filter(activity_uuid_product_uuid == "76269c17-78d6-420b-991a-aa38c51b45b7")
+  toy_europages_companies <- read_csv(toy_europages_companies())
+  toy_ecoinvent_activities <- read_csv(toy_ecoinvent_activities())
+  toy_ecoinvent_europages <- read_csv(toy_ecoinvent_europages())
+  toy_ecoinvent_inputs <- read_csv(toy_ecoinvent_inputs())
+  toy_isic_name <- read_csv(toy_isic_name())
+  toy_all_activities_scenario_sectors <- read_csv(toy_all_activities_scenario_sectors()) |>
+    filter(activity_uuid_product_uuid == "76269c17-78d6-420b-991a-aa38c51b45b7")
+
+  toy_emissions_profile <- profile_emissions(
+    companies = toy_emissions_profile_any_companies,
+    co2 = toy_emissions_profile_products_ecoinvent,
+    europages_companies = toy_europages_companies,
+    ecoinvent_activities = toy_ecoinvent_activities,
+    ecoinvent_europages = toy_ecoinvent_europages,
+    isic = toy_isic_name
+  )
+  toy_sector_profile <- profile_sector(
+    companies = toy_sector_profile_companies,
+    scenarios = toy_sector_profile_any_scenarios,
+    europages_companies = toy_europages_companies,
+    ecoinvent_activities = toy_ecoinvent_activities,
+    ecoinvent_europages = toy_ecoinvent_europages,
+    isic = toy_isic_name
+  )
+
+  output <- transition_risk_profile(
+    emissions_profile = toy_emissions_profile,
+    sector_profile = toy_sector_profile,
+    co2 = toy_emissions_profile_products_ecoinvent,
+    all_activities_scenario_sectors = toy_all_activities_scenario_sectors,
+    scenarios = toy_sector_profile_any_scenarios
+  ) |>
+    unnest_product() |>
+    filter(activity_uuid_product_uuid == "76269c17-78d6-420b-991a-aa38c51b45b7")
+
+  # Transition risk score is `NA` for uuid "76269c17-78d6-420b-991a-aa38c51b45b7"
+  expect_true(is.na(unique(output$transition_risk_score)))
+  # `transition_risk_category` is `NA` for `NA` transition risk score
+  expect_true(is.na(unique(output$transition_risk_category)))
+})
+
+test_that("outputs `NA` in `avg_transition_risk_best_case` and `avg_transition_risk_worst_case` for `NA` at company level if `transition_risk_score` and `transition_risk_category` are `NA` at product level", {
+  restore <- options(list(tiltIndicatorAfter.output_co2_footprint = TRUE))
+  toy_emissions_profile_products_ecoinvent <- read_csv(toy_emissions_profile_products_ecoinvent()) |>
+    filter(activity_uuid_product_uuid != "76269c17-78d6-420b-991a-aa38c51b45b7")
+  toy_emissions_profile_any_companies <- read_csv(toy_emissions_profile_any_companies())
+  toy_sector_profile_any_scenarios <- read_csv(toy_sector_profile_any_scenarios())
+  toy_sector_profile_companies <- read_csv(toy_sector_profile_companies()) |>
+    filter(activity_uuid_product_uuid == "76269c17-78d6-420b-991a-aa38c51b45b7")
+  toy_europages_companies <- read_csv(toy_europages_companies())
+  toy_ecoinvent_activities <- read_csv(toy_ecoinvent_activities())
+  toy_ecoinvent_europages <- read_csv(toy_ecoinvent_europages())
+  toy_ecoinvent_inputs <- read_csv(toy_ecoinvent_inputs())
+  toy_isic_name <- read_csv(toy_isic_name())
+  toy_all_activities_scenario_sectors <- read_csv(toy_all_activities_scenario_sectors()) |>
+    filter(activity_uuid_product_uuid == "76269c17-78d6-420b-991a-aa38c51b45b7")
+
+  toy_emissions_profile <- profile_emissions(
+    companies = toy_emissions_profile_any_companies,
+    co2 = toy_emissions_profile_products_ecoinvent,
+    europages_companies = toy_europages_companies,
+    ecoinvent_activities = toy_ecoinvent_activities,
+    ecoinvent_europages = toy_ecoinvent_europages,
+    isic = toy_isic_name
+  )
+  toy_sector_profile <- profile_sector(
+    companies = toy_sector_profile_companies,
+    scenarios = toy_sector_profile_any_scenarios,
+    europages_companies = toy_europages_companies,
+    ecoinvent_activities = toy_ecoinvent_activities,
+    ecoinvent_europages = toy_ecoinvent_europages,
+    isic = toy_isic_name
+  )
+
+  output <- transition_risk_profile(
+    emissions_profile = toy_emissions_profile,
+    sector_profile = toy_sector_profile,
+    co2 = toy_emissions_profile_products_ecoinvent,
+    all_activities_scenario_sectors = toy_all_activities_scenario_sectors,
+    scenarios = toy_sector_profile_any_scenarios
+  )
+
+  product_level_output <- output |>
+    unnest_product()
+
+  company_level_output <- output |>
