# Course materials

### Main reference

**“Discovering Statistics Using R”**(Field, A., Miles, J., & Field Zoe, 2012, 1st Edtn.) This textbook offers an accessible and comprehensive introduction to statistics and will be the main reference for this class.

### Further readings

In addition to the main readings, there are many excellent books available (many of them for free) that focus on different aspects of R. In case you would like to learn more about the capabilities of R, I can recommend the following books:

**“R for Data Science”**An excellent book by Hadley Wickham, which introduces you to R as a tool for doing data science, focussing on a consistent set of packages known as the tidyverse. [FREE online version]**“An Introduction to Statistical Learning”**This book provides an introduction to statistical learning methods and covers basic methods (e.g., linear regression) as well as more advanced methods (e.g., Support Vector Machines). [FREE online version]**“R for Marketing Research and Analytics”**A great book that is designed to teach R to marketing practitioners and data scientists.echo=**“Text Mining with R”**This book explains how you can analyse unstrunctured data (texts) using R. [FREE online version]**“Advanced R”**another great book written by Hadley Wickham. Explains more advanced R concepts. [FREE online version]**“Using R For Introductory Econometrics”**This book covers a nice introduction to R with a focus on the implementation of standard tools and methods used in econometrics. [FREE online version]**“R Packages”**which teaches you how to make the most of R’s fantastic package system. [FREE online version]**“R Graphics Cookbook”**a practical guide that provides more than 150 recipes to help you generate high-quality graphs quickly. [FREE online version]**“More books”**For more recommendations, please refer to this list of excellent books on specific topics related to R

### DataCamp

Please also make use of the abundance of web resources. For students who would like to further train the materials covered in class, we recommend DataCamp, an online platform that offers interactive courses in data science at different levels. To facilitate the learning process you will obtain full access to the entire DataCamp course curriculum for the duration of the course.

**“https://campus.datacamp.com/courses/free-introduction-to-r”**free interactive tutorials

### Other web-resources

**“https://www.r-project.org/”**official website**“http://www.statmethods.net/”**R reference by the author of “R in action”**“http://www.rdocumentation.org/”**R documentation aggregator**“http://stackoverflow.com/”**general discussion forum for programmers incl. R**“http://stats.stackexchange.com/”**discussion forum on statistics and data analytics**“http://www.r-bloggers.com/”**R blog aggregator**“http://www.cookbook-r.com/”**useful examples for all kind of R problems**“https://ggplot2.tidyverse.org/reference/index.html”**reference for data visualization