Help for package edeaR

Type:

Package

Title:

Exploratory and Descriptive Event-Based Data Analysis

Version:

0.9.5

Description:

Exploratory and descriptive analysis of event based data. Provides methods for describing and selecting process data, and for preparing event log data for process mining. Builds on the S3-class for event logs implemented in the package 'bupaR'.

License:

MIT + file LICENSE

Depends:

R(≥ 3.5.0)

Imports:

bupaR (≥ 0.5.1), dplyr, data.table, ggplot2, ggthemes, glue, tibble, shiny, miniUI, tidyr, shinyTime, lubridate, purrr, stringr, rlang (≥ 1.0.0), cli (≥ 3.2.0), zoo, hms, lifecycle, magrittr

Encoding:

UTF-8

RoxygenNote:

7.3.2

URL:

https://bupar.net/, https://github.com/bupaverse/edeaR/, https://bupaverse.github.io/edeaR/

Suggests:

knitr, eventdataR, rmarkdown, covr, testthat (≥ 3.0.0)

VignetteBuilder:

knitr

BugReports:

https://github.com/bupaverse/edeaR/issues/

Config/testthat/edition:

NeedsCompilation:

Packaged:

2025-07-07 13:33:41 UTC; lucp8407

Author:

Gert Janssenswillen [aut, cre], Gerard van Hulzen [ctb], Marijke Swennen [ctb], Ivan Esin [ctb], Hasselt University [cph]

Maintainer:

Gert Janssenswillen <gert.janssenswillen@uhasselt.be>

Repository:

CRAN

Date/Publication:

2025-07-09 13:40:02 UTC

edeaR - Exploratory and Descriptive Event-based data Analysis in R

Description

This package provides several useful techniques for Exploratory and Descriptive analysis of event based data in R, developed by the Business Informatics Research Group of Hasselt University.

Author(s)

Maintainer: Gert Janssenswillen gert.janssenswillen@uhasselt.be

Other contributors:

Gerard van Hulzen gerard.vanhulzen@uhasselt.be [contributor]
Marijke Swennen marijke.swennen@uhasselt.be [contributor]
Ivan Esin ivan.esin@student.uhasselt.be [contributor]
Hasselt University [copyright holder]

Activity Frequency

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

log

log: Object of class log or derivatives (grouped_log, eventlog, activitylog, etc.).

level

character (default "log"): Level of granularity for the analysis: "log" (default), "trace", "case", or "activity". For more information, see vignette("metrics", "edeaR") and 'Details' below.

append

logical (default FALSE) : The arguments append and append_column have been deprecated in favour of augment.
Indicating whether to append results to original log. Ignored when level is "log" or "trace".

append_column

The arguments append and append_column have been deprecated in favour of augment.
Which of the output columns to append to log, if append = TRUE. Default column depends on chosen level.

sort

logical (default TRUE): Sort output on count. Only for levels with frequency count output.

eventlog

; please use log instead.

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.

Metric: Activity Presence

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

log

log: Object of class log or derivatives (grouped_log, eventlog, activitylog, etc.).

append

append_column

The arguments append and append_column have been deprecated in favour of augment.
Which of the output columns to append to log, if append = TRUE. Default column depends on chosen level.

sort

logical (default TRUE): Sort output on count. Only for levels with frequency count output.

eventlog

; please use log instead.

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.

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)

Add fixed holiday to work schedule

Description

Add fixed holiday to work schedule

Usage

add_fixed_holiday(work_schedule, name, month, day)

Arguments

work_schedule

Work schedule created with create_work_schedule

name

Name of holiday

month

Month in which fixed holiday takes place

day

Day of fixed holiday

Add floating holiday to work schedule

Description

Add floating holiday to work schedule

Usage

add_floating_holiday(work_schedule, name, dates)

Arguments

work_schedule

Work schedule created with create_work_schedule

name

Name of holiday

dates

Dates of floating holiday. Make sure to list all dates relevant to your time frame

Add holiday period to work schedule

Description

Add holiday period to work schedule

Usage

add_holiday_periods(work_schedule, from, to)

Arguments

work_schedule

Work schedule created with create_work_schedule

from

Start of holiday period (included)

to

End of holiday period (included)

Augment Log

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

metric

Metric computed by edeaR

log

log: Object of class log or derivatives (grouped_log, eventlog, activitylog, etc.) that was used to compute the metric.

columns

character vector: Column names from the metric that you want to add to the log. If missing, defaults to all columns.

prefix

character: Prefix to be added to the newly added metric columns in the log.

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)

Calculate queuing length

Description

Usage

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

Arguments

queueing_times

Object of class queuing_times, returned by calculate_queuing_times.

level

character (default "log"): Level of granularity for the analysis: "log", "activity", "resource". For more information, see 'Details' below.

time_interval

The time interval after which the queue length should be calculated. For more information, see 'Details' below and the by argument of seq.Date.

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.

Calculate queuing times

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

log

log: Object of class log or derivatives (grouped_log, eventlog, activitylog, etc.).

units

character (default "auto"): The time unit in which the throughput times should be reported. Should be one of the following values: "auto" (default), "secs", "mins", "hours", "days", "weeks". See also the units argument of difftime.

eventlog

; please use log instead.

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.

