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Thank You R Hackers of NYC

“Never doubt that a small group of thoughtful, committed citizens can change the world. Indeed, it is the only thing that ever has.” --Margaret Mead. Thank You R Hackers of NYC. Harvesting & Analyzing Interaction Data in R: The Case of MyLyn. Sean P. Goggins, PhD Drexel University

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Thank You R Hackers of NYC

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  1. “Never doubt that a small group of thoughtful, committed citizens can change the world. Indeed, it is the only thing that ever has.”--Margaret Mead

  2. Thank You R Hackers of NYC

  3. Harvesting & Analyzing Interaction Data in R: The Case of MyLyn Sean P. Goggins, PhD Drexel University outdoors@acm.org MyLyn Research Collaborators: PeppoValetto, PhD (PI) & Kelly Blincoe

  4. I Study Small Groups I use electronic trace data, interviews, field notes, electronic content & surveys for raw data

  5. Coolest Open* Data to Me • Group’s Emerging & Evolving • Group Formation & Development • The long tail of social computing, which I describe as everything *except* Wikipedia & Facebook • Groups constructing knowledge, creating information and forming identity. *Available, but not always easy to get in an analyzable form

  6. Points • Harvesting Small, Open Data [MyLyn] • Analyzing • Temporal Changes in theMyLyn Network • Work • Talk • Libraries Used & Source Code • StatNet • iGraph • TNET • R Sourcecode and Data will be available for download at http://www.groupinformatics.org . If you use this data or scripts please cite: • Goggins, S. P., Laffey, J., Amelung, C., and Gallagher, M. 2010. Social Intelligence In Completely Online Groups. IEEE International Conference on Social Computing. 500-507. DOI=10.1109/SocialCom.2010.79. • Blincoe, K., Valetto, G., and Goggins, S. 2011. Leveraging Task Contexts for Managing Developers’ Coordination. Under Review.

  7. Data for R An Example From the MyLyn Project

  8. More About MyLyn: http://tasktop.com/blog/ http://www.eclipse.org/mylyn/ .zip file MyLyn Context Uploads Work Bug Database Talk MySQL Database Talk Talk HTML Parser

  9. Talk Cues Work Talk

  10. Coordination Requirements & Dependencies • MyLyn Data Has 2 Advantages • for Analysis compared to source • Control systems analysis: • You see files *viewed* together • Discourse on a Bug is directly connected to the files read and edited • Closer connection between analysis of work & talk. Talk Work

  11. Harvesting Data for R An Example From the MyLyn Project

  12. MyLyn Interaction Datamart Talk Work MyLyn Interaction Warehouse ETC CANS Work Talk

  13. Analyzing Open Data with R An Example From the MyLyn Project

  14. Analysis Tools • Eight Mylyn Releases (Temporal Analysis) • R Packages Used • TNET • iGraph • Statnet

  15. Weighted Network: TNET

  16. The Dense Graph (Work) • Developers create a dense graph. Not a complete graph, but dense. Work

  17. A Sparser Graph (Talk) • Commenter's create a sparse graph Talk

  18. Release One (2.0) Analysis Release 1 Discussion Code Talk Work iGraph

  19. STATNET for Discussion • StatNet Red = Bug Commenter Blue = Bug Opener Release 1 Talk StatNET

  20. Release One Work & Talk

  21. Release 1 (2.0) iGraph & Statnet Talk Red = Bug Commenter Blue = Bug Opener Clusters Release 1 StatNET In Degree & Out Degree iGraph

  22. Release One (2.0): Filtered Code Discussion Talk Release 1 Work Google Summer Coder 304, 373, 399 & 143 form The Strongest Connections In both networks Red = Bug Commenter Blue = Bug Opener

  23. Release One (2.0): Filtered Code Discussion Work Talk Release 1 457, 391 & 159 – Comment & Open Google Summer Coder 304, 373, 399 & 143 form The Strongest Connections In both networks Red = Bug Commenter Blue = Bug Opener

  24. Compare Over Time First & Last Release

  25. Release 1 (2.0) Compared to Release 8 (3.3) Release 1 Talk Release 8 304, 399, 143, 159, 173, 373 399, 118, 304, 159, 391, 416 StatNET & ordinary plotting

  26. Release 1 (2.0) Compared to Release 8 (3.3) Work Release 1 143 & 304 disengaged Or missing entirely Release 8 304, 373, 399 & 143 Two disconnected Graphs in release 8 iGraph

  27. Release Eight Work & Talk

  28. Release 8 (3.3): Filtered Discussion Talk Code Release 8 Nobody is “Just Blue” Work Red = Bug Commenter Blue = Bug Opener

  29. Release 8 (3.3): Filtered Discussion Release 8 Talk Code Work Notice 416 in Talk & Second Coder Graph Red = Bug Commenter Blue = Bug Opener

  30. Release 8 (3.3) iGraph & Statnet 399, 118 & 159 are significant, But play with different clusters of Other people. Release 8 Talk Red = Bug Commenter Blue = Bug Opener Clusters StatNET In Degree & Out Degree Blue Cluster iGraph

  31. Releases One  Eight High Level Views Over Time

  32. Discussion, Releases 1 – 8 Where there is no color, There are multiple, incomplete Graphs.

  33. Code, Releases 1 – 8 One Possible explanation: A few central People who slowly but Observably begin to engage Other contributors in An open source software Development project. Structure evolves Key Groups Evolve iGraph

  34. Next Step: The Story But that’s the research part, not the cool “R Stuff” Part

  35. The People 373 304 399 159 143 Our next step is piecing together a narrative about the groups that emerged on this project, and describing each of the individuals. This is all open data. When we finish this part, we will publish one or more papers. For now, Let’s look at the cool “R Stuff”

  36. Interaction Traces from Small Groups: The Case of MyLyn Sean P. Goggins, PhD Drexel University outdoors@acm.org Collaborators: PeppoValetto, PhD & Kelly Blincoe Questions? In the after session.

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