NTRES 6100

Collaborative and Reproducible Data Science in R

Course Description

Course information provided by the Courses of Study 2020-2021.

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.

When Offered Spring.

• Describe strategies for ensuring that their data analysis is reproducible.
• Demonstrate best practices for coding and project-oriented workflows in RStudio.
• Import and clean messy data files using a variety of packages and functions in R.
• Subset, reorganize, and merge diverse datasets in R.
• Effectively explore and visualize patterns in complex datasets with ggplot in R.
• Write simple functions/programs and data analysis pipelines in R.
• Automate repeated analysis tasks in R.
• Track the history of file changes (version control) and collaborate effectively on scripts with others with Git and GitHub.
• Use R Markdown to combine text, equations, code, tables, and figures into reports, websites, and presentations.

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