diff --git a/NAMESPACE b/NAMESPACE index 2c41cb1..7278350 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -9,12 +9,16 @@ export(map_region_risk) export(modes) export(plot_sankey) export(scatter_plot_financial) +export(scenarios) +export(scenarios_financial) export(theme_tiltplot) +export(years) importFrom(dplyr,arrange) importFrom(dplyr,bind_rows) importFrom(dplyr,case_when) importFrom(dplyr,distinct) importFrom(dplyr,filter) +importFrom(dplyr,first) importFrom(dplyr,group_by) importFrom(dplyr,inner_join) importFrom(dplyr,left_join) diff --git a/NEWS.md b/NEWS.md index c9532ce..2c277e3 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,5 +1,11 @@ +# tiltPlot 0.0.0.9003 (2024-07-09) + +* tiltPlot data fosters a new toy data set without financials (`without_financial`) (#137). +* `bar_plot_emission_profile()` has two new arguments: `scenario` and `year`. +* `map_region_risk()` has two new arguments: `scenario` and `year`. + # tiltPlot 0.0.0.9002 (2024-06-04) * `bar_plot_emission_profile()` has now modes that the user can choose from (#134). diff --git a/R/bar_plot_emission_profile.R b/R/bar_plot_emission_profile.R index f91d101..5e8d673 100644 --- a/R/bar_plot_emission_profile.R +++ b/R/bar_plot_emission_profile.R @@ -8,6 +8,8 @@ #' emission profiles will be plotted. The user can choose from one to several #' benchmark(s) to be plotted. #' @param mode A character vector: `r toString(modes())`. +#' @param scenario A character vector: `r toString(scenarios())`. +#' @param year A character vector: `r toString(years())`. #' #' @return A [ggplot] object. #' @@ -18,14 +20,19 @@ #' bar_plot_emission_profile(without_financial, benchmarks) bar_plot_emission_profile <- function(data, benchmarks = benchmarks(), - mode = modes()) { + mode = modes(), + scenario = scenarios(), + year = years()) { benchmarks <- arg_match(benchmarks, multiple = TRUE) mode <- mode |> - arg_match() + arg_match() |> + switch_mode_emission_profile() + scenario <- arg_match(scenario) + year <- year data |> - check_bar_plot_emission_profile() |> - prepare_bar_plot_emission_profile(benchmarks, mode) |> + check_bar_plot_emission_profile(mode) |> + prepare_bar_plot_emission_profile(benchmarks = benchmarks, mode = mode, scenario = scenario, year = year) |> plot_bar_plot_emission_profile_impl() } @@ -35,10 +42,10 @@ bar_plot_emission_profile <- function(data, #' #' @return A data frame #' @noRd -check_bar_plot_emission_profile <- function(data) { +check_bar_plot_emission_profile <- function(data, mode) { crucial <- c( "benchmark", - modes(), + mode, aka("risk_category") ) data |> check_crucial_names(names_matching(data, crucial)) @@ -53,14 +60,16 @@ check_bar_plot_emission_profile <- function(data) { #' @return A data frame. #' #' @noRd -prepare_bar_plot_emission_profile <- function(data, benchmarks, mode) { - risk_var <- names_matching(data, aka("risk_category")) +prepare_bar_plot_emission_profile <- function(data, benchmarks, mode, scenario, year) { + risk_var <- get_colname(data, aka("risk_category")) data <- data |> mutate(risk_category_var = as_risk_category(.data[[risk_var]])) data <- data |> - filter(.data$benchmark %in% benchmarks) |> + filter((.data$benchmark %in% .env$benchmarks & + .data$scenario == .env$scenario & + .data$year == .env$year)) |> group_by(.data$risk_category_var, .data$benchmark) |> summarise(total_mode = sum(.data[[mode]])) |> group_by(.data$benchmark) |> @@ -80,5 +89,5 @@ plot_bar_plot_emission_profile_impl <- function(data) { geom_col(position = position_stack(reverse = TRUE), width = width_bar()) + fill_score_colors() + theme_tiltplot() + - xlim(0, 1) + xlim(0, NA) } diff --git a/R/custom_gradient_color.R b/R/custom_gradient_color.R index a976137..0630df9 100644 --- a/R/custom_gradient_color.R +++ b/R/custom_gradient_color.R @@ -24,7 +24,6 @@ custom_gradient_color <- function(risk_high = 1, risk_medium = 1, risk_low = 1) # interpolate the colors based on proportions : 1 is highest intensity final_color <- high_color * risk_high + medium_color * risk_medium + low_color * risk_low - final_color <- do.call(rgb, as.list(final_color)) - - return(final_color) + final_color <- do.call(rgb, c(as.list(final_color))) + final_color } diff --git a/R/example_data.R b/R/example_data.R index 853c8b9..98d2049 100644 --- a/R/example_data.R +++ b/R/example_data.R @@ -25,16 +25,20 @@ default_financial <- function(bank_id = "a", default_without_financial <- function(company_name = "a", emission_profile = "medium", benchmark = "all", - equal_weight = 0.1, - worst_case = 0.1, - best_case = 0.1) { + scenario = "1.5C RPS", + year = 2030, + equal_weight_emission_profile = 0.1, + worst_case_emission_profile = 0.1, + best_case_emission_profile = 0.1) { tibble( company_name = company_name, emission_profile = emission_profile, benchmark = benchmark, - equal_weight = equal_weight, - worst_case = worst_case, - best_case = best_case + scenario = scenario, + year = year, + equal_weight_emission_profile = equal_weight_emission_profile, + worst_case_emission_profile = worst_case_emission_profile, + best_case_emission_profile = best_case_emission_profile ) } diff --git a/R/map_region_risk.R b/R/map_region_risk.R index a16dd3e..aa226ca 100644 --- a/R/map_region_risk.R +++ b/R/map_region_risk.R @@ -10,6 +10,8 @@ #' default mode. #' @param mode The mode to plot. It can be one of "equal_weight", "worst_case" #' or "best_case". If nothing is chosen, "equal_weight" is the default mode. +#' @param scenario A character vector: `r toString(scenarios())`. +#' @param year A character vector: `r toString(years())`. #' #' @return A ggplot2 object representing the country data plot. #' @export @@ -22,20 +24,17 @@ map_region_risk <- function(data, # TODO: plot for other countries country_code = c("DE"), - benchmark = c( - "all", - "isic_4digit", - "tilt_sector", - "unit", - "unit_isic_4digit", - "unit_tilt_sector" - ), - mode = c("equal_weight", "worst_case", "best_case")) { + benchmark = benchmarks(), + mode = modes(), + scenario = scenarios(), + year = years()) { prepared_data <- prepare_geo_data( data, country_code, benchmark, - mode + mode, + scenario, + year ) shp_1 <- prepared_data[[1]] aggregated_data <- prepared_data[[2]] diff --git a/R/prepare_geo_data.R b/R/prepare_geo_data.R index 05f51a5..eb00922 100644 --- a/R/prepare_geo_data.R +++ b/R/prepare_geo_data.R @@ -10,23 +10,27 @@ prepare_geo_data <- function(data, country_code = c("DE"), benchmark = benchmarks(), - mode = c( - "equal_weight", - "worst_case", - "best_case" - )) { + mode = modes(), + scenario = scenarios(), + year = years()) { benchmark <- arg_match(benchmark) - mode <- arg_match(mode) + mode <- mode |> + arg_match() |> + switch_mode_emission_profile() country_code <- arg_match(country_code) + scenario <- arg_match(scenario) + year <- year crucial <- c( aka("risk_category"), - "company_name", + aka("companies_id"), "postcode", - "benchmark" + "benchmark", + "scenario", + aka("year") ) data |> check_crucial_names(names_matching(data, crucial)) - risk_var <- names_matching(data, aka("risk_category")) + risk_var <- get_colname(data, aka("risk_category")) data <- data |> mutate(risk_category_var = as_risk_category(data[[risk_var]])) @@ -50,7 +54,11 @@ prepare_geo_data <- function(data, # merge shapefile with financial data geo <- data |> - filter(benchmark == .env$benchmark) |> + filter( + .data$benchmark == .env$benchmark, + .data$scenario == .env$scenario, + .data$year == .env$year + ) |> left_join(shp_1, by = "postcode") |> st_as_sf() @@ -59,7 +67,6 @@ prepare_geo_data <- function(data, list(shp_1, aggregated_data) } - #' Aggregate Geo Data #' #' @param geo A data frame containing geographical data. @@ -84,52 +91,28 @@ prepare_geo_data <- function(data, #' #' aggregate_geo(geo, mode = "worst_case") aggregate_geo <- function(geo, mode) { - if (mode %in% c("worst_case", "best_case")) { - aggregated_data <- geo |> - group_by(.data$postcode, .data$company_name) |> - mutate( - # Choose the worst or best risk category and set the others to 0. - proportion = calculate_case_proportions(.data$risk_category_var, mode) - ) |> - group_by(.data$postcode, .data$risk_category_var) |> - summarize(proportion = sum(.data$proportion)) |> - ungroup() - } else if (mode == "equal_weight") { - aggregated_data <- geo |> - group_by(.data$postcode, .data$risk_category_var) |> - summarize(count = n()) |> - # Do not group by company here since all of them have equal weights. - group_by(.data$postcode) |> - mutate(proportion = .data$count / sum(.data$count)) |> - ungroup() - } + aggregated_data <- geo |> + group_by(.data$postcode, .data$risk_category_var) |> + summarise(total_mode = sum(.data[[mode]])) |> + group_by(.data$postcode) |> + mutate(proportion = total_mode / sum(total_mode)) |> + ungroup() - # apply custom_gradient_color to each row + # Pivot aggregated_data <- aggregated_data |> pivot_wider(names_from = "risk_category_var", values_from = "proportion", values_fill = 0) |> - mutate(color = pmap(list(.data$high, .data$medium, .data$low), custom_gradient_color)) -} + filter(.data$total_mode != 0) -#' Calculate Proportions for Worst or Best Case Scenarios -#' -#' @param categories A factor vector of risk categories. -#' @param mode A character string specifying the mode. -#' -#' @return A numeric vector representing the calculated proportions for each -#' category. -#' -#' @examples -#' categories <- as_risk_category(c("low", "medium", "medium", "high")) -#' calculate_case_proportions(categories, mode = "worst_case") -#' @noRd -calculate_case_proportions <- function(categories, mode) { - if (mode == "worst_case") { - extreme_risk <- levels(categories)[max(as.integer(categories))] - } else if (mode == "best_case") { - extreme_risk <- levels(categories)[min(as.integer(categories))] - } - - is_extreme <- categories == extreme_risk - proportions <- ifelse(is_extreme, 1 / sum(is_extreme), 0) - proportions + # Calculate color row by row + aggregated_data <- aggregated_data |> + group_by(.data$postcode) |> + summarise( + total_mode = add(.data$total_mode), + geometry = first(.data$geometry), + low = add(.data$low), + medium = add(.data$medium), + high = add(.data$high) + ) |> + mutate(color = pmap(list(.data$high, .data$medium, .data$low), custom_gradient_color)) + aggregated_data } diff --git a/R/scatter_plot_financial.R b/R/scatter_plot_financial.R index 4214bcb..2e47a68 100644 --- a/R/scatter_plot_financial.R +++ b/R/scatter_plot_financial.R @@ -21,13 +21,9 @@ #' ) scatter_plot_financial <- function(data, benchmarks = benchmarks(), - mode = c( - "equal_weight", - "worst_case", - "best_case" - ), - scenario = c("IPR", "WEO"), - year = c(2030, 2050)) { + mode = modes(), + scenario = scenarios_financial(), + year = years()) { # FIXME: .env$ instead of _arg seems to cause a bug only for benchmarks. benchmarks_arg <- arg_match(benchmarks, multiple = TRUE) scenario <- arg_match(scenario) diff --git a/R/tiltPlot-package.R b/R/tiltPlot-package.R index a7f61b8..f699fae 100644 --- a/R/tiltPlot-package.R +++ b/R/tiltPlot-package.R @@ -7,6 +7,7 @@ #' @importFrom dplyr case_when #' @importFrom dplyr distinct #' @importFrom dplyr filter +#' @importFrom dplyr first #' @importFrom dplyr group_by #' @importFrom dplyr inner_join #' @importFrom dplyr left_join diff --git a/R/utils.R b/R/utils.R index 42c52c7..d779023 100644 --- a/R/utils.R +++ b/R/utils.R @@ -61,6 +61,27 @@ switch_mode <- function(mode) { ) } +#' Switch mode function for emission profile plots +#' +#' @param mode A character string. +#' +#' @return A character string. +#' +#' @examples +#' switch_mode("equal_weight") +#' # Returns: "equal_weight_emission_profile" +#' +#' switch_mode("worst_case") +#' # Returns: "worst_case_emission_profile" +#' @noRd +switch_mode_emission_profile <- function(mode) { + switch(mode, + "equal_weight" = "equal_weight_emission_profile", + "worst_case" = "worst_case_emission_profile", + "best_case" = "best_case_emission_profile" + ) +} + #' Get column names matching a specific pattern #' #' This function takes a data frame and a pattern as input and returns the column names @@ -81,6 +102,24 @@ names_matching <- function(data, pattern) { names(select(data, matches(pattern))) } +#' Get column name +#' +#' @param data A data frame. +#' @param column_name A character vector. +#' +#' @return A character vector. +#' +#' @examples +#' data <- data.frame(ab = 1:3, abc = 4:6) +#' +#' # Get column name "abc" +#' get_colname(data, "abc") +#' @noRd +get_colname <- function(data, column_name) { + exact_match <- which(names(data) == column_name) + names(data)[exact_match] +} + #' Convert vector to risk category #' #' @param x @@ -102,6 +141,8 @@ check_levels <- function(x) { plot_data <- function(p, name) p$data[[name]] +add <- function(x) sum(x, na.rm = TRUE) + risk_category_levels <- function() c("low", "medium", "high") #' Benchmarks used in the functions' arguments. @@ -129,15 +170,19 @@ benchmarks <- function() { dictionary <- function() { #styler: off tibble::tribble( - ~aka, ~column, - "europages_product", "ep_product", - "profile_ranking", "profile_ranking", - "risk_category", "emission_profile", - "scenario", "scenario", - "transition_risk_score", "transition_risk_score", - "transition_risk", "transition_risk", - "tilt_sector", "tilt_sector", - "year", "year" + ~aka, ~column, + "best_case", "best_case_emission_profile", + "companies_id", "companies_id", + "europages_product", "ep_product", + "equal_weight", "equal_weight_emission_profile", + "profile_ranking", "profile_ranking", + "risk_category", "emission_profile", + "scenario", "scenario", + "transition_risk_score", "transition_risk_score", + "transition_risk", "transition_risk", + "tilt_sector", "tilt_sector", + "year", "year", + "worst_case", "worst_case_emission_profile" ) # styler: on } @@ -162,3 +207,36 @@ modes <- function() { "worst_case" ) } + +#' Scenarios +#' +#' @keywords internal +#' @export +scenarios <- function() { + c( + "1.5C RPS", + "NZ 2050" + ) +} + +#' Scenarios Financial +#' +#' @keywords internal +#' @export +scenarios_financial <- function() { + c( + "IPR", + "WEO" + ) +} + +#' Years +#' +#' @keywords internal +#' @export +years <- function() { + c( + 2030, + 2050 + ) +} diff --git a/README.Rmd b/README.Rmd index 02126b9..0fbe678 100644 --- a/README.Rmd +++ b/README.Rmd @@ -110,18 +110,19 @@ The user can choose any number of benchmark to be plotted. ```{r} no_fin <- without_financial -benchmarks <- c("all", "isic_4digit", "unit") +benchmarks <- c("unit", "unit_tilt_sector") +company_name <- no_fin$companies_id[1] no_fin |> - filter(company_name == "peter") |> - bar_plot_emission_profile(benchmarks, mode = "equal_weight") + + filter(companies_id == company_name) |> + bar_plot_emission_profile(benchmarks, mode = "equal_weight", scenario = "1.5C RPS", year = 2030) + labs(title = "Emission profile of all products on a company level") ``` Plot on a portfolio level. ```{r} -bar_plot_emission_profile(no_fin, benchmarks, mode = "equal_weight") + +bar_plot_emission_profile(no_fin, benchmarks, mode = "equal_weight", scenario = "1.5C RPS", year = 2030) + labs(title = "Emission profile of all products on a portfolio level") ``` @@ -130,7 +131,7 @@ bar_plot_emission_profile(no_fin, benchmarks, mode = "equal_weight") + ```{r} fin <- financial -scenario <- "WEO" +scenario <- "IPR" year <- 2030 benchmarks <- c("all", "unit") mode <- "best_case" @@ -152,7 +153,7 @@ If nothing is chosen, equal_weight the default mode. ```{r} no_fin <- without_financial -map_region_risk(no_fin, "DE", benchmark = "tilt_sector", mode = "best_case") + +map_region_risk(no_fin, "DE", benchmark = "unit_tilt_sector", mode = "worst_case", scenario = "NZ 2050", year = 2030) + labs(title = "German map of high, medium and low proportions of the companies that are found in one region. © EuroGeographics for the administrative boundaries ") diff --git a/README.md b/README.md index 31577d4..4e1e396 100644 --- a/README.md +++ b/README.md @@ -143,23 +143,26 @@ bar_plot_emission_profile_financial(fin, benchmarks, mode = "equal_weight") + ``` r without_financial -#> # A tibble: 114 × 12 -#> company_name postcode emission_profile benchmark ep_product tilt_sector -#> -#> 1 bruno 27568 high all car D -#> 2 bruno 27568 high all steel C -#> 3 bruno 27568 medium all wheat B -#> 4 mauro 39221 high all steel C -#> 5 mauro 39221 high all machine C -#> 6 mirja 34117 high all tractor D -#> 7 mirja 34117 high all cattle A -#> 8 pasant 80337 high all tractor D -#> 9 pasant 80337 low all apple A -#> 10 peter 88131 high all banana A -#> # ℹ 104 more rows -#> # ℹ 6 more variables: tilt_subsector , isic_4digit , -#> # isic_4digit_name , equal_weight , worst_case , -#> # best_case +#> # A tibble: 252 × 35 +#> companies_id country postcode main_activity ep_product activity_uuid_produc…¹ +#> +#> 1 %ef%bb%bfma… germany 12043 wholesaler surface c… a62eb0d6-9120-541c-97… +#> 2 %ef%bb%bfma… germany 12043 wholesaler surface