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Uploaded byTony Hirst
Visual Conversations
This document discusses visual conversations with data through explanatory and exploratory data visualizations. It provides examples of visualization tools like ggplot2 and d3.js and techniques like exploiting hierarchical structure with treemaps, using pivot tables, and creating macroscopes. The document advocates having visual conversations with one's own data to discover patterns and differences in an emergent and interactive way.
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Visual Conversations
- 1. Visual Conversations withData Tony Hirst Dept of Communication and Systems, The Open University
- 4. Figure 1 SensorTraces is a chart that’s designed to be read
- 5. IT TELLS ASTORY
- 6. IT TELLS THE ROBOT’SSTORY
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- 15. Presentation Graphics vs. Visual Analysis
- 16. Explanatory visualization Data visualizationsthat 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 visualization Data 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.
- 17. Visual Analysis or Presentation Graphics?
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- 25. ggplot() + geom_linerange(data =d1,aes(x= car, ymin = ymin,ymax = ymax)) + geom_point(data = d2,aes(x= car, y= value,shape = variable),size = 2) + opts(title="F1 2011 Korea nRace Summary Chart",axis.text.x=theme_text(angle=-90, hjust=0)) + labs(x = NULL, y = "Position", shape = "")
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- 30. Hierarchical data andtreemaps - medals Pivot tables
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- 35. Show me thedifference… Let me see the difference…
- 39. Can I seethe difference..?
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- 43. Emergent views ofstructural properties
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- 52. Have you hada visual conversation with any of YOUR data lately?
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Editor's Notes
- #2 Visual Conversations with Data Datasets often contain amyriad number of stories,but how can we best make sense of them? Maybe avisual conversation can help? 30 mins
- #3 Example of data powered storytelling in YXR175/TXR120 robotics activity
- #4 Brief explanation of chart and what the labels are.
- #15 Wheel diameter, actual distance travelled
- #18 Livescribes, process of creation of the rich picture… the diagramming is an active storytelling process that builds on itself amd has potentially many narrative threads
- #27 Also how you position marks on a canvas in relation to each other
- #31 Collaborative commentary
- #39 The top, blue strip shows the gear (1 to 7); the green strip shows the throttle pedal depression (0-100%), and the red strip shows the brake (0-100%). The light blue strip is a composite of the previous three strips. The whiter the pixel, the closer it is to 100% throttle in 7th gear with no braking.The bottom two traces show the longitudinal and lateral g-force respectively. For the longitudinal trace, red shows braking – being forced into the steering wheel; green shows acceleration – being forced back into your seat. You’ll see the greatest g-force under braking occurs when the brakes are slapped full on… (the red bits in the third and fifth traces line up). For the latitudinal g-force, the red shows the driving being flung to the left (i.e. right hand corner), the green shows them being pushed out to the right.
- #47 Emergent Social Positioning: origins: 1.5 degree egonet (how followers follow each other, how hashtaggers follow each other)- projection maps from followers to folk they commonly follow;-- projection maps from hashtaggers to folk they commonly follow- projection maps from friends to folk who commonly follow them