bupaR Docs | Data Quality
Data Quality
Despite the extensive opportunities that process mining techniques
provide, the garbage in - garbage out principle still applies. Data
quality issues are widespread in real-life data and can generate
misleading results when used for analysis purposes. daqapo
- Data Quality Assessment for Process-Oriented data - provides a set of
assessment functions to identify a wide array of quality issues.
Getting started
In the examples below, we use the dataset
hospital_actlog, which is an artificial event log with data
quality issues provided by daqapo.
library(daqapo)
library(dplyr)
data("hospital_actlog")
data("hospital_events")
hospital_actlog <- activitylog(hospital_actlog)Attribute Dependencies
Detect violations of dependencies between attributes (i.e. condition(s) that should hold when (an)other condition(s) hold(s)).
Example: when the activity is “Registration”, the originator should start with “Clerk”.
hospital_actlog %>%
detect_attribute_dependencies(antecedent = activity == "Registration",
consequent = startsWith(originator,"Clerk"))## *** OUTPUT ***## The following statement was checked: if condition(s) ~activity == "Registration" hold(s), then ~startsWith(originator, "Clerk") should also hold.## This statement holds for 12 (85.71%) of the rows in the activity log for which the first condition(s) hold and does not hold for 2 (14.29%) of these rows.## For the following rows, the first condition(s) hold(s), but the second condition does not:## # Log of 10 events consisting of:
## 2 traces
## 4 cases
## 5 instances of 1 activity
## 5 resources
## Events occurred from 2017-11-21 18:10:17 until 2017-11-22 18:37:00
##
## # Variables were mapped as follows:
## Case identifier: patient_visit_nr
## Activity identifier: activity
## Resource identifier: originator
## Timestamps: start, complete
##
## # A tibble: 5 × 8
## patient_visi…¹ activ…² origi…³ start complete triag…⁴
## <dbl> <chr> <chr> <dttm> <dttm> <dbl>
## 1 528 Regist… Nurse 6 2017-11-21 18:10:17 2017-11-21 18:15:04 3
## 2 535 Regist… Clerk 3 2017-11-22 10:04:57 2017-11-22 10:06:46 2
## 3 536 Regist… Clerk 9 2017-11-22 10:26:41 2017-11-22 10:32:56 5
## 4 535 Regist… Clerk 6 2017-11-22 11:05:42 2017-11-22 11:11:11 2
## 5 534 Regist… <NA> 2017-11-22 18:35:00 2017-11-22 18:37:00 0
## # … with 2 more variables: specialization <chr>, .order <int>, and abbreviated
## # variable names ¹patient_visit_nr, ²activity, ³originator, ⁴triagecodeCase ID Sequence Gaps
Detect gaps in the sequence of case identifiers.
hospital_actlog %>%
detect_case_id_sequence_gaps()## *** OUTPUT ***## It was checked whether there are gaps in the sequence of case IDs## From the 27 expected cases in the activity log, ranging from 510 to 536, 5 (18.52%) are missing.## These missing case numbers are:## # A tibble: 2 × 3
## from to n_missing
## <dbl> <dbl> <dbl>
## 1 511 511 1
## 2 513 516 4Conditional Activity Presence
Check whether certain activities are present when a specific condition is satisfied.
For example, if specialization is “TRAU”, then the activity “Clinical exam” must take place.
hospital_actlog %>%
detect_conditional_activity_presence(condition = specialization == "TRAU",
activities = "Clinical exam")## *** OUTPUT ***## The following statement was checked: if condition(s) ~specialization == "TRAU" hold(s), then activity/activities Clinical exam should be recorded## The condition(s) hold(s) for 2 cases. From these cases:## - the specified activity/activities is/are recorded for 2 case(s) (100%)## - the specified activity/activities is/are not recorded for 0 case(s) (0%)Duration Outliers
Detect duration outliers for particular activities.
