- 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: 85 customer reviews
- Amazon Best Sellers Rank: #51,428 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.
Top customer reviews
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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.
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.
While "R in Action" might be better at easing the reader into the R language and environment, this book starts with enough R basics in its first two chapters, such as installing packages and loading data, that most will find it sufficient for getting their feet wet, and the author's introduction to ggplot2 in the appendix combined with Chapter 15 (Getting Your Data into Shape) are not only well done, but very succinct for busy professionals. After discussing bar graphs, line graphs, scatter plots, and summarized data distributions, the author presents annotations, axes, legends, and controlling the overall appearance of graphs, followed by discussions on facets, miscellaneous graphs, and outputting for presentation. If you are new to ggplot2, you will probably need to touch a majority of these chapters to some degree in order to get up to speed, but information is easy to find. Very well recommended.
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.