Interactive Graphics stat/engl 332 Zoology of tasks Botanist Stamp collector Photographer Ref: Buja (1996) The botanist The botanist collects specimens and looks up the botanical guide. Linking information Photographer The photographer directs their lens to the target and then focuses the image. Aspect ratio, histogram bin size Stamp Collector A stamp collector sorts, reorganizes and groups similar objects together. Multiple views, small multiples Linked Charts • Plots are linked, i.e. visual changes to one plot are propagated to the others • Charts become (marginal) views of different aspects of data • Selection & Highlighting help find relationships in higher dimensions !!! Warning !!! Graphics created for interaction are not as pretty as graphics made for presentation. 10 Presentation ● r=0.68 8 ● ● ● ● ● 6 ● ● ● ● ●● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ●● ● ●● ● ● ● ●● ● ● ● ● ●●●● ●●● ● ● ● ● ●● ● ●● ●●●● ● ●● ● ● ●● ● ● ●● ● ● ● ●● ● ● ● ●●● ● ●●●●● ●● ●● ● ● ● ● ● ● ● ●● ●●● ● ●● ● ● ● ●●● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ●● ●● ●● ● ● ●● ● ● ● ● ● ● ●● ● ● ●● ● ● ●● ● ● ● ● ●●●● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ●● ● ●● ●● ● ● ● ● ● ● ●● ● 2 4 ● ● ● 0 Total Tip ● 0 10 20 30 Total Bill 40 50 Interactive Linked Displays • Software for Interactive Statistical Graphics since late 1960s, early 1970s • Video Library: http://stat-graphics.org/movies/prim9.html (Interactive Graphics Hardware) • more modern software: ggobi, mondrian, iplots, ... Your turn: The Unusual Episode • Dataset contains a “story” • We are going to use interactive tools to figure out what happened • Ask questions that can be answered with a graphic • Once you know (or suspect) the story behind this data, collect graphical evidence for your theory. How do we Explore? • See what we find ... • ... compare to what we know • Start with simple, low-dimensional summaries, increase complexity step by step 1d Barcharts ! 1500 2000 1200 1500 500 ! 600 1000 500 600 400 200 0 ! Sex 400 800 200 0 Female 1000 count count ! 1000 count count 1400 800 Male 0 Adult Child Age 0 I II Treatment III IV died Outcome • strange gender distribution (does not match 50-50 distribution we’d expect for gender), i.e. not random • too few children to be random selection (could check with US Census) • treatment assignments not random, not designed experiment (would expect margins to be closer together) • observation: 1/3 of people exposed died survived 2d Associations • Conclusions: • Women & Children have higher survival chances (preferential treatment?) • Survival &Treatment are strongly associated: with higher treatment number survival rates decrease • Women in all groups have higher survival chances, • All Children in treatment 1 & 2 survived, no children in treatment 4 • Men in treatment 2 have very low chances of survival UNusual Episode Stamp collector: Barcharts or spine plots of different variables laid out on screen! Photographer: Bar for a category is shifted in a barchart, the view is re-focused! Biologist: Plots are probed for more details on counts/percentages in a bar. Plots are linked, highlighting in one plot corresponds to highlighting in other plots. How does it work? • The graphics window needs to listen to user actions. • The actions need to be related to the data. How Linking Works OBS 1 2 3 4 5 6 7 8 9 10 TOTBILL 16.99 10.34 21.01 23.68 24.59 25.29 8.77 32.83 15.04 14.78 TIP 1.01 1.66 3.5 3.31 3.61 4.71 2 1.17 1.96 3.24 SEX F M M M F M M M M M SMOKER no no no yes yes no no yes no no DAY sun thurs sun sun sat sat sun sat sun sun TIME dinner lunch dinner dinner dinner dinner dinner dinner dinner dinner SIZE 2 3 3 2 4 4 2 2 2 2