processcheckR

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

Cardinality rules:

• absent: activity does not occur more than n - 1 times,
• contains_between: activity occurs between n and m times,
• contains_exactly: activity occurs exactly n 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 happened 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 and check_rules functions (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 CRAN with:

install.packages("processcheckR")

Development Version

You can also install the latest (stable) development version with bugfixes and new features directly from GitHub:

# install.packages("devtools")
devtools::install_github("bupaverse/processcheckR")

Example

library(bupaR)
#> 
#> Attaching package: 'bupaR'
#> The following object is masked from 'package:stats':
#> 
#>     filter
#> The following object is masked from 'package:utils':
#> 
#>     timestamp
library(processcheckR)
#> 
#> Attaching package: 'processcheckR'
#> The following object is masked from 'package:base':
#> 
#>     xor
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() 
#> # A tibble: 4 × 3
#>   r1    r2    n_cases
#>   <lgl> <lgl>   <int>
#> 1 FALSE FALSE      10
#> 2 FALSE TRUE       45
#> 3 TRUE  FALSE     137
#> 4 TRUE  TRUE      858