SETAC Student Conference 2025

Published

14 July, 2025

Introduction to Reproducibility with R

Training workshop for the SETAC 13th Young Environmental Scientists Meeting 2025-08-12. It is designed and delivered by Emma Rand of The University of York.

Overview

An increase in the complexity and scale of biological data means biologists are increasingly required to develop the data skills needed to design reproducible workflows for the simulation, collection, organisation, processing, analysis and presentation of data. Developing such data skills requires at least some coding, also known as scripting. This makes your work (everything you do with your raw data) explicitly described, totally transparent and completely reproducible. However, learning to code can be a daunting prospect for many biologists! That’s where Reproducibility with R comes in!

R is a free and open source language especially well-suited to data analysis and visualisation and has a relatively inclusive and newbie-friendly community. R caters to users who do not see themselves as programmers, but then allows them to slide gradually into programming.

This workshop will introduce you to R and RStudio, the most widely used interface for working with R. You will learn how to import data, manipulate it, summarise it and plot it. You will learn how to use an organised project-oriented workflow with well commented scripts so that you can understand your work in the future, and share it with others. In addition, you will learn what a working directory and a file path are - these are key concepts in computing generally but ones which are often not taught to biologists.

Philosophy and approach

It is impossible to cover everything you might ever need! Different people will use different methods and tools. Topics have been chosen because they are: foundational, widely applicable and transferable conceptually.

Learning outcomes

After this workshop the successful learner will be able to:

  • Find their way around the RStudio windows
  • Create and plot data using ggplot
  • Explain the rationale for scripting analysis
  • Know how to load packages
  • Understand what is meant by the working directory, absolute and relative paths and be able to apply these concepts to data import
  • Summarise data in a single group or in multiple groups
  • Develop highly organised analyses including well-commented scripts that can be understood by future you and others