Package 'edeaR'

Description

Provides summary statistics about the frequency of activity types at the level of log, traces, cases, activity types.

Usage

activity_frequency(
  log,
  level = c("log", "trace", "activity", "case"),
  append = deprecated(),
  append_column = NULL,
  sort = TRUE,
  eventlog = deprecated()
)

## S3 method for class 'eventlog'
activity_frequency(
  log,
  level = c("log", "trace", "activity", "case"),
  append = deprecated(),
  append_column = NULL,
  sort = TRUE,
  eventlog = deprecated()
)

## S3 method for class 'grouped_eventlog'
activity_frequency(
  log,
  level = c("log", "trace", "activity", "case"),
  append = deprecated(),
  append_column = NULL,
  sort = TRUE,
  eventlog = deprecated()
)

## S3 method for class 'activitylog'
activity_frequency(
  log,
  level = c("log", "trace", "activity", "case"),
  append = deprecated(),
  append_column = NULL,
  sort = TRUE,
  eventlog = deprecated()
)

## S3 method for class 'grouped_activitylog'
activity_frequency(
  log,
  level = c("log", "trace", "activity", "case"),
  append = deprecated(),
  append_column = NULL,
  sort = TRUE,
  eventlog = deprecated()
)

Arguments

Details

Argument level has the following options:

• At log level, this metric shows the summary statistics of the frequency of activities throughout the complete log.

• On case level, this metric shows the absolute and relative number of times the different activity types occur in each case. The absolute number shows the number of distinct activity types that occur in each of the cases. The relative number is calculated based on the total activity executions in the case.

• On trace level, this metric presents the absolute and relative number of times a specific activity type occurs in each trace.

• On activity level, this metric provides the absolute and relative frequency of a specific activity in the complete log.

Methods (by class)

• activity_frequency(eventlog): Computes the activity frequency for an eventlog.

• activity_frequency(grouped_eventlog): Computes the activity frequency for a grouped_eventlog.

• activity_frequency(activitylog): Computes the activity frequency for an activitylog.

• activity_frequency(grouped_activitylog): Computes the activity frequency for a grouped_activitylog.

References

Swennen, M. (2018). Using Event Log Knowledge to Support Operational Exellence Techniques (Doctoral dissertation). Hasselt University.

See Also

Other metrics: activity_presence(), end_activities(), idle_time(), number_of_repetitions(), number_of_selfloops(), number_of_traces(), processing_time(), resource_frequency(), resource_involvement(), resource_specialisation(), start_activities(), throughput_time(), trace_coverage(), trace_length()

Description

Calculates for each activity type in what percentage of cases it is present.

Usage

activity_presence(
  log,
  append = deprecated(),
  append_column = NULL,
  sort = TRUE,
  eventlog = deprecated()
)

## S3 method for class 'eventlog'
activity_presence(
  log,
  append = deprecated(),
  append_column = NULL,
  sort = TRUE,
  eventlog = deprecated()
)

## S3 method for class 'grouped_eventlog'
activity_presence(
  log,
  append = deprecated(),
  append_column = NULL,
  sort = TRUE,
  eventlog = deprecated()
)

## S3 method for class 'activitylog'
activity_presence(
  log,
  append = deprecated(),
  append_column = NULL,
  sort = TRUE,
  eventlog = deprecated()
)

## S3 method for class 'grouped_activitylog'
activity_presence(
  log,
  append = deprecated(),
  append_column = NULL,
  sort = TRUE,
  eventlog = deprecated()
)

Arguments

Details

An indication of variance can be the presence of the activities in the different cases. This metric shows for each activity the absolute number of cases in which each activity occurs together with its relative presence.

Methods (by class)

• activity_presence(eventlog): Compute activity presence for an eventlog.

• activity_presence(grouped_eventlog): Compute activity presence for a grouped_eventlog.

• activity_presence(activitylog): Compute activity presence for an activitylog.

• activity_presence(grouped_activitylog): Compute activity presence for a grouped_activitylog.

References

Swennen, M. (2018). Using Event Log Knowledge to Support Operational Exellence Techniques (Doctoral dissertation). Hasselt University.

See Also

Other metrics: activity_frequency(), end_activities(), idle_time(), number_of_repetitions(), number_of_selfloops(), number_of_traces(), processing_time(), resource_frequency(), resource_involvement(), resource_specialisation(), start_activities(), throughput_time(), trace_coverage(), trace_length()

Examples

## Not run: 
data <- data.frame(case = rep("A",5),
activity_id = c("A","B","C","D","E"),
activity_instance_id = 1:5,
lifecycle_id = rep("complete",5),
timestamp = 1:5,
resource = rep("resource 1", 5))

log <- bupaR::eventlog(data,case_id = "case",
activity_id = "activity_id",
activity_instance_id = "activity_instance_id",
lifecycle_id = "lifecycle_id",
timestamp = "timestamp",
resource_id = "resource")

activity_presence(log)

## End(Not run)

Description

Add fixed holiday to work schedule

Usage

add_fixed_holiday(work_schedule, name, month, day)

Arguments

Description

Add floating holiday to work schedule

Usage

add_floating_holiday(work_schedule, name, dates)

Arguments

Description

Add holiday period to work schedule

Usage

add_holiday_periods(work_schedule, from, to)

Arguments

Description

Augment log with results from metric computation.

Usage

augment(metric, log, columns, prefix = "")

## S3 method for class 'log_metric'
augment(metric, log, columns, prefix = "")

## S3 method for class 'case_metric'
augment(metric, log, columns, prefix = "")

## S3 method for class 'activity_metric'
augment(metric, log, columns, prefix = "")

## S3 method for class 'resource_metric'
augment(metric, log, columns, prefix = "")

## S3 method for class 'resource_activity_metric'
augment(metric, log, columns, prefix = "")

## S3 method for class 'trace_metric'
augment(metric, log, columns, prefix = "")

Arguments

Value

Object of class log or derivatives (grouped_log, eventlog, activitylog, etc.). Same class as the log input.

Methods (by class)

• augment(log_metric): Augment log metric

• augment(case_metric): Augment case metric

• augment(activity_metric): Augment activity metric

• augment(resource_metric): Augment resource metric

• augment(resource_activity_metric): Augment resource-activity metric

• augment(trace_metric): Augment trace metric

Examples

## Not run: 
sepsis %>%
	throughput_time("case") %>%
	augment(sepsis)

## End(Not run)

Description

Usage

calculate_queuing_length(
  queueing_times,
  level = c("log", "activity", "resource"),
  time_interval
)

Arguments

Details

Argument level has the following options:

• At log level, this metric calculates the total number of activity instances that are queued at a given moment in time.

• At resource level, this metric calculates the total number activity instances that are queued for a given resource.

• On activity level, this metric calculates the total number of activity instances that are queue for a given activity type.

Argument time_interval has the following options (see also the by argument of seq.Date):

• A numeric as number of days.

• An object of class difftime.

• A character string, which could be one of "day", "week", "month", "quarter", or "year". The first day for which queue length is calculated, is the first timestamp found in the log.

See Also

calculate_queuing_times, seq.Date

Description

Usage

calculate_queuing_times(
  log,
  units = c("auto", "secs", "mins", "hours", "days", "weeks"),
  eventlog = deprecated()
)

## S3 method for class 'eventlog'
calculate_queuing_times(
  log,
  units = c("auto", "secs", "mins", "hours", "days", "weeks"),
  eventlog = deprecated()
)

## S3 method for class 'activitylog'
calculate_queuing_times(
  log,
  units = c("auto", "secs", "mins", "hours", "days", "weeks"),
  eventlog = deprecated()
)

Arguments

Value

Returns a list of all the activity instances, with the time they started, and the time since they were queued. Notice that this does not take into account any process model notion! The time since they are queued is the completion time of the previous activity in the log.

Methods (by class)

• calculate_queuing_times(eventlog): Calculate queueing times for eventlog and grouped_eventlog.

• calculate_queuing_times(activitylog): Calculate queueing times for activitylog and grouped_activitylog.

References

Swennen, M. (2018). Using Event Log Knowledge to Support Operational Exellence Techniques (Doctoral dissertation). Hasselt University.

See Also

difftime

Description

Adjust days in work schedule

Usage

change_day(work_schedule, day, start_time, end_time)

Arguments

Description

Create work schedule

Usage

create_work_schedule(start_time = "9:00:00", end_time = "17:00:00")

Arguments

Description

Analyse the end activities in the process.

Usage

end_activities(
  log,
  level = c("log", "case", "activity", "resource", "resource-activity"),
  append = deprecated(),
  append_column = NULL,
  sort = TRUE,
  eventlog = deprecated()
)

## S3 method for class 'eventlog'
end_activities(
  log,
  level = c("log", "case", "activity", "resource", "resource-activity"),
  append = deprecated(),
  append_column = NULL,
  sort = TRUE,
  eventlog = deprecated()
)

## S3 method for class 'grouped_eventlog'
end_activities(
  log,
  level = c("log", "case", "activity", "resource", "resource-activity"),
  append = deprecated(),
  append_column = NULL,
  sort = TRUE,
  eventlog = deprecated()
)

## S3 method for class 'activitylog'
end_activities(
  log,
  level = c("log", "case", "activity", "resource", "resource-activity"),
  append = deprecated(),
  append_column = NULL,
  sort = TRUE,
  eventlog = deprecated()
)

## S3 method for class 'grouped_activitylog'
end_activities(
  log,
  level = c("log", "case", "activity", "resource", "resource-activity"),
  append = deprecated(),
  append_column = NULL,
  sort = TRUE,
  eventlog = deprecated()
)

Arguments

Details

Argument level has the following options:

• At log level, this metric shows the absolute and relative number of activities that are the last activity in one or more of the cases.

