1. The Art of Graphical Presentation
Types of Variables
Guidelines for Good Graphics Charts
Common Mistakes in Graphics
Pictorial Games
Special-Purpose Charts

2. Types of Variables Qualitative
Ordered (e.g., modem, Ethernet, satellite)
Unordered (e.g., CS, math, literature)
Quantitative
Discrete (e.g., number of terminals)
Continuous (e.g., time)

3. Charting Based on Variable Types
Qualitative variables usually work best with bar charts or Kiviat graphs
If ordered, use bar charts to show order
Quantitative variables work well in X-Y graphs
Use points if discrete, lines if continuous
Bar charts sometimes work well for discrete

4. Guidelines for Good Graphics Charts
Principles of graphical excellence
Principles of good graphics
Specific hints for specific situations
Aesthetics
Friendliness

5. Principles of Graphical Excellence
Graphical excellence is the well-designed presentation of interesting data:
Substance
Statistics
Design

6. Graphical Excellence (2)
Complex ideas get communicated with:
Clarity
Precision
Efficiency

7. Graphical Excellence (3)
Viewer gets:
Greatest number of ideas
In the shortest time
With the least ink
In the smallest space

8. Graphical Excellence (4)
Is nearly always multivariate
Requires telling truth about data

9. Principles of Good Graphics
Above all else show the data
Maximize the data-ink ratio
Erase non-data ink
Erase redundant data ink
Revise and edit

10. Above All Else Show the Data

11. Above All Else Show the Data

12. Maximize the Data-Ink Ratio

13. Maximize the Data-Ink Ratio

14. Erase Non-Data Ink

15. Erase Non-Data Ink
North
East
West

16. Erase Redundant Data Ink
North
East
West

17. Erase Redundant Data Ink
North
East
West

18. Revise and Edit

19. Revise and Edit

20. Revise and Edit

21. Revise and Edit

22. Revise and Edit

23. Revise and Edit

24. Specific Things to Do Give information the reader needs
Limit complexity and confusion
Have a point
Show statistics graphically
Don’t always use graphics
Discuss it in the text

25. Give Information the Reader Needs
Show informative axes
Use axes to indicate range
Label things fully and intelligently
Highlight important points on the graph

26. Giving Information the Reader Needs

27. Giving Information the Reader Needs

28. Limit Complexity and Confusion
Not too many curves
Single scale for all curves
No “extra” curves
No pointless decoration (“ducks”)

29. Limiting Complexity and Confusion

30. Limiting Complexity and Confusion

31. Have a Point
Graphs should add information not otherwise available to reader
Don’t plot data just because you collected it
Know what you’re trying to show, and make sure the graph shows it

32. Having a Point
Sales were up 15% this quarter:

33. Having a Point

34. Having a Point

35. Having a Point

36. Show Statistics Graphically
Put bars in a reasonable order
Geographical
Best to worst
Even alphabetic
Make bar widths reflect interval widths
Hard to do with most graphing software
Show confidence intervals on the graph
Examples will be shown later

37. Don’t Always Use Graphics
Tables are best for small sets of numbers
e.g., 20 or fewer
Also best for certain arrangements of data
e.g., 10 graphs of 3 points each
Sometimes a simple sentence will do
Always ask whether the chart is the best way to present the information
And whether it brings out your message

38. Text Would Have Been Better

39. Discuss It in the Text Figures should be self-explanatory
Many people scan papers, just look at graphs
Good graphs build interest, “hook” readers
But text should highlight and aid figures
Tell readers when to look at figures
Point out what figure is telling them
Expand on what figure has to say

40. Aesthetics Not everyone is an artist
But figures should be visually pleasing
Elegance is found in
Simplicity of design
Complexity of data

41. Principles of Aesthetics
Use appropriate format and design
Use words, numbers, drawings together
Reflect balance, proportion, relevant scale
Keep detail and complexity accessible
Have a story about the data (narrative quality)
Do a professional job of drawing
Avoid decoration and chartjunk

42. Use Words, Numbers, Drawings Together
Put graphics near or in text that discusses them
Even if you have to murder your word processor
Integrate text into graphics
Tufte: “Data graphics are paragraphs about data and should be treated as such”

43. Reflect Balance, Proportion, Relevant Scale
Much of this boils down to “artistic sense”
Make sure things are big enough to read
Tiny type is OK only for young people!
Keep lines thin
But use heavier lines to indicate important information
Keep horizontal larger than vertical
About 50% larger works well

44. Poor Balance and Proportion
Sales in the North and West districts were steady through all quarters
East sales varied widely, significantly outperforming the other districts in the third quarter

45. Better Proportion
Sales in the North and West districts were steady through all quarters
East sales varied widely, significantly outperforming the other districts in the third quarter

46. Keep Detail and Complexity Accessible
Make your graphics friendly:
Avoid abbreviations and encodings
Run words left-to-right
Explain data with little messages
Label graphic, don’t use elaborate shadings and a complex legend
Avoid red/green distinctions
Use clean, serif fonts in mixed case

47. An Unfriendly Graph

48. A Friendly Version

49. Even Friendlier

50. Have a Story About the Data (Narrative Quality)
May be difficult in technical papers
But think about why you are drawing graph
Example:
Performance is controlled by network speed
But it tops out at the high end
And that’s because we hit a CPU bottleneck

