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rogerchenfz / awesome-r-learning-resources

A curated collection of free resources to help deepen your understanding of the R programming language. Updated regularly. Contributions encouraged via a pull request (see contributing.md).

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Awesome R Learning Resources Awesome

Useful R learning resources meant to help users from all skill levels and backgrounds deepen their understanding of R, resulting in a more knowledgable programming population that benefits everybody involved.

R is a programming language and environment for statistical computing and graphics.

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Contents

Resources

Functions

Math

Spatial

Shiny

Viz

Wrangling

Uncategorized

Blogs

  • Avery Robbins - 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.
  • Tony ElHabr - Tony ElHabr is passionate mostly about energy markets and sports analytics. His blog provides detailed tutorials, project explanations, and presentations.
  • Cédric Scherer - Cédric Scherer is a computational ecologist by training and a data visualization designer by heart with more than 9 years of hypothesis-driven research experience and strong skills in data wrangling, statistical analysis, model development and data visualization.
  • Data Imaginist - Thomas Lin Pedersen is a data scientist turned software engineer who focuses on improving researchers interactions with the data they produce.
  • Data meets Narrative - 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.
  • HighlandR - 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.
  • Julia Silge - Julia Silge is a data scientist and software engineer at RStudio where she work on open source modeling tools. She is passionate about making beautiful charts, the statistical programming language R, Jane Austen, black coffee, and red wine.
  • rweekly - Weekly Updates from the Entire R Community by Bruce Zhao, Colin Fay, Eric Nantz, Hao Zhu, Jon Calder, Jonathan Carroll, Maëlle Salmon, Ryo Nakagawara, and Wolfram Qin.
  • r-bloggers - R-Bloggers.com was created by Tal Galili and is a blog aggregator of content contributed by bloggers who write about R (in English). The site helps R bloggers and users to connect and follow the R blogosphere.
  • Ryo Nakagawara - Ryo Nakagawara is a Data Scientist and has been doing work as both a reporting analyst and a software developer in R and SQL to improve ACDI and VOCA data pipelines, create R packages, reproducible reports, dashboards, and Shiny apps to communicate how his projects worldwide are progressing.
  • Statistics Globe - Joachim Schork started this platform to share his statistical know-how and to improve his own statistical skills by discussing with other statisticians and programmers.

Books

  • A Sufficient Introduction to R - 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.
  • An Introduction to Statistical Learning - This book provides an introduction to statistical learning methods. Authors: Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani.
  • Advanced R - 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. Author: Hadley Wickham.
  • An Introduction to R - 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.
  • Answering Questions with Data - 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.
  • Exploratory Data Analysis with R - This book covers the essential exploratory techniques for summarizing data with R. These techniques are typically applied before formal modeling commences and can help inform the development of more complex statistical models. Author: Roger D. Peng.
  • Geocomputation with R - This book is about using the power of computers to do things with geographic data. It teaches a range of spatial skills, including: reading, writing and manipulating geographic data; making static and interactive maps; applying geocomputation to solve real-world problems; and modeling geographic phenomena. Authors: Robin Lovelace, Jakub Nowosad, Jannes Muenchow.
  • ggplot2: Elegant Graphics for Data Analysis - This book provides a hands-on introduction to ggplot2 with lots of example code and graphics. It also explains the grammar on which ggplot2 is based. Author: Hadley Wickham.
  • Handling and Processing Strings in R - This ebook aims to help you get started with manipulating strings in R. Author: Gaston Sanchez.
  • Introduction to R & Spatial Data with Raster and Terra - This document provides a concise introduction to R. It emphasizes what you need to know to be able to use the language in any context. Author: Professor Robert Hijmans.
  • Learning Statistics with R - Learning Statistics with R covers the contents of an introductory statistics class, as typically taught to undergraduate psychology students, focusing on the use of the R statistical software. Author: Danielle Navarro.
  • Mastering Shiny - This is the online version of Mastering Shiny, a book currently under early development and intended for a late 2020 release by O’Reilly Media. This book complements Shiny’s online documentation and is intended to help app authors develop a deeper understanding of Shiny. Author: Hadley Wickham.
  • Modern R with the tidyverse - The idea of Chapters 1 to 7 is to make you efficient with R as quickly as possible, especially if you already have prior programming knowledge. Starting with Chapter 8 you will learn more advanced topics, especially programming with R. Author: Bruno Rodrigues.
  • Practical Regression and Anova using R - The emphasis of this text is on the practice of regression and analysis of variance. The objective is to learn what methods are available and more importantly, when they should be applied. Author: Julian Faraway.
  • Practicals and Exercises - This series of exercises reviews some of the content discussed during the author's lectures, and introduces some other basic concepts about working with data in R. Author: Charles DiMaggio, PhD.
  • R for Data Science - This book will teach you how to do data science with R. You will learn how to get your data into R, get it into the most useful structure, transform it, visualise it and model it. Authors: Garrett Grolemund and Hadley Wickham.
  • R for Data Science: Exercise Solutions - Solutions and explanations for the exercises included in R for Data Science. Author: Jeffrey B. Arnold.
  • R Packages - In this book you will learn how to turn your code into packages that others can easily download and use. Author: Hadley Wickham.
  • R Programming for Data Science - This book brings the fundamentals of R programming to you, using the same material developed as part of the industry-leading Johns Hopkins Data Science Specialization. Author: Roger Peng.
  • Statistical Inference via Data Science - This is intended to be a gentle introduction to the practice of analyzing data and answering questions using data the way data scientists, statisticians, data journalists, and other researchers would. Authors: Chester Ismay and Albert Y. Kim.
  • Supervised Machine Learning for Text Analysis in R - This book focuses on supervised or predictive modeling for text, using text data to make predictions about the world around us. Authors: Emil Hvitfeldt and Julia Silge.
  • Text Mining with R - This book serves as an introduction of text mining using the tidytext package and other tidy tools in R. Authors: Julia Silge and David Robinson.
  • The Art of R Programming - This book is for those who wish to learn about developing software in R. Author: Norman Matloff.
  • The R Inferno - A book about trouble spots, oddities, traps, and glitches in R. Author: Patrick Burns.
  • The R Language - An introduction to R written by the authors of the R language.
  • Tidy Modeling with R - This book is a guide to using a new collection of software in the R programming language for model building.