Adjust days in work schedule

Description

Adjust days in work schedule

Usage

change_day(work_schedule, day, start_time, end_time)

Arguments

work_schedule

Work schedule created with create_work_schedule

day

A numeric vector containing the days to be changed. 1 = monday.

start_time

The new start time for selected days (hh:mm:ss)

end_time

The new end time for selected days (hh:mm:ss)

Create work schedule

Description

Create work schedule

Usage

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

Arguments

start_time

Character indicating the usual start time for workdays (hh:mm:ss)

end_time

Character indicating the usual end time for workdays (hh:mm:ss)

End activities

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

log

log: Object of class log or derivatives (grouped_log, eventlog, activitylog, etc.).

level

character (default "log"): Level of granularity for the analysis: "log" (default), "case", "activity", "resource", or "resource-activity". For more information, see vignette("metrics", "edeaR") and 'Details' below.

append

append_column

The arguments append and append_column have been deprecated in favour of augment.
Which of the output columns to append to log, if append = TRUE. Default column depends on chosen level.

sort

logical (default TRUE): Sort output on count. Only for levels with frequency count output.

eventlog

; please use log instead.

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.

Filter Activity

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

log

log: Object of class log or derivatives (grouped_log, eventlog, activitylog, etc.).

activities

character vector: Containing one or more activity identifiers.

reverse

logical (default FALSE): Indicating whether the selection should be reversed.

eventlog

; please use log instead.

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.

Filter Activity Frequency

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

log

log: Object of class log or derivatives (grouped_log, eventlog, activitylog, etc.).

percentage, interval

The target coverage of activity instances. Provide either percentage or interval.
percentage (numeric): A percentile of p will return the most common activity types of the log, which account for at least p% of the activity instances.
interval (numeric vector of length 2): An activity frequency interval. Half open interval can be created using NA.
For more information, see 'Details' below.

reverse

logical (default FALSE): Indicating whether the selection should be reversed.

eventlog

; please use log instead.

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

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.

Filter Activity Instance

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

log

eventlog: Object of class eventlog or derivatives (grouped_eventlog).

activity_instances

A vector of activity instance identifiers.

reverse

logical (default FALSE): Indicating whether the selection should be reversed.

eventlog

; please use log instead.

Value

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.

Filter Activity Presence

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

log

log: Object of class log or derivatives (grouped_log, eventlog, activitylog, etc.).

activities

character vector: Containing one or more activity identifiers.

method

character (default "all"): Filter method: "all" (default), "none", "one_of", "exact", or "only". For more information, see Details below.

reverse

logical (default FALSE): Indicating whether the selection should be reversed.

eventlog

; please use log instead.

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

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.

Filter Case

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

log

log: Object of class log or derivatives (grouped_log, eventlog, activitylog, etc.).

cases

character vector: A vector of cases identifiers.

reverse

logical (default FALSE): Indicating whether the selection should be reversed.

eventlog

; please use log instead.

Value

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.

Filter Case Condition

Description

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

Usage

filter_case_condition(log, ..., reverse = FALSE)

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

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

Arguments

log

log: Object of class log or derivatives (grouped_log, eventlog, activitylog, etc.).

...

Expressions that return a logical value, and are defined in terms of the variables in log. If multiple expressions are included, they are combined with the & operator. Only rows for which all conditions evaluate to TRUE are kept. For more information, see filter.

reverse

logical (default FALSE): Indicating whether the selection should be reversed.

Value

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.

Filter Start and End Activities

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

log

log: Object of class log or derivatives (grouped_log, eventlog, activitylog, etc.).

start_activities, end_activities

character vector (default NULL): A vector of activity identifiers, or NULL.

percentage

numeric (default NULL): A percentage p to be used as percentile cut off. When this is used, the most common endpoint-pairs will be selected until at least the p% of the cases are selected.

reverse

logical (default FALSE): Indicating whether the selection should be reversed.

eventlog

; please use log instead.

Value

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.

Filter Start and End Conditions

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

log

log: Object of class log or derivatives (grouped_log, eventlog, activitylog, etc.).

start_condition, end_condition

A logical condition.

reverse

logical (default FALSE): Indicating whether the selection should be reversed.

eventlog

; please use log instead.

Value

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.

Filter directly follows with time interval

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

log

log: Object of class log or derivatives (grouped_log, eventlog, activitylog, etc.).

from, to

character vector of length 1: The antecendent and consequent to filter on. Both are character vectors containing exactly one activity identifier.

interval

numeric vector of length 2: A duration interval. Half open interval can be created using NA.

reverse

logical (default FALSE): Indicating whether the selection should be reversed.

units

character (default "secs"): The time unit in which the processing times should be reported. Should be one of the following values: "secs" (default), "mins", "hours", "days", "weeks". See also the units argument of difftime().

Value

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.

Filter Idle Time

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

log

log: Object of class log or derivatives (grouped_log, eventlog, activitylog, etc.).

interval, percentage

Provide either interval or percentage.
interval (numeric vector of length 2): A duration interval. Half open interval can be created using NA.
percentage (numeric): A percentage to be used for relative filtering.

reverse

logical (default FALSE): Indicating whether the selection should be reversed.

units

Value

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.

