Code Rigor and Reproducibility with R at Columbia University 2025

 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:

Table of Content

• Summary
• Benefits
• Requirments
• Application Deadline
• How To Apply

Summary

• Application DeadlineJune 24, 2025
• Study LevelTraining
• SponsorColumbia University
• City to studyNew York

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Benefits

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. 

Requirements

Investigators at all career stages are welcome to attend, but to get the most out of this workshop, you should have experience in R programming and be actively using R for research. As part of the workshop we will ask you to bring and work on your own research code. There are a few requirements to attend this training:

1. 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.
2. 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. If you have R or RStudio installed already, but they were installed over a year ago, please install updated versions before the workshop:
   1. Download and install R(link is external and opens in a new window)
   2. RStudio is an open-source Integrated Development Environment (IDE) for R. Install the free version of the RStudio IDE for your laptop(link is external and opens in a new window).
3. Optional: Instructors will provide a basic introduction to git during day 2. If this is something you are interested in, download and install git(link is external and opens in a new window). 

Application Deadline

How To Apply

• 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

For more details visit Columbia University training webpage