Beyond Basics: R Fundamentals 5-Part Series

Feb. 14, 2022, 9:30 a.m. - Feb. 25, 2022, 11:30 a.m.

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DataLab: Data Science and Informatics

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This 5-part workshop series (in which the 5th workshop is an optional online Q&A) aims to help learners understand the relatively small but fundamental computational model underlying the R language. This will help you reason about code before you write and run it, and to debug it if it doesn’t do what you want. A sound understanding of this computational model makes programming in R much easier and more productive! This workshop series is intended for active engagement – be prepared to come to each session with questions about why some things worked, and others didn’t. All researchers (students, postdocs, faculty and staff) who meet the ‘prerequisites’ are welcome to register for this workshop series, which is being offered in person at the UC Davis DataLab, and will be broadcast as part of UC Love Data Week. The optional final 5th session will be offered entirely remotely for additional Q&A by all audiences. Learners who can attend all sessions will receive priority registration for the limited in-person seats. Broadcast participants still need to register to receive the Zoom link.

Workshop dates are February 14, February 16, February 18, February 23, and February 25, 2022, 9:30 AM – 11:30 AM.

Learning Objectives

After this workshop series learners should be able to:


Another title for this workshop could be “Everything You Should Have Learned About R.” These workshops are not an introduction to R. Participants are expected to have prior experience using R, be comfortable with basic R syntax, and to have it pre-installed and running on their laptops. This series is appropriate for motivated intermediate to advanced users who want a better understanding of base R.


R programming language

Instructors: Nick Ulle, Duncan Temple Lang, Pamela Reynolds

Instructors’ Biographies

Nick Ulle is a statistician and computer scientist. Prior to DataLab he was a visiting assistant professor of Statistics at UC Berkeley, where he designed and taught courses in data science. During his PhD in Statistics at UC Davis, he developed source code analysis techniques for the R programming language. His research interests include statistical computing, programming languages, data visualization, and pedagogy.

Duncan Temple Lang is a Professor of Statistics and Associate Dean for Graduate Programs at UC Davis. He was the director of DataLab's precursor (the Data Science Initiative) and is a member of the [R Core Team](

Registration is closed for this event