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  3. Intermediate Data Visualization with ggplot2
  • 1

    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

Wind rose plots

Polar coordinate plots are well-suited to scales like compass direction or time of day. A popular example is the "wind rose".

The wind dataset is taken from the openair package and contains hourly measurements for windspeed (ws) and direction (wd) from London in 2003. Both variables are factors.

Instructions 1/3

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  • Make a classic bar plot mapping wd onto the x aesthetic and ws onto fill.
  • Use a geom_bar() layer, since we want to aggregate over all date values, and set the width argument to 1, to eliminate any spaces between the bars.