Week 10

More purrring

Topics

Part 10:

• Use map() functions from the purrr package to iterate functions
• Nest datasets into list-columns, and iterate statistical modeling and/or visualization functions over those elements
• Use map2() from the purrr package to iterate functions across two inputs
• Learn how to iterate regression modeling across different model variables

Announcements

• Please fill out the course evaluations for the class.
  • Course evaluations are very helpful for making improvements to our classes.
  • If too few students fill out the evaluations, they are not released to me.
• Please complete the SPH’s Annual Student Survey
  • Responses help us improve our SPH programs!

• Cascadia R Conf in June 26-27 this year. It will be held at OHSU in RLSB. This is a great conference to meet other R enthusiasts in the area and learn more about what they are working on.

Class materials

• Class materials in OneDrive folder BSTA_526_W26_class_materials_public.
• For today’s class, make sure to download to your computer the folder called part10.
• Open RStudio by double-clicking on the project file called BSTA_526_W26_class_materials_public.Rproj in the main OneDrive folder.

Note: The link to the slides version of the class notes is not working. Please see the OneDrive folder for the html of the slides.

Part	OneDrive folder	Slides	Webpage
10

Readings

Required

• purrr tutorial by Jenny Bryan
  • This was listed as optional last week. If you haven’t read it, I strongly recommend the Core purrr lessons, and in particular Introduction to map(): extract elements.
    • Note: it uses map_dfr() which is the “newer” version of map_df(), but both are superseded as of purrr 1.0.0 though. Instead used map() combined with list_rbind() as mentioned in part 9.
• gtsummary:
  • tbl_regression() tutorial
  • Merge tables with tbl_merge()

Optional

• Great classic purrr resources. The drawback is that some of the functions have newer versions in the mean time. I am still recommending them though since they are classics and give great overviews of purrr and map().
  • Joy of Functional programming talk by Hadley Wickham - more on nesting/iteration
  • More on using map and nested data with modeling: Many Models
    • This is from the 1st edition in the R4DS book and was removed in the 2nd edition.
    • It’s a great introduction to using map() on nested data.
• Combining bromm’s tidy() with purrr:::map(): broom and dplyr

Suggested additional R readings

Miscellaneous R

• Useful vignettes on table output: gtsummary intro to tbl_summary
• tidy evaluation: Programming with dplyr - in case I can’t get to tidy evaluation use for functions with tidyverse
• ggplot2 in packages
  • Examples how to use ggplot inside functions. It’s meant for people writing packages, but’s helpful in general.
• Ted Laderas’s interactive workbook on learning rowwise and nested data: learning rowwise
• [Dates and times in R for Data Science](https://r4ds.hadley.nz/datetimes.html
• Cross tables with gtsummary::tbl_cross()

Statistics with R

For learning more about statistics with R:

• Modern Dive / Statistical Inference via Data Science by Chester Ismay and Albert Y Kim is a nice place to start: https://moderndive.com/v2/
• Danielle Navarro’s Learning Statistics with R is excellent and talks much more about statistics: https://learningstatisticswithr.com/
• Model Basics from R for Data Science
• More on survival analysis in R
• Survival Analysis in R Tutorial by Dr. Emily C Zabor
• Survminer survival plot vignette
• UVA’s Survival Workshop materials
• Rview’s Survival Analysis
• tidymodels package
  • Introduction to Machine Learning with the Tidyverse
  • Tidy Modeling with R
  • UMAP and Cocktail Recipes
  • Tidymodels: K-means
  • PCA and Penguins

Post-class survey

• Please fill out the post-class survey to provide feedback. Thank you!
• Previous muddiest points and clearest points with responses are collected here.

Homework

• See OneDrive folder for homework assignment.
• HW 10 due on 03/12.

Recording

• In-class recording links are on Sakai. Navigate to Course Materials -> Schedule with links to in-class recordings.