• On case level, this metric provides an overview of the end activity of each case.

• On activity level, this metric calculates for each activity the absolute and relative number of cases that end with this activity type. Similar to the start_activities metric, the relative number is calculated as a portion of the number of cases, being the number of "opportunities" that an activity could be the end activity. The cumulative sum is added to have an insight in the number of activities that is required to cover a certain part of the total.

• At resource level, an overview of which resources execute the last activity per case is provided.

• On resource-activity level, this metric shows for each occurring resource-activity combination the absolute and relative number of times this resource executes this activity as an end activity in a case.

Methods (by class)

• end_activities(eventlog): Computes the end activities for an eventlog.

• end_activities(grouped_eventlog): Computes the end activities for a grouped_eventlog.

• end_activities(activitylog): Computes the end activities for an activitylog.

• end_activities(grouped_activitylog): Computes the end activities for a grouped_activitylog.

References

Swennen, M. (2018). Using Event Log Knowledge to Support Operational Exellence Techniques (Doctoral dissertation). Hasselt University.

See Also

start_activities

Other metrics: activity_frequency(), activity_presence(), idle_time(), number_of_repetitions(), number_of_selfloops(), number_of_traces(), processing_time(), resource_frequency(), resource_involvement(), resource_specialisation(), start_activities(), throughput_time(), trace_coverage(), trace_length()

Description

Filters the log based on activities

Usage

filter_activity(log, activities, reverse = FALSE, eventlog = deprecated())

## S3 method for class 'log'
filter_activity(log, activities, reverse = FALSE, eventlog = deprecated())

## S3 method for class 'grouped_log'
filter_activity(log, activities, reverse = FALSE, eventlog = deprecated())

Arguments

Value

When given an object of type log, it will return a filtered log. When given an object of type grouped_log, the filter will be applied in a stratified way (i.e. each separately for each group). The returned log will be grouped on the same variables as the original log.

Methods (by class)

• filter_activity(log): Filters activities for a log.

• filter_activity(grouped_log): Filters activities for a grouped_log.

References

Swennen, M. (2018). Using Event Log Knowledge to Support Operational Exellence Techniques (Doctoral dissertation). Hasselt University.

See Also

vignette("filters", "edeaR")

Other filters: filter_activity_frequency(), filter_activity_instance(), filter_activity_presence(), filter_case(), filter_case_condition(), filter_endpoints(), filter_endpoints_condition(), filter_flow_time(), filter_idle_time(), filter_infrequent_flows(), filter_lifecycle(), filter_lifecycle_presence(), filter_precedence(), filter_precedence_condition(), filter_precedence_resource(), filter_processing_time(), filter_resource(), filter_resource_frequency(), filter_throughput_time(), filter_time_period(), filter_trace(), filter_trace_frequency(), filter_trace_length(), filter_trim(), filter_trim_lifecycle()

Description

Filters the log based on frequency of activities.

Usage

filter_activity_frequency(
  log,
  interval = NULL,
  percentage = NULL,
  reverse = FALSE,
  eventlog = deprecated()
)

## S3 method for class 'log'
filter_activity_frequency(
  log,
  interval = NULL,
  percentage = NULL,
  reverse = FALSE,
  eventlog = deprecated()
)

## S3 method for class 'grouped_log'
filter_activity_frequency(
  log,
  interval = NULL,
  percentage = NULL,
  reverse = FALSE,
  eventlog = deprecated()
)

Arguments

Details

Filtering the log based on activity frequency can be done in two ways: using an interval of allowed frequencies, or specify a coverage percentage:

• percentage: When filtering using a percentage p%, the filter will return p% of the activity instances, starting from the activity labels with the highest frequency. The filter will retain additional activity labels as long as the number of activity instances does not exceed the percentage threshold.

• interval: When filtering using an interval, activity labels will be retained when their absolute frequency fall in this interval. The interval is specified using a numeric vector of length 2. Half open intervals can be created by using NA, e.g., c(10, NA) will select activity labels which occur 10 times or more.

Value

When given an object of type log, it will return a filtered log. When given an object of type grouped_log, the filter will be applied in a stratified way (i.e. each separately for each group). The returned log will be grouped on the same variables as the original log.

Methods (by class)

• filter_activity_frequency(log): Filters activities for a log.

• filter_activity_frequency(grouped_log): Filters activities for a grouped_log.

References

Swennen, M. (2018). Using Event Log Knowledge to Support Operational Exellence Techniques (Doctoral dissertation). Hasselt University.

See Also

Other filters: filter_activity(), filter_activity_instance(), filter_activity_presence(), filter_case(), filter_case_condition(), filter_endpoints(), filter_endpoints_condition(), filter_flow_time(), filter_idle_time(), filter_infrequent_flows(), filter_lifecycle(), filter_lifecycle_presence(), filter_precedence(), filter_precedence_condition(), filter_precedence_resource(), filter_processing_time(), filter_resource(), filter_resource_frequency(), filter_throughput_time(), filter_time_period(), filter_trace(), filter_trace_frequency(), filter_trace_length(), filter_trim(), filter_trim_lifecycle()

Description

Filters the log based on activity instance identifier. This method has an activity_instances argument, to which a vector of identifiers can be given. The selection can be negated with the reverse argument.

Usage

filter_activity_instance(
  log,
  activity_instances,
  reverse = FALSE,
  eventlog = deprecated()
)

## S3 method for class 'eventlog'
filter_activity_instance(
  log,
  activity_instances,
  reverse = FALSE,
  eventlog = deprecated()
)

## S3 method for class 'grouped_eventlog'
filter_activity_instance(
  log,
  activity_instances,
  reverse = FALSE,
  eventlog = deprecated()
)

Arguments

Value

When given an object of type log, it will return a filtered log. When given an object of type grouped_log, the filter will be applied in a stratified way (i.e. each separately for each group). The returned log will be grouped on the same variables as the original log.

Methods (by class)

• filter_activity_instance(eventlog): Filters activities for an eventlog.

• filter_activity_instance(grouped_eventlog): Filters activities for a grouped_eventlog.

References

Swennen, M. (2018). Using Event Log Knowledge to Support Operational Exellence Techniques (Doctoral dissertation). Hasselt University.

See Also

Other filters: filter_activity(), filter_activity_frequency(), filter_activity_presence(), filter_case(), filter_case_condition(), filter_endpoints(), filter_endpoints_condition(), filter_flow_time(), filter_idle_time(), filter_infrequent_flows(), filter_lifecycle(), filter_lifecycle_presence(), filter_precedence(), filter_precedence_condition(), filter_precedence_resource(), filter_processing_time(), filter_resource(), filter_resource_frequency(), filter_throughput_time(), filter_time_period(), filter_trace(), filter_trace_frequency(), filter_trace_length(), filter_trim(), filter_trim_lifecycle()

Description

Filters cases based on the presence (or absence) of activities.

Usage

filter_activity_presence(
  log,
  activities = NULL,
  method = c("all", "none", "one_of", "exact", "only"),
  reverse = FALSE,
  eventlog = deprecated()
)

## S3 method for class 'log'
filter_activity_presence(
  log,
  activities = NULL,
  method = c("all", "none", "one_of", "exact", "only"),
  reverse = FALSE,
  eventlog = deprecated()
)

## S3 method for class 'grouped_log'
filter_activity_presence(
  log,
  activities = NULL,
  method = c("all", "none", "one_of", "exact", "only"),
  reverse = FALSE,
  eventlog = deprecated()
)

Arguments

Details

This 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 with following options:

• "all" 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.

• "one_of" means that at least one of them must be present.

• "exact" means that only exactly these activities can be present (although multiple times and in random orderings).

• "only" means that only (a set of) these activities are allowed to be present.

When only one activity label is supplied, note that methods "all" and "one_of" will be identical.

Value

When given an object of type log, it will return a filtered log. When given an object of type grouped_log, the filter will be applied in a stratified way (i.e. each separately for each group). The returned log will be grouped on the same variables as the original log.

Methods (by class)

• filter_activity_presence(log): Filters activities for a log.

• filter_activity_presence(grouped_log): Filters activities for a grouped_log.

References

Swennen, M. (2018). Using Event Log Knowledge to Support Operational Exellence Techniques (Doctoral dissertation). Hasselt University.

