Generic filtering

library(bupaR)

filter

Generic filtering of events can be done using the filter function, which takes an event log and any number of logical conditions. The example below filters events which have vehicleclas “C” and amount greater than 300. More process-specific filtering methods can be found here.

traffic_fines %>%
    filter(vehicleclass == "C", amount > 300)

## # Log of 20 events consisting of:
## 1 trace 
## 20 cases 
## 20 instances of 1 activity 
## 10 resources 
## Events occurred from 2006-08-10 until 2008-02-09 
##  
## # Variables were mapped as follows:
## Case identifier:     case_id 
## Activity identifier:     activity 
## Resource identifier:     resource 
## Activity instance identifier:    activity_instance_id 
## Timestamp:           timestamp 
## Lifecycle transition:        lifecycle 
## 
## # A tibble: 20 × 18
##    case_id activity    lifecycle resource timestamp           amount article
##    <chr>   <fct>       <fct>     <fct>    <dttm>              <chr>    <dbl>
##  1 A10060  Create Fine complete  541      2007-03-08 00:00:00 36.0       157
##  2 A10497  Create Fine complete  558      2007-03-30 00:00:00 36.0       157
##  3 A10818  Create Fine complete  561      2007-04-08 00:00:00 36.0       157
##  4 A11707  Create Fine complete  550      2007-04-24 00:00:00 36.0       157
##  5 A11936  Create Fine complete  557      2007-04-29 00:00:00 36.0       157
##  6 A12073  Create Fine complete  557      2007-05-03 00:00:00 36.0       157
##  7 A1408   Create Fine complete  559      2006-08-20 00:00:00 35.0       157
##  8 A14883  Create Fine complete  561      2007-06-29 00:00:00 36.0       157
##  9 A17130  Create Fine complete  541      2007-07-15 00:00:00 36.0       157
## 10 A1815   Create Fine complete  563      2006-08-10 00:00:00 35.0       157
## 11 A19109  Create Fine complete  556      2007-07-17 00:00:00 36.0       157
## 12 A23000  Create Fine complete  550      2007-12-29 00:00:00 36.0       157
## 13 A24247  Create Fine complete  561      2007-12-03 00:00:00 36.0       157
## 14 A24366  Create Fine complete  541      2008-02-09 00:00:00 36.0       157
## 15 A24634  Create Fine complete  537      2007-11-21 00:00:00 36.0       157
## 16 A24942  Create Fine complete  561      2007-12-30 00:00:00 36.0       157
## 17 A25581  Create Fine complete  559      2007-11-23 00:00:00 36.0       157
## 18 A25599  Create Fine complete  559      2007-11-24 00:00:00 36.0       157
## 19 A26099  Create Fine complete  559      2007-12-09 00:00:00 36.0       157
## 20 A26277  Create Fine complete  538      2008-01-07 00:00:00 36.0       157
## # ℹ 11 more variables: dismissal <chr>, expense <chr>, lastsent <chr>,
## #   matricola <dbl>, notificationtype <chr>, paymentamount <dbl>, points <dbl>,
## #   totalpaymentamount <chr>, vehicleclass <chr>, activity_instance_id <chr>,
## #   .order <int>

slice

An eventlog can be sliced, which mean returning a slice, i.e. a subset, from the eventlog, based on row number. There are three ways to slice event logs

Using slice: take a slice of cases
Using slice_activities: take a slice of activity instances
Using slice_events: take a slice of events

The next piece of code returns the first 10 cases. Note that first here is defined by the current order of the data set, not by time.

patients %>%
    slice(1:10)

## # Log of 110 events consisting of:
## 2 traces 
## 10 cases 
## 55 instances of 7 activities 
## 7 resources 
## Events occurred from 2017-01-02 11:41:53 until 2017-01-11 11:39:30 
##  
## # 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: 110 × 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 100 more rows
## # ℹ 1 more variable: .order <int>

slice_activities

The next piece of code returns the first 10 activity instances.

patients %>%
    slice_activities(1:10)

## # Log of 20 events consisting of:
## 1 trace 
## 10 cases 
## 10 instances of 1 activity 
## 1 resource 
## Events occurred from 2017-01-02 11:41:53 until 2017-01-06 09:13:28 
##  
## # 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: 20 × 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
## 11 Registrat… 1       r1       1           complete          2017-01-02 12:40:20
## 12 Registrat… 2       r1       2           complete          2017-01-02 15:16:38
## 13 Registrat… 3       r1       3           complete          2017-01-04 06:36:54
## 14 Registrat… 4       r1       4           complete          2017-01-04 04:25:06
## 15 Registrat… 5       r1       5           complete          2017-01-04 20:07:50
## 16 Registrat… 6       r1       6           complete          2017-01-04 18:12:46
## 17 Registrat… 7       r1       7           complete          2017-01-05 06:27:49
## 18 Registrat… 8       r1       8           complete          2017-01-05 07:58:17
## 19 Registrat… 9       r1       9           complete          2017-01-06 07:18:32
## 20 Registrat… 10      r1       10          complete          2017-01-06 09:13:28
## # ℹ 1 more variable: .order <int>

slice_events

The next piece of code returns the first 10 events.