For example, the duration of “Treatment” should be within 1 standard deviation of its mean duration.
hospital_actlog %>%
detect_duration_outliers(Treatment = duration_within(bound_sd = 1))## *** OUTPUT ***## Outliers are detected for following activities## Treatment Lower bound: 5.06 Upper bound: 22.2## A total of 1 is detected (1.89% of the activity executions)## For the following activity instances, outliers are detected:## # Log of 2 events consisting of:
## 1 trace
## 1 case
## 1 instance of 1 activity
## 1 resource
## Events occurred from 2017-11-21 18:26:04 until 2017-11-21 18:55:00
##
## # Variables were mapped as follows:
## Case identifier: patient_visit_nr
## Activity identifier: activity
## Resource identifier: originator
## Timestamps: start, complete
##
## # A tibble: 1 × 14
## patient_visi…¹ activ…² origi…³ start complete triag…⁴
## <dbl> <chr> <chr> <dttm> <dttm> <dbl>
## 1 523 Treatm… Nurse … 2017-11-21 18:26:04 2017-11-21 18:55:00 3
## # … with 8 more variables: specialization <chr>, .order <int>, duration <dbl>,
## # mean <dbl>, sd <dbl>, bound_sd <dbl>, lower_bound <dbl>, upper_bound <dbl>,
## # and abbreviated variable names ¹patient_visit_nr, ²activity, ³originator,
## # ⁴triagecodeOr, the duration of “Treatment” should be within 0 to 15 minutes.
hospital_actlog %>%
detect_duration_outliers(Treatment = duration_within(lower_bound = 0, upper_bound = 15))## *** OUTPUT ***## Outliers are detected for following activities## Treatment Lower bound: 0 Upper bound: 15## A total of 1 is detected (1.89% of the activity executions)## For the following activity instances, outliers are detected:## # Log of 2 events consisting of:
## 1 trace
## 1 case
## 1 instance of 1 activity
## 1 resource
## Events occurred from 2017-11-21 18:26:04 until 2017-11-21 18:55:00
##
## # Variables were mapped as follows:
## Case identifier: patient_visit_nr
## Activity identifier: activity
## Resource identifier: originator
## Timestamps: start, complete
##
## # A tibble: 1 × 14
## patient_visi…¹ activ…² origi…³ start complete triag…⁴
## <dbl> <chr> <chr> <dttm> <dttm> <dbl>
## 1 523 Treatm… Nurse … 2017-11-21 18:26:04 2017-11-21 18:55:00 3
## # … with 8 more variables: specialization <chr>, .order <int>, duration <dbl>,
## # mean <dbl>, sd <dbl>, bound_sd <dbl>, lower_bound <dbl>, upper_bound <dbl>,
## # and abbreviated variable names ¹patient_visit_nr, ²activity, ³originator,
## # ⁴triagecodeInactive Periods
Detect periods of time in which no activity executions are recorded, using a threshold specified in minutes.
For example, detect whether there are periods of more than 30 minutes without any activity executions.
hospital_actlog %>%
detect_inactive_periods(threshold = 30)## Selected timestamp parameter value: both## Selected inactivity type:arrivals## *** OUTPUT ***## Specified threshold of 30 minutes is violated 9 times.## Threshold is violated in the following periods:## period_start period_end time_gap
## 1 2017-11-20 10:20:06 2017-11-21 11:35:16 1515.16667
## 2 2017-11-21 11:22:16 2017-11-21 11:59:41 37.41667
## 3 2017-11-21 12:05:52 2017-11-21 13:43:16 97.40000
## 4 2017-11-21 14:06:09 2017-11-21 15:12:17 66.13333
## 5 2017-11-21 15:18:19 2017-11-21 16:42:08 83.81667
## 6 2017-11-21 17:06:10 2017-11-21 18:02:10 56.00000
## 7 2017-11-21 18:15:04 2017-11-22 10:04:57 949.88333
## 8 2017-11-22 10:32:56 2017-11-22 16:30:00 357.06667
## 9 2017-11-22 17:00:00 2017-11-22 18:00:00 60.00000Incomplete Cases
Check whether there are cases that miss a specific activity.
