Resources

Review material

• Lisa’s Intro DS course
  
• Prof. Brianna Heggeseth’s Machine Learning course - she’ll be building the site throughough the fall 2021 semester and is using tidymodels
• Hands on Machine Learning with R by Bradley Boehmke and Brandon Greenwell
  
• Johns Hopkins SPH ADS course site by Jeff Leek and Roger Peng (Has both review and new material)

Getting help/advise

• Create a reprex! (reproducible example)
  
• Build a Career in Data Science podcast by Jacqueline Nolis and Emily Robinson

git/GitHub in R

• Lisa’s GitHub for collaboration
  
• Lisa’s more in-depth post and video that goes more in-depth on how to collaborate
• Happy git with R by Jenny Bryan
• R Packages - making a PR from the R Packages boook by Hadley Wickham and Jenny Bryan
  
• Pull request helpers from the usethis package

Building a website

• Sharing on Short Notice with Alison Hill and Desirée De Leon from R Studio
• Lisa’s Building a distill website blog post
  
• Tom Mock’s distill post
  
• The Distillery showcase featuring websites made with distill and their source code.
• Emily Zabor’s tutorial
  
• blogdown book site by Yihui Xie, Amber Thomas, and Alison Hill and check out recent updates here

Building an R package

• Lisa’s example of building a data package
• Your first R package in 1 hour blog post by Shannon Pileggi
• Tom Westlake’s Writing an R Package From Scratch
  
• R Packages book by Wickham and Bryan

Custom ggplot2 themes

• theme() documentation (if you’re like me, you might as well keep this tab open at all times on your computer)
• Joey Stanley’s blog post
  
• Maddie Pickens’ tutorial

Shiny

• Lisa’s intro to building a shiny app
  
• Lisa’s Shiny tips for a “bit more than the basics” in Shiny
  
• R Studio tutorials by Garret Grolemund
  
• a gRadual intRoduction to Shiny by Ted Laderas
  
• Intro to Shiny by Katherine Hébert, Andrew MacDonald, Jake Lawlor, and Vincent Bellevance
  
• Mastering Shiny by Hadley Wickham
• Theming Shiny and R markdown with {thematic} and {bslib} with Carson Sievert from R Studio (formerly, I had a similar but older talk posted)
• Using CSS to style your shiny app by Sharla Gelfand

Tidymodels

• tidymodels.org main page
• Tidy Models with R textbook by Julia Silge and Max Kuhn
• Lisa’s tidymodels noRth presentation
• Julia Silge’s blog with great examples

Model stacking / ensembles

• stacks R package documentation by Simon Couch and Max Kuhn and Simon’s lightnight talk.
• Hands on Machine Learning by Bradley Boehmke & Brandon Greenwell

Interpretable Machine Learning

• DALExtra R package by Szymon Maksymiuk, Przemyslaw Biecek
• lime R package tutorial by Bradley Boemke
• Interpretable Machine Learning by Christoph Molnar
• Explanatory Model Analysis by Przemyslaw Biecek and Tomasz Burzykowski
• Hands on Machine Learning

Data ethics / justice

• fast.ai ethics course by Rachel Thomas
• Data Justice intro by Linnet Taylor
• Prediction-Based Decisions and Fairness by Shira Mitchell, Eric Potash, Solon Barocas, Alexander D’Amour, Kristian Lum
  
• teachdatascience blog
  
• Data Science Ethics chapter of Modern Data Science with R by Baumer, Kaplan, and Horton
• Calling Bullshit course materials by Bergstrom and West.

Using databases with R / SQL

• Vebash Naidoo’s blog posts
  
• Dr. Simona Picardi’s course material from WILD 6900: Tools for Reproducible Science, Spring 2021, Utah State University
  
• SQL chapter of Modern Data Science with R by Baumer, Kaplan, and Horton
  
• Jacqueline Nolis blog post: Pretending to have a SQL database with RStudio
  
• Databases using R from RStudio

Putting R into production

• Put R in prod
• Simple model example on Ray Buhr’s blog
  
• Simple plumber example on R Studio’s site (thanks for the link, Thomas Mock)

Gradient boosted machines (GBM) and H20

• Hands on Machine Learning
  
• xgboost R package documentation
  
• xgboost with tidymodels example by Julia Silge.
  
• H20 documentation
• H20 booklet

Deep Learning

• Deep Learning with R
• Hands on Machine Learning
  
• Neural Networks YouTube video series by 3Blue1Brown
  
• Neural Networks and Deep Learning by Michael Nielsen
  
• TensorFlow for R tutorials