#
Readings

The following required books will be used during the course. For all books, a free (open source) online version is available.

### Introduction to Statistical Learning

The first book we are using in this course is Introduction to Statistical Learning, abbreviated as ISLR. The link will direct you to the website of the book, where a pdf of the (first edition of the) book is available online for free and can be downloaded. Under ‘resources’, you can also find a link to a free online course on the book which includes a very nice series of (short) lectures!

### Data Visualization - A practical Introduction

The second book we are using in this course is Data Visualization - A practical introduction by Kieran Healy, which we will abbreviate as DatVis. The link will direct you to a preprint version of the book which is available online for free. Next week we will start with chapter 1, learning all about the basic principles of data visualization and perception.

### Mastering Shiny

In week 7, we will explore making interactive visualizations uisng R shiny apps. For this, we will use the book Mastering Shiny by Hadley Wickham. This book is currently still under development (and intended for a late 2020 release by O’Reilly Media), but can already be red online.

### Text Mining with R - A Tidy Approach

At the end of the course, we will have a look at some basic text mining, for which we will use the book Text Mining with R by Julia Silge and David Robinson. Nice to know: this entire book and its website were made using R Markdown! We will start text mining in week 9, and cover chapter 1 to 3.

## Optional Literature

Another useful resource for this course is the book R for Data Science by Hadley Wickham and Garrett Grolemund, or R4DS. Again, this entire book and its website were made using R Markdown! Hadley Wickham and R4DS use a specific *dialect* of `R`

, a set of packages called the tidyverse. Because these packages are very useful and great for data science, we will be using the tidyverse throughout this course.

### Practical Data Science with R

For more guidence on using R, we reccomend the book *Practical Data Science with R* by N. Zumel, J. Mount and J. Porzak (2014). Manning Publications. No online version is available for this book.