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appendix.qmd
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# Resources for Learning R {#sec-appendix-1}
To further your R learning journey, I would recommend a review of the following freely available resources. This list will constantly be updated as new material becomes available.
## 1. Online repositories with R/RStudio-related links
- [Big Book of R](https://www.bigbookofr.com/)
- [Free R Reading Material](https://committedtotape.shinyapps.io/freeR/)
- [Awesome R 1](https://github.com/qinwf/awesome-R)
- [Awesome R 2](https://github.com/uhub/awesome-r)
- [Awesome R 3](https://github.com/ktaranov/AwesomeR)
- [Awesome R 4](https://github.com/iamericfletcher/awesome-r-learning-resources)
- [resouRces: Database of Resources to Learn & Teach R](https://www.resourcesdatabase.com/)
- [R-universe](https://r-universe.dev/search/)
- [R Community Public Library](https://rviews.rstudio.com/2021/11/04/bookdown-org/)
## 2. R Communities
- [Posit (formerly RStudio) Community](https://community.rstudio.com/)
- [Stack Overflow R](https://stackoverflow.com/questions/tagged/r)
- [Stack Overflow RStudio](https://stackoverflow.com/questions/tagged/rstudio)
- [Cross Validated](https://stats.stackexchange.com/?tags=r)
- [R-bloggers](https://www.r-bloggers.com/)
- [R for Data Science Slack Group](https://rfordatasci.com/)
- [R User Community](https://www.linkedin.com/company/r-user-community/)
- [R-Ladies Global](https://rladies.org/)
- [satRday](https://satrdays.org/)
- [rOpenSci](https://ropensci.org/community/)
- **Twitter (now X)** – #rstats and #rstudio
- **Reddit** – r/rstats and r/RStudio
## 3. Conferences and Meetups
- [R Foundation Conferences (useR! and DSC)](https://www.r-project.org/conferences/)
- [R Conference](https://rstats.ai/)
- [Posit Conference](https://posit.co/conference/)
- [R User Group Meetups](https://www.meetup.com/pro/r-user-groups/)
## 4. Cheatsheets
- [R Views Cheatsheets](https://rviews.rstudio.com/2021/03/10/rstudio-open-source-resorurces/)
- [Posit Cheatsheets](https://posit.co/resources/cheatsheets/)
- [30 Essential Data Science, Machine Learning & Deep Learning Cheat Sheets](https://www.kdnuggets.com/2017/09/essential-data-science-machine-learning-deep-learning-cheat-sheets.html)
## 5. Tutorials
- [Swirl: Learn or Teach R, in R](https://swirlstats.com/)
- [RStudio Education Learning](https://education.rstudio.com/learn/)
- [RStudio Education Teaching](https://education.rstudio.com/teach/)
- [R Workflow](https://hbiostat.org/rflow/)
- [R Screencasts](https://www.rscreencasts.com/)
- [Quick-R](https://www.statmethods.net/)
- [RTutor: Interactive R Problem Sets](https://skranz.github.io/RTutor/)
## 6. Newsletters and Blogs
- [R Weekly](https://rweekly.org/)
- [Data Elixir](https://dataelixir.com/)
- [Revolution Analytics Blog](https://blog.revolutionanalytics.com/)
## 7. Data Visualization
- [The R Graph Gallery](https://r-graph-gallery.com/)
- [R Charts](https://r-charts.com/)
- [ggplot2 extensions](https://exts.ggplot2.tidyverse.org/)
- [Telling Stories with Data](https://tellingstorieswithdata.com/)
- [R Graphics Cookbook](https://r-graphics.org/)
- [Fundamentals of Data Visualization](https://clauswilke.com/dataviz/)
- [ggplot2: Elegant Graphics for Data Analysis](https://ggplot2-book.org/)
- [Data Visualization: A practical introduction](https://socviz.co/)
- [Modern Data Visualization with R](https://rkabacoff.github.io/datavis/)
## 8. Data Science
- [R for Data Science](https://r4ds.hadley.nz/)
