When I set out to learn R, one question came up with almost every client and programmer I met — myself included: is R package validation actually required? Because of this burning question, I wanted to outline and explain in simple terms the history and testing methods of R so that clients and programmers can be more educated about their options!

Anyone in pharma knows that IQ, OQ, and PQ are standard practice for SAS — and for almost any software we install — so it is natural to expect the same for R. Parallel programming of SDTMs, ADaMs, and TLGs likewise demands confirmation that deliverables meet output specifications.

Remember, though, that regulators such as the FDA do require validation but do not mandate any particular software or the method: 21 CFR Part 11, EU Annex 11, and ICH E6 all require validation of computerized systems used in GxP work. What they require is that the software you use is fit for its intended purpose — and that you can prove it.

What may not be obvious at first is why the R packages and functions themselves also need to be validated. The answer lies in understanding how R packages are developed and tested.

One Language, Thousands of Independent Developers

Unlike SAS, where a single organization — SAS Institute — develops, tests, and stands behind the software, R packages are independently developed and tested by the global R programming community. This means there is no standard method for documenting function requirements, test plans, or validation acceptance criteria.

R also presents unique challenges because the ecosystem never stands still. Packages continue to evolve, new packages and functions emerge, and old packages and functions are archived. User demands and operating systems change as well. In a GxP regulatory environment, this constant flux can make an R environment complicated to maintain long term.

Keep in mind that most R packages are developed to meet specific programming tasks — filling gaps in data input, data management, and analysis — often with no maintenance commitments at all. A package may work well for general purposes yet behave inappropriately for a specific clinical trial or regulatory submission.

Code Coverage Is a Rating, Not Validation

Just as Yelp reduces a restaurant to a five-star rating, the R world often reduces "validation" to a single number: code coverage. It is comforting to know that many of the common R packages have generally acceptable code coverage. However, coverage alone is no guarantee that a package meets your organization's standards for documentation, design, or testing.

Code coverage tells you how much of the code was exercised by tests — not whether the right things were tested, whether results are numerically correct, or whether the package meets your specific needs. A five-star restaurant rating does not tell you whether the restaurant serves the dish you need.

CRAN Tests Are Only a Starting Point

Many R packages ship with automated unit tests, but CRAN does not actually require them — CRAN's submission checks confirm that a package builds and its examples run, not that its functions are correct or thoroughly tested. Even where tests exist, organizations still need to review and approve them to confirm they meet internal standards and SOPs. This is where prior experience developing and testing SAS macros can be applied directly to R package validation. When testing SAS macros, SAS macros are updated to correct issues as an iterative process.

Unlike R packages, however, if organizations find high and unacceptable risks with packages, then their options are limited and may require extra resource to:

Build a corrected version into an internal repository using version pin via renv package and Posit Package Manger
Wrap the function in a validated internal layer
File an issue or pull request upstream

Important decisions need to be made to retire and replace the package or document the issues and the reasons for keeping these packages in production.

Important decisions need to be made to retire and replace the package or document the issues and the reasons for keeping these packages in production. Beyond the built-in tests, a defensible validation process must also control for regression testing, numerical accuracy, robustness, and error handling.

Consider the rigor most organizations already apply to SAS macros: consistent standards to ensure macros are designed for their target tasks, and — in my experience — testing at least ten times more extensive than for ordinary SAS programs, scaled to the number and complexity of parameters. When organizations adopt R packages without equivalent rigor, they are blindly trusting the results.

The general method is to run a series of test_that() function calls with passed and failed tests with user messages. For best practices, data frames with test variables, values and expected data frame results should be derived within test script function calls. The output object type, variable and values should be compared with the expected data frame results. This helps to assure independent, self-contained and reproducible test results.

To build genuine confidence in an R function, a single function may require hundreds of test cases. Test categories should include:

Input object types
Missing values
Internal object attributes
Boundary conditions
Single-parameter and multi-parameter combinations
Proper triggering of error and warning messages

No One to Call: Accountability in Open Source

In open source, no one is centrally accountable for the questions and issues that arise during R package validation testing. There is no vendor hotline when a function behaves unexpectedly in a submission dataset.

