Data Visualisation with R

This book introduces readers to the fundamentals of creating presentation graphics using R, based on 111 detailed and complete scripts. It shows how bar and column charts, population pyramids, Lorenz curves, box plots, scatter plots, time series, radial polygons, Gantt charts, heat maps, bump charts, mosaic and balloon charts, and a series of different thematic map types can be created using R’s Base Graphics System. Every example uses real data and includes step-by-step explanations of the figures and their programming. This second edition contains additional examples for cartograms, chord-diagrams and networks, and interactive visualizations with Javascript.

The open source software R is an established standard and a powerful tool for various visualizing applications, integrating nearly all technologies relevant for data visualization. The basic software, enhanced by more than 14000 extension packs currently freely available, is intensively used by organizations including Google, Facebook and the CIA. The book serves as a comprehensive reference guide to a broad variety of applications in various fields.

This book is intended for all kinds of R users, ranging from experts, for whom especially the example codes are particularly useful, to beginners, who will find the finished graphics most helpful in learning what R can actually deliver.

• Statistical Software
• Visualization Application Domains
• Visualisation Toolkits
• Data Visualisation
• R(Language)

“The book can be one of the favourites of a wide range of users from beginners with basic R knowledge to experts. It is especially recommended for students and researchers from social, environmental, and economic fields requiring a consistent and thorough reference always within reach.” (Márta Ladányi, ISCB News, iscb.info, Issue 69, July, 2020)

“Provides the reader with a broad and excellent overview of the various possibilities offered by R’s standard graphics package. … Overall, the book can especially be recommended for students and researchers that are already familiar with R and who intend to find suitable methods for visualizing their data in real world applications. Furthermore, it can also be helpful for students who want a broad overview of R and its visualization capabilities.” (Marc Hüsch, Statistical Papers, April, 2018)

“I definitely recommend this book. The example-based approach is very successful for introducing readers to R’s graphical capabilities; readers can learn proficiency in using base R graphics for obtaining exactly the static presentation figure they envision – including ambitious infographics.  … Last but (by far) not least, the book can be used as a collection of ideas for useful, informative and beautiful graphical displays.” (Ulrike Grömping, Journal of Statistical Software, Vol. 81, October, 2017)

“This is a well written book for designers. … Enjoy this book. I am having fun getting the code to work on other data.” (Mary Anne Thygesen, Cats and Dogs with Data, maryannedata.com, May, 2017)

“If you’ve ever been interested in coding using R, then this book is for you. … If like me, you need hands-on guidance the examples (which use real data) are shown with a step-by-step walkthrough teaching you everything that you need to know.” (Sandro Sorrentino, Medium, medium.com, February, 2017)

“I really like this book: those 100 interesting real data examples with the detailed R-scripts for producing beautiful graphical visualisation are particularly helpful for learning R graphics. I will use it as a main textbook for data visualization for my statistics course.” (Professor Qiwei Yao, Department of Statistics, London School of Economics)

“I like the book. With my two doctoral course students, I use it as a text book for my ‚Doctoral Dissertation Research‘ course in this semester.“ (Prof Dae-Heung Jang, Department of Statistics, Pukyong National University, Busan, South Korea, 2017)

• Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany

  Thomas Rahlf

Thomas Rahlf has 20 years of experience in data visualization. Holding a PhD in the history of statistics and econometrics, his areas of expertise include also historical statistics, time series analysis and statistical inference. He is director in the department of quality and program management at the German Research Foundation as well as a lecturer at the University of Bonn, Germany.
Graphics and statistics have been a key element of his work for a long time. Recent publications include “Statistical Inference“ in the Handbook of Cliometrics (Springer Reference Series, 2016) and “The German Time Series Dataset, 1834-2012” (Journal of Economics and Statistics, Vol. 236/1). He is also the editor of a book about historical statistics of Germany.

Policies and ethics