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R Graphics Cookbook 1st Edition

4.5 out of 5 stars 76 customer reviews
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ISBN-13: 978-1449316952
ISBN-10: 1449316956
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Editorial Reviews

Amazon.com Review

Q&A with Winston Chang, author of "R Graphics Cookbook: Practical Recipes for Visualizing Data"

Winston Chang

Q. Why is your book timely?

A. Interest in R for data analysis and visualization has exploded in recent years. In the computer-tech world, computers and networks have made it much easier to gather and organize data, and more and more people have recognized that there's useful information to be found. To illustrate, consider the job "data scientist": this is a job title that didn't even exist five years ago, and now it's one of the hottest tickets on the market.

At the same time, there's been a swell of interest in R in its more traditional setting, in science and engineering. I think there are many reasons for this. One, is that there's a growing recognition outside of the computer-programmer world that learning a little programming can save you a lot of time and reduce errors. Another reason is that the last few years have seen an improvement in the user-friendliness of tools for using R.

So there's a lot of interest in using R for finding information in data, and visualization an essential tool for doing this. Data visualizations can help you understand your data and find patterns when you're in the exploratory phase of data analysis, and they are essential for communicating your findings to others.

Q. What information do you hope that readers of your book will walk away with?

A. As my book is a Cookbook, the primary goal is to efficiently present solutions for visualizing data, without demanding a large investment of time from the reader. For many readers, the goal is to just figure out how to make a particular type of graph and be done with it.

There are others who will want to gain a deeper understanding of how graphing works in R. For these readers, I've written an appendix on the graphing package ggplot2, which is used extensively in the recipes in the book. This appendix explains some of the concepts in the grammar of graphics, and how they relate to structures common to data visualizations in general.

Finally, I hope that readers will find ideas and inspiration for visualizing their data by browsing the pages and looking at the pictures.

Q. What's the most exciting/important thing happening in your space?

A. I'm excited that R is becoming more and more accessible to users who don't primarily identify as programmers. Many scientists, engineers, and data analysts have outgrown programs that provide canned data analysis routines, and they're turning increasingly to R. The growing popularity of R is part of a virtuous circle: as R gains a larger user base, it encourages people to create better educational materials and programming tools for R, which in turn helps to grow the number of R users.

Technology-wise, I'm excited by Shiny, which is a framework for bringing R analyses to the web. (I should mention that this it's part of my job to work on the development of Shiny.) This makes it possible to build interactive applications for data analysis and visualization for users who don't need to know R, or even that the application is backed by R.

Book Description

Practical Recipes for Visualizing Data
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Product Details

  • Paperback: 416 pages
  • Publisher: O'Reilly Media; 1 edition (January 6, 2013)
  • Language: English
  • ISBN-10: 1449316956
  • ISBN-13: 978-1449316952
  • Product Dimensions: 7 x 0.9 x 9.2 inches
  • Shipping Weight: 1.6 pounds (View shipping rates and policies)
  • Average Customer Review: 4.5 out of 5 stars  See all reviews (76 customer reviews)
  • Amazon Best Sellers Rank: #43,381 in Books (See Top 100 in Books)

Customer Reviews

Top Customer Reviews

Format: Paperback
Even if you know R, learning to do graphs well in R is like learning (yet another) new language - that of ggplot2.

You could learn a new language by first studying its grammar and building some vocabulary. In that case, you might want to start with ggplot2 creator's book ggplot2: Elegant Graphics for Data Analysis (Use R!). That's an excellent book and that's where I started.

Still, there are many loose ends in my understanding of ggplot2 and sometimes I struggle to find the exact technique to achieve the effect I want. e.g. do I set fill or colour? What does grouping exactly do? How do I rearrange factors? How do I remove the legends and clean up the grid lines?

I am sure answers to all such questions could be found by googling and reading Hadley's original text carefully. However, the beauty of Winston Chang's book is that it has compiled tons of such examples in recipe format and is a huge time saver. Now this is my first stop reference, even before hitting google or stackoverflow.

