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 morecontains_exactly
: activity occurs exactly n timescontains_between
: activity occures between min and max
number of timesabsent
: activity does not occur more than n - 1
timesOrdering rules:
starts
: case starts with activityends
: case ends with activitysuccession
: if activity A happens, B should happen
after. If B happens, A should have happened before.response
: if activity A happens, B should happen
afterprecedence
: if activity B happens, A should have
happend beforeresponded_existence
: if activity A happens, B should
also (have) happen(ed) (i.e. before or after A)Exclusiveness:
and
: two activities always exist togetherxor
: two activities are not allowed to exist
togetherRules 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
.
You can install processcheckR from github with:
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