What is the ggplot2 package in R?

Overview

The programming language R is popular among statisticians and mathematicians for mathematical computations and programming.

The language has many diverse libraries that help make a user’s programming journey easy. It also has built-in datasets that can be used for experimenting and learning.

The ggplot2 is one of R’s most popular libraries among others. The library is a practical and useful implementation of the “Grammar of Graphics book” in R and is used for data visualization and plotting charts. The ggplot2 is considered versatile because it allows for easy customization since we add the plot’s aesthetics, layer by layer.

How to install and use the ggplot2 package

Installation

There are two ways to install ggplot2:

• We can use the tidyverse library. The ggplot2 package is contained within the tidyverse library, so installing it automatically installs ggplot2.

install.packages("tidyverse")
library(tidyverse)

• We can directly use the ggplot2 library.

install.packages("ggplot2")
library(ggplot2)

Implementation

After we install and load the library, using either of the options shown above, the next step is to use the package.

We’ll need to invoke the ggplot() function for this.

Syntax

ggplot(data = <DATA>, mapping = aes(<MAPPINGS>)) +  <GEOM_FUNCTION>()

• The function ggplot() wraps together the data and the aesthetics needed.
• data specifies the data file being used.
• The mapping specifies the variables being used to build the chart. These are x and y variables where needed.
• The geom_function can be any function such as geom_point for scatterplots, geom_line for line plots or even geom_boxplot for boxplots.

Code example

Let’s look at the code below:

Code explanation

• Line 1: We load the library tidyverse. The ggplot2 can also be used here instead.
• Line 2: We plot the iris data, Sepal length, and width using the geom_function geom_point().
• Line 5: We add a title to the plot using ggtitle().
• Line 6: We center the title since, by default, the title is left_aligned.
• Lines 7 and 8: We add labels on x and y axes.

Note: We can access the built-in datasets and their descriptions using the function data().