# Whole game {#sec-whole-game-intro .unnumbered}

```{r}

#| results: "asis"

#| echo: false

source("_common.R")

```

Our goal in this part of the book is to give you a rapid overview of the main tools of data science: **importing**, **tidying**, **transforming**, and **visualizing data**, as shown in @fig-ds-whole-game.

We want to show you the "whole game" of data science giving you just enough of all the major pieces so that you can tackle real, if simple, data sets.

The later parts of the book, will hit each of these topics in more depth, increasing the range of data science challenges that you can tackle.

```{r}

#| label: fig-ds-whole-game

#| echo: false

#| out.width: NULL

#| fig-cap: >

#| In this section of the book, you'll learn how to import,

#| tidy, transform, and visualize data.

#| fig-alt: >

#| A diagram displaying the data science cycle: Import -> Tidy ->

#| Understand (which has the phases Transform -> Visualize -> Model in a

#| cycle) -> Communicate. Surrounding all of these is Program

#| Import, Tidy, Transform, and Visualize is highlighted.

knitr::include_graphics("diagrams/data-science/whole-game.png", dpi = 270)

```

Five chapters focus on the tools of data science:

- 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 @sec-data-visualisation you'll dive into visualization, learning the basic structure of a ggplot2 plot, and powerful techniques for turning data into plots.

- Visualisation alone is typically not enough, so in @sec-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 @sec-data-tidy, you'll learn about tidy data, a consistent way of storing your data that makes transformation, visualization, and modelling easier.

You'll learn the underlying principles, and how to get your data into a tidy form.

- Before you can transform and visualize your data, you need to first get your data into R.

In @sec-data-import you'll learn the basics of getting `.csv` files into R.

- Finally, in @sec-exploratory-data-analysis, you'll combine visualization and transformation with your curiosity and skepticism to ask and answer interesting questions about data.

Nestled among these chapters that are five other chapters that focus on your R workflow.

In @sec-workflow-basics, @sec-workflow-pipes, @sec-workflow-style, and @sec-workflow-scripts-projects, 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.