Book Review: R Graphs Cookbook 64
RickJWagner writes "Once upon a time, I thought communication was one of my strong suits. Alas, a few years into my programming career I realized I'm more of the head-down codeslinging type, not one of the schmoozing managerial types. So when I have a point to make, I really like to have my data ready to do the talking for me. In that capacity, this book is a very good weapon to have in my arsenal." Read on for the rest of Rick's review.
Right away, you should realize this is not a book that teaches R. R (an excellent open source statistical language) is a great tool for any technician. I've used it to analyze logs, find performance bottlenecks, and make sense of mountains of nearly unrecognizable data. But this book doesn't teach R, it teaches R graphing.| R Graphs Cookbook | |
| author | Hrishi Mittal |
| pages | 272 |
| publisher | Packt Publishing |
| rating | 8/10 |
| reviewer | RickJWagner |
| ISBN | 1849513066 |
| summary | An invaluable reference book for expert R users |
It turns out R has excellent graphing capabilities. You can draw scatter plots, line plots, pie graphs, bar charts, histograms, box and whisker plots, heat maps, contour maps and 'regular' maps. These are all good for demonstrating data in different ways, and the book lightly explains which graph will help you illustrate which point.
If you're getting a little interested, you'll also want to know that all this graphing can be scripted and scheduled. So you can get data-driven reports on a schedule, easily accomplished once you know how to write the graphing scripts (which are then scheduled using cron or a similar facility). One small caveat: To prepare your data for presentation, I think it's usually necessary to partner R with another language that's better for text extracting and manipulation. I prefer Python for this task, you might like another language.
The book is exceptionally easy to read and work with. This doesn't mean it's simplistic, though. Anyone who's tangled with R's graphing without a good example will testify that figuring out the various functions and arguments necessary to wrangle a descriptive graph can be really difficult. This book gives you the kind of graphs you need, with the bells and whistles you're going to want, in a series of snippets you can run immediately.
The book is written in Packt's "Recipe" format. In a nutshell, this means that it's a series of how-to sections worded in a templated form. There are headings for sections that inform you what you're going to accomplish, how it's done, and why it worked. You quickly realize it's a repetitive format, but it serves to make the book an excellent resource for quick reference.
Another really nice feature of the book is the downloadable source code and matching data. Knowing the data is half the battle, really. The specific formulas given are certainly useful, but without knowing how the underlying data is formatted you really wouldn't get nearly the practical value. For that reason, I urge anyone using this book to be sure they examine the underlying data for at least the first few formulas. After that, it'll be automatic, you'll know you want to look at that data when you're trying to master some graph type. Then when you go to make your own data ready for graphing, you reach for that secondary language like Python, extract the fields you want in a way similar to your example data set, and presto-- you've got the graph you want.
The book starts out with a first chapter that introduces the kinds of graphs you'll be able to produce and situations where each type is most useful. The next chapters, up until the final one, are in-depth sections on each of the graph types. Maps are treated to a different chapter than pie graphs, for instance. The final chapter covers putting final touches on your graphs, including saving them in different formats (PDF, PNG, JPEG, etc.) and niceties like adding scientific notations, mathematical symbols, etc.
The book states that the target audience is experienced R programmers. I really don't think that's necessary, though. There is an obligatory R installation section, and I think that a reasonably competent programmer with Google at his disposal could get off the ground (for graphing purposes) with this book and a little bumbling. If you already know R, then you needn't worry at all, there is nothing here that will look foreign to you.
If I could change one thing about the book, I'd want a comprehensive index of all the functions and arguments that augment the basic core functions that produce the example graphs. These functions and arguments tweak the basic function in ways that make them much more appealing than what the basic function alone can provide. But the book isn't able to show each and every combination with each graphing function, so it's up to the reader to figure out how to pick some of the options from one recipe and apply it to another. It's not difficult to do, but having an index to help you find the options you want would make this process easier.
You can purchase R Graphs Cookbook from amazon.com. Slashdot welcomes readers' book reviews -- to see your own review here, read the book review guidelines, then visit the submission page.
This afternoon's Slashdot content... (Score:4, Insightful)
...is brought to you once again by the letter Packt and the number RickJWagner.
Re:Or you can use Excel (Score:1, Insightful)
Re:Or you can use Excel (Score:2, Insightful)
If your data set is so small that a spreadsheet can open it, then your data set is a toy data set.
Where's the +1,Smugly Superior mod option when you need it...
Seriously, any data set that you encounter "in the wild" is by definition not a toy data set. There's many instances where using a spreadsheet to quickly visualize some figures is fine, just like there's many instances where using a word processor to write a letter instead of firing up TeX is fine.
(Granted, you can easily write letters with TeX, too, and better-looking ones than what LibreOffice etc. will come up with, but that's because TeX has packages for just that sort of thing; you don't actually have to wrestle with raw TeX. R has a much steeper learning curve and thus may well be overkill. The right tool for the right job, people!)