+    unnest_company()
+
+  # `avg_transition_risk_equal_weight` and `transition_risk_category` are `NA`at product level
+  expect_true(is.na(unique(product_level_output$transition_risk_score)))
+  expect_true(is.na(unique(product_level_output$transition_risk_category)))
+  # `avg_transition_risk_best_case` and `avg_transition_risk_worst_case` are `NA` are `NA` at company level
+  expect_true(is.na(unique(company_level_output$avg_transition_risk_best_case)))
+  expect_true(is.na(unique(company_level_output$avg_transition_risk_worst_case)))
+})
+
+test_that("with `*.output_co2_footprint` unset, `for_webtool = TRUE` yields no error", {
+  unset <- NULL
+  withr::local_options(list(tiltIndicatorAfter.output_co2_footprint = unset))
+
+  toy_emissions_profile_products_ecoinvent <- read_csv(toy_emissions_profile_products_ecoinvent()) |>
+    filter(activity_uuid_product_uuid != "76269c17-78d6-420b-991a-aa38c51b45b7")
+  toy_emissions_profile_any_companies <- read_csv(toy_emissions_profile_any_companies())
+  toy_sector_profile_any_scenarios <- read_csv(toy_sector_profile_any_scenarios())
+  toy_sector_profile_companies <- read_csv(toy_sector_profile_companies()) |>
+    filter(activity_uuid_product_uuid == "76269c17-78d6-420b-991a-aa38c51b45b7")
+  toy_europages_companies <- read_csv(toy_europages_companies())
+  toy_ecoinvent_activities <- read_csv(toy_ecoinvent_activities())
+  toy_ecoinvent_europages <- read_csv(toy_ecoinvent_europages())
+  toy_ecoinvent_inputs <- read_csv(toy_ecoinvent_inputs())
+  toy_isic_name <- read_csv(toy_isic_name())
+  toy_all_activities_scenario_sectors <- read_csv(toy_all_activities_scenario_sectors()) |>
+    filter(activity_uuid_product_uuid == "76269c17-78d6-420b-991a-aa38c51b45b7")
+
+  toy_emissions_profile <- profile_emissions(
+    companies = toy_emissions_profile_any_companies,
+    co2 = toy_emissions_profile_products_ecoinvent,
+    europages_companies = toy_europages_companies,
+    ecoinvent_activities = toy_ecoinvent_activities,
+    ecoinvent_europages = toy_ecoinvent_europages,
+    isic = toy_isic_name
+  )
+  toy_sector_profile <- profile_sector(
+    companies = toy_sector_profile_companies,
+    scenarios = toy_sector_profile_any_scenarios,
+    europages_companies = toy_europages_companies,
+    ecoinvent_activities = toy_ecoinvent_activities,
+    ecoinvent_europages = toy_ecoinvent_europages,
+    isic = toy_isic_name
+  )
+
+  expect_no_error(
+    transition_risk_profile(
+      emissions_profile = toy_emissions_profile,
+      sector_profile = toy_sector_profile,
+      co2 = toy_emissions_profile_products_ecoinvent,
+      all_activities_scenario_sectors = toy_all_activities_scenario_sectors,
+      scenarios = toy_sector_profile_any_scenarios,
+      for_webtool = TRUE
+    )
+  )
+})
+
+test_that("doesn't output `co2_footprint` at product level and `co2e_avg` at company level if `*.output_co2_footprint` unset and `for_webtool = TRUE`", {
+  unset <- NULL
+  withr::local_options(list(tiltIndicatorAfter.output_co2_footprint = unset))
+
+  toy_emissions_profile_products_ecoinvent <- read_csv(toy_emissions_profile_products_ecoinvent()) |>
+    filter(activity_uuid_product_uuid == "76269c17-78d6-420b-991a-aa38c51b45b7")
+  toy_emissions_profile_any_companies <- read_csv(toy_emissions_profile_any_companies())
+  toy_sector_profile_any_scenarios <- read_csv(toy_sector_profile_any_scenarios())
+  toy_sector_profile_companies <- read_csv(toy_sector_profile_companies()) |>
+    filter(activity_uuid_product_uuid == "76269c17-78d6-420b-991a-aa38c51b45b7")
+  toy_europages_companies <- read_csv(toy_europages_companies())
+  toy_ecoinvent_activities <- read_csv(toy_ecoinvent_activities())
+  toy_ecoinvent_europages <- read_csv(toy_ecoinvent_europages())
+  toy_ecoinvent_inputs <- read_csv(toy_ecoinvent_inputs())
+  toy_isic_name <- read_csv(toy_isic_name())
+  toy_all_activities_scenario_sectors <- read_csv(toy_all_activities_scenario_sectors()) |>
+    filter(activity_uuid_product_uuid == "76269c17-78d6-420b-991a-aa38c51b45b7")
+
+  toy_emissions_profile <- profile_emissions(
+    companies = toy_emissions_profile_any_companies,
+    co2 = toy_emissions_profile_products_ecoinvent,
+    europages_companies = toy_europages_companies,
+    ecoinvent_activities = toy_ecoinvent_activities,