For example, does any of the cases miss the 5 listed activities?
hospital_actlog %>%
detect_incomplete_cases(activities = c("Registration","Triage","Clinical exam","Treatment","Treatment evaluation"))## *** OUTPUT ***## It was checked whether the activities Clinical exam, Registration, Treatment, Treatment evaluation, Triage are present for cases.## These activities are present for 4 (39.62%) of the cases and are not present for 18 (60.38%) of the cases.## Note: this function only checks the presence of activities for a particular case, not the completeness of these entries in the activity log or the order of activities.## For cases for which the aforementioned activities are not all present, the following activities are recorded (ordered by decreasing frequeny of occurrence):## # A tibble: 9 × 3
## activity n case_ids
## <chr> <int> <chr>
## 1 Triage 11 510 - 512 - 517 - 521 - 524 - 525 - 526 - 527 - 52…
## 2 Registration 9 512 - 518 - 518 - 518 - 521 - 522 - 527 - 528 - 534
## 3 Clinical exam 5 512 - 510 - 527 - 528 - 512
## 4 Treatment evaluation 2 529 - 532
## 5 0 1 533
## 6 Trage 1 520
## 7 Treatment 1 532
## 8 Triaga 1 522
## 9 registration 1 510Incorrect Activity Names
Given a set of allowed activities, are there any activities that are incorrect?
hospital_actlog %>%
detect_incorrect_activity_names(allowed_activities = c("Registration","Triage","Clinical exam","Treatment","Treatment evaluation"))## *** OUTPUT ***## 4 out of 9 (44.44% ) activity labels are identified to be incorrect.## These activity labels are:
## registration - Trage - Triaga - 0## Given this information, 4 of 53 (7.55%) rows in the activity log are incorrect. These are the following:## # Log of 8 events consisting of:
## 4 traces
## 4 cases
## 4 instances of 4 activities
## 4 resources
## Events occurred from 2017-11-20 10:18:17 until 2017-11-22 18:37:00
##
## # Variables were mapped as follows:
## Case identifier: patient_visit_nr
## Activity identifier: activity
## Resource identifier: originator
## Timestamps: start, complete
##
## # A tibble: 4 × 8
## patient_visi…¹ activ…² origi…³ start complete triag…⁴
## <dbl> <chr> <chr> <dttm> <dttm> <dbl>
## 1 510 regist… Clerk 9 2017-11-20 10:18:17 2017-11-20 10:20:06 3
## 2 520 Trage Nurse … 2017-11-21 13:43:16 2017-11-21 13:39:00 5
## 3 522 Triaga Nurse 5 2017-11-21 15:15:25 2017-11-21 15:18:04 2
## 4 533 0 <NA> 2017-11-22 18:35:00 2017-11-22 18:37:00 7
## # … with 2 more variables: specialization <chr>, .order <int>, and abbreviated
## # variable names ¹patient_visit_nr, ²activity, ³originator, ⁴triagecodeMissing Values
Analyse the missing values of the log. This can be done in general, or at the level of activities or specific columns.
hospital_actlog %>%
detect_missing_values()## Selected level of aggregation:overview## *** OUTPUT ***## Absolute number of missing values per column:##
## patient_visit_nr 0
## activity 0
## originator 2
## start 1
## complete 0
## triagecode 1
## specialization 0
## .order 0## Relative number of missing values per column (expressed as percentage):##
## patient_visit_nr 0.000000
## activity 0.000000
## originator 3.773585
## start 1.886792
## complete 0.000000
## triagecode 1.886792
## specialization 0.000000
## .order 0.000000## Overview of activity log rows which are incomplete:## # Log of 7 events consisting of:
## 3 traces
## 4 cases
## 4 instances of 3 activities
## 2 resources
## Events occurred from NA until NA
##
## # Variables were mapped as follows:
## Case identifier: patient_visit_nr
## Activity identifier: activity
## Resource identifier: originator
## Timestamps: start, complete
##
## # A tibble: 4 × 8
## patient_visi…¹ activ…² origi…³ start complete triag…⁴
## <dbl> <chr> <chr> <dttm> <dttm> <dbl>
## 1 510 Clinic… Doctor… 2017-11-20 11:35:01 2017-11-20 11:36:09 NA
## 2 533 0 <NA> 2017-11-22 18:35:00 2017-11-22 18:37:00 7
## 3 534 Regist… <NA> 2017-11-22 18:35:00 2017-11-22 18:37:00 0
## 4 512 Clinic… Doctor… NA 2017-11-20 11:33:57 3
## # … with 2 more variables: specialization <chr>, .order <int>, and abbreviated
## # variable names ¹patient_visit_nr, ²activity, ³originator, ⁴triagecodehospital_actlog %>%
detect_missing_values(level_of_aggregation = "activity")## Selected level of aggregation:activity## *** OUTPUT ***## Absolute number of missing values per column (per activity):## # A tibble: 9 × 8