Filter Infrequent Flows

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

log

log: Object of class log or derivatives (grouped_log, eventlog, activitylog, etc.).

min_n

numeric (default 2): Cases containing a flow that occurs less than min_n times are discarded.

eventlog

; please use log instead.

Value

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.

Filter Life Cycle

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

log

eventlog: Object of class eventlog or derivatives (grouped_eventlog).

lifecycles

character vector: A vector of life cycle identifiers.

reverse

logical (default FALSE): Indicating whether the selection should be reversed.

lifecycle

; please use lifecycles instead.

eventlog

; please use log instead.

Value

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.

Filter Life Cycle Presence

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

log

eventlog: Object of class eventlog or derivatives (grouped_eventlog).

lifecycles

character vector: A vector of life cycle identifiers.

method

character (default "all"): Filter method: "all" (default), "none", "one_of", "exact", or "only". For more information, see Details below.

reverse

logical (default FALSE): Indicating whether the selection should be reversed.

lifecycle

; please use lifecycles instead.

eventlog

; please use log instead.

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

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.

Filter Precedence Relations

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

log

log: Object of class log or derivatives (grouped_log, eventlog, activitylog, etc.).

antecedents, consequents

character vector: The set of antecendent and consequent activities. Both are character vectors containing at least one activity identifier. All pairs of antecedents and consequents are turned into seperate precedence rules.

precedence_type

character (default "directly_follows"): When "directly_follows", the consequent activity should happen immediately after the antecedent activities.
When "eventually_follows", other events are allowed to happen in between.

filter_method

character (default "all"): When "all", only cases where all the relations are valid are preserved.
When "one_of", all the cases where at least one of the conditions hold, are preserved.
When "none", none of the relations are allowed.

reverse

logical (default FALSE): Indicating whether the selection should be reversed.

eventlog

; please use log instead.

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

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.

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()

Filter Precedence Relations

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

log

log: Object of class log or derivatives (grouped_log, eventlog, activitylog, etc.).

antecedent_condition, consequent_condition

The antecendent and consequent conditions.

precedence_type

reverse

logical (default FALSE): Indicating whether the selection should be reversed.

eventlog

; please use log instead.

Value

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.

Filter Precedence Relations with Identical Resources

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

log

log: Object of class log or derivatives (grouped_log, eventlog, activitylog, etc.).

antecedents, consequents

precedence_type

filter_method

reverse

logical (default FALSE): Indicating whether the selection should be reversed.

eventlog

; please use log instead.

Value

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.

Filter Processing Time

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

log

log: Object of class log or derivatives (grouped_log, eventlog, activitylog, etc.).

interval, percentage

reverse

logical (default FALSE): Indicating whether the selection should be reversed.

units

eventlog

; please use log instead.

Value

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.

Filter Resource

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

log

log: Object of class log or derivatives (grouped_log, eventlog, activitylog, etc.).

resources

character vector: A vector of resources identifiers.

reverse

logical (default FALSE): Indicating whether the selection should be reversed.

eventlog

; please use log instead.

Value

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.

Filter Resource Frequency

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

log

log: Object of class log or derivatives (grouped_log, eventlog, activitylog, etc.).

percentage, interval

The target coverage of activity instances. Provide either percentage or interval.
percentage (numeric): A percentile of p will return the most common resource types of the log, which account for at least p% of the activity instances.
interval (numeric vector of length 2): A resource frequency interval. Half open interval can be created using NA.
For more information, see 'Details' below.

reverse

logical (default FALSE): Indicating whether the selection should be reversed.

eventlog

; please use log instead.

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

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.

Filter Throughput Time

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

log

log: Object of class log or derivatives (grouped_log, eventlog, activitylog, etc.).

interval, percentage

reverse

logical (default FALSE): Indicating whether the selection should be reversed.

units

eventlog

; please use log instead.

Value

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.

Filter Time Period

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

log

log: Object of class log or derivatives (grouped_log, eventlog, activitylog, etc.).

interval

Date or POSIXct vector: A time interval (vector of length 2 of type Date or POSIXct). Half-open intervals can be created with NA.

filter_method

character (default "contained"): Filtering method: "contained" (default), "intersecting", "start", "complete", or "trim". For more information, see 'Details' below.

force_trim

logical (default FALSE): If TRUE in combination with filter_method "trim", activity instances on the edges of the interval are cut at the exact edge of the interval.

reverse

logical (default FALSE): Indicating whether the selection should be reversed.

eventlog

; please use log instead.

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

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.

Filter Trace

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

log

log: Object of class log or derivatives (grouped_log, eventlog, activitylog, etc.).

trace_ids

character vector: A vector of trace identifiers

reverse

logical (default FALSE): Indicating whether the selection should be reversed.

eventlog

; please use log instead.

Value

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.

Filter Trace Frequency

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

log

log: Object of class log or derivatives (grouped_log, eventlog, activitylog, etc.).

percentage, interval

The target coverage of activity instances. Provide either percentage or interval.
percentage (numeric): A percentile of p will select the most common traces of the log, until at least p% of the cases is covered.
interval (numeric vector of length 2): A trace frequency interval. The filter will select cases of which the trace has a frequency inside the interval. Half open interval can be created using NA.
For more information, see 'Details' below.

reverse

logical (default FALSE): Indicating whether the selection should be reversed.

eventlog

; please use log instead.

