Post on 10-Jun-2020
How to display data badly
Karl W BromanBiostatistics & Medical InformaticsUniversity of Wisconsin – Madison
kbroman.org
github.com/kbroman
@kwbroman
Using Microsoft Excel toobscure your data and
annoy your readers
Karl W BromanBiostatistics & Medical InformaticsUniversity of Wisconsin – Madison
kbroman.org
github.com/kbroman
@kwbroman
Inspiration
This lecture was inspired by
H Wainer (1984) How to display data badly. American Statistician38(2): 137–147
Dr. Wainer was the first to elucidate the principles of thebad display of data.
The now widespread use of Microsoft Excel has resulted inremarkable advances in the field.
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General principles
The aim of good data graphics:
Display data accurately and clearly.
Some rules for displaying data badly:
• Display as little information as possible.
• Obscure what you do show (with chart junk).
• Use pseudo-3d and color gratuitously.
• Make a pie chart (preferably in color and 3d).
• Use a poorly chosen scale.
• Ignore sig figs.
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Example 1
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Example 2
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Example 2
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Example 3
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Example 3
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Example 3
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Example 3
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Example 4
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Example 4
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Example 4
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Example 5
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Example 5
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Example 5
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Example 6
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Example 6
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Example 7
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Example 7
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Displaying data well
• Be accurate and clear.
• Let the data speak.– Show as much information as possible, taking care not to obscure
the message.
• Science not sales.– Avoid unnecessary frills (esp. gratuitous 3d).
• In tables, every digit should be meaningful. Don’t dropending 0’s.
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More on data visualization
Karl W BromanBiostatistics & Medical InformaticsUniversity of Wisconsin – Madison
kbroman.org
github.com/kbroman
@kwbroman
Ease comparisons(things to be compared should be adjacent)
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Phe
noty
pe
Female Male Female Male Female Male
AA AB BB
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16
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Phe
noty
pe
AA AB BB AA AB BB
Female Male
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Ease comparisons(add a bit of color)
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Phe
noty
pe
Female Male Female Male Female Male
AA AB BB
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Phe
noty
pe
AA AB BB AA AB BB
Female Male
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Which comparison is easiest?
A B
0
50
100
150
A B
0
50
100
150
0
100
200
300
400
AB
0
100
200
300
400
A
B
0
100
200
300
400
A
B
B
A
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Don’t distort the quantities(value ∝ radius)
Wheat (17 Gbp)
Arabidopsis (0.145 Gbp)
Human (3.2 Gbp)
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Don’t distort the quantities(value ∝ area)
Wheat (17 Gbp)
Arabidopsis (0.145 Gbp)
Human (3.2 Gbp)
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Don’t use areas at all(value ∝ length)
Gen
ome
size
(G
bp)
0
5
10
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Arabidopsis Human Wheat
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Encoding data
Quantities
• Position
• Length
• Angle
• Area
• Luminance (light/dark)
• Chroma (amount of color)
Categories
• Shape
• Hue (which color)
• Texture
• Width
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Ease comparisons(align things vertically)
Women
Height (in)
55 60 65 70 75
Men
Height (in)
55 60 65 70 75
Men
Height (in)
55 60 65 70 75
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Ease comparisons(use common axes)
Women
Height (in)
55 60 65 70 75
Men
Height (in)
60 65 70 75
Women
Height (in)
55 60 65 70 75
Men
Height (in)
55 60 65 70 75
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Use labels not legends
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Petal length (cm)
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(cm
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setosaversicolorvirginica
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Petal length (cm)
Pet
al w
idth
(cm
)
setosa
versicolor
virginica
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Don’t sort alphabetically
0 5 10 15
Health care spending (% GDP)
United StatesUnited Kingdom
TurkeySwitzerland
SwedenSpain
Russian FederationPoland
NorwayNetherlands
MexicoKorea, Rep.
JapanItaly
IndonesiaIndia
GermanyFranceChina
CanadaBrazil
BelgiumAustria
AustraliaArgentina ●
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0 5 10 15
Health care spending (% GDP)
IndonesiaIndia
ChinaMexico
Russian FederationTurkeyPoland
Korea, Rep.Argentina
BrazilAustraliaNorway
JapanUnited Kingdom
SwedenSpain
ItalyBelgiumAustria
SwitzerlandGermany
CanadaFrance
NetherlandsUnited States ●
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Must you include 0?
Method
Det
ectio
n ra
te (
%)
0
20
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60
80
100
120
A B C
96.5% 98.1% 99.2%
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Method
Det
ectio
n ra
te (
%)
A B C
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Summary
• Put the things to be compared next to each other
• Use color to set things apart, but consider color blind folks
• Use position rather than angle or area to represent quantities
• Align things vertically to ease comparisons
• Use common axis limits to ease comparisons
• Use labels rather than legends
• Sort on meaningful variables (not alphabetically)
• Must 0 be included in the axis limits?
• Consider taking logs and/or differences
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Further reading
• ER Tufte (1983) The visual display of quantitative information. Graphics Press.
• ER Tufte (1990) Envisioning information. Graphics Press.
• ER Tufte (1997) Visual explanations. Graphics Press.
• WS Cleveland (1993) Visualizing data. Hobart Press.
• WS Cleveland (1994) The elements of graphing data. CRC Press.
• A Gelman, C Pasarica, R Dodhia (2002) Let’s practice what we preach: Turningtables into graphs. The American Statistician 56:121-130
• NB Robbins (2004) Creating more effective graphs. Wiley
• Nature Methods columns: http://bang.clearscience.info/?p=546
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