# How to Be an Rtist: A Beginner’s Guide to Writing Beautiful, Intentional R Code

*2022-10-26*

# Chapter 1 Preface

This is a *friendly* book on how to work with data from start to finish. It is intended to be easy reading for a beginner who is not aware of any technical vocabulary.

## 1.1 Why *This* Book?

How do you get inspired to write in R? Many books teach you what to write, but not how to find inspiration. This book starts from the ground up. You’ve just installed R and RStudio - what do you do now? What’s the quickest, smartest way to get results? I’ll show you how to get there, line-by-line, step-by-step. At some point, in trying to get your own work done, you will get stuck. I’ll show you how to get unstuck in mere minutes. I’ll also show you how to write in a way that will prevent you from getting stuck in the first place! This will keep you in the flow of writing beautiful, intentional R code.

## 1.3 *For the Love of R!*

Please remember that the R code taught in Statistics courses does not focus on data generally; it focuses on Statistics. Statistics has a reputation for being hard, especially on those who are learning the subject only because it is required in a non-mathematical university program. R has two completely different reputations. Among students in Statistics courses, it is usually hated. For those who use the tool for work, it is usually *loved*.

### 1.3.1 Why?

The above difference may be due to the fact that students pay to learn R, and workers are paid to learn R. But this is not the only reason for the two different reputations. The workers realize, once they use R day-to-day, that there is an amazing community of other workers who help each other. And not only do they help each other do the work that is currently needed to be done, but they help each other do the work in a smart and beautiful way that is nothing like the work needed in university Statistics courses.

### 1.3.2 Statistics vs. Data

Note that the professor teaching university Statistics probably uses R for **Statistics**. The person learning R outside of university uses R for **Data**. Data is not Statistics! Statistics is specific to the mathematics used on the data; it is not about work-flow, project-management, and coding to get data and reports ready. All the things that Statistics is not about, R does *beautifully*. Hence people who pick up R to work and who are not university professors, end up passionate about R and teaching others about R.