patients %>% 
    slice_events(1:10)

## # Log of 10 events consisting of:
## 1 trace 
## 10 cases 
## 10 instances of 1 activity 
## 1 resource 
## Events occurred from 2017-01-02 11:41:53 until 2017-01-06 05:58:54 
##  
## # 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: 10 × 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 more variable: .order <int>

first_n, last_n

The slice function select events, cases or activity instances based on their current position in the event data. As such, the result can be changed using the arrange function. More often, we want to select the first n activity instances, or the last ones. This is achieved with the first_n or last_n functions, which return the first, resp. last, n activity instances of a log based on time, not on position.

patients %>% 
    first_n(n = 5)

## # Log of 10 events consisting of:
## 2 traces 
## 3 cases 
## 5 instances of 2 activities 
## 2 resources 
## Events occurred from 2017-01-02 11:41:53 until 2017-01-04 04:25:06 
##  
## # 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: 10 × 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 Triage an… 1       r2       501         start             2017-01-02 12:40:20
##  4 Registrat… 1       r1       1           complete          2017-01-02 12:40:20
##  5 Registrat… 2       r1       2           complete          2017-01-02 15:16:38
##  6 Triage an… 2       r2       502         start             2017-01-02 22:32:25
##  7 Triage an… 1       r2       501         complete          2017-01-02 22:32:25
##  8 Triage an… 2       r2       502         complete          2017-01-03 12:34:01
##  9 Registrat… 4       r1       4           start             2017-01-04 01:34:04
## 10 Registrat… 4       r1       4           complete          2017-01-04 04:25:06
## # ℹ 1 more variable: .order <int>

This is not impacted by a different ordering of the data since it will take the time aspect into account.

patients %>%
    arrange(desc(time)) %>%
    first_n(n = 5)

## # Log of 10 events consisting of:
## 2 traces 
## 3 cases 
## 5 instances of 2 activities 
## 2 resources 
## Events occurred from 2017-01-02 11:41:53 until 2017-01-04 04:25:06 
##  
## # 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: 10 × 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 Triage an… 1       r2       501         start             2017-01-02 12:40:20
##  4 Registrat… 1       r1       1           complete          2017-01-02 12:40:20
##  5 Registrat… 2       r1       2           complete          2017-01-02 15:16:38
##  6 Triage an… 2       r2       502         start             2017-01-02 22:32:25
##  7 Triage an… 1       r2       501         complete          2017-01-02 22:32:25
##  8 Triage an… 2       r2       502         complete          2017-01-03 12:34:01
##  9 Registrat… 4       r1       4           start             2017-01-04 01:34:04
## 10 Registrat… 4       r1       4           complete          2017-01-04 04:25:06
## # ℹ 1 more variable: .order <int>

Incombination with group_by_case, it is very easy to select the heads or tails of each case. Below, we explore the 95% most common first 3 activities in the sepsis log.

sepsis %>%
    group_by_case() %>%
    first_n(3) %>%
    trace_explorer(coverage = 0.95)

sample_n

The sample_n function allows to take a sample of the event log containing n cases. The code below returns a sample of 10 patients.

patients %>%
    sample_n(size = 10)

## # Log of 108 events consisting of:
## 2 traces 
## 10 cases 
## 54 instances of 7 activities 
## 7 resources 
## Events occurred from 2017-03-17 05:57:32 until 2018-03-18 10:48:34 
##  
## # 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: 108 × 7
##    handling   patient employee handling_id registration_type time               
##    <fct>      <chr>   <fct>    <chr>       <fct>             <dttm>             
##  1 Registrat… 70      r1       70          start             2017-03-17 05:57:32
##  2 Registrat… 78      r1       78          start             2017-03-25 18:07:50
##  3 Registrat… 127     r1       127         start             2017-05-05 23:40:40
##  4 Registrat… 155     r1       155         start             2017-06-03 10:05:28
##  5 Registrat… 261     r1       261         start             2017-09-16 15:08:03
##  6 Registrat… 319     r1       319         start             2017-11-04 06:23:34
##  7 Registrat… 325     r1       325         start             2017-11-09 03:47:37
##  8 Registrat… 350     r1       350         start             2017-11-24 12:22:18
##  9 Registrat… 362     r1       362         start             2017-12-05 18:53:44
## 10 Registrat… 455     r1       455         start             2018-03-14 21:04:28
## # ℹ 98 more rows
## # ℹ 1 more variable: .order <int>

Note that this function can also be used with a sample size bigger than the number of cases in the event log, if you allow for the replacements of drawn cases.

A more extensive list of subsetting methods is provided by edeaR. Look here for more information.