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(21 reviews)
Author: Winston Chang
ISBN : 1449316956
New from $29.20
Format: PDF
Download file now Free R Graphics Cookbook [Paperback] for everyone book mediafire, rapishare, and mirror link
This practical guide provides more than 150 recipes to help you generate high-quality graphs quickly, without having to comb through all the details of R’s graphing systems. Each recipe tackles a specific problem with a solution you can apply to your own project, and includes a discussion of how and why the recipe works.
Most of the recipes use the ggplot2 package, a powerful and flexible way to make graphs in R. If you have a basic understanding of the R language, you’re ready to get started.
- Use R’s default graphics for quick exploration of data
- Create a variety of bar graphs, line graphs, and scatter plots
- Summarize data distributions with histograms, density curves, box plots, and other examples
- Provide annotations to help viewers interpret data
- Control the overall appearance of graphics
- Render data groups alongside each other for easy comparison
- Use colors in plots
- Create network graphs, heat maps, and 3D scatter plots
- Structure data for graphing
Direct download links available for Free R Graphics Cookbook [Paperback]
- Paperback: 416 pages
- Publisher: O'Reilly Media; 1 edition (January 3, 2013)
- Language: English
- ISBN-10: 1449316956
- ISBN-13: 978-1449316952
- Product Dimensions: 0.3 x 7.3 x 9.5 inches
- Shipping Weight: 1.6 pounds (View shipping rates and policies)
Free R Graphics Cookbook
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.
... 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 could use a little more editing. A natural introduction is found in an appendix at the very end - as the book is organized as a series of cases, this is a missed opportunity to set up context, explain ggplot2's style, and perhaps include a few tables listing all the geoms, etc. The functions of Hadley Wickham's "plyr" package, if they really belonged in the book, should have been explained better.
The main cause of ambivalence is that ggplot2 is just one package, and facing a choice between learning ggplot2 and learning the "old school" R graphics, I would go for the latter, as much better documented. (I can point to the (overpriced) books by Murrell, Mittal and Takezawa, material in Robert Kabacoff's "R in Action" and on his Quick-R web site, graphics coverage in Crawley's "R Book" - but, most obviously, to the built-in help, which is naturally more evolved than that of the upstart ggplot2). Then, one could take a look at ggplot2.
By Dimitri Shvorob
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