c… a62eb0d6-9120-541c-97… +#> 3 %ef%bb%bfma… germany 12043 wholesaler surface c… a62eb0d6-9120-541c-97… +#> 4 %ef%bb%bfma… germany 12043 wholesaler surface c… a62eb0d6-9120-541c-97… +#> 5 %ef%bb%bfma… germany 12043 wholesaler surface c… a62eb0d6-9120-541c-97… +#> 6 %ef%bb%bfma… germany 12043 wholesaler surface c… a62eb0d6-9120-541c-97… +#> 7 %ef%bb%bfma… germany 12043 wholesaler surface c… a62eb0d6-9120-541c-97… +#> 8 %ef%bb%bfma… germany 12043 wholesaler surface c… a62eb0d6-9120-541c-97… +#> 9 %ef%bb%bfma… germany 12043 wholesaler hand tool… 7c082396-1f14-5674-86… +#> 10 %ef%bb%bfma… germany 12043 wholesaler hand tool… 7c082396-1f14-5674-86… +#> # ℹ 242 more rows +#> # ℹ abbreviated name: ¹​activity_uuid_product_uuid +#> # ℹ 29 more variables: matched_activity_name , +#> # matched_reference_product , unit , co2e_lower , +#> # co2e_upper , emission_profile , benchmark , +#> # profile_ranking , tilt_sector , tilt_subsector , +#> # sector_profile , scenario , year , … ``` Plot on a company level. The user can choose any number of benchmark to @@ -168,11 +171,12 @@ be plotted. ``` r no_fin <- without_financial -benchmarks <- c("all", "isic_4digit", "unit") +benchmarks <- c("unit", "unit_tilt_sector") +company_name <- no_fin$companies_id[1] no_fin |> - filter(company_name == "peter") |> - bar_plot_emission_profile(benchmarks, mode = "equal_weight") + + filter(companies_id == company_name) |> + bar_plot_emission_profile(benchmarks, mode = "equal_weight", scenario = "1.5C RPS", year = 2030) + labs(title = "Emission profile of all products on a company level") ``` @@ -181,7 +185,7 @@ no_fin |> Plot on a portfolio level. ``` r -bar_plot_emission_profile(no_fin, benchmarks, mode = "equal_weight") + +bar_plot_emission_profile(no_fin, benchmarks, mode = "equal_weight", scenario = "1.5C RPS", year = 2030) + labs(title = "Emission profile of all products on a portfolio level") ``` @@ -191,7 +195,7 @@ bar_plot_emission_profile(no_fin, benchmarks, mode = "equal_weight") + ``` r fin <- financial -scenario <- "WEO" +scenario <- "IPR" year <- 2030 benchmarks <- c("all", "unit") mode <- "best_case" @@ -214,12 +218,11 @@ Different modes can be chosen: “equal_weight”, “worst_case” and ``` r no_fin <- without_financial -map_region_risk(no_fin, "DE", benchmark = "tilt_sector", mode = "best_case") + +map_region_risk(no_fin, "DE", benchmark = "unit_tilt_sector", mode = "worst_case", scenario = "NZ 2050", year = 2030) + labs(title = "German map of high, medium and low proportions of the companies that are found in one region. © EuroGeographics for the administrative boundaries ") #> Extracting data using giscoR package, please report issues on https://github.com/rOpenGov/giscoR/issues -#> Cache management as per giscoR. see 'giscoR::gisco_get_nuts()' ``` diff --git a/data-raw/without_financial.R b/data-raw/without_financial.R index cc85b4d..93a8309 100644 --- a/data-raw/without_financial.R +++ b/data-raw/without_financial.R @@ -1,121 +1,258 @@ # styler: off without_financial <- tibble::tribble( - ~company_name, ~postcode, ~emission_profile, ~benchmark, ~ep_product, ~tilt_sector, ~tilt_subsector, ~isic_4digit, ~isic_4digit_name, ~equal_weight, ~worst_case, ~best_case, - "bruno", 27568L, "high", "all", "car", "D", "d", "4100", "d", 0.3333333, 0.5, 0, - "bruno", 27568L, "high", "all", "steel", "C", "c", "4100", "c", 0.3333333, 0.5, 0, - "bruno", 27568L, "medium", "all", "wheat", "B", "b", "4100", "b", 0.3333333, 0, 1, - "mauro", 39221L, "high", "all", "steel", "C", "c", "4100", "c", 0.5, 0.5, 0.5, - "mauro", 39221L, "high", "all", "machine", "C", "c", "4100", "c", 0.5, 0.5, 0.5, - "mirja", 34117L, "high", "all", "tractor", "D", "d", "4100", "d", 0.5, 0.5, 0.5, - "mirja", 34117L, "high", "all", "cattle", "A", "a", "4100", "a", 0.5, 0.5, 0.5, - "pasant", 80337L, "high", "all", "tractor", "D", "d", "4100", "d", 0.5, 1, 0, - "pasant", 80337L, "low", "all", "apple", "A", "a", "4100", "a", 0.5, 0, 1, - "peter", 88131L, "high", "all", "banana", "A", "a", "4100", "a", 0.5, 1, 0, - "peter", 88131L, "medium", "all", "wheat", "B", "b", "4100", "b", 0.5, 0, 1, - "peter peasant", 53773L, "high", "all", "car", "D", "d", "4100", "d", 0.5, 0.5, 0.5, - "peter peasant", 53773L, "high", "all", "car", "D", "d", "4100", "d", 0.5, 0.5, 0.5, - "tilman", 12043L, "high", "all", "car", "D", "d", "4100", "d", 0.1666667, 0.3333333, 0, - "tilman", 12043L, "high", "all", "tractor", "D", "d", "4100", "d", 0.1666667, 0.3333333, 0, - "tilman", 12043L, "high", "all", "steel", "C", "c", "4100", "c", 0.1666667, 0.3333333, 0, - "tilman", 12043L, "low", "all", "truck", "D", "d", "4100", "d", 0.1666667, 0, 0.5, - "tilman", 12043L, "low", "all", "truck", "D", "d", "4100", "d", 0.1666667, 0, 0.5, - "tilman", 12043L, "medium", "all", "iron", "C", "c", "4100", "c", 0.1666667, 0, 0, - "bruno", 27568L, "high", "isic_4digit", "car", "D", "d", "4100", "d", 0.3333333, 0.5, 0, - "bruno", 27568L, "high", "isic_4digit", "steel", "C", "c", "4100", "c", 0.3333333, 0.5, 0, - "bruno", 27568L, "medium", "isic_4digit", "wheat", "B", "b", "4100", "b", 0.3333333, 0, 1, - "mauro", 39221L, "high", "isic_4digit", "steel", "C", "c", "4100", "c", 0.5, 0.5, 0.5, - "mauro", 39221L, "high", "isic_4digit", "machine", "C", "c", "4100", "c", 0.5, 0.5, 0.5, - "mirja", 34117L, "high", "isic_4digit", "tractor", "D", "d", "4100", "d", 0.5, 0.5, 0.5, - "mirja", 34117L, "high", "isic_4digit", "cattle", "A", "a", "4100", "a", 0.5, 0.5, 0.5, - "pasant", 80337L, "high", "isic_4digit", "tractor", "D", "d", "4100", "d", 0.5, 1, 0, - "pasant", 80337L, "low", "isic_4digit", "apple", "A", "a", "4100", "a", 0.5, 0, 1, - "peter", 88131L, "high", "isic_4digit", "banana", "A", "a", "4100", "a", 0.5, 1, 0, - "peter", 88131L, "medium", "isic_4digit", "wheat", "B", "b", "4100", "b", 0.5, 0, 1, - "peter peasant", 53773L, "high", "isic_4digit", "car", "D", "d", "4100", "d", 0.5, 0.5, 0.5, - "peter peasant", 53773L, "high", "isic_4digit", "car", "D", "d", "4100", "d", 0.5, 0.5, 0.5, - "tilman", 12043L, "high", "isic_4digit", "car", "D", "d", "4100", "d", 0.1666667, 0.3333333, 0, - "tilman", 12043L, "high", "isic_4digit", "tractor", "D", "d", "4100", "d", 0.1666667, 0.3333333, 0, - "tilman", 12043L, "high", "isic_4digit", "steel", "C", "c", "4100", "c", 0.1666667, 0.3333333, 0, - "tilman", 12043L, "low", "isic_4digit", "truck", "D", "d", "4100", "d", 0.1666667, 0, 0.5, - "tilman", 12043L, "low", "isic_4digit", "truck", "D", "d", "4100", "d", 0.1666667, 0, 0.5, - "tilman", 12043L, "medium", "isic_4digit", "iron", "C", "c", "4100", "c", 0.1666667, 0, 0, - "bruno", 27568L, "high", "tilt_sector", "car", "D", "d", "4100", "d", 0.3333333, 0.5, 0, - "bruno", 27568L, "high", "tilt_sector", "steel", "C", "c", "4100", "c", 0.3333333, 0.5, 0, - "bruno", 27568L, "medium", "tilt_sector", "wheat", "B", "b", "4100", "b", 0.3333333, 0, 1, - "mauro", 39221L, "high", "tilt_sector", "steel", "C", "c", "4100", "c", 0.5, 0.5, 0.5, - "mauro", 39221L, "high", "tilt_sector", "machine", "C", "c", "4100", "c", 0.5, 0.5, 0.5, - "mirja", 34117L, "high", "tilt_sector", "tractor", "D", "d", "4100", "d", 0.5, 0.5, 0.5, - "mirja", 34117L, "high", "tilt_sector", "cattle", "A", "a", "4100", "a", 0.5, 0.5, 0.5, - "pasant", 80337L, "high", "tilt_sector", "tractor", "D", "d", "4100", "d", 0.5, 1, 0, - "pasant", 80337L, "low", "tilt_sector", "apple", "A", "a", "4100", "a", 0.5, 0, 1, - "peter", 88131L, "high", "tilt_sector", "banana", "A", "a", "4100", "a", 0.5, 1, 0, - "peter", 88131L, "medium", "tilt_sector", "wheat", "B", "b", "4100", "b", 0.5, 0, 1, - "peter peasant", 53773L, "high", "tilt_sector", "car", "D", "d", "4100", "d", 0.5, 0.5, 0.5, - "peter peasant", 53773L, "high", "tilt_sector", "car", "D", "d", "4100", "d", 0.5, 0.5, 0.5, - "tilman", 12043L, "high", "tilt_sector", "car", "D", "d", "4100", "d", 0.1666667, 0.3333333, 0, - "tilman", 12043L, "high", "tilt_sector", "tractor", "D", "d", "4100", "d", 0.1666667, 0.3333333, 0, - "tilman", 12043L, "high", "tilt_sector", "steel", "C", "c", "4100", "c", 0.1666667, 0.3333333, 0, - "tilman", 12043L, "low", "tilt_sector", "truck", "D", "d", "4100", "d", 0.1666667, 0, 0.5, - "tilman", 12043L, "low", "tilt_sector", "truck", "D", "d", "4100", "d", 0.1666667, 0, 0.5, - "tilman", 12043L, "medium", "tilt_sector", "iron", "C", "c", "4100", "c", 0.1666667, 0, 0, - "bruno", 27568L, "high", "unit", "car", "D", "d", "4100", "d", 0.3333333, 0.5, 0, - "bruno", 27568L, "high", "unit", "steel", "C", "c", "4100", "c", 0.3333333, 