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Designing Better Graphs

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Designing Better Graphs Matthew Wettergreen, PhD
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Page 1: Designing Better Graphs

Designing Better Graphs

Matthew Wettergreen, PhD

Page 2: Designing Better Graphs

Components of a Good Graph

● Proper data organization (MOST IMPORTANT)● Formatted without default settings● Designed for ease of readability for others

● Correct use of “preattentive stimulus”● Visual Hierarchy● Readable text● Properly formatted axes● Properly weighted lines● Appropriate color (only when necessary)

Page 3: Designing Better Graphs

Identifying the Proper Format for Your Data:What Relationships are you trying to Show?

Relationship Example Question

RankIn what order are our proposed device designs based on the summed values from the Pugh matrix from high to low?

Part-to-whole

What portion of the market does our company command, and how does that compare to our competitors?

Time-series Is the traffic on our website increasing or decreasing?

DeviationTo what degree do our device’s readings vary relative to the expected numbers?

Distribution

What is the range of our employees’ salaries, and how many employees fall into each subset of that range in increments of $10,000?

Correlation

Is there a relationship between the stiffness of our construction material on the strength of our constructed shape?

Page 4: Designing Better Graphs

Do Not Use Program Defaults!

● Thin lines

● Too small of text

● Box around data

● Often lacks labels

● Grey background

● Gridlines

● Bad colors

● Boxes around everything

Page 5: Designing Better Graphs

Pre-attentive Processing

extremely fast, pre-conscious visual processing

Page 6: Designing Better Graphs

How many 5’s are in this figure?

385720939823728196837293827

382912358383492730122894839

909020102032893759273091428

938309762965817431869241024

Page 7: Designing Better Graphs

How many 5’s are in this figure?

385720939823728196837293827

382912358383492730122894839

909020102032893759273091428

938309762965817431869241024

Page 8: Designing Better Graphs

Preattentive Processing

• There are some basic visual properties that can be detected immediately by low-level visual system

• These are identified from a “Pop-out” instead of a “Serial Search”

• Preattentive Processing relates to tasks that can be performed in less than 200 to 250 milliseconds on a complex display

Page 9: Designing Better Graphs

Color (hue) is preattentive

Detection of red circle in group of blue circles is preattentive

Page 10: Designing Better Graphs

Form (curvature) is preattentive

Curved form “pops out” of display

Page 11: Designing Better Graphs

Don't Over Do It!Conjunction of Attributes

Conjunction targets generally cannot be detected preattentively

The red circle hides in the sea of red square and blue circle

Page 12: Designing Better Graphs

Other Preattentive Examples

Page 13: Designing Better Graphs

Which Preattentive Stimulus is Best for Quantitative/Qualitative Data?

Type Attribute Quantitatively Perceived?

Form Line Length Yes

Line width Yes, limited

Orientation No

Size Yes, limited

Shape No

Curvature No

Added Marks No

Enclosure No

Color Hue No

Intensity Yes, limited

Position 2-D Position Yes

Page 14: Designing Better Graphs

Preattentive Stimuli has a Visual Heirarchy

Which preattentive stimulus is stronger?

Page 15: Designing Better Graphs

Visual Hierarchy

Page 16: Designing Better Graphs

Highlighting Trends Using Sorting

• Sorting and rearranging data lets users visually draw meaningful comparisons from the data

• Sort based on the relationship and pattern you are trying to flush out in this graph, not by convention (ie alphabetically)

Page 17: Designing Better Graphs

Before After

Page 18: Designing Better Graphs

Visual Hierarchy for Text

Page 19: Designing Better Graphs

Amount of Sales by Salesperson

0

500

1000

1500

2000

2500

Dodsworth Suyama Davolio Fuller Peacock Leverling Arcoll Callahan Dusty

Salesperson

Am

ou

nt

of

Sal

es (

$)

