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Infographics are usually terrible... but they don't have to be

Data Visualization 101. MAR Boost Box 4/8/14
by Daina Bouquin on 10 April 2014

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Transcript of Infographics are usually terrible... but they don't have to be

conclusion
Objectives
What is Data Visualization?
Infographics are Usually Terrible
Graphical representation of data!

Goal:
Provide the viewer with a
thorough
understanding of the information contents
Data Visualization Tools
R
Example Case
Infographic time!
Choose a tool
Daina Bouquin, MLIS, CAS
Weill Cornell Medical College
Cornell University
Agenda
Different Types of data visualization
Basic data visualization concepts
Infographic examples
Tools
Demo Piktochart
Some basic principles should be applied no matter what
Understand the types of questions you should be asking yourself when considering using an infographic
Avoid common data visualization cognitive pitfalls
Feel more comfortable approaching new tools
This will be a broad overview!
InfoVis
Infographics
http://hint.fm/wind/

Interactive
http://nyti.ms/1li5Si2
http://projects.flowingdata.com/timeuse/
http://bit.ly/1iSiXxH
Streaming Data
Scientific Visuals
Assess what type of visual we might want to use
Open source statistical programming language
Cognitive Pitfalls
Maximize information,
minimize ink and space
Facilitate comparisons
Repetition of basic design
Assume the reader's interest and intelligence
20
20
30
Official reports
Marketing, Presenting, etc.
Emphasis on fun, exploration, beauty, “wow”
The Value of Hospital Libraries
Data Visualization 101
...but they don't have to be
Data visualization is a great way to get people interested in other data-related topics
(Plus it's really fun)

You can't do anything with your data if it isn't well curated, organized, analyzed etc.
Color
HSV
hue, saturation, value
20
25
50
75
Find the Data Sources
Get Feedback
Aim for flexibility that fits your skill-level

Consider how much time you have to dedicate to the project and how much you're willing to learn

Think about everything you want to do and be sure the tool meets as many of your needs as possible


Choosing a Tool
D3.js
http://blog.revolutionanalytics.com/2012/02/creating-beautiful-maps-with-r.html
http://bl.ocks.org/mbostock/4060606
http://d3js.org/
D3.js is a JavaScript library for manipulating documents based on data.
Adobe Creative Cloud
Software package for creating and publishing visuals
"Freemium" Tools
Online tools with free and paid-for options Typically very low bar of entry
Ex: Piktochart
Structured comparison, precision, and inference
http://bit.ly/1jo27ak
Pretty, but kind of terrible
Getting Better
References

Adolph, C. (2014). CSSS 569 Visualizing Data. University of Washington.
http://faculty.washington.edu/cadolph/vis/VMIRlec.pdf

Lyons, Ray. 2013, Nov 6. American Library Association. Seeing is Believing: Understanding Data Visualization [Webinar]
https://ala.adobeconnect.com/_a1087453682/p3wrhqtzb5f/?launcher=false&fcsContent=true&pbMode=normal

Tufte, E. R. (1990). Envisioning information. Cheshire, Conn. (P.O. Box 430, Cheshire 06410: Graphics Press.

Tufte, E. R. (1997). Visual explanations: Images and quantities, evidence and narrative. Cheshire, Conn: Graphics Press

Tufte, E. R. (2001). The visual display of quantitative information. Cheshire, Conn: Graphics Press.

Tufte, E. R. (2006). Beautiful evidence. Cheshire, Conn: Graphics Press.

Wong, D. M. (2010). The Wall Street Journal Guide to Information Graphics: The Dos and Don't of Presenting Data, Facts and Figures. New York: W.W. Norton and Co.

colors on opposite sides are aesthetically pleasing
use smooth gradients
avoid using colors with similar brightness (value)
See the full transcript