R Basics: Introduction to Programming for Researchers (4-part series)
May 18, 2021, 4 p.m. - May 18, 2021, 6 p.m.
Organizer -
DataLab: Data Science and Informatics
Contact -
DataLab datalab-training@ucdavis.edu
Location -
Zoom links will be sent to the learner's email address as listed on their registration.
Description
This 4-part workshop series provides an introduction to using the R programming language for reproducible data analysis and scientific computing. Topics include programming basics, how to work with tabular data, how to break down programming problems, and how to organize code for clarity and reproducibility.
Learning Objectives
After this workshop series, learners will be able to load tabular data sets into R, compute simple summaries and visualizations, do common data-tidying tasks, write reusable functions, and identify where to go to learn more.
Prerequisites
No prior programming experience is necessary. All learners will need access to an internet-connected computer and the latest version of Zoom, R, and RStudio.
Software
Before the workshop, learners should install the latest versions of the R and RStudio software. Download R for free from <https://www.r-project.org/>. After installing R, download RStudio Desktop (Open-Source Edition) for free from <https://rstudio.com/products/rstudio/download/>. If you need help with or want to check your installation, please stop by our drop-in office hours prior to the workshop.
Instructors
Nick Ulle and Pamela Reynolds
Instructors' Biographies
Nick Ulle is a statistician at DataLab. During his graduate studies in Statistics at UC Davis, he developed source code analysis techniques for the R programming language. Before returning to join the DataLab, he was a Visiting Assistant Professor of Statistics at UC Berkeley, where he designed and taught courses in data science. His research interests include statistical computing, programming languages, data visualization, and pedagogy.
Pamela Reynolds is the Associate Director of DataLab and an experimental ecologist with a background in project management and team science. Reynolds received her PhD in Biology from the University of North Carolina at Chapel Hill where she studied the biodiversity of the world’s oceans. Her research interests include applications of data science to promote interdisciplinary research, and developing pedagogy for technical skills and computational thinking.