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