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

8. The Tidyverse

9. Coherent system of packages for
data manipulation, exploration,
and visualization that share a
common design philosophy

11. Tidyverse = ?

12. Tidyverse = !

13. Tidyverse != Hadleyverse

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

25. Solution: stringr::str_split() …

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

29. + forcats::fct_reorder

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/

35. Review
stringr::str_split
tidyr::unnest
coord_flip()
forcats::fct_reorder
tibble::tribble
reprex::reprex
tibble::as_tibble
purrr:map_df
dplyr::na_if
dplyr::select_if
skimr::skim

36. Resources

37. R4ds.had.co.nz

38. #rstats twitter

39. #rstats twitter

40. Rstudio.com/resources/cheatsheets

41. DataCamp.com

42. Learn | https://datacamp.com/courses

43. Conclusion

44. The tidyverse
Come for the stickers and
package names …
Stay for the friendly
community and happy
workflow

45. Thank you!
tiny.cc/nyrtalk
hookedondata.org
@robinson_es