See Also

Other filters: filter_activity(), filter_activity_frequency(), filter_activity_instance(), filter_case(), filter_case_condition(), filter_endpoints(), filter_endpoints_condition(), filter_flow_time(), filter_idle_time(), filter_infrequent_flows(), filter_lifecycle(), filter_lifecycle_presence(), filter_precedence(), filter_precedence_condition(), filter_precedence_resource(), filter_processing_time(), filter_resource(), filter_resource_frequency(), filter_throughput_time(), filter_time_period(), filter_trace(), filter_trace_frequency(), filter_trace_length(), filter_trim(), filter_trim_lifecycle()

Description

Filters the log based on case identifier. This method has a cases argument, to which a vector of identifiers can be given. The selection can be negated with the reverse argument.

Usage

filter_case(log, cases, reverse = FALSE, eventlog = deprecated())

## S3 method for class 'log'
filter_case(log, cases, reverse = FALSE, eventlog = deprecated())

## S3 method for class 'grouped_log'
filter_case(log, cases, reverse = FALSE, eventlog = deprecated())

Arguments

Value

When given an object of type log, it will return a filtered log. When given an object of type grouped_log, the filter will be applied in a stratified way (i.e. each separately for each group). The returned log will be grouped on the same variables as the original log.

Methods (by class)

• filter_case(log): Filters cases for a log.

• filter_case(grouped_log): Filters cases for a grouped_log.

References

Swennen, M. (2018). Using Event Log Knowledge to Support Operational Exellence Techniques (Doctoral dissertation). Hasselt University.

See Also

Other filters: filter_activity(), filter_activity_frequency(), filter_activity_instance(), filter_activity_presence(), filter_case_condition(), filter_endpoints(), filter_endpoints_condition(), filter_flow_time(), filter_idle_time(), filter_infrequent_flows(), filter_lifecycle(), filter_lifecycle_presence(), filter_precedence(), filter_precedence_condition(), filter_precedence_resource(), filter_processing_time(), filter_resource(), filter_resource_frequency(), filter_throughput_time(), filter_time_period(), filter_trace(), filter_trace_frequency(), filter_trace_length(), filter_trim(), filter_trim_lifecycle()

Description

Filters cases using a condition. Only keeps cases if the condition is valid for at least one event.

Usage

filter_case_condition(
  log,
  ...,
  condition = NULL,
  reverse = FALSE,
  eventlog = deprecated()
)

## S3 method for class 'log'
filter_case_condition(
  log,
  ...,
  condition = deprecated(),
  reverse = FALSE,
  eventlog = deprecated()
)

## S3 method for class 'grouped_log'
filter_case_condition(
  log,
  ...,
  condition = deprecated(),
  reverse = FALSE,
  eventlog = deprecated()
)

Arguments

Value

When given an object of type log, it will return a filtered log. When given an object of type grouped_log, the filter will be applied in a stratified way (i.e. each separately for each group). The returned log will be grouped on the same variables as the original log.

Methods (by class)

• filter_case_condition(log): Filters cases for a log.

• filter_case_condition(grouped_log): Filters cases for a grouped_log.

References

Swennen, M. (2018). Using Event Log Knowledge to Support Operational Exellence Techniques (Doctoral dissertation). Hasselt University.

See Also

filter

Other filters: filter_activity(), filter_activity_frequency(), filter_activity_instance(), filter_activity_presence(), filter_case(), filter_endpoints(), filter_endpoints_condition(), filter_flow_time(), filter_idle_time(), filter_infrequent_flows(), filter_lifecycle(), filter_lifecycle_presence(), filter_precedence(), filter_precedence_condition(), filter_precedence_resource(), filter_processing_time(), filter_resource(), filter_resource_frequency(), filter_throughput_time(), filter_time_period(), filter_trace(), filter_trace_frequency(), filter_trace_length(), filter_trim(), filter_trim_lifecycle()

Description

Filters the log based on a provided set of start and end activities

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 log accordingly.

Usage

filter_endpoints(
  log,
  start_activities = NULL,
  end_activities = NULL,
  percentage = NULL,
  reverse = FALSE,
  eventlog = deprecated()
)

## S3 method for class 'log'
filter_endpoints(
  log,
  start_activities = NULL,
  end_activities = NULL,
  percentage = NULL,
  reverse = FALSE,
  eventlog = deprecated()
)

## S3 method for class 'grouped_log'
filter_endpoints(
  log,
  start_activities = NULL,
  end_activities = NULL,
  percentage = NULL,
  reverse = FALSE,
  eventlog = deprecated()
)

Arguments

Value

When given an object of type log, it will return a filtered log. When given an object of type grouped_log, the filter will be applied in a stratified way (i.e. each separately for each group). The returned log will be grouped on the same variables as the original log.

Methods (by class)

• filter_endpoints(log): Filters cases for a log.

• filter_endpoints(grouped_log): Filters cases for a grouped_log.

References

Swennen, M. (2018). Using Event Log Knowledge to Support Operational Exellence Techniques (Doctoral dissertation). Hasselt University.

See Also

Other filters: filter_activity(), filter_activity_frequency(), filter_activity_instance(), filter_activity_presence(), filter_case(), filter_case_condition(), filter_endpoints_condition(), filter_flow_time(), filter_idle_time(), filter_infrequent_flows(), filter_lifecycle(), filter_lifecycle_presence(), filter_precedence(), filter_precedence_condition(), filter_precedence_resource(), filter_processing_time(), filter_resource(), filter_resource_frequency(), filter_throughput_time(), filter_time_period(), filter_trace(), filter_trace_frequency(), filter_trace_length(), filter_trim(), filter_trim_lifecycle()

Description

Filters cases where the first and/or last activity adhere to the specified conditions.

Usage

filter_endpoints_condition(
  log,
  start_condition = NULL,
  end_condition = NULL,
  reverse = FALSE,
  eventlog = deprecated()
)

## S3 method for class 'eventlog'
filter_endpoints_condition(
  log,
  start_condition = NULL,
  end_condition = NULL,
  reverse = FALSE,
  eventlog = deprecated()
)

## S3 method for class 'grouped_log'
filter_endpoints_condition(
  log,
  start_condition = NULL,
  end_condition = NULL,
  reverse = FALSE,
  eventlog = deprecated()
)

## S3 method for class 'activitylog'
filter_endpoints_condition(
  log,
  start_condition = NULL,
  end_condition = NULL,
  reverse = FALSE,
  eventlog = deprecated()
)

filter_endpoints_conditions(
  log,
  start_condition = NULL,
  end_condition = NULL,
  reverse = FALSE,
  eventlog = deprecated()
)

Arguments

Value

When given an object of type log, it will return a filtered log. When given an object of type grouped_log, the filter will be applied in a stratified way (i.e. each separately for each group). The returned log will be grouped on the same variables as the original log.

Methods (by class)

• filter_endpoints_condition(eventlog): Filters cases for an eventlog.

• filter_endpoints_condition(grouped_log): Filters cases for a grouped_log.

• filter_endpoints_condition(activitylog): Filters cases for an activitylog.

References

Swennen, M. (2018). Using Event Log Knowledge to Support Operational Exellence Techniques (Doctoral dissertation). Hasselt University.

See Also

Other filters: filter_activity(), filter_activity_frequency(), filter_activity_instance(), filter_activity_presence(), filter_case(), filter_case_condition(), filter_endpoints(), filter_flow_time(), filter_idle_time(), filter_infrequent_flows(), filter_lifecycle(), filter_lifecycle_presence(), filter_precedence(), filter_precedence_condition(), filter_precedence_resource(), filter_processing_time(), filter_resource(), filter_resource_frequency(), filter_throughput_time(), filter_time_period(), filter_trace(), filter_trace_frequency(), filter_trace_length(), filter_trim(), filter_trim_lifecycle()

Description

Filter cases where the activity from is followed by activity to within a certain time interval.

Usage

filter_flow_time(
  log,
  from,
  to,
  interval,
  reverse = FALSE,
  units = c("secs", "mins", "hours", "days", "weeks")
)

## S3 method for class 'log'
filter_flow_time(
  log,
  from,
  to,
  interval,
  reverse = FALSE,
  units = c("secs", "mins", "hours", "days", "weeks")
)

## S3 method for class 'grouped_log'
filter_flow_time(
  log,
  from,
  to,
  interval,
  reverse = FALSE,
  units = c("secs", "mins", "hours", "days", "weeks")
)

Arguments

Value

When given an object of type log, it will return a filtered log. When given an object of type grouped_log, the filter will be applied in a stratified way (i.e. each separately for each group). The returned log will be grouped on the same variables as the original log.

Methods (by class)

• filter_flow_time(log): Filters on flow time for a bupaR::log.

• filter_flow_time(grouped_log): Filters on flow time for a bupaR::grouped_log.

References

Swennen, M. (2018). Using Event Log Knowledge to Support Operational Exellence Techniques (Doctoral dissertation). Hasselt University.

See Also

processing_time(),difftime()

Other filters: filter_activity(), filter_activity_frequency(), filter_activity_instance(), filter_activity_presence(), filter_case(), filter_case_condition(), filter_endpoints(), filter_endpoints_condition(), filter_idle_time(), filter_infrequent_flows(), filter_lifecycle(), filter_lifecycle_presence(), filter_precedence(), filter_precedence_condition(), filter_precedence_resource(), filter_processing_time(), filter_resource(), filter_resource_frequency(), filter_throughput_time(), filter_time_period(), filter_trace(), filter_trace_frequency(), filter_trace_length(), filter_trim(), filter_trim_lifecycle()

Description

Filters cases based on their idle_time.

