OU Stats Conference - Quick Ways In to DataViz

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  • OU Statistics Conference, May 18th, 2011For many people, the ability to visualise data is limited to generating chart types provided by spreadsheet applications and statistical software tools, or delving into the intricacies of scientific programming libraries. However, the rapid growth in the publication of public data sets on the one hand, and the development of powerful graphics generating libraries capable of running in web browsers as well as in desktop applications on the other, has resulted in the proliferation of tools supporting interactive visual data exploration and analysis. The growth in "data journalism" has also spurred news media organisation such as the New York Times to invest in the development of online interactive data exploration tools.In this presentation, I shall review some of the available tools and libraries, including the open source Gephi application (a graphical network analysis package) and IBM's Many Eyes suite of interactive charts, and demonstrate how they can be used to support interactive exploration of data sets without the requirement of programming knowledge. If time allows, the presentation will also cover the rise in visual "data cleansing" applications such as Google Refine and the Stanford Visualisation Lab's Data Wrangler tool.
  • Shape into representation
  • Google Fusion Tables also allows the “typing” of data contained within columns. One very useful type is a location type. By declaring location type columns, Google Fusion Tables will try to geoode your data fro you, based on that location information. So what does that mean?
  • 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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