- Series: Use R!
- Paperback: 260 pages
- Publisher: Springer; 2nd ed. 2016 edition (June 9, 2016)
- Language: English
- ISBN-10: 331924275X
- ISBN-13: 978-3319242750
- Product Dimensions: 6.1 x 0.6 x 9.2 inches
- Shipping Weight: 1 pounds (View shipping rates and policies)
- Average Customer Review: 19 customer reviews
- Amazon Best Sellers Rank: #31,223 in Books (See Top 100 in Books)
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ggplot2: Elegant Graphics for Data Analysis (Use R!) 2nd ed. 2016 Edition
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“The book is an excellent and very comprehensive manual of … one of the most popular R packages. It is currently the only book describing ggplot2 in such depth. The book contains many examples and is very nicely illustrated, demonstrating the strength of the package.” (Klaus Galensa, Computing Reviews, May, 2017)
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Hadley has rewritten the book on ggplot2 completely and utilized the examples and questions from the communities on StackOverflow and GoogleGroups as a guide. The book starts off gentle, but does assume you have basic knowledge of R (installation of packages, some base functions, loading libraries and simple syntax). The components of the grammar are brought in piecewise and in a logical way that should help early learners and refresh those of us who have used the package for a while. There are tons of code examples which are colored coded for legibility and syntax reasons. Each block of code is followed by the output from that code, which helps the user understand what is expected. At the end of major sections, there are exercises which not only help you understand what you've learned, but also get you thinking about how you would analyze a similar dataset. This is really important because if you do these exercises, you will be well prepared to implement the visualization strategies herein on any dataset.
Chapters 9-11 introduce auxiliary packages in the tidyverse (formerly "Hadleyverse") including dplyr, tidyr, and broom, which are used to discuss an entire data analysis pipeline. This sections does a good job of introducing these tools and what you would use them for. If you're interested in digging further into these packages, Hadley has been writing another book called R for Data Science which will hopefully be on sale in late 2016. An early version can be found here: [...]
The last chapter is about programming with ggplot2. Hadley introduces some very useful, more advanced methods for plotting with ggplot2 from creating your own functions to using standard evaluation. A very useful introduction for more advanced users.
Overall, the book is a gentle and thorough introduction to the ggplot2 package for beginners and a very useful references to all of the updates introduced in the last few years since the last ggplot book (Winston Chang's R Graphics Cookbook)R Graphics Cookbook.
Writing style is very clear. Examples of each concept along with review questions that really make you think about what has just been covered rather than just regurgitating facts.
Overall style is concept, some details, and examples.
Sometimes I wish Hadley would use more of an primitive breakdown approach to examples. For example one example starts with using loess to build some data for an example. I'd rather just see some plain data rather than a building some data from line fitting. That would make it easer to see how data flows through an example. I appreciate that what Hadley ends up with is real world data, but I, and this may just be me, I like things explained at a more primitive level.
But in any case this book is not just showing you some neat plots, even though it has many, it is giving you the fundamentals you need to be able to implement from scratch the plots you think up in your head to point out statistical features in your data.
For whatever it's worth, my other current favorite textbook is An Introduction to Statistical Learning: with Applications in R. I highly recommend that, too, for anybody in the data / stat learning / R space!