We conclude each chapter with links to additional resources. In this introduction, these are pointers to the materials and software requirements of this book, as well as related resources on R (R Core Team, 2021) and the tidyverse (Wickham et al., 2019).
This book and course
Resources related to this book and course at the University of Konstanz, 2020/2021:
- Neth, H. (2021). ds4psy: Data Science for Psychologists.
Social Psychology and Decision Sciences, University of Konstanz, Germany.
Textbook and R package (version 0.7.0, May 12, 2021).
Retrieved from https://bookdown.org/hneth/ds4psy/.
Software and packages
Working through this book assumes an installation of three types of software programs:
The RStudio IDE
The distinctions between R, R packages, and RStudio are somewhat confusing at first and will be explained in more detail in Chapter 1: Basic R concepts and commands (see Section 1.1.3). At this point, it is good to know that we can interact with R and manage our R packages withing the RStudio IDE. Given the large variety of functions and levels, this interface is divided into many sub-windows that can be arranged and expanded in various ways. To get started, we only need to distinguish between the main Editor window (typically located on the top left), the Console (for entering R commands), and a few auxiliary windows that may display outputs (e.g., a Viewer for showing visualizations) and provide information on our current Environment or the Packages available on our computer. A useful window is Help: Although its main page provides mostly links to online materials, any R package contains detailed documentations on and examples of its functions that can be browsed in this window.
Figure 0.4 shows the RStudio cheatsheet on the RStudio IDE and illustrates that there are dozens of other functions available. As you get more experienced, you will discover lots of nifty features and shortcuts. Especially foldable sections and keyboard shortcuts (see
Alt + Shift + K for an overview) can make your life in R a lot easier.
But don’t let the abundance of options overwhelm you — I have yet to meet a person who needs or uses all of them.
A useful feature of RStudio is that collections of files can be combined into projects. For instance, it makes sense to store everything related to this course in a dedicated directory on your hard drive (e.g., in a folder “ds4psy”) and create an RStudio project (also named ds4psy) that uses this directory as its root. An immediate benefit of using projects is that your entire workflow gets more organized.7
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:
A typical R Markdown document consists of three distinct parts:
- A header for setting global document options;
- Text that may contain headings, paragraphs, and itemized lists; and
- Code chunks that contain and evaluate R code.
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 (i.e.,
.html files), as they can easily be exchanged and shown on most devices and platforms.
Fortunately, the range of commands required to benefit from R Markdown is very limited. For instance, the commands in the help file Help > Markdown Quick Reference of RStudio provide a good start for creating beautiful and functional documents. Beyond these basics, the R Markdown Cheatsheet — also available in RStudio by selecting Help > Cheatsheets > R Markdown Cheat Sheet — provides a more comprehensive overview of R Markdown functionality and commands:
This book and course were originally based on R for Data Science (Wickham & Grolemund, 2017). The contents of this book are more general and more tidyverse-centric, but still a classic and readworthy reference:
- 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.]
There are many other excellent books (and even more fragmentary and bad books) on data science in R for various audiences. Here are some recommendations for finding additional texts and courses on learning data science or statistics with R:
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. Due to the grass-roots nature of the site, many books are unfinished and of low quality. However, there are also many excellent ones. Some 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.
Answering questions with data (by Matthew J.C. Crump et al.) is a free textbook teaching introductory statistics for undergraduates in Psychology (with lots of additional material).
R you Ready for R? (by Wade Roberts) does not teach statistics from scratch, but provides helpful recipes for conducting particular analyses.
Statistical Inference via Data Science (by Chester Ismay and Albert Y. Kim) teaches statistical inference from a tidyverse perspective.
Web sites and blogs
Online information on R is abundant, but can be hard to navigate. Useful starting points include:
Intro2R provides a gentle 3-day introduction to R.
Quick-R (by Robert Kabacoff) is a popular website on R programming that also provides many pointers for using R in statistics.
R-bloggers collects blog posts on R.
The Simply statistics blog (by Rafa Irizarry, Roger Peng, and Jeff Leek) provides insightful and inspiring articles on many data science topics.
The Win vector blog (by John Mount and Nina Zumel) provides noteworthy observations on particular problems and data science in general.
The Learning Machines blog (by Holger K. von Jouanne-Diedrich) contains many readworthy articles on using R for modeling and machine learning.
Towards data science provides background articles on current data science issues.
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:
RStudio cheatsheets 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 2021-06-15 15:18:53 by hn.]
See the introductory chapters of R for Data Science (Wickham & Grolemund, 2017) for short, but helpful instructions on organizing your workflow with RStudio — especially the even-numbered chapters basics (Chapter 4), scripts (Chapter 6), and projects (Chapter 8).↩︎