Histograms – How to do it in R

Getting Started

First, be sure you have installed ggformula. Remember, you only need to install the package once on your machine.

Then, be sure to load the package ggformula. Remember, you need to do this with each new Quarto/RMarkdown document or R Session.

Data for Examples

As a reminder (see Overview of Data Visualization), we will be using the penguins data from the palmerpenguins package:

library(palmerpenguins)

Here is a snippet of the data:

Palmer Penguins
species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex	year
Chinstrap	Dream	50.0	19.5	196	3900	male	2007
Adelie	Biscoe	42.0	19.5	200	4050	male	2008
Adelie	Dream	38.1	17.6	187	3425	female	2009
Gentoo	Biscoe	45.5	15.0	220	5000	male	2008
Adelie	Biscoe	40.5	17.9	187	3200	female	2007

Histograms with One Quantitative Variable

Basic Code

For a single quantitative variable, x, here is the general structure for a histogram.

gf_histogram(~x, 
             data = mydata)

Run the code below to see an example using the quantitative variable bill_length_mm from the penguins data. Then replace bill_length_mm with another quantitative variable from the penguins data (e.g. bill_depth_mm)

Notice the warning produced from running the code. This is just a warning that there were rows (penguins) ignored due to missing data for the variables visualized.

Bin Widths

One of the features of a histogram is the bin width. The bin width is something that is automatically generated for most histogram functions, but it is not always ideal. You can add an argument to the R function gf_histogram() to modify the bin width.

gf_histogram(~ x, 
             data = mydata, 
             binwidth = 10)  #adjust this number

Run the code below to see an example using the quantitative variable bill_length_mm from the penguins data with a bin width of 5 (mm). Modify the bin width and see how it affects the histogram.

Adding Labels

Descriptive labels are important for any visualization. We can always add them to any visualization by adding xlab = and ylab = to your function.

gf_histogram(~x, 
             data = mydata,
             xlab = "X Axis Label",
             ylab = "Y Axis Label",
             title = "Descriptive Title")

Add labels and a title to the histogram for bill_length_mm.

Other Modifications

We can add a few other modifications that purely aesthetic - just to make our graphs look nicer or easier to read.

Outlining the Bars

We can add a color that outlines the bars by telling R to outline the color of the bars in black.

gf_histogram(~x, 
             data = mydata,
             xlab = "X Axis Label",
             ylab = "Y Axis Label",
             title = "Descriptive Title",
             color = "black")

Filling the Bars with Color

We can add a color to fill the bars by telling R to fill the bars with a specified color either using a built in color from R or using a hex code for colors .

gf_histogram(~x, 
             data = mydata,
             xlab = "X Axis Label",
             ylab = "Y Axis Label",
             title = "Descriptive Title",
             fill = "darkcyan")

Changing the Theme

The package ggformula is built on top of another package called ggplot2 and so any ggplot2 function can be added to a ggformula generated graphic. For example, we can change the theme to a built-in theme.

Try changing the theme to the following graph:

Try It Out: Modifications

Try adding some modifications for the histogram of bill_length_mm.

Histograms for Comparisons Across Groups

When we have a quantitative variable that has been measured across multiple groups, we may be interested in comparing histograms across the values/groups of a categorical variable. We can do this using two different features of data visualization:

Color Differences
Facets

Adding Color to Groups

Similar to changing the color of bars to a single color, we can use the fill = argument but instead specify our categorical variable z.

gf_histogram(~x, 
             data = mydata,
             fill = ~z) #don't forget the ~ before the variable name

Here is the histogram of bill_length_mm with color varied by species a categorical variable with values of Adelie, Chinstrap, and Gentoo. Modify the code below to change the fill color to another categorical variable such as island or sex and see what happens.

Faceting by Groups

Faceting in visualization is a tool that allows you to easily split up data across multiple panels of the same type. To do this in ggformula you add | z after the formula which conditions the graph on the categorical variable z and splits the graph by the groups/values of z.

gf_histogram(~x | z, 
             data = mydata)

Here is the histogram of bill_length_mm with facets based on species, a categorical variable with values of Adelie, Chinstrap, and Gentoo. Modify the code below to change the facets to another categorical variable such as island or sex and see what happens. Try adding fill by the facet variable as well.

Check Your Understading: Histograms

Question 1. Which of the following characteristics of the distribution of values for a variable can be evaluated using a histogram?

Click Here for Hint

Question 2: Which of the following can we determine from the y-axis of a histogram?