16.5

13

13

108

Graph Text Must Have a Consistent Visual Hierarchy

The visual hierarchy of this graph is inconsistent

Page 20: Designing Better Graphs

Visual Hierarchy of Text

Actual

Amount of Sales by Salesperson

Amount of Sales ($)

2500, 2000, 1500, 1000, 500, 0

Dodsworth, Suyama, Davolio, Fuller, Peacock, Leverling, Arcoll, Callahan, Dusty

Salesperson

Correct

Title (1st)

X-Axis Title (2nd)

Y-Axis Title (2nd)

X-Axis Data (3rd)

Y-Axis Data (3rd)

Data Labels (4th)

Page 21: Designing Better Graphs

Choosing a Readable Font

Fine Legibility Poor Legibility

Serif San-Serif Serif San-Serif

Times New Roman Arial Script Gill Sans ITC

Palatino Verdana Broadway Papyrus

Courier Tahoma Old English Tempus Sans ITC

Page 22: Designing Better Graphs

Amount of Sales by Salesperson

0

500

1000

1500

2000

2500

Dodsworth Suyama Davolio Fuller Peacock Leverling Arcoll Callahan Dusty

Salesperson

Am

ou

nt o

f Sa

les

($)

The visual hierarchy of this graph has been fixedand is now consistent

Page 23: Designing Better Graphs

Properly Formatting Axes

Page 24: Designing Better Graphs

Formatting of Axes• Scaling and aspect ratio of your axes is important to

bring out the relevant relationships and patterns

• Axes should have consistent labeling – no interval jumps

• Breaks in axes should be clearly indicated

Don't Do

Page 25: Designing Better Graphs

Use a Correct Y-Axis Range

Don't Do

Page 26: Designing Better Graphs

Select the Correct Aspect Ratio

D

Don't Do

Page 27: Designing Better Graphs

Properly Formatted Lines

Page 28: Designing Better Graphs

Lines

• Use reference lines (grids) only when an important values should be seen across an entire graph

• Tick marks should generally face outward

• Don’t clutter the interior of the scale-line rectangle with legends, labels, and lines

Page 29: Designing Better Graphs

Grid Lines Can Obscure Data

Don't Do

Page 30: Designing Better Graphs

Proper Use of Color

Page 31: Designing Better Graphs

Color Choices

• Use color only to serve a specific communication goal

• Use more than one color only when the data means different things

• Use a mix of soft colors for the data and brighter colors to accentuate specific features

Page 32: Designing Better Graphs

This is What Bad Color Use Looks Like

Page 33: Designing Better Graphs

Quantitative Use of Color

● Color is mostly arbitrary, that is, non-directional● The human eye cannot perceive quantitative

differences in hue or color

What do the different shades of green mean in reference to the red?

Page 34: Designing Better Graphs

Color Perception Can Be Tricked

All five of these grey boxes are the same shade

Page 35: Designing Better Graphs

Use one color per series

Don't

Do

Page 36: Designing Better Graphs

Color Palette Examples

Standard palette for data

For accentuating features and trends

Page 37: Designing Better Graphs

Use Hues to Order Data

vs

Page 38: Designing Better Graphs

Some Rules About Using Color

● To speed visual search

● To improve object recognition

● To enhance meaning

● To convey structure

● To establish identity

● To … symbolism

● To improve usability

● To communicate mood

● To show associations

● To express metaphors

Page 39: Designing Better Graphs

Components of a Good Graph

●Proper data organization (MOST IMPORTANT)●Formatted without default settings●Designed for ease of readability for others

● Correct use of “preattentive stimulus”● Visual Hierarchy● Readable text● Properly formatted axes● Properly weighted lines● Appropriate color (only when necessary)

Page 40: Designing Better Graphs

Resources for Better Graphs

● Chart Chooser– Extreme Presentation: Chart Suggestions

● http://extremepresentation.typepad.com/files/choosing-a-good-chart-09.pdf

– Juice Analytics: Chart Chooser● http://labs.juiceanalytics.com/chartchooser.html

● Color– Color Scheme Designer

● http://colorschemedesigner.com/


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