Tools & techniquesfor working withdatasets                             Tony Hirst              Dept of Communication and S...
Quick wins andhalf-hour hacks
Building atoolbox…
http://mashe.hawksey.info/2012/11/mining-and-openrefineing-jiscmail-a-look-at-oer-discuss//via Martin Hawksey/@mhawksey
“You can quickly create an online 3-Dvisualisation (with Google Earth) ofthese rare documents”
R-Studio
All at once      orone at a time?
Macroscopes
@mediaczar             (Accession Plot)
Google Maps, 1884 edition?
Overview first,            zoom and filter,    then details-on-demandFrom: The Eyes Have It:A Task by Data Type Taxonomy f...
•   X and Y (at a push, Z)•   Node size and colour•   (Node label size and colour)•   Edge thickness and colour•   (Edge l...
Group by  Hierarchy inside(implied) containment
Treemap in R
Similarities    anddifferences
Single page   app +  linkage
Templated data views
blog.ouseful.info @psychemedia
B llabs
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B llabs
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  • Let pi,j be the rate at which word i occurs in document j, and pj be the average across documents( sum Pij/ndocs)The size of each word is mapped to its maximum deviation ( maxi(pi,j- pj ) ), and its angular position is determined by the document where that maximum occurs.
  • B llabs

    1. 1. Tools & techniquesfor working withdatasets Tony Hirst Dept of Communication and Systems The Open University
    2. 2. Quick wins andhalf-hour hacks
    3. 3. Building atoolbox…
    4. 4. http://mashe.hawksey.info/2012/11/mining-and-openrefineing-jiscmail-a-look-at-oer-discuss//via Martin Hawksey/@mhawksey
    5. 5. “You can quickly create an online 3-Dvisualisation (with Google Earth) ofthese rare documents”
    6. 6. R-Studio
    7. 7. All at once orone at a time?
    8. 8. Macroscopes
    9. 9. @mediaczar (Accession Plot)
    10. 10. Google Maps, 1884 edition?
    11. 11. Overview first, zoom and filter, then details-on-demandFrom: The Eyes Have It:A Task by Data Type Taxonomy for Information Visualizations
    12. 12. • X and Y (at a push, Z)• Node size and colour• (Node label size and colour)• Edge thickness and colour• (Edge label and colour)• Node proximity/grouping• Clustering• Filtering and differential application of the above
    13. 13. Group by  Hierarchy inside(implied) containment
    14. 14. Treemap in R
    15. 15. Similarities anddifferences
    16. 16. Single page app + linkage
    17. 17. Templated data views
    18. 18. blog.ouseful.info @psychemedia
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