The filters for event data subsetting can mostly be divided into two type: event filters and case filters. Event filters will subset parts of cases based on criteria applied on the events (e.g. the resource which performed it), while case filters will subset complete cases, based on criteria applied on the cases (e.g. the trace length).
Each filter has a reverse argument, which allows to reverse the filter very easily. Furthermore, each filter has an interface-alternative, which can be called by adding a i before the function name.
The filter activity function can be used to filter activities by name. It has three arguments
## Number of events: 996
## Number of cases: 498
## Number of traces: 2
## Number of distinct activities: 2
## Average trace length: 2
##
## Start eventlog: 2017-01-05 08:59:04
## End eventlog: 2018-05-05 01:34:30## handling patient employee handling_id
## Blood test :474 Length:996 r1: 0 Length:996
## Check-out : 0 Class :character r2: 0 Class :character
## Discuss Results : 0 Mode :character r3:474 Mode :character
## MRI SCAN : 0 r4: 0
## Registration : 0 r5:522
## Triage and Assessment: 0 r6: 0
## X-Ray :522 r7: 0
## registration_type time .order
## complete:498 Min. :2017-01-05 08:59:04.00 Min. : 1.0
## start :498 1st Qu.:2017-05-06 12:31:43.00 1st Qu.:249.8
## Median :2017-09-08 00:10:11.00 Median :498.5
## Mean :2017-09-03 07:11:55.96 Mean :498.5
## 3rd Qu.:2017-12-23 02:06:20.50 3rd Qu.:747.2
## Max. :2018-05-05 01:34:30.00 Max. :996.0
## As one can see, there are only 2 distinct activities left in the event log.
It is also possible to filter on activity frequency. This filter uses a percentile cut off, and will look at those activities which are most frequent until the required percentage of events has been reached. Thus, a percentile cut off of 80% will look at the activities needed to represent 80% of the events. In the example below, the least frequent activities covering 50% of the event log are selected, since the reverse argument is true.
patients %>%
filter_activity_frequency(percentage = 0.5, reverse = T) %>%
activity_frequency("activity")## # A tibble: 4 × 3
## handling absolute relative
## <fct> <int> <dbl>
## 1 Check-out 492 0.401
## 2 X-Ray 261 0.213
## 3 Blood test 237 0.193
## 4 MRI SCAN 236 0.192The filter_attributes function is a very generic function an can be
supplied with conditions on the data set, in the same way as the
dplyr::filter function. As such, it allows you to filter on
event or case attributes. Multiple conditions can be listed, separated
by a comma. In that case, the comma will be treated as “and”. You can
use the |-symbol to state “OR”. Since the patients dataset does not have
many additional attributes, the example below uses the resource and
activity. This filter is thus the same as the combination of
filter_activity and filter_resource, in case both conditions were
required. However, it has the advantange of stating both conditions as
OR.
## Warning: `filter_attributes()` was deprecated in bupaR 0.5.0.
## ℹ Please use `filter()` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.## # Log of 1522 events consisting of:
## 2 traces
## 500 cases
## 761 instances of 2 activities
## 2 resources
## Events occurred from 2017-01-02 11:41:53 until 2018-05-05 01:34:30
##
## # Variables were mapped as follows:
## Case identifier: patient
## Activity identifier: handling
## Resource identifier: employee
## Activity instance identifier: handling_id
## Timestamp: time
## Lifecycle transition: registration_type
##
## # A tibble: 1,522 × 7
## handling patient employee handling_id registration_type time
## <fct> <chr> <fct> <chr> <fct> <dttm>
## 1 Registrat… 1 r1 1 start 2017-01-02 11:41:53
## 2 Registrat… 2 r1 2 start 2017-01-02 11:41:53
## 3 Registrat… 3 r1 3 start 2017-01-04 01:34:05
## 4 Registrat… 4 r1 4 start 2017-01-04 01:34:04
## 5 Registrat… 5 r1 5 start 2017-01-04 16:07:47
## 6 Registrat… 6 r1 6 start 2017-01-04 16:07:47
## 7 Registrat… 7 r1 7 start 2017-01-05 04:56:11
## 8 Registrat… 8 r1 8 start 2017-01-05 04:56:11
## 9 Registrat… 9 r1 9 start 2017-01-06 05:58:54
## 10 Registrat… 10 r1 10 start 2017-01-06 05:58:54
## # ℹ 1,512 more rows
## # ℹ 1 more variable: .order <int>Similar to the activity filter, the resource filter can be used to filter events by listing on or more resources.
## # A tibble: 2 × 3
## employee absolute relative
## <fct> <int> <dbl>
## 1 r1 500 0.679
## 2 r4 236 0.321The trim filter is a special event filter, as it also take into account the notion of cases. In fact, it trim cases such that they start with a certain activities until they end with a certain activity. It requires two list: one for possible start activities and one for end activities. The cases will be trimmed from the first appearance of a start activity till the last appearance of an end activity. When reversed, these slices of the event log will be removed instead of preserved.
patients %>%
filter_trim(start_activities = "Registration", end_activities = c("MRI SCAN","X-Ray")) %>%
traces()## # A tibble: 2 × 3
## trace absolute_frequency relative_frequency
## <chr> <int> <dbl>
## 1 Registration,Triage and Assessment,X-Ray 261 0.525
## 2 Registration,Triage and Assessment,Bloo… 236 0.475This functions allows to filter cases that contain certain
activities. It requires as input a vector containing one or more
activity labels and it has a method argument. The latter
can have the values all, none or one_of. When
set to all, it means that all the specified activity labels
must be present for a case to be selected, none means that they
are not allowed to be present, and one_of means that at least
one of them must be present.
The case filter allows to subset a set of case identifiers. As
arguments it only requires a vector of case id’s. The selection can also
be negated using reverse = T.
The filter_endpoints method filters cases based on the
first and last activity label. It can be used in two ways: by specifying
vectors with allowed start activities and/or allowed end activities, or
by specifying a percentile. In the latter case, the percentile value
will be used as a cut off. For example, when set to 0.9, it will select
the most common endpoint pairs which together cover at least 90% of the
cases, and filter the event log accordingly. This filter can also be
reversed.
In order to extract a subset of an event log which conforms with a
set of precedence rules, one can use the filter_precedence
method. There are two types of precendence relations which can be
tested: activities that should directly follow each other, or
activities that should eventually follow each other. The type
can be set with the precedence_type argument. Further, the
filter requires a vector of one or more antecedents (containing activity
labels), and one or more consequents. Finally, also a
filter_method argument can be set. This argument is relevant
when there is more than one antecedent or consequent. In such a case,
you can specify that all possible precedence combinations must be
present (all), or at least one of them (_one_of).
There are three different filters which take into account the length of a case:
Each of these filters can work in two ways, similar to the endpoints
filter: either by using an interval or by using a percentile cut off.
The percentile cut off will always start with the shortest cases first
and stop including cases when the specified percentile is reached. The
processing and throughput time filters also have a units
attribute to specify the time unit used when defining an interval. All
the methods can be reversed by setting reverse = T.
Cases can also be filtered by supplying a time window to the method
filter_time_period. There are four different filter
methods, of which one can be used as argument:
The selection can also be reversed. Note that there is a 5 filter method, trim, but this is actually an event filter and will thus be discussed in the next section.
The last case filter can be used to filter cases based on the frequency of the corresponding trace. A trace is a sequence of activity labels, and will be discussed in more detail in Section . There are again two ways to select cases based on trace frequency, by interval or by percentile cut off. The percentile cut off will start with the most frequent traces. This filter also contains the reverse argument.