Code Rigor and Reproducibility with R Boot Camp

July 1-2, 2024 | Subscribe to hear about the next training

code rigor and reproducibility with r boot camp

The most recent Code Rigor and Reproducibility with R Boot Camp was on July 1-2, 2024. Sign up below to hear about the next training!

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. 

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Training Overview

Summer 2024 dates: In-person training July 1-2, 2024; 9am - ~5:30pm EDT

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

Audience and 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
    2. RStudio is an open-source Integrated Development Environment (IDE) for R. Install the free version of the RStudio IDE for your laptop.
  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

R Tutorials

Knowing basic R platform and commands is required for the training as noted in requirements above. If you are new to R or need a refresher, you can review the below tutorials to be well prepared. This course will be based on the “tidyverse” style of coding in R. If you would like to learn more or refresh on this style of R coding, we recommend the following resources:  

  • R for Data Science by Hadley Wickham and Garrett Grolemund (Book available in print or for free online.)
  • Brooke Anderson’s YouTube Channel (Short video lectures on R programming, created by the instructor for this workshop for an introductory R class.)


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.

Rachel Severson, MS, Colorado Department of Public Health and Environment. Rachel Severson is the Immunization Branch Data Unit Manager at the Colorado Department of Public Health and Environment in the Division of Disease Control and Public Health Response. Her work involves working with Immunization Information System data to develop and supervise Colorado's immunization data analyses, infrastructure, and governance. Rachel received a Bachelor of Arts degree in Environmental Chemistry from Colorado College, and a Master of Science degree in Environmental Epidemiology from Colorado State University.


Training scholarships are available for the Code Rigor and Reproducibility with R Boot Camp.


Summer 2024: The Code Rigor and Reproducibility with R Boot Camp is a live, in-person training taking place July 1-2 from 9am- ~5:30pm EDT at the Columbia University Irving Medical Campus in NYC. All training start and end times are in EDT.

More information on travel, lodging, and getting around NYC.


"This is the best professional development I've ever participated in and would recommend it to anyone using R for their work. Brooke Anderson and her TAs were wonderful and made this training engaging, accessible, and satisfying (I left with better code than I came in with!)." - Research Specialist at VA Puget Sound Health Sciences Research & Development, 2023

"The instructor and TAs showed us many, many better ways to write our code that are simpler and more reproducible. I'm self-taught beyond a few intro R classes from Coursera, and I wish I had taken something like this years ago." - Faculty member at University of Illinois Urbana-Champaign, 2023

"Professor Anderson is an enthusiastic and engaging teacher, and the lecture content and labs was very practical. I was able to immediately apply what I learned to my current research project." - Postdoc at Penn Medicine, 2023

Registration Fees

  Early-Bird Rate (through 5/10/24) Regular Rate (5/11/24 - 6/24/24) Columbia Discount*
Student/Postdoc/Trainee $1,195 $1,395 10%
Faculty/Academic Staff/Non-ProfitOrganizations/Government Agencies $1,395 $1,595 10%
Corporate/For-Profit Organizations $1,595 $1,795 NA


*Columbia Discount: This discount is valid for any active student, postdoc, staff, or faculty at Columbia University. If paying by credit card, use your Columbia email address during the registration process to automatically have the discount applied. If paying by internal transfer within Columbia, submit this Columbia Internal Transfer Request form to receive further instructions. Please note: filling out this form is not the same as registering for a training and does not guarantee a training seat.  

Invoice Payment: If you would prefer to pay by invoice/check, please submit this Invoice Request form to receive further instructions. Please note: filling out this form is not the same as registering for a training and does not guarantee a training seat.

Registration Fee: Registration Fee includes course material, breakfast, and lunch on training days. Course material will be available to all attendees during and after the workshop. Lodging and transportation are not included.

Cancellations: Cancellation notices must be received via email at least 30 days prior to the training start date in order to receive a full refund, minus a $75 administrative fee. Cancellation notices received via email 14-29 days prior to the training will receive a 75% refund, minus a $75 administrative fee. Please email your cancellation notice to Due to workshop capacity and preparation, we regret that we are unable to refund registration fees for cancellations <14days prior to the training.  

If you are unable to attend the training, we encourage you to send a substitute within the same registration category. Please inform us of the substitute via email at least one week prior to the training to include them on attendee communications, updated registration forms, and materials. Should the substitute fall within a different registration category your credit card will be credited/charged respectively. Please email substitute inquiries to In the event Columbia must cancel the event, your registration fee will be fully refunded.

Additional Information

The Code Rigor and Reproducibility with R Boot Camp is hosted by Columbia University's SHARP Program at the Mailman School of Public Health.

Jump to:  OverviewAudience and Requirements | R TutorialsInstructor  |  Scholarships  |  Locations   |  Testimonials  |  Registration Fees  |  Additional Information