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

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.

Filter Trace Length

Description

Filters cases on trace_length, using a percentile threshold or 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

log

log: Object of class log or derivatives (grouped_log, eventlog, activitylog, etc.).

interval, percentage

Provide either interval or percentage.
interval (numeric vector of length 2): A trace length interval. Half open interval can be created using NA.
percentage (numeric): A percentage p to be used for relative filtering.

reverse

logical (default FALSE): Indicating whether the selection should be reversed.

eventlog

; please use log instead.

Value

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.

Trim Cases

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

log

log: Object of class log or derivatives (grouped_log, eventlog, activitylog, etc.).

start_activities, end_activities

character vector (default NULL): A vector of activity identifiers, or NULL.

reverse

logical (default FALSE): Indicating whether the selection should be reversed.

eventlog

; please use log instead.

Value

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.

Filter Trim Life Cycle

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

log

eventlog: Object of class eventlog or derivatives (grouped_eventlog).

start_lifecycles, end_lifecycles

character vector (default NULL): A vector of life cycle identifiers, or NULL.

reverse

logical (default FALSE): Indicating whether the selection should be reversed.

start_lifecycle

; please use start_lifecycles instead.

end_lifecycle

; please use end_lifecycles instead.

eventlog

; please use log instead.

Value

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.

Idle Time

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

log

log: Object of class log or derivatives (grouped_log, eventlog, activitylog, etc.).

level

character (default "log"): Level of granularity for the analysis: "log" (default), "trace", "case", or "resource". For more information, see vignette("metrics", "edeaR") and Details below.

append

append_column

The arguments append and append_column have been deprecated in favour of augment.
Which of the output columns to append to log, if append = TRUE. Default column depends on chosen level.

units

sort

logical (default TRUE): Sort by decreasing idle time. Only relevant for "trace" and "resource" level.

eventlog

; please use log instead.

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.

ifilter functions

Description

ifilter functions

Usage

ifilter_activity(eventlog)

ifilter_activity_frequency(eventlog)

ifilter_activity_instance(eventlog)

ifilter_activity_presence(eventlog)

ifilter_case(eventlog)

ifilter_endpoints(eventlog)

ifilter_lifecycle(eventlog)

ifilter_lifecycle_presence(eventlog)

ifilter_precedence(eventlog)

ifilter_processing_time(eventlog)

ifilter_resource(eventlog)

ifilter_resource_frequency(eventlog)

ifilter_throughput_time(eventlog)

ifilter_time_period(eventlog)

ifilter_trace_frequency(eventlog)

ifilter_trace_length(eventlog)

ifilter_trim(eventlog)

ifilter_trim_lifecycle(eventlog)

Number of Repetitions

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

log

log: Object of class log or derivatives (grouped_log, eventlog, activitylog, etc.).

type

character (default "all"): The type of repetitions: "all" (default), "repeat", or "redo". For more information, see 'Details' below.

level

append

append_column

The arguments append and append_column have been deprecated in favour of augment.
Which of the output columns to append to log, if append = TRUE. Default column depends on chosen level.

sort

logical (default TRUE): Sort output on count. Only for levels with frequency count output.

eventlog

; please use log instead.

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.

Number of Self-loops

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()
)

Arguments

log

log: Object of class log or derivatives (grouped_log, eventlog, activitylog, etc.).

type

character (default "all"): The type of repetitions: "all" (default), "repeat", or "redo". For more information, see 'Details' below.

level

append

append_column

The arguments append and append_column have been deprecated in favour of augment.
Which of the output columns to append to log, if append = TRUE. Default column depends on chosen level.

sort

logical (default TRUE): Sort output on count. Only for levels with frequency count output.

eventlog

; please use log instead.

Details

Two types of self-loops are defined, which can be chosen using the type argument:

"repeat" self-loops are activity executions of the same activity type that are executed immediately following each other by the same resource.
"redo" self-loops are activity executions of the same activity type that are executed immediately following each other by a different resource.

Argument level has the following options:

At "log" level, the summary statistics of the number of self-loops within a trace can give a first insight in the amount of waste in a log. As stated earlier, each combination of two occurrences of the same activity executed by the same resource will be counted as one repeat self-loop of this activity.
On "case" level, an overview is provided of the absolute and relative number of repeat and redo self-loops in each case. To calculate the relative number, each (repeat or redo) self-loop is counted as 1 occurrence, and the other activity instances are also counted as 1.
On "activity" level, the absolute and relative number of self-loops per activity can be an indication for which activities are causing the most waste in the process.
On "resource" level, this metric can give insights into which resources needs to repeat their work most often within a case, or for which resource the work they did should be redone by another resource within the same case. This metric shows the absolute and relative number of both repeat and redo self-loops for each resource in the log.
On "resource-activity" level, this metric can be used to get an insight in which activities are the most crucial for which resources. This metric shows the absolute and relative number of both repeat and redo self-loops for each of the resource-activity combinations that occur in the log. Two different relative numbers are provided here, one from the resource perspective and one from the activity perspective. At the resource perspective, the denominator is the total number of executions by the resource under consideration. At the activity perspective, the denominator is the total number of occurrences of the activity under consideration.

