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# What is gghalves?

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

# gghalves Video TutorialFor those that prefer Full YouTube Video Tutorials.

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

# 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.

# 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`.

## 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)

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.

### Half-Plot Visualization

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

### 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`.

### 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

# 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

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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