- This event has passed.
R Fundamentals Part 3 of 3
March 12 @ 2:10 pm - 4:00 pm
Instructor: Professor Duncan Temple Lang
Dates: February 27, March 5 and 12, 2020
Location: DataLab classroom (Shields Library room 360)
Recording and materials available after the event
This 3-part workshop series explores the underlying computational model of the R statistical language. The aim of this series is 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’ (below) are welcome to register for this workshop series. DataLab Affiliates and learners who can attend all 3 sessions will receive priority registration.
* define REPL
* interpret error messages
* navigate the global environment
* explain data type hierarchy
* evaluate the consequences of implicit coercion
* describe how function calls work
* define scoping rules
* distinguish between S3 and S4
* write a function
* plan an approach for debugging code
* increase your productivity and confidence in using R
* identify where to go to learn more!
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.
About the instructor:
Dr. Duncan Temple Lang is a professor of Statistics and Associate Dean of Graduate Programs at the University of California at Davis. Prior to UC Davis he worked in the Statistics and Data Mining group at Bell Labs. He holds a PhD in Statistics from UC Berkeley specializing in statistical computing systems. His research interests include statistical and scientific computing infrastructure, parallel and distributed computing, data technologies, data visualization, and research reproducibility. He has authored several books including XML and Web Technologies for Data Science with R (2013) and Data Science in R: A Case Studies Approach to Computational Reasoning and Problem Solving (2015). He is a core developer for the R statistical language, and was the Director of the UC Davis Data Science Initiative.