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
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:
- Create interactive visualizations
- Publish dynamic graphs to websites
- Collect and manipulate data from almost any website
- 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).
- 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.
- Accompanying PDFs to use as reference materials
- R code templates from the instructional videos and exercises
- Data sets used in the instructional videos and exercises
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
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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