How I analyze 100+ ggplots at once

Written by Matt Dancho



Visualizing big data is next to impossible. As soon as I have 12 plots, that’s where my ability to use native ggplot suffers. That is until I found trelliscopejs.

trelliscopejs is like ggplot2 faceting on steroids. This may seem crazy, but the benefit is that when you have 20, 30, or even 100+ plots you need to analyze, trelliscopejs is the solution!

And, I’m going to get you up and running with trelliscopejs in under 5-minutes:

  1. I’ll teach you how to make 20+ ggplot facets using trelliscopejs
  2. BONUS: I’ll not only show you how to make static ggplots, but I’ll even show you how to use the plotly integration for interactivity

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 trelliscopejs package in my 5-minute YouTube video tutorial.

What you make in this R-Tip

By the end of this tutorial, you’ll make the 20+ ggplots for exposing insights in your big data.

Analyzing 20+ ggplots (made with trelliscopejs)

Thank You Developers.

Before we move on, please recognize that trelliscopejs was developed by Ryan Hafen (follow Ryan on Twitter). Thank you for everything you do!

Also, the full documentation for trelliscopejs can be accessed here.

trelliscopejs Tutorial

Let’s dive into using trelliscopejs so we can analyze 100+ ggplots.

Step 1: Load the Libraries and Data

First, run this code to:

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

Get the code.

Our data looks like this. We want to understand how Highway Fuel Economy (hwy) varies with displ (engine size) but we want to see if there is any differences between manufacturers.

The mpg dataset

Step 2: Make a ggplot

Next, let’s make a basic ggplot of fuel economy vs engine displacement.

Get the code.

This produces the following plot of hwy vs displ.

Our basic ggplot

Step 3: Apply the trelliscopejs magic!

Listen, I’m telling you this next part is straight-up magic!

Seriously, I now use this simple trick to analyze 100+ ggplots at once.

  • Use the facet_trelliscope() function
  • This replaces a facet_wrap() or facetgrid()
  • And makes 100’s of ggplots (as many as your heart desires)
  • In this case, we facet by manufacturer and end up with 15 plots to analyze.

Get the code.

The result is the trelliscope plot with 15 ggplots by manufacturer.

We've transformed our ggplot into a faceted trelliscope with 15 plots by manufacturer

Step 4: Customize the Trelliscopejs

This is really cool!! You can add additional labels like max/min displacement by plot.

Customize the trelliscope with labels & filters

BONUS: Make your trelliscope interactive!!!

If you thought you were done…

We’re just gettin’ started!

THIS is the magic of trelliscope!!

  • Add interactivity with the Plotly integration inside of facet_trelliscope().
  • Simply add as_plotly = TRUE

Get the code.

Check out the interactivity from plotly!!

Interactivity with the plotly-trelliscopejs integration

Conclusions

You learned how to use the trelliscopejs library to not only create 100’s of static ggplots but create 100’s of interactive plotly plots. Great work! But, there’s a lot more to becoming a data scientist.

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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.

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What I found out is that:

  1. Data Science does not have to be difficult, it just has to be taught from a business perspective
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