Your SlideShare is downloading. ×
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
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
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×
Saving this for later? Get the SlideShare app to save on your phone or tablet. Read anywhere, anytime – even offline.
Text the download link to your phone
Standard text messaging rates apply

Iwmw12 data viz taster

3,735

Published on

Published in: Education, Technology
0 Comments
1 Like
Statistics
Notes
  • Be the first to comment

No Downloads
Views
Total Views
3,735
On Slideshare
0
From Embeds
0
Number of Embeds
4
Actions
Shares
0
Downloads
7
Comments
0
Likes
1
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
No notes for slide
  • 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

    ×