1  R and RStudio

This page provides a comprehensive collection of R/RStudio resources. On this page, you will find a list of personal websites and blogs that do some great work in R/RStudio. Additionally, there is a list of resources (books, tutorials, and blogs) focused on the basics of R/RStudio.

1.1 Personal Websites and Blogs

  1. Silvia Canelon
  2. Meghan Hall
  3. Paul Villanueva
  4. Shannon Pileggi
  5. Katherine (Kat) Hoffman
  6. Deisy Gysi
  7. Simon J
  8. Ryo Nakagawara
  9. Kieran Healy - Some Favorite Things
  10. François Husson
  11. Rebecca Barter
  12. Alboukadel Kassambara
  13. Michael Toth
  14. Sam Abbott
  15. Shel Kariuki
  16. Austin Wehrwein
  17. Nicola Rennie
  18. R for Ecology
  19. Stats and R
  20. Roland Krasser
  21. Martin Chan - Musings on R
  22. Rsquared Academy
  23. Olivier Gimenez
  24. David Schoch

More to come…

1.2 Basics

1.2.1 Curated list of resources

  1. Big Book of R
  2. FREE R READING MATERIAL
  3. An R Community Public Library
  4. Awesome R Learning Resources
  5. R Cheatsheets
  6. Posit Cheatsheets
  7. R4theRestofUs
  8. EDUCATORS R LEARNERS
  9. R and Data Mining

1.2.2 Books

  1. R4DataScience
  2. R4DataScience - Solutions
  3. R4DataScience, 2nd Edition
  4. R Workflow A
  5. R Workflow B
  6. What They Forgot to Teach You About R
  7. R for Researchers: An Introduction
  8. Supervised Machine Learning for Text Analysis in R
  9. Reproducible Analytical Pipelines - Master’s of Data Science
  10. R for Data Analysis
  11. Introduction to Data Science: Data Analysis and Prediction Algorithms with R
  12. Hands-On Programming with R
  13. R for Health Data Science
  14. R for Health Data Science Resources
  15. Data Science in Education Using R
  16. A Succinct Intro to R
  17. Data Science: A First Introduction
  18. The Epidemiologist R Handbook
  19. Introduction to R, version 2
  20. Technical Foundations of Informatics
  21. Data Science for Economists and Other Animals
  22. Efficient R programming
  23. An Introduction to R
  24. YaRrr! The Pirate’s Guide to R
  25. Modern Data Science with R
  26. Hands-On Machine Learning with R
  27. Modern Dive
  28. Advanced R
  29. Yet Again: R + Data Science
  30. Public Policy Analytics
  31. Hands-On Machine Learning with R
  32. Deep R Programming
  33. Urban Informatics
  34. Mastering Spark with R
  35. Modern Statistics with R
  36. R for the Rest of Us: A Statistics-Free Introduction
  37. Agile Data Science with R
  38. R for Non-Programmers: A Guide for Social Scientists

1.2.3 R and GPT

  1. R and gpttools

1.2.4 Snowflake

  1. Analyze Data with R using Posit Workbench and Snowflake
  2. Analyze Data in R with Posit Workbench and Snowflake

1.2.5 Conference Workshops

  1. Talk recordings and workshop materials from rstudio::conf(2022)
  2. Links to slides from rstudio::conf 2020
  3. NHS-R
  4. useR! 2019 Highlights
  5. ToulouseR 2019

1.2.6 Package Development

  1. R Packages
  2. Introduction to Package Development
  3. Your first R package in 1 hour
  4. Developing R packages
  5. Reflections on building my first few R packages
  6. R Package Tutorial

1.2.7 Newsletters

  1. RWeekly

1.2.8 Basic Intro Courses

  1. Introducing R to a non-programmer in one hour
  2. 6 Lessons that I learned from teaching R to non-programmers
  3. What Is RStudio? A Beginner’s Guide
  4. Data Carpentry - R for Social Scientists
  5. Software Carpentry - R for Reproducible Scientific Analysis
  6. Library Carpentry - Introduction to R
  7. R for Novices

