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Data Visualisation: A TasterTony Hirst                                         Martin HawkseyDept of Communication and Sys...
<A QUICK NOTE>
“The most interestingvisualisationsof your data   will be produced by     someone else”
Presentation Graphics         vs.   Visual Analysis
Explanatory visualizationData visualizations that are used totransmit information or a point ofview from the designer to t...
Data sketches  [ Amanda Cox, New York Times ]
Infographics             ≠(Exploratory) Visualisation
Macroscopes
Expressions of  Structure
Hierarchical data and treemaps - medalsPivot tables
O’Reilly Annual Review of Book Sales
Network structure                Node and edges                 All nodes the same sort of thing                    Edges ...
Dynamics
TrendsAutocorrelation
@mediaczar             (Accession Plot)
“Literate visualisation”  (writing diagrams)
ggplot( mydata,aes(x=xVal,y=yVal)) +geom_point() +facet_wrap(~mygroup)
Data   Application   OutputData     [Code]      Output
Iwmw12 data viz taster
Iwmw12 data viz taster
Iwmw12 data viz taster
Iwmw12 data viz taster
Iwmw12 data viz taster
Iwmw12 data viz taster
Iwmw12 data viz taster
Iwmw12 data viz taster
Iwmw12 data viz taster
Iwmw12 data viz taster
Iwmw12 data viz taster
Iwmw12 data viz taster
Iwmw12 data viz taster
Iwmw12 data viz taster
Iwmw12 data viz taster
Iwmw12 data viz taster
Iwmw12 data viz taster
Iwmw12 data viz taster
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Iwmw12 data viz taster

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  • 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 of "Iwmw12 data viz taster"

    1. 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. 2. <A QUICK NOTE>
    3. 3. “The most interestingvisualisationsof your data will be produced by someone else”
    4. 4. Presentation Graphics vs. Visual Analysis
    5. 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. 6. Data sketches [ Amanda Cox, New York Times ]
    7. 7. Infographics ≠(Exploratory) Visualisation
    8. 8. Macroscopes
    9. 9. Expressions of Structure
    10. 10. Hierarchical data and treemaps - medalsPivot tables
    11. 11. O’Reilly Annual Review of Book Sales
    12. 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. 13. Dynamics
    14. 14. TrendsAutocorrelation
    15. 15. @mediaczar (Accession Plot)
    16. 16. “Literate visualisation” (writing diagrams)
    17. 17. ggplot( mydata,aes(x=xVal,y=yVal)) +geom_point() +facet_wrap(~mygroup)
    18. 18. Data Application OutputData [Code] Output
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