Align naming of metric-like columns for output of target_* functions

[jdhoffa]

Current behaviour

target_market_share() outputs a column metric
target_sda() outputs a column emission_factor_metric

Desired behaviour

Align so that both columns either output:

• production_metric and emission_factor_metric
• both metric

Supersedes #311

---

Brief description of the problem

library(r2dii.data)
library(r2dii.match)
library(r2dii.analysis)

matched <- loanbook_demo %>%
  match_name(abcd_demo) %>%
  prioritize()

# Calculate targets at portfolio level
matched %>%
  target_market_share(
    abcd = abcd_demo,
    scenario = scenario_demo_2020,
    region_isos = region_isos_demo
    )

#> # A tibble: 792 × 10
#>    sector     technology  year region scenario_source metric     production
#>    <chr>      <chr>      <int> <chr>  <chr>           <chr>           <dbl>
#>  1 automotive electric    2020 global demo_2020       projected     324592.
#>  2 automotive electric    2020 global demo_2020       target_cps    324592.
#>  3 automotive electric    2020 global demo_2020       target_sds    324592.
#>  4 automotive electric    2020 global demo_2020       target_sps    324592.
#>  5 automotive electric    2021 global demo_2020       projected     339656.
#>  6 automotive electric    2021 global demo_2020       target_cps    329191.
#>  7 automotive electric    2021 global demo_2020       target_sds    352505.
#>  8 automotive electric    2021 global demo_2020       target_sps    330435.
#>  9 automotive electric    2022 global demo_2020       projected     354720.
#> 10 automotive electric    2022 global demo_2020       target_cps    333693.
#> # ℹ 782 more rows
#> # ℹ 3 more variables: technology_share <dbl>, scope <chr>,
#> #   percentage_of_initial_production_by_scope <dbl>

# Calculate targets at company level
out_market_share <- matched %>%
  target_market_share(
  abcd = abcd_demo,
  scenario = scenario_demo_2020,
  region_isos = region_isos_demo
  ) 

out_sda <- matched %>% 
  target_sda(
    abcd = abcd_demo,
    co2_intensity_scenario = co2_intensity_scenario_demo,
    region_isos = region_isos_demo
  )
#> Warning: Removing rows in abcd where `emission_factor` is NA

# note that the column names of the metrics are different
all_names <- c(names(out_sda), names(out_market_share))
all_names[grep("metric", all_names)]
#> [1] "emission_factor_metric" "metric"

^{Created on 2024-02-06 with reprex v2.1.0}

AB#9902

[jdhoffa]

See also #313 for some useful discussion.

[cjyetman]

@jdhoffa and @jacobvjk noting that the documentation of the output of these functions in pacta.loanbook somewhat increases the urgency of deciding whether or not to rename these columns

[jacobvjk]

While I agree that the naming choices here are far from optimal, changing these will lead to downstream issues at the very lest in pacta.multi.loanbook and any other workflow we have built for supervisors which depend on the given variable names. We could update all of them, but is it really worth it? This has been around for years and does not seem to have generated confused feedback from users.

[jdhoffa]

Happy to close this without action