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.
- Visualisation is a great place to start with R programming, because the payoff is so clear: you get to make elegant and informative plots that help you understand data.
In Chapter \@ref(data-visualisation) you'll dive into visualisation, learning the basic structure of a ggplot2 plot, and powerful techniques for turning data into plots.
- Visualisation alone is typically not enough, so in Chapter \@ref(data-transform) you'll learn the key verbs that allow you to select important variables, filter out key observations, create new variables, and compute summaries.
- In Chapter \@ref(data-tidy), you'll learn about tidy data, a consistent way of storing your data that makes transformation, visualisation, and modelling easier.
You'll learn the underlying principles, and how to get your data into a tidy form.
- Finally, in Chapter \@ref(exploratory-data-analysis), you'll combine visualisation and transformation with your curiosity and scepticism to ask and answer interesting questions about data.
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.
In Chapters \@ref(workflow-basics), \@ref(workflow-pipes), \@ref(workflow-style), and \@ref(workflow-scripts-projects), you'll learn good workflow practices for writing and organising your R code.
