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* Reflect new part structure

* Mention all chapters

* Hide the ruler

* Crossref diagram

* Fix crossref

* Mention all import chapters

* Fix link to following chapter

* Fix title and summary

* Add intros

* Consistent chunk style?
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data-visualize.qmd
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@@ -15,7 +15,7 @@ R has several systems for making graphs, but ggplot2 is one of the most elegant
ggplot2 implements the **grammar of graphics**, a coherent system for describing and building graphs.
With ggplot2, you can do more and faster by learning one system and applying it in many places.
This chapter will teach you how to visualize your data using ggplot2.
This chapter will teach you how to visualize your data using **ggplot2**.
We will start by creating a simple scatterplot and use that to introduce aesthetic mappings and geometric objects -- the fundamental building blocks of ggplot2.
We will then walk you through visualizing distributions of single variables as well as visualizing relationships between two or more variables.
We'll finish off with saving your plots and troubleshooting tips.
@@ -567,7 +567,7 @@ In the following sections you will learn about commonly used plots for visualizi
To visualize the relationship between a numerical and a categorical variable we can use side-by-side box plots.
A **boxplot** is a type of visual shorthand for a distribution of values that is popular among statisticians.
Each boxplot consists of:
As shown in @fig-eda-boxplot, each boxplot consists of:
- A box that stretches from the 25th percentile of the distribution to the 75th percentile, a distance known as the interquartile range (IQR).
In the middle of the box is a line that displays the median, i.e. 50th percentile, of the distribution.
@@ -579,7 +579,10 @@ Each boxplot consists of:
- A line (or whisker) that extends from each end of the box and goes to the farthest non-outlier point in the distribution.
```{r}
#| label: fig-eda-boxplot
#| echo: false
#| fig-cap: >
#| Diagram depicting how a boxplot is created.
#| fig-alt: >
#| A diagram depicting how a boxplot is created following the steps outlined
#| above.
@@ -848,7 +851,7 @@ We started with the basic idea that underpins ggplot2: a visualization is a mapp
You then learned about increasing the complexity and improving the presentation of your plots layer-by-layer.
You also learned about commonly used plots for visualizing the distribution of a single variable as well as for visualizing relationships between two or more variables, by levering additional aesthetic mappings and/or splitting your plot into small multiples using faceting.
We'll use visualizations again and again through out this book, introducing new techniques as we need them as well as do a deeper dive into creating visualizations with ggplot2 in @sec-layers through @sec-eda.
We'll use visualizations again and again through out this book, introducing new techniques as we need them as well as do a deeper dive into creating visualizations with ggplot2 in @sec-layers through @sec-exploratory-data-analysis.
With the basics of visualization under your belt, in the next chapter we're going to switch gears a little and give you some practical workflow advice.
We intersperse workflow advice with data science tools throughout this part of the book because it'll help you stay organize as you write increasing amounts of R code.