Methods (by class)

number_of_selfloops(eventlog): Computes the number of self-loops for an eventlog.
number_of_selfloops(grouped_eventlog): Computes the number of self-loops for a grouped_eventlog.
number_of_selfloops(activitylog): Computes the number of self-loops for an activitylog.
number_of_selfloops(grouped_activitylog): Computes the number of self-loops for a grouped_activitylog.

References

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

Number of Traces

Description

Computes how many traces there are.

This metric provides two values, the absolute and relative number of traces that occur in the log. The relative number shows expected number of traces needed to cover 100 cases.

Usage

number_of_traces(log, eventlog = deprecated())

## S3 method for class 'log'
number_of_traces(log, eventlog = deprecated())

## S3 method for class 'grouped_log'
number_of_traces(log, eventlog = deprecated())

Arguments

log

log: Object of class log or derivatives (grouped_log, eventlog, activitylog, etc.).

eventlog

; please use log instead.

Methods (by class)

number_of_traces(log): Number of traces in a log.
number_of_traces(grouped_log): Number of traces in a grouped_log.

References

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

Plot Methods

Description

Visualize metric

Usage

## S3 method for class 'activity_frequency'
plot(x, ...)

## S3 method for class 'activity_presence'
plot(x, ...)

## S3 method for class 'end_activities'
plot(x, ...)

## S3 method for class 'idle_time'
plot(x, ...)

## S3 method for class 'processing_time'
plot(x, ...)

## S3 method for class 'referral_matrix'
plot(x, ...)

## S3 method for class 'resource_frequency'
plot(x, ...)

## S3 method for class 'resource_involvement'
plot(x, ...)

## S3 method for class 'resource_specialisation'
plot(x, ...)

## S3 method for class 'start_activities'
plot(x, ...)

## S3 method for class 'throughput_time'
plot(x, ...)

## S3 method for class 'trace_coverage'
plot(x, ...)

## S3 method for class 'trace_length'
plot(x, ...)

## S3 method for class 'number_of_selfloops'
plot(x, ...)

## S3 method for class 'number_of_repetitions'
plot(x, ...)

Arguments

x

Data to plot

...

Additional variables

Value

A ggplot object, which can be customized further, if deemed necessary.

Print work schedule

Description

Print work schedule

Usage

## S3 method for class 'work_schedule'
print(x, ...)

Arguments

x

Work schedule to print

...

Additional arguments (ignored)

Processing Time

Description

Provides summary statistics about the processing time of the process.

In contrast to the throughput_time() of the cases in a log, the metrics concerning the active time or the actual processing time provide summary statistics on the processing time of events on the level of the complete log, the specific cases, traces, the activities, and the resource-activity combinations.

Usage

processing_time(
  log,
  level = c("log", "trace", "case", "activity", "resource", "resource-activity",
    "activity-instance"),
  append = deprecated(),
  append_column = NULL,
  units = c("auto", "secs", "mins", "hours", "days", "weeks"),
  sort = TRUE,
  work_schedule = NULL,
  eventlog = deprecated()
)

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

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

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

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

Arguments

log

log: Object of class log or derivatives (grouped_log, eventlog, activitylog, etc.).

level

character (default "log"): Level of granularity for the analysis: "log" (default), "trace", "case", "activity", "resource", or "resource-activity". For more information, see vignette("metrics", "edeaR") and Details below.

append

append_column

The arguments append and append_column have been deprecated in favour of augment.
Which of the output columns to append to log, if append = TRUE. Default column depends on chosen level.

units

character (default "auto"): The time unit in which the processing times should be reported. Should be one of the following values: "auto" (default), "secs", "mins", "hours", "days", "weeks". See also the units argument of difftime().

sort

logical (default TRUE): Sort on decreasing processing time. For "case" level only.

work_schedule

A schedule of working hours. If provided, only working hours are counted as processing time.

eventlog

; please use log instead.

Details

Argument level has the following options:

At "log" level, this metric calculates the summary statistics of the actual processing time per case, summarised over the complete event log.
On "trace" level, the summary statistics of processing time can be calculated for each possible sequence of activities that appears in the event log.
On "case" level, a list of cases with their processing time are provided.
On "activity" level, an overview of the average processing time -or the service time- of each activity can be calculated.
At "resource" level, this metric calculates the processing time per resource.
On "resource-activity" level, the efficiency of resources by looking at the combination of each resource with each activity can be investigated.

Methods (by class)

processing_time(eventlog): Computes processing time for an eventlog.
processing_time(grouped_eventlog): Computes processing time for a grouped_eventlog.
processing_time(activitylog): Computes processing time for an activitylog.
processing_time(grouped_activitylog): Computes processing time for a grouped_activitylog.

References

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

Referral matrix repetitons

Description

Provides a list of initatiors and completers of redo repetitons

Usage

redo_repetitions_referral_matrix(log, eventlog = deprecated())

## S3 method for class 'eventlog'
redo_repetitions_referral_matrix(log, eventlog = deprecated())

## S3 method for class 'activitylog'
redo_repetitions_referral_matrix(log, eventlog = deprecated())

Arguments

log

log: Object of class log or derivatives (grouped_log, eventlog, activitylog, etc.).

eventlog

; please use log instead.

