Kelly Bodwin (Cal Poly San Luis Obispo), Tyson Barrett (Highmark Health and Utah State University), Allison Theobold (Cal Poly San Luis Obispo)
Abstract
This 1 and a half day 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.