tiltPlot

The goal of tiltPlot is to provide plots for the TILT project.

Installation

You can install the development version of tiltPlot from GitHub with:

# install.packages("pak")
pak::pak("2DegreesInvesting/tiltPlot")

Example

library(ggplot2)
library(dplyr, warn.conflicts = FALSE)
library(tiltPlot)

1. Sankey Plot with financial data

financial
#> # A tibble: 114 × 13
#>    bank_id amount_total company_name postcode wz    xctr_risk_category benchmark
#>    <chr>          <int> <chr>           <int> <chr> <chr>              <chr>    
#>  1 bank_b           500 peter peasa…    53773 A     high               all      
#>  2 bank_b           500 peter peasa…    53773 A     high               unit     
#>  3 bank_b           500 peter peasa…    53773 A     medium             tilt_sec 
#>  4 bank_b           500 peter peasa…    53773 A     medium             unit_til…
#>  5 bank_b           500 peter peasa…    53773 A     low                isic_sec 
#>  6 bank_b           500 peter peasa…    53773 A     medium             unit_isi…
#>  7 bank_b           500 tilman          12043 D     low                all      
#>  8 bank_b           500 tilman          12043 D     low                unit     
#>  9 bank_b           500 tilman          12043 D     medium             tilt_sec 
#> 10 bank_b           500 tilman          12043 D     medium             unit_til…
#> # ℹ 104 more rows
#> # ℹ 6 more variables: product_name <chr>, tilt_sector <chr>,
#> #   amount_of_distinct_products <int>, equal_weight_finance <dbl>,
#> #   worst_case_finance <int>, best_case_finance <int>

Here is the default Sankey Plot. By default the function plots with companies and uses an “equal_weight” mode.

fin <- financial
plot_sankey(fin)

You can also choose to have the plot without the company node.

plot_sankey(fin, with_company = FALSE)

Finally, the user can choose different modes to plot the Sankey plot with financial data available.

plot_sankey(fin, with_company = FALSE, mode = "best_case")

2. XCTR plots with financial data

financial
#> # A tibble: 114 × 13
#>    bank_id amount_total company_name postcode wz    xctr_risk_category benchmark
#>    <chr>          <int> <chr>           <int> <chr> <chr>              <chr>    
#>  1 bank_b           500 peter peasa…    53773 A     high               all      
#>  2 bank_b           500 peter peasa…    53773 A     high               unit     
#>  3 bank_b           500 peter peasa…    53773 A     medium             tilt_sec 
#>  4 bank_b           500 peter peasa…    53773 A     medium             unit_til…
#>  5 bank_b           500 peter peasa…    53773 A     low                isic_sec 
#>  6 bank_b           500 peter peasa…    53773 A     medium             unit_isi…
#>  7 bank_b           500 tilman          12043 D     low                all      
#>  8 bank_b           500 tilman          12043 D     low                unit     
#>  9 bank_b           500 tilman          12043 D     medium             tilt_sec 
#> 10 bank_b           500 tilman          12043 D     medium             unit_til…
#> # ℹ 104 more rows
#> # ℹ 6 more variables: product_name <chr>, tilt_sector <chr>,
#> #   amount_of_distinct_products <int>, equal_weight_finance <dbl>,
#> #   worst_case_finance <int>, best_case_finance <int>

On a company level:

fin <- financial

plot_xctr_financial(fin, "peter", mode = "worst_case") +
  labs(title = "Risk distribution of all products on a company level, on a financial weight")

On a portfolio level:

plot_xctr_financial(fin, mode = "worst_case") +
  labs(title = "Risk distribution of all products on a portfolio level, on a financial weight")

3. XCTR plots without financial data

without_financial
#> # A tibble: 58 × 5
#>    company_name  xctr_risk_category benchmark     product_name tilt_sector
#>    <chr>         <chr>              <chr>         <chr>        <chr>      
#>  1 peter peasant high               all           car          D          
#>  2 peter peasant high               unit          car          D          
#>  3 peter peasant medium             tilt_sec      car          D          
#>  4 peter peasant medium             unit_tilt_sec car          D          
#>  5 peter peasant low                isic_sec      car          D          
#>  6 peter peasant low                unit_isic_sec car          D          
#>  7 peter         high               all           banana       A          
#>  8 peter         high               unit          banana       A          
#>  9 peter         medium             tilt_sec      banana       A          
#> 10 peter         medium             unit_tilt_sec banana       A          
#> # ℹ 48 more rows

To plot on a company level:

no_fin <- without_financial

no_fin |>
  filter(company_name == "peter") |>
  plot_xctr() +
  labs(title = "Risk distribution of all products on a company level")

On a portfolio level :

plot_xctr(no_fin) +
  labs(title = "Risk distribution of all products on a portfolio level")

4. Create a German map with risk categories color gradient

map_region_risk(financial, "DE", benchmark = "unit_isic_sec") +
  labs(title = "German map of high, medium and low propotion of the companies 
  that are found in one region.
  © EuroGeographics for the administrative boundaries ")
#> Object cached at /tmp/Rtmp2M7MqW/eurostat/sf10320163035.RData
#> Reading cache file /tmp/Rtmp2M7MqW/eurostat/sf10320163035.RData
#> sf at resolution 1: 10  from year  2016  read from cache file:  /tmp/Rtmp2M7MqW/eurostat/sf10320163035.RData