This filter can be used by using an interval or by using a percentage. The percentage will always start with the cases with the lowest idle time first and stop including cases when the specified percentile is reached. On the other hand, an absolute interval can be defined instead to filter cases which have an idle time in this interval. The time units in which this interval is defined can be supplied with the units argument.

Usage

filter_idle_time(
  log,
  interval = NULL,
  percentage = NULL,
  reverse = FALSE,
  units = c("secs", "mins", "hours", "days", "weeks")
)

## S3 method for class 'log'
filter_idle_time(
  log,
  interval = NULL,
  percentage = NULL,
  reverse = FALSE,
  units = c("secs", "mins", "hours", "days", "weeks")
)

## S3 method for class 'grouped_log'
filter_idle_time(
  log,
  interval = NULL,
  percentage = NULL,
  reverse = FALSE,
  units = c("secs", "mins", "hours", "days", "weeks")
)

Arguments

Value

When given an object of type log, it will return a filtered log. When given an object of type grouped_log, the filter will be applied in a stratified way (i.e. each separately for each group). The returned log will be grouped on the same variables as the original log.

Methods (by class)

• filter_idle_time(log): Filters cases for a log.

• filter_idle_time(grouped_log): Filters cases for a grouped_log.

References

Swennen, M. (2018). Using Event Log Knowledge to Support Operational Exellence Techniques (Doctoral dissertation). Hasselt University.

See Also

idle_time(),difftime()

Other filters: filter_activity(), filter_activity_frequency(), filter_activity_instance(), filter_activity_presence(), filter_case(), filter_case_condition(), filter_endpoints(), filter_endpoints_condition(), filter_flow_time(), filter_infrequent_flows(), filter_lifecycle(), filter_lifecycle_presence(), filter_precedence(), filter_precedence_condition(), filter_precedence_resource(), filter_processing_time(), filter_resource(), filter_resource_frequency(), filter_throughput_time(), filter_time_period(), filter_trace(), filter_trace_frequency(), filter_trace_length(), filter_trim(), filter_trim_lifecycle()

Description

Filter cases based on infrequent flows.

Usage

filter_infrequent_flows(log, min_n = 2, eventlog = deprecated())

## S3 method for class 'eventlog'
filter_infrequent_flows(log, min_n = 2, eventlog = deprecated())

## S3 method for class 'grouped_eventlog'
filter_infrequent_flows(log, min_n = 2, eventlog = deprecated())

## S3 method for class 'activitylog'
filter_infrequent_flows(log, min_n = 2, eventlog = deprecated())

## S3 method for class 'grouped_activitylog'
filter_infrequent_flows(log, min_n = 2, eventlog = deprecated())

Arguments

Value

When given an object of type log, it will return a filtered log. When given an object of type grouped_log, the filter will be applied in a stratified way (i.e. each separately for each group). The returned log will be grouped on the same variables as the original log.

Methods (by class)

• filter_infrequent_flows(eventlog): Filters infrequent flows for an eventlog.

• filter_infrequent_flows(grouped_eventlog): Filters infrequent flows for a grouped_eventlog.

• filter_infrequent_flows(activitylog): Filters infrequent flows for an activitylog.

• filter_infrequent_flows(grouped_activitylog): Filters infrequent flows for a grouped_activitylog.

References

Swennen, M. (2018). Using Event Log Knowledge to Support Operational Exellence Techniques (Doctoral dissertation). Hasselt University.

See Also

Other filters: filter_activity(), filter_activity_frequency(), filter_activity_instance(), filter_activity_presence(), filter_case(), filter_case_condition(), filter_endpoints(), filter_endpoints_condition(), filter_flow_time(), filter_idle_time(), filter_lifecycle(), filter_lifecycle_presence(), filter_precedence(), filter_precedence_condition(), filter_precedence_resource(), filter_processing_time(), filter_resource(), filter_resource_frequency(), filter_throughput_time(), filter_time_period(), filter_trace(), filter_trace_frequency(), filter_trace_length(), filter_trim(), filter_trim_lifecycle()

Description

Filters the log based on the life cycle identifier.

Usage

filter_lifecycle(
  log,
  lifecycles,
  reverse = FALSE,
  lifecycle = deprecated(),
  eventlog = deprecated()
)

## S3 method for class 'eventlog'
filter_lifecycle(
  log,
  lifecycles,
  reverse = FALSE,
  lifecycle = deprecated(),
  eventlog = deprecated()
)

## S3 method for class 'grouped_eventlog'
filter_lifecycle(
  log,
  lifecycles,
  reverse = FALSE,
  lifecycle = deprecated(),
  eventlog = deprecated()
)

Arguments

Value

When given an object of type log, it will return a filtered log. When given an object of type grouped_log, the filter will be applied in a stratified way (i.e. each separately for each group). The returned log will be grouped on the same variables as the original log.

Methods (by class)

• filter_lifecycle(eventlog): Filters based on life cycle identifiers for an eventlog.

• filter_lifecycle(grouped_eventlog): Filters based on life cycle identifiers a grouped_eventlog.

References

Swennen, M. (2018). Using Event Log Knowledge to Support Operational Exellence Techniques (Doctoral dissertation). Hasselt University.

See Also

lifecycle_id

Other filters: filter_activity(), filter_activity_frequency(), filter_activity_instance(), filter_activity_presence(), filter_case(), filter_case_condition(), filter_endpoints(), filter_endpoints_condition(), filter_flow_time(), filter_idle_time(), filter_infrequent_flows(), filter_lifecycle_presence(), filter_precedence(), filter_precedence_condition(), filter_precedence_resource(), filter_processing_time(), filter_resource(), filter_resource_frequency(), filter_throughput_time(), filter_time_period(), filter_trace(), filter_trace_frequency(), filter_trace_length(), filter_trim(), filter_trim_lifecycle()

Description

Filters activity instances based on the presence (or absence) of life cycles.

Usage

filter_lifecycle_presence(
  log,
  lifecycles,
  method = c("all", "none", "one_of", "exact", "only"),
  reverse = FALSE,
  lifecycle = deprecated(),
  eventlog = deprecated()
)

## S3 method for class 'eventlog'
filter_lifecycle_presence(
  log,
  lifecycles,
  method = c("all", "none", "one_of", "exact", "only"),
  reverse = FALSE,
  lifecycle = deprecated(),
  eventlog = deprecated()
)

## S3 method for class 'grouped_eventlog'
filter_lifecycle_presence(
  log,
  lifecycles,
  method = c("all", "none", "one_of", "exact", "only"),
  reverse = FALSE,
  lifecycle = deprecated(),
  eventlog = deprecated()
)

Arguments

Details

This function allows to filter activity instances that (do not) contain certain life cycle identifiers. It requires as input a vector containing one or more life cycle labels and it has a method argument with following options:

• "all" means that all the specified life cycle labels must be present for an activity instance to be selected.

• "none" means that they are not allowed to be present.

• "one_of" means that at least one of them must be present.

• "exact" means that only exactly these life cycle labels can be present (although multiple times and in random orderings).

• "only" means that only (a set of) these life cycle labels are allowed to be present.

Value

When given an object of type log, it will return a filtered log. When given an object of type grouped_log, the filter will be applied in a stratified way (i.e. each separately for each group). The returned log will be grouped on the same variables as the original log.

Methods (by class)

• filter_lifecycle_presence(eventlog): Filters activity instances on the presence of life cycle labels for an eventlog.

• filter_lifecycle_presence(grouped_eventlog): Filters activity instances on the presence of life cycle labels for a grouped_eventlog.

References

Swennen, M. (2018). Using Event Log Knowledge to Support Operational Exellence Techniques (Doctoral dissertation). Hasselt University.

See Also

lifecycle_id

Other filters: filter_activity(), filter_activity_frequency(), filter_activity_instance(), filter_activity_presence(), filter_case(), filter_case_condition(), filter_endpoints(), filter_endpoints_condition(), filter_flow_time(), filter_idle_time(), filter_infrequent_flows(), filter_lifecycle(), filter_precedence(), filter_precedence_condition(), filter_precedence_resource(), filter_processing_time(), filter_resource(), filter_resource_frequency(), filter_throughput_time(), filter_time_period(), filter_trace(), filter_trace_frequency(), filter_trace_length(), filter_trim(), filter_trim_lifecycle()

Description

Filters cases based on the precedence relations between two sets of activities.

Usage

filter_precedence(
  log,
  antecedents,
  consequents,
  precedence_type = c("directly_follows", "eventually_follows"),
  filter_method = c("all", "one_of", "none"),
  reverse = FALSE,
  eventlog = deprecated()
)

## S3 method for class 'log'
filter_precedence(
  log,
  antecedents,
  consequents,
  precedence_type = c("directly_follows", "eventually_follows"),
  filter_method = c("all", "one_of", "none"),
  reverse = FALSE,
  eventlog = deprecated()
)

## S3 method for class 'grouped_log'
filter_precedence(
  log,
  antecedents,
  consequents,
  precedence_type = c("directly_follows", "eventually_follows"),
  filter_method = c("all", "one_of", "none"),
  reverse = FALSE,
  eventlog = deprecated()
)

Arguments

Details

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 ("directly_follows") each other, or activities that should eventually follow ("eventually_follows") 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, 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"), at least one of them ("one_of"), or none ("none").

