Preface

What is the use of a book, without pictures or conversations?

Alice, Alice in Wonderland

It’s wondrous, with treasures to satiate desires both subtle and gross; but it’s not for the timid.

Q, “Q Who?” Stark Trek: The Next Generation

When I began writing this book, I spent quite a bit of time searching for a good quote to start things off. I ended up with two. R is a wonderfully flexible platform and language for exploring, visualizing, and understanding data. I chose the quote from Alice in Wonderland to capture the flavor of statistical analysis today—an interactive process of exploration, visualization, and interpretation.

The second quote reflects the generally held notion that R is difficult to learn. What I hope to show you is that is doesn’t have to be. R is broad and powerful, with so many analytic and graphic functions available (more than 50,000 at last count) that it easily intimidates both novice and experienced users alike. But there is rhyme and reason to the apparent madness. With guidelines and instructions, you can navigate the tremendous resources available, selecting the tools you need to accomplish your work with style, elegance, efficiency—and more than a little coolness.

I first encountered R several years ago, when applying for a new statistical consulting position. The prospective employer asked in the pre-interview material if I was conversant in R. Following the standard advice of recruiters, I immediately said yes, and set off to learn it. I was an experienced statistician and researcher, had 25 years experience as an SAS and SPSS programmer, and was fluent in a half dozen programming languages. How hard could it be? Famous last words.

As I tried to learn the language (as fast as possible, with an interview looming), I found either tomes on the underlying structure of the language or dense treatises on specific advanced statistical methods, written by and for subject-matter experts. The online help was written in a Spartan style that was more reference than tutorial. Every time I thought I had a handle on the overall organization and capabilities of R, I found something new that made me feel ignorant and small.

To make sense of it all, I approached R as a data scientist. I thought about what it takes to successfully process, analyze, and understand data, including

• Accessing the data (getting the data into the application from multiple sources)
• Cleaning the data (coding missing data, fixing or deleting miscoded data, transforming variables into more useful formats)
• Annotating the data (in order to remember what each piece represents)
• Summarizing the data (getting descriptive statistics to help characterize the data)
• Visualizing the data (because a picture really is worth a thousand words)
• Modeling the data (uncovering relationships and testing hypotheses)
• Preparing the results (creating publication-quality tables and graphs)

Then I tried to understand how I could use R to accomplish each of these tasks. Because I learn best by teaching, I eventually created a website (www.statmethods.net) to document what I had learned.

Then, about a year ago, Marjan Bace (the publisher) called and asked if I would like to write a book on R. I had already written 50 journal articles, 4 technical manuals, numerous book chapters, and a book on research methodology, so how hard could it be? At the risk of sounding repetitive—famous last words.

The book you’re holding is the one that I wished I had so many years ago. I have tried to provide you with a guide to R that will allow you to quickly access the power of this great open source endeavor, without all the frustration and angst. I hope you enjoy it.

P.S. I was offered the job but didn’t take it. However, learning R has taken my career in directions that I could never have anticipated. Life can be funny.