library("reprex")
library("evaluator")
library("readr")

wd = "~/Documents/Code/Evaluator"
setwd(wd)



# create Excel templates in base directory

create_templates(base_directory = wd)
#> # A tibble: 5 x 2
#>   filename                 copied
#>   <chr>                    <lgl> 
#> 1 domains.csv              FALSE 
#> 2 qualitative_mappings.csv FALSE 
#> 3 risk_tolerances.csv      FALSE 
#> 4 run_analysis.R           FALSE 
#> 5 survey.xlsx              FALSE


##  Modify/customize input spreadsheet manually before completing next steps


## If you have a different domain structure (e.g. ISO2700x, NIST CSF, or COBIT), you will need to edit the inputs/domains.csv file to include the domain names and the domain IDs,
### and a shorthand abbreviation for the domain (such as POL for the Policy domain).

# read domains from CSV files
domains = read_csv("./inputs/domains.csv")
#> Parsed with column specification:
#> cols(
#>   domain_id = col_character(),
#>   domain = col_character()
#> )

# read the import spreadsheet and generate CSV files from it
import_spreadsheet("./inputs/survey.xlsx", domains, output_dir = "./inputs")
#> # A tibble: 2 x 11
#>   filename  size isdir mode  mtime               ctime              
#>   <chr>    <dbl> <lgl> <oct> <dttm>              <dttm>             
#> 1 ./input…  8017 FALSE 644   2020-04-16 17:36:59 2020-04-16 17:36:59
#> 2 ./input… 10352 FALSE 644   2020-04-16 17:36:59 2020-04-16 17:36:59
#> # … with 5 more variables: atime <dttm>, uid <int>, gid <int>, uname <chr>,
#> #   grname <chr>

#  but ... does not update risk_tolerances.csv or domains.csv  (maybe customize)

# read in the FAIR parameters from CSV files, then validate
qualitative_scenarios <- readr::read_csv("./inputs/qualitative_scenarios.csv")
#> Parsed with column specification:
#> cols(
#>   scenario_id = col_character(),
#>   scenario = col_character(),
#>   tcomm = col_character(),
#>   tef = col_character(),
#>   tc = col_character(),
#>   lm = col_character(),
#>   domain_id = col_character(),
#>   controls = col_character()
#> )
mappings <- readr::read_csv("./inputs/qualitative_mappings.csv")
#> Parsed with column specification:
#> cols(
#>   type = col_character(),
#>   label = col_character(),
#>   l = col_double(),
#>   ml = col_double(),
#>   h = col_double(),
#>   conf = col_double()
#> )
capabilities <- readr::read_csv("./inputs/capabilities.csv")
#> Parsed with column specification:
#> cols(
#>   capability_id = col_character(),
#>   domain_id = col_character(),
#>   capability = col_character(),
#>   diff = col_character()
#> )
validate_scenarios(qualitative_scenarios, capabilities, domains, mappings)

# Encode scenarios, coverting qualitative (ordinal) variables to quantitative 
quantitative_scenarios <- encode_scenarios(qualitative_scenarios, 
                                           capabilities, mappings)

## QUESTION -- where are the parameters/rules for converting qualitative FAIR to quant?


# Run the simulation 

simulation_results <- run_simulations(quantitative_scenarios, 
                                      iterations = 100L)
#> Error: All scenarios must be tidyrisk_scenario objects
# Error: All scenarios must be tidyrisk_scenario objects



# saveRDS(simulation_results, file = "./results/simulation_results.rds")

^{Created on 2020-04-16 by the reprex package (v0.3.0)}