+    ecoinvent_europages = toy_ecoinvent_europages,
+    isic = toy_isic_name
+  )
+  toy_sector_profile <- profile_sector(
+    companies = toy_sector_profile_companies,
+    scenarios = toy_sector_profile_any_scenarios,
+    europages_companies = toy_europages_companies,
+    ecoinvent_activities = toy_ecoinvent_activities,
+    ecoinvent_europages = toy_ecoinvent_europages,
+    isic = toy_isic_name
+  )
+
+  output <- transition_risk_profile(
+    emissions_profile = toy_emissions_profile,
+    sector_profile = toy_sector_profile,
+    co2 = toy_emissions_profile_products_ecoinvent,
+    all_activities_scenario_sectors = toy_all_activities_scenario_sectors,
+    scenarios = toy_sector_profile_any_scenarios,
+    for_webtool = TRUE
+  )
+
+  expect_false("co2_footprint" %in% names(unnest_product(output)))
+  expect_false("co2e_avg" %in% names(unnest_company(output)))
+})
+
+
+test_that("outputs `co2_footprint` at product level and `co2e_avg` at company level if `*.output_co2_footprint = TRUE` and `for_webtool = FALSE`", {
+  withr::local_options(list(tiltIndicatorAfter.output_co2_footprint = TRUE))
+
+  toy_emissions_profile_products_ecoinvent <- read_csv(toy_emissions_profile_products_ecoinvent()) |>
+    filter(activity_uuid_product_uuid == "76269c17-78d6-420b-991a-aa38c51b45b7")
+  toy_emissions_profile_any_companies <- read_csv(toy_emissions_profile_any_companies())
+  toy_sector_profile_any_scenarios <- read_csv(toy_sector_profile_any_scenarios())
+  toy_sector_profile_companies <- read_csv(toy_sector_profile_companies()) |>
+    filter(activity_uuid_product_uuid == "76269c17-78d6-420b-991a-aa38c51b45b7")
+  toy_europages_companies <- read_csv(toy_europages_companies())
+  toy_ecoinvent_activities <- read_csv(toy_ecoinvent_activities())
+  toy_ecoinvent_europages <- read_csv(toy_ecoinvent_europages())
+  toy_ecoinvent_inputs <- read_csv(toy_ecoinvent_inputs())
+  toy_isic_name <- read_csv(toy_isic_name())
+  toy_all_activities_scenario_sectors <- read_csv(toy_all_activities_scenario_sectors()) |>
+    filter(activity_uuid_product_uuid == "76269c17-78d6-420b-991a-aa38c51b45b7")
+
+  toy_emissions_profile <- profile_emissions(
+    companies = toy_emissions_profile_any_companies,
+    co2 = toy_emissions_profile_products_ecoinvent,
+    europages_companies = toy_europages_companies,
+    ecoinvent_activities = toy_ecoinvent_activities,
+    ecoinvent_europages = toy_ecoinvent_europages,
+    isic = toy_isic_name
+  )
+  toy_sector_profile <- profile_sector(
+    companies = toy_sector_profile_companies,
+    scenarios = toy_sector_profile_any_scenarios,
+    europages_companies = toy_europages_companies,
+    ecoinvent_activities = toy_ecoinvent_activities,
+    ecoinvent_europages = toy_ecoinvent_europages,
+    isic = toy_isic_name
+  )
+
+  output <- transition_risk_profile(
+    emissions_profile = toy_emissions_profile,
+    sector_profile = toy_sector_profile,
+    co2 = toy_emissions_profile_products_ecoinvent,
+    all_activities_scenario_sectors = toy_all_activities_scenario_sectors,
+    scenarios = toy_sector_profile_any_scenarios
+  )
+
+  expect_true("co2_footprint" %in% names(unnest_product(output)))
+  expect_true("co2e_avg" %in% names(unnest_company(output)))
+})
+
+test_that("removes `co2_footprint` at product level and `co2e_avg` at company level if `*.output_co2_footprint = TRUE` and `for_webtool = TRUE`", {
+  withr::local_options(list(tiltIndicatorAfter.output_co2_footprint = TRUE))
+
+  toy_emissions_profile_products_ecoinvent <- read_csv(toy_emissions_profile_products_ecoinvent()) |>
+    filter(activity_uuid_product_uuid == "76269c17-78d6-420b-991a-aa38c51b45b7")
+  toy_emissions_profile_any_companies <- read_csv(toy_emissions_profile_any_companies())
+  toy_sector_profile_any_scenarios <- read_csv(toy_sector_profile_any_scenarios())
+  toy_sector_profile_companies <- read_csv(toy_sector_profile_companies()) |>
+    filter(activity_uuid_product_uuid == "76269c17-78d6-420b-991a-aa38c51b45b7")
+  toy_europages_companies <- read_csv(toy_europages_companies())
+  toy_ecoinvent_activities <- read_csv(toy_ecoinvent_activities())
+  toy_ecoinvent_europages <- read_csv(toy_ecoinvent_europages())
+  toy_ecoinvent_inputs <- read_csv(toy_ecoinvent_inputs())
+  toy_isic_name <- read_csv(toy_isic_name())
