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.match

r2dii 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:
#> y

It 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>