Book and course
Resources related to this book and course at the University of Konstanz, Summer 2020:
- Wickham, H., & Grolemund, G. (2017). R for data science: Import, tidy, transform, visualize, and model data. Sebastopol, Canada: O’Reilly Media, Inc. [Available at http://r4ds.had.co.nz.]
Software and packages
Working through this book assumes an installation of three types of software programs:
An R interface: R Studio provides an integrated development environment (IDE) for R.
Especially foldable sections and many keyboard shortcuts (see
Alt + Shift + Kfor an overview) can make your life in R a lot easier.
R Markdown allows weaving text and code into reproducible research documents. For quick instructions on combining text and code, see Appendix E, or read the more detailed introduction of Chapter 27: R Markdown of the r4ds textbook. Alternatively, just start with one of the following templates:
When using R Markdown (typically saved as with the file extension .Rmd), you can generate various output formats to show and transfer your work. I recommend generating output documents in HTML format (.html files), as they can easily be exchanged and shown on most devices and platforms.
Some recommendations for additional books on R, the tidyverse, and various aspects of data science and statistics:
Bookdown.org is a major catalyst for data science in R, as it provides many great books on various topics at no charge. The archive page contains books on an even wider selection of topics, including many unfinished ones. Easy recommendations include:
The Art of Data Science (by Roger D. Peng and Elizabeth Matsui) is a thoughtful introduction to the principles behind data science.
Hands-On Programming with R (by Garrett Grolemund) provides a solid introduction to R.
Learning statistics with R (by D.J. Navarro) is an excellent starting point for psychology students wanting to learn more about statistics.
Statistical Inference via Data Science (by Chester Ismay and Albert Y. Kim) teaches statistical inference from a tidyverse perspective.
YaRrr! The Pirate’s Guide to R (by Nathaniel D. Phillips) is an introduction that approaches R in a funny and entertaining fashion. (See Rpository.com/learnR/ for a course with corresponding exercises and solutions.)
Free data science books links to many more books, many of which are classic textbooks.
Web sites and blogs
Online information on R is abundant. Useful starting points include:
R-bloggers collects blog posts on R.
Quick-R (by Robert Kabacoff) is a popular website on R programming that also provides many pointers for using R in statistics.
The Simply statistics blog (by Rafa Irizarry, Roger Peng, and Jeff Leek) provides insightful and inspiring articles on many data science topics.
Towards data science provides space for sharing concepts, ideas, and codes.
Other R courses and exercises include:
An introduction to statistical learning (by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani) provides an introduction to statistical learning methods with applications in R (and a corresponding ISLR package).
Computing for the social sciences is a course in Computational Social Science (taught by Benjamin Soltoff) as part of their Masters in computational social science program. The syllabus is more advanced than this course (and its pace much faster). But as the materials are of very high quality, they are a great way to explore additional topics.
fasteR: Fast lane to learning R (by Norman Matloff) for those who seek a quick, painless entree to the world of R.
R-exercises provides categorized sets of exercises to help people developing their R programming skills.
Other helpful links that do not fit into the above categories include:
R Studio cheat sheets provide visual summaries of many task domains and packages.
Automatic Help for R provides pointers and tools for teaching and managing R courses.
What they forgot to teach you about R is a book in the making (by Jennifer Bryan and Jim Hester) that provides many practical tips (e.g., regarding R maintenance, file names and paths, and workflow).
[index.Rmd updated on 2020-05-25 14:33:58 by hn.]
Neth, H. (2020). ds4psy: Data science for psychologists. Retrieved from https://CRAN.R-project.org/package=ds4psy
R Core Team. (2020). R: A language and environment for statistical computing. Retrieved from https://www.R-project.org
Wickham, H. (2014a). Advanced R (1st ed.). Retrieved from http://adv-r.had.co.nz/
Wickham, H. (2015). R packages: Organize, test, document, and share your code. Retrieved from http://adv-r.had.co.nz/
Wickham, H. (2019b). tidyverse: Easily install and load the ’tidyverse’. Retrieved from https://CRAN.R-project.org/package=tidyverse
Wickham, H., & Grolemund, G. (2017). R for data science: Import, tidy, transform, visualize, and model data. Retrieved from http://r4ds.had.co.nz