Methods (by class)

redo_repetitions_referral_matrix(eventlog): Compute matrix for eventlog
redo_repetitions_referral_matrix(activitylog): Compute matrix for activitylog

References

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

Referral matrix selfloops

Description

Provides a list of initatiors and completers of redo selfloops

Usage

redo_selfloops_referral_matrix(log, eventlog = deprecated())

## S3 method for class 'eventlog'
redo_selfloops_referral_matrix(log, eventlog = deprecated())

## S3 method for class 'activitylog'
redo_selfloops_referral_matrix(log, eventlog = deprecated())

Arguments

log

log: Object of class log or derivatives (grouped_log, eventlog, activitylog, etc.).

eventlog

; please use log instead.

Methods (by class)

redo_selfloops_referral_matrix(eventlog): Compute matrix for eventlog
redo_selfloops_referral_matrix(activitylog): Compute matrix for activitylog

References

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

Objects exported from other packages

Description

These objects are imported from other packages. Follow the links below to see their documentation.

magrittr: %>%

Resource Frequency

Description

Analyses the frequency of resources at different levels of analysis.

Usage

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

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

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

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

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

Arguments

log

log: Object of class log or derivatives (grouped_log, eventlog, activitylog, etc.).

level

append

append_column

The arguments append and append_column have been deprecated in favour of augment.
Which of the output columns to append to log, if append = TRUE. Default column depends on chosen level.

sort

logical (default TRUE): Sort output on count. Only for levels with frequency count output.

eventlog

; please use log instead.

Details

Argument level has the following options:

At "log" level, summary statistics show the number of times a resource executes an activity in the complete log.
On "case" level, summary statistics of the frequency of resources can be used to get a better view on the variance between the different cases, to get an insight into the number of different resources working on each case together with the number of activities a resource executes per case.
On "activity" level, the resource frequency states how many different resources are executing a specific activity in the complete log.
On "resource" level, this metric simply shows the absolute and relative frequency of occurrences of each resource in the complete log.
On "resource-activity" level, the absolute and relative number of times each resource-activity combination occurs in the complete log can be calculated. Two different relative numbers are provided here, one from the resource perspective and one from the activity perspective. At the resource perspective, the denominator is the total number of executions by the resource under consideration. At the activity perspective, the denominator is the total number of occurrences of the activity under consideration.

Methods (by class)

resource_frequency(eventlog): Computes the resource frequency for an eventlog.
resource_frequency(grouped_eventlog): Computes the resource frequency for a grouped_eventlog.
resource_frequency(activitylog): Computes the resource frequency for an activitylog.
resource_frequency(grouped_activitylog): Computes the resource frequency for a grouped_activitylog.

References

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

Resource Involvement

Description

Calculates for each resource or resource-activity combination in what percentage of cases it is present.

Next to the resource_frequency, the involvement of resources in cases can be of interest to, e.g., decide how "indispensable" they are. This metric is provided on three levels of analysis, which are the cases, the resources, and the resource-activity combinations.

Usage

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

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

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

Arguments

log

log: Object of class log or derivatives (grouped_log, eventlog, activitylog, etc.).

level

character (default "case"): Level of granularity for the analysis: "case" (default), "resource", or "resource-activity". For more information, see vignette("metrics", "edeaR") and 'Details' below.

append

append_column

The arguments append and append_column have been deprecated in favour of augment.
Which of the output columns to append to log, if append = TRUE. Default column depends on chosen level.

sort

logical (default TRUE): Sort output on count. Only for levels with frequency count output.

eventlog

; please use log instead.

Details

Argument level has the following options:

On "case" level, the absolute and relative number of distinct resources executing activities in each case is calculated, to get an overview of which cases are handled by a small amount of resources and which cases need more resources, indicating a higher level of variance in the process.
On "resource" level, this metric provides the absolute and relative number of cases in which each resource is involved, indicating which resources are more "necessary" within the process than the others.
On "resource-activity" level, this metric provides a list of all resource-activity combinations with the absolute and relative number of cases in which each resource-activity combination is involved.

Methods (by class)

resource_involvement(log): Computes the resource involvement for a log.
resource_involvement(grouped_log): Computes the resource involvement for a grouped_log.

References

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

Resource Specialisation

Description

Analyses whether resources specialise in specific activities.

This metric can give an overview of which resources are performing certain activities more than others, and which resources are responsible for containing all knowledge or capabilities on one topic.

Usage

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

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

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

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

Arguments

log

log: Object of class log or derivatives (grouped_log, eventlog, activitylog, etc.).

level

character (default "log"): Level of granularity for the analysis: "log" (default), , "activity", or "resource". For more information, see vignette("metrics", "edeaR") and 'Details' below.

append

append_column

The arguments append and append_column have been deprecated in favour of augment.
Which of the output columns to append to log, if append = TRUE. Default column depends on chosen level.

sort

logical (default TRUE): Sort output on count. Only for levels with frequency count output.

eventlog

; please use log instead.

Details

Argument level has the following options:

At "log" level, this metric provides summary statistics on the number of distinct activities executed per resource.
On "activity" level, this metric provides an overview of the absolute and relative number of different resources executing this activity within the complete log. This will give insights into which activities resources are specialised in.
On "resource" level, this metric shows the absolute and relative number of distinct activities that each resource executes.

