grafify: Make 5 powerful ggplot2 graphs quickly with R
Written by Matt Dancho on June 15, 2021
The grafify
package is a new R package that makes it easy to make 19-powerful ggplot2
graphs, ANOVAs, and comparison plots. I’ve been really enjoying it! In the next 10-minutes, we’ll learn how to make my 5 favorite grafify
plots (with one line of code!):
- Scatter-Bar (1), Scatter-Box (2), and Scatter-Violin (3) plots for plotting 2-variables
- Scatter-Box 3D plots (4) for plotting 3-variables
- BONUS: Before-and-After Plots (5) for showing State Change
This article was last updated on: February 15th, 2022.
R-Tips Weekly
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. 👇
Video Tutorial
Learn how to use the grafify
package in our 7-minute YouTube video tutorial.
What you make in this R-Tip
grafify
is a new R package for making great-looking ggplot2
graphs quickly in R. It has 19 plotting functions that simplify common ggplot graphs and provide color-blind friendly themes. By the end of the tutorial, you’ll make:
- Scatter Bar SD
- Scatter Box SD
- Scatter Violin SD
- Scatter Box 3D
- BONUS: Before and After Plots!!!
Thank You Developers.
Before we move on, please recognize that the grafify
package was generously built and maintained with many hours of work by Avinash R. Shenoy. Thank you for all that you do!
The grafify
Tutorial
The grafify
package extends ggplot2
by adding several simplified plotting functions. Let’s explore the package by making 5 powerful plots (with one line of code)!
Load the Libraries and Data
First, run this code to:
- Load Libraries: Load
grafify
andtidyverse
. - Import Data: We’re using the
mpg
dataset that comes withggplot2
.
Plot 1: Scatterbar SD Plot
First, we can make a Scatterbar Plot that shows the data points along with error bars at a standard deviation. Simply use plot_scatterbar_sd()
.
Plot 2: Scatterbox Plot
Next, we can make a Scatterbox Plot that shows a custom boxplot / jitter plot combination. I’ve added a jitter point to show the distribution. Simply use plot_scatterbox()
.
Plot 3: Dotviolin Plot
Next, we can make a Dotviolin Plot that shows a custom violin plot / dotplot combination. Simply use plot_dotviolin()
.
Plot 4: Scatterbox 3D Plot
Next, we can make a 3D Scatterbox Plot that shows three variables using boxplot / jitter plot combination. This is great for drilling into multiple categories. Simply use plot_3d_scatterbox()
.
BONUS: Plot 5 - Before-After Plot
As a super cool bonus, we can make a Before-After Plot that shows changes between two states (in this case how various models changed in MPG Fuel Efficiency from 1999 to 2008). This is great for comparing two states. Simply use plot_befafter_colors()
.
We can see that most vehicles are improving in MPG from 1999 to 2008. However, there are a few that are going down. Most notably is the New Beetle going from 35MPG to about 27MPG, a decline of -23%.
Recap
We learned how to make 5 powerful plots using the grafify
package. But, there’s a lot more to becoming a data scientist.
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