Whole game

The goal of the first part of this book is to introduce you the data science workflow including data importing, tidying, and data exploration as quickly as possible. Data exploration is the art of looking at your data, rapidly generating hypotheses, quickly testing them, then repeating again and again and again. The goal of data exploration is to generate many promising leads that you can later explore in more depth.

A diagram displaying the data science cycle: Import -> Tidy -> Explore (which has the phases Transform -> Visualize -> Model in a cycle) -> Communicate. Surrounding all of these is Communicate. Explore is highlighted.

In this part of the book, you will learn several useful tools that have an immediate payoff:

Modelling is an important part of the exploratory process, but you don’t have the skills to effectively learn or apply it yet and details of modeling fall outside the scope of this book.

Nestled among these five chapters that teach you the tools for doing data science are three chapters that focus on your R workflow. In Chapter Chapter 3, Chapter Chapter 5, Chapter Chapter 7, and Chapter Chapter 9, you’ll learn good workflow practices for writing and organizing your R code. These will set you up for success in the long run, as they’ll give you the tools to stay organised when you tackle real projects.