Workflows for reproducible data science

SFI Centre for Research and Training in Genomics Data Science

Transcript

1. Workflows for reproducible data science Mine Çetinkaya-Rundel University of Edinburgh + Duke University + RStudio mine-cetinkaya-rundel cetinkaya.mine@gmail.com @minebocek bit.ly/repro-ds
2. The results in Table 1 don’t seem to correspond to those in Figure 2!
3. 61 3 44 94 12 4 45 20
4. 70 have tried and failed to reproduce another scientist's experiments more than percent Monya Baker,. "1,500 scientists lift the lid on reproducibility." Nature News 533.7604 (2016): 452.
5. 50 have tried and failed to reproduce their own experiments more than percent Monya Baker. "1,500 scientists lift the lid on reproducibility." Nature News 533.7604 (2016): 452.
6. 640 Google Scholar Search, September 1, 2019. results containing the term reproducibility crisis just in 2019 Google Scholar yields
7. 1992 Jon Claerbout and Martin Karrenbach. "Electronic documents give reproducible research a new meaning." SEG Technical Program Expanded Abstracts 1992. Society of Exploration Geophysicists, 1992. 601-604. earliest reference reproducibility research* that I could find…
8. Jon Claerbout and Martin Karrenbach. "Electronic documents give reproducible research a new meaning." SEG Technical Program Expanded Abstracts 1992. Society of Exploration Geophysicists, 1992. 601-604.
9. Jon Claerbout and Martin Karrenbach. "Electronic documents give reproducible research a new meaning." SEG Technical Program Expanded Abstracts 1992. Society of Exploration Geophysicists, 1992. 601-604.
10. Michelle Lewis, et al. "Replication Study: Transcriptional amplification in tumor cells with elevated c-Myc." Elife 7 (2018): e30274.
11. Photo by Alexander Dummer on Unsplash setting the stage
12. replicability reproducibility same research question same research question same results same results new data same data
13. – Mark Holder “Your closest collaborator is you six months ago, but you don’t reply to emails.”
14. Term Estimate Std. Error Statistic p-value (Intercept) 9.06 0.929 9.76 1.13e-17 Sepal.Width -1.74 0.301 -5.77 4.51e- 8 Table 1. Regression output for predicting petal length from sepal width. Figure 2. Relationship between petal length and sepal width e.g.
15. Term Estimate Std. Error Statistic p-value (Intercept) 9.06 0.929 9.76 1.13e-17 Sepal.Width -1.74 0.301 -5.77 4.51e- 8 Table 1. Regression output for predicting petal length from sepal width. Figure 2. Relationship between petal length and sepal width e.g.
16. analysis report Term Estimate Std. Error Statistic p-value (Intercept) 9.06 0.929 9.76 1.13e-17 Sepal.Width -1.74 0.301 -5.77 4.51e- 8 Table 1. Regression output for predicting petal length from sepal width.
17. analysis report Term Estimate Std. Error Statistic p-value (Intercept) 9.06 0.929 9.76 1.13e-17 Sepal.Width -1.74 0.301 -5.77 4.51e- 8 Table 1. Regression output for predicting petal length from sepal width. Figure 2. Relationship between petal length and sepal width
18. analysis report Term Estimate Std. Error Statistic p-value (Intercept) 9.06 0.929 9.76 1.13e-17 Sepal.Width -1.74 0.301 -5.77 4.51e- 8 Table 1. Regression output for predicting petal length from sepal width. Figure 2. Relationship between petal length and sepal width
19. Term Estimate Std. Error Statistic p-value (Intercept) 9.06 0.929 9.76 1.13e-17 Sepal.Width -1.74 0.301 -5.77 4.51e- 8 Table 1. Regression output for predicting petal length from sepal width. Figure 2. Relationship between petal length and sepal width
20. making research reproducible
21. – David Donoho, paraphrasing Jon Claerbout “An article about computational science in a scientific publication is not the scholarship itself, it is merely advertising of the scholarship. The actual scholarship is the complete software development environment and the complete set of instructions which generated the figures.” Jonathan Buckheit and David Donoho. "Wavelab and reproducible research." Wavelets and statistics. Springer, New York, NY, 1995. 55-81.
22. raw data code & documentation to reproduce the analysis specifications of your computational environment make available and accessible Peng, Roger. "The reproducibility crisis in science: A statistical counterattack." Significance 12.3 (2015): 30-32. Gentleman, Robert, and Duncan Temple Lang. "Statistical analyses and reproducible research." Journal of Computational and Graphical Statistics 16.1 (2007): 1-23.
23. – Keith Baggerly “The most important tool is the mindset, when starting, that the end product will be reproducible.”
24. nobody, not even yourself, can recreate any part of your analysis push button reproducibility in published work
