PRDV200: Communicating with Data

Three principles for visualization:

1. be true to your research – design your display to illustrate a particular point
2. maximize information, minimize ink –use the simplest possible representation for the bits you want to convey
3. organize hierarchically – what should a viewer see first? what if they look deeper?

Worked example

• Participants heard examples from an artificial language
• Three different presentation methods for examples
  • index cards, list of sentences, mp3 files on ipod
• Task was to spread $100 of "bets" across different continuations for a new example
• Dependent measure was bet on the correct answer

Worked example

bar graph

bar graph with standard errors

bar graph with 95% CIs

box plot

viola plot

strip chart

strip chart with means

Morals of the example

• Summary statistics
  • almost always necessary
  • but at what level of analysis?
• Distribution is important
  • what is the form of the data?
  • is your summary misleading?
• Fancier is not always better
  • pretty pictures are awesome
  • but not if they obscure the data

Anscombe's Quartet

Conventional visualizations

Histogram

• Important first way of looking at your data
  
• One dimensional
  
• Shows shape by binning a continuous distribution

Grouped histogram

Pie chart

• A whole split into parts
• Emphasizes that all parts sum to a constant
• Single dimension with discrete categories

Stacked bar graph

• Wholes split into parts
• Easy to compare
  • often better than pie chart
• Can have multiple discrete dimensions

Venn diagram

• Shows overlap between discrete groups
• Sometimes the only way to display overlapping sets
• Unintuitive – no "popout"

Conventional visualizations

Scatter plot

• Relationship between observations on two continuous dimensions
• Can show multiple groups
• Can show trend lines etc.
• Uninformative with too much data

Line graph

• Also ubiquitous!
• Good for showing one variable (e.g., time) as continuous even though you have discrete measures
• Can compare several discrete groups

Bar graph

• aka "dynamite plot"
• Ubiquitous!
• Can be used for lots of discrete grouping factors
• Natural semantics of grouping
• Conceals data

^{More bar graphs}

Strip chart

Box plot

Conventional visualizations

Heat map

• Worksvery well when there are natural semantics
• Color mapping can be problematic
  • grayscale usually fine
• Can be unintuitive

Bubble plot

• Can be very intuitive
• Size is not perfectly quantitative

Trellis plots