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October 17, 2013 at Ryerson University, Toronto ON

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- 1. Everyday Analytics for Everyone Communicating Effectively with Data Visualization Myles Harrison Canadian Library Assessment Workshop October 17th, 2013
- 2. Why Are We Here? – A Story
- 3. The Process • • • • Question or Story Data Please Make with the visuals Communicate Question Data Visuals Winning
- 4. i.
- 5. Thinking Like an Analyst
- 6. Thinking about Data – The Exercise – – – – – – – Books Employees Students Budget Size / Capacity Age Location – – – – – – Date Published Subject Author Length Type Publisher – – – – – – Gender Age Program Level Income Nationality
- 7. Let’s talk about relationships Doing analysis is as simple as asking the question: “What is the relationship between x and y?”
- 8. Thinking about Data – Relationships Libraries Students Size / Capacity Books Nationality Location Employees Budget Income Age Level Students Gender Program Age ………….
- 9. Types of Variables • Categorical vs. Quantitative • Categorical: • Nominal • Ordinal • Quantitative: • Discrete • Continuous
- 10. Nominal • From nominalis – Latin for name • Named categories without quantity or order • e.g: - color of vehicle - gender of participant - flavour of ice cream
- 11. Ordinal • Think “ordered” • Not a quantitative measure but has specific order • e.g: - size (small, medium, large) - quality (poor, fair, good, excellent) - education (bachelor’s, master’s, PhD)
- 12. Quantitative Data • Discrete: set values at interval - e.g. number of people • Continuous: any value along the number line - e.g. temperature in degrees celsius
- 13. ii.
- 14. Visual Encoding Adapted from Show Me The Numbers, 2nd ed. by Stephen Few. Analytics Press, 2012
- 15. My God it’s full of graphs
- 16. Pie Chart • Probably most common type of chart • Used to compare relative quantities of categorical data using angular area • Consensus amongst data visualization experts: avoid if possible
- 17. Many versus few As the number of values of the categorical variable being compared increases, legibility and usefulness decrease rapidly.
- 18. Bar Chart • Used to compare absolute values of nominal or ordinal data • Horizontal or vertical (bar and column) • Colour should encode meaning • Bars should not overlap
- 19. Bar vs. Column • Either is acceptable • Horizontal (bar) charts may be more suitable if long labels in categorical variable • Vertical (column) charts should used for time intervals
- 20. Start axes from zero (please) Because the height of the bar from the x-axis is interpreted as being the total quantity, it is important to start using it as the zero point.
- 21. (in thirty seconds)
- 22. Scatterplot • For (and only for) showing relationship between quantitative variables • Sizing, colour and symbol shape are important • For large amounts of data, a trend line or other moving average helps to see the overall shape of the data
- 23. More data…?
- 24. Line Chart • Shows connection and continuity by connecting points with lines • Intervals should be equal and gaps depicted • As number of quantities being visualized increases, readability decreases
- 25. Visualizing with integrity Because we subconsciously “fill-in” data gaps it is important to depict data in such a way that this tendency does not result in misinterpretation.
- 26. Smooth move An often overlooked nuance is the difference between smooth and straight lines between data points. This becomes more salient as the number of data points decreases.
- 27. More lines…?
- 28. iii.
- 29. Simplicity Elegance Clarity Consistency
- 30. The Process Question Data Visualize Insight
- 31. Recommended Resources
- 32. Conclusion • Today have covered a small portion of realm of data visualization • Always give thought to both your data and design decisions around its visualization • Simplicity and clarity above all • The goal is communication
- 33. “Above all else, show the data.” – Edward R. Tufte

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