51. Showing a Story About the Data

52. Do a Professional Job of Drawing
This is easy with modern tools
But take the time to do it right
Align things carefully
Check the final version in the format you will use
I.e., print the Postscript one last time before submission
Or look at your slides on the projection screen

53. Avoid Decoration and Chartjunk
Powerpoint, etc. make chartjunk easy
Avoid clip art, automatic backgrounds, etc.
Remember: the data is the story
Statistics aren’t boring
Uninterested readers aren’t drawn by cartoons
Interested readers are distracted
Does removing it change the message?
If not, leave it out

54. Examples of Chartjunk Gridlines! In or out? Filled Labels Borders and
Fills Galore
Pointless
Fake 3-D Effects
Gridlines!
Vibration
Filled
“Walls”
Unintentional
Heavy or Double Lines
Filled “Floor”
Clip Art

55. Common Mistakes in Graphics
Excess information
Multiple scales
Using symbols in place of text
Poor scales
Using lines incorrectly

56. Excess Information Sneaky trick to meet length limits Rules of thumb:
6 curves on line chart
10 bars on bar chart
8 slices on pie chart
Extract essence, don’t cram things in

57. Way Too Much Information

58. What’s Important About That Chart?
Times for cp and rcp rise with number of replicas
Most other benchmarks are near constant
Exactly constant for rm

59. The Right Amount of Information

60. Multiple Scales Another way to meet length limits
Basically, two graphs overlaid on each other
Confuses reader (which line goes with which scale?)
Misstates relationships
Implies equality of magnitude that doesn’t exist

61. Some Especially Bad Multiple Scales

62. Using Symbols in Place of Text
Graphics should be self-explanatory
Remember that the graphs often draw the reader in
So use explanatory text, not symbols
This means no Greek letters!
Unless your conference is in Athens...

63. It’s All Greek To Me...

64. Explanation is Easy

65. Poor Scales Plotting programs love non-zero origins
But people are used to zero
Fiddle with axis ranges (and logarithms) to get your message across
But don’t lie or cheat
Sometimes trimming off high ends makes things clearer
Brings out low-end detail

66. Nonzero Origins (Chosen by Microsoft)

67. Proper Origins

68. A Poor Axis Range

69. A Logarithmic Range

70. A Truncated Range

71. Using Lines Incorrectly
Don’t connect points unless interpolation is meaningful
Don’t smooth lines that are based on samples
Exception: fitted non-linear curves

72. Incorrect Line Usage

73. Pictorial Games Non-zero origins and broken scales
Double-whammy graphs
Omitting confidence intervals
Scaling by height, not area
Poor histogram cell size

74. Non-Zero Origins and Broken Scales
People expect (0,0) origins
Subconsciously
So non-zero origins are a great way to lie
More common than not in popular press
Also very common to cheat by omitting part of scale
“Really, Your Honor, I included (0,0)”

75. Non-Zero Origins

76. The Three-Quarters Rule
Highest point should be 3/4 of scale or more

77. Double-Whammy Graphs Put two related measures on same graph
One is (almost) function of other
Hits reader twice with same information
And thus overstates impact

78. Omitting Confidence Intervals
Statistical data is inherently fuzzy
But means appear precise
Giving confidence intervals can make it clear there’s no real difference
So liars and fools leave them out

79. Graph Without Confidence Intervals

80. Graph With Confidence Intervals

81. Confidence Intervals
Sample mean value is only an estimate of the true population mean
Bounds c1 and c2 such that there is a high probability, 1-a, that the population mean is in the interval (c1,c2): Prob{ c1 < m < c2} =1-a where a is the significance level and 100(1-a) is the confidence level
Overlapping confidence intervals is interpreted as “not statistically different”

82. Graph With Confidence Intervals

83. Scaling by Height Instead of Area
Clip art is popular with illustrators:
Women in the Workforce
1980
1960

84. The Trouble with Height Scaling
Previous graph had heights of 2:1
But people perceive areas, not heights
So areas should be what’s proportional to data
Tufte defines a lie factor: size of effect in graphic divided by size of effect in data
Not limited to area scaling
But especially insidious there (quadratic effect)

85. Women in the Workforce Scaling by Area
Here’s the same graph with 2:1 area:
Women in the Workforce
1980
1960

86. Poor Histogram Cell Size
Picking bucket size is always a problem
Prefer 5 or more observations per bucket
Choice of bucket size can affect results:

87. Special-Purpose Charts
Histograms
Scatter plots
Gantt charts
Kiviat graphs

88. Histograms

89. Scatter Plots Useful in statistical analysis
Also excellent for huge quantities of data
Can show patterns otherwise invisible

90. Gantt Charts Shows relative duration of Boolean conditions
Arranged to make lines continuous
Each level after first follows FTTF pattern

91. Kiviat Graphs Also called “star charts” or “radar plots”
Useful for looking at balance between HB and LB metrics

92. Useful Reference Works
Edward R. Tufte, The Visual Display of Quantitative Information, Graphics Press, Cheshire, Connecticut, 1983.
Edward R. Tufte, Envisioning Information, Graphics Press, Cheshire, Connecticut, 1990.
Edward R. Tufte, Visual Explanations, Graphics Press, Cheshire, Connecticut, 1997.
Darrell Huff, How to Lie With Statistics, W.W. Norton & Co., New York, 1954