NY R Conference talk
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This document summarizes Emily Robinson's talk on the lesser known parts of the tidyverse. The talk goals were to introduce tidyverse terminology, prevent common problems, and share useful functions. The tidyverse is introduced as a coherent system of R packages that share a design philosophy. Common data analysis steps like viewing data, inspecting missing values, and making plots are demonstrated. Solutions to problems in these steps like printing only a subset of data, replacing values with NAs, ordering factors, and creating reproducible examples are presented. Useful tidyverse functions introduced include as_tibble(), select_if(), na_if(), str_split(), unnest(), coord_flip(), fct_reorder(), and reprex(). Resources like R
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NY R Conference talk
- 1. The Lesser Known s of the Tidyverse Emily Robinson @robinson_es
- 2. About Me ➔ R User ~ 6 years ➔ Data Scientist at DataCamp ➔ Enjoy talking about: ◆ A/B testing ◆ Building and finding data science community ◆ R
- 3. Talk Goals
- 4. 1. Keep you hip to the lingo
- 5. 2. Stop you from doing this ….
- 6. …. by sharing useful functions
- 7. 3. Point you to resources
- 9. Coherent system of packages for data manipulation, exploration, and visualization that share a common design philosophy
- 11. Tidyverse = ?
- 12. Tidyverse = !
- 14. Tidyverse != Hadleyverse Many other contributors
- 15. Demo
- 17. Some steps of a data analysis workflow ➔ View dataset in console ➔ Inspect missing values ➔ Examine some columns ➔ Make a plot ➔ Do something cool and new!
- 18. Problem: it takes over the console Step 1: print your dataset!
- 19. Prints only 10 rows and the columns that fit on the screen Solution: as_tibble()
- 20. Problem: how do you do this for every column? Step 2: examine your NAs
- 21. Problem: missing values aren’t actually NA Answer: purrr::map_df() to “map” function over each column
- 22. Solution: na_if() to replace certain values with NA
- 23. Problem: how I can I do this quickly? + Skimr Solution: dplyr::select_if() + skimr::skim() Step 3: examine your numeric columns
- 24. Problem: it has multiple answers in each row Step 4: examine a single column
- 26. Solution: stringr::str_split() and tidyr::unnest() +
- 27. Problem: it’s a mess Step 5: make a plot!
- 28. Solution: coord_flip … But they’re not ordered
- 30. Final step: do something cool and new! Problem:
- 31. One solution: make a minimal reproducible example +
- 32. Part 0 (optional): use tribble() to make a toy dataset
- 33. Part 1: Use reprex() to find any problems Credit: Nick Tiernay, https://www.njtierney.com/post/2017/01/11/magic-reprex/
- 34. Part 2: Use reprex() to post your question or issue Credit: Nick Tiernay, https://www.njtierney.com/post/2017/01/11/magic-reprex/
- 36. Resources
- 37. R4ds.had.co.nz
- 38. #rstats twitter
- 39. #rstats twitter
- 41. DataCamp.com
- 43. Conclusion
- 44. The tidyverse Come for the stickers and package names … Stay for the friendly community and happy workflow