R Graphics Cookbook, 2nd Edition

Chapter 6. Summarized Data Distributions

This chapter explores how to visualize summarized distributions of data.

6.1 Making a Basic Histogram

Problem

You want to make a histogram.

Solution

Use geom_histogram() and map a continuous variable to x (Figure 6-1):

ggplot(faithful, aes(x = waiting)) +
  geom_histogram()

Figure 6-1. A basic histogram

Discussion

All geom_histogram() requires is one column from a data frame or a single vector of data. For this example we’ll use the faithful data set, which contains two columns with data about the Old Faithful geyser: eruptions, which is the length of each eruption, and waiting, which is the length of time to the next eruption. We’ll only use the waiting variable in this example:

faithful
#>     eruptions waiting
#> 1       3.600      79
#> 2       1.800      54
#> 3       3.333      74
#>  ...<266 more rows>...
#> 270     4.417      90
#> 271     1.817      46
#> 272     4.467      74

If you just want to get a quick look at some data that isn’t in a data frame, you can get the same result by passing in NULL for the data frame and giving ggplot() a vector of values. This would have the same result as the previous code:

# Store the values in a simple vector
w <- faithful$waiting

ggplot(NULL, aes(x = w)) +
  geom_histogram()

By default, the data is grouped into 30 bins. This number of bins is an arbitrary default value, and may be too fine or too coarse for your data. You can change the size of the ...