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| 1 | +best_and_worst_cases <- function(data) { |
| 2 | + data |> |
| 3 | + mutate(amount_of_distinct_products = dplyr::n_distinct(.data$clustered), .by = "companies_id") |> |
| 4 | + mutate(equal_weight = (1/.data$amount_of_distinct_products)) |> |
| 5 | + lowest_risk_category_per_company(.by = "companies_id") |> |
| 6 | + highest_risk_category_per_company(.by = "companies_id") |> |
| 7 | + mutate(dummy_best = ifelse(.data$risk_category == .data$min_risk_category_per_company, 1, 0)) |> |
| 8 | + mutate(dummy_worst = ifelse(.data$risk_category == .data$max_risk_category_per_company, 1, 0)) |> |
| 9 | + mutate(count_best_case_products_per_company_benchmark = sum(.data$dummy_best), .by = c("companies_id", "grouped_by")) |> |
| 10 | + mutate(count_worst_case_products_per_company_benchmark = sum(.data$dummy_worst), .by = c("companies_id", "grouped_by")) |> |
| 11 | + mutate(best_case = ifelse(.data$count_best_case_products_per_company_benchmark == 0, NA, .data$dummy_best/.data$count_best_case_products_per_company_benchmark)) |> |
| 12 | + mutate(worst_case = ifelse(.data$count_worst_case_products_per_company_benchmark == 0, NA, .data$dummy_worst/.data$count_worst_case_products_per_company_benchmark)) |
| 13 | +} |
| 14 | + |
| 15 | +lowest_risk_category_per_company <- function(data, .by) { |
| 16 | + risk_order <- c("low", "medium", "high") |
| 17 | + mutate(data, min_risk_category_per_company = risk_order[which(risk_order %in% .data$risk_category)[1]], .by = all_of(.by)) |
| 18 | +} |
| 19 | + |
| 20 | +highest_risk_category_per_company <- function(data, .by) { |
| 21 | + risk_order <- c("high", "medium", "low") |
| 22 | + mutate(data, max_risk_category_per_company = risk_order[which(risk_order %in% .data$risk_category)[1]], .by = all_of(.by)) |
| 23 | +} |
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