0.5, 0, - "bruno", 27568L, "medium", "unit", "wheat", "B", "b", "4100", "b", 0.3333333, 0, 1, - "mauro", 39221L, "high", "unit", "steel", "C", "c", "4100", "c", 0.5, 0.5, 0.5, - "mauro", 39221L, "high", "unit", "machine", "C", "c", "4100", "c", 0.5, 0.5, 0.5, - "mirja", 34117L, "high", "unit", "tractor", "D", "d", "4100", "d", 0.5, 0.5, 0.5, - "mirja", 34117L, "high", "unit", "cattle", "A", "a", "4100", "a", 0.5, 0.5, 0.5, - "pasant", 80337L, "high", "unit", "tractor", "D", "d", "4100", "d", 0.5, 1, 0, - "pasant", 80337L, "low", "unit", "apple", "A", "a", "4100", "a", 0.5, 0, 1, - "peter", 88131L, "high", "unit", "banana", "A", "a", "4100", "a", 0.5, 1, 0, - "peter", 88131L, "medium", "unit", "wheat", "B", "b", "4100", "b", 0.5, 0, 1, - "peter peasant", 53773L, "high", "unit", "car", "D", "d", "4100", "d", 0.5, 0.5, 0.5, - "peter peasant", 53773L, "high", "unit", "car", "D", "d", "4100", "d", 0.5, 0.5, 0.5, - "tilman", 12043L, "high", "unit", "car", "D", "d", "4100", "d", 0.1666667, 0.3333333, 0, - "tilman", 12043L, "high", "unit", "tractor", "D", "d", "4100", "d", 0.1666667, 0.3333333, 0, - "tilman", 12043L, "high", "unit", "steel", "C", "c", "4100", "c", 0.1666667, 0.3333333, 0, - "tilman", 12043L, "low", "unit", "truck", "D", "d", "4100", "d", 0.1666667, 0, 0.5, - "tilman", 12043L, "low", "unit", "truck", "D", "d", "4100", "d", 0.1666667, 0, 0.5, - "tilman", 12043L, "medium", "unit", "iron", "C", "c", "4100", "c", 0.1666667, 0, 0, - "bruno", 27568L, "high", "unit_isic_4digit", "car", "D", "d", "4100", "d", 0.3333333, 0.5, 0, - "bruno", 27568L, "high", "unit_isic_4digit", "steel", "C", "c", "4100", "c", 0.3333333, 0.5, 0, - "bruno", 27568L, "medium", "unit_isic_4digit", "wheat", "B", "b", "4100", "b", 0.3333333, 0, 1, - "mauro", 39221L, "high", "unit_isic_4digit", "steel", "C", "c", "4100", "c", 0.5, 0.5, 0.5, - "mauro", 39221L, "high", "unit_isic_4digit", "machine", "C", "c", "4100", "c", 0.5, 0.5, 0.5, - "mirja", 34117L, "high", "unit_isic_4digit", "tractor", "D", "d", "4100", "d", 0.5, 0.5, 0.5, - "mirja", 34117L, "high", "unit_isic_4digit", "cattle", "A", "a", "4100", "a", 0.5, 0.5, 0.5, - "pasant", 80337L, "high", "unit_isic_4digit", "tractor", "D", "d", "4100", "d", 0.5, 1, 0, - "pasant", 80337L, "low", "unit_isic_4digit", "apple", "A", "a", "4100", "a", 0.5, 0, 1, - "peter", 88131L, "high", "unit_isic_4digit", "banana", "A", "a", "4100", "a", 0.5, 1, 0, - "peter", 88131L, "medium", "unit_isic_4digit", "wheat", "B", "b", "4100", "b", 0.5, 0, 1, - "peter peasant", 53773L, "high", "unit_isic_4digit", "car", "D", "d", "4100", "d", 0.5, 0.5, 0.5, - "peter peasant", 53773L, "high", "unit_isic_4digit", "car", "D", "d", "4100", "d", 0.5, 0.5, 0.5, - "tilman", 12043L, "high", "unit_isic_4digit", "car", "D", "d", "4100", "d", 0.1666667, 0.3333333, 0, - "tilman", 12043L, "high", "unit_isic_4digit", "tractor", "D", "d", "4100", "d", 0.1666667, 0.3333333, 0, - "tilman", 12043L, "high", "unit_isic_4digit", "steel", "C", "c", "4100", "c", 0.1666667, 0.3333333, 0, - "tilman", 12043L, "low", "unit_isic_4digit", "truck", "D", "d", "4100", "d", 0.1666667, 0, 0.5, - "tilman", 12043L, "low", "unit_isic_4digit", "truck", "D", "d", "4100", "d", 0.1666667, 0, 0.5, - "tilman", 12043L, "medium", "unit_isic_4digit", "iron", "C", "c", "4100", "c", 0.1666667, 0, 0, - "bruno", 27568L, "high", "unit_tilt_sector", "car", "D", "d", "4100", "d", 0.3333333, 0.5, 0, - "bruno", 27568L, "high", "unit_tilt_sector", "steel", "C", "c", "4100", "c", 0.3333333, 0.5, 0, - "bruno", 27568L, "medium", "unit_tilt_sector", "wheat", "B", "b", "4100", "b", 0.3333333, 0, 1, - "mauro", 39221L, "high", "unit_tilt_sector", "steel", "C", "c", "4100", "c", 0.5, 0.5, 0.5, - "mauro", 39221L, "high", "unit_tilt_sector", "machine", "C", "c", "4100", "c", 0.5, 0.5, 0.5, - "mirja", 34117L, "high", "unit_tilt_sector", "tractor", "D", "d", "4100", "d", 0.5, 0.5, 0.5, - "mirja", 34117L, "high", "unit_tilt_sector", "cattle", "A", "a", "4100", "a", 0.5, 0.5, 0.5, - "pasant", 80337L, "high", "unit_tilt_sector", "tractor", "D", "d", "4100", "d", 0.5, 1, 0, - "pasant", 80337L, "low", "unit_tilt_sector", "apple", "A", "a", "4100", "a", 0.5, 0, 1, - "peter", 88131L, "high", "unit_tilt_sector", "banana", "A", "a", "4100", "a", 0.5, 1, 0, - "peter", 88131L, "medium", "unit_tilt_sector", "wheat", "B", "b", "4100", "b", 0.5, 0, 1, - "peter peasant", 53773L, "high", "unit_tilt_sector", "car", "D", "d", "4100", "d", 0.5, 0.5, 0.5, - "peter peasant", 53773L, "high", "unit_tilt_sector", "car", "D", "d", "4100", "d", 0.5, 0.5, 0.5, - "tilman", 12043L, "high", "unit_tilt_sector", "car", "D", "d", "4100", "d", 0.1666667, 0.3333333, 0, - "tilman", 12043L, "high", "unit_tilt_sector", "tractor", "D", "d", "4100", "d", 0.1666667, 0.3333333, 0, - "tilman", 12043L, "high", "unit_tilt_sector", "steel", "C", "c", "4100", "c", 0.1666667, 0.3333333, 0, - "tilman", 12043L, "low", "unit_tilt_sector", "truck", "D", "d", "4100", "d", 0.1666667, 0, 0.5, - "tilman", 12043L, "low", "unit_tilt_sector", "truck", "D", "d", "4100", "d", 0.1666667, 0, 0.5, - "tilman", 12043L, "medium", "unit_tilt_sector", "iron", "C", "c", "4100", "c", 0.1666667, 0, 0 + ~companies_id, ~country, ~postcode, ~main_activity, ~ep_product, ~activity_uuid_product_uuid, ~matched_activity_name, ~matched_reference_product, ~unit, ~co2e_lower, ~co2e_upper, ~emission_profile, ~benchmark, ~profile_ranking, ~tilt_sector, ~tilt_subsector, ~sector_profile, ~scenario, ~year, ~reduction_targets, ~benchmark_tr_score, ~transition_risk_score, ~transition_risk, ~min_headcount, ~max_headcount, ~equal_weight_emission_profile_with_all_products, ~equal_weight_emission_profile, ~worst_case_emission_profile, ~best_case_emission_profile, ~equal_weight_sector_profile_with_all_products, ~worst_case_sector_profile, ~best_case_sector_profile, ~equal_weight_transition_risk, ~worst_case_transition_risk, ~best_case_transition_risk, + "%ef%bb%bfmathias-maschinenhandel-laserservice_00000005053892-001", "germany", 12043L, "wholesaler", "surface coating", "a62eb0d6-9120-541c-9772-9b03dd0428b8_88d505a1-3996-4b69-941c-ba500a9f0ee5", "market for metal coating facility", "metal coating facility", "unit", -1.08245, 6.17e+10, "high", "unit", 0.671853, "construction", "construction residential", "medium", "1.5C RPS", 2030L, 0.18, "1.5C RPS_2030_unit", 0.425926, "high", NA, NA, 0.1428571429, 0.1428571429, 0.5, 0, 0.1428571429, 0, 0, 0.1428571429, 0.5, 0, + "%ef%bb%bfmathias-maschinenhandel-laserservice_00000005053892-001", "germany", 12043L, "wholesaler", "surface coating", "a62eb0d6-9120-541c-9772-9b03dd0428b8_88d505a1-3996-4b69-941c-ba500a9f0ee5", "market for metal coating facility", "metal coating facility", "unit", -1.08245, 6.17e+10, "high", "unit", 0.671853, "construction", "construction residential", "high", "1.5C RPS", 2050L, 0.98, "1.5C RPS_2050_unit", 0.825926, "high", NA, NA, 0.1428571429, 0.1428571429, 0.5, 0, 0.1428571429, 0.1428571429, 0.1428571429, 0.1428571429, 0.1666666667, 0, + "%ef%bb%bfmathias-maschinenhandel-laserservice_00000005053892-001", "germany", 12043L, "wholesaler", "surface coating", "a62eb0d6-9120-541c-9772-9b03dd0428b8_88d505a1-3996-4b69-941c-ba500a9f0ee5", "market for metal coating facility", "metal coating facility", "unit", -1.08245, 6.17e+10, "high", "unit", 0.671853, "construction", "construction residential", "high", "NZ 2050", 2030L, 0.4, "NZ 2050_2030_unit", 0.535926, "high", NA, NA, 0.1428571429, 0.1428571429, 0.5, 0, 0.1428571429, 0.5, 0, 0.1428571429, 0.3333333333, 0, + "%ef%bb%bfmathias-maschinenhandel-laserservice_00000005053892-001", "germany", 12043L, "wholesaler", "surface coating", "a62eb0d6-9120-541c-9772-9b03dd0428b8_88d505a1-3996-4b69-941c-ba500a9f0ee5", "market for metal coating facility", "metal coating facility", "unit", -1.08245, 6.17e+10, "high", "unit", 0.671853, "construction", "construction residential", "high", "NZ 2050", 2050L, 0.97, "NZ 2050_2050_unit", 0.820926, "high", NA, NA, 0.1428571429, 0.1428571429, 0.5, 0, 0.1428571429, 0.1666666667, 0.1666666667, 0.1428571429, 0.2, 0, + "%ef%bb%bfmathias-maschinenhandel-laserservice_00000005053892-001", "germany", 12043L, "wholesaler", "surface coating", "a62eb0d6-9120-541c-9772-9b03dd0428b8_88d505a1-3996-4b69-941c-ba500a9f0ee5", "market for metal coating facility", "metal coating facility", "unit", -1.50924, 508583.8, "low", "unit_tilt_sector", 0.295858, "construction", "construction residential", "medium", "1.5C RPS", 2030L, 0.18, "1.5C