Hands-On Programming with R

Foreword

Learning to program is important if you’re serious about understanding data. There’s no argument that data science must be performed on a computer, but you have a choice between learning a graphical user interface (GUI) or a programming language. Both Garrett and I strongly believe that programming is a vital skill for everyone who works intensely with data. While convenient, a GUI is ultimately limiting, because it hampers three properties essential for good data analysis:

Reproducibility

The ability to re-create a past analysis, which is crucial for good science.

Automation

The ability to rapidly re-create an analysis when data changes (as it always does).

Communication

Code is just text, so it is easy to communicate. When learning, this makes it easy to get help—whether it’s with email, Google, Stack Overflow, or elsewhere.

Don’t be afraid of programming! Anyone can learn to program with the right motivation, and this book is organized to keep you motivated. This is not a reference book; instead, it’s structured around three hands-on challenges. Mastering these challenges will lead you through the basics of R programming and even into some intermediate topics, such as vectorized code, scoping, and S3 methods. Real challenges are a great way to learn, because you’re not memorizing functions void of context; instead, you’re learning functions as you need them to solve a real problem. You’ll learn by doing, not by reading.

As you learn to program, you are going ...