bupaR

Overview

bupaR is a free, open-source collection of R packages for analyzing business process data. It is best suited to analysts and process specialists who are comfortable working in R and need to examine event data, visualize processes, or check conformance. Its practical strength is a flexible analysis workflow; its clear boundary is that it does not provide root-cause analysis or process monitoring.

Key features

The workflow covers importing and preprocessing process data, descriptive statistics, process visualization, and conformance checking. bupaR accepts event logs, with one event per row, as well as activity logs, with one activity instance per row. Supporting both structures gives analysts options when preparing their process data, but the work still assumes an R-based workflow rather than a standalone business interface.

Analyses can address control flow, performance, and organizational aspects, and can incorporate custom process attributes. Process maps summarize processes visually, while trace explorer charts can help communicate an overall process picture. Maps can be used in Shiny apps and exported to PDF, PNG, PS, or EPS, which gives R users practical ways to share visual results beyond their analysis environment.

The bupaverse core brings together bupaR, edeaR, eventdataR, processcheckR, and processmapR. The related xesreadR package reads XES files from a local path or URL into an event log and can write event logs to XES. CRAN installation instructions are provided for bupaR and bupaverse, and GitHub instructions cover development versions.

There are a few technical considerations. The package requires R 3.5.0 or later, and validation of larger event logs can take time; when data meets the requirements, validation can be disabled with validate = FALSE. Predictive work using processpredictR also requires Python, TensorFlow, and Keras, so that path entails dependencies beyond R.

Pricing

bupaR's Free and open-source package costs free. It provides the R package for business process analysis without a paid tier or seat charge. That makes it a straightforward choice for individual analysts, research work, and teams able to manage their own R environment. The trade-off is that there is no commercial monitoring or root-cause analysis capability in the product.

Platforms

bupaR is self-hosted software delivered as R packages. Users install it through CRAN, or use GitHub for development versions, and should be prepared to work with R 3.5.0 or later. XES workflows are supported through xesreadR, while process maps can be incorporated into Shiny apps or exported in several formats.

Who it's for

bupaR fits data scientists, process architects, and other analysts who want to investigate process data in R. Its supported perspectives and custom attributes suit work that needs more than a simple process diagram, while conformance checking can help assess how recorded behavior relates to expected process behavior. The package has also been used in analytics, process optimization, healthcare datasets, and analysis of processes at scale. Its open-source approach can support repeatable analysis when monitoring the impact of change.

It is a weaker fit for teams seeking a hosted, business-facing process-mining service, built-in root-cause analysis, or process monitoring. Those needs call for a different kind of product.

Pros and cons

• Pros: Free and open source, with no seat or usage cap stated for the package.
• Pros: Handles both event-log and activity-log structures, with analysis across control flow, performance, and organizational aspects.
• Pros: Conformance checking, custom attributes, and process visualizations support a broad analysis workflow.
• Pros: Shiny use and multiple export formats make process maps easier to share.
• Cons: Requires R 3.5.0 or later and comfort with an R-based workflow, which may rule it out for teams wanting a standalone interface.
• Cons: Large-log validation can take time, and predictive work adds Python, TensorFlow, and Keras dependencies.
• Cons: It does not include root-cause analysis or process monitoring.

Alternatives

Process Mining Software is a useful starting point for comparing more options by category.

• Mindzie Process Mining is worth considering if a desktop option with a stated free allowance of up to 1,000 cases or a broader platform across web, Windows, macOS, API, and self-hosted environments better suits your setup.
• PM4Py is another free process-mining option for users who want Linux, macOS, Windows, or API support; its community version is AGPL v3 and covers teaching, research, and open-source projects, with covered work required to be published under AGPL.
• Celonis Process Mining Platform is an alternative with a free plan and API, web, and Windows platforms.
• ProM suits readers looking for a free desktop application for Linux, macOS, or Windows, provided they can install packages and plug-ins separately and meet its Java 8-or-later requirement.
• ProcessMind may suit a web-based workflow with a free plan for one member, unlimited models, and 20 AI credits per month, as well as a free trial.
• Cortado is another free option for Linux, macOS, or Windows.
• Process.Science is an alternative for teams considering a paid product with a free plan and trial, across self-hosted, web, and Windows platforms.
• RuM is another free option for Linux, macOS, or Windows.

Verdict

Choose bupaR if you work in R and want a no-cost, open-source toolkit for process-data preparation, analysis, visualization, and conformance checking. Its breadth across log formats and analysis perspectives is the strongest reason to choose it. Look elsewhere if your priority is built-in process monitoring, root-cause analysis, or a workflow that does not depend on R.