Another advantage of the book is that all recipes are self contained and you could quickly pick up a technique or two in any 5 minute of break time. After immersing myself in enough examples during last week, I feel I am getting better hang of grammar of graphics (philosophy behind ggplot2).

By the way, if you have not read Hadley's book or tutorial, please read Appendix A before you dive in.
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Even though I am really familiar with ggplot2, plyr and reshape, Winston Chang have some great tricks manipulating data and creating awesome graphics that I had never seen before. The recipe format of this book is great for beginners and advanced users because one can get straight answers without having to read too much.
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This book is essentially a collection of examples of doing very specific graphical methods using ggplot2. Perhaps that is all it seeks to be; the clue being in the "cookbook" of the title -- implying a collection of recipes. What this book lacks is a discussion of the rationale underlying the use of ggplot2, leaving the reader to infer as much as possible through the experience of multiple examples (much as a good cookbook will have an introductory discussion of how various ingredients work together). This is fine if all you intend is to stick to some very specific graph styles, but makes it challenging to tailor a graph to a specific problem. Considering the extent of the examples, a chapter summarizing the theory behind the use of ggplot2 would be really helpful. I'm hesitant to buy the official ggplot2 book (ggplot2: Elegant Graphics for Data Analysis), as it seems well-understood that ggplot2 has evolved dramatically since that book was published, and it seems a lot to pay for a book which doesn't match the package in current form. Based upon the reviews, I had assumed that this book would fill the gap.
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I have Hadley Wickham's book on ggplot2 ggplot2: Elegant Graphics for Data Analysis (Use R!), which is a great introduction to advanced R graphics. However, since that book was published in 2009, a few updates in R and ggplot2 have made some of the tricks I used for plots obsolete and I've had to refer to the online ggplot2 documentation to update my code. The R Graphics Cookbook makes this task of finding best practices for ggplot2 much easier, and provides some really concrete examples. This R Graphics Cookbook and the ggplot2 book make for a powerful combination.
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Format: Paperback
... "ggplot2" being a particular, fairly mature and popular R graphics package developed by Hadley Wickham, and described in his 2009 book "ggplot2: Elegant graphics for data analysis". Three years later, Winston Chang's accessible and inexpensive how-to book can push "ggplot2" into the mainstream. It is actually a different book, "R for Everyone" by Jared Lander, that brought me over the fence separating "old school" R graphics and "ggplot2" - it's a pity that, talking about a "syntax" of graphics, neither "ggplot2"-focused book manages to be textbook-systematic about it, and the reader has to pick up the general points as they go through examples - but once I am here, "R Graphics Cookbook" is a necessity.

UPD. With the benefit of a little more life experience, I would say: don't spend your time on *any* R book. Python is the way to go.
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This book is the third that I have recently used to get up to speed on the R language and environment. The other two texts include "R in a Nutshell" and "R in Action" (see my review). The main catalysts behind my decision to purchase another book on R was my need to get up to speed with using the language to create graphs, and that not only are plotting functions that come with base R not unified behind a common interface and set of options, there exist limitations with base R plotting functions. Of course, there are other plotting functions across the over 2500 packages that have become available to the community, but the ggplot2 package that is the focus of this book is well regarded and grown in use to become one of the most popular R packages since its release in 2005.

What Chang presents in this book is extremely practical. My recent pro bono work to move a client away from Microsoft Excel to this powerful open source platform and industry standard used for both small and big data analytics is partially testament to the usefulness of this book. While I have needed to peruse R package documentation as part of this work, it is not a stretch to say that a majority of what I needed was contained in this book, either as starting points or complete examples. The explanations are very well written and organized, and the fact that all of the pertinent graphs are in color was very helpful when it came time to understanding how ggplot2 can be used for tasks such as plotting multiple lines in one plot, and the data setup that is necessary to perform these tasks, which is a bit different than base R plotting functions, but worth the effort to use since ggplot2 employs universal usage patterns.
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