1. Introduction to Graphical Presentation Andy Wang CIS 5930-03 Computer Systems Performance Analysis

2. 2 The Art of Graphical Presentation Reference Works Types of Variables Guidelines for Good Graphics Charts Common Mistakes in Graphics Pictorial Games Special-Purpose Charts

3. 3 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

4. 4 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)

5. 5 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

6. 6 Guidelines for Good Graphics Charts Principles of graphical excellence Principles of good graphics Specific hints for specific situations Aesthetics Friendliness

7. 7 Principles of Graphical Excellence Graphical excellence is the well- designed presentation of interesting data: –Substance –Statistics –Design

8. 8 Graphical Excellence (2) Complex ideas get communicated with: –Clarity –Precision –Efficiency

9. 9 Graphical Excellence (3) Viewer gets: –Greatest number of ideas –In the shortest time –With the least ink –In the smallest space

10. 10 Graphical Excellence (4) Is nearly always multivariate Requires telling truth about data

11. 11 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

12. 12 Above All Else Show the Data

13. 13 Above All Else Show the Data

14. 14 Maximize the Data-Ink Ratio

15. 15 Maximize the Data-Ink Ratio

16. 16 Erase Non-Data Ink

17. 17 Erase Non-Data Ink East West North

18. 18 Erase Redundant Data Ink East West North

19. 19 Erase Redundant Data Ink East West North

20. 20 Revise and Edit

21. 21 Revise and Edit

22. 22 Revise and Edit

23. 23 Revise and Edit

24. 24 Revise and Edit

25. 25 Revise and Edit

26. 26 Revise and Edit

27. 27 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

28. 28 Give Information the Reader Needs Show informative axes –Use axes to indicate range Label things fully and intelligently Highlight important points on the graph

29. 29 Giving Information the Reader Needs

30. 30 Giving Information the Reader Needs

31. 31 Limit Complexity and Confusion Not too many curves Single scale for all curves No “extra” curves No pointless decoration (“ducks”)

32. 32 Limiting Complexity and Confusion

33. 33 Limiting Complexity and Confusion

34. 34 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

35. 35 Having a Point Sales were up 15% this quarter:

36. 36 Having a Point

37. 37 Having a Point

38. 38 Having a Point

39. 39 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

40. 40 Don’t Always Use Graphics Tables are best for small sets of numbers –Tufte says 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

41. 41 Text Would Have Been Better

42. 42 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

43. 43 Aesthetics Not everyone is an artist –But figures should be visually pleasing Elegance is found in –Simplicity of design –Complexity of data

44. 44 Principles of Aesthetics Use appropriate format and design Use words, numbers, drawings together Reflect balance, proportion, relevant scale Keep detail and complexity accessible Have story about the data (narrative quality) Do professional job of drawing Avoid decoration and chartjunk

45. 45 Use Appropriate Format and Design Don’t automatically draw a graph –Mentioned before Choose graphical format carefully Sometimes “text graphic” works best –Use text placement to communicate numbers –Very close to being a table

46. 46 GNP: +3.8IPG: +5.8CPI: +7.7Profits: +13.3 CEA: +4.7 DR: +4.5 NABE: +4.5 WEF: +4.5 CBO: +4.4 CB: +4.2 IBM: +4.1 CE: +2.9 NABE: +6.2 IBM: +5.9 CB: +5.5 DR: +5.2 WEF: +4.8 IBM: +6.6 NABE: +6.5 CB: +6.2 WEF: +21 DR: +10.5 IBM: +10.4 CE: +6.5 WEF: 6.8 CB: 6.7 NABE: 6.7 IBM: 6.6 DR: 6.5 CBO: 6.3 CEA: 6.3 Unempl: 6.0 About a year ago, eight forecasters were asked for their predictions on some key economic indicators. Here’s how the forecasts stack up against the probable 1978 results (shown in the black panel). (New York Times, Jan. 2, 1979) Using Text as a Graphic

47. 47 The Stem-and-Leaf Plot From Tukey, via Tufte, heights of volcanoes in feet: 0|98766562 1|97719630 2|99987766544422211009850 3|876655412099551426 4|9998844331929433361107 5|97666666554422210097731 6|898665441077761065 7|98855431100652108073 8|653322122937

48. 48 Choosing a Graphical Format Many options, more being invented all the time –Examples will be given later –See Jain for some commonly useful ones –Tufte shows ways to get creative Choose a format that reflects your data –Or that helps you analyze it yourself

49. 49 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”

50. 50 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

51. 51 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

52. 52 Better Proportion Sales in North and West districts were steady through all quarters East sales varied widely, significantly outperforming other districts in third quarter

53. 53 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

54. 54 An Unfriendly Graph

55. 55 A Friendly Version

56. 56 Even Friendlier

57. 57 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 high end –And that’s because we hit a CPU bottleneck

