4. Data
Graphics (scatterplot,
spectrum, micrograph, etc)
Presentation
(to your professor, other
researchers, general public)
Exploration:
“What conclusions
do my data support?”
Visual
Intuition
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5. Graphics reveal data.
• Dual purposes: to explore data, and to
present data.
• Excellent graphics do so with clarity,
efficiency and precision.
• Richness beyond what any summary
statistics (average, standard deviation,
correlation, etc) can provide.
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6. Anscombe’s quartet
• All datasets: mean, variance &
correlation are all identical
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7. Scientific graphics should:
• Show the data
• Allow the viewer to think about the
substance, rather than the
methodology of the experiment
(or something else- font/color/etc)
• Avoid distorting the data
• Reveal multiple layers of detail: big
picture & fine structure
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9. Some examples: the bad
Lie factor: effect shown in graphic
effect shown in data
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10. Some examples: the bad
• Bad data = bad
graphics!
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11. Types of graphics
• Most popular in mass media:
time series & data maps
• Excel is optimized for business users
(earnings reports, market share, etc).
•
• Beware “chartjunk”!
Chemistry is most concerted with relational
graphics.
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12. Effective strategies:
1. Maximize “data ink”: % of graphic
actually used to plot your data.
2. Maximize data density.
•
•
Use small multiples
Combine graphics, images &
numbers to tell a visual story.
3. Use color effectively
4. Revise & edit.
Tufte’s rules: http://www.sealthreinhold.com/tuftes-rules/index.php
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16. Data density
Frankel & DePace. “Visual Strategies: A Practical Guide to Graphics for Scientists and
Engineers” (Yale University Press 2012)
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17. Data density
• Not all data needs to
be presented as a
graphic
• For example, tables
are sometimes more
effective for small
data sets
(most infographics are silly)
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18. (some infographics
are pretty neat!)
from Wired Magazine’s best infographics
& scientific figures
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19. Using color effectively
Sequential: data that runs from low
to high
Diverging: emphasize max/min
extremes of data
Qualitative: no difference implied
between data classes (best for
nominal/categorical data)
• Colorbrewer (colorbrewer2.org)
• Colorblind people are scientists too!
• Beware the black & white photocopier
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20. Using color effectively
• Minimal color highlights data: builds a
visual story
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21. Tools of the trade
• Understand the limitations of Excel
• Lots of field-specific options: gnuplot,
Origin, R, Matlab, SPSS, Sigmaplot, etc.
(see handout)
• Revise and edit: develop your own
personal style
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