gghalves: Make Half Boxplot | Half Dotplot Visualizations with ggplot2

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This article is part of R-Tips Weekly, a weekly video tutorial that shows you step-by-step how to do common R coding tasks.

Here are the links to get set up. 👇

What is gghalves?

gghalves is a new R package that makes it easy to compose your own half-plots using ggplot2.

gghalves Video Tutorial
For those that prefer Full YouTube Video Tutorials.

Learn how to use gghalves in our free 8-minute YouTube video.

(Click image to play tutorial)

Watch our full YouTube Tutorial

What are Half Plots?
Combining two plots side-by-side.

Half/Half Plots are a way to showcase two plots side-by-side. Here’s a common example:

  1. Showing a Boxplot to identify outliers and quantiles

  2. Showing a Dotplot to identify distribution

We can easily do this with a half-plot thanks to gghalves.

Before we get started, get the R Cheat Sheet

gghalves is great for making customized ggplot2 plots. But, you’ll still need to learn how to wrangle data with dplyr and visualize data with ggplot2. For those topics, I’ll use the Ultimate R Cheat Sheet to refer to dplyr and ggplot2 code in my workflow.

Quick Example:

Download the Ultimate R Cheat Sheet Then Click the “CS” next to “ggplot2” opens the Data Visualization with GGplot2 Cheat Sheet.

Now you’re ready to quickly reference ggplot2 functions.

Onto the tutorial.

ggplot2 cheat sheet

How gghalves works

The gghalves package extends ggplot2 by adding several new “geoms” (ggplot geometries) that allow us to add half plots. In this tutorial, we’ll cover:

  • geom_half_boxplot(): For creating half-boxplots
  • geom_half_dotplot(): For creating half-dotplots
Pro Tip:

Simply type “geom_half” in your R console and hit Tab to show all of the half plotting geoms available.

Load the Libraries and Data

First, run this code to:

  1. Load Libraries: Load gghalves, tidyverse and tidyquant.
  2. Import Data: We’re using the mpg dataset that comes with ggplot2.

Get the code.

Make the Half-Boxplot / Half-Dotplot

Next, we can combine a half-boxplot and half-dotplot. This has the advantage of showing:

  • Quantiles and Outliers (Boxplot)
  • Distribution (Dotplot)

Business Goal

Suppose we have a question:

What effect does Engine Size (number of Cylinders) have on Vehicle Highway Fuel Economy (Highway MPG)?

We can visualize this with gghalves by making half-plots of Cylinder vs Highway.

Half-Plot Visualization Code

Using the Ultimate R Cheat Sheet, we can make a ggplot from the ggplot2 data visualization cheat sheet. We’ll add geom_half_boxplot() and geom_half_dotplot() to make the half-plots of Cylinder vs Highway.

Get the code.

Half-Plot Visualization

Here is the visualization. We can explore to find an interesting relationship between Engine Size and Fuel Economy.

Get the code.

Insights: Bimodal Distribution of 6-Cylinder Engine Class

Generally speaking, fuel economy goes down as engine size increases. But, the 6-Cylinder engine has something unique going on that has been uncovered by the gghalves::geom_half_dotplot().

The 6-Cylinder Engine class of car has a bimodal distribution, which is when there are two peaks. This generally indicates that there are two different populations within the group. We need to investigate with ggplot2.

Get the code.

Exploring the Bimodal Relationship

We can explore the 6 Cylinder Vehicle Class a bit further to identify the cause of the Bimodal Distribution. It looks like:

  • SUV and Pickup classes have much lower fuel economy
  • Compact, Midsize, Minivan, and Subcompact have much higher fuel economy

Get the code.

Why Learning ggplot2 is essential

I wouldn’t be nearly as effective as a data scientist without knowing ggplot2. In fact, data visualization has been one of two skills that have been critical to my career (with the other one being data transformation).

Case Study: This tutorial showcases exactly why visualization is important

Get the code.

Let’s just take this tutorial as a case study. Without being able to visualize with ggplot2:

  • We wouldn’t be able to visually identify the Bimodal Distribution. We needed to see that to know to explore the 6-Cylinder Engine Class.

  • We wouldn’t have been able to explore the 6-Cylinder Engine Class. This showed us the importance of the Vehicle Class (e.g. SUV, Pickups being lower and Compact, Subcompact being higher in fuel economy).

Career Tip: Learn ggplot2

If I had one piece of advice, it would be to start learning ggplot2. Let me explain.

Learning ggplot2 helped me to:

  • Explain complex topics to non-technical people
  • Develop good reports that showcased important points visually
  • Make persuasive arguments that got the attention of Senior Management and even my CEO

So, yes, learning ggplot2 was absolutely essential to my career. I received many promotions and got the attention of my CEO using ggplot2 effectively.

If you’d like to learn ggplot2 and data science for business, then read on. 👇

My Struggles with Learning Data Science

It took me a long time to learn data science. And I made a lot of mistakes as I fumbled through learning R. I specifically had a tough time navigating the ever increasing landscape of tools and packages, trying to pick between R and Python, and getting lost along the way.

If you feel like this, you’re not alone.

In fact, that’s the driving reason that I created Business Science and Business Science University (You can read about my personal journey here).

What I found out is that:

  1. Data Science does not have to be difficult, it just has to be taught smartly

  2. Anyone can learn data science fast provided they are motivated.

How I can help

If you are interested in learning R and the ecosystem of tools at a deeper level, then I have a streamlined program that will get you past your struggles and improve your career in the process.

It’s called the 5-Course R-Track System. It’s an integrated system containing 5 courses that work together on a learning path. Through 5+ projects, you learn everything you need to help your organization: from data science foundations, to advanced machine learning, to web applications and deployment.

The result is that you break through previous struggles, learning from my experience & our community of 2000+ data scientists that are ready to help you succeed.

Ready to take the next step? Then let’s get started.

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