Collaborative and Reproducible Data Science in R

2.0; 3.0 credits · Graduate

As datasets grow larger and more complex across all areas of science, computational skills are increasingly in high demand. This course introduces a series of practical tools that enable researchers to spend less time wrestling with software or repeating error-prone manual data processing and more time getting research done in efficient and transparent ways that facilitate collaboration and reproducibility. We will work in R/RStudio. Topics covered include 1) tidy data formatting, 2) rearrangement, filtering, exploration, and visualization of complex datasets, 3) basic programming, 4) version control with Git and GitHub, and 5) using R Markdown to combine text, code, tables, and figures into reports, websites, and presentations. The course emphasizes practical skill development and is structured around hands-on (the keyboard) learning.