Statistical Graphics! Who needs Visual Analytics? - PowerPoint PPT Presentation

Titel Event, Date Author Affiliation Statistical Graphics! Who needs Visual Analytics? martin@theusRus.de Telefónica O2 Germany 1

Statistical Graphics! Who needs Visual Analytics? useR! 2009, Rennes, FR Martin Theus www.theusRus.de 2 Outline • Data Visualization – who does not want to? • Is statistical graphics more or less than InfoVis? • From exploration to diagnostics and back? • What have R Graphics and Susan Boyle in common? • Where does R graphics head to? 2

Statistical Graphics! Who needs Visual Analytics? useR! 2009, Rennes, FR Martin Theus www.theusRus.de 3 Many Names – One Thing? Information Graphics Data Visualization Info Vis Visualization Statistical Graphics Visual Communication Visual Data Mining Visual Analytics 3

Statistical Graphics! Who needs Visual Analytics? useR! 2009, Rennes, FR Martin Theus www.theusRus.de 4 Many Names – Classification Information �� � � � � � Data � � � � � � Distributions Information Graphics Data Visualization Info Vis Statistical Graphics Visual Communication Visual Data Mining Visual Analytics 4

Statistical Graphics! Who needs Visual Analytics? useR! 2009, Rennes, FR Martin Theus www.theusRus.de 5 There are also Difference along other Dimensions … A Tree in R A Tree in Infovis Start>=8.5 | Start>=14.5 present Age< 55 absent 8/11 Age>=111 absent 29/0 12/0 absent present 12/2 3/4 5

Statistical Graphics! Who needs Visual Analytics? useR! 2009, Rennes, FR Martin Theus www.theusRus.de 6 Where Statistical Graphics trials Visual Analytics I • If a graphical display “only” shows the data it is much harder to go after certain properties we may expect to find in the data Water Softness hard medium soft No warm Yes Temperature No M-User cold Yes M X M X M X Preference 6

Statistical Graphics! Who needs Visual Analytics? useR! 2009, Rennes, FR Martin Theus www.theusRus.de 7 Where Statistical Graphics trials Visual Analytics II • As a trained statistician we can look at graphics with distributions in mind � sometimes we add explizit decision support Notched Model-based Clustering Boxplot 300 2 250 1 200 oleic 0 150 100 –1 50 –2 0 1 2 3 4 –1 0 1 2 3 7

Statistical Graphics! Who needs Visual Analytics? useR! 2009, Rennes, FR Martin Theus www.theusRus.de 8 Visual Communication • Common to all visualization efforts is to reduce the overall infor- mation to the relevant part that needs to be communicated 8

Statistical Graphics! Who needs Visual Analytics? useR! 2009, Rennes, FR Martin Theus www.theusRus.de 8 Visual Communication • Common to all visualization efforts is to reduce the overall infor- mation to the relevant part that needs to be communicated 8

Statistical Graphics! Who needs Visual Analytics? useR! 2009, Rennes, FR Martin Theus www.theusRus.de 8 Visual Communication • Common to all visualization efforts is to reduce the overall infor- mation to the relevant part that needs to be communicated 8

Statistical Graphics! Who needs Visual Analytics? useR! 2009, Rennes, FR Martin Theus www.theusRus.de 8 Visual Communication • Common to all visualization efforts is to reduce the overall infor- mation to the relevant part that needs to be communicated 8

Statistical Graphics! Who needs Visual Analytics? useR! 2009, Rennes, FR Martin Theus www.theusRus.de 9 Constructing Information Visualization • Graphics design may be based on construction rules, design dogma or aesthetics, but all these points are neither necessary nor sufficient criteria for a successful design – but certainly a good point to start off. • Milton Glaser puts it this way: “… All design basically is a strange combination of the intelligence and the intuition, where the intelligence only takes you so far and than your intuition has to reconcile some of the logic in some peculiar way. …” • Is teaching good graph design then (almost) impossible? 9

Statistical Graphics! Who needs Visual Analytics? useR! 2009, Rennes, FR Martin Theus www.theusRus.de 10 “Killer Applications” • We ungrudgingly can confirm that we looked at multi[ple|variate] time series for quite some time, but the “narrative power” of the Gapminder ani- mation is not met by any traditional display around • Of course, the applications are still limited (three continuous meas- ures for some do- zons of catego- ries) but in these cases they just work perfectly 10

Statistical Graphics! Who needs Visual Analytics? useR! 2009, Rennes, FR Martin Theus www.theusRus.de 11 Processing Pipeline • Perceptual aspects are central for the correct design and inter- pretation of graphics • As perception is subjective, we need to get it as unambiguous as possible • Reusing common building blocks eases the decoding of graphics Data Graph Decode ? -4.1 1.0 90 23.16 23.45 -3.0 1.6 87.7 23.14 23.71 -3.0 2.9 85.8 23.39 24.29 * -3.4 2.0 87.8 23.53 24.08 -3.2 3.1 87.2 23.71 24.25 * * -4.2 3.5 87.1 23.82 24.19 * * Code * -4.2 1.3 86.2 23.85 24.19 -3.2 2.6 85.9 23.80 24.14 * -3.5 2.8 87.2 23.65 23.90 -4.3 2.2 88.4 23.58 23.88 * -3.9 0.7 88.6 23.47 23.96 -3.5 3.1 89.1 23.77 24.01 -4.3 2.1 89.4 23.59 23.89 -4.1 0.6 87.8 23.65 24.00 … 11

Statistical Graphics! Who needs Visual Analytics? useR! 2009, Rennes, FR Martin Theus www.theusRus.de 12 Drawing immediate conclusions from graphs • Usually graphs are far weaker in communicating precise information than tables, such that the surplus of graphics must be the qualitative take home Dr. Snow’s cholera desease map from 1855 12

Statistical Graphics! Who needs Visual Analytics? useR! 2009, Rennes, FR Martin Theus www.theusRus.de 13 Snow’s Map: Visual Processing Pipeline How do we learn from the map? Three Steps 1.Mapping Cases 2.Mapping Pumps 3.Judging Distances 13

Statistical Graphics! Who needs Visual Analytics? useR! 2009, Rennes, FR Martin Theus www.theusRus.de 14 Some Eye Candy … parallel lines 14

Statistical Graphics! Who needs Visual Analytics? useR! 2009, Rennes, FR Martin Theus www.theusRus.de 15 Some Eye Candy … same Size 15

Statistical Graphics! Who needs Visual Analytics? useR! 2009, Rennes, FR Martin Theus www.theusRus.de 16 Some Eye Candy … same Color 16

Statistical Graphics! Who needs Visual Analytics? useR! 2009, Rennes, FR Martin Theus www.theusRus.de 16 Some Eye Candy … same Color 16

Statistical Graphics! Who needs Visual Analytics? useR! 2009, Rennes, FR Martin Theus www.theusRus.de 17 The eye isn’t equally good at judging different shapes • Angles are much harder than distances … 17

Statistical Graphics! Who needs Visual Analytics? useR! 2009, Rennes, FR Martin Theus www.theusRus.de 18 The wrong plot might obscure the message The graph shows yearly CO 2 The first differences (year to year concentrations. change) shows surprising but not quite reasonable results. What can you tell about the slope? 18