Validating Shiny Apps in Regulated Environments: Trustworthy Tools Without Stifling Innovation

Shiny applications have become a staple for delivering interactive tools in clinical and healthcare settings. However, when these applications are employed in regulated environments, validation becomes a critical component. Pedro Silva from Jumping Rivers Ltd, an experienced R and Shiny developer, recently addressed this topic during his presentation at R/Medicine 2025. He shared practical approaches to validating Shiny applications, discussing principles from software engineering, quality assurance, and risk-based validation.

Why Validation Matters

Validation is not just a bureaucratic hurdle; it’s a necessity in regulated environments such as pharma, clinical research, and healthcare. Shiny applications are everywhere, and with regulation comes responsibility—not only in terms of ownership but also in ensuring the integrity and trustworthiness of these applications.

Regulators often scrutinize several aspects, including:

Traceability: Who did what, when, and why?
Reproducibility: Can we get the same results tomorrow or in a production environment?
Documentation: What does the application do? How was it built? How do we know it works?

Without validation, organizations risk releasing buggy applications, generating incorrect outputs, and facing delays when regulators or reviewers ask for proof of correctness. The lack of validation evidence becomes a liability, underscoring the importance of integrating validation strategies from the start.

Building Validatable Shiny Apps

Silva emphasized that validation efforts become more manageable when developers incorporate specific strategies during the app’s development phase. Here are some best practices:

Modular Code: Keep UI and logic separate to enhance maintainability and testability.
Version Control: Use tools like Git to maintain environmental snapshots and track changes.
Avoid Common Mistakes: Avoid hard-coding file paths, manipulating data directly in the server, and using global variables with side effects.
Document Dependencies: Keep a record of dependencies, tests, and logs to facilitate troubleshooting and validation.

Challenges Unique to Shiny

Silva highlighted the unique challenges posed by Shiny applications due to their stateful, interactive, and user-driven nature. Unlike static reports, Shiny apps require a combination of UI testing tools, version control, logging, and sound software engineering discipline.

Mitigating Unique Challenges

Interactivity: Employ end-to-end tests to validate interactions.
Reactivity Chains: Break down logic into smaller, testable functions.
User Behavior: Log everything, validate inputs, and restrict them whenever possible.
Environment Variability: Use tools like ‘renv’ or Docker to freeze the environment and ensure consistency across different machines.
Lack of Built-in Audit Trail: Implement comprehensive logging to track user actions and input history.

Practical Validation Strategies

Drawing from traditional software development practices, Silva outlined several strategies to streamline Shiny app validation:

Unit Testing: Use packages like testthat for unit tests and shinytest2 for end-to-end tests.
Automation: Implement continuous integration and continuous deployment (CI/CD) pipelines to automate linting, testing, and deployment processes.
Code Review: Ensure at least two people review the code before it is merged into the main project.
Documentation: Maintain functional requirement specs, test plans, and user guides. Keep an audit trail using version control and document decisions and changes.

Tools to Support Validation

Silva shared a suite of tools and packages used by Jumping Rivers to support validation workflows:

testthat and shinytest2: Essential for catching problems early in the development process.
renv: For managing dependencies and ensuring reproducibility of the development environment.
lintr: To maintain clean, readable code that simplifies validation efforts.
riskmetric: For package-level risk assessments.
oysteR: To scan for security vulnerabilities.
diffify: To compare versions of packages and assess changes.
Litmus.dashboard: A package-level risk assessment explorer developed by Jumping Rivers to assess package dependencies.

Validation as a Continuous Process

Silva concluded by reminding the audience that validation should not be an afterthought. Instead, it should be integrated into the development workflow from the beginning. Automating processes wherever possible helps reduce human error and makes validation part of the routine. He emphasized that validation is not the enemy of innovation; rather, with the right tools and mindset, it enhances the development process and ensures the credibility of Shiny applications.

By following these practical strategies and leveraging the right tools, R users in regulated environments can build confidence in their Shiny applications without sacrificing innovation. As the R community continues to grow, these insights will help bridge the gap between developers and compliance teams, fostering a culture of collaboration and trust.