RPS_2030_unit_tilt_sector", 0.237929, "medium", NA, NA, 0.1428571429, 0.1428571429, 0, 0.5, 0.1428571429, 0, 0, 0.1428571429, 0, 0, + "%ef%bb%bfmathias-maschinenhandel-laserservice_00000005053892-001", "germany", 12043L, "wholesaler", "surface coating", "a62eb0d6-9120-541c-9772-9b03dd0428b8_88d505a1-3996-4b69-941c-ba500a9f0ee5", "market for metal coating facility", "metal coating facility", "unit", -1.50924, 508583.8, "low", "unit_tilt_sector", 0.295858, "construction", "construction residential", "high", "1.5C RPS", 2050L, 0.98, "1.5C RPS_2050_unit_tilt_sector", 0.637929, "medium", NA, NA, 0.1428571429, 0.1428571429, 0, 0.5, 0.1428571429, 0.1428571429, 0.1428571429, 0.1428571429, 0, 0.5, + "%ef%bb%bfmathias-maschinenhandel-laserservice_00000005053892-001", "germany", 12043L, "wholesaler", "surface coating", "a62eb0d6-9120-541c-9772-9b03dd0428b8_88d505a1-3996-4b69-941c-ba500a9f0ee5", "market for metal coating facility", "metal coating facility", "unit", -1.50924, 508583.8, "low", "unit_tilt_sector", 0.295858, 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"medium", "unit", 0.479602, "land use", "agriculture & livestock", "high", "1.5C RPS", 2030L, 0.62, "1.5C RPS_2030_unit", 0.549801, "high", NA, NA, 0.125, 0.125, 0.3333333333, 0, 0.125, 0.2, 0, 0.125, 0.3333333333, 0, + "getrankevertrieb-rothenburg-gmbh_00000004872771-001", "germany", 53773L, "wholesaler", "vegetable", "05aa297e-c17f-53ae-8aca-67722ead0b4f_f772b470-588c-4427-ade3-c4dad207f66b", "market for green asparagus", "green asparagus", "kg", 0.00109, 263338.8, "medium", "unit", 0.479602, "land use", "agriculture & livestock", "high", "1.5C RPS", 2050L, 1.32, "1.5C RPS_2050_unit", 0.899801, "high", NA, NA, 0.125, 0.125, 0.3333333333, 0, 0.125, 0.125, 0.125, 0.125, 0.3333333333, 0, + "getrankevertrieb-rothenburg-gmbh_00000004872771-001", "germany", 53773L, "wholesaler", "vegetable", "05aa297e-c17f-53ae-8aca-67722ead0b4f_f772b470-588c-4427-ade3-c4dad207f66b", "market for green asparagus", "green asparagus", "kg", -0.00225, 28546178, "medium", "unit_tilt_sector", 0.642713, "land use", "agriculture & livestock", "high", "1.5C RPS", 2030L, 0.62, "1.5C RPS_2030_unit_tilt_sector", 0.631356, "high", NA, NA, 0.125, 0.125, 0, 0, 0.125, 0.2, 0, 0.125, 0.3333333333, 0, + "getrankevertrieb-rothenburg-gmbh_00000004872771-001", "germany", 53773L, "wholesaler", "vegetable", "05aa297e-c17f-53ae-8aca-67722ead0b4f_f772b470-588c-4427-ade3-c4dad207f66b", "market for green asparagus", "green asparagus", "kg", -0.00225, 28546178, "medium", "unit_tilt_sector", 0.642713, "land use", "agriculture & livestock", "high", "1.5C RPS", 2050L, 1.32, "1.5C RPS_2050_unit_tilt_sector", 0.981356, "high", NA, NA, 0.125, 0.125, 0, 0, 0.125, 0.125, 0.125, 0.125, 0.3333333333, 0, + "getrankevertrieb-rothenburg-gmbh_00000004872771-001", "germany", 53773L, "wholesaler", "vegetable", "05aa297e-c17f-53ae-8aca-67722ead0b4f_f772b470-588c-4427-ade3-c4dad207f66b", "market for green asparagus", "green asparagus", "kg", 0.00109, 263338.8, "medium", "unit", 0.479602, "land use", "agriculture & livestock", NA, "NZ 2050", 2030L, NA, "NA_NA_NA", NA, NA, NA, NA, 0.125, 0.125, 0, 0, 0.125, 0, 0, 0.125, 0, 0, + "getrankevertrieb-rothenburg-gmbh_00000004872771-001", "germany", 53773L, "wholesaler", "vegetable", "05aa297e-c17f-53ae-8aca-67722ead0b4f_f772b470-588c-4427-ade3-c4dad207f66b", "market for green asparagus", "green asparagus", "kg", 0.00109, 263338.8, "medium", "unit", 0.479602, "land use", "agriculture & livestock", NA, "NZ 2050", 2050L, NA, "NA_NA_NA", NA, NA, NA, NA, 0.125, 0.125, 0, 0, 0.125, 0, 0, 0.125, 0, 0, + "getrankevertrieb-rothenburg-gmbh_00000004872771-001", "germany", 53773L, "wholesaler", "vegetable", "05aa297e-c17f-53ae-8aca-67722ead0b4f_f772b470-588c-4427-ade3-c4dad207f66b", "market for green asparagus", "green asparagus", "kg", -0.00225, 28546178, "medium", "unit_tilt_sector", 0.642713, "land use", "agriculture & livestock", NA, "NZ 2050", 2030L, NA, "NA_NA_NA", NA, NA, NA, NA, 0.125, 0.125, 0, 0, 0.125, 0, 0, 0.125, 0, 0, + "getrankevertrieb-rothenburg-gmbh_00000004872771-001", "germany", 53773L, "wholesaler", "vegetable", "05aa297e-c17f-53ae-8aca-67722ead0b4f_f772b470-588c-4427-ade3-c4dad207f66b", "market for green asparagus", "green asparagus", "kg", -0.00225, 28546178, "medium", "unit_tilt_sector", 0.642713, "land use", "agriculture & livestock", NA, "NZ 2050", 2050L, NA, "NA_NA_NA", NA, NA, NA, NA, 0.125, 0.125, 0, 0, 0.125, 0, 0, 0.125, 0, 0, + "getrankevertrieb-rothenburg-gmbh_00000004872771-001", "germany", 53773L, "wholesaler", "juice", "fa48ed8b-ca88-5835-b27c-fea02870db24_1f7bbd3e-fcd1-412d-8608-035b855ea735", "market for orange, fresh grade", "orange, fresh grade", "kg", -1.22516, 1491.734, "low", "unit", 0.250853, "land use", "agriculture & livestock", "high", "1.5C RPS", 2030L, 0.62, "1.5C RPS_2030_unit", 0.435426, "medium", NA, NA, 0.125, 0.125, 0, 0.2, 0.125, 0.2, 0, 0.125, 0, 0, + "getrankevertrieb-rothenburg-gmbh_00000004872771-001", "germany", 53773L, "wholesaler", "juice", "fa48ed8b-ca88-5835-b27c-fea02870db24_1f7bbd3e-fcd1-412d-8608-035b855ea735", "market for orange, fresh grade", "orange, fresh grade", "kg", -1.22516, 1491.734, "low", "unit", 0.250853, "land use", "agriculture & livestock", "high", "1.5C RPS", 2050L, 1.32, "1.5C RPS_2050_unit", 0.785426, "medium", NA, NA, 0.125, 0.125, 0, 0.2, 0.125, 0.125, 0.125, 0.125, 0, 0.2, + "getrankevertrieb-rothenburg-gmbh_00000004872771-001", "germany", 53773L, "wholesaler", "juice", "fa48ed8b-ca88-5835-b27c-fea02870db24_1f7bbd3e-fcd1-412d-8608-035b855ea735", "market for orange, fresh grade", "orange, fresh grade", "kg", -1.50924, 508583.8, "low", "unit_tilt_sector", 0.316802, "land use", "agriculture & livestock", "high", "1.5C RPS", 2030L, 0.62, "1.5C RPS_2030_unit_tilt_sector", 0.468401, "medium", NA, NA, 0.125, 0.125, 0, 0.1666666667, 0.125, 0.2, 0, 0.125, 0, 0, + "getrankevertrieb-rothenburg-gmbh_00000004872771-001", "germany", 53773L, "wholesaler", "juice", "fa48ed8b-ca88-5835-b27c-fea02870db24_1f7bbd3e-fcd1-412d-8608-035b855ea735", "market for orange, fresh grade", "orange, fresh grade", "kg", -1.50924, 508583.8, "low", "unit_tilt_sector", 0.316802, "land use", "agriculture & livestock", "high", "1.5C RPS", 2050L, 1.32, "1.5C RPS_2050_unit_tilt_sector", 0.818401, "medium", NA, NA, 0.125, 0.125, 0, 0.1666666667, 0.125, 0.125, 0.125, 0.125, 0, 0.2, + "getrankevertrieb-rothenburg-gmbh_00000004872771-001", "germany", 53773L, "wholesaler", "juice", "fa48ed8b-ca88-5835-b27c-fea02870db24_1f7bbd3e-fcd1-412d-8608-035b855ea735", "market for orange, fresh grade", "orange, fresh grade", "kg", -1.22516, 1491.734, "low", "unit", 0.250853, "land use", "agriculture & livestock", NA, "NZ 2050", 2030L, NA, "NA_NA_NA", NA, NA, NA, NA, 0.125, 0.125, 0, 0, 0.125, 0, 0, 0.125, 0, 0, + "getrankevertrieb-rothenburg-gmbh_00000004872771-001", "germany", 53773L, "wholesaler", "juice", "fa48ed8b-ca88-5835-b27c-fea02870db24_1f7bbd3e-fcd1-412d-8608-035b855ea735", "market for orange, fresh grade", "orange, fresh grade", "kg", -1.22516, 1491.734, "low", "unit", 0.250853, "land use", "agriculture & livestock", NA, "NZ 2050", 2050L, NA, "NA_NA_NA", NA, NA, NA, NA, 0.125, 0.125, 0, 0, 0.125, 0, 0, 0.125, 0, 0, + "getrankevertrieb-rothenburg-gmbh_00000004872771-001", "germany", 53773L, "wholesaler", "juice", "fa48ed8b-ca88-5835-b27c-fea02870db24_1f7bbd3e-fcd1-412d-8608-035b855ea735", "market for orange, fresh grade", "orange, fresh grade", "kg", -1.50924, 508583.8, "low", "unit_tilt_sector", 0.316802, "land use", "agriculture & livestock", NA, "NZ 2050", 2030L, NA, "NA_NA_NA", NA, NA, NA, NA, 0.125, 0.125, 0, 0, 0.125, 0, 0, 0.125, 0, 0, + "getrankevertrieb-rothenburg-gmbh_00000004872771-001", "germany", 53773L, "wholesaler", "juice", "fa48ed8b-ca88-5835-b27c-fea02870db24_1f7bbd3e-fcd1-412d-8608-035b855ea735", "market for orange, fresh grade", "orange, fresh grade", "kg", -1.50924, 508583.8, "low", "unit_tilt_sector", 0.316802, "land use", "agriculture & livestock", NA, "NZ 2050", 2050L, NA, "NA_NA_NA", NA, NA, NA, NA, 0.125, 0.125, 0, 0, 0.125, 0, 0, 0.125, 0, 0, + "getrankevertrieb-rothenburg-gmbh_00000004872771-001", "germany", 53773L, "wholesaler", "fruit", "31a43832-e06a-597c-b6f9-05164dd585e3_e4c082c9-dec1-41d1-8fca-0457c8f630e7", "market for jatropha seed", "jatropha seed", "kg", 0.00109, 263338.8, "medium", "unit", 0.514315, "land use", "agriculture & livestock", "high", "1.5C RPS", 2030L, 0.62, "1.5C RPS_2030_unit", 0.567158, "high", NA, NA, 