Interactive Visualizations

Sample video: What is rCharts?

Interactive Visualizations


Create interactive visualizations with clickable charts, graphs, maps and networks


Publish dynamic graphs and applications to your blog or website


Collect and manipulate data from almost any website, saving you time and creating new possibilities

PrerequisitesIntro to R
Instruction1 hour 30 minutes
Practice3 to 4 hours

Syllabus: Interactive Visualizations

This course is designed for students with a basic familiarity with R and some experience with data analysis and data manipulation. In less than 90 minutes of instructional time, students learn how to create dynamic visualizations in R and embed them into websites or host them on other sites. This course also teaches students how to collect data from different websites and manipulate that data so it can be visualized in R.

By the end of this course, students will be able to:

  1. Create interactive visualizations
  2. Publish dynamic graphs to websites
  3. Collect and manipulate data from almost any website
  1. Concept reviews: these are comprised of short five question quizzes that cover the most important concepts and ideas in each lesson. They encourage holistic understanding and are multi-faceted question types (i.e. drag and drop, fill-in-the-blanks, matching, etc).
  2. Exercises: these are additional videos that cover the coding functions in the instructional video in more depth. They are project-based and include coding templates for students to strengthen their skills outside of the course.
Materials provided:
  1. Accompanying PDFs to use as reference materials
  2. R code templates from the instructional videos and exercises
  3. Data sets used in the instructional videos and exercises

Course Outline

1. Dynamic graphs with rCharts  (31 min)

Why interactive graphs?
Introduction to rCharts
Visualizing health with rCharts

2. Interactive Shiny web applications  (25 min)

Building applications with Shiny – UI
Building applications with Shiny – server
Visualizing healthcare with Shiny
Adding pages to your Shiny application

3. Web scraping, networks, and mapping  (23 min)

Web scraping in R
Visualizing networks
Creating interactive maps


Total instructional time:              1 hr, 11 min

Dr. Keegan Hines

Keegan Hines is a data scientist with IronNet Cybersecurity, which focuses on large-scale machine learning applications in cyber defense. He is passionate about solving challenging problems in machine learning and distributed computing, as well as creating more effective methods for data visualization and communication. When not doing data science, he is probably on a road bike or performing improv comedy.

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