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  3. Intermediate Data Visualization with ggplot2
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    Statistics

    Free

    A picture paints a thousand words, which is why R ggplot2 is such a powerful tool for graphical data analysis. In this chapter, you’ll progress from simply plotting data to applying a variety of statistical methods. These include a variety of linear models, descriptive and inferential statistics (mean, standard deviation and confidence intervals) and custom functions.

  • 2

    Coordinates

    The Coordinates layers offer specific and very useful tools for efficiently and accurately communicating data. Here we’ll look at the various ways of effectively using these layers, so you can clearly visualize lognormal datasets, variables with units, and periodic data.

  • 3

    Facets

    Facets let you split plots into multiple panes, each displaying subsets of the dataset. Here you'll learn how to wrap facets and arrange them in a grid, as well as providing custom labeling.

  • 4

    Best Practices

    Now that you have the technical skills to make great visualizations, it’s important that you make them as meaningful as possible. In this chapter, you’ll review three plot types that are commonly discouraged in the data viz community: heat maps, pie charts, and dynamite plots. You’ll learn the pitfalls with these plots and how to avoid making these mistakes yourself.

Initializing

Exercise

Aspect ratio II: setting ratios

When values are not on the same scale it can be a bit tricky to set an appropriate aspect ratio. A classic William Cleveland (inventor of dot plots) example is the sunspots data set. We have 3200 observations from 1750 to 2016.

sun_plot is a plot without any set aspect ratio. It fills up the graphics device.

To make aspect ratios clear, we've drawn an orange box that is 75 units high and 75 years wide. Using a 1:1 aspect ratio would make the box square. That aspect ratio would make things harder to see the oscillations: it is better to force a wider ratio.

Instructions 1/2

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    • Fix the coordinates to a 1:1 aspect ratio.
  • 2

    The y axis is now unreadably small. Make it bigger!

    • Change the aspect ratio to 20:1. This is the aspect ratio recommended by Cleveland to help make the trend among oscillations easiest to see.