## activity patient_vi…¹ origi…² start compl…³ triag…⁴ speci…⁵ .order
## <chr> <int> <int> <int> <int> <int> <int> <int>
## 1 0 0 1 0 0 0 0 0
## 2 Clinical exam 0 0 1 0 1 0 0
## 3 Registration 0 1 0 0 0 0 0
## 4 Trage 0 0 0 0 0 0 0
## 5 Treatment 0 0 0 0 0 0 0
## 6 Treatment evaluation 0 0 0 0 0 0 0
## 7 Triaga 0 0 0 0 0 0 0
## 8 Triage 0 0 0 0 0 0 0
## 9 registration 0 0 0 0 0 0 0
## # … with abbreviated variable names ¹patient_visit_nr, ²originator, ³complete,
## # ⁴triagecode, ⁵specialization## Relative number of missing values per column (per activity, expressed as percentage):## # A tibble: 9 × 8
## activity patient_vi…¹ origi…² start compl…³ triag…⁴ speci…⁵ .order
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0 0 1 0 0 0 0 0
## 2 Clinical exam 0 0 0.111 0 0.111 0 0
## 3 Registration 0 0.0714 0 0 0 0 0
## 4 Trage 0 0 0 0 0 0 0
## 5 Treatment 0 0 0 0 0 0 0
## 6 Treatment evaluation 0 0 0 0 0 0 0
## 7 Triaga 0 0 0 0 0 0 0
## 8 Triage 0 0 0 0 0 0 0
## 9 registration 0 0 0 0 0 0 0
## # … with abbreviated variable names ¹patient_visit_nr, ²originator, ³complete,
## # ⁴triagecode, ⁵specialization## Overview of activity log rows which are incomplete:## # Log of 7 events consisting of:
## 3 traces
## 4 cases
## 4 instances of 3 activities
## 2 resources
## Events occurred from NA until NA
##
## # Variables were mapped as follows:
## Case identifier: patient_visit_nr
## Activity identifier: activity
## Resource identifier: originator
## Timestamps: start, complete
##
## # A tibble: 4 × 8
## patient_visi…¹ activ…² origi…³ start complete triag…⁴
## <dbl> <chr> <chr> <dttm> <dttm> <dbl>
## 1 510 Clinic… Doctor… 2017-11-20 11:35:01 2017-11-20 11:36:09 NA
## 2 533 0 <NA> 2017-11-22 18:35:00 2017-11-22 18:37:00 7
## 3 534 Regist… <NA> 2017-11-22 18:35:00 2017-11-22 18:37:00 0
## 4 512 Clinic… Doctor… NA 2017-11-20 11:33:57 3
## # … with 2 more variables: specialization <chr>, .order <int>, and abbreviated
## # variable names ¹patient_visit_nr, ²activity, ³originator, ⁴triagecodehospital_actlog %>%
detect_missing_values(
level_of_aggregation = "column",
column = "triagecode")## Selected level of aggregation:column## *** OUTPUT ***## Absolute number of missing values in columntriagecode:1## Relative number of missing values in columntriagecode(expressed as percentage):1.88679245283019## ## Overview of activity log rows in whichtriagecodeis missing:## # Log of 2 events consisting of:
## 1 trace
## 1 case
## 1 instance of 1 activity
## 1 resource
## Events occurred from 2017-11-20 11:35:01 until 2017-11-20 11:36:09
##
## # Variables were mapped as follows:
## Case identifier: patient_visit_nr
## Activity identifier: activity
## Resource identifier: originator
## Timestamps: start, complete
##
## # A tibble: 1 × 8
## patient_visi…¹ activ…² origi…³ start complete triag…⁴
## <dbl> <chr> <chr> <dttm> <dttm> <dbl>
## 1 510 Clinic… Doctor… 2017-11-20 11:35:01 2017-11-20 11:36:09 NA
## # … with 2 more variables: specialization <chr>, .order <int>, and abbreviated
## # variable names ¹patient_visit_nr, ²activity, ³originator, ⁴triagecodeMultiregistration
Detect whether there are multiple activity executions registered by the same resource (or for the same case), in a short period of time. This period of time can be specified with a threshold in seconds.
