awesome-r-learning-resources

In 2019, William Chase began a project to make a new series of artwork every month made entirely with R. In this project, he explored different techniques, developed algorithms, and provided detailed posts detailing the development process for each month.

A comprehensive and easy to follow tutorial that covers working with axes, titles, legends, backgrounds, grid lines, margins, multi-panel plots, colors, themes, lines, text, coordinates, chart types, ribbons, smoothings, and interactive plots. Author: Cédric Scherer.

Discover the best Color Palette & Color Tools. Author: meetqy.

Author: Henry Cann.

A curated list of awesome ggplot2 tutorials, packages etc. Author: Erik Gahner Larsen.

A curated list of resources for R Shiny. Author: Rob Gilmore.

This book is designed for R programmers who want to deepen their understanding of the language, and programmers experienced in other languages who want to understand what makes R different and special.

This introduction to R is derived from an original set of notes describing the S and S-Plus environments written in 1990–2 by Bill Venables and David M. Smith when at the University of Adelaide.

The aim of this book is to introduce you to using R, a powerful and flexible interactive environment for statistical computing and research. Authors: Alex Douglas, Deon Roos, Francesca Mancini, Ana Couto & David Lusseau

This book provides an introduction to statistical learning methods. Authors: Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani.

This is a free textbook teaching introductory statistics for undergraduates in Psychology. The textbook was written with math-phobia in mind and attempts to reduce the phobia associated with arithmetic computations. Author: Matthew J. C. Crump.

This book is intended to guide people that are completely new to programming along a path towards a useful skill level using R. Author: Derek L. Sonderegger.

Alex Cookson loves making beautiful visualizations and easy-to-read walkthroughs of R concepts. He's particularly interested in data about media, like books, movies, and musicals.

Avery Robbins loves to learn and to share useful or awesome things that have benefited him personally. This website is a tool for him to actively do just that: share knowledge, ideas, and tips that are helpful.

Cédric Scherer is a graduated computational ecologist and freelance data visualization expert who has created visualizations across all disciplines, purposes, and styles and regularly teaches data visualization principles, R, and ggplot2.

Thomas Lin Pedersen is a data scientist turned software engineer who focuses on improving researchers’ interactions with the data they produce.

Rebecca Barter enjoys making sense of complex, messy and sometimes nonsensical datasets, such as electronic health records, and insurance claims. Her dual passions are explaining “seemingly complicated” concepts to others in plain English, and exploring and uncovering the stories that underlie complex datasets.

John Mackintosh's blog is a place for him to showcase demonstrations or workshops, notes he's learned at work, chart makeovers, and techniques and technology that he doesn't currently use in his role.

Topics include modeling, creating functions, dashboards, and forecasting.

Topics include saving and reading data, map functions in purrr, t-tests, item response theory, and the basics of R and the tidyverse.

A collection of talks and seminars about R-related topics such as ggplot2 or Shiny, and data visualization in general.

Topics include regular expressions, data types, Shiny, and gganimate.

Topics for the online course Data Analysis and Visualization Using R.

Topics include time series, analyzing word relationships with ggraph and tidytext, and tidymodels.

A data science podcast where Roger Peng and Hilary Parker talk about the latest in data science and data analysis in academia and industry.

Practical advice on how to take advantage of R to accomplish innovative and robust data analyses. Hosted by Eric Nantz.

Founded by Jessie Mostipak (@kierisi) to create a supportive and responsive online space for learners and mentors to gather and work through the R for Data Science book by Garrett Grolemund and Hadley Wickham. Grown into a community of R learners at all skill levels working together to improve their skills.