This paper introduces bupaR which is a collection of Rpackages which can be used for exploring and visualizing event data and monitoring processes. It creates a bridge between the BPM community and the broader R and data science communities. Furthermore, it stimulates the use of process mining in corporations, as R is a very wide used analysis tool in business, with its popularity still strongly increasing.
Over the past decades, the open source statistical language R [
This paper introduces bupaR, which is a set of R-packages for business process analysis in R. The importance of these for the BPM community is twofold.
Firstly, it allows researchers and data analysts to tap into the wide range of functionalities in the R-ecosystem. This does not only include traditional data mining techniques, such as clustering, classi cation etc, but also new developments, such as text mining, spark, deep learning, etc. Furthermore, since R originated as a statistical language, a vast amount of statistical tests becomes available, allowing straightforward con rmatory analyses on event data.
Secondly, the introduced packages will allow to bridge the gap between business and academics, as R is increasingly used as a tool for data analysis in corporations. As such, the packages will provide a low threshold for companies to discover the bene ts of business process analysis and to become more acquainted with the BPM community.
The next section will provide an overview of the packages and their functionalities, while Section 3 discusses the maturity of the tools and some use cases. Section 4 then provides more practical information on how to get started, including a link to a screencast and to the website with further documentation.
Overview of functionalities bupaR consists of di erent R-packages, each with their own purpose. The package bupaR itselfs acts as the central heart of the suite, providing the basic functionality for handling event data which is used by the other packages. The other packages are listed below, and are brie y introduced in the next paragraphs. { edeaR: for exploratory and descriptive analysis of event data { xesreadR: for reading and writing xes les { processmapR: for creating process visualizations { processmonitoR: for creating process dashboards for monitoring { eventdataR: a data repository containing both real-life and arti cial event logs 2.1
bupaR The central package includes basic functionality for creating eventlog-objects in R. An event log in this context is a dataset combined with a mapping. The mapping contains the characteristics of the speci c event log, i.e. the case identi er, activity identi er, timestamp, etc. Each element of this mapping refers to a variable in the data set. Figure 1 shows how an event log is created using the user interface of bupaR1.
The package further contains several functions to get information about an event log. Furthermore, bupaR also provides speci c event log versions for the generic R functions print and summary. For a complete list of all the functions, please look here: http://bupar.net/bupar.html. 1 The function used in Figure 1 is called ieventlog, in which the rst letter i indicates that it opens a user interface, which is useful when doing interactive analyis. However, each of these interface functions has a non-interface alternative (in this case the function eventlog, which can be used both interactively and for scripting). 2.2
edeaR
The name of the package edeaR stands for Exploratory and Descriptive
Eventdata Analysis in R. It was rst introduced at SIMPDA 2016 [
The metric functions are largely build around the concepts of Lean Six
Sigma en Operational Excellence [
While each of the metrics returns numeric results (an output table or a set
of numbers), the generic plot function of R has been customized to provided
tailored plots for each of the metrics at each level of granularity [
The subsetting functions can be found easily as each of them starts with the pre x filter . Di erent lter functions are available: ltering speci c case identi ers, speci c attributes (event or case attributes), ltering on throughput time, ltering activities based on their frequencies, etc. Note that for each of the lter functions, also an interface is provided for straightforward interactive use. A complete list of functions and guide for edeaR can be found here: http: //bupar.net/edeaR.html. 2.3
processmapR The package processmapR provides a straightforward way to create visualizations of processes, using customizable map pro les. The default pro le is the frequency pro le with absolute frequencies on both arcs and nodes, as can be seen in the example in Figure 2. The return object is a dgr graph object from the DiagrammeR package, and can be further customized by the user if needed. Complex graphs can be simpli ed by combining the processmapR functions with subsetting functions of edeaR. More information can be found http://bupar.net/processmapR.html. 2.4
xesreadR
As is clear from the name, the purpose of xesreadR is to read and write xes- les.
This makes bupar compatible with the IEEE standard for sharing and storing
event data, and thus with other process mining tools. For example, this is useful
if you want to combine bupaR with RapidProM, as RapidMiner also supports
the use of R-scripts.
With the purpose of testing new techniques and algorithms, the package
eventdataR contains a data repository of both arti cial and real-life event log.
Each of them can be loaded very straightforwardly with the data function in R.
The available datasets are listed here: http://bupar.net/eventdataR.html.
While the other packages can be used both interactively and in production,
processmonitoR is mostly intended for the latter case, as it provides
building blocks for online process monitoring dashboards. The available dashboards,
which build upon the Shiny framework [
As explained, the packages can be used for several purposes, ranging from a
onetime interactive analysis of event data to building a full- edged online process
monitoring dashboard. edeaR, the oldest package included in bupaR was rst
published on CRAN in January 2016 and has since been downloaded more than
5000 times wordwide. Its use is also illustrated in several submissions for the
BPI challenge [
Although the other packages have been shared publicly more recently, the underlying functionality has already proved useful in several projects. A process monitoring dashboard was developed to analyze the process of connecting new customers at a Belgian utility network operator, while the visualizations of processes have been used for visualizing train deviations at a European railway infrastructure manager.
Screencast A screencast discussing the installation and use of bupaR and the related packages can be found here: https://goo.gl/huHTGE.
Prerequisites In order to use bupaR, R and Rstudio (or another IDE) need to be installed. More information can be found on cran.rstudio.com and www. rstudio.com.
Installing bupaR Installing bupaR can be done easily by executing install.packages("bupaR") in the R-console. You can then load it using library(bupaR). The rst time you will be asked to also install the related packages, on which you should answer Yes (Y).
Further guidance The website www.bupar.net contains ample documentation and examples of the tools discussed.
Special thanks and appreciation go to Mieke Jans and Marijke Swennen for catalyzing the development of the initial version of edeaR and for their suggestions and feedback during the further evolution of bupaR.