R Beginner’s Guide (Best Practices)

Preface

After introducing R to a few beginners, and following up with them after they started their journey, I noticed a recurring pattern of frustration: they felt overwhelmed by the amount of learning they thought they needed in order to start their projects; and, even after starting, they were overwhelmed by abstract errors that could have been avoided with a bit more insight. At the time of writing this book, project quick start guides using best practices were non-existent or scattered. Hence this guide.

You should benefit from this book if you are 1) a complete beginner, or 2) were only introduced to R through statistics courses. Courses often emphasize analytical techniques, but spend less time on workflow, project organization, and coding practices. As a result, even familiar users can struggle with messy files, invalid code, confusing output, and unnecessary frustration—especially when their primary goal is to analyze data, not to debug programs.

I learned that R has two very different reputations: for many students, it is seen as difficult or discouraging; but among practitioners who use it regularly, it is frequently appreciated and even enjoyed. A key difference is not ability, but context: day-to-day use encourages better habits, clearer workflows, and engagement with a generous community that openly shares knowledge and support.

In sum, this book is intended to support your first, serious data project by emphasizing practical workflow, clarity, and reproducibility from the very beginning. That way, you can avoid the vicious cycle below.