practical considerations for displaying quantitative data
DESCRIPTION
Many librarians need to express data visually in reports, papers, and presentations. The goal of this talk is to cover the basics of effectively displaying quantitative data visually. It will include an overview of quantitative data types and common quantitative relationships that can be expressed visually. The talk will emphasize practical considerations and guidance for effectively selecting and designing data visualizations, such as those found in everyday tools like Microsoft Excel and the Google Visualization API.TRANSCRIPT
Practical Considerations for Displaying Quantitative Data
Cory LownNCSU Libraries
Maryland SLA21 October 2010
Outline
• History and context• Things to consider• Good questions• What is data?• What kind of chart?• Visual perception
• Data visualization tools• Where to learn more
History and context
16,500 BCE
6,200 BCE
950
1637
1786
1991 – in Maryland
2005
Data visualization isn't new
What is new
1. Amount of data
1. Computer processing ubiquity
2. Desktop and Web applications
Computers are useless. They can only give you answers.
— Pablo Picasso
Good questions
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• Image of something built
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• Image of a tool
What is
data?
*See Stephen Few's Show Me the Numbers
155,741
155,741Searches
Quantitative information always expresses
relationships
Quantitative relationships are:
1. An association between quantitative values and categories
1. Associations among multiple sets of quantitative values
Relationships among quantities
• Nominal comparison• Time series• Ranking• Part to whole (%)• Deviation• Distribution• Correlation
What kind
of chart?
*See Stephen Few's Show Me the Numbers
Charts
• Tables
• Graphs
Tables
• Look up individual values
• Compare individual values
• Precision is important
• Multiple units of measure
A table with mixed units
Graphs
• Meaning is revealed by the shape of the values
• Show relationships among many values
1 of 13,000 pages of data
Same data in a graph
Visual
perception
*See Stephen Few's Show Me the Numbers and Christopher G. Healey's Perception in Visualization
http://www.csc.ncsu.edu/faculty/healey/PP/index.html
Sti
mulus
Sti
mulation
Perception
Preattentive processing
Extremely fast, pre-conscious visual processing
Example
9128732198432789543287
6784905043267812837698
7843928364382398731092
3478957438298374209123
0980934591283754845645
8934678238328009748349
Example
9128732198432789543287
6784905043267812837698
7843928364382398731092
3478957438298374209123
0980934591283754845645
8934678238328009748349
Some preattentive attributes
Form:• Orientation• Line length• Line width• Size• Shape• Curvature• Added marks• Enclosure
Color:• Hue• Intensity
Spatial Position:• 2D
Some preattentive attributes
Form:• Orientation• Line length• Line width• Size• Shape• Curvature• Added marks• Enclosure
Color:• Hue• Intensity
Spatial Position:• 2D
Some preattentive attributes
Form:• Orientation• Line length• Line width• Size• Shape• Curvature• Added marks• Enclosure
Color:• Hue• Intensity
Spatial Position:• 2D
Scatterplot
• Correlation• Nominal comparisons
Line chart
• Time series• Deviation• Distribution
Bar chart
• Nominal comparison• Ranking• Part to whole• Deviation• Distribution
Stacked bar chart
• Part to whole
The humble pie chart
Is B or C larger?
3D effects distort 2D proportions
Advice from Edward Tufte
• Show the data• Make large datasets coherent• Emphasize substance over method• Don't distort• Reveal several levels of detail• Serve a clear purpose
Data visualization tools
Docs
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Many
Eyes
Many
Eyes
Many
Eyes
Many
Eyes
Many
Eyes
Visualization
API
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Untitled Image LayoutSome JavaScript – not so bad, right?
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Web tools (no coding)
• Google Docs/Gadgets* http://docs.google.com/
• Many Eyes http://manyeyes.alphaworks.ibm.com/manyeyes/
Web tools (coding)
• Google Visualization API* http://code.google.com/apis/visualization/documentation/gallery.html
• Protovis* http://vis.stanford.edu/protovis/• Flotr
http://www.solutoire.com/experiments/flotr/examples/
• Flot http://people.iola.dk/olau/flot/examples/
Web tools (coding)
• MIT Simile widgets http://www.simile-widgets.org/
• Rgraph http://www.rgraph.net/• jQuery Visualize
http://www.filamentgroup.com/lab/update_to_jquery_visualize_accessible_charts_with_html5_from_designing_with
Desktop apps (easier to use)
• OpenOffice Spreadsheet / MS Excel• Adobe Illustrator• OmniGraffle (diagramming - Mac)• Visio (diagramming – PC)
Desktop apps (harder to use)
• GraphViz (network graphs)• JMP (stats)• R (stats)• Processing* http://processing.org/
Where to learn
more
Books
• Show Me the Numbers* (Few, 2004)• Now You See It (Few, 2009)• The Visual Display of Quantitative
Information (Tufte, 1983)• Beautiful Data (Segaran
& Hammerbacher, 2009) • Visualizing Data (Fry, 2008)
Websites
• http://flowingdata.com• http://infosthetics.com/• http://www.visualcomplexity.com/vc/• http://www.gapminder.org/• http://www.visualizing.org/• http://understandinggraphics.com/