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OU Stats Conference - Quick Ways In to DataViz

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OU Stats Conference - Quick Ways In to DataViz

  1. 1. Visualisations for the Rest of Us - How to Create Rich Interactive Visualisations Without Any of the Pain<br />Tony Hirst<br />Communication and Systems,<br />The Open University<br />
  2. 2. Storytelling<br />Snsemaking<br />
  3. 3.
  4. 4. ( Data Driven Journalism )<br />
  5. 5. “Personal dashboards”<br />
  6. 6. BIG DATA<br />
  7. 7. Taking aMACROSCOPICview<br />
  8. 8. Visual AnalysisorPresentation Graphics?<br />
  9. 9.
  10. 10. Data<br />VisualAnalysis<br />
  11. 11. Data<br />(Visual)Analysis<br />PresentationGraphics<br />
  12. 12. Data<br />(Visual)Analysis<br />PresentationGraphics<br />
  13. 13. Data<br />Interactive<br />Visualisation<br />
  14. 14. For the creator<br />Spreadsheet chartsVisualisation applicationsOff-the-shelf visualisation componentsBespoke programmed visualisations<br />
  15. 15. Data Shapes<br />“Shape Sorter”, by Ella’s Dad<br />
  16. 16.
  17. 17. Data representation is key…<br />“Key”, by dewitt<br />
  18. 18. What makes for a good visualisation tool?<br />
  19. 19.
  20. 20. ComparingSortingAdding variablesFilteringHighlightingAggregatingRe-expressingRe-visualisingZooming and PanningRe-scaling<br />Key Properties of an EffectiveVisualisation Environment<br />
  21. 21. So let’s start at the beginning……with the DATA<br />
  22. 22. GetTheData.org<br />
  23. 23.
  24. 24.
  25. 25.
  26. 26.
  27. 27.
  28. 28. Manipulating Data<br />Spreadsheet<br />Text editor<br />Database/query language<br />Maths/stats tool<br />Programming language<br />(Data cleansing tool)<br />
  29. 29. Stanford Data Wrangler<br />GoogleRefine<br />
  30. 30. In the right format, or in the right environment, a “dataset as document” can become a queryable database<br />
  31. 31.
  32. 32. http://bit.ly/dFCKxM<br />
  33. 33. Fishing for ideas and keeping up…<br />
  34. 34. http://www.improving-visualisation.org/<br />
  35. 35. http://flowingdata.com/<br />http://infosthetics.com/<br />
  36. 36.
  37. 37. IBM’sMany Eyes<br />
  38. 38.
  39. 39.
  40. 40. Tableau<br />
  41. 41. Google Public Data Explorer<br />
  42. 42. Gephi<br />
  43. 43.
  44. 44. Link reception (2 receipts)<br />
  45. 45. Using colour for a reason<br />
  46. 46. So how many dimensions does support?<br />
  47. 47. X and Y (at a push, Z)<br />Node size and colour<br />(Node label size and colour)<br />Edge thickness and colour<br />(Edge label and colour)<br />Node proximity/grouping<br />Clustering<br />Filtering and differential application of the above<br />
  48. 48. Progressive Enhancement<br />
  49. 49. Data filtering<br />
  50. 50.
  51. 51.
  52. 52. Programmed visualisations<br />
  53. 53. Protovis<br />
  54. 54. Hand-coded visualisations:Protovis<br />
  55. 55. Processing<br />
  56. 56.
  57. 57. http://bit.ly/iMZF75<br />Using R for creating visualisations<br />
  58. 58. R-Studio<br />
  59. 59. Data Journalism Developer Studio<br />Data Science Toolkit<br />
  60. 60. (Probably no time for)QUESTIONS…?<br />http://blog.ouseful.info<br />@psychemedia<br />

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