---
title: "Rule-Based Conformance Checking"
author: "Gert Janssenswillen"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Rule-Based Conformance Checking}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

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

The goal of processcheckR is to support rule-based conformance checking. Currently the following declarative rules can be checked:

Cardinality rules:

* `contains`: activity occurs n times or more
* `contains_exactly`: activity occurs exactly n times
* `contains_between`: activity occures between min and max number of times
* `absent`: activity does not occur more than n - 1 times

Ordering rules:

* `starts`: case starts with activity
* `ends`: case ends with activity
* `succession`: if activity A happens, B should happen after. If B happens, A should have happened before.
* `response`: if activity A happens, B should happen after
* `precedence`: if activity B happens, A should have happend before
* `responded_existence`: if activity A happens, B should also (have) happen(ed) (i.e. before or after A)

Exclusiveness:

* `and`: two activities always exist together
* `xor`: two activities are not allowed to exist together


Rules can be checked using the  `check_rule` function (see example below). It will create a new logical variable to indicate for which cases the rule holds. The name of the variable can be configured using the `label` argument in  `check_rule`. 

## Installation

You can install processcheckR from github with:

```{r gh-installation, eval = FALSE}
# install.packages("devtools")
devtools::install_github("gertjanssenswillen/processcheckR")
```

## Example


```{r example}
library(bupaR)
library(processcheckR)
sepsis %>%
  # check if cases starts with "ER Registration"
  check_rule(starts("ER Registration"), label = "r1") %>%
  # check if activities "CRP" and "LacticAcid" occur together
  check_rule(and("CRP","LacticAcid"), label = "r2") %>%
  group_by(r1, r2) %>%
  n_cases() 
```