A **relative frequency histogram** is a graph that displays the relative frequencies of values in a dataset.

This tutorial explains how to create a relative frequency histogram in R by using the **histogram()** function from the **lattice**, which uses the following syntax:

**histogram(x, type)**

where:

**x:**data**type:**type of relative frequency histogram you’d like to create; options include percent, count, and density.

**Default Histogram**

First, load the **lattice **package:

library(lattice)

By default, this package creates a relative frequency histogram with **percent **along the y-axis:

#create data data <- c(0, 0, 2, 3, 4, 4, 5, 6, 7, 12, 12, 14) #create relative frequency histogram histogram(data)

**Modifying the Histogram**

We can modify the histogram to include a title, different axes labels, and a different color using the following arguments:

**main:**the title**xlab:**the x-axis label**ylab:**the y-axis label**col:**the fill color to use in the histogram

For example:

#modify the histogram histogram(data, main='Points per Game by Player', xlab='Points per Game', col='steelblue')

**Modifying the Numbers of Bins**

We can specify the number of bins to use in the histogram using the **breaks **argument:

#modify the number of bins histogram(data, main='Points per Game by Player', xlab='Points per Game', col='steelblue', breaks=15)

The more bins you specify, the more you will be able to get a granular look at your data. Conversely, the fewer number of bins you specify, the more aggregated the data will become:

#modify the number of bins histogram(data, main='Points per Game by Player', xlab='Points per Game', col='steelblue', breaks=3)

**Related:** Use Sturges’ Rule to identify the optimal number of bins to use in a histogram.

a relative frequency histogram by definition is normalized by height, so it shows proportion. This is just a regular frequency histogram.