NTRES 6100 - Collaborative and Reproducible Data Science in R

Spring. 3 credits. S/U grades only.

Forbidden Overlap: due to an overlap in content, students will receive credit for only one course in the following group: AEM 2850, GDEV 4290, GDEV 5290, NTRES 6100, STSCI 3040, STSCI 5040.
N. O. Therkildsen.

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.

Outcome 1: Describe strategies for ensuring that their data analysis is reproducible.

Outcome 2: Demonstrate best practices for coding and project-oriented workflows in RStudio.

Outcome 3: Import and clean messy data files using a variety of packages and functions in R.

Outcome 4: Subset, reorganize, and merge diverse datasets in R.

Outcome 5: Effectively explore and visualize patterns in complex datasets with ggplot in R.

Outcome 6: Write simple functions/programs and data analysis pipelines in R.

Outcome 7: Automate repeated analysis tasks in R.

Outcome 8: Track the history of file changes (version control) and collaborate effectively on scripts with others with Git and GitHub.

Outcome 9: Use R Markdown to combine text, equations, code, tables, and figures into reports, websites, and presentations.