1.2.9 Training Courses and Tutorials

  1. RStudio Education
  2. RStudio Education Beginners
  3. RStudio Education Intermediates
  4. RStudio Education Experts
  5. Quick-R Tutorial
  6. Learn R in R
  7. Learning R With Education Datasets
  8. STAT 545 Data wrangling, exploration, and analysis with R
  9. STHDA
  10. datascienceplus
  11. Statistical Computing in R
  12. Statistical Programming Paradigms and Workflows
  13. Teaching Statistics and Data Science Online
  14. An Introduction to Docker for R Users
  15. R Bootcamp
  16. Kane’s Data Science Course
  17. Andrew Heiss R-based courses
  18. STREAMLINING WITH R
  19. R Bootcamp
  20. Advanced R Topics
  21. R for Water Resources Data Science
  22. Reproducible science workshop
  23. Think R
  24. R 4 Beginners
  25. Applied Economics with R
  26. Ready for R
  27. Interpretable Machine Learning
  28. R basics: a practical introduction to R
  29. Introduction to R
  30. fasteR: Fast Lane to Learning R!
  31. Analysis of community ecology data in R
  32. R Screencasts
  33. R for better science in less time
  34. R and Stats
  35. Palmer Archipelago Penguins Data
  36. We are R-Ladies
  37. Web Scraping in R: A Quick Guide
  38. Word Clouds in R
  39. Data science tutorials
  40. MARKET ANALYSIS COURSE
  41. RTutor: Interactive R Problem Sets
  42. Data Wrangling and dplyr
  43. Intro to R for Programming
  44. purrr tutorial
  45. Intro to R: Shiny App
  46. Spatial Data Science
  47. Generalized Additive Models in R
  48. Visualizing the Bayesian workflow in R
  49. Create machine learning models: An R version
  50. Wrangling Unruly Data
  51. Cleaning UK Office for National Statistics data in R
  52. How to Automate EDA with DataExplorer in R
  53. Rachael’s R Tutorials
  54. Manipulating Data with dplyr
  55. Get me to the church on time with R spatial
  56. Multivariate data analysis with R and vegan
  57. A Course in Exploratory Data Analysis
  58. Exploratory Data Analysis with explore package
  59. Getting Started with R
  60. R Crash Course
  61. Four ways to streamline your R workflows
  62. One billion row challenge using base R
  63. Let’s talk about joins
  64. How to Get Good with R?
  65. S-Cubed Courses
  66. 5 Lines of Code to Convince You to Learn R

1.2.10 Tools

  1. R Universe Authors and Packages
  2. R-hub
  3. R in Visual Studio Code
  4. R Libraries Every Data Scientist Should Know
  5. Packages for Exploratory Data Analysis in R
  6. 15 Essential packages in R for Data Science
  7. WebR - R in the Browser
  8. Five useful R functions
  9. Six not-so-basic base R functions
  10. Get Good with R: Typing Skills and Shortcuts

1.2.11 Troubleshooting / Tips and Tricks

  1. How to Use “not in” operator in Filter
  2. Common R Mistakes in Data Viz
  3. R Examples
  4. R Tips and Tricks

1.2.12 Hackathons and Competitions

  1. Kaggle Housing Price Tutorial

1.3 Tidyverse

1.3.1 Basics

  1. A very short introduction to Tidyverse
  2. The Tidyverse in Action
  3. A Gentle Guide to Tidy Statistics in R
  4. An Introduction to R through the tidyverse
  5. C’est quoi, le tidyverse?
  6. Basic usage of tidycensus

1.3.2 Books

  1. Tidyverse Skills for Data Science
  2. Modern R with the tidyverse
  3. Text Mining with R: A Tidy Approach
  4. Tidy Finance with R

1.3.3 Workshops

  1. Tidyverse Workshop Series

1.3.4 Tips and Tricks

  1. Eight R Tidyverse tips for everyday data engineering
  2. Tidyverse Tips

1.3.5 Tutorials

  1. Data Science LADAL Tutorials
  2. Pivoting data from columns to rows (and back!) in the tidyverse
  3. Reshaping data frames using pivot functions from {tidyr} and tally from {dplyr}
  4. Finding the modal school district
  5. Teaching the tidyverse in 2021
  6. Data manipulation with the tidyverse
  7. The TidyVerse in Action
  8. Transitioning into the tidyverse (part 1)
  9. Transitioning into the tidyverse (part 2)
  10. Introduction to using Databases in R, with Tidyverse tools
  11. Wrangling penguins: some basic data wrangling in R with dplyr
  12. Visualize {dplyr}
  13. Visualize the Tidyverse
  14. Tidy Intelligence

1.4 Tidymodels in R

1.4.1 Basics

  1. A Gentle Introduction to tidymodels
  2. Tidymodels
  3. The Case for tidymodels
  4. Caret vs. tidymodels - comparing the old and new
  5. Tidymodels: tidy machine learning in R

1.4.2 Books

  1. Tidy Modeling with R
  2. ISLR tidymodels labs
  3. Modeling Data in the Tidyverse
  4. Feature Engineering
  5. Forecasting: Principles and Practice

1.4.3 Conferences

  1. Introduction to Machine Learning with the Tidyverse
  2. RStudio Conf Workshop Materials
  3. Tidymodels package for survival models

1.4.4 Tutorials

  1. Sample Story: When Will NYC’s Subway Ridership Recover?
  2. #TidyTuesday and tidymodels
  3. tidypredict
  4. Feature Engineering and Selection: A Practical Approach for Predictive Models
  5. Experimenting with machine learning in R with tidymodels and the Kaggle titanic dataset