If All the World Were a Monorepo The R ecosystem and the case for extreme empathy in software maintenance by Julie, Sep 08, 2025 --- Introduction Julie, a software engineer with a background in traditional languages like C, Java, and Lisp, describes her challenging and ultimately rewarding experience learning R. While many find R difficult—some even argue it’s “not actually a programming language”—Julie grew to appreciate R's unique ecosystem, bold design choices, and strong community. --- Reverse Dependency Checks in R’s CRAN Unlike many package ecosystems (npm, PyPI), R’s CRAN runs extensive build and test checks before publishing packages. CRAN tests packages against various R versions, operating systems, and even runs unit tests on reverse dependencies—the packages depending on the one being published. Julie received an email that her package grf failed tests in a downstream package (policytree) due to an API change she introduced during version 2.0. This strict vetting delayed release as she coordinated fixes in dependent packages, illustrating CRAN's meticulous maintenance approach. --- The Ecosystem as a Monorepo CRAN functions like a massive monorepo: most R packages don't specify upper version bounds, enabling users to update all packages simultaneously with minimal version conflicts. Reverse dependency checks create a high coordination cost for package maintainers but drastically reduce "dependency hell" for users. This approach imposes a burden on developers but benefits predominantly researchers and data scientists focusing on analysis rather than package management. --- Points on the Trade-off Curve Julie contrasts R’s approach with her experience at Elasticsearch, where a major API break was handled in a "federated" manner, shifting upgrade burdens onto users. The federated approach accelerates software evolution but causes long-term fragmentation and upgrade challenges, with many projects stuck on old versions years later. R’s approach favors integration cost reduction at the expense of ease of evolution. Many R packages might have rough edges or inconsistent APIs, but users rarely worry about version conflicts or breakage. --- Or a Different Curve Entirely? Julie posits the trade-off might be more than just a single spectrum: R’s ecosystem encourages "extreme empathy," treating users’ code as the developers’ responsibility too. With her new role at Databricks, she sees parallels in centralized migrations within large organizations, where ownership of migrating dependent code results in smoother updates. Large migrations centralized and assisted by tools (like LLMs) complete faster and with fewer regressions. The CRAN model exemplifies deep empathy for dependency users and improved overall ecosystem health. --- Conclusion Julie appreciates R’s unique ecosystem and its demanding but empathetic package maintenance culture. The monorepo-like mindset underlying CRAN’s reverse dependency checks promotes responsibility for downstream users and smoother dependency management. This approach inspires a broader perspective on software migrations and maintenance, valuing the success of users as inseparable from the success of package maintainers. --- Acknowledgments Thanks to Brett Wines and Stefan Wager for their valuable suggestions. --- Footnotes R’s syntax and language design feature many "rough edges," explored in the book The R Inferno. Statisticians widely favor R for productivity, evidenced by Julie’s personal connections. R’s complex and diverse object orientation systems contrast with Python’s cleaner but breaking changes in version 3. --- This post was published on Julie’s Substack and reflects on the unique culture, tooling, and maintenance practices of the R ecosystem through personal experience with package development, release, and ecosystem-wide coordination.