tiltIndicatorAfter
The goal of tiltIndicatorAfter is to conduct post-processing of four indicators created from the tiltIndicator package. The post-processing process cleans the useless data and adds additional columns. The processed output from the tiltIndicatorAfter package is the final output for the user.
This repository hosts only public code and may only show only fake data.
Installation
You can install the development version of tiltIndicator from GitHub with:
# install.packages("devtools")
devtools::install_github("2DegreesInvesting/tiltIndicatorAfter")Example
library(tiltIndicatorAfter)
library(tiltToyData)
library(readr, warn.conflicts = FALSE)
options(readr.show_col_types = FALSE)
packageVersion("tiltIndicatorAfter")
#> [1] '0.0.0.9014'
companies <- read_csv(toy_emissions_profile_any_companies())
products <- read_csv(toy_emissions_profile_products())
result <- profile_emissions(
companies,
products,
# TODO: Move to tiltToyData
europages_companies = tiltIndicatorAfter::ep_companies,
ecoinvent_activities = tiltIndicatorAfter::ecoinvent_activities,
ecoinvent_europages = tiltIndicatorAfter::matches_mapper |> head(100),
isic_tilt = tiltIndicatorAfter::isic_tilt_mapper
)
result |> unnest_product()
#> # A tibble: 49 × 26
#> companies_id company_name country PCTR_risk_category benchmark ep_product
#> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 fleischerei-sti… <NA> <NA> high all stove
#> 2 fleischerei-sti… <NA> <NA> high isic_4di… stove
#> 3 fleischerei-sti… <NA> <NA> high tilt_sec… stove
#> 4 fleischerei-sti… <NA> <NA> high unit stove
#> 5 fleischerei-sti… <NA> <NA> high unit_isi… stove
#> 6 fleischerei-sti… <NA> <NA> high unit_til… stove
#> 7 fleischerei-sti… <NA> <NA> high all oven
#> 8 fleischerei-sti… <NA> <NA> medium isic_4di… oven
#> 9 fleischerei-sti… <NA> <NA> medium tilt_sec… oven
#> 10 fleischerei-sti… <NA> <NA> medium unit oven
#> # ℹ 39 more rows
#> # ℹ 20 more variables: matched_activity_name <chr>,
#> # matched_reference_product <chr>, unit <chr>, multi_match <lgl>,
#> # matching_certainty <chr>, matching_certainty_company_average <chr>,
#> # tilt_sector <chr>, tilt_subsector <chr>, isic_4digit <chr>,
#> # isic_4digit_name <chr>, company_city <chr>, postcode <dbl>, address <chr>,
#> # main_activity <chr>, activity_uuid_product_uuid <chr>, …
result |> unnest_company()
#> # A tibble: 129 × 11
#> companies_id company_name country PCTR_share PCTR_risk_category benchmark
#> <chr> <chr> <chr> <dbl> <lgl> <lgl>
#> 1 fleischerei-sti… <NA> <NA> 1 NA NA
#> 2 fleischerei-sti… <NA> <NA> 0 NA NA
#> 3 fleischerei-sti… <NA> <NA> 0 NA NA
#> 4 fleischerei-sti… <NA> <NA> 0.5 NA NA
#> 5 fleischerei-sti… <NA> <NA> 0.5 NA NA
#> 6 fleischerei-sti… <NA> <NA> 0 NA NA
#> 7 fleischerei-sti… <NA> <NA> 0.5 NA NA
#> 8 fleischerei-sti… <NA> <NA> 0.5 NA NA
#> 9 fleischerei-sti… <NA> <NA> 0 NA NA
#> 10 fleischerei-sti… <NA> <NA> 0.5 NA NA
#> # ℹ 119 more rows
#> # ℹ 5 more variables: matching_certainty_company_average <chr>,
#> # company_city <chr>, postcode <dbl>, address <chr>, main_activity <chr>
inputs <- read_csv(toy_emissions_profile_upstream_products())
result <- profile_emissions_upstream(
companies,
inputs,
# TODO: Move to tiltToyData
europages_companies = tiltIndicatorAfter::ep_companies,
ecoinvent_activities = tiltIndicatorAfter::ecoinvent_activities,
ecoinvent_inputs = tiltIndicatorAfter::ecoinvent_inputs,
ecoinvent_europages = tiltIndicatorAfter::matches_mapper |> head(100),
isic_tilt = tiltIndicatorAfter::isic_tilt_mapper
)
result |> unnest_product()
#> # A tibble: 319 × 27
#> companies_id company_name country ICTR_risk_category benchmark ep_product
#> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 fleischerei-sti… <NA> <NA> high all stove
#> 2 fleischerei-sti… <NA> <NA> high all stove
#> 3 fleischerei-sti… <NA> <NA> medium all stove
#> 4 fleischerei-sti… <NA> <NA> high all stove
#> 5 fleischerei-sti… <NA> <NA> high all stove
#> 6 fleischerei-sti… <NA> <NA> low all stove
#> 7 fleischerei-sti… <NA> <NA> low all stove
#> 8 fleischerei-sti… <NA> <NA> high all stove
#> 9 fleischerei-sti… <NA> <NA> high input_is… stove
#> 10 fleischerei-sti… <NA> <NA> high input_is… stove
#> # ℹ 309 more rows
#> # ℹ 21 more variables: matched_activity_name <chr>,
#> # matched_reference_product <chr>, unit <chr>, multi_match <lgl>,
#> # matching_certainty <chr>, matching_certainty_company_average <chr>,
#> # input_name <chr>, input_unit <chr>, input_tilt_sector <chr>,
#> # input_tilt_subsector <chr>, input_isic_4digit <chr>,
#> # input_isic_4digit_name <chr>, company_city <chr>, postcode <dbl>, …
result |> unnest_company()
#> # A tibble: 127 × 11
#> companies_id company_name company_city country ICTR_share ICTR_risk_category
#> <chr> <chr> <chr> <chr> <dbl> <chr>
#> 1 fleischerei-… <NA> <NA> <NA> 0.571 high
#> 2 fleischerei-… <NA> <NA> <NA> 0.214 medium
#> 3 fleischerei-… <NA> <NA> <NA> 0.214 low
#> 4 fleischerei-… <NA> <NA> <NA> 0.357 high
#> 5 fleischerei-… <NA> <NA> <NA> 0.357 medium
#> 6 fleischerei-… <NA> <NA> <NA> 0.286 low
#> 7 fleischerei-… <NA> <NA> <NA> 0.429 high
#> 8 fleischerei-… <NA> <NA> <NA> 0.357 medium
#> 9 fleischerei-… <NA> <NA> <NA> 0.214 low
#> 10 fleischerei-… <NA> <NA> <NA> 0.429 high
#> # ℹ 117 more rows
#> # ℹ 5 more variables: benchmark <chr>,
#> # matching_certainty_company_average <chr>, postcode <dbl>, address <chr>,
#> # main_activity <chr>