Statistical Analysis with R

I highly enjoy the way the author introduces R to users - by having the users analyze a set of data with the role of saving a kingdom during the ancient China era. All the functions introduced in this book are the basic functions anyone needs to start exploring their data in R. I find myself using these functions all the time even though I am now more experienced in R. The user do not have to learn functions that they will never use when it comes to analyzing their own dataset.

Overall, the book covers the fundamentals required to understand how R works and provide the basic functions for users to start using R on their own. The author assumes that the user has no prior background on any programming language or even statistics. Hence, this book is suitable for anyone who is thinking of using R but has no idea where to start.

My only complain is the way the book was organized. Starting from chapter 4, the book becomes continuous in terms of the R codes and data analysis. The R output from one chapter is required for the next. This does not allow the user to simply flip to a chapter of interest and start following the author. It is definitely not written as a quick reference.

One of many good references for using R. Others may be better depends on user

A little chaotic!

Sooner or later, every R beginner will come across An Introduction to R, which appears as the first link under Manuals on the R website. If you work your way through this introduction, you will get a good grounding in the essentials for using R. Unfortunately, it is very dry and it can be a challenge to get through. I certainly never managed to read it from start to finish in one sitting, but having used R for more than 10 years, I regularly return to read bits and pieces, so by now I have read and re-read it all many times. So, useful though this introduction is, it is not always a great place to start for R beginners.

There are many books available about R, including books focusing on the language itself, books on graphics in R, books on implementing particular statistical techniques in R and more than one introduction to R. A few weeks ago I was offered an electronic review copy of Statistical Analysis With R, a new beginner's introduction to R by John M. Quick. Curious to see whether it could offer a good springboard into R, I decided to take up the offer.

At around 300 pages and covering a little less ground, it certainly takes a more leisurely pace than An Introduction to R. It also attempts a more engaging style by building a narrative around the premise that you have become a strategist for the Shu army in 3rd century China. The worked examples are all built around the challenge of looking at past battle statistics to determine the best strategy for a campaign against the rival Wei kingdom. Given how hard it can be to make an introduction to a statistical programming language exciting, it is certainly worth trying a novel approach. Still, some readers may find the Shu theme a little corny.

The book begins with instructions for downloading and installing R and goes on to explore the basics of importing and manipulating data, statistical exploration of the data (means, standard deviations and correlations), linear regression and finishes with a couple of chapters on producing and customising charts. This is a good selection of topics: mastery of these will provide beginners good grounding in the core capabilities of R. Readers with limited experience with statistics may be reassured that no assumptions are made about mathematical knowledge. The exploration of the battle data is used to provide a simple explanation of what linear regression is as well as the techniques available in R to perform the computations. While this approach certainly makes the book accessible to a broader audience, it is not without risks. Statistical tools are notorious for being abused by people who do not understand them properly. As a friend of mine likes to say, "drive-by regressions" can do a lot of damage!

Each chapter adopts the same structure: a brief introduction advancing the Shu story; a list of the topics covered in the chapter; a series of worked examples with sample commands to be entered into the R console followed by an explanatory "What just happened?" section and a "Pop quiz"; suggestions for further tasks for the readers to try; and finally a chapter summary. At times this approach feels a little repetitive (and the recurring heading "Have a go hero" for the suggested further tasks section may sound a little sarcastic to Australian readers at least), but it is thorough.

If I were to write my own introduction to R (one day perhaps?), I would do some things a little differently. I would try to explain a bit more about the semantics of the language, particularly the difference between the various data type (vectors, lists, data frames and so on). But perhaps that just end up being as dry as An Introduction to R. Also, though I certainly agree with Quick that commenting your code is a very important discipline (even if no-one else ever reads it, you might have to read it again yourself!), I do think that he takes this principle too far in expecting readers to type all of the comments in the worked examples into the console!

Statistical Analysis With R is a very gentle introduction to R. If you have no prior experience of R, reading this book will certainly get you started. On the other hand, if you have already started experimenting with R, the pace may just be a little too slow.

[To all of the R-bloggers out there who recognize this, I apologize. To those that don't, This is at least the 5th review of this book to go on the feed. The author is linking to the others here.]

Long Version: I have a Bachelor's degree in Computer Science. I'm pretty handy when it comes to programming. So when I look for a book about a language, I'm looking for a book that will dispense with a whirlwind review of code logic within the first Chapter, if at all. That said, R isn't just a programming language, now is it? This book comes across as a Programming textbook, in large part because R comes across as a Programming environment. However, there's a very fine line the book walks between Programming and Statistical Analysis. The book makes no effort to explain control structures like loops, conditional statements, and logic. In this, it is an entirely honest title-the book teaches how to use R for the usual suspects of statistics. But I'm getting ahead of myself.

Quick (the author) starts the book by framing you as the successor to a 3rd century CE Chinese General, preparing for war against another kingdom. The exercises all follow this cheesy lively narrative. It's a creative presentation of sample data, but it does occasionally distract from the general utility of the tools he is presenting.

After a couple of plodding chapters on installing R (all screenshots from an apple, annoyingly), Quick delves into the statistics. He moves quickly from basic arithmetic to correlation analysis, regression, and ANOVA. There's no explanation of hypothesis testing or probability theory. There's no discussion of the underlying mechanics of the output. This is exactly as advertised. Note the tagline: less theory, more results. For this reason, I wouldn't recommend this book for statistics beginners. What's the point of getting Regression output if you have no idea what your p-value, or R-squared mean? However, for anyone who already understands these statistics in principle, it is fairly straightforward to pick up these R commands.

As I previously mentioned, there's also no discussion of looping or conditionality (though he uses an if statement in one of the exercises). I find it puzzling on this note that the author includes a brief tutorial on function writing, but this is a small comment-it seems a strange place to halt the discussion of the language.

Quick then shifts to a fantastic tutorial on how to use the built-in graphing tools. Slow, but fairly comprehensive for R's default graphing functionality. If you're looking for a way to graph something without jumping into ggplot2, this book can show you a reasonable alternative. Although, if you are looking to make even higher quality graphs, Quick sends you out on a brief tutorial on installing new packages, along with web resources for finding what information you could need to get functionality from them. The brave can take it from there. The less adventuresome can look forward to Packt Publishing's other book on R, R Graphs Cookbook. I'm working through it now and will post a similar review in due time.

I recommend this book if you fit the profile of someone who could benefit from it. Read the tl;dr version for clarification on this point.

tl;dr Version: Fantastic for fans of the ... for Dummies approach to learning. Deliberate pacing, yet fairly comprehensive. Good for those with a rudimentary understanding of statistics. Not good for programmers.