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# Making pretty charts that actually mean something

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Learn how to make visually compelling charts that still convey great meaning using the best data visualization techniques.

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• very neat. I like the section on pie/bar graphs and have been reading tufte's book, although it's been a bit dated.

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• Cognos dashboard
• Microstrategy
• A central part of analyzing data is thinking about how we need to look at the numbers to understandthem. Data visualization is powerful because it can condense a lot of complicated information into asmall space and so answer important questions. But this can only happen when the design allows thoseanswers to show through.The variety of charts out there may seem endless, but they really boil down to six core visual elements:grouping, size, position, angle, color, and annotation. When we visualize data, these elements are ourbuilding blocks. In this post, I’ll step through each of these visual elements and show how we can usethem to design thoughtful visualizations of our data.
• The visual differences between the shapes of the various numbers that appear (for instance, the difference in shape between a &quot;3&quot; and a &quot;5&quot;) are too complex to process preattentively.
• This time perception was easy and immediate, because the 5&apos;s were encoded with a different preattentive visual attribute from the other numbers—in this case, a different color. Why is this important to note? Because if you want to visually encode information in a manner that can be perceived instantly and easily by your readers, you now know that you should visually encode the data using preattentive attributes, and if you want some of the data to stand out from the rest, you should encode it using different preattentive attributes.
• Data-Ink Ratio
• Data-Ink Ratio
• Data-Ink Ratio
• Data-Ink Ratio
• How we aggregate underlying dataShould we look at data in a yearly, quarterly, monthly, weekly granularity?
• How we aggregate underlying dataShould we look at data in a yearly, quarterly, monthly, weekly granularity?
• It all depends about the answer you’re looking for. The chart on the left is great if you want to see quarterly trends, but what if you want to find out how the Facebook ad campaign you launched last Tuesday did?
• How we aggregate underlying dataShould we look at data in a yearly, quarterly, monthly, weekly granularity?
• How we aggregate underlying dataShould we look at data in a yearly, quarterly, monthly, weekly granularity?
• ### Making pretty charts that actually mean something

1. 1. Making pretty charts that actually mean something ben@bensullins.com @bensullins
2. 2. warm-up
3. 3. Name an opposite Tree River
4. 4. Name an opposite couch rug
5. 5. Name an opposite Grumpy Cat Boo
6. 6. List 3 alternate uses Mask Recycle Bin Speaker Paper Weight Hockey Puck Table Leveler
7. 7. How did the chicken cross the road? He walked…
8. 8. How did the legless chicken cross the road?
9. 9. On his wing tips!
10. 10. what is beauty?
11. 11. cognos
12. 12. Microstrategy
13. 13. http://vizcandy.blogspot.com/2013/07/replicating-new-york-times-d3js-chart.html
14. 14. Oracle PeopleSoft
15. 15. http://vizcandy.blogspot.ca/p/tableau-8-redoux.html
17. 17. QlickView
18. 18. Why is data beautiful?
19. 19. “Beauty is about perception, not about make-up.” Kevyn Aucoin (makeup artist / photographer)
20. 20. Selective Attention Test http://www.youtube.com/watch?v=vJG698U2Mvo&feature=youtu.be&fullscreen=1
21. 21. Count how many times the number “5” appears
22. 22. Now count the number of time “5” appears in the same set of numbers
23. 23. Attentive Processing Pre-attentive Processing
24. 24. Which product categories are trending up?
25. 25. Which product categories are trending up?
26. 26. Attentive Pre-attentive
27. 27. What are our best Product Sub-Categories?
28. 28. What are our best Product Sub-Categories?
29. 29. Which product categories are doing good this year?
30. 30. Which product categories are doing good this year?
31. 31. why stats aren’t enough
32. 32. Data Stats
33. 33. Stats Visualized Data Visualized Francis Anscombe, 1973
34. 34. How to make data beautiful?
35. 35. Reduce non-data pixels Enhance data pixels
36. 36. “Above all else, show the data” Edward Tufte, 1983
37. 37. Data-ink ratio Edward Tufte, 1983
38. 38. Data-pixel ratio Stephen Few, 2004
39. 39. How can we increase the data-pixel ratio?
40. 40. How can we increase the data-pixel ratio?
41. 41. How can we increase the data-pixel ratio?
42. 42. enhance data-pixels
43. 43. granularity
44. 44. Granularity, which is right? Monthly Daily
45. 45. Granularity, which is right? Sub-Category Category
46. 46. size & scale
47. 47. Bubbles on a map
48. 48. Sizing Bars – preserve true portions
49. 49. position
50. 50. angle
51. 51. Connect dots/bars to show trends over time
52. 52. color
53. 53. Color – proper use
54. 54. Color – improper use
55. 55. Color – what does your mind do here?
56. 56. annotation
57. 57. Annotation – reference lines
58. 58. Annotation – grid lines
59. 59. Annotation – grid lines
60. 60. Darkhorse Analytics Example
61. 61. bensullins.com
62. 62. Sources Information Visualization: Perception for Design - Colin Ware Designing Effective Tables and Graphs - Stephen Few Which chart or graph is right for you? - Tableau Software Now You See It: Tableau 2008 Users Conference - Stephen Few Tapping the Power of Visual Perception - Stephen Few Designing Data – Typekit blog - Mike Sall Anscombe’s Quartet – Wikipedia - Francis Anscombe Data Viz Techniques in Tableau - Andy Kriebel