Your Guide to Quality Data Science Products

If your statistical computing environment was designed before R became a viable language for submissions, before cloud infrastructure had the prominent position it has today, and before "data engineering bottleneck" entered everyone's vocabulary you're probably already feeling the pressure. It's obvious that to overcome some of these challenges, a serious modernization effort needs to ensue. The questions that remain are how, how fast, and whether to build, buy, or partner. To answer those questions, Appsilon interviewed statistical computing leaders at top pharmaceutical companies. We coupled this with internal expertise and released a comprehensive report last year: The Anatomy of Modern Statistical Computing Environments in Pharma.

The conversation around AI readiness typically centers on data quality, talent acquisition, and executive buy-in. While these factors matter, they overlook a more fundamental question: does the organization have the technical and cultural infrastructure to operationalize AI at scale? 

Novo Nordisk, Roche, GSK, Johnson & Johnson: the biggest names in pharma have moved off SAS. This post breaks down what's actually driving that shift, what open source delivers beyond cost savings, and what separates a successful transition from one that stalls.

Discover Tapyr 0.2’s new package management with uv and Shiny for Python testing API, enhancing deployment and performance.

Learn how to validate R Shiny apps in pharma, following FDA and GxP guidelines for compliance, performance, and security.

Learn how to validate R packages for GxP compliance in pharma. Ensure accuracy, reproducibility, and regulatory adherence with our step-by-step guide.

This blog explores the top 5 popular pharmaverse packages for clinical reporting featuring {rtables}, {admiral}, {teal}, {riskmetric}, and {tidyCDISC} for improving data analysis and to ensure compliance.

Learn how to quickly develop interactive and reproducible clinical data dashboards using {teal} and {pharmaverseadam}.

Learn how to profile R and Shiny code to boost performance. Discover tools, techniques, and tips to optimize your Shiny applications for a smoother user experience.

{shiny.telemetry} 0.3.0 introduces enhanced Shiny app analytics with advanced user tracking, error monitoring, MongoDB support, and improved security.

Explore Rhino 1.9.0's enhanced formating tools and seamless {bslib} integration for superior flexibility and styling in your Shiny applications.

Learn more about Tapyr, an open-source Shiny for Python framework, in this recap of a Shiny Gathering exploring its features for building deployment-ready interactive web apps.