bupaR

bupaR is a free, open-source suite of R packages for analyzing business process data. It supports importing and preparing event data, calculating descriptive statistics, visualizing processes, and checking conformance. Users can examine control flow, performance, and organizational aspects, and include custom process attributes in their analysis. It works with event logs, in which each row is an event, and activity logs, in which each row represents an activity instance. Process maps can help summarize processes; they can be used in Shiny apps and exported to PDF, PNG, PS, or EPS. The bupaverse core includes bupaR, edeaR, eventdataR, processcheckR, and processmapR. The xesreadR package can read XES files from a local path or URL and write event logs to XES. Installation guidance covers CRAN and GitHub development versions. The package requires R 3.5.0 or later and is licensed under MIT plus a license file. Validation can take time on larger event logs, but the documentation describes disabling it when data meets the requirements. The project is based in Hasselt, Belgium, and was founded in 2019.

bupaR provides a free R-based workflow for importing, analyzing, and visualizing business process data. Users should account for its R requirement and the possible time needed to validate large logs.

It suits data scientists, process analysts, and process architects working with business process data in R. Its documented use cases include analytics, process optimization, and healthcare datasets.

Overview

bupaR is a free collection of R packages for analyzing business-process data, best suited to analysts and process specialists who can work in R. It covers a useful path from event data to process views and conformance checks, but it is not a fit for teams seeking a hosted monitoring or root-cause analysis product.

Key features

The workflow spans importing and preprocessing event data, descriptive statistics, process visualization, and conformance checking. It accepts event logs with one event per row and activity logs with one activity instance per row, giving R users room to work with either representation. The trade-off is that data preparation and analysis remain part of an R-based workflow rather than a standalone business interface.

Analysis can focus on control flow, performance, and organizational aspects, with custom process attributes included where they matter. Process maps summarize processes visually, while trace explorer charts can help convey an overall process picture. Maps can also be used in Shiny apps or exported to PDF, PNG, PS, or EPS, which makes results easier to share outside an analysis session.

The bupaverse core groups bupaR with edeaR, eventdataR, processcheckR, and processmapR. The broader package ecosystem includes xesreadR, which can import XES files from a local path or URL and write event logs back to XES. CRAN installation instructions are available for bupaR and bupaverse, with GitHub instructions for development versions. The package requires R 3.5.0 or later. Large event logs can take time to validate; validation can be disabled when the data already meets the requirements. Using processpredictR also requires Python, TensorFlow, and Keras.

Pricing

bupaR's Free and open-source package costs nothing and provides the R package for business-process analysis. Its MIT license makes it a practical choice for teams that want an open-source toolkit without a software subscription. The cost is not the only consideration: users need an R environment, and the product is self-hosted rather than a hosted service.

Platforms

bupaR is self-hosted and R-based, with R 3.5.0 or later required. It supports conformance checking, but not root-cause analysis or process monitoring. For usage questions, users can turn to the bupar-tagged Stack Overflow or [email protected]; package bugs belong in the relevant GitHub repository.

Who it's for

Data scientists, process architects, and analytics teams are the natural audience, particularly those analyzing healthcare datasets or studying process optimization. Its open-source package structure also suits teams that need to reproduce analyses when assessing the effect of change. Readers who need process monitoring as a product capability, root-cause analysis, or a workflow outside R should look elsewhere.

Pros and cons

• Pros: Free and open source, with no paid tier needed for the core process-analysis package.
• Pros: Covers event-data preparation, descriptive analysis, visualization, and conformance checking in one R-based ecosystem.
• Pros: Supports both event and activity log structures, custom attributes, and several process perspectives.
• Cons: Requires R 3.5.0 or later, so it is unsuitable for teams seeking a ready-to-use hosted application.
• Cons: Validation may take time on larger logs, and processpredictR adds Python, TensorFlow, and Keras requirements.
• Cons: It does not provide root-cause analysis or process monitoring.

Alternatives

Process Mining Software is a useful starting point for comparing products across the category.

Choose Celonis Process Mining Platform if API, web, or Windows availability matters more than bupaR's R-package workflow; it also has a free plan.

Mindzie Process Mining is worth considering for a desktop or cloud edition, including a free desktop option with full functionality for up to 1,000 cases.

ProcessMind may suit readers who want a web-based option with a free plan for one member, unlimited models, and 20 AI credits per month.

PM4Py is another free process-mining option; its Community Version is covered by AGPL v3, including a requirement to publish covered work under that license.

ProM is a free alternative for users comfortable installing packages and plug-ins separately and meeting its Java 8 or later requirement.

UiPath Maestro Case Management is an alternative to consider.

ARIS is a paid alternative.

Pega CRM is a paid web-based alternative with a free trial.

Verdict

Choose bupaR if your team can work in R and wants a free, repeatable toolkit for analyzing process data, visualizing flows, and checking conformance. Look elsewhere if you need built-in process monitoring, root-cause analysis, or a product that does not depend on an R environment.