58. 58 Showing a Story About the Data

59. 59 Do a Professional Job of Drawing This is easy with modern tools –But take the time to do it right Align things carefully Check final version in format you will use –I.e., print Postscript one last time before submission –Or look at your slides on projection screen Preferably in presentation room Color balance varies by projector

60. 60 Avoid Decoration and Chartjunk Powerpoint, etc. make chartjunk easy Avoid clip art, automatic backgrounds, etc. Remember: data is the story –Statistics aren’t boring –Uninterested readers aren’t drawn by cartoons –Interested readers are distracted Does removing it change message? –If not, leave it out

61. 61 Examples of Chartjunk Gridlines! Vibration Pointless Fake 3-D Effects Filled “Floor”Clip Art In or out? Filled “Walls” Borders and Fills Galore Unintentional Heavy or Double Lines Filled Labels Serif Font with Thin & Thick Lines

62. 62 Common Mistakes in Graphics Excess information Multiple scales Using symbols in place of text Poor scales Using lines incorrectly

63. 63 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 But note that Tufte hates pie charts Extract essence, don’t cram things in

64. 64 Way Too Much Information

65. 65 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

66. 66 The Right Amount of Information

67. 67 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

68. 68 Some Especially Bad Multiple Scales

69. 69 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...

70. 70 It’s All Greek To Me...

71. 71 Explanation is Easy

72. 72 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

73. 73 Nonzero Origins (Chosen by Microsoft)

74. 74 Proper Origins

75. 75 A Poor Axis Range

76. 76 A Logarithmic Range

77. 77 A Truncated Range

78. 78 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

79. 79 Incorrect Line Usage

80. 80 Pictorial Games Non-zero origins and broken scales Double-whammy graphs Omitting confidence intervals Scaling by height, not area Poor histogram cell size

81. 81 Non-Zero Origins and Broken Scales People expect (0,0) origins –Subconsciously So non-zero origins are 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)”

82. 82 Non-Zero Origins

83. 83 The Three-Quarters Rule Highest point should be 3/4 of scale or more

84. 84 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

85. 85 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

86. 86 Graph Without Confidence Intervals

87. 87 Graph With Confidence Intervals

88. 88 Scaling by Height Instead of Area Clip art is popular with illustrators: Women in the Workforce 1960 1980

89. 89 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 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)

90. 90 Scaling by Area Same graph with 2:1 area: Women in the Workforce 1960 1980

91. 91 Poor Histogram Cell Size Picking bucket size is always problem Prefer 5 or more observations per bucket Choice of bucket size can affect results:

92. 92 Principles of Graphics Integrity (Tufte) Proportional representation of numbers Clear, detailed, thorough labeling Show data variation, not design variation Use deflated money units Don’t have more dimensions than data has Don’t quote data out of context

93. 93 Proportional Representation of Numbers Maintain lie factor of 1.0 Use areas, not heights, with clip art Avoiding “decorative” graphs will do wonders –Not too hard for most engineers!

94. 94 Clear, Detailed, Thorough Labeling Goal is to defeat distortion and ambiguity Write explanations on graphic itself Label important events in the data

95. 95 Show Data Variation, Not Design Variation Use one design for entire graphic In papers, try to use one design for all graphs Again, artistic license is big culprit

96. 96 Use Deflated Money Units Often necessary to show money over time –Even in computer science –E.g., price/performance over time –Or expected future cost of a disk Nominal dollars are meaningless Derate by some standard inflation measure –That’s what the WWW is for!

97. 97 Don’t Have More Dimensions Than Data Has This gets back to the Lie Factor 1-D data (e.g., money) should occupy one dimension on the graph: not Clip art is prohibited by this rule –But if you have to, use an area measure $1.00 $2.00

98. 98 Don’t Quote Data Out of Context Tufte’s example:

99. 99 The Same Data in Context

100. 100 Special-Purpose Charts Tukey’s box plot Histograms Scatter plots Gantt charts Kiviat graphs

101. 101 Tukey’s Box Plot Shows range, median, quartiles all in one: Tufte can’t resist improvements: or or even minim um maxim um quarti le medi an

102. 102 Histograms Tufte improves everything about them:

103. 103 Scatter Plots Useful in statistical analysis Also excellent for huge quantities of data –Can show patterns otherwise invisible

104. 104 Better Scatter Plots Again, Tufte improves the standard –But it can be a pain with automated tools Can use modified Tukey box plot for axes

105. 105 Gantt Charts Shows relative duration of Boolean conditions Arranged to make lines continuous –Each level after first follows FTTF pattern

106. 106 Kiviat Graphs Also called “star charts” or “radar plots” Useful for looking at balance between HB and LB metrics

107. 107 A Few Examples A bad graph Two good graphs

108. 108 A Very Bad Graph

109. 109 A Good Graph: Sunspots

110. 110 A Superb Graph: DEC Traces

111. White Slide