Value

When given an object of type log, it will return a filtered log. When given an object of type grouped_log, the filter will be applied in a stratified way (i.e. each separately for each group). The returned log will be grouped on the same variables as the original log.

Methods (by class)

• filter_precedence(log): Filters cases for a log.

• filter_precedence(grouped_log): Filters cases for a grouped_log.

References

Swennen, M. (2018). Using Event Log Knowledge to Support Operational Exellence Techniques (Doctoral dissertation). Hasselt University.

See Also

Other filters: filter_activity(), filter_activity_frequency(), filter_activity_instance(), filter_activity_presence(), filter_case(), filter_case_condition(), filter_endpoints(), filter_endpoints_condition(), filter_flow_time(), filter_idle_time(), filter_infrequent_flows(), filter_lifecycle(), filter_lifecycle_presence(), filter_precedence_condition(), filter_precedence_resource(), filter_processing_time(), filter_resource(), filter_resource_frequency(), filter_throughput_time(), filter_time_period(), filter_trace(), filter_trace_frequency(), filter_trace_length(), filter_trim(), filter_trim_lifecycle()

Examples

eventdataR::patients %>%
	filter_precedence(antecedents = "Triage and Assessment",
					  consequents = "Blood test",
					  precedence_type = "directly_follows") %>%
	bupaR::traces()

eventdataR::patients %>%
	filter_precedence(antecedents = "Triage and Assessment",
					  consequents = c("Blood test", "X-Ray", "MRI SCAN"),
					  precedence_type = "eventually_follows",
					  filter_method = "one_of") %>%
	bupaR::traces()

Description

Filters cases based on the precedence relations between two sets of activities. For more information, see filter_precedence.

Usage

filter_precedence_condition(
  log,
  antecedent_condition,
  consequent_condition,
  precedence_type = c("directly_follows", "eventually_follows"),
  reverse = FALSE,
  eventlog = deprecated()
)

## S3 method for class 'log'
filter_precedence_condition(
  log,
  antecedent_condition,
  consequent_condition,
  precedence_type = c("directly_follows", "eventually_follows"),
  reverse = FALSE,
  eventlog = deprecated()
)

## S3 method for class 'grouped_log'
filter_precedence_condition(
  log,
  antecedent_condition,
  consequent_condition,
  precedence_type = c("directly_follows", "eventually_follows"),
  reverse = FALSE,
  eventlog = deprecated()
)

Arguments

Value

When given an object of type log, it will return a filtered log. When given an object of type grouped_log, the filter will be applied in a stratified way (i.e. each separately for each group). The returned log will be grouped on the same variables as the original log.

Methods (by class)

• filter_precedence_condition(log): Filters cases for a log.

• filter_precedence_condition(grouped_log): Filters cases for a grouped_log.

References

Swennen, M. (2018). Using Event Log Knowledge to Support Operational Exellence Techniques (Doctoral dissertation). Hasselt University.

See Also

Other filters: filter_activity(), filter_activity_frequency(), filter_activity_instance(), filter_activity_presence(), filter_case(), filter_case_condition(), filter_endpoints(), filter_endpoints_condition(), filter_flow_time(), filter_idle_time(), filter_infrequent_flows(), filter_lifecycle(), filter_lifecycle_presence(), filter_precedence(), filter_precedence_resource(), filter_processing_time(), filter_resource(), filter_resource_frequency(), filter_throughput_time(), filter_time_period(), filter_trace(), filter_trace_frequency(), filter_trace_length(), filter_trim(), filter_trim_lifecycle()

Description

Filters cases based on the precedence relations between two sets of activities, where both antecendent and consequent have to be executed by the same resource.

Usage

filter_precedence_resource(
  log,
  antecedents,
  consequents,
  precedence_type = c("directly_follows", "eventually_follows"),
  filter_method = c("all", "one_of", "none"),
  reverse = FALSE,
  eventlog = deprecated()
)

## S3 method for class 'eventlog'
filter_precedence_resource(
  log,
  antecedents,
  consequents,
  precedence_type = c("directly_follows", "eventually_follows"),
  filter_method = c("all", "one_of", "none"),
  reverse = FALSE,
  eventlog = deprecated()
)

## S3 method for class 'activitylog'
filter_precedence_resource(
  log,
  antecedents,
  consequents,
  precedence_type = c("directly_follows", "eventually_follows"),
  filter_method = c("all", "one_of", "none"),
  reverse = FALSE,
  eventlog = deprecated()
)

## S3 method for class 'grouped_log'
filter_precedence_resource(
  log,
  antecedents,
  consequents,
  precedence_type = c("directly_follows", "eventually_follows"),
  filter_method = c("all", "one_of", "none"),
  reverse = FALSE,
  eventlog = deprecated()
)

Arguments

Value

When given an object of type log, it will return a filtered log. When given an object of type grouped_log, the filter will be applied in a stratified way (i.e. each separately for each group). The returned log will be grouped on the same variables as the original log.

Methods (by class)

• filter_precedence_resource(eventlog): Filters cases for an eventlog.

• filter_precedence_resource(activitylog): Filters cases for an activitylog.

• filter_precedence_resource(grouped_log): Filters cases for a grouped_log.

References

Swennen, M. (2018). Using Event Log Knowledge to Support Operational Exellence Techniques (Doctoral dissertation). Hasselt University.

See Also

filter_precedence()

Other filters: filter_activity(), filter_activity_frequency(), filter_activity_instance(), filter_activity_presence(), filter_case(), filter_case_condition(), filter_endpoints(), filter_endpoints_condition(), filter_flow_time(), filter_idle_time(), filter_infrequent_flows(), filter_lifecycle(), filter_lifecycle_presence(), filter_precedence(), filter_precedence_condition(), filter_processing_time(), filter_resource(), filter_resource_frequency(), filter_throughput_time(), filter_time_period(), filter_trace(), filter_trace_frequency(), filter_trace_length(), filter_trim(), filter_trim_lifecycle()

Description

Filters cases based on their processing_time.

This filter can be used by using an interval or by using a percentage. The percentage will always start with the shortest cases first and stop including cases when the specified percentile is reached. On the other hand, an absolute interval can be defined instead to filter cases which have a processing time in this interval. The time units in which this interval is defined can be supplied with the units argument.

Usage

filter_processing_time(
  log,
  interval = NULL,
  percentage = NULL,
  reverse = FALSE,
  units = c("secs", "mins", "hours", "days", "weeks"),
  eventlog = deprecated()
)

## S3 method for class 'log'
filter_processing_time(
  log,
  interval = NULL,
  percentage = NULL,
  reverse = FALSE,
  units = c("secs", "mins", "hours", "days", "weeks"),
  eventlog = deprecated()
)

## S3 method for class 'grouped_log'
filter_processing_time(
  log,
  interval = NULL,
  percentage = NULL,
  reverse = FALSE,
  units = c("secs", "mins", "hours", "days", "weeks"),
  eventlog = deprecated()
)

Arguments

Value

When given an object of type log, it will return a filtered log. When given an object of type grouped_log, the filter will be applied in a stratified way (i.e. each separately for each group). The returned log will be grouped on the same variables as the original log.

Methods (by class)

• filter_processing_time(log): Filters cases for a log.

• filter_processing_time(grouped_log): Filters cases for a grouped_log.

References

Swennen, M. (2018). Using Event Log Knowledge to Support Operational Exellence Techniques (Doctoral dissertation). Hasselt University.

See Also

processing_time(),difftime()

Other filters: filter_activity(), filter_activity_frequency(), filter_activity_instance(), filter_activity_presence(), filter_case(), filter_case_condition(), filter_endpoints(), filter_endpoints_condition(), filter_flow_time(), filter_idle_time(), filter_infrequent_flows(), filter_lifecycle(), filter_lifecycle_presence(), filter_precedence(), filter_precedence_condition(), filter_precedence_resource(), filter_resource(), filter_resource_frequency(), filter_throughput_time(), filter_time_period(), filter_trace(), filter_trace_frequency(), filter_trace_length(), filter_trim(), filter_trim_lifecycle()

Description

Filters the log based on resource identifiers

This method can be used to filter on resource identifiers. It has a resources argument, to which a vector of identifiers can be given. The selection can be negated with the reverse argument.

Usage

filter_resource(log, resources, reverse = FALSE, eventlog = deprecated())

## S3 method for class 'log'
filter_resource(log, resources, reverse = FALSE, eventlog = deprecated())

## S3 method for class 'grouped_log'
filter_resource(log, resources, reverse = FALSE, eventlog = deprecated())

Arguments

Value

When given an object of type log, it will return a filtered log. When given an object of type grouped_log, the filter will be applied in a stratified way (i.e. each separately for each group). The returned log will be grouped on the same variables as the original log.

Methods (by class)

• filter_resource(log): Filters resources for a log.

• filter_resource(grouped_log): Filters resources for a grouped_log.

References

Swennen, M. (2018). Using Event Log Knowledge to Support Operational Exellence Techniques (Doctoral dissertation). Hasselt University.

