2019-09-17 Creating Reproducible Data Science

A webinar for the John Deere company on creating reproducible data science; code at github.com/akgold/2019-09-17_john_deere_webinar.

Transcript

1. Creating Reproducible Data Science Alex K. Gold Solutions Engineer @alexkgold github.com/akgold/2019-09-17_john_deere_webinar
2. Why care about reproducibility? Or portability?
3. Why care about reproducibility? Or portability? • With Colleagues • With Future You • They don’t answer ☎ Sharing!
4. How much reproducibility is enough? More Reproducible Not at all Somewhat Fully More Work
5. A Taxonomy Of Irreproducibility
6. Code that won’t run on someone else’s machine c://Documents/ /usr/home/agold ~/agold
7. Difficulty Finding Latest Version ninja_analysis_final2_alex_final.Rmd “Well, the version I have…” “Let me just send you the newest…”
8. Things that are tedious and you’ll need again “Well, I had to set up these variables like this…” model <- glm(outcome ~ input, family = “gamma”, data = my_dat, weights = wgt, subset = my_set, na_action = “na.exclude”, offset = 7, model = TRUE)
9. Environmental factors that will break “Oh, so you just need to get this system dependency configured…” “Right, I used version 0.7.6 of the package, not 0.7.8…” “Oh yeah, this kinda doesn’t work on Windows…”
10. Solutions
11. Code that breaks on someone else’s machine
12. Code that breaks on someone else’s machine -based Workflow
13. If the first line of your R script is setwd(“C:\Users\jenny\path\that\only\I\have”) I will come into your office and SET YOUR COMPUTER ON FIRE
14. If the first line of your R script is rm(list = ls()) I will come into your office and SET YOUR COMPUTER ON FIRE
15. Avoiding Computer fires Project-based Workflow and here::here
16. Avoiding Computer fires Demo!
17. Code Structure
18. Project-Based Workflow Learn More tidyverse.org/articles/2017/12/workflow-vs-script
19. Difficulty Finding Latest Version
20. Difficulty Finding Latest Version Version Control
21. Version Control
22. Version Control 1. Distinct versions. 2. Remote backups. 3. Collaborating on code. Why would I use it?
23. Version Control A few terms. (Sorry) Origin
24. Cute dog break.
25. Version Control Branching Master “Feature”
26. Git vs Github
27. Where can I git? In RStudio On Github Command Line
28. Version Control Demo!
29. Version Control Command Review
30. Version Control Branching master fix_model Git checkout -c fix_model git merge
31. Version Control Learn More www.happygitwithr.com
32. Things that are tedious and you’ll need again
33. Things that are tedious and you’ll need again R Tools
34. When you’ve used the same • function • RMarkdown document • boilerplate Shiny code 3x write a package
35. Code Snippets, Functions, And Templates Demo!
36. R Packages r-pkgs.had.co.nz
37. Environmental factors that will break
38. Environmental factors that will break Controlling Environments
39. Why would my environment break?
40. Reproducing Environments (Advanced Reproducibility) Save package state using packrat/renv
41. Reproducing Environments (Advanced Reproducibility)
42. Reproducing Environments Learn More environments.rstudio.com
43. The End Code only for your machine -based workflow tidyverse.org/articles/2017/12/workflow-vs-script Trying to find and coordinate versions Use happygitwithr.com Reusing tedious work Write a r-pkgs.had.co.nz Reproducing environments packrat/renv environments.rstudio.com github.com/akgold/2019-09-17_john_deere_webinar