Summer 2023 dates: In-person training July 31-August 1, 2023; 8:30am - ~5:30pm EDT
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.
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.
ADDITIONAL INFORMATION
Capicity is limited. Paid registration is required to attend.