Foreword

Rhas had tremendous growth in popularity over the last three years. Based on that, you’d think that it was a new, up-and-coming language. But surprisingly, R has been around since 1993. Why the sudden uptick in popularity? The somewhat obvious answer seems to be the emergence of data science as a career and a field of study. But the underpinnings of data science have been around for many decades. Statistics, linear algebra, operations research, artificial intelligence, and machine learning all contribute parts to the tools that a modern data scientist uses. R, more than most languages, has been built to make most of these tools only a single function call away.

That’s why I’m very excited to have this book as one of the first in the Addison-Wesley Data and Analytics Series. R is indispensable for many data science tasks. Many algorithms useful for prediction and analysis can be accessed through only a few lines of code, which makes it a great fit for solving modern data challenges. Data science as a field isn’t just about math and statistics, and it isn’t just about programming and infrastructure. This book provides a well-balanced introduction to the power and expressiveness of R and is aimed at a general audience.

I can’t think of a better author to provide an introduction to R than Jared Lander. Jared and I first met through the New York City machine learning community in late 2009. Back then, the New York City data community was small enough to fit in a single conference room, and many of the other data meetups had yet to be formed. Over the last four years, Jared has been at the forefront of the emerging data science profession.

Through running the Open Statistical Programming Meetup, speaking at events, and teaching a course at Columbia on R, Jared has helped grow the community by educating programmers, data scientists, journalists, and statisticians alike. But Jared’s expertise isn’t limited to teaching. As an everyday practitioner, he puts these tools to use while consulting for clients big and small.

This book provides an introduction both to programming in R and to the various statistical methods and tools an everyday R programmer uses. Examples use publicly available datasets that Jared has helpfully cleaned and made accessible through his Web site. By using real data and setting up interesting problems, this book stays engaging to the end.

—Paul Dix, Series Editor