Unnest product- and company-level results
Arguments
- data
A nested data frame, e.g. the output of
sector_profile().
See also
Other helpers:
exclude(),
jitter_range(),
join_to(),
summarize_range()
Examples
library(tiltToyData)
library(readr)
options(readr.show_col_types = FALSE)
companies <- read_csv(toy_sector_profile_companies())
scenarios <- read_csv(toy_sector_profile_any_scenarios())
both <- sector_profile(companies, scenarios)
both
#> # A tibble: 72 × 3
#> companies_id product company
#> * <chr> <list> <list>
#> 1 antimonarchy_canine <tibble [4 × 10]> <tibble [16 × 3]>
#> 2 celestial_lovebird <tibble [4 × 10]> <tibble [16 × 3]>
#> 3 nonphilosophical_llama <tibble [8 × 10]> <tibble [16 × 3]>
#> 4 asteria_megalotomusquinquespinosus <tibble [4 × 10]> <tibble [16 × 3]>
#> 5 quasifaithful_amphiuma <tibble [4 × 10]> <tibble [16 × 3]>
#> 6 spectacular_americanriverotter <tibble [4 × 10]> <tibble [16 × 3]>
#> 7 contrite_silkworm <tibble [4 × 10]> <tibble [16 × 3]>
#> 8 harmless_owlbutterfly <tibble [4 × 10]> <tibble [16 × 3]>
#> 9 fascist_maiasaura <tibble [4 × 10]> <tibble [16 × 3]>
#> 10 charismatic_islandwhistler <tibble [4 × 10]> <tibble [16 × 3]>
#> # ℹ 62 more rows
both |> unnest_product()
#> # A tibble: 304 × 11
#> companies_id grouped_by risk_category profile_ranking clustered
#> <chr> <chr> <chr> <dbl> <chr>
#> 1 antimonarchy_canine ipr_1.5C RPS_… medium 0.18 tent
#> 2 antimonarchy_canine ipr_1.5C RPS_… high 0.98 tent
#> 3 antimonarchy_canine weo_NZ 2050_2… high 0.4 tent
#> 4 antimonarchy_canine weo_NZ 2050_2… high 0.97 tent
#> 5 celestial_lovebird ipr_1.5C RPS_… medium 0.18 table hi…
#> 6 celestial_lovebird ipr_1.5C RPS_… high 0.98 table hi…
#> 7 celestial_lovebird weo_NZ 2050_2… high 0.4 table hi…
#> 8 celestial_lovebird weo_NZ 2050_2… high 0.97 table hi…
#> 9 nonphilosophical_llama ipr_1.5C RPS_… low 0.09 surface …
#> 10 nonphilosophical_llama ipr_1.5C RPS_… high 0.95 surface …
#> # ℹ 294 more rows
#> # ℹ 6 more variables: activity_uuid_product_uuid <chr>, tilt_sector <chr>,
#> # scenario <chr>, year <dbl>, type <chr>, tilt_subsector <chr>
both |> unnest_company()
#> # A tibble: 1,152 × 4
#> companies_id grouped_by risk_category value
#> <chr> <chr> <chr> <dbl>
#> 1 antimonarchy_canine ipr_1.5C RPS_2030 high 0
#> 2 antimonarchy_canine ipr_1.5C RPS_2030 medium 1
#> 3 antimonarchy_canine ipr_1.5C RPS_2030 low 0
#> 4 antimonarchy_canine ipr_1.5C RPS_2030 NA 0
#> 5 antimonarchy_canine ipr_1.5C RPS_2050 high 1
#> 6 antimonarchy_canine ipr_1.5C RPS_2050 medium 0
#> 7 antimonarchy_canine ipr_1.5C RPS_2050 low 0
#> 8 antimonarchy_canine ipr_1.5C RPS_2050 NA 0
#> 9 antimonarchy_canine weo_NZ 2050_2030 high 1
#> 10 antimonarchy_canine weo_NZ 2050_2030 medium 0
#> # ℹ 1,142 more rows