As a SME R thought leader and developer, I have been invited to give international presentations and podcasts!  I have also been honored to write four R book reviews.

Podcasts

Conferences & Presentations

R Made Easier for SAS Programmers [WUSS Best Paper Award, 2025]

Reach for R Low Hanging Fruit for Faster Results [WUSS Best Paper Award, 2025]

R and Pharmaverse Trends [Invited PHUSE Speaker, Jul 31, 2024]

R made easier for SAS Users [Invited SAS Speaker, Oct 23, 2024]

Mar 14 - 18, 2027, PHUSE US Connect, Dallas TX

June 6-9, 2027, PharmaSUG, Anaheim, CA

The SDTM Army Knife: A Dashboard for Reviewing, Visualizing and Understanding SDTM Structure for CDISC Compliance and Clinical Data for Data Issues [Invited PHUSE Speaker, Jan 28, 2026]

Ready for Next Level SDTMs and ADaMs Compliance with End-to-End Processing? [Invited PHUSE Speaker, Mar 2026] [AI Summary by Stuart Malcolm]

Jun 22 - Aug 28, 2026, CDISC Standards for Clinical Data Class, UCSD on-line class

Jul 7 - Aug 22, 2026, Practical R for the Pharmaceutical Industry, UCSD on-line class

Sep 1 - 3, 2026, WUSS, Burlingame, CA

Is Double Programming Really Required For Validation? [Sunil Gupta, Clinical Tech Leader Blog, CDISC and Verisian CORE News]

Is Double Programming Really Required for Validation? [Invited PhUSE Speaker, April 2025, Verisian Paper]

Does SDTM Validation Really Require Double Programming? [Invited PharmaSUG Speaker, San Diego, 2025]

Book Reviews

As a SME R thought leader and developer, I have been honored to write several R book reviews! 

Data Wrangling with R book review by Sunil Gupta 

I have the honor to review Data Wrangling with R by Gustavo Santos! Being new to R, I was afraid this book was going to be too technical and only on statistical modeling. On the contrary, I was happy to find this R book easy to read, understand and apply whether you are new or an intermediate R programmer. The logical flow between chapters and sections with useful summaries and tips showcase Gustavo's in-depth knowledge and expertise in R. I really like how Gustavo compares similar R methods which help to reinforce R syntax understanding. Gustavo's approach will be liked by Data Scientists since all traditional methods of data cleaning, structure and operations before statistical modeling are followed. The graph gallery makes it easy to look up the correct R graph code without writing R code from scratch. I highly recommend Data Wrangling with R book to establish a strong foundation of R learning!

R Bioinformatics Book review by Sunil Gupta

While this book is ideal for biomedical applications, the author transforms this complex topic to a language that is easier to read! The step-by-step instructions follow a cookbook similar to recipes. It is obvious the author has in-depth knowledge and real-world hands-on experience with R. The book has sections for each bioinformatics subtopic to explain tasks, how and what R codes to execute, line by line, and references for more information. This blueprint is ideal to help guide readers of all levels.

This book has good structure and programming style as well as logical content. A complete end-to-end work flow process is reviewed with the appropriate R packages as solutions to complete tasks. This saves reader’s time so they do not have to search and review R packages. In addition, when ready, readers can produce publication quality tables or graphs to share or to post online as Shiny apps. Readers in this field can jump start their understanding and progress toward getting projects started as well as keeping to tight deadlines!

The authors do a good job of explaining why and how programmers should use R!  This book is ideal for social scientists but also good for all industries since it does not assume prior knowledge of R and also addresses R learning pain points.  The book examples are based on real-world applications and the R syntax is explained in easy to understand language.  The book is unique because it divides exercises into three levels: Easy, Intermediate and Advanced for all levels of R programmers.  The step-by-step guide helps new R programmers stay on the workflow as well as apply best practices.  The R examples show various options for each function which helps R programmers understand the function better.  Finally, the essential programming chapter is great since all R programmers need to learn and master these R concepts.