Communities of Practice

A community of practice is a group of people who share a concern or a passion for something they do and learn how to do it better as they interact regularly.

  • TidyTuesday - TidyTuesday is a weekly data project aimed at the R ecosystem with an emphasis placed on understanding how to summarize and arrange data to make meaningful charts.

Podcasts

  • Not so Standard Deviations - A data science podcast where Roger Peng and Hilary Parker talk about the latest in data science and data analysis in academia and industry.
  • The R-Podcast - Practical advice on how to take advantage of R to accomplish innovative and robust data analyses. Hosted by Eric Nantz.

YouTube

  • Andrew Couch - YouTube channel created by Andrew Couch. Topics include modeling, creating functions, dashboards, and forecasting.
  • Ben Stenhaug - YouTube channel created by Ben Stenhaug. Topics include saving and reading data, map functions in purrr, t-tests, item response theory, and the basics of R and the tidyverse.
  • Colin Quirk - YouTube channel created by Colin Quirk. Topics include regular expressions, data types, Shiny, and gganimate.
  • Data Analysis and Visualization Using R - YouTube channel created by David Robinson. Videos for the online course Data Analysis and Visualization Using R.
  • Data Science with Tom - YouTube channel created by Tom. Topics include time series, analyzing word relationships with ggraph and tidytext, and tidymodels.
  • David Jablonski - YouTube channel created by David Jablonski. His UC Berkeley R Bootcamp playlists include videos on R basics, handling data, performing calculations, programming, graphics, workflows, and statistics.
  • David Robinson - YouTube channel created by David Robinson. Topics include graphing for EDA, data manipulation, animated mapping, visualization, text mining, time series, forecasting, regression, bootstrapping, package development, network graphs, ANOVA, JSON, simulation, survival analysis, and tidymetrics. Click here for screencast annotations.
  • Dragonfly Statistics - YouTube channel created by Kevin O'Brien. Topics include numerical computing, generating random walks, markov chains, encoding categorical variables, probability, correlation plots, feature engineering, time series, binary classifiers, models, data.table, confusion matrices, machine learning, geocoding, summary statistics, and simulation.
  • Julia Silge - YouTube channel created by Julia Silge. Topics include predictive text modeling, impute missing data, tidymodels, sentiment analysis, multinomial classification, principal component analysis, data preprocessing and resampling, and multinomial classification.
  • Numyard - YouTube channel created by the folks at Numyard. Topics include working with dataframes, for loops, basic math, vectors, lists, creating functions, data types, and random sampling.
  • Richard Webster - YouTube channel created by Richard Webster. Topics include the paste function, the apply family of functions, while and for loops, conditional statements, visualization, removing NAs, and combining data.
  • Simplilearn - YouTube channel created by the folks at Simplilearn. The R Programming for Beginners playlist includes topics dealing with data science, charting, data visualization, algorithms, business analytics, regression, random forest, SVM, clustering, time series, modeling, and analytical techniques.
  • Statistics Globe - YouTube channel created by Joachim Schork. It is a collection of short but detailed tutorials on how to work through common problems you will face while using R. Topics include data formatting, reordering data, strings, and ggplot2.
  • StatQuest with Josh Starmer - YouTube channel created by Josh Starmer. The Statistics and Machine Learning in R playlist deals with principal component analysis, random forest, regression, ROC and AUC, and ridge, lasso and elastic-net.
  • TidyX - YouTube channel created by Ellis Hughes and Patrick Ward. TidyX is a screen cast where the hosts select code from the TidyTuesday project and go through their code line-by-line, explaining what they did and how the functions they used work. They also break down the visualizations they create and talk about how to apply similar approaches to other data sets. The objective is to help more people learn R and get involved in the TidyTuesday community.

Contributing

  • Your contributions are always welcome! Please visit our contributing.md to learn how to contribute to this list.

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A curated collection of free resources to help deepen your understanding of the R programming language. Updated regularly. Contributions encouraged via a pull request (see contributing.md).

License:Creative Commons Zero v1.0 Universal