0.125, 0.125, 0.3333333333, 0, 0.125, 0.2, 0, 0.125, 0.3333333333, 0, + "getrankevertrieb-rothenburg-gmbh_00000004872771-001", "germany", 53773L, "wholesaler", "fruit", "31a43832-e06a-597c-b6f9-05164dd585e3_e4c082c9-dec1-41d1-8fca-0457c8f630e7", "market for jatropha seed", "jatropha seed", "kg", 0.00109, 263338.8, "medium", "unit", 0.514315, "land use", "agriculture & livestock", "high", "1.5C RPS", 2050L, 1.32, "1.5C RPS_2050_unit", 0.917158, "high", NA, NA, 0.125, 0.125, 0.3333333333, 0, 0.125, 0.125, 0.125, 0.125, 0.3333333333, 0, + "getrankevertrieb-rothenburg-gmbh_00000004872771-001", "germany", 53773L, "wholesaler", "fruit", "31a43832-e06a-597c-b6f9-05164dd585e3_e4c082c9-dec1-41d1-8fca-0457c8f630e7", "market for jatropha seed", "jatropha seed", "kg", 0.002175, 6.88e+10, "high", "unit_tilt_sector", 0.673077, "land use", "agriculture & livestock", "high", "1.5C RPS", 2030L, 0.62, "1.5C RPS_2030_unit_tilt_sector", 0.646538, "high", NA, NA, 0.125, 0.125, 1, 0, 0.125, 0.2, 0, 0.125, 0.3333333333, 0, + "getrankevertrieb-rothenburg-gmbh_00000004872771-001", "germany", 53773L, "wholesaler", "fruit", "31a43832-e06a-597c-b6f9-05164dd585e3_e4c082c9-dec1-41d1-8fca-0457c8f630e7", "market for jatropha seed", "jatropha seed", "kg", 0.002175, 6.88e+10, "high", "unit_tilt_sector", 0.673077, "land use", "agriculture & livestock", "high", "1.5C RPS", 2050L, 1.32, "1.5C RPS_2050_unit_tilt_sector", 0.996538, "high", NA, NA, 0.125, 0.125, 1, 0, 0.125, 0.125, 0.125, 0.125, 0.3333333333, 0, + "getrankevertrieb-rothenburg-gmbh_00000004872771-001", "germany", 53773L, "wholesaler", "fruit", "31a43832-e06a-597c-b6f9-05164dd585e3_e4c082c9-dec1-41d1-8fca-0457c8f630e7", "market for jatropha seed", "jatropha seed", "kg", 0.00109, 263338.8, "medium", "unit", 0.514315, "land use", "agriculture & livestock", NA, "NZ 2050", 2030L, NA, "NA_NA_NA", NA, NA, NA, NA, 0.125, 0.125, 0, 0, 0.125, 0, 0, 0.125, 0, 0, + "getrankevertrieb-rothenburg-gmbh_00000004872771-001", "germany", 53773L, "wholesaler", "fruit", "31a43832-e06a-597c-b6f9-05164dd585e3_e4c082c9-dec1-41d1-8fca-0457c8f630e7", "market for jatropha seed", "jatropha seed", "kg", 0.00109, 263338.8, "medium", "unit", 0.514315, "land use", "agriculture & livestock", NA, "NZ 2050", 2050L, NA, "NA_NA_NA", NA, NA, NA, NA, 0.125, 0.125, 0, 0, 0.125, 0, 0, 0.125, 0, 0, + "getrankevertrieb-rothenburg-gmbh_00000004872771-001", "germany", 53773L, "wholesaler", "fruit", "31a43832-e06a-597c-b6f9-05164dd585e3_e4c082c9-dec1-41d1-8fca-0457c8f630e7", "market for jatropha seed", "jatropha seed", "kg", 0.002175, 6.88e+10, "high", "unit_tilt_sector", 0.673077, "land use", "agriculture & livestock", NA, "NZ 2050", 2030L, NA, "NA_NA_NA", NA, NA, NA, NA, 0.125, 0.125, 0, 0, 0.125, 0, 0, 0.125, 0, 0, + "getrankevertrieb-rothenburg-gmbh_00000004872771-001", "germany", 53773L, "wholesaler", "fruit", "31a43832-e06a-597c-b6f9-05164dd585e3_e4c082c9-dec1-41d1-8fca-0457c8f630e7", "market for jatropha seed", "jatropha seed", "kg", 0.002175, 6.88e+10, "high", "unit_tilt_sector", 0.673077, "land use", "agriculture & livestock", NA, "NZ 2050", 2050L, NA, "NA_NA_NA", NA, NA, NA, NA, 0.125, 0.125, 0, 0, 0.125, 0, 0, 0.125, 0, 0, + "getrankevertrieb-rothenburg-gmbh_00000004872771-001", "germany", 53773L, "wholesaler", "mineral water", "6d83f0d2-8498-538a-beee-67e9f5cd7e54_dee1a118-12e5-47eb-8daa-f4cf683e4a5a", "market for water, harvested from rainwater", "water, harvested from rainwater", "kg", -1.22516, 1491.734, "low", "unit", 0.004302, "industry", "other industry", "low", "1.5C RPS", 2030L, 0.09, "1.5C RPS_2030_unit", 0.047151, "low", NA, NA, 0.125, 0.125, 0, 0.2, 0.125, 0, 0.5, 0.125, 0, 0.5, + "getrankevertrieb-rothenburg-gmbh_00000004872771-001", "germany", 53773L, "wholesaler", "mineral water", "6d83f0d2-8498-538a-beee-67e9f5cd7e54_dee1a118-12e5-47eb-8daa-f4cf683e4a5a", "market for water, harvested from rainwater", "water, harvested from rainwater", "kg", -1.22516, 1491.734, "low", "unit", 0.004302, "industry", "other industry", "high", "1.5C RPS", 2050L, 0.95, "1.5C RPS_2050_unit", 0.477151, "medium", NA, NA, 0.125, 0.125, 0, 0.2, 0.125, 0.125, 0.125, 0.125, 0, 0.2, + "getrankevertrieb-rothenburg-gmbh_00000004872771-001", "germany", 53773L, "wholesaler", "mineral water", "6d83f0d2-8498-538a-beee-67e9f5cd7e54_dee1a118-12e5-47eb-8daa-f4cf683e4a5a", "market for water, harvested from rainwater", "water, harvested from rainwater", "kg", -1.22516, 1491.734, "low", "unit", 0.004302, "industry", "other industry", "medium", "NZ 2050", 2030L, 0.22, "NZ 2050_2030_unit", 0.112151, "medium", NA, NA, 0.125, 0.125, 0, 0.2, 0.125, 0, 0.25, 0.125, 0, 0.3333333333, + "getrankevertrieb-rothenburg-gmbh_00000004872771-001", "germany", 53773L, "wholesaler", "mineral water", "6d83f0d2-8498-538a-beee-67e9f5cd7e54_dee1a118-12e5-47eb-8daa-f4cf683e4a5a", "market for water, harvested from rainwater", "water, harvested from rainwater", "kg", -1.22516, 1491.734, "low", "unit", 0.004302, "industry", "other industry", "high", "NZ 2050", 2050L, 0.96, "NZ 2050_2050_unit", 0.482151, "medium", NA, NA, 0.125, 0.125, 0, 0.2, 0.125, 0.25, 0.25, 0.125, 0, 0.3333333333, + "getrankevertrieb-rothenburg-gmbh_00000004872771-001", "germany", 53773L, "wholesaler", "mineral water", "6d83f0d2-8498-538a-beee-67e9f5cd7e54_dee1a118-12e5-47eb-8daa-f4cf683e4a5a", "market for water, harvested from rainwater", "water, harvested from rainwater", "kg", -1.50924, 508583.8, "low", "unit_tilt_sector", 0.006014, "industry", "other industry", "low", "1.5C RPS", 2030L, 0.09, "1.5C RPS_2030_unit_tilt_sector", 0.048007, "low", NA, NA, 0.125, 0.125, 0, 0.1666666667, 0.125, 0, 0.5, 0.125, 0, 0.5, + "getrankevertrieb-rothenburg-gmbh_00000004872771-001", "germany", 53773L, "wholesaler", "mineral water", "6d83f0d2-8498-538a-beee-67e9f5cd7e54_dee1a118-12e5-47eb-8daa-f4cf683e4a5a", "market for water, harvested from rainwater", "water, harvested from rainwater", "kg", -1.50924, 508583.8, "low", "unit_tilt_sector", 0.006014, "industry", "other industry", "high", "1.5C RPS", 2050L, 0.95, "1.5C RPS_2050_unit_tilt_sector", 0.478007, "medium", NA, NA, 0.125, 0.125, 0, 0.1666666667, 0.125, 0.125, 0.125, 0.125, 0, 0.2, + "getrankevertrieb-rothenburg-gmbh_00000004872771-001", "germany", 53773L, "wholesaler", "mineral water", "6d83f0d2-8498-538a-beee-67e9f5cd7e54_dee1a118-12e5-47eb-8daa-f4cf683e4a5a", "market for water, harvested from rainwater", "water, harvested from rainwater", "kg", -1.50924, 508583.8, "low", "unit_tilt_sector", 0.006014, "industry", "other industry", "medium", "NZ 2050", 2030L, 0.22, "NZ 2050_2030_unit_tilt_sector", 0.113007, "medium", NA, NA, 0.125, 0.125, 0, 0.1666666667, 0.125, 0, 0.3333333333, 0.125, 0, 0.3333333333, + "getrankevertrieb-rothenburg-gmbh_00000004872771-001", "germany", 53773L, "wholesaler", "mineral water", "6d83f0d2-8498-538a-beee-67e9f5cd7e54_dee1a118-12e5-47eb-8daa-f4cf683e4a5a", "market for water, harvested from rainwater", "water, harvested from rainwater", "kg", -1.50924, 508583.8, "low", "unit_tilt_sector", 0.006014, "industry", "other industry", "high", "NZ 2050", 2050L, 0.96, "NZ 2050_2050_unit_tilt_sector", 0.483007, "medium", NA, NA, 0.125, 0.125, 0, 0.1666666667, 0.125, 0.25, 0.25, 0.125, 0, 0.3333333333 ) # styler: on - usethis::use_data(without_financial, overwrite = TRUE) diff --git a/data/without_financial.rda b/data/without_financial.rda index 2c6bcf1..ef5e597 100644 Binary files a/data/without_financial.rda and b/data/without_financial.rda differ diff --git a/man/bar_plot_emission_profile.Rd b/man/bar_plot_emission_profile.Rd index 2002d90..4b7a024 100644 --- a/man/bar_plot_emission_profile.Rd +++ b/man/bar_plot_emission_profile.Rd @@ -4,7 +4,13 @@ \alias{bar_plot_emission_profile} \title{Create a horizontal stacked bar chart without financial data} \usage{ -bar_plot_emission_profile(data, benchmarks = benchmarks(), mode = modes()) +bar_plot_emission_profile( + data, + benchmarks = benchmarks(), + mode = modes(), + scenario = scenarios(), + year = years() +) } \arguments{ \item{data}{A data frame like \link{without_financial}.} @@ -14,6 +20,10 @@ emission profiles will be plotted. The user can choose from one to several benchmark(s) to be plotted.} \item{mode}{A character vector: equal_weight, best_case, worst_case.} + +\item{scenario}{A character vector: 1.5C RPS, NZ 2050.} + +\item{year}{A character vector: 2030, 2050.