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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

Bar plots: dynamite plots

In the video we saw many reasons why "dynamite plots" (bar plots with error bars) are not well suited for their intended purpose of depicting distributions. If you really want error bars on bar plots, you can of course get them, but you'll need to set the positions manually. A point geom will typically serve you much better.

Nonetheless, you should know how to handle these kinds of plots, so let's give it a try.

Instructions

100 XP
  • Using mtcars,, plot wt versus fcyl.
  • Add a bar summary stat, aggregating the wts by their mean, filling the bars in a skyblue color.
  • Add an errorbar summary stat, aggregating the wts by mean_sdl.