Week 10 Readings
More stats, broom, and tables (gt, gtsummary)
Required
- Introduction to map() - Jenny Bryan
- Purrr Tips and Tricks by Emil Hvitfeldt.
- Introduction to broom
Suggested
- More on using map and nested data with modeling: R for Data Science: Many Models, First edition
- broom and dplyr
- Joy of Functional programming talk by Hadley Wickham - more on nesting/iteration
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/
- 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
- Programming with dplyr - in case I can’t get to tidyeval use for functions with tidyverse
- ggplot2 in packages - examples how to use ggplot inside functions
- Resources for learning more statistics:
- Modern Dive / Statistical Inference via Data Science by Chester Ismay and Albert Y Kim is a nice place to start: https://moderndive.com/
- Danielle Navarro’s Learning Statistics with R is excellent and talks much more about statistics: https://learningstatisticswithr.com/
- More on using map and nested data with modeling: R for Data Science: Many Models
- broom and dplyr
- Useful vignettes on table output: gtsummary intro to tbl_summary
- Ted Laderas’s interactive workbook on learning rowwise and nested data: learning rowwise
- We might not get to dates but in case you want to learn more: Dates and times in R for Data Science
- If you want to learn about tidymodels: Introduction to Machine Learning with the Tidyverse
- Joy of Functional programming talk by Hadley Wickham - more on nesting/iteration
- More on model building and machine learning
- Model Building from R for Data Science
- Many Models - from R for Data science. Covers
group_by()/nest()and list-columns - Tidymodels with R: Recipes
- Tidymodels with R: Fitting Models with Parsnip
- Tidymodels with R: Judging Model Effectiveness
- UMAP and Cocktail Recipes
- Tidymodels: K-means
- PCA and Penguins