See Also

Other filters: filter_activity(), filter_activity_frequency(), filter_activity_instance(), filter_activity_presence(), filter_case(), filter_case_condition(), filter_endpoints(), filter_endpoints_condition(), filter_flow_time(), filter_idle_time(), filter_infrequent_flows(), filter_lifecycle(), filter_lifecycle_presence(), filter_precedence(), filter_precedence_condition(), filter_precedence_resource(), filter_processing_time(), filter_resource_frequency(), filter_throughput_time(), filter_time_period(), filter_trace(), filter_trace_frequency(), filter_trace_length(), filter_trim(), filter_trim_lifecycle()

Description

Filters the log based on frequency of resources

Usage

filter_resource_frequency(
  log,
  interval = NULL,
  percentage = NULL,
  reverse = FALSE,
  eventlog = deprecated()
)

## S3 method for class 'log'
filter_resource_frequency(
  log,
  interval = NULL,
  percentage = NULL,
  reverse = FALSE,
  eventlog = deprecated()
)

## S3 method for class 'grouped_log'
filter_resource_frequency(
  log,
  interval = NULL,
  percentage = NULL,
  reverse = FALSE,
  eventlog = deprecated()
)

Arguments

Details

Filtering the log based on resource frequency can be done in two ways: using an interval of allowed frequencies, or specify a coverage percentage:

• percentage: When filtering using a percentage p%, the filter will return p% of the activity instances, starting from the resource labels with the highest frequency. The filter will retain additional resource labels as long as the number of activity instances does not exceed the percentage threshold.

• interval: When filtering using an interval, resource labels will be retained when their absolute frequency fall in this interval. The interval is specified using a numeric vector of length 2. Half open intervals can be created by using NA, e.g., c(10, NA) will select resource labels which occur 10 times or more.

Value

When given an object of type log, it will return a filtered log. When given an object of type grouped_log, the filter will be applied in a stratified way (i.e. each separately for each group). The returned log will be grouped on the same variables as the original log.

Methods (by class)

• filter_resource_frequency(log): Filters resources for a log.

• filter_resource_frequency(grouped_log): Filters resources for a grouped_log.

References

Swennen, M. (2018). Using Event Log Knowledge to Support Operational Exellence Techniques (Doctoral dissertation). Hasselt University.

See Also

Other filters: filter_activity(), filter_activity_frequency(), filter_activity_instance(), filter_activity_presence(), filter_case(), filter_case_condition(), filter_endpoints(), filter_endpoints_condition(), filter_flow_time(), filter_idle_time(), filter_infrequent_flows(), filter_lifecycle(), filter_lifecycle_presence(), filter_precedence(), filter_precedence_condition(), filter_precedence_resource(), filter_processing_time(), filter_resource(), filter_throughput_time(), filter_time_period(), filter_trace(), filter_trace_frequency(), filter_trace_length(), filter_trim(), filter_trim_lifecycle()

Description

Filters cases based on their throughput_time.

This filter can be used by using an interval or by using a percentage. The percentage will always start with the shortest cases first and stop including cases when the specified percentile is reached. On the other hand, an absolute interval can be defined instead to filter cases which have a throughput time in this interval. The time units in which this interval is defined can be supplied with the units argument.

Usage

filter_throughput_time(
  log,
  interval = NULL,
  percentage = NULL,
  reverse = FALSE,
  units = c("secs", "mins", "hours", "days", "weeks"),
  eventlog = deprecated()
)

## S3 method for class 'log'
filter_throughput_time(
  log,
  interval = NULL,
  percentage = NULL,
  reverse = FALSE,
  units = c("secs", "mins", "hours", "days", "weeks"),
  eventlog = deprecated()
)

## S3 method for class 'grouped_log'
filter_throughput_time(
  log,
  interval = NULL,
  percentage = NULL,
  reverse = FALSE,
  units = c("secs", "mins", "hours", "days", "weeks"),
  eventlog = deprecated()
)

Arguments

Value

When given an object of type log, it will return a filtered log. When given an object of type grouped_log, the filter will be applied in a stratified way (i.e. each separately for each group). The returned log will be grouped on the same variables as the original log.

Methods (by class)

• filter_throughput_time(log): Filters cases for a log.

• filter_throughput_time(grouped_log): Filters cases for a grouped_log.

References

Swennen, M. (2018). Using Event Log Knowledge to Support Operational Exellence Techniques (Doctoral dissertation). Hasselt University.

See Also

throughput_time(),difftime()

Other filters: filter_activity(), filter_activity_frequency(), filter_activity_instance(), filter_activity_presence(), filter_case(), filter_case_condition(), filter_endpoints(), filter_endpoints_condition(), filter_flow_time(), filter_idle_time(), filter_infrequent_flows(), filter_lifecycle(), filter_lifecycle_presence(), filter_precedence(), filter_precedence_condition(), filter_precedence_resource(), filter_processing_time(), filter_resource(), filter_resource_frequency(), filter_time_period(), filter_trace(), filter_trace_frequency(), filter_trace_length(), filter_trim(), filter_trim_lifecycle()

Description

Function to filter the log using a time period.

Usage

filter_time_period(
  log,
  interval = NULL,
  filter_method = c("contained", "intersecting", "start", "complete", "trim"),
  force_trim = FALSE,
  reverse = FALSE,
  eventlog = deprecated()
)

## S3 method for class 'eventlog'
filter_time_period(
  log,
  interval = NULL,
  filter_method = c("contained", "intersecting", "start", "complete", "trim"),
  force_trim = FALSE,
  reverse = FALSE,
  eventlog = deprecated()
)

## S3 method for class 'grouped_eventlog'
filter_time_period(
  log,
  interval = NULL,
  filter_method = c("contained", "intersecting", "start", "complete", "trim"),
  force_trim = FALSE,
  reverse = FALSE,
  eventlog = deprecated()
)

## S3 method for class 'activitylog'
filter_time_period(
  log,
  interval = NULL,
  filter_method = c("contained", "intersecting", "start", "complete", "trim"),
  force_trim = FALSE,
  reverse = FALSE,
  eventlog = deprecated()
)

## S3 method for class 'grouped_activitylog'
filter_time_period(
  log,
  interval = NULL,
  filter_method = c("contained", "intersecting", "start", "complete", "trim"),
  force_trim = FALSE,
  reverse = FALSE,
  eventlog = deprecated()
)

Arguments

Details

Event data can be filtered by supplying a time window to the method filter_time_period. There are 5 different values for filter_method:

• "contained": Keeps all the events related to cases contained in the time period.

• "intersecting": Keeps all the events related to cases in which at least one event started and/or ended in the time period.

• "start": Keeps all the events related to cases started in the time period.

• "complete": Keeps all the events related to cases complete in the time period.

• "trim": Keeps all the events which started and ended in the time frame.

Value

When given an object of type log, it will return a filtered log. When given an object of type grouped_log, the filter will be applied in a stratified way (i.e. each separately for each group). The returned log will be grouped on the same variables as the original log.

Methods (by class)

• filter_time_period(eventlog): Filters activity instances for an eventlog.

• filter_time_period(grouped_eventlog): Filters activity instances for a grouped_eventlog.

• filter_time_period(activitylog): Filters activity instances for an activitylog.

• filter_time_period(grouped_activitylog): Filters activity instances for a grouped_activitylog.

References

Swennen, M. (2018). Using Event Log Knowledge to Support Operational Exellence Techniques (Doctoral dissertation). Hasselt University.

See Also

Other filters: filter_activity(), filter_activity_frequency(), filter_activity_instance(), filter_activity_presence(), filter_case(), filter_case_condition(), filter_endpoints(), filter_endpoints_condition(), filter_flow_time(), filter_idle_time(), filter_infrequent_flows(), filter_lifecycle(), filter_lifecycle_presence(), filter_precedence(), filter_precedence_condition(), filter_precedence_resource(), filter_processing_time(), filter_resource(), filter_resource_frequency(), filter_throughput_time(), filter_trace(), filter_trace_frequency(), filter_trace_length(), filter_trim(), filter_trim_lifecycle()

Description

Filters the log based on trace identifier.

This method can be used to filter on trace identifier, which can be obtained from case_list. It has a trace_ids argument, to which a vector of identifiers can be given. The selection can be negated with the reverse argument.

Usage

filter_trace(log, trace_ids, reverse = FALSE, eventlog = deprecated())

## S3 method for class 'log'
filter_trace(log, trace_ids, reverse = FALSE, eventlog = deprecated())

## S3 method for class 'grouped_log'
filter_trace(log, trace_ids, reverse = FALSE, eventlog = deprecated())

Arguments

Value

When given an object of type log, it will return a filtered log. When given an object of type grouped_log, the filter will be applied in a stratified way (i.e. each separately for each group). The returned log will be grouped on the same variables as the original log.

Methods (by class)

• filter_trace(log): Filters cases for a log.

• filter_trace(grouped_log): Filters cases for a grouped_log.

References

Swennen, M. (2018). Using Event Log Knowledge to Support Operational Exellence Techniques (Doctoral dissertation). Hasselt University.

