The scenarioSelector app helps you find and visualize a scenario. You can use it online or as an R package following the installation and example below.
Install the development version of scenarioSelector from GitHub with:
# install.packages("devtools")
devtools::install_github("2DegreesInvesting/scenarioSelector")
Use the scenarioSelector package with:
library(scenarioSelector)
You can run the app with:
if (interactive()) {
run_app()
}
The tidy data is available with:
scenarios
#> # A tibble: 56,680 × 12
#> scenario model is_net0 is_2dii region year sector technology target variable
#> <chr> <chr> <lgl> <lgl> <chr> <dbl> <chr> <chr> <chr> <chr>
#> 1 etp_2017 2ds FALSE TRUE global 2020 autom… electric produ… smsp
#> 2 etp_2017 2ds FALSE TRUE global 2020 autom… electric produ… tmsr
#> 3 etp_2017 2ds FALSE TRUE global 2020 autom… hybrid produ… smsp
#> 4 etp_2017 2ds FALSE TRUE global 2020 autom… hybrid produ… tmsr
#> 5 etp_2017 2ds FALSE TRUE global 2020 autom… ice produ… smsp
#> 6 etp_2017 2ds FALSE TRUE global 2020 autom… ice produ… tmsr
#> 7 etp_2017 2ds FALSE TRUE global 2021 autom… electric produ… smsp
#> 8 etp_2017 2ds FALSE TRUE global 2021 autom… electric produ… tmsr
#> 9 etp_2017 2ds FALSE TRUE global 2021 autom… hybrid produ… smsp
#> 10 etp_2017 2ds FALSE TRUE global 2021 autom… hybrid produ… tmsr
#> # … with 56,670 more rows, and 2 more variables: value <dbl>, unit <chr>
To track the source of the data see data-raw/.
For a discussion including related work see the channel
# scenario-selector-tool
on 2DII’s (private) Slack or ADO
#552.
This app is small but implements some big ideas I learned from Mastering Shiny, by Hadley Wickham:
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