ggforce: Make a Hull Plot to Visualize Clusters in ggplot2
Written by Matt Dancho
ggforce package is a
ggplot2 extension that adds many exploratory data analysis features. In this tutorial, we’ll learn how to make hull plots for visualizing clusters or groups within our data.
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. 👇
Follow along with our Full YouTube Video Tutorial.
Learn how to use
ggforce in our 7-minute YouTube video tutorial.
What is a Hull Plot?
The Hull Plot is a visualization that produces a shaded areas around clusters (groups) within our data. It gets the name because of the Convex Hull shape. It’s a great way to show customer segments, group membership, and clusters on a Scatter Plot.
Hull Plot (We'll make in this tutorial)
We’ll go through a short tutorial to get you up and running with
ggforce to make a hull plot.
Hull plots with
This tutorial showcases the awesome power of
ggforce for visualizing distributions.
This tutorial wouldn’t be possible without the excellent work of Thomas Lin Pedersen, creator of
ggforce. Check out the ggforce package here.
Before we get started, get the R Cheat Sheet
ggforce is great for extending ggplot2 with advanced features. But, you’ll need to learn
ggplot2 to take full advantage. For these topics, I’ll use the Ultimate R Cheat Sheet to refer to
ggplot2 code in my workflow.
Download the Ultimate R Cheat Sheet. Then Click the “CS” hyperlink to “ggplot2”.
Now you’re ready to quickly reference the
ggplot2 cheat sheet. This shows you the core plotting functions available in the ggplot library.
Onto the tutorial.
Load the Libraries and Data
First, run this code to:
- Load Libraries: Load
- Import Data: We’re using the
mpgdataset that comes with
mpg dataset. We’ll focus on “hwy” (fuel economy in Miles Per Gallon), “displ” (engine displacement volume in liters), and “cyl” (number of engine cylinders).
hull plot: Using ggplot
Next, we’ll make a hull plot that highlights the Vehicle Fuel Economy (MPG) for Engine Size (Number of Cylinders and Engine Displacement). It helps if you have
ggplot2 visualization experience. If you are interested in learning
ggplot2 in-depth, check out our R for Business Analysis Course (DS4B 101-R) that contains over 30-hours of video lessons on learning R for data analysis.
Step 1: Make the Base Scatter Plot
The first step is to make the scatter plot using
- Prep the Data: Using
mutate()to add a descriptive Engine Size column that will display the Number of Cylinders.
- Map the columns: Using
ggplot(), we map the displ and hwy column.
- Make the scatter points: Using
geom_point(), we add scatter plot points to our base plot. Refer to the Ultimate R Cheat Sheet and ggplot2 “CS” for more geoms.
This produces our base plot, which is a scatter plot of displacement vs highway fuel economy.
Step 2: Add the Hull Plot with
Next, we add our hull plot geometry layer using
ggforce::geom_mark_hull(). This produces the hull plot shaded regions indicating the groups. We map the descriptive engine size column to the
label aesthetics. We adjust the
concavity to smooth out the concavity.
And here’s the output. We can see that the hull plot shows the cylinder class membership for the vehicles scatter points.
Step 3: Make the plot look professional
It’s a good idea to spruce up our plot, especially if we are going to present to business stakeholders in a presentation or report. We’ll leverage
ggplot for theme customization. Refer to the Ultimate R Cheat Sheet and
ggplot2 documentation for more customizations.
And here’s the output. We have our final plot that tells the story of how highway fuel economy varies with the vehicle’s number of cylinders and engine displacement volume.
You learned how to make hull plots with
ggforce. But, there’s a lot more to becoming a data scientist.
If you’d like to have an exciting career making 6-figures as a data scientist (or over $150,000+ as a senior data scientist), then read on.👇
My Struggles with Learning Data Science
It took me a long time to learn how to apply data science to business. 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:
- Data Science does not have to be difficult, it just has to be taught from a business perspective
- 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 my 5-Course R-Track System. It’s an integrated system containing 5 courses that work together on a learning path. Through 8 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 2653 data scientists that are ready to help you succeed.
Ready to take the next step? Then let’s get started.