bupaR Docs | Inspect log
Inspect logs
Getting metadata
The mapping function can be used to retrieve all the meta data from a
log object, i.e. the relation between log identifiers and
the corresponding data fields.
patients %>% mapping## Case identifier: patient
## Activity identifier: handling
## Resource identifier: employee
## Activity instance identifier: handling_id
## Timestamp: time
## Lifecycle transition: registration_typeIn this case, we see that the handling field is the activity identifier in the event log, while the patient field is used as case identifier. We can also obtain each of these identifiers individually.
patients %>% activity_id
patients %>% case_id
patients %>% resource_id## [1] "handling"
## [1] "patient"
## [1] "employee"Getting basic information
We can look at a general summary of the event log with
summary().
patients %>% summary## Number of events: 5442
## Number of cases: 500
## Number of traces: 7
## Number of distinct activities: 7
## Average trace length: 10.884
##
## Start eventlog: 2017-01-02 11:41:53
## End eventlog: 2018-05-05 07:16:02## handling patient employee handling_id
## Blood test : 474 Length:5442 r1:1000 Length:5442
## Check-out : 984 Class :character r2:1000 Class :character
## Discuss Results : 990 Mode :character r3: 474 Mode :character
## MRI SCAN : 472 r4: 472
## Registration :1000 r5: 522
## Triage and Assessment:1000 r6: 990
## X-Ray : 522 r7: 984
## registration_type time .order
## complete:2721 Min. :2017-01-02 11:41:53.00 Min. : 1
## start :2721 1st Qu.:2017-05-06 17:15:18.00 1st Qu.:1361
## Median :2017-09-08 04:16:50.00 Median :2722
## Mean :2017-09-02 20:52:34.40 Mean :2722
## 3rd Qu.:2017-12-22 15:44:11.25 3rd Qu.:4082
## Max. :2018-05-05 07:16:02.00 Max. :5442
## The basic counts which show up in the summary can also be retrieved individual as a numeric vector of length one.
patients %>% n_activities
patients %>% n_activity_instances
patients %>% n_cases
patients %>% n_events
patients %>% n_traces
patients %>% n_resources## [1] 7
## [1] 2721
## [1] 500
## [1] 5442
## [1] 7
## [1] 7More detailed information about activities ,
cases, resources and traces can
be obtained using the functions named accordingly, as in the examples
below for the patients event log.
Activities
patients %>% activities()## # A tibble: 7 × 3
## handling absolute_frequency relative_frequency
## <fct> <int> <dbl>
## 1 Registration 500 0.184
## 2 Triage and Assessment 500 0.184
## 3 Discuss Results 495 0.182
## 4 Check-out 492 0.181
## 5 X-Ray 261 0.0959
## 6 Blood test 237 0.0871
## 7 MRI SCAN 236 0.0867Cases
patients %>% cases()## # A tibble: 500 × 10
## patient trace_length number_of_activities start_timestamp
## <chr> <int> <int> <dttm>
## 1 1 6 6 2017-01-02 11:41:53
## 2 10 5 5 2017-01-06 05:58:54
## 3 100 5 5 2017-04-11 16:34:31
## 4 101 5 5 2017-04-16 06:38:58
## 5 102 5 5 2017-04-16 06:38:58
## 6 103 6 6 2017-04-19 20:22:01
## 7 104 6 6 2017-04-19 20:22:01
## 8 105 6 6 2017-04-21 02:19:09
## 9 106 6 6 2017-04-21 02:19:09
## 10 107 5 5 2017-04-22 18:32:16
## # ℹ 490 more rows
## # ℹ 6 more variables: complete_timestamp <dttm>, trace <chr>, trace_id <dbl>,
## # duration <drtn>, first_activity <fct>, last_activity <fct>Resources
patients %>% resources()## # A tibble: 7 × 3
## employee absolute_frequency relative_frequency
## <fct> <int> <dbl>
## 1 r1 500 0.184
## 2 r2 500 0.184
## 3 r6 495 0.182
## 4 r7 492 0.181
## 5 r5 261 0.0959
## 6 r3 237 0.0871
## 7 r4 236 0.0867Traces
patients %>% traces()## # A tibble: 7 × 3
## trace absolute_frequency relative_frequency
## <chr> <int> <dbl>
## 1 Registration,Triage and Assessment,X-Ra… 258 0.516
## 2 Registration,Triage and Assessment,Bloo… 234 0.468
## 3 Registration,Triage and Assessment,Bloo… 2 0.004
## 4 Registration,Triage and Assessment,X-Ray 2 0.004
## 5 Registration,Triage and Assessment 2 0.004
## 6 Registration,Triage and Assessment,X-Ra… 1 0.002
## 7 Registration,Triage and Assessment,Bloo… 1 0.002Read more:
Copyright © 2023 bupaR - Hasselt University