Resources

We conclude this introductory chapter with some pointers to the materials and software requirements of this book, and related resources on R (R Core Team, 2020) and the tidyverse (Wickham, 2019c).

Book and course

Resources related to this book and course at the University of Konstanz, Summer 2020:

uni.kn

Textbooks

The official textbook of this course is Data Science for Psychologists (Neth, 2020), freely available at https://bookdown.org/hneth/ds4psy/.

A more general and in-depth introduction is R for Data Science (Wickham & Grolemund, 2017):

  • 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.]

The ebook R for data science: Exercise solutions (by Jeffrey B. Arnold) provides exercise solutions to the exercises in r4ds.

Software and packages

Working through this book assumes an installation of three types of software programs:

  1. An R engine: The R project for statistical computing is the origin of all things R. A current distribution of R (e.g., R version 3.6.3) for your machine can be downloaded from one if its mirrors.

  2. An R interface: R Studio provides an integrated development environment (IDE) for R.
    Especially foldable sections and many keyboard shortcuts (see Alt + Shift + K for an overview) can make your life in R a lot easier.

  3. Additional tools: The R packages of the tidyverse (Wickham, 2019c) and the ds4psy package (Neth, 2020).

The terminology of R packages is explained in more detail in Chapter 1: Basic R concepts and commands (Section 1.1.3).

R Markdown

R Markdown allows weaving text and code into reproducible research documents. For quick instructions on combining text and code, see Appendix F, or read the more detailed introduction of Chapter 27: R Markdown of the r4ds textbook. Alternatively, just start with one of the following templates:

  • minimal template: rmd_template_s [in .Rmd | .html format]

  • medium template: rmd_template_m [in .Rmd | .html format]

  • explicit explanations: Rmarkdown_basics [in .Rmd | .html format]

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.

Books

Some recommendations for additional books on R, the tidyverse, and various aspects of data science and statistics:

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.

Educational resources

Other R courses and exercises include:

Miscellaneous

Other helpful links that do not fit into the above categories include:


ds4psy

[index.Rmd updated on 2020-07-30 20:24:56 by hn.]

References

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. (2019c). 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