R is widely used in clinical research, yet many analysis workflows remain fragmented, undocumented, and difficult to reproduce. This presentation introduces best practices for developing R packages that support clinical studies and translational research. Drawing from real-world examples, we explore how packaging functions, documentation, and test routines can improve reusability, collaboration, and regulatory compliance.

The session covers key aspects of package development — from structure and version control to testing, metadata, and automation of reporting pipelines. We also demonstrate how R packages can be extended with Shiny apps to provide user-friendly interfaces for researchers and clinicians. The presentation concludes with lessons learned from developing R tools for sample size estimation, meta-analysis, and personalised medicine.

Whether you’re building a lightweight helper package or a validated tool for broader distribution, this talk provides practical guidance to move from ad hoc scripts to robust, shareable R software.