- 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: 83 customer reviews
- Amazon Best Sellers Rank: #19,764 in Books (See Top 100 in Books)
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R Graphics Cookbook: Practical Recipes for Visualizing Data 1st Edition
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Q&A with Winston Chang, author of "R Graphics Cookbook: Practical Recipes for Visualizing Data"
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.
About the Author
Winston Chang is a software engineer at RStudio, where he works on data visualization and software development tools for R. He holds a Ph.D. in Psychology from Northwestern University. During his time as a graduate student, he created a website called "Cookbook for R", which contains recipes for handling common tasks in R. In previous lives, he was a philosophy graduate student and a computer programmer.
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I spent a solid year learning and exploring R as a graduate student before I cracked open Winston Chang's R Graphics Cookbook and started learning ggplot2's little oddities. ggplot2 is itself almost like another language within R, but it's thankfully a very simple language -- far more simple and far more flexible, I feel, than the built-in graphics options.
Since you'll be printing your graphics step by step -- your boundaries before your lines; your lines separately from points; etc. -- it's easy to keep track of where every impact on the output image is occurring, allowing you to easily tweak the code and get immediate results. E.g., if annotations are not lining up where you want, or font size needs to be reduced.
Chang's cookbook is separated by what feature you need to either edit or create, making it easy to jump to what the reader needs. Full sections are devoted to bar graphs, line graphs, scatter plots, data distribution graphics, customizing annotations, axes, legends, color options, and cetera. Nearly 400 pages of text and images showing different ways of customizing and displaying every piece of your graphics. It's not a book you read cover to cover -- just the resource that 'cookbook' implies, meeting the reader's specific needs.
If you want to just jump into the code and see what you can do with your own data, there's no better place to start. Almost no time is devoted to unnecessary exercises or teaching you the fundamentals of the R programming language. Exploring the far reaches of the Internet is a free alternative that's likely just as helpful, but Chang's book serves as a great reference, and contains almost everything you need all in one.
He likes that there are not only recipes, but underlying reasons. The book provides insight into how R is intended to work. This is the difference between understanding the basic concept of cut-and-paste as opposed to knowing just the steps for doing it in one operating system.
He also likes that the explanations provide gobs of useful search terms. These are great for solving challenges not contained in the recipes.
Must be a good book. He no longer grouses about how "easy things used to be with SAS," and tends to build complex charts with R now, instead of Libreoffice.
The reason for missing a star:
After 3 chapters, I notice that there are a lot of similarities among of the plots. It would be immensely helpful if the author added a brief introductions on ggplot: the philosophy of the developers and the common features of different geoms. To me, it works like a photoshop, things work in layers and the order of layers affect the output. An overview of ggplot is helpful is because before you plot anything, it is a good idea to have a holistic picture in mind what layers I would be needing and which is the best way to organize the layers. Recipes are easy for instant hands-on, but to figure out the principles based on discrete recipes is a demanding job for average users.
Updated: in the end of the book, there is appendix A, which explains the philosophy of ggplot. -This is exactly what I wanted. I realized this after finishing the first 6 chapter. It really helps.
That said, there are a few quibbles I should point out.
- The code is largely pretty good, but not always the most graceful. For example, the author appears not to have known about the bquote() function, and at one point he uses a very tortuous technique to solve a problem that bquote() would have handled with simplicity and grace. There are other places where I would have done things differently, but chacun à son goût, I suppose.
- This is really a book about ggplot2 and the hadleyverse, which is a dynamic place at the very least. The chapter on data manipulation with plyr and reshape2 should probably be replaced by one on dplyr and tidyr, which are now the preferred tools (partly for simplicity, partly for efficiency and partly for generality). The author can hardly be blamed for this, but the next edition, if there is one, should probably look fairly different in this part, at least
- Lattice is largly absent, which is a shame, and low-level methods with grid itself, which would have been useful in some places, is not mentioned.
I recommend this book, but to be most useful it should be taken along with Paul Murrell's more in-depth book, R Graphics (2nd Ed).