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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

Aspect ratio I: 1:1 ratios

We can set the aspect ratio of a plot with coord_fixed(), which uses ratio = 1 as a default. A 1:1 aspect ratio is most appropriate when two continuous variables are on the same scale, as with the iris dataset.

All variables are measured in centimeters, so it only makes sense that one unit on the plot should be the same physical distance on each axis. This gives a more truthful depiction of the relationship between the two variables since the aspect ratio can change the angle of our smoothing line. This would give an erroneous impression of the data. Of course the underlying linear models don't change, but our perception can be influenced by the angle drawn.

A plot using the iris dataset, of sepal width vs. sepal length colored by species, is shown in the viewer.

Instructions

100 XP
  • Add a fixed coordinate layer to force a 1:1 aspect ratio.