+  toy_all_activities_scenario_sectors <- read_csv(toy_all_activities_scenario_sectors()) |>
+    filter(activity_uuid_product_uuid == "76269c17-78d6-420b-991a-aa38c51b45b7")
+
+  toy_emissions_profile <- profile_emissions(
+    companies = toy_emissions_profile_any_companies,
+    co2 = toy_emissions_profile_products_ecoinvent,
+    europages_companies = toy_europages_companies,
+    ecoinvent_activities = toy_ecoinvent_activities,
+    ecoinvent_europages = toy_ecoinvent_europages,
+    isic = toy_isic_name
+  )
+  toy_sector_profile <- profile_sector(
+    companies = toy_sector_profile_companies,
+    scenarios = toy_sector_profile_any_scenarios,
+    europages_companies = toy_europages_companies,
+    ecoinvent_activities = toy_ecoinvent_activities,
+    ecoinvent_europages = toy_ecoinvent_europages,
+    isic = toy_isic_name
+  )
+
+  output <- transition_risk_profile(
+    emissions_profile = toy_emissions_profile,
+    sector_profile = toy_sector_profile,
+    co2 = toy_emissions_profile_products_ecoinvent,
+    all_activities_scenario_sectors = toy_all_activities_scenario_sectors,
+    scenarios = toy_sector_profile_any_scenarios,
+    for_webtool = TRUE
+  )
+
+  expect_true(!("co2_footprint" %in% names(unnest_product(output))))
+  expect_true(!("co2e_avg" %in% names(unnest_company(output))))
+})
+
+
+test_that("At company level the `emission*` column are of type double", {
+  withr::local_options(list(tiltIndicatorAfter.output_co2_footprint = TRUE))
+
+  toy_emissions_profile_products_ecoinvent <- read_csv(toy_emissions_profile_products_ecoinvent()) |>
+    filter(activity_uuid_product_uuid == "76269c17-78d6-420b-991a-aa38c51b45b7")
+  toy_emissions_profile_any_companies <- read_csv(toy_emissions_profile_any_companies())
+  toy_sector_profile_any_scenarios <- read_csv(toy_sector_profile_any_scenarios())
+  toy_sector_profile_companies <- read_csv(toy_sector_profile_companies()) |>
+    filter(activity_uuid_product_uuid == "76269c17-78d6-420b-991a-aa38c51b45b7")
+  toy_europages_companies <- read_csv(toy_europages_companies())
+  toy_ecoinvent_activities <- read_csv(toy_ecoinvent_activities())
+  toy_ecoinvent_europages <- read_csv(toy_ecoinvent_europages())
+  toy_ecoinvent_inputs <- read_csv(toy_ecoinvent_inputs())
+  toy_isic_name <- read_csv(toy_isic_name())
+  toy_all_activities_scenario_sectors <- read_csv(toy_all_activities_scenario_sectors()) |>
+    filter(activity_uuid_product_uuid == "76269c17-78d6-420b-991a-aa38c51b45b7")
+
+  toy_emissions_profile <- profile_emissions(
+    companies = toy_emissions_profile_any_companies,
+    co2 = toy_emissions_profile_products_ecoinvent,
+    europages_companies = toy_europages_companies,
+    ecoinvent_activities = toy_ecoinvent_activities,
+    ecoinvent_europages = toy_ecoinvent_europages,
+    isic = toy_isic_name
+  )
+  toy_sector_profile <- profile_sector(
+    companies = toy_sector_profile_companies,
+    scenarios = toy_sector_profile_any_scenarios,
+    europages_companies = toy_europages_companies,
+    ecoinvent_activities = toy_ecoinvent_activities,
+    ecoinvent_europages = toy_ecoinvent_europages,
+    isic = toy_isic_name
+  )
+
+  type <- transition_risk_profile(
+    emissions_profile = toy_emissions_profile,
+    sector_profile = toy_sector_profile,
+    co2 = toy_emissions_profile_products_ecoinvent,
+    all_activities_scenario_sectors = toy_all_activities_scenario_sectors,
+    scenarios = toy_sector_profile_any_scenarios
+  ) |>
+    unnest_company() |>
+    select(matches("emission_")) |>
+    lapply(typeof) |>
+    unlist() |>
+    unique()
+  expect_equal(type, "double")
+})
+
+test_that("the output at product level has all the new required columns (#189)", {
+  toy_emissions_profile_products_ecoinvent <- read_csv(toy_emissions_profile_products_ecoinvent()) |>
+    filter(activity_uuid_product_uuid == "76269c17-78d6-420b-991a-aa38c51b45b7")
+  toy_emissions_profile_any_companies <- read_csv(toy_emissions_profile_any_companies())
+  toy_sector_profile_any_scenarios <- read_csv(toy_sector_profile_any_scenarios())
+  toy_sector_profile_companies <- read_csv(toy_sector_profile_companies()) |>
+    filter(activity_uuid_product_uuid == "76269c17-78d6-420b-991a-aa38c51b45b7")
+  toy_europages_companies <- read_csv(toy_europages_companies())
