Introduction and Principles of reproducibility

Published

4 June, 2026

Introduction

Programme Overview

What this training is and is not

It is

  • ✅ An introduction to R for those without previous experience
  • ✅ About using RStudio Projects and good practice for code
  • ✅ An introduction to the tidyverse

It is not

  • ❌ An introduction to statistics
  • ❌ Magic

Learning Objectives

After this workshop the successful learner will be able to:

  • Explain the rationale for scripting analysis
  • Find their way around the RStudio windows
  • Create and plot data using ggplot
  • 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

Principles of reproducibility

What is reproducibility?

Research reproducibility is central to the scientific method. It is achieved in a variety of ways.

In this context I mean “computational reproducibility” (National Academies of Sciences et al. 2019):

Reproducible: Same data + same analysis = identical results

Two by Two cell matrix. Columns are Data, either same or different. Rows are Analysis either same or different. Each of cells contain one of the definitions for reproducibility

The Turing Way’s definitions of reproducible research

  • Replicable: Different data + same analysis = qualitatively similar results. The work is not dependent on the specificities of the data.

  • Robust: Same data + different analysis = qualitatively similar or identical results. The work is not dependent on the specificities of the analysis.

  • Generalisable: Different data + different analysis = qualitatively similar results and same conclusions.

Why does reproducibility matter?

  • Many high profile cases of work which did not reproduce e.g. Anil Potti unravelled by Baggerly and Coombes (2009)

  • Five selfish reasons to work reproducibly (Markowetz 2015). Alternatively, see the very entertaining talk

  • Will become standard in Science and publishing e.g OECD Global Science Forum Building digital workforce capacity and skills for data-intensive science (OECD Global Science Forum 2020)

How to achieve reproducibility

  • Reproducibility is a continuum. Some is better than none!

  • Script everything

  • Organisation: Project-oriented workflows with file and folder structure, naming things

  • Code: follow a consistent style, organise into sections and scripts (be modular), Code algorithmically

  • Documentation: Readme files, code comments, metadata,

  • More advanced: version, control, continuous integration and testing

Further reading

Pages made with R (R Core Team 2026), Quarto (Allaire et al. 2026), knitr (Xie 2025, 2015, 2014), kableExtra (Zhu 2024)

References

Allaire, J. J., Charles Teague, Carlos Scheidegger, Yihui Xie, Christophe Dervieux, and Gordon Woodhull. 2026. Quarto. V. 1.9. Released March. https://doi.org/10.5281/zenodo.5960048.
Baggerly, Keith A, and Kevin R Coombes. 2009. “DERIVING CHEMOSENSITIVITY FROM CELL LINES: FORENSIC BIOINFORMATICS AND REPRODUCIBLE RESEARCH IN HIGH-THROUGHPUT BIOLOGY.” Ann. Appl. Stat. 3 (4): 1309–34. https://doi.org/10.2307/27801549.
Broman, Karl W, and Kara H Woo. 2018. “Data Organization in Spreadsheets.” Am. Stat. 72 (1): 2–10. https://doi.org/10.1080/00031305.2017.1375989.
Bryan, Jennifer. 2018. “Excuse Me, Do You Have a Moment to Talk about Version Control?” Am. Stat. 72 (1): 20–27.
Markowetz, Florian. 2015. “Five Selfish Reasons to Work Reproducibly.” Genome Biol. 16 (December): 274. https://doi.org/10.1186/s13059-015-0850-7.
National Academies of Sciences, Engineering, Medicine, Policy, et al. 2019. Understanding Reproducibility and Replicability. National Academies Press (US). https://www.ncbi.nlm.nih.gov/books/NBK547546/.
OECD Global Science Forum. 2020. Building Digital Workforce Capacity and Skills for Data-Intensive Science. http://www.oecd.org/officialdocuments/publicdisplaydocumentpdf/?cote=DSTI/STP/GSF(2020)6/FINAL&docLanguage=En.
R Core Team. 2026. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. https://www.R-project.org/.
Sandve, Geir Kjetil, Anton Nekrutenko, James Taylor, and Eivind Hovig. 2013. “Ten Simple Rules for Reproducible Computational Research.” PLoS Comput. Biol. 9 (10): e1003285.
Wilson, Greg, D A Aruliah, C Titus Brown, et al. 2014. “Best Practices for Scientific Computing.” PLoS Biol. 12 (1): e1001745.
Wilson, Greg, Jennifer Bryan, Karen Cranston, Justin Kitzes, Lex Nederbragt, and Tracy K Teal. 2017. “Good Enough Practices in Scientific Computing.” PLoS Comput. Biol. 13 (6): e1005510.
Xie, Yihui. 2014. “Knitr: A Comprehensive Tool for Reproducible Research in R.” In Implementing Reproducible Computational Research, edited by Victoria Stodden, Friedrich Leisch, and Roger D. Peng. Chapman; Hall/CRC.
Xie, Yihui. 2015. Dynamic Documents with R and Knitr. 2nd ed. Chapman; Hall/CRC. https://yihui.org/knitr/.
Xie, Yihui. 2025. Knitr: A General-Purpose Package for Dynamic Report Generation in r. https://yihui.org/knitr/.
Zhu, Hao. 2024. kableExtra: Construct Complex Table with ’Kable’ and Pipe Syntax. https://CRAN.R-project.org/package=kableExtra.