just because you can doesn\'t mean you should - graphing data well

$: Just Because You Can Doesn\'t Mean You Should - Graphing Data Well$
Steve Figard, Ph.D., Abbott Laboratories, Abbott Park, IL

Just Because You Can, Doesn’t Mean You Just Because You Can, Doesn’t Mean You ShouldShould

––The Elements of Graphing Data WellThe Elements of Graphing Data Well

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Agenda/Objectives of Presentation

Introduction Terminology The Ten Commandments of Good Graphics A Word about PowerPoint The “Best” & The “Worst” Concluding Statement

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Introduction

The Problem: a plethora of options/features/capabilities Confusion of what can be done with what ought to be done

Attributable to ignorance…“Programming today is a race between software engineers striving to build bigger and better idiot-proof programs, and the Universe trying to produce bigger and better idiots. So far, the Universe is winning.” -Richard Cook, science fiction author, The Wizardry Compiled

Due to lack of trainingNot usually covered in university classes

• Introduction• Terminology• 10 Commandments• PowerPoint• Best/Worst• Conclusion

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Introduction

Two goals of good graphicsClarity revealing the story in the dataEase of visualizing the plotted data


“When a graph is made, quantitative and categorical information is encoded by a display method. Then the information is visually decoded. This visual perception is a vital link. No matter how clever the choice of the information, and no matter how technologically impressive the encoding, a visualization fails if the decoding fails. Some display methods lead to efficient, accurate decoding, and others lead to inefficient, inaccurate decoding.” - William Cleveland

The “Grand Unification Philosophy” of good graphicsminimize the mental gymnastics that the viewer must go through to understand the graph

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Terminology• Introduction• Terminology• 10 Commandments• PowerPoint• Best/Worst• Conclusion

aka, y axis

aka, x axis

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Terminology• Introduction• Terminology• 10 Commandments• PowerPoint• Best/Worst• Conclusion

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The Ten Commandments…of good graphics

Folded, spindled, and mutilated from:


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1. Thou shalt pay very close attention to thy axes, for therein lieth great opportunity to succeed or to fail.

The units of measure employed-alternate labels


How relate to log2?

Now these numbers we understand!

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The units of measure employed-what to plot


The raw data: interesting but not as informative…

…as the actual difference between the lines.

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The units of measure employed-plotting differences


The optical illusion: The lines are only one unit apart across the entire range.

Why? Because the eye is good at perceiving perpendicular

distances between two curves, but not the difference in height.

Lesson to be learned: plotting themetric of interest may be moreInformative than the raw data.

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The range of those units of measure

Choose your range so thatthe data rectangle fills upas much of the scale-linerectangle as possibleDo not insist that the zero always be included on a scale showing magnitude, but…


“Get your facts first, and then you can distort them as much as you please. (Facts are stubborn, but statistics are more pliable).” - Mark Twain

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The number of tick marks shownToo many = clutterToo few = “guesstimation” difficulties3-10 usually sufficientBeware abuse of time-scaletick marks by changing theinterval shown…


one year interval five year interval

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The presence or absence of breaks in the axisOnly when necessary…try log scale firstIf used, do a full scale breakIf used, do NOT connect numerical values across the break!


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The presence or absence of breaks in the axisPay close attention to ranges especially when breaks are clearly present – they will impact the interpretation of the data and may alter the message conveyed…


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The size or length of the axis on the pageSometimes the default square or rectangle may hide important features in the data


JMP is particularly good at allowing“on the fly” adjustment of axes

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2. Thou shalt use color to categorize, not accessorize.Only two uses of color that transmit useful information to the viewer

Encoding a categorical variable


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2. Thou shalt use color to categorize, not accessorize.Only two uses of color that transmit useful information to the viewer

Encoding a quantitative variable: contour plotsthe choice of color for a contour plot must achieve two goals:effortless perception of the order of the values(i.e., we do not want to be constantly referring to a key)clearly perceived boundaries between adjacent levels


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3. Thou shalt choose symbols that can be easily distinguished from one another.

Concerned with plots in which the data overlaps so that discerning the different datasets being plotted becomes critical


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3. Thou shalt choose symbols that can be easily distinguished from one another.

“Texture” based on micropatterns inherent in the symbol: boundaries


JMP provides several sets of markers that should be evaluated with this commandment in mind

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4. Thou shalt not employ “chartjunk.”Category 1: unintentional optical art and the moiré effect


Anyone recognizeExcel bar chartoptions here?!

