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  3. Visualization Best Practices in R
  • 1

    Proportions of a whole

    Free

    In this chapter, we focus on visualizing proportions of a whole; we see that pie charts really aren't so bad, along with discussing the waffle chart and stacked bars for comparing multiple proportions.

  • 2

    Point data

    We shift our focus now to single-observation or point data and go over when bar charts are appropriate and when they are not, what to use when they are not, and general perception-based enhancements for your charts.

  • 3

    Single distributions

    We now move on to visualizing distributional data, we expose the fragility of histograms, discuss when it is better to shift to a kernel density plots, and how to make both plots work best for your data.

  • 4

    Comparing distributions

    Finishing off we take a look at comparing multiple distributions to each other. We see why the traditional box plots are very dangerous and how to easily improve them, along with investigating when you should use more advanced alternatives like the beeswarm plot and violin plots.

Initializing

Exercise

Orienting with the data

Let's take our first look at the new speeding dataset.

First, print the data frame to your screen and try and get a sense of it. You can use filter()s, group_by()s or any of your tidyverse functions to do this.

The supplied code is what we used to make the histogram of blue car speeds in the slides. Modify this code to look at how many miles-per-hour red cars were going over the speed limit (speed_over). Give the plot a title while you're at it to let people know what they're looking at.

Instructions

100 XP
  • Print the md_speeding data frame to the console and investigate it.
  • Change filter() to 'RED' cars.
  • Change column of interest to speed_over.
  • Title plot 'MPH over speed limit | Red cars'