Iwmw12 data viz taster
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  • Collaborative commentary
  • library(ggplot2)mydata=with(anscombe,data.frame(xVal=c(x1,x2,x3,x4), yVal=c(y1,y2,y3,y4), mygroup=gl(4,nrow(anscombe))))

Transcript

  • 1. Data Visualisation: A TasterTony Hirst Martin HawkseyDept of Communication and Systems, JISC CETISThe Open University@psychemedia/blog.ouseful.info @mhawksey/mashe.hawksey.info
  • 2. <A QUICK NOTE>
  • 3. “The most interestingvisualisationsof your data will be produced by someone else”
  • 4. Presentation Graphics vs. Visual Analysis
  • 5. Explanatory visualizationData visualizations that are used totransmit information or a point ofview from the designer to thereader. Explanatory visualizationstypically have a specific “story” orinformation that they are intendedto transmit.Exploratory visualizationData visualizations that are used bythe designer for self-informativepurposes to discoverpatterns, trends, or sub-problemsin a dataset. Exploratoryvisualizations typically don’t havean already-known story.
  • 6. Data sketches [ Amanda Cox, New York Times ]
  • 7. Infographics ≠(Exploratory) Visualisation
  • 8. Macroscopes
  • 9. Expressions of Structure
  • 10. Hierarchical data and treemaps - medalsPivot tables
  • 11. O’Reilly Annual Review of Book Sales
  • 12. Network structure Node and edges All nodes the same sort of thing Edges may be directed or undirected Edges may be weighted Bipartite graph – two sorts of nodes Can collapse a bipartite graph to get a new view over the data
  • 13. Dynamics
  • 14. TrendsAutocorrelation
  • 15. @mediaczar (Accession Plot)
  • 16. “Literate visualisation” (writing diagrams)
  • 17. ggplot( mydata,aes(x=xVal,y=yVal)) +geom_point() +facet_wrap(~mygroup)
  • 18. Data Application OutputData [Code] Output