Financial toy data
Examples
financial
#> # A tibble: 264 × 23
#> bank_id amount_total company_name postcode grouping_emission ep_product
#> <chr> <int> <chr> <int> <chr> <chr>
#> 1 bank_a 1000 tilman 12043 all car
#> 2 bank_a 1000 tilman 12043 all tractor
#> 3 bank_a 1000 tilman 12043 all steel
#> 4 bank_a 1000 tilman 12043 all car
#> 5 bank_a 1000 tilman 12043 all tractor
#> 6 bank_a 1000 tilman 12043 all steel
#> 7 bank_a 1000 tilman 12043 all car
#> 8 bank_a 1000 tilman 12043 all tractor
#> 9 bank_a 1000 tilman 12043 all steel
#> 10 bank_a 1000 tilman 12043 all car
#> # ℹ 254 more rows
#> # ℹ 17 more variables: co2_footprint_product <dbl>, tilt_sector <chr>,
#> # tilt_subsector <chr>, isic_4digit <int>, isic_4digit_name <chr>,
#> # amount_of_distinct_products <int>, equal_weight_finance <dbl>,
#> # worst_case_finance <int>, best_case_finance <int>, emission_category <chr>,
#> # profile_ranking <dbl>, sector_profile <chr>, scenario <chr>, year <int>,
#> # reduction_targets <dbl>, transition_risk_score <dbl>, …