## 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.

### Visualization

### 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:

**be true to your research**– design your display to illustrate a particular point**maximize information, minimize ink**–use the simplest possible representation for the bits you want to convey**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

… 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

^{More bar graphs}

#### Strip chart

Very useful for showing individual subject means

#### Box plot

Shows the shape of distribution but not focused on individual subjects

#### 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

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

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