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ggplot2: Elegant Graphics for Data Analysis (Use R!)

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Review

“The versatility and efficiency of ggplot have led to the development of ggplot2 and this book which overviews the standard use and presentation secrets of functions developed in the last 5 years. … The book is written in an accessible manner and it is suitable for undergraduates, postgraduates and researchers with some R experience. All theoretical concepts are accompanied by code making it easy to learn by reproducing the examples.” (Irina Ioana Mohorianu, zbMATH 1397.62006, 2018)“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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From the Back Cover

This new edition to the classic book by ggplot2 creator Hadley Wickham highlights compatibility with knitr and RStudio. ggplot2 is a data visualization package for R that helps users create data graphics, including those that are multi-layered, with ease. With ggplot2, it's easy to:* produce handsome, publication-quality plots with automatic legends created from the plot specification* superimpose multiple layers (points, lines, maps, tiles, box plots) from different data sources with automatically adjusted common scales* add customizable smoothers that use powerful modeling capabilities of R, such as loess, linear models, generalized additive models, and robust regression* Â save any ggplot2 plot (or part thereof) for later modification or reuse* create custom themes that capture in-house or journal style requirements and that can easily be applied to multiple plots* approach a graph from a visual perspective, thinking about how each component of the data is represented on the final plotThis book will be useful to everyone who has struggled with displaying data in an informative and attractive way. Some basic knowledge of R is necessary (e.g., importing data into R). Â ggplot2 is a mini-language specifically tailored for producing graphics, and you'll learn everything you need in the book. After reading this book you'll be able to produce graphics customized precisely for your problems, and you'll find it easy to get graphics out of your head and on to the screen or page. Â New to this edition:* Brings the book up-to-date with ggplot2 1.0, including major updates to the theme system* New scales, stats and geoms added throughout* Additional practice exercises* A revised introduction that focuses on ggplot() instead of qplot()* Updated chapters on data and modeling using tidyr, dplyr and broom

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Product details

Series: Use R!

Paperback: 276 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:

3.9 out of 5 stars

25 customer reviews

Amazon Best Sellers Rank:

#72,598 in Books (See Top 100 in Books)

This review is for the 2nd Edition of the book. ggplot2 has changed a lot in recent years and the old book is no longer useful.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.

I've been working with this book for a couple of months now -- lots of tabs and highlights. It is amazingly comprehensive for it's length but if you are pretty new to ggplot, this is a challenging introduction in the following way: the examples start at a pretty complex level of aesthetic combos and they are a bit too clever. As an example, things like the geom 'label' uses a dataframe named 'label' to place a label of 'label' on a plot -- cute but not helpful to the reader. This happens too much for beginners. It's like someone who is excellent in algebra naming all the different variables in an equation "x" -- they can keep track easily in their head because they breathe algebra and it saves them the trouble of thinking up a bunch of different variable names that they don't need for clarity. But it makes it hard going for a newbie trying to learn the pieces. In this way and others, many portions of the book are like a second-level of ggplot use; it's easier to learn the basics on one of the websites or one of the other cookbooks, then come here for clever ways to combine lots of things into a couple of lines of code. Sometimes Wickham gives the impression of wanting to teach the material but can't avoid opportunities to show some cleverness -- that's great for experienced people looking for tricks but it makes this book initially a bit frustrating at points for some users (like me). I still use it a lot but it's not a great reference for real basics. This is in part because the indexed refs to geoms are to an initial intro of the term but if you want to see how to use that geom beyond the single initial use, you need to read the whole book because the specifics are embedded in many other examples. This is no doubt on purpose but, again, be aware it's not a very good reference-type book.

This book 2016 version of the 2009 book and is really good. Great examples! Updated to the current version of R so all the examples and references are to functions and packages in the current R version that I am using today... which makes it much easier to follow.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.

It's hard to call yourself a statastician or data scientist without strong data visualization skills. Tools like Tableau or Microsoft office can only take you so far. ggplot is an excellent option that allows you to make highly customizable graphs at the cost of a slightly increased learning curve. As a statistics graduate student I found this book to be an excellent introduction to this R package and found the 2nd edition to be very up to date. It is by no means an exhaustive reference, but that makes the book readable and easy to follow. It provides just enough information to get you started and to point out major features and pitfalls, but there is no excess of words. I also appreciated the writer's familiarity with Tufte's philosophy. I highly recommend this book to anyone with at least a basic familiarity with R who looking to up their visualizations skills.

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