bupaR

bupaR is an open-source R package suite for examining business-process data, from raw event records to process maps and conformance checks. It is aimed at analysts, data scientists and process specialists who are comfortable building a repeatable workflow in R. The trade-off is a capable analytical toolkit rather than a hosted monitoring product or a visual root-cause platform.

Overview

bupaR covers the main preparation and analysis stages for process data: importing and preprocessing logs, calculating descriptive statistics, visualising process behaviour and checking conformance. It can work with event logs, where each row is an event, or activity logs, where each row represents an activity instance. That accommodates different source-system exports, but the user still needs to structure and manage the data within an R workflow.

The project dates to 2019 and is headquartered in Hasselt, Belgium. Its repository uses the MIT license alongside a license file, making it suitable for teams that need an open-source foundation they can inspect and adapt.

Key features

Analysis spans three useful perspectives: control flow, performance and organisational behaviour. Custom process attributes can be carried through the workflow, so analysis is not restricted to a fixed set of fields. This makes bupaR more useful for domain-specific process questions than a narrowly predefined dashboard.

Process maps provide a compact visual summary, while trace explorer charts can communicate the overall shape of a process to colleagues. Maps can be embedded in a Shiny app and exported to PDF, PNG, PS or EPS, which helps turn R analysis into shareable reports and interfaces. These are practical presentation options, although they do not replace a full operational monitoring environment.

The bupaverse core combines bupaR with edeaR, eventdataR, processcheckR and processmapR. Related packages extend the workflow: xesreadR reads XES files from a local path or URL and writes event logs back to XES. CRAN instructions cover bupaR and bupaverse, while GitHub instructions support development versions. Users considering process prediction should note that processpredictR requires Python, TensorFlow and Keras, adding a substantial dependency burden beyond R.

Validation protects data quality, but it can take time on larger event logs. When the data already meets the required structure, validation can be disabled with validate = FALSE. That gives experienced users a performance option, not a shortcut for malformed data.

Pricing

There are no paid tiers, seat charges or trial expiry to weigh up. The free package is the right fit for individuals, research, and teams able to operate their own R-based environment. It gives up the convenience of a commercial hosted service: deployment, ongoing data operations and user-facing monitoring remain the buyer's responsibility.

Platforms

bupaR is self-hosted within the R ecosystem and requires R 3.5.0 or later. Installation is documented through CRAN, with GitHub available for development builds. XES support is provided through xesreadR. Because processpredictR also needs Python, TensorFlow and Keras, prediction work may require maintaining two language environments.

Who it's for

Choose bupaR if you are a data scientist, process architect or analytics team that wants transparent, scriptable process analysis and repeatable comparisons when a process changes. Its support for healthcare datasets and process analysis at scale makes it relevant where logs are substantial and reproducibility matters.

Look elsewhere if business users need a browser-first experience, if you require continuous process monitoring, or if root-cause analysis is a primary requirement. bupaR is an analysis workbench, not an ERP-connected operations suite; it has no ERP connectors, and process monitoring and root-cause analysis are not part of its stated capabilities.

Pros and cons

• Pro: The end-to-end path from event-data preparation through descriptive analysis and conformance checking keeps core process work in one R ecosystem.
• Pro: Control-flow, performance and organisational views, plus custom attributes, support more than a single process perspective.
• Pro: Open-source licensing and scriptable workflows support repeatable analysis when measuring the effect of change.
• Con: R 3.5.0 or later is mandatory, so teams without R expertise face a steeper starting point than with a hosted visual product.
• Con: Large-log validation can be slow, and prediction adds Python, TensorFlow and Keras dependencies.
• Con: There is no stated process monitoring, root-cause analysis or ERP connectivity for operational deployments.

Alternatives

For a broad comparison, see Process Mining Software.

Mindzie Process Mining is the better choice when you want a desktop or cloud product, with a free desktop edition capped at 1,000 cases and full functionality.

PM4Py suits users seeking a free Python-based option, especially for teaching, research and open-source projects under AGPL v3.

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

Celonis Process Mining Platform is worth considering when a web and API-oriented commercial platform is preferred.

ProcessMind fits teams wanting a hosted freemium service, including a free tier for one member, unlimited models and 20 AI credits per month.

Cortado is another free desktop process-mining option.

RuM is a free alternative for users seeking a cross-platform desktop application.

StereoLOGIC Process Mining is an alternative for organisations evaluating paid, self-hosted or web deployment.

Verdict

bupaR is a strong choice for R-capable analysts who value open-source, reproducible process analysis and need conformance checking alongside visual exploration. Its main reason to choose it is the breadth of an extensible package ecosystem at no licence cost. Its main reason to look elsewhere is the need for a ready-made hosted monitoring, root-cause or ERP-connected platform.