feat: loanbook_demo drops columns that are not crucial to r2dii.analysis functions

[jdhoffa]

Relates to feedback from a bank.
Since our data preparation guidance is to prepare input data exactly as loanbook_demo, we should either:

• Keep all columns that are currently in loanbook_demo, but indicate which are actually crucial to target_market_share and target_sda
  OR
• Remove the non-crucial columns

AB#10167

[maurolepore]

Note we have r2dii.match::crucial_lbk() but those are the minimum columns you need to run r2dii.match::match_name() -- it fails with prioritize() and thus with anything downstream.

Maybe we should modify crucial_lbk()?

library(dplyr, warn.conflicts = FALSE)
library(r2dii.data)
library(r2dii.match)

loanbook <- r2dii.data::loanbook_demo
ald <- r2dii.data::ald_demo

crucial_lbk()
#> [1] "id_ultimate_parent"                    
#> [2] "name_ultimate_parent"                  
#> [3] "id_direct_loantaker"                   
#> [4] "name_direct_loantaker"                 
#> [5] "sector_classification_system"          
#> [6] "sector_classification_direct_loantaker"

matched_crucial <- loanbook %>% 
  select(crucial_lbk()) %>% 
  match_name(ald)

try(prioritize(matched_crucial))
#> Error : Must have missing names:
#> `id_loan`

^{Created on 2021-09-02 by the reprex package (v2.0.1)}

[jdhoffa]

Note that these seem to be the crucial columns:

library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
library(r2dii.data)
library(r2dii.match)
library(r2dii.analysis)
library(r2dii.plot)

loanbook <- dplyr::select(
  loanbook_demo,
  c(
    "id_loan",
    "id_direct_loantaker",
    "name_direct_loantaker",
    # "id_intermediate_parent_1",
    # "name_intermediate_parent_1",
    "id_ultimate_parent",
    "name_ultimate_parent",
    "loan_size_outstanding",
    "loan_size_outstanding_currency",
    "loan_size_credit_limit",
    "loan_size_credit_limit_currency",
    "sector_classification_system",
    # "sector_classification_input_type",
    "sector_classification_direct_loantaker",
    # "fi_type",
    # "flag_project_finance_loan",
    # "name_project",
    "lei_direct_loantaker", # optional, but may be used as a `join_id`
    "isin_direct_loantaker" # optional, but may be used as a `join_id`
    )
)

matched <- loanbook |> 
  match_name(abcd_demo) |> 
  prioritize()
  
matched |> 
  target_market_share(abcd_demo, scenario_demo_2020, region_isos_demo) |> 
  filter(sector == "power", region == "global", technology == "renewablescap") |> 
  prep_trajectory() |> 
  plot_trajectory()

matched |> 
  target_sda(
    abcd_demo, 
    co2_intensity_scenario_demo, 
    region_isos = region_isos_demo
    ) |> 
  filter(sector == "cement", region == "global") |> 
  prep_emission_intensity() |> 
  plot_emission_intensity()
#> Warning: Removing rows in abcd where `emission_factor` is NA

^{Created on 2024-03-20 with reprex v2.1.0}

[jacobvjk]

making this a priority so we can solve the connected issue #372 too