Three Channels, Deep Dependency Chains

R packages arrive through three channels, each with different governance and testing requirements: CRAN, Bioconductor, and GitHub. In addition, organizations may develop their own custom packages, which also require a standard validation process. Every package carries a network of dependencies, each with its own version and operating-system requirements. Some packages have a huge dependency chain, which makes them more difficult to maintain. Other than a few R control processing programming constructs, R is built on a deck of cards concept where R function calls a collection of other R functions. In general, there is more interdependence of R functions than there are with SAS macros calls. This makes the world of R functions more fragile!

Breaking Changes and the Moving Train

One issue with open-source languages like R is the breaking change — when a function or function parameter available in one version of a package is removed or altered in the next. Such changes mean that every package update carries some risk of breaking previously validated R programs that depend on it.

Organizations must balance the pace of package adoption against the cost of revalidating the entire repository. A pragmatic strategy is to start with a stable set of common packages and update them on a schedule — perhaps every six months or once a year. With this method, programmers don't fall too far behind, yet the disruption caused by revalidation is minimized. It helps that many common packages are more mature now and cause fewer breaking changes. Keep in mind, too, that some packages require specific — usually newer — versions of R itself.

Ideally, organizations keep a frozen snapshot of the complete production R package environment to control reproducibility and manage updates in a controlled, systematic manner. That snapshot becomes part of the reproducibility record for FDA submissions. Yet even a frozen environment is never final: each operating-system update should trigger an assessment of whether validation must be repeated. The whole process can feel like trying to board a moving train.

Even a slight change in a package's dependency hierarchy can alter results and the behavior of R functions — a potential nightmare for any IT department, and proof that change management for R cannot live with IT alone. Everyone — programmers, statisticians, and QA alike — shares responsibility for monitoring which packages are in use, internally and externally, how they are used, and how they behave after every update. SAS never demanded this discipline from the whole team; R does, because there are many more moving parts.

So How Do You Actually Do It?

So yes: R package validation is required, just as it always was for SAS.

Since FDA does not mandate package or function-level validation specifically nor do they set test-case volume levels, sponsors must define a risk-based determination method utilizing resources from GAMP 5 and the R Validation Hub. Based on years of experience of practical experience and discussions, pharma industry expert panels have created validation guidelines and framework for sponsors to follow. This means that sponsors must be confident with the types and scope of test scripts performed on R functions used. Using the analogy of testing SAS macros, this could be hundreds of test scripts for each R function.

The harder question — and the one I hear next from every client — is how to do it without an army. Add it up: hundreds of test cases per function, documented requirements and acceptance criteria, re-execution of the full test battery on every update cycle, frozen snapshots, and audit-ready evidence for every package in your repository. Doing all of this manually, package by package and function by function, simply does not scale.

Forward-looking organizations recognize that, for audit purposes, they must allocate internal resources and expertise to validate the R packages the company uses — or outsource this task. R package validation tools can greatly help to standardize and automate this process, and their outputs serve as objective evidence of validation - see appendix. Industry initiatives such as the R Validation Hub provide useful risk-assessment guidance, but risk scoring is not the same as generating, executing, and documenting actual tests.

Appendix: Tooling Note and Author Disclosure

As a trusted advisor to my clients, I wanted to learn and vet tools to solve this problem. Only by doing a deep dive to fully understand the history of R and the massive testing requirements of R, did I realize the significance of R package validation tools in having confidence in R! I was happy to learn that R-Assure was designed for regulated environments and that it automates the most labor-intensive parts of the work described in this article:

Risk-based test generation at scale — hundreds of test cases per function, systematically covering the very categories outlined above: input object types, missing values, object attributes, boundary conditions, parameter combinations, and correct triggering of errors and warnings.
A pre-built test corpus — nearly 200,000 test cases spanning base R and commonly used packages such as dplyr, stringr, and lubridate, built under a documented quality-gate process.
Audit-ready validation evidence — controlled execution with documented, time-stamped results, full traceability from function to test case to outcome, a hash-chained audit trail, and role-gated electronic signatures, following GAMP 5 risk-based principles.
Repeatable revalidation — when a package or R version updates, the same documented test battery can be regenerated and re-executed against your new snapshot, turning the "moving train" into a scheduled, manageable process.

R-Assure does not remove your obligation to validate — that responsibility, along with your SOPs and reviewer sign-off, stays with your organization. What it does is turn an unscalable manual process into a documented, repeatable one.

To learn more about R-Assure, IQ/OQ or PQ — or to request a sample validation report for a package your team already uses — reach out to me directly at GuptaProgramming@gmail.com or connect with me on https://www.linkedin.com/in/sassavvy.