## Types of Charts

These slides walk through a wide variety of charting options at your disposal. Again, note which charts are associated with each data type.

### Outline

• Why visualize?
• to understand data
• a worked example
• The visual vocabulary
• elements
• perceptual motivations
• Conventional modes of combination
• taxonomy of visualization
• Tips & Tricks, Tradeoffs, & Trouble

### Example: Movements of the French Army

Minard, 1861; Tufte, 2001

### 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

#### 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?
• Fancier is not always better
• pretty pictures are awesome
• but not if they obscure the data

#### Histogram

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

#### 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"

#### Scatter plot

• Relationship between observations on two continuous dimensions
• Can show multiple groups
• Can show trend lines etc.
• Uninformative with too much data
… with many discrete items (identity as a dimension)

#### 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

#### Strip chart

Very useful for showing individual subject means

#### Box plot

Shows the shape of distribution but not focused on individual subjects

#### 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

Source: Mike Frank and Ed Vul, https://ocw.mit.edu/courses/res-9-0002-statistics-and-visualization-for-data-analysis-and-inference-january-iap-2009/96df7f49cd50ed9bb0feb4793b9c8d89_lec1_visulzatn.pdf