- [Data 8: The Foundations of Data Science](https://www.data8.org/)
- [Introduction to Data Science](https://sml201.github.io/)
- [Readings in Applied Data Science](https://github.com/hadley/stats337)
- [Elements of Data Science](https://github.com/AllenDowney/ElementsOfDataScience)
- [The Art of Data Science](https://bookdown.org/rdpeng/artofdatascience/)
- [Data Science Cheatsheet](https://github.com/ml874/Data-Science-Cheatsheet)
- [Introduction to Data Science (for not-yet scientists)](https://florian-huber.github.io/data_science_course/book/cover.html)
- [data.org](https://data.org/)
- [Data Basic](https://databasic.io/en/)
- [Data Science in a Box](https://datasciencebox.org/)
- [Data Literacy](http://dataliteracy.rbind.io/)
## 9. Statistics
- [An Introduction to Statistical Learning with R or Python](https://www.statlearning.com/)
- [Modern Statistics with R](https://modernstatisticswithr.com/)
- [Introduction to Modern Statistics](https://openintro-ims.netlify.app/index.html)
- [Statistical Thinking](https://www.fharrell.com/)
- [Introduction to Modern Statistics](https://openintro-ims.netlify.app/index.html)
- [Biostatistics for Biomedical Research](https://hbiostat.org/bbr/)
- [STA 212: Statistical Models](https://sta-212-f19.lucymcgowan.com/)
- [STA 312: Linear Models](https://sta-312-f20.netlify.app/)
- [STA 363: Statistical Learning](https://sta-363-s20.lucymcgowan.com/)
- [Introduction to Probability for Data Science](https://probability4datascience.com/)
- [Statistics 431: Advanced Statistical Computing with R](https://cal-poly-advanced-r.github.io/STAT-431/)
- [Statistics and R](https://tinystats.github.io/teacups-giraffes-and-statistics/index.html)
- [Tidy Statistics in R](https://themockup.blog/posts/2018-12-10-a-gentle-guide-to-tidy-statistics-in-r/) - [Datamethods](https://discourse.datamethods.org/)
- [Statistical Computing](https://www.stephaniehicks.com/jhustatcomputing2022/schedule)
## 10. Datasets
- [Awesome Public Datasets](https://github.com/awesomedata/awesome-public-datasets)
- [Dataverse Project](https://dataverse.org/)
- [Data Commons](https://datacommons.org/)
- [Open Africa Data](https://africaopendata.org/)
- [Data Africa](https://dataafrica.io/)
- [Our World in Data](https://ourworldindata.org/)
- [California Data Sources](https://hillcrestadvisory.com/2019/01/20/california-data-sources/)
- [DataSF](https://datasf.org/opendata/)
- [NYC Open Data](https://opendata.cityofnewyork.us/)
- [The Humanitarian Data Exchange](https://data.humdata.org/)
- [Global Data Barometer](https://globaldatabarometer.org/)
- [Microsoft Research Open Data](https://msropendata.com/)
- [Kaggle Datasets](https://www.kaggle.com/datasets)
- [Google Research Datasets](https://datasetsearch.research.google.com/)
- [NHS R-community Datasets](https://nhs-r-community.github.io/NHSRdatasets/)
- [IHME](https://www.healthdata.org/)
- [Best Public Datasets for Health Data Science Projects](https://healthdatascience.substack.com/p/best-public-datasets-for-public-health-225)
- [World Bank Open Data](https://data.worldbank.org/)
## 11. Tidyverse
- [A very short introduction to Tidyverse](https://dominicroye.github.io/en/2020/a-very-short-introduction-to-tidyverse/)
- [Tidyverse Workshop Series](https://github.com/nuitrcs/r-tidyverse)
- [Tidyverse Skills for Data Science](https://jhudatascience.org/tidyversecourse/)
- [Eight R Tidyverse tips for everyday data engineering](https://tomaztsql.wordpress.com/2022/07/14/eight-r-tidyverse-tips-for-everyday-data-engineering/)
- [Tidyverse Tips](https://oliviergimenez.github.io/tidyverse-tips/)
