Post on 27-Jan-2015
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Martin HawkseyJISC CETIS
@mhawksey/mashe.hawksey.info
Tony HirstDept of Communication and Systems,The Open University
@psychemedia/blog.ouseful.info
Data Visualisation: A Taster
<A QUICK NOTE>
Your Data is Increasingly Out There
“The most interesting visualisations of your data
will be produced by someone else”
Presentation Graphicsvs.
Visual Analysis
Explanatory visualizationData visualizations that are used to transmit information or a point of view from the designer to the reader. Explanatory visualizations typically have a specific “story” or information that they are intended to transmit.
Exploratory visualizationData visualizations that are used by the designer for self-informative purposes to discover patterns, trends, or sub-problems in a dataset. Exploratory visualizations typically don’t have an already-known story.
Data sketches[ Amanda Cox, New York Times ]
Infographics≠
(Exploratory) Visualisation
Embody a Model
Macroscopes
Natural Views
Expressions of Structure
Documents as a Database
Structure in data - h
ierarchies
Hierarchical data and treemaps - medals
Pivot tables
IBM Many Eyes
O’Reilly Annual Review of Book Sales
Network structure
All nodes the same sort of thing
Bipartite graph – two sorts of nodesCan collapse a bipartite graph to get a new view over the dataStru
cture in data - graphs
Node and edges
Edges may be directed or undirected
Edges may be weighted
Dynamics
aka “Seasonal Subseries”
Trends
Autocorrelation
(Accession Plot)
@mediaczar
Follower count nonsense
Who talks to whom
“Literate visualisation”(writing diagrams)
GraphViz
ggplot( mydata,aes(x=xVal,y=yVal))
+geom_point() +facet_wrap(~mygroup)
Data Application Output
Data [Code] Output