r2dii.analysis 0.1.0 is now on CRAN. r2dii.analysis provides analysis tools to help you study how financial assets align to climate scenarios.
This release signals that r2dii.analysis is now maturing. You can install it from CRAN with:
install.packages("r2dii.analysis")Then use it with:
library(r2dii.analysis)Let’s also load the rest of the r2dii suite.
library(r2dii.data)
library(r2dii.match)I also use the package dplyr; it is not necessary but convenient.
library(dplyr)All user-facing changes are listed in the changelog; here I’ll show the most important ones.
We fixed a bug in the dataset target_sda; it now outputs targets calculated using the ald-adjusted scenarios.
match_result <- loanbook_demo %>%
match_name(ald_demo) %>%
prioritize()
match_result %>%
target_sda(
ald_demo,
co2_intensity_scenario_demo
)
# A tibble: 208 x 4
sector year emission_factor_metric emission_factor_value
<chr> <dbl> <chr> <dbl>
1 automotive 2002 projected 0.228
2 automotive 2003 projected 0.226
3 automotive 2004 projected 0.224
4 automotive 2005 projected 0.222
5 automotive 2006 projected 0.220
6 automotive 2007 projected 0.218
7 automotive 2008 projected 0.216
8 automotive 2009 projected 0.214
9 automotive 2010 projected 0.212
10 automotive 2011 projected 0.210
# … with 198 more rowsWe have also added the parameter weight_production to target_market_share. This allows you to choose between outputting production values, weighted by relative loan size (weight_production == TRUE), or to output the actual raw company production values (weight_production == FALSE).
match_result <- loanbook_demo %>%
match_name(ald_demo) %>%
prioritize()
weighted_production <- TRUE
match_result %>%
target_market_share(
ald_demo,
scenario_demo_2020,
region_isos_demo,
weight_production = weighted_production
) %>%
filter(metric == "projected")
# A tibble: 234 x 8
sector technology year region scenario_source metric production
<chr> <chr> <int> <chr> <chr> <chr> <dbl>
1 autom… electric 2020 global demo_2020 proje… 145942.
2 autom… electric 2021 global demo_2020 proje… 148212.
3 autom… electric 2022 global demo_2020 proje… 150481.
4 autom… electric 2023 global demo_2020 proje… 152751.
5 autom… electric 2024 global demo_2020 proje… 155020.
6 autom… electric 2025 global demo_2020 proje… 157289.
7 autom… electric 2026 global demo_2020 proje… 159559.
8 autom… hybrid 2020 global demo_2020 proje… 141768.
9 autom… hybrid 2021 global demo_2020 proje… 138687.
10 autom… hybrid 2022 global demo_2020 proje… 135606.
# … with 224 more rows, and 1 more variable: technology_share <dbl>
unweighted_production <- FALSE
match_result %>%
target_market_share(
ald_demo,
scenario_demo_2020,
region_isos_demo,
weight_production = unweighted_production
) %>%
filter(metric == "projected")
# A tibble: 234 x 8
sector technology year region scenario_source metric production
<chr> <chr> <int> <chr> <chr> <chr> <dbl>
1 autom… electric 2020 global demo_2020 proje… 6630404.
2 autom… electric 2021 global demo_2020 proje… 6731628.
3 autom… electric 2022 global demo_2020 proje… 6832852.
4 autom… electric 2023 global demo_2020 proje… 6934075.
5 autom… electric 2024 global demo_2020 proje… 7035299.
6 autom… electric 2025 global demo_2020 proje… 7136523.
7 autom… electric 2026 global demo_2020 proje… 7237746.
8 autom… hybrid 2020 global demo_2020 proje… 7009476.
9 autom… hybrid 2021 global demo_2020 proje… 6883774.
10 autom… hybrid 2022 global demo_2020 proje… 6758072.
# … with 224 more rows, and 1 more variable: technology_share <dbl>
thank <- function(x) {
x <- sort(x)
paste0("[", x, "]", sprintf("(https://github.com/%s)", x), collapse = ", ")
}
contributors <- c(
"jdhoffa",
"maurolepore"
)While this release includes commits from only a few of us (jdhoffa, maurolepore), it is thanks to ideas and feedback from many more colleagues at 2DII – particularly George Harris.