A Guide to Reproducible Code in Ecology and Evolution

Abstract

The way we do science is changing — data are getting bigger, analyses are getting more complex, and governments, funding agencies and the scientific method itself demand more transparency and accountability in research. One way to deal with these changes is to make our research more reproducible, especially our code. Although most of us now write code to perform our analyses, it is often not very reproducible. We have all come back to a piece of work we have not looked at for a while and had no idea what our code was doing or which of the many "final_ analysis" scripts truly was the final analysis! Unfortunately, the number of tools for reproducibility and all the jargon can leave new users feeling overwhelmed, with no idea how to start making their code more reproducible. So, we have put together this guide to help. A Guide to Reproducible Code covers all the basic tools and information you will need to start making your code more reproducible. We focus on R and Python, but many of the tips apply to any programming language. Anna Krystalli introduces some ways to organise files on your computer and to document your workflows. Laura Graham writes about how to make your code more reproducible and readable. François Michonneau explains how to write reproducible reports. Tamora James breaks down the basics of version control. Finally, Mike Croucher describes how to archive your code. We have also included a selection of helpful tips from other scientists. True reproducibility is really hard. But do not let this put you off. We would not expect anyone to follow all of the advice in this booklet at once. Instead, challenge yourself to add one more aspect to each of your projects. Remember, partially reproducible research is much better than completely non-reproducible research.