Building Trust with Code: Validating Shiny Apps in Regulated Environments

This blog post is a follow up to my 2025 R/Medicine talk on Validating Shiny Apps in Regulated Environments.

Over the last years Shiny has become a cornerstone in data science applications, from dashboards and review tools to interactive decision making apps. But in regulated environments like pharma, healthcare, or finance, the stakes are higher. A clever visualization isn’t enough. We need to prove the app works reliably, reproducibly, and transparently.

So, what does it actually mean to validate a Shiny app?

Why Validation Matters

Validation isn’t about ticking a box. It’s about building trust.

In regulated settings, apps influence real world decisions. Regulators expect traceability, reproducibility, and documentation. Without these, you’re not just at risk of bugs, you risk noncompliance. And that means delays, rework, or worse.

Think of validation as a safety net. It ensures the app behaves as expected, be it under edge cases, months down the line, or even when someone else deploys it.

We once helped a client whose Shiny app was blocked from deployment by their compliance team because there was no documentation of who had last changed a calculation. Adding logging and a simple GitHub workflow solved it overnight.

Validation doesn’t have to be complex. It just has to be intentional.

What Makes a Shiny App Validatable?

Not every Shiny app is born equal. But some design choices from the start can make validation easier down the line:

• Modular, testable code: Keep logic in functions, not tangled in server.R.
• Clear separation: UI, logic, and data should live in separate spaces.
• Version control: For both code and data.
• Reproducible environments: Ensure the development environment can be replicated.
• Minimal hidden state:Avoid global variables or side effects.

These practices aren’t just about validation, they also make your codebase more maintainable and collaborative.

Common Pitfalls (and How to Avoid Them)

As someone that has seen a lot of Shiny applications over the years, some common patterns come up again and again, especially when validating legacy apps.

• Hardcoded file paths that break in production
• Ad hoc data wrangling inside server functions
• Global variables causing unpredictable behavior
• No formal record of package dependencies
• No tests. No logs. No idea who changed what or why

Sound familiar? You’re not alone. These are solvable problems, often with small changes that pay off in the long run.

The Unique Challenge of Shiny

Shiny is interactive by nature, which makes it harder to validate than static scripts. Here’s what makes it tricky and what to do about it:

• Reactive chains hide logic. Break them down and add logging.
• User controlled outputs might produce unexpected results. Validate downloadable content and limit inputs.
• Deployment differences matter. Validate the version that’s actually in production.
• No audit trail by default. Packages like {logger}, {loggit}, or custom logging can give you a starting point.

In Shiny apps, testing isn’t just about code, it’s about behavior. Think about what the user sees, clicks, and downloads. All of that needs to be validated.

Software Engineering for Validation

Good engineering habits go a long way:

• Use {testthat} for logic
• Combine with {shinytest2} for UI workflows
• Use {lintr} and CI/CD pipelines to catch issues early
• Set up a code review process
• Automate documentation and testing reports

With that in mind, an example of a minimal validation stack could look something like:

• {testthat} for unit testing
• {shinytest2} for end to end checks
• {renv} or Docker for environments
• {logger} for audit trails
• GitHub Actions (or similar) for automation

Easier to implement when you build it in from the start.

Documentation: The Backbone of Validation

Documentation doesn’t have to be bureaucratic. It just has to be clear.

A great way to get started would be:

• Functional Requirements Spec (FRS): What the app should do
• Test Plan & Summary (TP/TSR): How you know it does it
• README/User Guide: For both users and reviewers
• Audit trail: Who changed what, when, and why
• Reproducibility artifacts: renv.lock, Dockerfiles, Git commits

Matching Effort to Risk

Not every app needs the same level of scrutiny. That’s where a risk based approach comes in. (Risk Appetite)

• Low risk: sandbox tools, exploratory dashboards → lighter touch
• High risk: decision support, outputs used in reports or submissions → full validation

Start by defining the app’s intended use, data sensitivity, and audience. It helps you make smart trade offs.

“But it’s just an internal tool!”

Internal tools often evolve into production tools. Validation future proofs them.

“It slows us down!”

Done right, validation saves time. It catches bugs early and reduces friction with compliance teams.

Tools for Risk & Security

Beyond testing and documentation, assessing package level risk and security is essential, especially when your app depends on external libraries.

There are some tools out there that can help with this, including:

• riskmetric: Evaluate risk across R packages using metrics like maintenance, documentation, and testing.
• oysteR: Scan R packages for known security vulnerabilities via CVEs.
• diffify – Compare changes between versions of R packages to identify what’s changed and what might break.
• Litmus.dashboard – Explore package-level risk scores interactively and track changes over time.

How we deal with Shiny Validation in Jumping Rivers

At Jumping Rivers, we’ve been validating R packages for quite some time now, and have in the meanwhile developed the Litmusverse, a toolkit designed to make R package validation easier, more transparent, and aligned with regulatory expectations.

But how is that related to Shiny Validation? While a Shiny app doesn’t have to be a package, treating it as one simplifies validation a lot. It lets us apply the same best practices used for standard R packages: version control, documentation, testing, and reproducible environments. From there, we just add application specific validation steps.

• Validate the Shiny application package dependencies using the Litmusverse workflow, using a scoring strategy that suits the application risk appetite.
• Validate the application code itself using a separate scoring strategy more focused on code quality, documentation and not on popularity or CRAN metrics as we would use for dependencies (Litmus allows for scoring strategies to be tweaked at will or even include custom metrics if needed).
• Generate a report with the validation results from both the dependencies validation and the application validation.

Final Thoughts: Start Validated, Stay Validated

The best time to think about validation is at the start of your project. The second best time is right now.

• Build with validation in mind.
• Document as you go.
• Automate wherever possible.
• Choose tools that support transparency and traceability.

Validation isn’t a one time hurdle. It’s a habit you build with each commit, each test, each documented decision.

Validation isn’t a blocker, it’s a confidence booster. For you, your team, and your reviewers.

Get in Touch

If you’re interested in learning more about R validation and how it can be used to unleash the power of open source in your organisation, contact us.