I never liked most programming books. The same with most programming courses. The reason is that many of them assume you already have a programming background.
That assumption doesn’t work well with the R programming language. It’s unique because it’s a statistical programming language. That means the people often needing to learn it are researchers, statisticians, and data analysts – not computer engineers.
That’s why I wrote this book. I wanted to explain R programming to people who don’t know how to program. I wanted to strip away all the bloated, technical jargon used in most texts and explain R programming “in plain English.”
It’s hard enough to learn statistics. The same for psychology, business, sociology, medicine, economics, marketing, or whatever you were crazy enough to devote the best years of your life to learning. We don’t need the programming language we use to analyze our data to be as hard to learn as the subject itself.
Few things I write about in this book are original. I heavily relied on the default R documentation1, as well as many other package-specific documentation pages, to write this book. (See References page for complete list of sources.)
What makes this book a better resource though is the way it’s written. I make things easy for beginners to understand. R documentation is often written with terminology that sounds foreign to people who don’t already know R programming.
The other key benefit to this book is the depth (or lack thereof) of the content. This book is not a reference book. It’s about teaching the core concepts of R programming that come up again and again. Understanding this foundation makes it easier for you to use reference materials later.
In other words, you’ll know what to “Google.” :)
This book would make a great companion piece to your first statistics class. I don’t cover the common statistical functions until the end of the book. If your class book or professor references code to perform certain calculations, this will help you understand what it’s doing.
That said, the final chapter does assume you know these concepts already. So I won’t spend much time defining what a p-value is or what makes a fixed-effects model.