More bars

With the last plot, 40 bins make it roughly look like we have a skewed but unimodal distribution. Remember the rule-of-thumb from the slides: if you have more than 150 data points you should usually shoot straight up to 100 bins. Let's do that here.

Edit the plot to have 100 bins instead of 40 and also change the color of the bars to 'steelblue' just because it's a good color and sometimes that's important.

Do you notice anything about the range around 30% now? If you want to get a little more of an idea of what could be happening, try filtering the data to the percentage over the speed limit of 30.

md_speeding %>% filter(percentage_over_limit == 30)

See anything surprising for a continuous value?

This exercise is part of the course

Visualization Best Practices in R

View Course

Exercise instructions

Change bin number to 100
Set fill of bars to 'steelblue'

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

ggplot(md_speeding) +
    geom_histogram(
        aes(x = percentage_over_limit),
        bins = 40 ,         # switch to 100 bins
        ___                 # set the fill of the bars to 'steelblue'
        alpha = 0.8 )

Edit and Run Code