JSM 2017

Guiding Principles for Interactive Graphics Based on LIBD Data Science Projects

Transcript

1. 11 Guiding Principles for Interactive Graphics Based on LIBD Data Science Projects Leonardo Collado-Torres @fellgernon https://speakerdeck.com/lcolladotor
2. CHALLENGES 2 Before you jump in the world of interactive graphics
3. CHALLENGES 3 Before you jump in the world of interactive graphics make sure that you are aware of some of the drawbacks
4. eQTL browser 4 http://www.nature.com/nbt/journal/v35/n4/full/nbt.3838.html
5. CHALLENGES 5 D3: new language, new tools http://nvd3.org//examples/lineWithFocus.html https://github.com/ramnathv/rCharts
6. CHALLENGES 6 https://github.com/nachocab/clickme https://github.com/lcolladotor/ballgownR-devel Interpretability
7. CHALLENGES 7 Data resolution https://github.com/nachocab/clickme https://github.com/lcolladotor/ballgownR-devel
8. CHALLENGES 8
9. CHALLENGES 9 Dependencies https://github.com/nachocab/clickme https://github.com/lcolladotor/ballgownR-devel
10. CHALLENGES 10 rMaps case https://github.com/ramnathv/rMaps
11. CHALLENGES 11 Nowadays: no map! https://github.com/ramnathv/rMaps http://lcolladotor.github.io/
12. CHALLENGES 12 External dependencies Now missing: https://dl.dropboxusercontent.com/u/10794332/mx_states.json https://github.com/ramnathv/rMaps http://lcolladotor.github.io/
13. CHALLENGES 13 Quick summary • Interpretability • Data resolution: loading and sharing • Dependencies for deploying • External dependencies: less control
14. 29 eQTL browser Scenario I: for publication • Bill Ulrich 14
15. eQTL browser 15 http://www.nature.com/nbt/journal/v35/n4/full/nbt.3838.html
16. eQTL browser Thousands of eQTLs 16 http://www.biorxiv.org/content/early/2017/04/05/124321
17. eQTL browser Search for a gene 17 http://eqtl.brainseq.org/
18. eQTL browser Choose an eQTL to explore 18 http://eqtl.brainseq.org/
19. eQTL browser View the data 19 http://eqtl.brainseq.org/
20. eQTL browser View all the data + mouse-over display 20 http://eqtl.brainseq.org/
21. eQTL browser Collection of data 21 http://eqtl.brainseq.org/
22. eQTL browser Link to major community tools 22 http://eqtl.brainseq.org/
23. eQTL browser Customized view with LIBD’s data 23 http://eqtl.brainseq.org/
24. eQTL browser Scenario 1 summary 24 http://eqtl.brainseq.org/ • Re-use established tools as much as possible • Keep the information users will look at • Simple graphics • Speed is important • While developing: keep in mind users will want changes
25. Scenario II: for common use 25 • Stephen Semick shinycsv
26. Lots of tabular data: how can we make it easy to explore? 26 shinycsv
27. Excel’s recommended charts 27 shinycsv
28. Our solution: shinycsv 28 shinycsv https://jhubiostatistics.shinyapps.io/shinycsv/
29. Simple summary statistics 29 shinycsv https://jhubiostatistics.shinyapps.io/shinycsv/
30. Subset, search, re-order 30 shinycsv https://jhubiostatistics.shinyapps.io/shinycsv/
31. Check new summary statistics after sub setting 31 shinycsv https://jhubiostatistics.shinyapps.io/shinycsv/
32. Visualize a variable: just select it 32 shinycsv https://jhubiostatistics.shinyapps.io/shinycsv/
33. Includes summary statistics 33 shinycsv https://jhubiostatistics.shinyapps.io/shinycsv/
34. Plot changes by variable type 34 shinycsv https://jhubiostatistics.shinyapps.io/shinycsv/
35. Plot two variables and change colors 35 shinycsv https://jhubiostatistics.shinyapps.io/shinycsv/
36. Upload your data: handles different formats 36 shinycsv https://jhubiostatistics.shinyapps.io/shinycsv/ Download data if you edited it
37. Reproduce and learn from shinycsv 37 shinycsv https://jhubiostatistics.shinyapps.io/shinycsv/
38. Our solution: shinycsv 38 shinycsv https://jhubiostatistics.shinyapps.io/shinycsv-showcase/
39. Using shinycsv non-interactively 39 shinycsv https://jhubiostatistics.shinyapps.io/shinycsv/ Create PDFs with many plots, then explore manually
40. Scenario II summary 40 shinycsv http://lcolladotor.github.io/2017/01/20/Easily-explore-a-table-with-shinycsv • Re-use established tools as much as possible • Simple graphics: can be made prettier later • Minimize user options: try to make best guess • Might lead to unexpected use cases
41. Collaborators 41 Hopkins Jeff Leek Shannon Ellis Ben Langmead Chris Wilks Kai Kammers Kasper Hansen Margaret Taub OHSU Abhinav Nellore LIBD Andrew Jaffe Emily Burke Stephen Semick Carrie Wright Amanda Price Nina Rajpurohit Bill Ulrich
42. 11 Guiding Principles for Interactive Graphics Based on LIBD Data Science Projects Leonardo Collado-Torres @fellgernon https://speakerdeck.com/lcolladotor