A Histogram is a graphical display of continuous data using bars of different heights.

It is similar to a bar graph, except a histogram groups the data into bins. The height of each bar shows the number of elements in the bin.

They are a great way to display the distribution or variation of data over a range.

The hist() function

In R, you can create a histogram using the hist() function.

It has many options and arguments to control many things, such as bin size, labels, titles and colors.

Syntax

The syntax for the hist() function is:

hist(x,breaks,freq,labels,density,angle,col,border,main,xlab,ylab,…)

Parameters

Create a Histogram

To get started with plot, you need a set of data to work with. Let’s consider the built-in faithful data set as an example data set.

Here are the first six observations of the data set.

# First six observations of the 'Faithful' data set
head(faithful)
  eruptions waiting
1     3.600      79
2     1.800      54
3     3.333      74
4     2.283      62
5     4.533      85
6     2.883      55

To create a histogram just specify the vector in hist() function.

# Create a histogram of time between eruptions of Old Faithful
hist(faithful$waiting)

Choose the Number of Bins

The accuracy of a histogram depends solely upon the number of bins used to plot the data.

Large number of bins hides important details about distribution, while small number of bins causes a lot of noise and hides important information about the distribution as well.

By default, the hist() function chooses an appropriate number of bins to cover the range of values.

However, there are a couple of ways to manually set the number of bins.

1. You can tell R the number of bars you want in the histogram by giving a single number as a value to the breaks argument.

# Specify the number of bars you want in the histogram
hist(faithful$waiting,
     breaks = 20)

2. You can tell R exactly where to put the breaks by giving a vector with the break points as the argument.

# Histogram with custom breaks
hist(faithful$waiting,
     breaks = c(40,45,55,60,65,70,75,85,90,100))

Coloring a Histogram

Use col argument to change the colors used for the bars.

hist(faithful$waiting,
     col="dodgerblue3")

By using the border argument, you can even change the color used for the border of the bars.

hist(faithful$waiting,
     col="lightblue1",
     border="dodgerblue3")

Create a Hatched Histogram

Creating hatched charts in R is rather easy, just specify the density argument in the hist() function.

By default the plot is hatched with 45° slanting lines, however, you can change it with the angle argument.

# Create a hatched histogram with 60° slanting lines
hist(faithful$waiting,
     col="dodgerblue3",
     density=25,
     angle=60)

Adding Titles and Axis Labels

You can add your own title and axis labels easily by specifying following arguments.

hist(faithful$waiting,
     col="dodgerblue3",
     main="Time between eruptions of Old Faithful",
     xlab="Time (minutes)")

Add Value Markers

Often you want to draw attention to specific values or observations in your graphic to provide unique insight. You can do this by adding markers to your graphic.

For example, adding mean line will give you an idea about how much of the distribution is above and below the average.

You can add such marker by using the abline() function.

# Add mean line in the histogram
hist(faithful$waiting,
     col="lightblue1")
abline(v=mean(faithful$waiting),
       col="dodgerblue3",
       lty=2,
       lwd=2)

Another example is placing values on top of bars; which will help you interpret the graph correctly.

You can add them by setting the labels argument to TRUE.

# Show values on top of each bar in the histogram
hist(faithful$waiting,
     col="dodgerblue3",
     labels=TRUE)

Plotting a Kernel Density Estimate (KDE)

A histogram gives you a rough sense of the density of the underlying distribution of your data.

The most complete way of describing your data is by estimating the probability density function (PDF) or density of your variable.

Use the density() function to approximate the sample density and then use lines() function to draw the approximation.

By default, the hist() function plots the counts in the histogram. By setting freq argument to FALSE, you can plot the densities.

# Add a kernel density estimate to a histogram
hist(faithful$waiting,
     col="lightblue1",
     freq = FALSE)
lines(density(faithful$waiting),
      col="dodgerblue3",
      lwd=2)

To fill the density plot, use the polygon() function.

# Fill the density plot
hist(faithful$waiting,
     col="lightblue1",
     freq = FALSE)
lines(density(faithful$waiting))
polygon(density(faithful$waiting),
        col=rgb(1,0,1,.2))

Plot Multiple Histograms

Often you want to compare the distributions of different variables within your data.

You can overlay the histograms by setting the add argument of the second histogram to TRUE.

# random numbers
h1 <- rnorm(1000,6)
h2 <- rnorm(1000,4)

# Overlay two histograms
hist(h1,
     col=rgb(1,0,0,0.5))
hist(h2,
     col=rgb(0,0,1,0.5),
     add=TRUE)