+  toy_ecoinvent_activities <- read_csv(toy_ecoinvent_activities())
+  toy_ecoinvent_europages <- read_csv(toy_ecoinvent_europages())
+  toy_ecoinvent_inputs <- read_csv(toy_ecoinvent_inputs())
+  toy_isic_name <- read_csv(toy_isic_name())
+  toy_all_activities_scenario_sectors <- read_csv(toy_all_activities_scenario_sectors()) |>
+    filter(activity_uuid_product_uuid == "76269c17-78d6-420b-991a-aa38c51b45b7")
+
+  toy_emissions_profile <- profile_emissions(
+    companies = toy_emissions_profile_any_companies,
+    co2 = toy_emissions_profile_products_ecoinvent,
+    europages_companies = toy_europages_companies,
+    ecoinvent_activities = toy_ecoinvent_activities,
+    ecoinvent_europages = toy_ecoinvent_europages,
+    isic = toy_isic_name
+  )
+  toy_sector_profile <- profile_sector(
+    companies = toy_sector_profile_companies,
+    scenarios = toy_sector_profile_any_scenarios,
+    europages_companies = toy_europages_companies,
+    ecoinvent_activities = toy_ecoinvent_activities,
+    ecoinvent_europages = toy_ecoinvent_europages,
+    isic = toy_isic_name
+  )
+
+  product <- transition_risk_profile(
+    emissions_profile = toy_emissions_profile,
+    sector_profile = toy_sector_profile,
+    co2 = toy_emissions_profile_products_ecoinvent,
+    all_activities_scenario_sectors = toy_all_activities_scenario_sectors,
+    scenarios = toy_sector_profile_any_scenarios
+  ) |>
+    unnest_product()
+
+  expect_true(any(matches_name(product, "postcode")))
+  expect_true(any(matches_name(product, "address")))
+  expect_true(any(matches_name(product, "min_headcount")))
+  expect_true(any(matches_name(product, "max_headcount")))
+  expect_true(any(matches_name(product, "emissions_profile_best_case")))
+  expect_true(any(matches_name(product, "emissions_profile_worst_case")))
+  expect_true(any(matches_name(product, "transition_risk_profile_best_case")))
+  expect_true(any(matches_name(product, "transition_risk_profile_worst_case")))
+  expect_true(any(matches_name(product, "isic_4digit")))
+  expect_true(any(matches_name(product, "matching_certainty")))
+  expect_true(any(matches_name(product, "company_name")))
+  expect_true(any(matches_name(product, "emissions_profile_equal_weight")))
+  expect_true(any(matches_name(product, "amount_of_distinct_products_matched")))
+})
+
+test_that("the output at company level has has all the new required columns (#189, #290)", {
+  toy_emissions_profile_products_ecoinvent <- read_csv(toy_emissions_profile_products_ecoinvent()) |>
+    filter(activity_uuid_product_uuid == "76269c17-78d6-420b-991a-aa38c51b45b7")
+  toy_emissions_profile_any_companies <- read_csv(toy_emissions_profile_any_companies())
+  toy_sector_profile_any_scenarios <- read_csv(toy_sector_profile_any_scenarios())
+  toy_sector_profile_companies <- read_csv(toy_sector_profile_companies()) |>
+    filter(activity_uuid_product_uuid == "76269c17-78d6-420b-991a-aa38c51b45b7")
+  toy_europages_companies <- read_csv(toy_europages_companies())
+  toy_ecoinvent_activities <- read_csv(toy_ecoinvent_activities())
+  toy_ecoinvent_europages <- read_csv(toy_ecoinvent_europages())
+  toy_ecoinvent_inputs <- read_csv(toy_ecoinvent_inputs())
+  toy_isic_name <- read_csv(toy_isic_name())
+  toy_all_activities_scenario_sectors <- read_csv(toy_all_activities_scenario_sectors()) |>
+    filter(activity_uuid_product_uuid == "76269c17-78d6-420b-991a-aa38c51b45b7")
+
+  toy_emissions_profile <- profile_emissions(
+    companies = toy_emissions_profile_any_companies,
+    co2 = toy_emissions_profile_products_ecoinvent,
+    europages_companies = toy_europages_companies,
+    ecoinvent_activities = toy_ecoinvent_activities,
+    ecoinvent_europages = toy_ecoinvent_europages,
+    isic = toy_isic_name
+  )
+  toy_sector_profile <- profile_sector(
+    companies = toy_sector_profile_companies,
+    scenarios = toy_sector_profile_any_scenarios,
+    europages_companies = toy_europages_companies,
+    ecoinvent_activities = toy_ecoinvent_activities,
+    ecoinvent_europages = toy_ecoinvent_europages,
+    isic = toy_isic_name
+  )
+
+  company <- transition_risk_profile(
+    emissions_profile = toy_emissions_profile,
+    sector_profile = toy_sector_profile,
+    co2 = toy_emissions_profile_products_ecoinvent,
+    all_activities_scenario_sectors = toy_all_activities_scenario_sectors,
