How to generate Boxplots in R using ggpubr package

A Boxplot, also known as a Whisker plot, is a standardized way of displaying the distribution of data based on five number summary (“minimum”, first quartile (Q1), median, third quartile (Q3), and “maximum”). It can be useful to visualize the spread and skewness of the data and to identify outliers. In R, there are several ways to generate boxplots, and one of them is by using the ggpubr package.

The ggpubr package is a package that provides a simple and easy-to-use interface for creating ggplot2-based graphics. It provides a set of functions for creating different types of plots, including boxplots.

To generate boxplots in R using the ggpubr package, you first need to install and load the package. Once the package is loaded, you can use the ggboxplot() function to create a boxplot of a given data set. The function takes the data as an input, as well as other optional parameters such as the color of the box, the size of the points and the labels for the x and y axes.

It’s worth noting that boxplots are useful when you have continuous data and you want to see the distribution of the data and identify outliers. The ggpubr package provides a simple and easy-to-use interface for creating boxplots and other types of plots. It’s a good idea to consult with experts before visualizing data in boxplots.

In summary, Boxplots, also known as Whisker plots, are a standardized way of displaying the distribution of data based on five number summary. They can be useful to visualize the spread and skewness of the data and to identify outliers. In R, there are several ways to generate boxplots, and one of them is by using the ggpubr package. The ggpubr package provides a set of functions for creating boxplots, such as the ggboxplot() function. It’s worth noting that boxplots are useful when you have continuous data and you want to see the distribution of the data and identify outliers. The ggpubr package provides a simple and easy-to-use interface for creating boxplots and other types of plots. It’s a good idea to consult with experts before visualizing data in boxplots.

 

In this Applied Machine Learning Recipe, you will learn: How to generate Boxplots in R using ggpubr package.