ggplot

The function ggplot() allows us to graph most kinds of data relatively simply. Its syntax is slightly odd but very flexible. We’ll show specific commands for several types of plots below.

To begin, remember to load ggplot2 with:

library(ggplot2)

## Registered S3 methods overwritten by 'ggplot2':
##   method         from 
##   [.quosures     rlang
##   c.quosures     rlang
##   print.quosures rlang

To make a graph with ggplot(), you need to specify at least two elements in your command. The first uses the function ggplot() itself, to specify which data frame you want to use and also which variables are to be plotted. The second part tells R what kind of graph to make, using a geom function. The odd part is that these two parts are put together with a + sign. It’s simplest to see this with an example. We’ll draw a histogram with ggplot() in the next section.

Histograms

A histogram represents the frequency distribution of a numerical variable in a sample.

Let’s see how to make a basic histogram using the age data from the Titanic data set. Make sure you have loaded the data (using read.csv) into a data frame called titanicData.

titanicData <- read.csv("DataForLabs/titanic.csv")

Here’s the code to make a simple histogram of age:

ggplot(titanicData, aes(x=age)) + geom_histogram()

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## Warning: Removed 680 rows containing non-finite values (stat_bin).

Notice that there are two functions called here, put together in a single command with a plus sign. The first function is ggplot(), and it has two input arguments. Listed first is titanicData; this is the name of the data frame containing the variables that we want to graph. The second input to ggplot is an aes() function. In this case, the aes() function tells R that we want Age to be the x-variable (i.e. the variable that is displayed along the x-axis). (The “aes” stands for “aesthetics”,” but if you’re like us this won’t help you remember it any better.)

The second function in this command is geom_histogram(). This is the part that tells R that the “geometry” of our plot should be a histogram.

This is not the most beautiful graph in the world, but it conveys the information. At the end of this tutorial we’ll see a couple of options that can make a ggplot graph look a little better.

Bar graphs

A bar graph plots the frequency distribution of a categorical variable.

In ggplot(), the syntax for a bar graph is very similar to that for a histogram. For example, here is a bar graph for the categorical variable Sex in the titanic data set. Aside from specifying a different variable for x, we use a different geom function here, geom_bar.

ggplot(titanicData, aes(x=sex)) + geom_bar(stat="count")

Boxplots

A boxplot is a convenient way of showing the frequency distribution of a numerical variable in multiple groups. Here’s the code to draw a boxplot for age in the titanic data set, separately for each sex:

ggplot(titanicData, aes(x=sex, y=age)) + geom_boxplot()

## Warning: Removed 680 rows containing non-finite values (stat_boxplot).

Notice that the y variable here is age, and x is the categorical variable sex that winds up on the x-axis. See the result below, and look at where the variables are. The other new feature here is the new geom function, geom_boxplot().

Here the thick bar in the middle of each boxplot is the median of that group. The upper and lower bounds of the box extend from the first to the third quartile. The vertical lines are called whiskers, and they cover most of the range of the data (except when data points are pretty far from the median (see text), when they are plotted as individual dots, as on the male boxplot).

Scatterplots

The last graphical style that we will cover here is the scatter plot, which shows the relationship between two numerical variables.

The titanic data set does not have two numerical variables, so let’s use a different data set—the example from Figure 2.3-2 of Whitlock and Schluter, showing the relationship between the ornamentation of father guppies and the sexual attractiveness of their sons. You can load the data for that example with

guppyFatherSonData <-read.csv("DataForLabs/chap02e3bGuppyFatherSonAttractiveness.csv")

To make a scatter plot of the variables fatherOrnamentation and sonAttractiveness with ggplot, you need to specify the x and y variables, and use geom_point():

ggplot(guppyFatherSonData,   
  aes(x = fatherOrnamentation, 
  y = sonAttractiveness)) +
  geom_point()

Better looking graphics with options

The code we have listed here for graphics barely scratches the surface of what ggplot, and R as a whole, are capable of. Not only are there far more choices about the kinds of plots available, but there are many, many options for customizing the look and feel of each graph. You can choose the font, the font size, the colors, the style of the axes labels, etc., and you can customize the legends and axes legends nearly as much as you want.

Let’s dig a little deeper into just a couple of options that you can add to any of the forgoing graphs to make them look a little better. For example, you can change the text of the x-axis label or the y-axis label by using xlab or ylab. Let’s do that for the scatterplot, to make the labels a little nicer to read for humans.

ggplot(guppyFatherSonData,   
    aes(x = fatherOrnamentation, y = sonAttractiveness)) +
    geom_point() +
    xlab("Father's ornamentation") + 
    ylab("Son’s attractiveness")

The labels that we want to add are included in quotes inside the xlab and ylab functions. Here is what appears:

It can also be nice to remove the default gray background, to make what some feel is a cleaner graph. Try adding

+ theme_classic()

to the end of one of your lines of code making a graph, to see whether you prefer the result to the default design.

ggplot(guppyFatherSonData,   
    aes(x = fatherOrnamentation, y = sonAttractiveness)) +
    geom_point() +
    xlab("Father's ornamentation") + 
    ylab("Son’s attractiveness") + 
    theme_classic()

Getting help

The help pages in R are the main source of help, but the amount of detail might be off-putting for beginners. For example, to explore the options for ggplot(), enter the following into the R Console.

help(ggplot)

This will cause the contents of the manual page for this function to appear in the Help window in RStudio. These manual pages are often frustratingly technical. What many of us do instead is simply google the name of the function—there are a great number of resources online about R.

There are also many introductory books available. A good one is Dalgaard (2008) Introductory Statistics with R, 2nd ed.