r2dii 
2 ° Investing Initiative maintains a
collection or R packages called r2dii. To help you install and use all
those packages at once, we also built the meta package r2dii. Run
library(r2dii) to see the name of all packages in r2dii. Learn more
about each package at a link of the form
https://2degreesinvesting.github.io/<package name goes here>/ – for
example, https://2degreesinvesting.github.io/r2dii.data/, or
https://2degreesinvesting.github.io/r2dii.match/.
Installation
# install.packages("devtools")
devtools::install_github("2DegreesInvesting/r2dii")How to minimize installation errors?
Example
These examples provide a high level overview of the main features. For more details see Get started.
Attaching r2dii automatically attaches other r2dii packages.
library(r2dii)
#> Loading required package: r2dii.data
#> Loading required package: r2dii.dataraw
#> Loading required package: r2dii.utils
#> Loading required package: r2dii.matchr2dii includes some utility functions.
degrees()
#> [1] "°"
sprintf("Welcome to 2%s Investing Initiative!", degrees())
#> [1] "Welcome to 2° Investing Initiative!"
clean_column_names(dplyr::group_by(tibble::tibble(x.x = 1), x.x))
#> # A tibble: 1 x 1
#> # Groups: x_x [1]
#> x_x
#> <dbl>
#> 1 1
# Fails
check_crucial_names(c(x = 1), expected_names = c("x", "y"))
#> Error: Must have missing names:
#> yIt also includes datasets for examples and tests.
# Column definitions for all datasets
data_dictionary
#> # A tibble: 70 x 4
#> dataset column typeof definition
#> <chr> <chr> <chr> <chr>
#> 1 ald_demo ald_timestamp charac… Date at which asset data was pulled from…
#> 2 ald_demo country_of_domici… charac… Country where company is registered
#> 3 ald_demo emission_factor double Company level emission factor of the tec…
#> 4 ald_demo is_ultimate_liste… logical Flag if company is the listed ultimate p…
#> 5 ald_demo is_ultimate_owner logical Flag if company is the ultimate parent i…
#> 6 ald_demo name_company charac… The name of the company owning the asset
#> 7 ald_demo number_of_assets integer Number of assets of a given technology o…
#> 8 ald_demo plant_location charac… Country where asset is located
#> 9 ald_demo production double Company level production of the technolo…
#> 10 ald_demo production_unit charac… The units that production is measured in
#> # … with 60 more rows
# Some example datasets
loanbook_demo
#> # A tibble: 320 x 19
#> id_loan id_direct_loant… name_direct_loa… id_intermediate… name_intermedia…
#> <chr> <chr> <chr> <chr> <chr>
#> 1 L1 C294 Yuamen Xinneng … <NA> <NA>
#> 2 L2 C293 Yuamen Changyua… <NA> <NA>
#> 3 L3 C292 Yuama Ethanol L… IP5 Yuama Inc.
#> 4 L4 C299 Yudaksel Holdin… <NA> <NA>
#> 5 L5 C305 Yukon Energy Co… <NA> <NA>
#> 6 L6 C304 Yukon Developme… <NA> <NA>
#> 7 L7 C227 Yaugoa-Zapadnay… <NA> <NA>
#> 8 L8 C303 Yueyang City Co… <NA> <NA>
#> 9 L9 C301 Yuedxiu Corp One IP10 Yuedxiu Group
#> 10 L10 C302 Yuexi County AA… <NA> <NA>
#> # … with 310 more rows, and 14 more variables: id_ultimate_parent <chr>,
#> # name_ultimate_parent <chr>, loan_size_outstanding <dbl>,
#> # loan_size_outstanding_currency <chr>, loan_size_credit_limit <dbl>,
#> # loan_size_credit_limit_currency <chr>, sector_classification_system <chr>,
#> # sector_classification_input_type <chr>,
#> # sector_classification_direct_loantaker <dbl>, fi_type <chr>,
#> # flag_project_finance_loan <chr>, name_project <lgl>,
#> # lei_direct_loantaker <lgl>, isin_direct_loantaker <lgl>
ald_demo
#> # A tibble: 17,368 x 13
#> name_company sector technology production_unit year production
#> <chr> <chr> <chr> <chr> <dbl> <dbl>
#> 1 aba hydropo… power hydrocap MW 2013 133340.
#> 2 aba hydropo… power hydrocap MW 2014 131582.
#> 3 aba hydropo… power hydrocap MW 2015 129824.
#> 4 aba hydropo… power hydrocap MW 2016 128065.
#> 5 aba hydropo… power hydrocap MW 2017 126307.
#> 6 aba hydropo… power hydrocap MW 2018 124549.
#> 7 aba hydropo… power hydrocap MW 2019 122790.
#> 8 aba hydropo… power hydrocap MW 2020 121032.
#> 9 aba hydropo… power hydrocap MW 2021 119274.
#> 10 aba hydropo… power hydrocap MW 2022 117515.
#> # … with 17,358 more rows, and 7 more variables: emission_factor <dbl>,
#> # country_of_domicile <chr>, plant_location <chr>, number_of_assets <dbl>,
#> # is_ultimate_owner <lgl>, is_ultimate_listed_owner <lgl>,
#> # ald_timestamp <chr>And it provides tools to match financial portfolios with climate data.
match_name(loanbook_demo, ald_demo) %>%
prioritize()
#> # A tibble: 267 x 27
#> id_loan id_direct_loant… name_direct_loa… id_intermediate… name_intermedia…
#> <chr> <chr> <chr> <chr> <chr>
#> 1 L151 C168 Shaanxi Auto <NA> <NA>
#> 2 L152 C169 Shandong Auto <NA> <NA>
#> 3 L153 C170 Shandong Kama <NA> <NA>
#> 4 L154 C171 Shandong Tangju… <NA> <NA>
#> 5 L155 C173 Shanghai Automo… <NA> <NA>
#> 6 L156 C176 Shanxi Dayun <NA> <NA>
#> 7 L157 C178 Shenyang Polars… <NA> <NA>
#> 8 L158 C180 Shuanghuan Auto <NA> <NA>
#> 9 L159 C182 Sichuan Auto <NA> <NA>
#> 10 L160 C184 Singulato <NA> <NA>
#> # … with 257 more rows, and 22 more variables: id_ultimate_parent <chr>,
#> # name_ultimate_parent <chr>, loan_size_outstanding <dbl>,
#> # loan_size_outstanding_currency <chr>, loan_size_credit_limit <dbl>,
#> # loan_size_credit_limit_currency <chr>, sector_classification_system <chr>,
#> # sector_classification_input_type <chr>,
#> # sector_classification_direct_loantaker <dbl>, fi_type <chr>,
#> # flag_project_finance_loan <chr>, name_project <lgl>,
#> # lei_direct_loantaker <lgl>, isin_direct_loantaker <lgl>, id_2dii <chr>,
#> # level <chr>, sector <chr>, sector_ald <chr>, name <chr>, name_ald <chr>,
#> # score <dbl>, source <chr>