hospital_actlog %>%
detect_multiregistration(threshold_in_seconds = 10)## Selected level of aggregation: resource## Selected timestamp parameter value: complete## *** OUTPUT ***## Multi-registration is detected for 4 of the 12 resources (33.33%). These resources are:## Doctor 7 - Nurse 27 - Nurse 5 - NA## For the following rows in the activity log, multi-registration is detected:## # Log of 17 events consisting of:
## 5 traces
## 7 cases
## 9 instances of 5 activities
## 4 resources
## Events occurred from NA until NA
##
## # Variables were mapped as follows:
## Case identifier: patient_visit_nr
## Activity identifier: activity
## Resource identifier: originator
## Timestamps: start, complete
##
## # A tibble: 9 × 8
## originator patient_v…¹ activ…² start complete triag…³
## <chr> <dbl> <chr> <dttm> <dttm> <dbl>
## 1 Doctor 7 512 Clinic… 2017-11-20 11:27:12 2017-11-20 11:33:57 3
## 2 Doctor 7 512 Clinic… NA 2017-11-20 11:33:57 3
## 3 Nurse 27 536 Triage 2017-11-22 15:15:39 2017-11-22 15:25:01 5
## 4 Nurse 27 536 Treatm… 2017-11-22 15:15:41 2017-11-22 15:25:03 5
## 5 Nurse 5 524 Triage 2017-11-21 17:04:03 2017-11-21 17:06:05 3
## 6 Nurse 5 525 Triage 2017-11-21 17:04:13 2017-11-21 17:06:08 3
## 7 Nurse 5 526 Triage 2017-11-21 17:04:15 2017-11-21 17:06:10 4
## 8 <NA> 533 0 2017-11-22 18:35:00 2017-11-22 18:37:00 7
## 9 <NA> 534 Regist… 2017-11-22 18:35:00 2017-11-22 18:37:00 0
## # … with 2 more variables: specialization <chr>, .order <int>, and abbreviated
## # variable names ¹patient_visit_nr, ²activity, ³triagecodeOverlaps
Check if a resource has performed two or more activities in parallel.
hospital_actlog %>%
detect_overlaps()## # A tibble: 7 × 4
## activity_a activity_b n avg_overlap_mins
## <chr> <chr> <int> <dbl>
## 1 Clinical exam Treatment 2 8.17
## 2 Registration Clinical exam 1 1.9
## 3 Registration Triaga 1 2.65
## 4 Registration Triage 1 1.93
## 5 Triage Clinical exam 2 5.63
## 6 Triage Registration 1 0.817
## 7 Triage Treatment 1 9.33Similar Labels
Check for similar labels in a specific column. Both the column and the maximum allowed edit distance for two labels to consider similar can be configured.
hospital_actlog %>%
detect_similar_labels(column_labels = "activity", max_edit_distance = 3)## Warning in detect_similar_labels.activitylog(., column_labels = "activity", :
## Not all provided columns are of type character or factor and will be ignored:
## patient_visit_nr,start,complete,.order## # A tibble: 16 × 3
## column_labels labels similar_to
## <chr> <chr> <chr>
## 1 activity registration Registration
## 2 activity Registration registration
## 3 activity Triage Trage - Triaga
## 4 activity Trage Triage - Triaga
## 5 activity Triaga Triage - Trage
## 6 originator Clerk 9 Clerk 12 - Clerk 6 - Clerk 3
## 7 originator Clerk 12 Clerk 9 - Clerk 6 - Clerk 3
## 8 originator Nurse 27 Nurse 17 - Nurse 5 - Nurse 6
## 9 originator Doctor 7 Doctor 4 - Doctor 1
## 10 originator Nurse 17 Nurse 27 - Nurse 5 - Nurse 6
## 11 originator Clerk 6 Clerk 9 - Clerk 12 - Clerk 3
## 12 originator Doctor 4 Doctor 7 - Doctor 1
## 13 originator Clerk 3 Clerk 9 - Clerk 12 - Clerk 6
## 14 originator Nurse 5 Nurse 27 - Nurse 17 - Nurse 6
## 15 originator Nurse 6 Nurse 27 - Nurse 17 - Nurse 5
## 16 originator Doctor 1 Doctor 7 - Doctor 4Time Anomalies
Detect activity executions with negative or zero duration.
