Code Rigor and Reproducibility with R
Enhance research code in a two-day R Boot Camp. Dive deep into strategies for efficiency, bug prevention, and reproducibility.
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Course Description
The Code Rigor and Reproducibility with R Boot Camp is a two-day intensive workshop for researchers who are currently using R in their research, focused on diving into strategies to improve research code so it will be more efficient, less likely to harbor hidden bugs, and ready to share as a reproducible documentation of your analysis.
- Grasp essential concepts for ensuring code quality and reproducibility in health research projects.
- Apply strategies to identify bugs and enhance code clarity for better readability.
- Explore functional programming in R to streamline code, enhancing efficiency and maintainability.
- Understand principles of file organization for consistent project structuring and maintenance of a robust code base.
To contact support for this course, please email [email protected].
Course Prerequisites
- Experience with R and RStudio required for the Boot Camp. To get the most out of this workshop, it is recommended that you have used R within the context of research projects, rather than only in classroom settings.
- Each participant is required to bring a personal laptop as all lab sessions will be done on your personal laptop. Each participant must have R and RStudio downloaded and installed prior to attending the Boot Camp.
What You Will Learn
More and more health researchers are learning and using open-source software like R for research. Most training in this software, however, focuses on introductory tools, leaving researchers to run into challenges when they scale their code to research projects, including challenges in making research code efficient, bug-free, reproducible, and ready to share.
This two-day intensive boot camp fills a critical gap—many health researchers are using open-source code for substantial and complex data analysis projects, yet their training in coding did not cover techniques for efficient, rigorous, and reproducible code when scaling to large and complex projects. Led by an expert in open-source programming for environmental health research, this workshop will cover techniques that you can use to make R code more rigorous and reproducible for research projects. The workshop will alternate between seminar lectures and applied computational work, with approximately equal amounts of lecture and hands-on work over the course of the workshop. In addition, participants will have the option to apply the principles from day 1 of the workshop to an example of their own research code as an optional homework, with time reserved in day 2 of the workshop for one-on-one evaluations of their progress on making their own code more rigorous and reproducible.
By the end of the workshop, participants will be familiar with the following topics:
- Fundamentals of how research code can be made rigorous and reproducible.
- Approaches to tackle messy code, using an editing process to identify bugs and clarify code for human readers.
- Strategies to use functional programming in R to dramatically improve the efficiency and concision of research code, making it easier to maintain and keep bug-free.
- How to find and build on existing code examples while maintaining a rigorous and reproducible code base.
- Basic principles of file system architecture, how to leverage it to structure project files consistently, and how code to this structure.
- Strategies to develop a personal set of fundamental tools (functions, packages, data structures) as a basis to scale rigorously to larger coding projects.
- How to prepare data and code to be published as part of a peer-reviewed article.
Instructors
Brooke Anderson, PhD, Colorado State University. Brooke Anderson is a tenured Associate Professor at Colorado State University in the Department of Environmental and Radiological Health Sciences. Previously, she completed a postdoctoral appointment in biostatistics at the Johns Hopkins Bloomberg School of Public Health and a Ph.D. in engineering at Yale University. Her research focuses on the health risks associated with climate-related exposures, including heat waves and air pollution, for which she has conducted several national-level studies. Recently, she has expanded her research to include work on collaborative teams investigating immunology and tuberculosis. She has published over 50 peer-reviewed papers and has served as a member of the editorial boards of Epidemiology and Environmental Health Perspectives. As part of her research, she has also published a number of open-source R software packages to facilitate environmental epidemiology research.