# (PART) Explore {-}

# Introduction {#explore-intro}

The goal of the first part of this book is to get you up to speed with the basic tools of data exploration as quickly as possible:

```{r echo = FALSE, out.width = "75%"}
knitr::include_graphics("diagrams/data-science-explore.png")
```

You will get frustrated when you start programming in R, because it is such a stickler for mistakes. Even one character out of place will cause it to complain. However, that frustration is both typical and temporary. It happens to everyone, and the only way to get over it is to keep trying.

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 [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 [data transformation] you'll learn the key verbs that allow you to select important variables, filter out key observations, create new variables, and compute summaries.
  
Finally, in [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. We'll come back to it in [modelling](#model-intro), once you're better equipped with more data wrangling and programming tools.
