R Histogram Tutorial: Create and Customize Plots with Base R

In this tutorial, I'll walk you through creating and customizing histograms in the R programming language. We'll cover what a histogram is, how to set up your data, how to build a basic histogram, and how to adjust colors, labels, bins, and axis limits. 

We will be using the base R programming language with no additional packages. This approach is especially useful when additional packages cannot be used or when you are looking for quick exploratory analyses. In other cases, you might consider using ggplot2, as covered in our How to Make a ggplot2 Histogram in R tutorial.

To easily run all the example code in this tutorial yourself, you can create a DataLab workbook for free that has R pre-installed and contains all code samples. For more practice on how to make a histogram in R, check out this hands-on DataCamp exercise.

Working with histograms in other tools? See our guides on Histograms in Matplotlib for Python and How to Create a Histogram in Excel.

TL;DR

• Use hist(x) to create a basic histogram in base R with no extra packages

• Customize appearance with col (fill color), border (outline), main (title), xlab, and ylab

• Control binning with the breaks argument: pass a number, a vector of breakpoints, or a method name ("Sturges", "Scott", "Freedman-Diaconis")

• Overlay descriptive statistics using abline() for the mean and lines(density()) for a density curve

• For grouped or publication-quality histograms, switch to ggplot2

What Is a Histogram?

A histogram is a graph that shows frequency distributions across continuous (numeric) variables. Histograms allow us to see the count of observations in data within ranges that the variable spans.

Histograms look similar to bar charts. A key difference between the two is that bar charts have a value associated with a specific category or discrete variable, while a histogram visualizes frequencies for continuous variables. 

Setting Up Data for Histograms

We will be using this housing dataset which includes details about different house listings, including the size of the house, the number of rooms, the price, and location information. We can read the data using the read.csv() function, either directly from the URL or by downloading the csv file into a directory and reading it from our local storage. We can also specify that we only want to store the columns we are interested in for this tutorial: price and condition.

home_data <- read.csv("https://raw.githubusercontent.com/rashida048/Datasets/master/home_data.csv")[ ,c('price', 'condition')]

Let’s look at the first few rows of data using the head() function.

head(home_data, 5)

Creating Histograms with Base R

Next, we will create a histogram using the hist() function to look at the distribution of prices in our dataset.

hist(home_data$price)

Basic histogram of home prices. Image by Author.

Adding descriptive statistics

We can add descriptive statistics to the histogram using the abline() function. This adds a vertical line to the plot.

• Set the v argument to the position on the x-axis for the vertical line. Here, we get the mean house price using mean().

• The col argument sets the line color, in this case to red.

• The lwd argument sets the line width. A value of 3 increases the thickness of the line to make it easier to see.

hist(home_data$price)

abline(v = mean(home_data$price), col='red', lwd = 3)

Plotting probability densities

To add a probability density line to the histogram, we first change the y-axis to be scaled to density. In the call to hist() , we set the probability argument to TRUE.

The probability density line is made with a combination of density(), which calculates the position of the probability density curve, and lines(), which adds the line to the existing plot.

hist(home_data$price, probability = TRUE)
abline(v = mean(home_data$price), col='red', lwd = 3)
lines(density(home_data$price), col = 'green', lwd = 3)

Histogram of home prices with the average highlighted. Image by Author.

Notice that the numbers on the y-axis have changed.

Customizing the color

The col parameter of hist() sets the fill color of the bars, and border sets the outline color. Here I'll set the fill to blue and the borders to white.

hist(home_data$price, col = 'blue', border = "white")

Histogram of home prices with color added. Image by Author.

Adding labels and titles

We can change the labels on the plot to make it more readable and presentable. This is useful if you share the plot with others.

• xlab sets the x-axis label

• ylab sets the y-axis label

• main sets the plot title

hist(home_data$price, xlab = 'Price (USD)', ylab = 'Number of Listings', main = 'Distribution of House Prices')