} } \value{ A \link{ggplot} object. diff --git a/man/figures/README-unnamed-chunk-11-1.png b/man/figures/README-unnamed-chunk-11-1.png index 3e60cd0..5ed0d55 100644 Binary files a/man/figures/README-unnamed-chunk-11-1.png and b/man/figures/README-unnamed-chunk-11-1.png differ diff --git a/man/figures/README-unnamed-chunk-12-1.png b/man/figures/README-unnamed-chunk-12-1.png index 477b698..b3728e8 100644 Binary files a/man/figures/README-unnamed-chunk-12-1.png and b/man/figures/README-unnamed-chunk-12-1.png differ diff --git a/man/figures/README-unnamed-chunk-14-1.png b/man/figures/README-unnamed-chunk-14-1.png index 1c717b1..8114e04 100644 Binary files a/man/figures/README-unnamed-chunk-14-1.png and b/man/figures/README-unnamed-chunk-14-1.png differ diff --git a/man/map_region_risk.Rd b/man/map_region_risk.Rd index 7cd00ff..c769545 100644 --- a/man/map_region_risk.Rd +++ b/man/map_region_risk.Rd @@ -8,9 +8,10 @@ financial data.} map_region_risk( data, country_code = c("DE"), - benchmark = c("all", "isic_4digit", "tilt_sector", "unit", "unit_isic_4digit", - "unit_tilt_sector"), - mode = c("equal_weight", "worst_case", "best_case") + benchmark = benchmarks(), + mode = modes(), + scenario = scenarios(), + year = years() ) } \arguments{ @@ -26,6 +27,10 @@ default mode.} \item{mode}{The mode to plot. It can be one of "equal_weight", "worst_case" or "best_case". If nothing is chosen, "equal_weight" is the default mode.} + +\item{scenario}{A character vector: 1.5C RPS, NZ 2050.} + +\item{year}{A character vector: 2030, 2050.} } \value{ A ggplot2 object representing the country data plot. diff --git a/man/scatter_plot_financial.Rd b/man/scatter_plot_financial.Rd index 575b357..a3a464a 100644 --- a/man/scatter_plot_financial.Rd +++ b/man/scatter_plot_financial.Rd @@ -7,9 +7,9 @@ scatter_plot_financial( data, benchmarks = benchmarks(), - mode = c("equal_weight", "worst_case", "best_case"), - scenario = c("IPR", "WEO"), - year = c(2030, 2050) + mode = modes(), + scenario = scenarios_financial(), + year = years() ) } \arguments{ diff --git a/man/scenarios.Rd b/man/scenarios.Rd new file mode 100644 index 0000000..3b1a6b5 --- /dev/null +++ b/man/scenarios.Rd @@ -0,0 +1,12 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/utils.R +\name{scenarios} +\alias{scenarios} +\title{Scenarios} +\usage{ +scenarios() +} +\description{ +Scenarios +} +\keyword{internal} diff --git a/man/scenarios_financial.Rd b/man/scenarios_financial.Rd new file mode 100644 index 0000000..6401e31 --- /dev/null +++ b/man/scenarios_financial.Rd @@ -0,0 +1,12 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/utils.R +\name{scenarios_financial} +\alias{scenarios_financial} +\title{Scenarios Financial} +\usage{ +scenarios_financial() +} +\description{ +Scenarios Financial +} +\keyword{internal} diff --git a/man/without_financial.Rd b/man/without_financial.Rd index e51374b..28ae48c 100644 --- a/man/without_financial.Rd +++ b/man/without_financial.Rd @@ -5,7 +5,7 @@ \alias{without_financial} \title{Without financial data set} \format{ -An object of class \code{tbl_df} (inherits from \code{tbl}, \code{data.frame}) with 114 rows and 12 columns. +An object of class \code{tbl_df} (inherits from \code{tbl}, \code{data.frame}) with 252 rows and 35 columns. } \source{ \verb{@lindadelacombaz} diff --git a/man/years.Rd b/man/years.Rd new file mode 100644 index 0000000..d3ce2e6 --- /dev/null +++ b/man/years.Rd @@ -0,0 +1,12 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/utils.R +\name{years} +\alias{years} +\title{Years} +\usage{ +years() +} +\description{ +Years +} +\keyword{internal} diff --git a/tests/testthat/_snaps/example_data.md b/tests/testthat/_snaps/example_data.md index 24c232c..ab201c5 100644 --- a/tests/testthat/_snaps/example_data.md +++ b/tests/testthat/_snaps/example_data.md @@ -20,11 +20,13 @@ Code str(example_without_financial()) Output - tibble [1 x 6] (S3: tbl_df/tbl/data.frame) - $ company_name : chr "a" - $ emission_profile: chr "medium" - $ benchmark : chr "all" - $ equal_weight : num 0.1 - $ worst_case : num 0.1 - $ best_case : num 0.1 + tibble [1 x 8] (S3: tbl_df/tbl/data.frame) + $ company_name : chr "a" + $ emission_profile : chr "medium" + $ benchmark : chr "all" + $ scenario : chr "1.5C RPS" + $ year : num 2030 + $ equal_weight_emission_profile: num 0.1 + $ worst_case_emission_profile : num 0.1 + $ best_case_emission_profile : num 0.1 diff --git a/tests/testthat/_snaps/without_financial.md b/tests/testthat/_snaps/without_financial.md index 3255d59..c371c24 100644 --- a/tests/testthat/_snaps/without_financial.md +++ b/tests/testthat/_snaps/without_financial.md @@ -3,21 +3,24 @@ Code without_financial Output - # A tibble: 114 x 12 - company_name postcode emission_profile benchmark ep_product tilt_sector - - 1 bruno 27568 high all car D - 2 bruno 27568 high all steel C - 3 bruno 27568 medium all wheat B - 4 mauro 39221 high all steel C - 5 mauro 39221 high all machine C - 6 mirja 34117 high all tractor D - 7 mirja 34117 high all cattle A - 8 pasant 80337 high all tractor D - 9 pasant 80337 low all apple A - 10 peter 88131 high all banana A - # i 104 more rows - # i 6 more variables: tilt_subsector , isic_4digit , - # isic_4digit_name , equal_weight , worst_case , - # best_case + # A tibble: 252 x 35 + companies_id country postcode main_activity ep_product activity_uuid_produc~1 + + 1 %ef%bb%bfma~ germany 12043 wholesaler surface c~ a62eb0d6-9120-541c-97~ + 2 %ef%bb%bfma~ germany 12043 wholesaler surface c~ a62eb0d6-9120-541c-97~ + 3 %ef%bb%bfma~ germany 12043 wholesaler surface c~ a62eb0d6-9120-541c-97~ + 4 %ef%bb%bfma~ germany 12043 wholesaler surface c~ a62eb0d6-9120-541c-97~ + 5 %ef%bb%bfma~ germany 12043 wholesaler surface c~ a62eb0d6-9120-541c-97~ + 6 %ef%bb%bfma~ germany 12043 wholesaler surface c~ a62eb0d6-9120-541c-97~ + 7 %ef%bb%bfma~ germany 12043 wholesaler surface c~ a62eb0d6-9120-541c-97~ + 8 %ef%bb%bfma~ germany 12043 wholesaler surface c~ a62eb0d6-9120-541c-97~ + 9 %ef%bb%bfma~ germany 12043 wholesaler hand tool~ 7c082396-1f14-5674-86~ + 10 %ef%bb%bfma~ germany 12043 wholesaler hand tool~ 7c082396-1f14-5674-86~ + # i 242 more rows + # i abbreviated name: 1: activity_uuid_product_uuid + # i 29 more variables: matched_activity_name , + # matched_reference_product , unit , co2e_lower , + # co2e_upper , emission_profile , benchmark , + # profile_ranking , tilt_sector , tilt_subsector , + # sector_profile , scenario , year , ... diff --git a/tests/testthat/test-bar_plot_emission_profile.R b/tests/testthat/test-bar_plot_emission_profile.R index ee04b05..adde97e 100644 --- a/tests/testthat/test-bar_plot_emission_profile.R +++ b/tests/testthat/test-bar_plot_emission_profile.R @@ -1,12 +1,20 @@ test_that("returns an object of the expected class", { data <- example_without_financial() - plot <- bar_plot_emission_profile(data, benchmarks(), mode = "equal_weight") + plot <- bar_plot_emission_profile(data, benchmarks(), + mode = modes()[1], + scenarios()[1], years()[1] + ) expect_s3_class(plot, "ggplot") }) test_that("returns correct risk category values for equal weight mode", { data <- example_without_financial(!!aka("risk_category") := risk_category_levels()) - data <- prepare_bar_plot_emission_profile(data, benchmarks(), "equal_weight") + mode <- "equal_weight" |> + switch_mode_emission_profile() + data <- prepare_bar_plot_emission_profile( + data, benchmarks(), mode, + scenarios()[1], years()[1] + ) risk_categories <- levels(data$risk_category_var) expected_risk_categories <- risk_category_levels() expect_true(setequal(risk_categories, expected_risk_categories)) @@ -14,7 +22,12 @@ test_that("returns correct risk category values for equal weight mode", { test_that("returns correct risk category values for best case mode", { data <- example_without_financial(!!aka("risk_category") := risk_category_levels()) - data <- prepare_bar_plot_emission_profile(data, benchmarks(), "best_case") + mode <- "best_case" |> + switch_mode_emission_profile() + data <- prepare_bar_plot_emission_profile( + data, benchmarks(), mode, + scenarios()[1], years()[1] + ) risk_categories <- levels(data$risk_category_var) expected_risk_categories <- risk_category_levels() expect_true(setequal(risk_categories, expected_risk_categories)) @@ -22,7 +35,12 @@ test_that("returns correct risk category values for best case mode", { test_that("returns correct risk category values for worst_case mode", { data <- example_without_financial(!!aka("risk_category") := risk_category_levels()) - data <- prepare_bar_plot_emission_profile(data, benchmarks(), "worst_case") + mode <- "worst_case" |> + switch_mode_emission_profile() + data <- prepare_bar_plot_emission_profile( + data, benchmarks(), mode, + scenarios()[1], years()[1] + ) risk_categories <- levels(data$risk_category_var) expected_risk_categories <- risk_category_levels() expect_true(setequal(risk_categories, expected_risk_categories)) @@ -30,7 +48,12 @@ test_that("returns correct risk category values for worst_case mode", { test_that("returns correct benchmarks values for equal weight mode", { data <- example_without_financial() - data <- prepare_bar_plot_emission_profile(data, benchmarks(), "equal_weight") + mode <- "equal_weight" |> + switch_mode_emission_profile() + data <- prepare_bar_plot_emission_profile( + data, benchmarks(), mode, + scenarios()[1], years()[1] + ) benchmarks <- unique(data$benchmark) expected_benchmarks <- example_without_financial() |> pull(benchmark) |> @@ -40,7 +63,15 @@ test_that("returns correct benchmarks values for equal weight mode", { test_that("returns correct benchmarks values for best case mode", { data <- example_without_financial() - data <- prepare_bar_plot_emission_profile(data, benchmarks(), "best_case") + mode <- "best_case" |> + switch_mode_emission_profile() + data <- prepare_bar_plot_emission_profile( + data, + benchmarks(), + mode, + scenarios()[1], + years()[1] + ) benchmarks <- unique(data$benchmark) expected_benchmarks <- example_without_financial() |> pull(benchmark) |> @@ -50,7 +81,12 @@ test_that("returns correct benchmarks values for best case mode", { test_that("returns correct benchmarks values for worst case mode", { data <- example_without_financial() - data <- prepare_bar_plot_emission_profile(data, benchmarks(), "worst_case") + mode <- "worst_case" |> + switch_mode_emission_profile() + data <- prepare_bar_plot_emission_profile( + data, benchmarks(), mode, + scenarios()[1], years()[1] + ) benchmarks <- unique(data$benchmark) expected_benchmarks <- example_without_financial() |> pull(benchmark) |> @@ -60,17 +96,24 @@ test_that("returns correct benchmarks values for worst case mode", { test_that("proportions are less or equal to 1 for equal weight mode", { data <- example_without_financial() - data <- data |> - prepare_bar_plot_emission_profile(benchmarks(), mode = "equal_weight") - + mode <- "equal_weight" |> + switch_mode_emission_profile() + data <- prepare_bar_plot_emission_profile( + data, benchmarks(), mode, + scenarios()[1], years()[1] + ) proportions <- data$proportion expect_true(all(proportions >= 0 & proportions <= 1)) }) test_that("proportions are less or equal to 1 for best case mode", { data <- example_without_financial() - data <- data |> - prepare_bar_plot_emission_profile(benchmarks(), mode = "best_case") + mode <- "best_case" |> + switch_mode_emission_profile() + data <- prepare_bar_plot_emission_profile( + data, benchmarks(), mode, + scenarios()[1], years()[1] + ) proportions <- data$proportion expect_true(all(proportions >= 0 & proportions <= 1)) @@ -78,9 +121,12 @@ test_that("proportions are less or equal to 1 for best case mode", { test_that("proportions are less or equal to 1 for worst case mode", { data <- example_without_financial() - data <- data |> - prepare_bar_plot_emission_profile(benchmarks(), mode = "worst_case") - + mode <- "worst_case" |> + switch_mode_emission_profile() + data <- prepare_bar_plot_emission_profile( + data, benchmarks(), mode, + scenarios()[1], years()[1] + ) proportions <- data$proportion expect_true(all(proportions >= 0 & proportions <= 1)) }) diff --git a/tests/testthat/test-map_region_risk.R b/tests/testthat/test-map_region_risk.R index e7660aa..f54898f 100644 --- a/tests/testthat/test-map_region_risk.R +++ b/tests/testthat/test-map_region_risk.R @@ -9,7 +9,7 @@ test_that("returns correct risk category values colors", { medium = medium_hex(), high = high_hex() ) - plot <- map_region_risk(data) + plot <- map_region_risk(data, "DE", "all", "equal_weight", scenarios()[1], years()[1]) layers <- ggplot_build(plot)$data colors <- layers[[1]]$fill @@ -24,7 +24,7 @@ test_that("plots the correct companies", { company_name = letters[1:3], !!aka("risk_category") := risk_category_levels() ) - plot <- map_region_risk(data) + plot <- map_region_risk(data, "DE", "all", "equal_weight", scenarios()[1], years()[1]) company_names <- unique(plot$plot_env$data$company_name) expected_company_names <- unique(data$company_name) @@ -38,7 +38,7 @@ test_that("plots the selected benchmark", { postcode = c(53773L, 53774L, 53775L), !!aka("risk_category") := risk_category_levels() ) - plot <- map_region_risk(data, "DE", "all") + plot <- map_region_risk(data, "DE", "all", "equal_weight", scenarios()[1], years()[1]) benchmark <- unique(plot$plot_env$benchmark) expected_benchmark <- unique(data$benchmark) @@ -52,7 +52,7 @@ test_that("plots the selected mode", { postcode = c(53773L, 53774L, 53775L), !!aka("risk_category") := risk_category_levels() ) - plot <- map_region_risk(data, "DE", "all", "equal_weight") + plot <- map_region_risk(data, "DE", "all", "equal_weight", scenarios()[1], years()[1]) mode <- unique(plot$plot_env$mode) expected_mode <- "equal_weight" diff --git a/tests/testthat/test-prepare_geo_data.R b/tests/testthat/test-prepare_geo_data.R index 265cc4b..4427d6d 100644 --- a/tests/testthat/test-prepare_geo_data.R +++ b/tests/testthat/test-prepare_geo_data.R @@ -1,27 +1,32 @@ test_that("returns an object of the expected class", { skip_on_ci() - data <- tibble( + data <- example_without_financial( postcode = c(53773L, 53774L, 53775L), - emission_profile = risk_category_levels(), - benchmark = rep("all", 3) + !!aka("risk_category") := risk_category_levels() + ) + prepared_data <- prepare_geo_data( + data, "DE", "all", "equal_weight", scenarios()[1], + years()[1] ) - prepared_data <- prepare_geo_data(data) expect_type(prepared_data, "list") }) test_that("aggregation returns correct risk category values colors", { skip_on_ci() - data <- tibble( + data <- example_without_financial( postcode = c(53773L, 53774L, 53775L), - risk_category_var = risk_category_levels(), - benchmark = rep("all", 3) + !!aka("risk_category") := risk_category_levels() ) expected_colors <- list( low = low_hex(), medium = medium_hex(), high = high_hex() ) - aggregated_data <- aggregate_geo(data, mode = "equal_weight") + prepared_data <- prepare_geo_data( + data, "DE", "all", "equal_weight", scenarios()[1], + years()[1] + ) + aggregated_data <- prepared_data[[2]] colors <- aggregated_data$color names(colors) <- names(expected_colors) @@ -35,7 +40,11 @@ test_that("returns the correct postcodes", { postcode = c(53773L, 53774L, 53775L), !!aka("risk_category") := risk_category_levels() ) - aggregated_data <- prepare_geo_data(data)[[2]] + prepared_data <- prepare_geo_data( + data, "DE", "all", "equal_weight", scenarios()[1], + years()[1] + ) + aggregated_data <- prepared_data[[2]] postcodes <- unique(aggregated_data$postcode) expected_postcodes <- unique(data$postcode) diff --git a/vignettes/articles/integration-with-tiltIndicator.Rmd b/vignettes/articles/integration-with-tiltIndicator.Rmd deleted file mode 100644 index 9b7560a..0000000 --- a/vignettes/articles/integration-with-tiltIndicator.Rmd +++ /dev/null @@ -1,62 +0,0 @@ ---- -title: "Integration with other components of the tilt system" ---- - -```{r, include = FALSE} -knitr::opts_chunk$set( - collapse = TRUE, - comment = "#>" -) -``` - -Setup. - -```{r setup} -library(tiltToyData) -library(tiltIndicatorAfter) -library(tiltPlot) -library(dplyr, warn.conflicts = FALSE) -library(readr, warn.conflicts = FALSE) - -options(readr.show_col_types = FALSE) -``` - - -Data. - -```{r} -companies <- read_csv(toy_emissions_profile_any_companies()) -products <- read_csv(toy_emissions_profile_products_ecoinvent()) -co2 <- 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()) -``` - -Apply tilt methodology. - -```{r} -result <- profile_emissions( - companies, - products, - europages_companies = europages_companies, - ecoinvent_activities = ecoinvent_activities, - ecoinvent_europages = ecoinvent_europages, - isic = isic_name - ) |> - unnest_product() -``` - -Visualize all companies. - -```{r} -bar_plot_emission_profile(result, benchmarks = c("all", "unit"), mode = "equal_weight") -``` - -Visualize one specific company. - -```{r} -first_company <- filter(result, companies_id %in% first(companies_id)) -bar_plot_emission_profile(first_company, benchmarks = c("all", "unit"), mode = "equal_weight") -```