Methods (by class)

resource_specialisation(log): Computes the resource specialisation for a log.
resource_specialisation(grouped_log): Computes the resource specialisation for a grouped_log.

References

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

Set operations

Description

Set operations

Usage

setdiff(x, y, ...)

## S3 method for class 'eventlog'
setdiff(x, y, ...)

Arguments

x

Eventlog

y

Eventlog

...

Ignored

Value

Eventlog of activity instances in x which do not belong to y

Methods (by class)

setdiff(eventlog): Setdiff for eventlogs

Metric: Size of repetitions

Description

Provides summary statistics on the sizes of repetitions.

Usage

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

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

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

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

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

Arguments

log

log: Object of class log or derivatives (grouped_log, eventlog, activitylog, etc.).

type

character (default "all"): The type of repetitions: "all" (default), "repeat", or "redo". For more information, see 'Details' below.

level

append

append_column

The arguments append and append_column have been deprecated in favour of augment.
Which of the output columns to append to log, if append = TRUE. Default column depends on chosen level.

eventlog

; please use log instead.

Methods (by class)

size_of_repetitions(eventlog): Size of repetitions for eventlog
size_of_repetitions(grouped_eventlog): Size of repetitions for grouped event log
size_of_repetitions(activitylog): Size of repetitions for activitylog
size_of_repetitions(grouped_activitylog): Size of repetitions for grouped activitylog

References

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

Metric: Size of selfloops

Description

Provides summary statistics on the sizes of selfloops

Usage

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

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

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

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

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

Arguments

log

log: Object of class log or derivatives (grouped_log, eventlog, activitylog, etc.).

type

character (default "all"): The type of repetitions: "all" (default), "repeat", or "redo". For more information, see 'Details' below.

level

append

append_column

The arguments append and append_column have been deprecated in favour of augment.
Which of the output columns to append to log, if append = TRUE. Default column depends on chosen level.

eventlog

; please use log instead.

Methods (by class)

size_of_selfloops(eventlog): Size of selfloops for eventlog
size_of_selfloops(grouped_eventlog): Size of selfloops for grouped eventlog
size_of_selfloops(activitylog): Size of selfloops for activitylog
size_of_selfloops(grouped_activitylog): Size of selfloops for grouped activitylog

References

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

Start Activities

Description

Analyse the start activities in the process.

Usage

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

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

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

Arguments

log

log: Object of class log or derivatives (grouped_log, eventlog, activitylog, etc.).

level

append

append_column

The arguments append and append_column have been deprecated in favour of augment.
Which of the output columns to append to log, if append = TRUE. Default column depends on chosen level.

sort

logical (default TRUE): Sort output on count. Only for levels with frequency count output.

eventlog

; please use log instead.

Details

Argument level has the following options:

On "log" level, this metric shows the absolute and relative number of activities that are the first activity in one or more of the cases.
On "case" level, this metric provides an overview of the start activity of each case.
On "activity" level, this metric calculates for each activity the absolute and relative number of cases that start with this activity type. Similar to the end_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 start 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.
On "resource" level, an overview of which resources execute the first activity per case are 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 start activity in a case.

Methods (by class)

start_activities(eventlog): Computes the start activities for an eventlog.
start_activities(grouped_eventlog): Computes the start activities for a grouped_eventlog.
start_activities(activitylog): Computes the start activities for an activitylog.
start_activities(grouped_activitylog): Computes the start activities for a grouped_activitylog.

References

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

Throughput Time of Cases

Description

Provides summary statistics concerning the throughput times of cases.

Usage

throughput_time(
  log,
  level = c("log", "trace", "case", "activity", "activity-instance"),
  append = deprecated(),
  append_column = NULL,
  units = c("auto", "secs", "mins", "hours", "days", "weeks"),
  sort = TRUE,
  work_schedule = NULL,
  eventlog = deprecated()
)

## S3 method for class 'eventlog'
throughput_time(
  log,
  level = c("log", "trace", "case", "activity", "activity-instance"),
  append = deprecated(),
  append_column = NULL,
  units = c("auto", "secs", "mins", "hours", "days", "weeks"),
  sort = TRUE,
  work_schedule = NULL,
  eventlog = deprecated()
)

## S3 method for class 'grouped_eventlog'
throughput_time(
  log,
  level = c("log", "trace", "case", "activity", "activity-instance"),
  append = deprecated(),
  append_column = NULL,
  units = c("auto", "secs", "mins", "hours", "days", "weeks"),
  sort = TRUE,
  work_schedule = NULL,
  eventlog = deprecated()
)

## S3 method for class 'activitylog'
throughput_time(
  log,
  level = c("log", "trace", "case", "activity", "activity-instance"),
  append = deprecated(),
  append_column = NULL,
  units = c("auto", "secs", "mins", "hours", "days", "weeks"),
  sort = TRUE,
  work_schedule = NULL,
  eventlog = deprecated()
)

## S3 method for class 'grouped_activitylog'
throughput_time(
  log,
  level = c("log", "trace", "case", "activity", "activity-instance"),
  append = deprecated(),
  append_column = NULL,
  units = c("auto", "secs", "mins", "hours", "days", "weeks"),
  sort = TRUE,
  work_schedule = NULL,
  eventlog = deprecated()
)

Arguments

log

log: Object of class log or derivatives (grouped_log, eventlog, activitylog, etc.).

level

character (default "log"): Level of granularity for the analysis: "log" (default), "trace", or "case". For more information, see vignette("metrics", "edeaR") and Details below.

append

append_column

The arguments append and append_column have been deprecated in favour of augment.
Which of the output columns to append to log, if append = TRUE. Default column depends on chosen level.

units

sort

logical (default TRUE): Sort output on count. Only for levels with frequency count output.

work_schedule

A schedule of working hours. If provided, only working hours are counted as processing time.

eventlog

; please use log instead.