hospital_actlog %>%
detect_time_anomalies()## Selected anomaly type: both## *** OUTPUT ***## For 5 rows in the activity log (9.43%), an anomaly is detected.## The anomalies are spread over the activities as follows:## # A tibble: 3 × 3
## activity type n
## <chr> <chr> <int>
## 1 Registration negative duration 3
## 2 Clinical exam zero duration 1
## 3 Trage negative duration 1## Anomalies are found in the following rows:## # Log of 10 events consisting of:
## 3 traces
## 3 cases
## 5 instances of 3 activities
## 5 resources
## Events occurred from 2017-11-21 11:22:16 until 2017-11-21 19:00:00
##
## # Variables were mapped as follows:
## Case identifier: patient_visit_nr
## Activity identifier: activity
## Resource identifier: originator
## Timestamps: start, complete
##
## # A tibble: 5 × 10
## patient_visi…¹ activ…² origi…³ start complete triag…⁴
## <dbl> <chr> <chr> <dttm> <dttm> <dbl>
## 1 518 Regist… Clerk … 2017-11-21 11:45:16 2017-11-21 11:22:16 4
## 2 518 Regist… Clerk 6 2017-11-21 11:45:16 2017-11-21 11:22:16 4
## 3 518 Regist… Clerk 9 2017-11-21 11:45:16 2017-11-21 11:22:16 4
## 4 520 Trage Nurse … 2017-11-21 13:43:16 2017-11-21 13:39:00 5
## 5 528 Clinic… Doctor… 2017-11-21 19:00:00 2017-11-21 19:00:00 3
## # … with 4 more variables: specialization <chr>, .order <int>, duration <dbl>,
## # type <chr>, and abbreviated variable names ¹patient_visit_nr, ²activity,
## # ³originator, ⁴triagecodeUnique Values
List all unique combinations of the specified columns.
hospital_actlog %>%
detect_unique_values(column_labels = "activity")## *** OUTPUT ***## Distinct entries are computed for the following columns:
## activity## # Log of 105 events consisting of:
## 14 traces
## 22 cases
## 53 instances of 9 activities
## 12 resources
## Events occurred from NA until NA
##
## # Variables were mapped as follows:
## Case identifier: patient_visit_nr
## Activity identifier: activity
## Resource identifier: originator
## Timestamps: start, complete
##
## # A tibble: 53 × 6
## activity patien…¹ origi…² start complete .order
## <chr> <dbl> <chr> <dttm> <dttm> <int>
## 1 registration 510 Clerk 9 2017-11-20 10:18:17 2017-11-20 10:20:06 1
## 2 Registration 512 Clerk … 2017-11-20 10:33:14 2017-11-20 10:37:00 2
## 3 Triage 510 Nurse … 2017-11-20 10:34:08 2017-11-20 10:41:48 3
## 4 Triage 512 Nurse … 2017-11-20 10:44:12 2017-11-20 10:50:17 4
## 5 Clinical exam 512 Doctor… 2017-11-20 11:27:12 2017-11-20 11:33:57 5
## 6 Clinical exam 510 Doctor… 2017-11-20 11:35:01 2017-11-20 11:36:09 6
## 7 Triage 517 Nurse … 2017-11-21 11:35:16 2017-11-21 11:39:00 7
## 8 Registration 518 Clerk … 2017-11-21 11:45:16 2017-11-21 11:22:16 8
## 9 Registration 518 Clerk 6 2017-11-21 11:45:16 2017-11-21 11:22:16 9
## 10 Registration 518 Clerk 9 2017-11-21 11:45:16 2017-11-21 11:22:16 10
## # … with 43 more rows, and abbreviated variable names ¹patient_visit_nr,
## # ²originatorhospital_actlog %>%
detect_unique_values(column_labels = c("activity", "originator"))## *** OUTPUT ***## Distinct entries are computed for the following columns:
