1 Introduction

This book provides an introduction to R and also serves as a first course in the study of experimental design and applied statistical analysis. We only assume a basic understanding of statistics, but no prior knowledge of R.

The first few sections of this book will guide you through the installation of R and RMarkdown, and introduce you to the syntax of R and RMarkdown, functions, data types, and basic data visualization. We will then review statistical concepts needed and discuss experiment design, data collection, how to import and export data, and data cleaning. Finally, we introduce a range of commonly used tests and analyses and learn how to perform them using R.

Statistics are used to answer questions that a non-expert might bring to us. We therefor place great emphasis on producing professional looking reports, interpreting any results correctly, and communicating our conclusions clearly without using technical jargon.

You cannot learn how to “do statistics” by just reading this book, statistics need practice. You should replicate any code and examples, work the exercises, and access and study the data sets and sample code.

Any files I reference in the book are available online, organized by chapter.

1.1 How to get help

There are many ways you can get help with anything R related:

If you need help with statistics, I recommend

  • Mathematical Statistics with Resampling and R by Laura Chihara and Tim Hesterberg (Wiley)
  • Modern Mathematical Statistics by Edward Dudewicz and Satyan Mishra (Wiley)

Of course, you could always ask me :)

1.2 Disclaimer

This book is intended as a companion text for Math 333 - Applied Statistical and Experimental Design, not as a stand alone textbook. Not everything that is covered in class is included in this book. Also, this book is still a work in progress.