Details

Argument level has the following options:

At "log" level, the summary statistics describing the throughput time of cases in an aggregated fashion.
On "trace" level, the throughput time of the different process variants or traces in the log are calculated.
On "case" level, the throughput time is defined as the total duration of the case, or the difference between the timestamp of the end event and the timestamp of the start event of the case. Possible idle_time() is also included in this calculation.
On "activity-instance" level, the throughput time of each activity instance. Throughput here is defined as the difference between the first and last event, without considering the lifecycle status. For the lifecycle-aware throughput time (e.g. not incorporating the time the activity is "suspended"), see processing time.
on ⁠'activity⁠ level, summary statistics describing the throuhgput time of activity instances (see above) per activity type. For other levels (e.g. "activity", "resource", or "resource-activity"), the throughput time is equal to the processing_time() and are, therefore, not supported by this method.

Methods (by class)

throughput_time(eventlog): Computes throughput time for an eventlog.
throughput_time(grouped_eventlog): Computes throughput time for a grouped_eventlog.
throughput_time(activitylog): Computes throughput time for an activitylog.
throughput_time(grouped_activitylog): Computes throughput time for a grouped_activitylog.

References

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

Trace Coverage

Description

Analyses the structuredness of a log by use of trace frequencies.

Usage

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

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

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

Arguments

log

log: Object of class log or derivatives (grouped_log, eventlog, activitylog, etc.).

level

character (default "log"): Level of granularity for the analysis: "log" (default), "trace", or "case". For more information, see vignette("metrics", "edeaR") and Details below.

append

append_column

The arguments append and append_column have been deprecated in favour of augment.
Which of the output columns to append to log, if append = TRUE. Default column depends on chosen level.

sort

logical (default TRUE): Sort output on count. Only for levels with frequency count output.

eventlog

; please use log instead.

Details

Argument level has the following options:

At "log" level, summary statistics of the coverage of traces are returned.
On "trace" level, the absolute and relative frequency of each trace are returned.
On "case" level, the coverage of the corresponding trace is returned for each case.

Methods (by class)

trace_coverage(log): Calculates trace coverage metric for a log.
trace_coverage(grouped_log): Calculates trace coverage metric for a grouped_log.

References

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

Trace Length

Description

Analysis of trace lengths

This metric provides an overview of the number of activities that occur in each trace.

An important remark is that this metric takes into account each instance of an activity, but not the individual lifecycle events.

Usage

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

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

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

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

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

Arguments

log

log: Object of class log or derivatives (grouped_log, eventlog, activitylog, etc.).

level

character (default "log"): Level of granularity for the analysis: "log" (default), "trace", or "case". For more information, see vignette("metrics", "edeaR") and Details below.

append

append_column

The arguments append and append_column have been deprecated in favour of augment.
Which of the output columns to append to log, if append = TRUE. Default column depends on chosen level.

sort

logical (default TRUE): Sort output on count. Only for levels with frequency count output.

eventlog

; please use log instead.

Details

Argument level has the following options:

At "log" level, the summary statistics describing the trace length of cases in an aggregated fashion.
On "trace" level, the trace length of the different process variants or traces in the log are calculated.
On "case" level, the trace lengths for each case are computed.

Methods (by class)

trace_length(eventlog): Computes trace length for an eventlog.
trace_length(grouped_eventlog): Computes trace length for a grouped_eventlog.
trace_length(activitylog): Computes trace length for an activitylog.
trace_length(grouped_activitylog): Computes trace length for a grouped_activitylog.

References

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

edeaR - Exploratory and Descriptive Event-based data Analysis in R

Description

Author(s)

See Also

Activity Frequency

Description

Usage

Arguments

Details

Methods (by class)

References

See Also

Metric: Activity Presence

Description

Usage

Arguments

Details

Methods (by class)

References

See Also

Examples

Add fixed holiday to work schedule

Description

Usage

Arguments

Add floating holiday to work schedule

Description

Usage

Arguments

Add holiday period to work schedule

Description

Usage

Arguments

Augment Log

Description

Usage

Arguments

Value

Methods (by class)

Examples

Calculate queuing length

Description

Usage

Arguments

Details

See Also

Calculate queuing times

Description

Usage

Arguments

Value

Methods (by class)

References

See Also

Adjust days in work schedule

Description

Usage

Arguments

Create work schedule

Description

Usage

Arguments

End activities

Description

Usage

Arguments

Details

Methods (by class)

References

See Also

Filter Activity

Description

Usage

Arguments

Value

Methods (by class)

References

See Also

Filter Activity Frequency

Description