Statistics 744, McMaster University

Overview

What are interactive graphics?

• Visualizations
• Combined with ways to click or hover and get more information
• e.g., the internet

DataViz interactive goals

• Tell a clear story
• Allow viewers to see more than would be shown in two static dimensions
• Or to obtain more detailed information
• Or to explore things that go beyond the story you’re telling

Some interaction techniques

• Scaling and panning: allow viewers to look at different parts of an image
• Labelling: attach labels with detailed information
• Subsetting
  • Brushing: highlight categories by selecting them
  • Linking: link selections across several plots
  • Animation: selections in sequence; ideally can be played automatically or controlled by user
• Exploration: users can set parameters and try new things

Principles

No substitute for good design

• Good design still aids comprehension and usability

• People are busy!
  • You need your story to be clear and readable
• Keep things simple
  • Details can be hidden
  • Structure and logic should be visible

Acceptance

• Why don’t people do this more?

• In what contexts can you reach an audience with interactive graphics?

• How can interactive graphics help you with your research?

Exploring

• Interactive graphics can be very useful for certain kinds of exploration

• Principles of friendliness may no longer apply, since users will become very familiar with the tool

• Principles of clarity probably still do
  • It’s possible to mislead yourself

Telling stories

• You may want to lead your readers through an interactive visualization

• Several versions of your plot

• Or even several default views of the same plot

Remember Cleveland

• What do people need to compare?

• How can you help them do it?
  • Choose scales
  • Make information persistent or retrievable

Examples

Examples

• Schoolchildren example

• Tax example

• Linking example with cars

• gapminder animation

Scaling and panning

• Except for exploration, you don’t want graphs that need to be looked over piece by piece

• But it can be a useful option

• Many packages just provide this

Labeling

• Someone using interactively does not need to see more than 3 or 4 significant figures

• They also do not need to see everything in your data frame

• You should not drop precision before making the plot, though

• Solution: create dummy columns and limit what is shown to users on labels

Brushing

• Pick out a particular data series

• Needs good communication and usability

• Particularly useful if combined with linking

Linking

• Connect information across different pictures

Animation

• Best when it’s under user control

• A really good brushing interface can often be better

• Can be really nice for talks, though

Tools and resources

Resources

• Gap minder
• Gap minder and plotly
• Web book: Interactive web-based data visualization with R, plotly, and shiny
• htmlwidgets examples

Packages

• plotly
  • Straightforward and popular (esp. via ggplotly)
• gganimate
  • very “gg” (grammar of graphics)-ish; not so interactive
• crosstalk
  • for letting different html widgets talk to each other (linking)
• leaflet
  • maps (more later)