25. “There’s no one-size-fits-all solution for computational reproducibility.” Perkel, Jeffrey M. "A toolkit for data transparency takes shape." Nature 560 (2018): 513-515.
26. but the following might help… 8 principles
27. organize your project 1
28. – Jenny Bryan “File organization and naming are powerful weapons against chaos.”
29. level of organization
30. simpler analysis raw-data processed-data manuscript "|- manuscript.Rmd more complex analysis raw-data processed-data scripts manuscript figures "|- manuscript.Rmd stick with the conventions of your peers
31. Lucy D’Agostino McGowan. “One year to dissertate”, livefreeordichotomize.com/2018/09/14/one-year-to-dissertate. recommended reading
32. write READMEs liberally 2
33. raw-data processed-data scripts manuscript figures "|- README.md "|- airports.csv "|- flights.csv "|- planes.csv "|- weather.csv # README This folder contains the raw data for the project. All datasets were downloaded from openflights.org/data.html on 2019-04-01. - airlines: Airline names - airports: Airports metadata - flights: Flight data - planes: Plane metadata - weather: Hourly weather data "|- airlines.csv
34. keep data tidy & machine readable 3
35. Student Exam Grade Name 1 2 Major Barney Donaldson 89 76 Data Science, Public Policy Clay Whelan 67 83 Public Policy Simran Bass 82 90 Statistics Chante Munro 45 72 Political Science, Statistics Gabrielle Cherry 32 79 . Kush Piper 98 sick Statistics Faizan Ratliff 82 75 Data Science Torin Ruiz 70 80 Sociology, Statistics Reiss Richardson missed exam 34 Neuroscience Ajwa Cochran 50 65 Data Science Low participation name exam_1 exam_2 first_major second_major participation Barney Donaldson 89 76 Data Science Public Policy ok Clay Whelan 67 83 Public Policy NA ok Simran Bass 82 90 Statistics NA ok Chante Munro 45 72 Political Science Statistics Low Gabrielle Cherry 32 79 NA NA ok Kush Piper 98 NA Statistics NA ok Faizan Ratliff 82 75 Data Science NA ok Torin Ruiz 70 80 Sociology Statistics ok Reiss Richardson NA 34 Neuroscience NA low Ajwa Cochran 50 65 Data Science NA low record code + document non-code steps + write tests
36. Mark Ziemann, Yotam Eren, and Assam El-Osta. "Gene name errors are widespread in the scientific literature." Genome biology 17.1 (2016): 177. doi.org/10.1186/s13059-016-1044-7. recommended reading
37. Karl Broman and Kara Woo. "Data organization in spreadsheets." The American Statistician 72.1 (2018): 2-10. doi.org/10.1080/00031305.2017.1375989. recommended reading
38. comment your code 4
40. use literate programming 5
44. Yihui Xie, JJ Allaire, and Garrett Grolemund. “R Markdown: The Definitive Guide”, bookdown.org/yihui/rmarkdown. recommended reading
45. use version control 6
46. changes tracked by hosted on
47. Jenny Bryan, et. al. “Happy Git with R”, happygitwithr.com. recommended reading
48. automate your process 7
49. raw-data processed-data scripts manuscript figures "|- 01-load-packages.R "|- 03-clean-data.R "|- 04-explore.R "|- 05-model.R "|- 06-summarise.R "|- 02-load-data.R "|- 00-analyse.R
50. Karl Broman. “Minimal Make”, kbroman.org/minimal_make. recommended reading
51. share computing environment 8
57. Carl Boettiger. "An introduction to Docker for reproducible research." ACM SIGOPS Operating Systems Review 49.1 (2015): 71-79. Ben Marwick, Carl Boettiger, and Lincoln Mullen. "Packaging data analytical work reproducibly using R (and friends)." The American Statistician 72.1 (2018): 80-88. recommended reading
58. 1 organize your project 2 write READMEs liberally 3 keep data tidy & machine readable 4 comment your code 5 use literate programming 6 use version control 7 automate your process 8 share computing environment
59. Greg Wilson, Jennifer Bryan, Karen Cranston, Justin Kitzes, Lex Nederbragt, Tracy K. Teal “Good enough practices in scientific computing." PLoS computational biology 13.6 (2017): e1005510. recommended reading
60. painful bits
61. > coming up with good names > stages of data cleaning > going back and redoing stuff > adding interim steps > keeping track of the order of things > clutter of unneeded old stuff Karl Broman, tools4RR. kbroman.org/Tools4RR
62. looking into the future Photo by Sweet Ice Cream Photography on Unsplash.
63. – Keith Baggerly “The most important tool is the mindset, when starting, that the end product will be reproducible.”
64. – Karl Broman “The second most important tool is training.”
65. #1: Convince data scientists to adopt a reproducible data analysis workflow #2: Train new data scientists who don’t have any other workflow
66. statistics and data science educators who teach data analysis should be instilling best practices in students before they set out to do research
67. → → → →
68. → → → → → →
69. Workflows for reproducible data science mine-cetinkaya-rundel cetinkaya.mine@gmail.com @minebocek bit.ly/repro-ds