Resources
Here you will find a list of resources which will be useful during this course.
Books
In short my recommendation is:
Focus your efforts on IMS2 as it covers experimental design, statistics and how to present results. To better understand a data analysis workflow, use R4DS and MD2. For help with a basic programming concept, consult HOPR. For alternative explanations of statistical approaches, use MD2 and StatBio.
Introduction to Modern Statistics, 2nd ed,. Çetinkaya-Rundel & Hardin (2024)
Main course book (IMS2)
Good for:
- All the statistical methods we cover in this course
- General advice for making figures and presenting results
- Introductory level explanations of theory
- Simple practical exercises (see Applications sections)
Not so good for:
- How to do something in R
- Deeper explanations of statistical methods
- Biological examples (most examples are from social sciences / business)
Statistical Inference via Data Science 2nd ed., Ismay, Kim & Valdivia (2025)
(MD2)
Good for:
- Alternative explanation of some statistical methods we cover in this course
- R code examples simple data handling, plotting and analysis
- Simple coding exercises to help understand core concepts
Not so good for:
- Anything multivariate
- Biological examples (most examples are from social sciences / business)
- More advanced R problems
R for Data Science, 2nd ed., Wickham, Çetinkaya-Rundel & Grolemund (2023)
(R4DS)
Good for:
- R code for importing, handling, cleaning and plotting data with
tidyverse - Help with Quarto documents
Not so good for:
- Statistics
- Core R programming concepts
Hands-On Programming with R, Grolemund (2018)
(HOPR)
Good for:
- Basic introduction to R
- Core R programming concepts
Not so good for:
- Tidyverse packages
- Statistics
Fundamental statistical concepts and techniques in the biological and environmental sciences, 1st ed. Duthie (2025)
(StatBio)
Good for:
- Theory based statistics
- Biological examples
- Basic introduction to classical frequentist statistical methods
- Also available as an audiobook
Not so good for:
- R code
- Randomisation and simulation based inference
Software
During the course we will use R and RStudio as tools to handle data, make plots and do statistics.
If you want to use another software package for these purposes, you are very welcome to, but you do so at your own undertaking.
We will make use of a set of R packages that are part of the extended tidyverse set of packages. Below are the websites for the main ones we will use, which contain guides, “cheatsheets” and reference materials.