+    scenarios = toy_sector_profile_any_scenarios
+  ) |>
+    unnest_company()
+
+  expect_true(any(matches_name(company, "postcode")))
+  expect_true(any(matches_name(company, "address")))
+  expect_true(any(matches_name(company, "min_headcount")))
+  expect_true(any(matches_name(company, "max_headcount")))
+  expect_true(any(matches_name(company, "company")))
+  expect_true(any(matches_name(company, "emission_rank_avg_best_case")))
+  expect_true(any(matches_name(company, "emission_rank_avg_worst_case")))
+  expect_true(any(matches_name(company, "sector_target_avg_best_case")))
+  expect_true(any(matches_name(company, "sector_target_avg_worst_case")))
+  expect_true(any(matches_name(company, "cov_transition_risk")))
+  expect_true(any(matches_name(company, "cov_emission_rank")))
+  expect_true(any(matches_name(company, "cov_sector_target")))
+})
+
+test_that("At product level, when either `sector_category` is NA or `emission_category` is NA, tilt and isic sectors are not NA", {
+  toy_emissions_profile_products_ecoinvent <- read_csv(toy_emissions_profile_products_ecoinvent()) |>
+    filter(activity_uuid_product_uuid != "76269c17-78d6-420b-991a-aa38c51b45b7")
+  toy_emissions_profile_any_companies <- read_csv(toy_emissions_profile_any_companies())
+  toy_sector_profile_any_scenarios <- read_csv(toy_sector_profile_any_scenarios())
+  toy_sector_profile_companies <- read_csv(toy_sector_profile_companies()) |>
+    filter(activity_uuid_product_uuid == "76269c17-78d6-420b-991a-aa38c51b45b7")
+  toy_europages_companies <- read_csv(toy_europages_companies())
+  toy_ecoinvent_activities <- read_csv(toy_ecoinvent_activities())
+  toy_ecoinvent_europages <- read_csv(toy_ecoinvent_europages())
+  toy_ecoinvent_inputs <- read_csv(toy_ecoinvent_inputs())
+  toy_isic_name <- read_csv(toy_isic_name())
+  toy_all_activities_scenario_sectors <- read_csv(toy_all_activities_scenario_sectors()) |>
+    filter(activity_uuid_product_uuid == "76269c17-78d6-420b-991a-aa38c51b45b7")
+
+  toy_emissions_profile <- profile_emissions(
+    companies = toy_emissions_profile_any_companies,
+    co2 = toy_emissions_profile_products_ecoinvent,
+    europages_companies = toy_europages_companies,
+    ecoinvent_activities = toy_ecoinvent_activities,
+    ecoinvent_europages = toy_ecoinvent_europages,
+    isic = toy_isic_name
+  )
+  toy_sector_profile <- profile_sector(
+    companies = toy_sector_profile_companies,
+    scenarios = toy_sector_profile_any_scenarios,
+    europages_companies = toy_europages_companies,
+    ecoinvent_activities = toy_ecoinvent_activities,
+    ecoinvent_europages = toy_ecoinvent_europages,
+    isic = toy_isic_name
+  )
+
+  output_product <- transition_risk_profile(
+    emissions_profile = toy_emissions_profile,
+    sector_profile = toy_sector_profile,
+    co2 = toy_emissions_profile_products_ecoinvent,
+    all_activities_scenario_sectors = toy_all_activities_scenario_sectors,
+    scenarios = toy_sector_profile_any_scenarios
+  ) |> unnest_product()
+
+  # when `sector_category` is NA then, tilt and isic sectors are not NA
+  out_sector_na <- filter(output_product, is.na(sector_category))
+  expect_true(nrow(filter(out_sector_na, is.na(out_sector_na$tilt_sector))) == 0)
+  expect_true(nrow(filter(out_sector_na, is.na(out_sector_na$tilt_subsector))) == 0)
+  expect_true(nrow(filter(out_sector_na, is.na(out_sector_na$isic_4digit))) == 0)
+
+  # when `emission_category` is NA then, tilt and isic sectors are not NA
+  out_emissions_na <- filter(output_product, is.na(emission_category))
+  expect_true(nrow(filter(out_emissions_na, is.na(out_emissions_na$tilt_sector))) == 0)
+  expect_true(nrow(filter(out_emissions_na, is.na(out_emissions_na$tilt_subsector))) == 0)
+  expect_true(nrow(filter(out_emissions_na, is.na(out_emissions_na$isic_4digit))) == 0)
+})
+
+test_that("`transition_risk_NA_share` is not NA for all cases of benchmark combinations", {
+  toy_emissions_profile_products_ecoinvent <- read_csv(toy_emissions_profile_products_ecoinvent()) |>
+    filter(activity_uuid_product_uuid != "833caa78-30df-4374-900f-7f88ab44075b")
+  toy_emissions_profile_any_companies <- read_csv(toy_emissions_profile_any_companies())
+  toy_sector_profile_any_scenarios <- read_csv(toy_sector_profile_any_scenarios())
