Reproducible Computing (ADDED FEE) — Professional Development Continuing Education Course
ASA, Biometrics Section
Success in statistics and data science is dependent on the development of both analytical and computational skills. This workshop will cover:
- Recognizing the problems that reproducible research helps address.
- Identifying pain points in getting your analysis to be reproducible.
- The role of documentation, sharing, version control, automation, and organization in making your research more reproducible.
- Introducing tools to solve these problems, specifically R, RStudio, RMarkdown, git, GitHub, and make.
- Strategies for scaling these tools and methods for larger more complex projects.
Workshop attendees will work through several exercises and get first-hand experience with using relevant tool-chains and techniques, including R/RStudio, literate programming with R Markdown, automation with make, and collaboration and version control with git/GitHub.