The moiré effect describes the phenomenon when the graphic design interacts with the physiological tremor of the eye to generate the distracting appearance of vibration and movement.

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4. Thou shalt not employ “chartjunk.”Category 1: unintentional optical art and the moiré effect


Can you say “garish”?!

garish adj. 1 too bright or gaudy; showy; glaring 2 gaudily or showily dressed, decorated, written, etc.(Webster’s New World College Dictionary, 4th Ed.)

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4. Thou shalt not employ “chartjunk.”Category 2: the dreaded grid (especially compared to symbol size)


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4. Thou shalt not employ “chartjunk.”Category 3: the self-promoting graphical duck


“When a graphic is taken over by decorative forms or computer debris, when the data measures and structures become Design Elements, when the overall design purveys Graphical Style rather than quantitative information, then that graphic may be called a duck in honor of the duck-form store, ‘Big Duck.’”- Edward Tufte

Based on an architectural observation that is valid for

graphics:“It is all right to decorate

construction but never construct decoration.”

Fortunately, this is really hard to do in JMP…you have to work at it. Our Worst case selection will further clarify this rule of thumb.

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4. Thou shalt not employ “chartjunk.”Category 3: the self-promoting graphical duck

The charts widely used in mass media and business publications, to wit, the pie chart, divided bar charts, and area charts, will, in most cases, violate this commandment when their use is attempted in science and technology.


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5. Thou shalt show variation in thy data, not in thy design.Avoid confounding design variation with data variation


Five different vertical scales are used to show the price and two different horizontal scales to show the passage of time without one indication of these changes (not even a scale break)!

(FYI: This qualifies as a “self-promoting graphical duck.”)

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6. Thou shalt maximize the data-ink ratio in thy graphs.The data-ink ratio = the amount of “ink” used to depict the actual data divided by the total “ink” used to print the graphicThe “Precision Marching Bandof 63 Mosquitoes”:data-ink ratio < 0.6


Some data that doesn’t fit the pattern, an important observation, is actually obscured by the Marching Band…

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6. Thou shalt maximize the data-ink ratio in thy graphs.The “Precision Marching Band of 63 Mosquitoes”: remove the elements below


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6. Thou shalt maximize the data-ink ratio in thy graphs.The “Precision Marching Band of 63 Mosquitoes”:

add a few labels and rotate y axis labels and numbers for easier reading


Data-ink ratio up to ~ 0.9

All data clearly visible

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7. Thou shalt maximize thy data density and the size of thy data matrix (within reason).

The human eye has the ability to detect large amounts of information in small spaces: take advantage of this phenomenon


Low data density:•data matrix contains only four entries

• the names (2) and the numbers (2) for the two bars on the right•bar on the left is the sum of the other two

•original graph covers 26.5 square inches•dividing 4 by 26.5 = data density of 0.15 numbers per square inch

NOT GOOD!

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Good data density: map of France


This map of France was originally 27 square inches (close to that of previous slide). It shows the location and boundaries of 30,000 French communes. To recreate the data of the map would require somewhere in the neighborhood of 240,000 numbers: 30,000 latitudes, 30,000 longitudes, and an average of six numbers describing the shape of each commune. The data density thus works out to be nearly 9,000 numbers per square inch.

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Of course, “within reason” applies…


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8. Thou shalt draw the viewer’s eye to the data, not to other design elements.

Use visually prominent graphical elements to show the dataDon’t clutter the interior of the scale-line rectangle with legends, labels, and linesTick marks should generally face outwardUse reference lines only when an important value must be seen across the entire graph, and then use a color, weight and style of line that does not overpower the data symbolsIf data labels are used inside the scale-line rectangle, don’t allow them to interfere with the data or to clutter the graphDon’t put notes and keys inside the scale-line rectangle; notes should go in a caption or the accompanying textWhen datasets are superimposed, choose color, symbol, line weights and styles, and other such graphical elements so that the datasets can be readily visually distinguished


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9. Thou shalt do and redo thy graphs to determine which one telleth the story best.

Experiment: this process is complex and multivariateNot only efficiency, but complexity, structure, density, and even beauty have a role to play in the generation of the final product.JMP, particularly the Graph Builder, is uniquely strong in this ability to “play” with visualization options.


Simplest:height by weight…

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“Playing” with Graph Builder


Wrap by sex:

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Adding overlay by age:

Eh, don’t like that?Hit undo button twice…

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Redo by reversingthe process use:wrap by ageoverlay by sex…

And if you stilldon’t like it orsee clearly the

story in the data,UNDO…REDO!

