---
title: "An Evaluator-driven Risk Management Process"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{An Evaluator-driven Risk Management Process}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

Working notes for building a risk assessment process using the Evaluator 
toolkit. This vignette details the basic flow used at some organizations to 
maintain reproducability of inputs and results. While other technologies and 
solutions may be employed, this workflow is intended to be easy to implement 
for users familiar with source control principles.

## Starting from Scratch

- Create a starting Evaluator working directory via `create_templates()`
- Customize the domains used in your organization by editing the `domains.csv` 
file to include the domains (and their abbreviations) which are appropriate for your 
risk domain and organization.
- Edit the `survey.xlsx` spreadsheet to include the risk scenarios and the threats 
for each scenario which you wish to monitor on an ongoing basis.
- Create a source code repository (such as a GitHub for Enterprise repo) for 
your ongoing risk analysis work.
- Add a tag to designate the just committed files (without analysis) as your reference 
documentation.

## Performing an Analysis
- Meet with subject matter experts for each domain and walk through each 
scenario and threat source.
- Record answers in the survey.xlsx spreadsheet.
- Run the analysis as detailed in the [usage](usage.html) vignette.
- Commit the inputs and results to your source code repository.
- Add a tag to reflect the state as of a certain point in time (i.e. FY18 Q1)

## What to Store

If using the sample workflow, you will have two subdirectories located under 
a parent `evaluator` directory in your home folder. `inputs` contains the 
qualitative scenarios and input data files, while `results` stores the 
simulation results, including the summarized data files. Both directories 
together represent an analysis snapshot.

## Experimental Quick Start Script

When using `create_templates()` an `run_analysis.R` file will be placed at the base 
of the `evaluator` directory. A sample ultra-quick workflow looks like:

1. Run `evaluator::create_templates()`
2. Edit the inputs for your situation.
3. Run the quick start script with code like the following:

```{r eval=FALSE}
base_dir <- "~/evaluator"
source("~/evaluator/run_analysis.R")
```
