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Rough first pass at summaries for all whole game chapters

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Hadley Wickham
2022-09-29 10:36:22 -05:00
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data-visualize.qmd
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@@ -15,8 +15,6 @@ 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.
If you'd like to learn more about the theoretical underpinnings of ggplot2, you might enjoy reading "The Layered Grammar of Graphics", <https://vita.had.co.nz/papers/layered-grammar.pdf>, the scientific paper that discusses the theoretical underpinnings..
### Prerequisites
This chapter focuses on ggplot2, one of the core packages in the tidyverse.
@@ -139,7 +137,8 @@ We will begin with the `<MAPPINGS>` component.
> "The greatest value of a picture is when it forces us to notice what we never expected to see." --- John Tukey
In the plot below, one group of points (highlighted in red) seems to fall outside of the linear trend.
These cars have a higher fuel efficiency than you might expect. That is, they have a higher miles per gallon than other cars with similar engine sizes.
These cars have a higher fuel efficiency than you might expect.
That is, they have a higher miles per gallon than other cars with similar engine sizes.
How can you explain these cars?
```{r}
@@ -1303,3 +1302,20 @@ knitr::include_graphics("images/visualization-grammar-3.png")
You could use this method to build *any* plot that you imagine.
In other words, you can use the code template that you've learned in this chapter to build hundreds of thousands of unique plots.
If you'd like to learn more about this theoretical underpinnings of ggplot2, you might enjoy reading "[The Layered Grammar of Graphics](https://vita.had.co.nz/papers/layered-grammar.pdf)", the scientific paper that describes the theory of ggplot2 in detail.
## Summary
In this chapter, you've learn the basics of data visualization with ggplot2.
We started with the basic idea that underpins ggplot2: a visualization is a mapping from variables in your data to aesthetic properties like position, colour, size and shape.
You then learned about facets, which allow you to create small multiples, where each panel contains a subgroup from your data.
We then gave you a whirlwind tour of the geoms and stats which control the "type" of graph you get, whether it's a scatterplot, line plot, histogram, or something else.
Position adjustment control the fine details of position when geoms might otherwise overlap, and coordinate systems allow you fundamentally change what `x` and `y` mean.
We'll use visualizations again and again through out this book, introducing new techniques as we need them.
If you want to get a comprehensive understand of ggplot2, we recommend reading the book, [*ggplot2: Elegant Graphics for Data Analysis*](https://ggplot2-book.org).
Other useful resources are the [*R Graphics Cookbook*](https://r-graphics.org) by Winston Chang and [*Fundamentals of Data Visualization*](https://clauswilke.com/dataviz/) by Claus Wilke.
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.