Interactive Course

Visualizing Big Data with Trelliscope

Learn how to visualize big data in R using ggplot2 and trelliscopejs.

  • 4 hours
  • 16 Videos
  • 46 Exercises
  • 2,323 Participants
  • 3,450 XP

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Course Description

Having honed your visualization skills by learning ggplot2, it's now time to tackle larger datasets. In this course, you will learn several techniques for visualizing big data, with particular focus on the scalable visualization technique of faceting. You will learn how to put this technique into action using the Trelliscope approach as implemented in the trelliscopejs R package. Trelliscope plugs seamlessly into standard R workflows and produces interactive visualizations that allow you to visually explore your data in detail. By the end of this course, you will be able to easily create interactive exploratory displays of large datasets that will help you and your colleagues gain new insights into your data.

  1. ggplot2 + TrelliscopeJS

    In the previous chapter you saw how faceting can be used as a powerful technique for visualizing a lot of data that can be naturally partitioned in some meaningful way. Now, using the trelliscopejs package with ggplot2, you will learn how to create faceted visualizations when the number of partitions in the data becomes too large to effectively view in a single screen.

  2. Case Study: Exploring Montreal BIXI Bike Data

    The Montreal BIXI bike network provides open data for every bike ride, including the date, time, duration, and start and end stations of the ride. In this chapter, you will analyze data from over 4 million bike rides in 2017, going between 546 stations. There are many interesting exploratory questions to ask from this data and you will create exploratory visualizations ranging from summary statistics to detailed Trelliscope visualizations that will give you interesting insight into the data.

  1. 1

    General strategies for visualizing big data

    Free

    Learn different strategies for plotting big data using ggplot2, including calculating and plotting summary statistics, various techniques to deal with overplotting, and principles of small multiples with faceting, which leads into Trelliscope.

  2. ggplot2 + TrelliscopeJS

    In the previous chapter you saw how faceting can be used as a powerful technique for visualizing a lot of data that can be naturally partitioned in some meaningful way. Now, using the trelliscopejs package with ggplot2, you will learn how to create faceted visualizations when the number of partitions in the data becomes too large to effectively view in a single screen.

  3. Trelliscope in the Tidyverse

    The ggplot2 + trelliscopejs interface is easy to use, but trelliscopejs also provides a faceted plotting mechanism that gives you much more flexibility in what plotting system you use and how to specify cognostics. You will learn all about that in this chapter!

  4. Case Study: Exploring Montreal BIXI Bike Data

    The Montreal BIXI bike network provides open data for every bike ride, including the date, time, duration, and start and end stations of the ride. In this chapter, you will analyze data from over 4 million bike rides in 2017, going between 546 stations. There are many interesting exploratory questions to ask from this data and you will create exploratory visualizations ranging from summary statistics to detailed Trelliscope visualizations that will give you interesting insight into the data.

What do other learners have to say?

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Lloyd's Banking Group

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Harvard Business School

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Decision Science Analytics @ USAA

Ryan Hafen
Ryan Hafen

Author of TrelliscopeJS

Ryan Hafen is a statistical consultant and a remote adjunct assistant professor in the Statistics Department at Purdue University. Ryan's research focuses on methodology, tools, and applications in exploratory analysis, statistical model building, and machine learning on large, complex datasets. He is the developer of the datadr and Trelliscope components of the Tessera project, as well as the rbokeh R visualization interface to the Bokeh plotting library. Prior to his work as a statistical consultant, Ryan worked at Pacific Northwest National Laboratory doing applied work on large complex data spanning many domains, including power systems engineering, nuclear forensics, high energy physics, biology, and cyber security.

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