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10.Thou shalt not create “unfriendly” but “friendly” data graphics.

Remember your audience


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10.Thou shalt not create “unfriendly” but “friendly” data graphics.

Regarding typography: a quote of Tufte quoting someone else:


The concept that “the simpler the form of a letter the simpler its reading” was an obsession of beginning constructivism. It became something like a dogma, and is still followed by “modernistic” typographers…. Ophthalmology has disclosed that the more the letters are differentiated from each other, the easier is the reading. Without going into comparisons and details, it should be realized that words consisting of only capital letters present the most difficult reading – because of their equal height, equal volume, and with most, their equal width. When comparing serif letters with sans-serif, the latter provide an uneasy reading. The fashionable preference for sans-serif in text shows neither historical nor practical competence.

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A Word about PowerPointThe charges: Cognitive style. Presenter-

focused, not content or audience focused.

Low resolution. Little info per slide - so more slides are needed. Data graphics are weak: average of 12 numbers per graphic.

Bullets. Bullet lists can show only 3 logical flows: sequence; priority; or membership. Multivariate models with feedback and simultaneity can’t be listed. This encourages lazy thinking, generic ideas and ignores critical relationships and assumptions.


Guilty as charged?

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A Word about PowerPoint

Yes and no: Some validity to these charges, BUT…they all seem to

ignore the fact that PowerPoint, or any other presentation software, is just a tool.

To blame the tool for its misuse is to kill the messenger for his message.What is needed is not condemnation of the tool but proper instruction of the use of that tool.


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Competitors for Best & WorstThe Best: Minard’s data map + time-series


Plots 6 (!) variables:1. size of army2/3. location on 2D surface4. direction of movement5. temperature6. dates during retreat (time)

Invasion starts with 422,000men at Polish-Russian border

A sacked and deserted Moscowreached with only 100,000 men

Retreat in dead of winterdepicted on lower darkerband and linked to temp

scale and dates on bottom

…defies “the pen of the historian by its brutal eloquence.”

Only 10,000 made it home!

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Competitors for Best & WorstThe Worst:


Only five pieces ofdata (not variables) in this“graphically preposterous”work of art.

Not one but two axis breaks!

3D effect = chartjunkSince numbersall sum to 100%,plotting both isredundant.

Colors signify nothing

…”delighted connoisseurs of the graphically preposterous.”

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Conclusion (in Tufte’s words)

“Design is choice. The theory of the visual display of quantitative information consists of principles that generate design options and that guide choices among options. The principles should not be applied rigidly or in a peevish spirit; they are not logically or mathematically certain; and it is better to violate any principle than to place graceless or inelegant marks on paper....

“What is to be sought in designs for the display of information is the clear portrayal of complexity. Not the complication of the simple; rather the task of the designer is to give visual access to the subtle and the difficult – that is,

the revelation of the complex.”


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Handy ReferencesCleveland, William S. 1994. The Elements of Graphing Data. Revised Edition. Summit, New Jersey: Hobart Press.Tufte, Edward R. 2001. The Visual Display of Quantitative Information. 2nd Edition. Cheshire, Connecticut: Graphics Press.Huff, Darrell. 1954. How to Lie with Statistics. New York, New York: W. W. Norton & Company.Zumel, Nina. 2009. Good Graphs: Graphical Perception and Data Visualization. http://www.win-vector.com/, accessed 4 June 2010.Pirrello, Chuck. 2010. Effective Visualization Techniques for Data Discovery and Analysis. Cary, North Carolina: SAS Institute, Inc.Few, Stephen. 2009. Now You See It: Simple Visualization Techniques for Quantitative Analysis. Oakland, California: Analytics Press.Cleveland, William S. 1993. Visualizing Data. Summit, New Jersey: Hobart Press.Tufte, Edward R. 1990. Envisioning Information. Cheshire, Connecticut: Graphics Press.Annesley, Thomas M. 2010. Put Your Best Figure Forward: Line Graphs and Scattergrams. Clinical Chemistry 56 (8): 1229-1233.Bessler, LeRoy. 2004. Communication-Effective Use of Color for Web Pages, Graphs, Tables, Maps, Text, and Print. SUGI 29, Montreal, Canada. http://www2.sas.com/proceedings/sugi29/176-29.pdf, accessed 2 July 2010.

http://www.win-vector.com/

http://www.win-vector.com/

http://www2.sas.com/proceedings/sugi29/176-29.pdf

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