See Also

case_list

Other filters: filter_activity(), filter_activity_frequency(), filter_activity_instance(), filter_activity_presence(), filter_case(), filter_case_condition(), filter_endpoints(), filter_endpoints_condition(), filter_flow_time(), filter_idle_time(), filter_infrequent_flows(), filter_lifecycle(), filter_lifecycle_presence(), filter_precedence(), filter_precedence_condition(), filter_precedence_resource(), filter_processing_time(), filter_resource(), filter_resource_frequency(), filter_throughput_time(), filter_time_period(), filter_trace_frequency(), filter_trace_length(), filter_trim(), filter_trim_lifecycle()

Description

Filters the log based the frequency of traces, using an interval or a percentile cut off.

Usage

filter_trace_frequency(
  log,
  interval = NULL,
  percentage = NULL,
  reverse = FALSE,
  eventlog = deprecated()
)

## S3 method for class 'log'
filter_trace_frequency(
  log,
  interval = NULL,
  percentage = NULL,
  reverse = FALSE,
  eventlog = deprecated()
)

## S3 method for class 'grouped_log'
filter_trace_frequency(
  log,
  interval = NULL,
  percentage = NULL,
  reverse = FALSE,
  eventlog = deprecated()
)

Arguments

Details

Filtering the log based on trace frequency can be done in two ways: using an interval of allowed frequencies, or specify a coverage percentage:

• percentage: When filtering using a percentage p%, the filter will return p% of the cases, starting from the traces with the highest frequency. The filter will retain additional traces as long as the number of activity instances does not exceed the percentage threshold.

• interval: When filtering using an interval, traces will be retained when their absolute frequency fall in this interval. The interval is specified using a numeric vector of length 2. Half open intervals can be created by using NA, e.g., c(10, NA) will select cases with a trace that occurs 10 times or more.

Value

When given an object of type log, it will return a filtered log. When given an object of type grouped_log, the filter will be applied in a stratified way (i.e. each separately for each group). The returned log will be grouped on the same variables as the original log.

Methods (by class)

• filter_trace_frequency(log): Filters cases for a log.

• filter_trace_frequency(grouped_log): Filters cases for a grouped_log.

References

Swennen, M. (2018). Using Event Log Knowledge to Support Operational Exellence Techniques (Doctoral dissertation). Hasselt University.

See Also

Other filters: filter_activity(), filter_activity_frequency(), filter_activity_instance(), filter_activity_presence(), filter_case(), filter_case_condition(), filter_endpoints(), filter_endpoints_condition(), filter_flow_time(), filter_idle_time(), filter_infrequent_flows(), filter_lifecycle(), filter_lifecycle_presence(), filter_precedence(), filter_precedence_condition(), filter_precedence_resource(), filter_processing_time(), filter_resource(), filter_resource_frequency(), filter_throughput_time(), filter_time_period(), filter_trace(), filter_trace_length(), filter_trim(), filter_trim_lifecycle()

Description

Filters cases on trace_length, using a percentile threshold or interval.

This filter can be used by using an interval or by using a percentage. The percentage will always start with the shortest cases first and stop including cases when the specified percentile is reached. On the other hand, an absolute interval can be defined instead to filter cases which have a length in this interval.

Usage

filter_trace_length(
  log,
  interval = NULL,
  percentage = NULL,
  reverse = FALSE,
  eventlog = deprecated()
)

## S3 method for class 'log'
filter_trace_length(
  log,
  interval = NULL,
  percentage = NULL,
  reverse = FALSE,
  eventlog = deprecated()
)

## S3 method for class 'grouped_log'
filter_trace_length(
  log,
  interval = NULL,
  percentage = NULL,
  reverse = FALSE,
  eventlog = deprecated()
)

Arguments

Value

When given an object of type log, it will return a filtered log. When given an object of type grouped_log, the filter will be applied in a stratified way (i.e. each separately for each group). The returned log will be grouped on the same variables as the original log.

Methods (by class)

• filter_trace_length(log): Filters cases for a log.

• filter_trace_length(grouped_log): Filters cases for a grouped_log.

References

Swennen, M. (2018). Using Event Log Knowledge to Support Operational Exellence Techniques (Doctoral dissertation). Hasselt University.

See Also

trace_length

Other filters: filter_activity(), filter_activity_frequency(), filter_activity_instance(), filter_activity_presence(), filter_case(), filter_case_condition(), filter_endpoints(), filter_endpoints_condition(), filter_flow_time(), filter_idle_time(), filter_infrequent_flows(), filter_lifecycle(), filter_lifecycle_presence(), filter_precedence(), filter_precedence_condition(), filter_precedence_resource(), filter_processing_time(), filter_resource(), filter_resource_frequency(), filter_throughput_time(), filter_time_period(), filter_trace(), filter_trace_frequency(), filter_trim(), filter_trim_lifecycle()

Description

Trim cases from the first event of a set of start activities to the last event of a set of end activities.

One can trim cases by removing one or more activity instances at the start and/or end of a case. Trimming is performed until all cases have a start and/or end point belonging to a set of allowed activity labels. This filter requires a set of allowed start activities and/or a set of allowed end activities. If one of them is not provided it will not trim the cases at this edge. The selection can be reversed, which means that only the trimmed events at the start and end of cases are retained. As such, this argument allows to cut intermediate parts out of traces.

Usage

filter_trim(
  log,
  start_activities = NULL,
  end_activities = NULL,
  reverse = FALSE,
  eventlog = deprecated()
)

## S3 method for class 'eventlog'
filter_trim(
  log,
  start_activities = NULL,
  end_activities = NULL,
  reverse = FALSE,
  eventlog = deprecated()
)

## S3 method for class 'grouped_eventlog'
filter_trim(
  log,
  start_activities = NULL,
  end_activities = NULL,
  reverse = FALSE,
  eventlog = deprecated()
)

## S3 method for class 'activitylog'
filter_trim(
  log,
  start_activities = NULL,
  end_activities = NULL,
  reverse = FALSE,
  eventlog = deprecated()
)

## S3 method for class 'grouped_activitylog'
filter_trim(
  log,
  start_activities = NULL,
  end_activities = NULL,
  reverse = FALSE,
  eventlog = deprecated()
)

Arguments

Value

When given an object of type log, it will return a filtered log. When given an object of type grouped_log, the filter will be applied in a stratified way (i.e. each separately for each group). The returned log will be grouped on the same variables as the original log.

Methods (by class)

• filter_trim(eventlog): Filters activity instances for an eventlog.

• filter_trim(grouped_eventlog): Filters activity instances for a grouped_eventlog.

• filter_trim(activitylog): Filters activity instances for an activitylog.

• filter_trim(grouped_activitylog): Filters activity instances for a grouped_activitylog.

References

Swennen, M. (2018). Using Event Log Knowledge to Support Operational Exellence Techniques (Doctoral dissertation). Hasselt University.

See Also

Other filters: filter_activity(), filter_activity_frequency(), filter_activity_instance(), filter_activity_presence(), filter_case(), filter_case_condition(), filter_endpoints(), filter_endpoints_condition(), filter_flow_time(), filter_idle_time(), filter_infrequent_flows(), filter_lifecycle(), filter_lifecycle_presence(), filter_precedence(), filter_precedence_condition(), filter_precedence_resource(), filter_processing_time(), filter_resource(), filter_resource_frequency(), filter_throughput_time(), filter_time_period(), filter_trace(), filter_trace_frequency(), filter_trace_length(), filter_trim_lifecycle()

Description

Trim activity instances from the first event of a set of start life cycle labels to the last event of a set of end life cycle labels.

One can trim activity instances by removing one or more events at the start and/or end of the activity instances. Trimming is performed until all activity instances have a start and/or end point belonging to a set of allowed life cycle labels. This filter requires a set of allowed start life cycle labels and/or a set of allowed life cycle labels. If one of them is not provided it will not trim the activity instances at this edge.The selection can be reversed, which means that only the trimmed events at the start and end of activity instances are retained. As such, this argument allows to cut intermediate parts out of activity instances.

Usage

filter_trim_lifecycle(
  log,
  start_lifecycles = NULL,
  end_lifecycles = NULL,
  reverse = FALSE,
  start_lifecycle = deprecated(),
  end_lifecycle = deprecated(),
  eventlog = deprecated()
)

## S3 method for class 'eventlog'
filter_trim_lifecycle(
  log,
  start_lifecycles = NULL,
  end_lifecycles = NULL,
  reverse = FALSE,
  start_lifecycle = deprecated(),
  end_lifecycle = deprecated(),
  eventlog = deprecated()
)

## S3 method for class 'grouped_eventlog'
filter_trim_lifecycle(
  log,
  start_lifecycles = NULL,
  end_lifecycles = NULL,
  reverse = FALSE,
  start_lifecycle = deprecated(),
  end_lifecycle = deprecated(),
  eventlog = deprecated()
)

Arguments

Value

When given an object of type log, it will return a filtered log. When given an object of type grouped_log, the filter will be applied in a stratified way (i.e. each separately for each group). The returned log will be grouped on the same variables as the original log.

Methods (by class)

• filter_trim_lifecycle(eventlog): Filters activity instances for an eventlog.

