Preface

Effectively handling, processing and analysing large amounts of data is an essential skill for modern evolutionary biologists. Large genomic and phenotypic datasets are now routine for the biosciences and we are no longer at point where a simple desktop program can suit our needs for data curation, statistical analysis and visualisation. To meet the challenge, it is necessary for biologists to learn how to program and the fundamentals of data science. As well as this, exploring, visualising and understanding data in a programming environment can help reinforce understanding of key concepts and mathematical or statistical relationships.

These reasons are the motivation for the online sections of our book, Evolutionary Genetics: Concepts, Analysis & Practice. Each of the ten tutorials hosted here are self-contained introductions to key concepts in evolutionary genetics and they are also designed to familiarise you with the basics of the R programming language. Each tutorial comes with a set of study questions which you can use to reflect on your learning and of course, we also provide the answers for you to check your work against.

The first two tutorials (Chapters 1 & 2) are genetics-free, providing an introduction to R and also the tidyverse approach. They are intended for as wide an audience as possible. We hope they will be of use to biologists and non-biologists alike.

Mark Ravinet & Glenn-Peter Sætre Oslo, October 2018

2021 version

This version is a revamped version of Mark Ravinet’s original tutorials, edited by Even Sletteng Garvang and Solveig Brochmann based on the feedback during the three years the course has been run at the University of Oslo. Even contributed with new code, revised sections and wrote a new tutorial for chapter 1. Solveig contributed with edits and feedback, and revised sections. In 2024 the tutorials were shortened to 7 assignments by Emily Enevoldsen and Marius F. Maurstad.

The code used to create this book can be found at https://github.com/BIOS1140/BIOS1140.github.io. The R and package versions used can be found in Appendix B.