- [An Introduction to R through the tidyverse](https://pmacdasci.github.io/r-intro-tidyverse/)
- [Modern R with the tidyverse](http://modern-rstats.eu/)
- [C’est quoi, le tidyverse?](https://thinkr.fr/c-est-quoi-le-tidyverse/#Mettre_en_forme_ses_donnees_avec_la_package_tidyr)
- [Transitioning into the tidyverse (part 1)](https://www.rebeccabarter.com/blog/2019-08-05_base_r_to_tidyverse/)
- [Transitioning into the tidyverse (part 2)](https://www.rebeccabarter.com/blog/2019-08-05_base_r_to_tidyverse_pt2/)
## 12. Programming: Functions, Loops, and Control Statements
- [Control Structures](https://cran.r-project.org/doc/manuals/r-release/R-lang.html#Control-structures)
- [Apply Functions](https://www.rdocumentation.org/packages/base/versions/3.6.2/topics/apply)
- [Defining your own functions](https://modern-rstats.eu/defining-your-own-functions.html)
- [Functional programming 1](https://modern-rstats.eu/functional-programming.html)
- [Functional programming 2](http://adv-r.had.co.nz/Functional-programming.html)
- [Statistical Programming Paradigms and Workflows](https://www.stephaniehicks.com/jhustatprogramming2022/schedule)
- [Hands-On Programming with R](https://jjallaire.github.io/hopr/)
- [purr tutorial](https://jennybc.github.io/purrr-tutorial/index.html)
- [Unlocking the Power of Functional Programming in R (Part 1)](https://appsilon.com/functional-programming-in-r-part-1/)
- [Unlocking the Power of Functional Programming in R (Part 2)](https://appsilon.com/functional-programming-in-r-part-2/)
## 13. Machine Learning and Artificial Intelligence
- [Hands-On Machine Learning with R](https://bradleyboehmke.github.io/HOML/)
- [Create machine learning models: An R version](https://rpubs.com/eR_ic/exploRe)
- [Interpretable Machine Learning](https://advanced-ds-in-r.netlify.app/posts/2021-03-31-imllocal/)
- [Supervised Machine Learning for Text Analysis in R](https://smltar.com/)
- [Posit AI Blog](https://blogs.rstudio.com/ai/)
- [Tidy Modeling with R](https://www.tmwr.org/)
- [Tidymodels](https://www.tidymodels.org/)
- [A Gentle Introduction to tidymodels](https://rviews.rstudio.com/2019/06/19/a-gentle-intro-to-tidymodels/)
- [The Case for tidymodels](https://rviews.rstudio.com/2020/04/21/the-case-for-tidymodels/)
- [ISLR tidymodels labs](https://emilhvitfeldt.github.io/ISLR-tidymodels-labs/index.html)
- [Introduction to Machine Learning with the Tidyverse](https://education.rstudio.com/blog/2020/02/conf20-intro-ml/)
- [tidypredict](https://tidypredict.tidymodels.org/)
## 14. Publishing
- [Quarto](https://quarto.org/)
- [R Markdown](https://rmarkdown.rstudio.com/)
- [Awesome Quarto](https://github.com/mcanouil/awesome-quarto)
## 15. R Shiny
- [Shiny Learning Resources](https://shiny.rstudio.com/tutorial/)
- [Shiny Tutorials](https://shiny.rstudio.com/tutorial/written-tutorial/lesson1/)
- [Mastering Shiny](https://mastering-shiny.org/)
- [Building Web Applications WITH SHINY](https://rstudio-education.github.io/shiny-course/)
- [a gRadual intRoduction to Shiny](https://laderast.github.io/gradual_shiny/)
- [Engineering Production-Grade Shiny Apps](https://engineering-shiny.org/)
- [ShinyUI Editor](https://rstudio.github.io/shinyuieditor/)
- [Shiny Examples](https://shinylive.io/r/examples/)
- [An Intro to Shiny](https://ucsb-meds.github.io/shiny-workshop/#1)
- [Shiny Gallery](https://shiny.posit.co/r/gallery/)
- [shinydashboard](https://rstudio.github.io/shinydashboard/structure.html)
- [Application layout guide](https://shiny.posit.co/r/articles/build/layout-guide/)