Literature and Links
Here you will find a selection of publications and web links, primarily in English. They are suitable for reviewing or acquiring foundational knowledge in statistics as well as for supplementing and deepening the content of the DSB modules. Additions to this subjective selection are welcome and can be sent to the DSB team!
Books
General R Books
- Ligges, U. (2014): Programmieren mit R, Springer Spektrum, Berlin. [German]
- Wickham, H. & Grolemund, G. (2023): R for Data Science, 2nd edition, O’Reilly Media Inc., CA, USA. Available online at r4ds.hadley.nz → The tidyverse book, covering all relevant tidyverse packages. Highly recommended for anyone working with R.
- Wickham, H. (2019): Advanced R, 2nd edition, Chapman & Hall/CRC The R Series, UK. Available online at adv-r.hadley.nz → If you want to learn more about R programming and writing your own functions, this book is highly recommended!
- Crawley, M.J. (2013): The R Book, 2nd edition, Wiley & Sons, West Sussex, UK. → At nearly 1000 pages, this is the most comprehensive R book on the market, covering inferential statistics and statistical modelling with R in detail.
Visualisation in R
- Wickham, H. (2016): ggplot2 – Elegant Graphics for Data Analysis, 2nd edition, Springer International Publishing, Switzerland. R code and text available on GitHub.
- Kassambara, A. (2013): Guide to Create Beautiful Graphics in R, 2nd edition, STHDA.
General Visualisation
- Kirk, A. (2019): Data Visualisation – A Handbook for Data Driven Design, 2nd edition, SAGE Publications Ltd.
- Berinato, S. (2019): Good Charts Workbook, HBR Press.
Statistics & Mathematics (German)
- Bärlocher, F. (1999): Biostatistik – Praktische Einführung in Konzepte und Methoden, Thieme Verlag. [German]
- Eickhoff-Schachtebeck, A. & Schöbel, A. (2014): Mathematik in der Biologie, Springer Spektrum. [German]
- Dormann, C. (2017): Parametrische Statistik – Verteilungen, Maximum Likelihood und GLM in R, Springer Spektrum. [German]
General Statistics
- Quinn, G.P. & Keough, M.J. (2002): Experimental Design and Data Analysis for Biologists, Cambridge University Press, UK.
- Lazic, S.E. (2017): Experimental Design for Laboratory Biologists – Maximising Information and Improving Reproducibility, Cambridge University Press.
- Cohen, J. (1988): Statistical Power Analysis for the Behavioral Sciences, 2nd edition, Lawrence Erlbaum Associates, Hillsdale, NJ.
Statistics with R
- Zuur, A.F., Ieno, E.N., Walker, N.J., Saveliev, A.A. & Smith, G.M. (2009): Mixed Effects Models and Extensions in Ecology with R, Springer, New York, USA. Further information and data files: highstat.com → Starting from the simple linear regression model and its limitations, this book describes various alternative modelling approaches such as Generalised Linear and Additive Models (GLM and GAM), Mixed Effects Models, and more.
Machine Learning
- James, G., Witten, D., Hastie, T. & Tibshirani, R. (2021): An Introduction to Statistical Learning with Applications in R, 2nd edition, Springer. Available online at statlearning.com
Style Guides
Improve R code readability – make errors easier to spot
A coding style defines, for example, where spaces are placed or how file and object names are structured. A consistent style has great benefits but no effect on the functionality of R code. Errors are much easier to find and readability improves – especially when multiple people work on the same code.
Several style guides for R code exist, including in some of the books listed above. Here is a selection of online resources:
- Wickham, H.: The tidyverse style guide
- Google: Google’s R Style Guide
Useful Websites
General
- R-bloggers – Blog aggregator with posts on latest developments, individual R packages, and online tutorials
- CRAN Task Views – Thematically organised overviews of relevant R packages (e.g., Environmetrics, Spatial, TimeSeries)
- fishR – R resources for fisheries sciences: packages, online courses, and tutorials
Functional Programming in R
- Jenny Bryan: purrr Tutorial – Recommended tutorial on the purrr package for functional programming in R
Visualisation
- ggplot2:
- Official documentation – Function reference and guides
- R Graph Gallery – ggplot2 – Gallery of ggplot2 graphics with code examples
- ColorBrewer – Colour scales for maps and charts
Quarto & R Markdown
- Quarto Documentation – Official Quarto website with comprehensive guides
- R Markdown Documentation – Official R Markdown website
- R Markdown: The Definitive Guide – Comprehensive online book by Xie, Allaire & Grolemund
Regular Expressions
- regular-expressions.info – Comprehensive resource on regular expressions (regex) with tutorials at beginner and advanced levels
Shiny
- Shiny Documentation – Official Shiny website with tutorials and examples
- Mastering Shiny – Online book by Hadley Wickham
- shinyapps.io – Platform for publishing Shiny applications