Kelly Bodwin (Cal Poly San Luis Obispo), Tyson Barrett (Highmark Health and Utah State University), Allison Theobold (Cal Poly San Luis Obispo)
Abstract
This workshop will provide structured learning goals, course material resources, and skills training for instructors of intermediate to advanced R courses. The target audience is educators who may have taught a first course in R or used it as supporting software in other statistics courses, and who are interested in teaching a second R course or beyond. We will first establish a suggested common curriculum for intermediate R based on five areas: Data types and sources beyond comma-separated files, advanced and dynamic data visualization, complexities of unclean or unstructured data, speed and efficiency concerns for large or repeated analyses, and reproducible workflow for long-term collaborative projects.
We will assume attendees have R fluency at the level of a typical introductory course, such as the textbook R for Data Science (Wickham, Çetinkaya-Rundel, & Grolemund 2023); as well as familiarity with some scattered intermediate to advanced topics, interpreted broadly. The focus of this workshop will not be training in intermediate R skills, but rather in preparing educators to deliver higher-level R courses. We will provide resources, examples, and frameworks to empower attendees to expand their R knowledge and design a meaningful hands-on classroom experience.