+  toy_sector_profile_companies <- read_csv(toy_sector_profile_companies()) |>
+    filter(activity_uuid_product_uuid == "76269c17-78d6-420b-991a-aa38c51b45b7")
+  toy_europages_companies <- read_csv(toy_europages_companies())
+  toy_ecoinvent_activities <- read_csv(toy_ecoinvent_activities())
+  toy_ecoinvent_europages <- read_csv(toy_ecoinvent_europages())
+  toy_ecoinvent_inputs <- read_csv(toy_ecoinvent_inputs())
+  toy_isic_name <- read_csv(toy_isic_name())
+  toy_all_activities_scenario_sectors <- read_csv(toy_all_activities_scenario_sectors()) |>
+    filter(activity_uuid_product_uuid == "76269c17-78d6-420b-991a-aa38c51b45b7")
+
+  toy_emissions_profile <- profile_emissions(
+    companies = toy_emissions_profile_any_companies,
+    co2 = toy_emissions_profile_products_ecoinvent,
+    europages_companies = toy_europages_companies,
+    ecoinvent_activities = toy_ecoinvent_activities,
+    ecoinvent_europages = toy_ecoinvent_europages,
+    isic = toy_isic_name
+  )
+  toy_sector_profile <- profile_sector(
+    companies = toy_sector_profile_companies,
+    scenarios = toy_sector_profile_any_scenarios,
+    europages_companies = toy_europages_companies,
+    ecoinvent_activities = toy_ecoinvent_activities,
+    ecoinvent_europages = toy_ecoinvent_europages,
+    isic = toy_isic_name
+  )
+
+  output <- transition_risk_profile(
+    emissions_profile = toy_emissions_profile,
+    sector_profile = toy_sector_profile,
+    co2 = toy_emissions_profile_products_ecoinvent,
+    all_activities_scenario_sectors = toy_all_activities_scenario_sectors,
+    scenarios = toy_sector_profile_any_scenarios
+  )
+
+  output_product <- output |> unnest_product()
+  benchmark_cases <- c("1.5C RPS_2030_all", "NA_NA_all", "NA_NA_NA")
+  expect_true(all(benchmark_cases %in% unique(output_product$grouping_transition_risk)))
+
+  out_na <- filter(output_product, is.na(transition_risk_NA_share))
+  expect_true(nrow(out_na) == 0)
+})
+
+test_that("`transition_risk_NA_share` is not greater than 1 and not less than 0 for all cases of benchmark combinations", {
+  toy_emissions_profile_products_ecoinvent <- read_csv(toy_emissions_profile_products_ecoinvent()) |>
+    filter(activity_uuid_product_uuid != "833caa78-30df-4374-900f-7f88ab44075b")
+  toy_emissions_profile_any_companies <- read_csv(toy_emissions_profile_any_companies())
+  toy_sector_profile_any_scenarios <- read_csv(toy_sector_profile_any_scenarios())
+  toy_sector_profile_companies <- read_csv(toy_sector_profile_companies()) |>
+    filter(activity_uuid_product_uuid == "76269c17-78d6-420b-991a-aa38c51b45b7")
+  toy_europages_companies <- read_csv(toy_europages_companies())
+  toy_ecoinvent_activities <- read_csv(toy_ecoinvent_activities())
+  toy_ecoinvent_europages <- read_csv(toy_ecoinvent_europages())
+  toy_ecoinvent_inputs <- read_csv(toy_ecoinvent_inputs())
+  toy_isic_name <- read_csv(toy_isic_name())
+  toy_all_activities_scenario_sectors <- read_csv(toy_all_activities_scenario_sectors()) |>
+    filter(activity_uuid_product_uuid == "76269c17-78d6-420b-991a-aa38c51b45b7")
+
+  toy_emissions_profile <- profile_emissions(
+    companies = toy_emissions_profile_any_companies,
+    co2 = toy_emissions_profile_products_ecoinvent,
+    europages_companies = toy_europages_companies,
+    ecoinvent_activities = toy_ecoinvent_activities,
+    ecoinvent_europages = toy_ecoinvent_europages,
+    isic = toy_isic_name
+  )
+  toy_sector_profile <- profile_sector(
+    companies = toy_sector_profile_companies,
+    scenarios = toy_sector_profile_any_scenarios,
+    europages_companies = toy_europages_companies,
+    ecoinvent_activities = toy_ecoinvent_activities,
+    ecoinvent_europages = toy_ecoinvent_europages,
+    isic = toy_isic_name
+  )
+
+  output <- transition_risk_profile(
+    emissions_profile = toy_emissions_profile,
+    sector_profile = toy_sector_profile,
+    co2 = toy_emissions_profile_products_ecoinvent,
+    all_activities_scenario_sectors = toy_all_activities_scenario_sectors,
+    scenarios = toy_sector_profile_any_scenarios
+  )
+
+  output_product <- output |> unnest_product()
+  benchmark_cases <- c("1.5C RPS_2030_all", "NA_NA_all", "NA_NA_NA")
+  expect_true(all(benchmark_cases %in% unique(output_product$grouping_transition_risk)))
+
+  out_na <- unique(output_product$transition_risk_NA_share)
