`summarize_scenarios.Rd`

Given a dataframe of raw results from `run_simulations`

, summarize
the individual results at a per-scenario level. This is generally the most
granular level of data for reporting and analysis (full simulation results
are rarely directly helpful).

summarize_scenarios(simulation_results)

simulation_results | Simulation results dataframe. |
---|

Dataframe.

Summary stats created include: * Mean/Min/Max/Median are calculated for loss events * Median/Max/VaR are calculated for annual loss expected (ALE) * Mean/Median/Max/Min are calculated for single loss expected (SLE) * Mean percentage of threat capability exceeding difficulty on successful threat events * Mean percentage of difficulty exceeding threat capability on defended events * Vulnerability percentage * Z-score of ALE (outliers flagged as 2 >= z-score)

data(simulation_results) summarize_scenarios(simulation_results)#> # A tibble: 56 x 18 #> domain_id scenario_id loss_events_mean loss_events_min loss_events_max #> <chr> <chr> <dbl> <int> <int> #> 1 AC 48 0 0 0 #> 2 AC 51 6.12 1 12 #> 3 ADM 56 0.672 0 1 #> 4 ASSET 45 6.12 1 12 #> 5 ASSET 46 0.672 0 1 #> 6 ASSET 47 1 1 1 #> 7 BC 31 0 0 0 #> 8 BC 55 0.656 0 1 #> 9 COMP 10 1.94 0 8 #> 10 COMP 11 24.6 9 47 #> # ... with 46 more rows, and 13 more variables: loss_events_median <dbl>, #> # ale_median <dbl>, ale_max <dbl>, ale_var <dbl>, sle_mean <dbl>, #> # sle_median <dbl>, sle_max <dbl>, sle_min <dbl>, mean_tc_exceedance <dbl>, #> # mean_diff_exceedance <dbl>, mean_vuln <dbl>, ale_var_zscore <dbl>, #> # outlier <lgl>