## activity - originator## # Log of 105 events consisting of:
## 14 traces
## 22 cases
## 53 instances of 9 activities
## 12 resources
## Events occurred from NA until NA
##
## # Variables were mapped as follows:
## Case identifier: patient_visit_nr
## Activity identifier: activity
## Resource identifier: originator
## Timestamps: start, complete
##
## # A tibble: 53 × 6
## activity origin…¹ patie…² start complete .order
## <chr> <chr> <dbl> <dttm> <dttm> <int>
## 1 registration Clerk 9 510 2017-11-20 10:18:17 2017-11-20 10:20:06 1
## 2 Registration Clerk 12 512 2017-11-20 10:33:14 2017-11-20 10:37:00 2
## 3 Triage Nurse 27 510 2017-11-20 10:34:08 2017-11-20 10:41:48 3
## 4 Triage Nurse 27 512 2017-11-20 10:44:12 2017-11-20 10:50:17 4
## 5 Clinical exam Doctor 7 512 2017-11-20 11:27:12 2017-11-20 11:33:57 5
## 6 Clinical exam Doctor 7 510 2017-11-20 11:35:01 2017-11-20 11:36:09 6
## 7 Triage Nurse 17 517 2017-11-21 11:35:16 2017-11-21 11:39:00 7
## 8 Registration Clerk 12 518 2017-11-21 11:45:16 2017-11-21 11:22:16 8
## 9 Registration Clerk 6 518 2017-11-21 11:45:16 2017-11-21 11:22:16 9
## 10 Registration Clerk 9 518 2017-11-21 11:45:16 2017-11-21 11:22:16 10
## # … with 43 more rows, and abbreviated variable names ¹originator,
## # ²patient_visit_nrValue Range Violations
Detect value range violation.
hospital_actlog %>%
detect_value_range_violations(triagecode = domain_numeric(from = 0, to = 5))## $triagecode
## $type
## [1] "numeric"
##
## $from
## [1] 0
##
## $to
## [1] 5
##
## attr(,"class")
## [1] "value_range" "list"## *** OUTPUT ***## The domain range for column triagecode is checked.## Values allowed between 0 and 5## The values fall within the specified domain range for 46 (86.79%) of the rows in the activity log and outside the domain range for 7 (13.21%) of these rows.## ## The following rows fall outside the specified domain range for indicated column:## # Log of 14 events consisting of:
## 5 traces
## 6 cases
## 7 instances of 5 activities
## 4 resources
## Events occurred from 2017-11-20 11:35:01 until 2017-11-23 18:33:00
##
## # Variables were mapped as follows:
## Case identifier: patient_visit_nr
## Activity identifier: activity
## Resource identifier: originator
## Timestamps: start, complete
##
## # A tibble: 7 × 9
## column_checked patie…¹ activ…² origi…³ start complete
## <chr> <dbl> <chr> <chr> <dttm> <dttm>
## 1 triagecode 510 Clinic… Doctor… 2017-11-20 11:35:01 2017-11-20 11:36:09
## 2 triagecode 529 Treatm… Doctor… 2017-11-22 16:30:00 2017-11-22 17:00:00
## 3 triagecode 530 Triage Nurse … 2017-11-22 18:00:00 2017-11-22 18:05:00
## 4 triagecode 531 Triage Nurse … 2017-11-22 18:05:00 2017-11-22 18:10:00
## 5 triagecode 532 Treatm… Nurse … 2017-11-22 18:15:00 2017-11-22 18:25:00
## 6 triagecode 532 Treatm… Doctor… 2017-11-22 18:27:00 2017-11-23 18:33:00
## 7 triagecode 533 0 <NA> 2017-11-22 18:35:00 2017-11-22 18:37:00
## # … with 3 more variables: triagecode <dbl>, specialization <chr>,
## # .order <int>, and abbreviated variable names ¹patient_visit_nr, ²activity,
## # ³originatorRead more:
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