R Beginner’s Guide (Best Practices)

Preface

I taught my spouse how to program in R (she had no previous knowledge despite completing a degree in Statistics). She, like many of us, was often frustrated while learning. I emphasized the kindness and generosity of the R community who write free books and provide support on forums. But as I started recommending books alongside our personal lessons, I noticed there wasn’t a book meant for a specific audience: a complete beginner who wishes to learn by doing and in a way that avoids future frustration.

Simply put, this book is a hands-on guide for your very first R projects.

For the Love of R

Without a code- and project-oriented data science course, many beginner analysts know only mathematical or methodological knowledge and how to start a program. They are bound to write invalid code. The resulting invalid output is especially demotivating for analysts, as their task is to analyse the data. They may have been exposed to R in a Statistics courses, but these courses typically focus on methods and potentially mathematics; not work-flow, project-management, and coding. Furthermore, Statistics has a reputation for being hard, especially on those who are learning the subject only because it is required in a non-mathematical program.

As a result, R has two completely different reputations. Among students in Statistics courses, it is often despised. Among those who use the tool for work, it is usually loved. The difference may be caused by students paying to learn R, and employees being paid. But employees also realize, once they use R day-to-day, that there is an amazing community of others who help each other. This book is also meant to play a small part in that community.