+  expect_false(all(out_na < 0))
+  expect_false(all(out_na > 1))
+})
+
+test_that("is sensitive to `for_webtool`", {
+  withr::local_options(list(tiltIndicatorAfter.output_co2_footprint = TRUE))
+
+  toy_emissions_profile_products_ecoinvent <- read_csv(toy_emissions_profile_products_ecoinvent()) |>
+    filter(activity_uuid_product_uuid == "76269c17-78d6-420b-991a-aa38c51b45b7")
+  toy_emissions_profile_any_companies <- read_csv(toy_emissions_profile_any_companies())
+  toy_sector_profile_any_scenarios <- read_csv(toy_sector_profile_any_scenarios())
+  toy_sector_profile_companies <- read_csv(toy_sector_profile_companies()) |>
+    filter(activity_uuid_product_uuid == "76269c17-78d6-420b-991a-aa38c51b45b7")
+  toy_europages_companies <- read_csv(toy_europages_companies())
+  toy_ecoinvent_activities <- read_csv(toy_ecoinvent_activities())
+  toy_ecoinvent_europages <- read_csv(toy_ecoinvent_europages())
+  toy_ecoinvent_inputs <- read_csv(toy_ecoinvent_inputs())
+  toy_isic_name <- read_csv(toy_isic_name())
+  toy_all_activities_scenario_sectors <- read_csv(toy_all_activities_scenario_sectors()) |>
+    filter(activity_uuid_product_uuid == "76269c17-78d6-420b-991a-aa38c51b45b7")
+
+  toy_emissions_profile <- profile_emissions(
+    companies = toy_emissions_profile_any_companies,
+    co2 = toy_emissions_profile_products_ecoinvent,
+    europages_companies = toy_europages_companies,
+    ecoinvent_activities = toy_ecoinvent_activities,
+    ecoinvent_europages = toy_ecoinvent_europages,
+    isic = toy_isic_name
+  )
+  toy_sector_profile <- profile_sector(
+    companies = toy_sector_profile_companies,
+    scenarios = toy_sector_profile_any_scenarios,
+    europages_companies = toy_europages_companies,
+    ecoinvent_activities = toy_ecoinvent_activities,
+    ecoinvent_europages = toy_ecoinvent_europages,
+    isic = toy_isic_name
+  )
+
+  not_for_webtool <- transition_risk_profile(
+    emissions_profile = toy_emissions_profile,
+    sector_profile = toy_sector_profile,
+    co2 = toy_emissions_profile_products_ecoinvent,
+    all_activities_scenario_sectors = toy_all_activities_scenario_sectors,
+    scenarios = toy_sector_profile_any_scenarios,
+    for_webtool = FALSE
+  )
+
+  for_webtool <- transition_risk_profile(
+    emissions_profile = toy_emissions_profile,
+    sector_profile = toy_sector_profile,
+    co2 = toy_emissions_profile_products_ecoinvent,
+    all_activities_scenario_sectors = toy_all_activities_scenario_sectors,
+    scenarios = toy_sector_profile_any_scenarios,
+    for_webtool = TRUE
+  )
+
+  expect_equal(names(for_webtool), names(not_for_webtool))
+
+  not_for_webtool_company <- not_for_webtool |>
+    unnest_company() |>
+    ncol()
+  for_webtool_company <- for_webtool |>
+    unnest_company() |>
+    ncol()
+  expect_true(for_webtool_company < not_for_webtool_company)
+})
+
+test_that("case 3 companies are identified correctly", {
+  # To identify which companies belong to Case 3, please follow this link:
+  # https://github.com/2DegreesInvesting/TiltDevProjectMGMT/issues/169#issuecomment-2284344632
+  case3_companies <- tribble(
+    # styler: off
+    ~companies_id, ~product, ~matched_activity_name, ~sector_target,
+           "comp",      "a", "market for tap water",            2.0,
+           "comp",      "b", "market for tap water",       NA_real_,
+           "comp",      "c",          NA_character_,            2.0,
+           "comp",      "d",          NA_character_,       NA_real_,
+    "case_3_comp",      "a",          NA_character_,       NA_real_,
+    "case_3_comp",      "b",          NA_character_,       NA_real_
+    # styler: on
+  )
+  result <- identify_case3_companies(case3_companies)
+
+  expected_case3_company <- "case_3_comp"
+  expect_true(unique(result$companies_id) == expected_case3_company)
+})
+
+test_that("All columns are rounded off to 4 decimal places", {
+  round_cols <- tribble(
+    # styler: off
+    ~character_col, ~logical_col, ~numerical_col,
+            "comp",        TRUE,       2.9393876,
+            "comp",       FALSE,        NA_real_,
+            "comp",        TRUE,       4.5895959
+    # styler: on
+  )
+
+  result <- round_off_to_4_decimal_places_impl(round_cols)
+  expect_true(all(result$numerical_col == round(result$numerical_col, 4), na.rm = TRUE))
+})
+