• filter_trim_lifecycle(grouped_eventlog): Filters activity instances for a grouped_eventlog.

References

Swennen, M. (2018). Using Event Log Knowledge to Support Operational Exellence Techniques (Doctoral dissertation). Hasselt University.

See Also

lifecycle_id

Other filters: filter_activity(), filter_activity_frequency(), filter_activity_instance(), filter_activity_presence(), filter_case(), filter_case_condition(), filter_endpoints(), filter_endpoints_condition(), filter_flow_time(), filter_idle_time(), filter_infrequent_flows(), filter_lifecycle(), filter_lifecycle_presence(), filter_precedence(), filter_precedence_condition(), filter_precedence_resource(), filter_processing_time(), filter_resource(), filter_resource_frequency(), filter_throughput_time(), filter_time_period(), filter_trace(), filter_trace_frequency(), filter_trace_length(), filter_trim()

Description

Calculates the amount of time that no activity occurs.

Usage

idle_time(
  log,
  level = c("log", "trace", "case", "resource", "flow"),
  append = deprecated(),
  append_column = NULL,
  units = c("auto", "secs", "mins", "hours", "days", "weeks"),
  sort = TRUE,
  eventlog = deprecated()
)

## S3 method for class 'eventlog'
idle_time(
  log,
  level = c("log", "trace", "case", "resource", "flow"),
  append = deprecated(),
  append_column = NULL,
  units = c("auto", "secs", "mins", "hours", "days", "weeks"),
  sort = TRUE,
  eventlog = deprecated()
)

## S3 method for class 'grouped_eventlog'
idle_time(
  log,
  level = c("log", "case", "trace", "resource", "flow"),
  append = deprecated(),
  append_column = NULL,
  units = c("auto", "secs", "mins", "hours", "days", "weeks"),
  sort = TRUE,
  eventlog = deprecated()
)

## S3 method for class 'activitylog'
idle_time(
  log,
  level = c("log", "trace", "case", "resource", "flow"),
  append = deprecated(),
  append_column = NULL,
  units = c("auto", "secs", "mins", "hours", "days", "weeks"),
  sort = TRUE,
  eventlog = deprecated()
)

## S3 method for class 'grouped_activitylog'
idle_time(
  log,
  level = c("log", "trace", "case", "resource", "flow"),
  append = deprecated(),
  append_column = NULL,
  units = c("auto", "secs", "mins", "hours", "days", "weeks"),
  sort = TRUE,
  eventlog = deprecated()
)

Arguments

Details

Argument level has the following options:

• At "log" level, the idle time metric provides an overview of summary statistics of the idle time per case, aggregated over the complete log.

• On "trace" level, the idle time metric provides an overview of the summary statistics of the idle time for each trace in the log.

• On "case" level, the idle time metric provides an overview of the total idle time per case

• On "resource" level, this metric can be used to get an insight in the amount of time each resource "wastes" during the process.

Methods (by class)

• idle_time(eventlog): Computes the idle time for an eventlog.

• idle_time(grouped_eventlog): Computes the idle time for a grouped_eventlog.

• idle_time(activitylog): Computes the idle time for an activitylog.

• idle_time(grouped_activitylog): Computes the idle time for a grouped_activitylog.

References

Swennen, M. (2018). Using Event Log Knowledge to Support Operational Exellence Techniques (Doctoral dissertation). Hasselt University.

See Also

throughput_time(),processing_time(),difftime()

Other metrics: activity_frequency(), activity_presence(), end_activities(), number_of_repetitions(), number_of_selfloops(), number_of_traces(), processing_time(), resource_frequency(), resource_involvement(), resource_specialisation(), start_activities(), throughput_time(), trace_coverage(), trace_length()

Description

Provides information statistics on the number of repetitions

A repetition is an execution of an activity within a case while that activity has already been executed before, but one or more other activities are executed in between.

Usage

number_of_repetitions(
  log,
  type = c("all", "repeat", "redo"),
  level = c("log", "case", "activity", "resource", "resource-activity"),
  append = deprecated(),
  append_column = NULL,
  sort = TRUE,
  eventlog = deprecated()
)

## S3 method for class 'eventlog'
number_of_repetitions(
  log,
  type = c("all", "repeat", "redo"),
  level = c("log", "case", "activity", "resource", "resource-activity"),
  append = deprecated(),
  append_column = NULL,
  sort = TRUE,
  eventlog = deprecated()
)

## S3 method for class 'grouped_eventlog'
number_of_repetitions(
  log,
  type = c("all", "repeat", "redo"),
  level = c("log", "case", "activity", "resource", "resource-activity"),
  append = deprecated(),
  append_column = NULL,
  sort = TRUE,
  eventlog = deprecated()
)

## S3 method for class 'activitylog'
number_of_repetitions(
  log,
  type = c("all", "repeat", "redo"),
  level = c("log", "case", "activity", "resource", "resource-activity"),
  append = deprecated(),
  append_column = NULL,
  sort = TRUE,
  eventlog = deprecated()
)

## S3 method for class 'grouped_activitylog'
number_of_repetitions(
  log,
  type = c("all", "repeat", "redo"),
  level = c("log", "case", "activity", "resource", "resource-activity"),
  append = deprecated(),
  append_column = NULL,
  sort = TRUE,
  eventlog = deprecated()
)

Arguments

Details

Argument level has the following options:

• At "log" level, this metric shows the summary statistics of the number of repetitions within a case, which can provide insights in the amount of waste in a log. Each combination of two or more occurrences of the same activity, executed not immediately following each other, by the same resource is counted as one repeat repetition of this activity.

• On "case" level, this metric provides the absolute and relative number of repetitions in each case.

• On "activity" level, this metric shows which activities occur the most in a repetition. The absolute and relative number of both repeat and redo repetitions is provided by this metric, giving an overview per activity.

• On "resource" level, it can be interesting to have an overview of which resources need more than one time to execute an activity in a case or which resources need to have an activity redone later on in the case by another resource. This metric provides the absolute and relative number of times each resource appears in a repetition.

• On "resource-activity" level, this metric provides specific information about which activities and which resources are involved in the repetitions. For this metric the absolute and relative number of repeat and redo repetitions is provided. Again, two difierent relative numbers are provided, one relative to the total number of executions of the activity in the complete log, and one relative to the total number of executions performed by the resource throughout the complete log.

Similar to the self-loop metric, a distinction should be made between "repeat" and "redo" repetitions, as can be set by the type argument:

• "repeat" repetitions are activity executions of the same activity type that are executed not immediately following each other, but by the same resource.

• "redo" repetitions are activity executions of the same activity type that are executed not immediately following each other and by a different resource than the first activity occurrence of this activity type.

Methods (by class)

• number_of_repetitions(eventlog): Computes the number of repetitions for an eventlog.

• number_of_repetitions(grouped_eventlog): Computes the number of repetitions for a grouped_eventlog.

• number_of_repetitions(activitylog): Computes the number of repetitions for an activitylog.

• number_of_repetitions(grouped_activitylog): Computes the number of repetitions for a grouped_activitylog.

References

Swennen, M. (2018). Using Event Log Knowledge to Support Operational Exellence Techniques (Doctoral dissertation). Hasselt University.

See Also

number_of_selfloops

Other metrics: activity_frequency(), activity_presence(), end_activities(), idle_time(), number_of_selfloops(), number_of_traces(), processing_time(), resource_frequency(), resource_involvement(), resource_specialisation(), start_activities(), throughput_time(), trace_coverage(), trace_length()

Description

Provides information statistics on the number of self-loops in a trace.

Activity instances of the same activity type that are executed more than once immediately after each other by the same resource are in a self-loop ("length-1-loop"). If an activity instance of the same activity type is executed 3 times after each other by the same resource, this is defined as a "size 2 self-loop".

Usage

number_of_selfloops(
  log,
  type = c("all", "repeat", "redo"),
  level = c("log", "case", "activity", "resource", "resource-activity"),
  append = deprecated(),
  append_column = NULL,
  sort = TRUE,
  eventlog = deprecated()
)

## S3 method for class 'eventlog'
number_of_selfloops(
  log,
  type = c("all", "repeat", "redo"),
  level = c("log", "case", "activity", "resource", "resource-activity"),
  append = deprecated(),
  append_column = NULL,
  sort = TRUE,
  eventlog = deprecated()
)

## S3 method for class 'grouped_eventlog'
number_of_selfloops(
  log,
  type = c("all", "repeat", "redo"),
  level = c("log", "case", "activity", "resource", "resource-activity"),
  append = deprecated(),
  append_column = NULL,
  sort = TRUE,
  eventlog = deprecated()
)

## S3 method for class 'activitylog'
number_of_selfloops(
  log,
  type = c("all", "repeat", "redo"),
  level = c("log", "case", "activity", "resource", "resource-activity"),
  append = deprecated(),
  append_column = NULL,
  sort = TRUE,
  eventlog = deprecated()
)

## S3 method for class 'grouped_activitylog'
number_of_selfloops(
  log,
  type = c("all", "repeat", "redo"),
  level = c("log", "case", "activity", "resource", "resource-activity"),
  append = deprecated(),
  append_column = NULL,
  sort = TRUE,
  eventlog = deprecated()
)