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

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

Contents

• Topic Areas
• Blogs
• Books
• Communities of Practice
• Podcasts
• YouTube

Topic Areas

Functions

• stat.berkeley - Introduction to Functions - An introduction to functions in the R language by the organizers of Integrating Computing into the Statistics Curricula (U.C. Berkeley).

Math

• Descriptive Statistics - A tutorial of descriptive statistics which are used to summarize data in a way that provides insight into the information contained in the data. Author: Salvatore S. Mangiafico.
• Essential Math for Data Science - An article discussing the key mathematical topics to master to become a better data scientist. Author: Tirthajyoti Sarkar.
• Gallery of Statistical Distributions - Author: NIST/SEMATECH.
• Plotting distributions (ggplot2) - A tutorial for plotting a distribution of data. Author: Winston Chang.

Spatial

• An Introduction to Choropleth maps in R - Author: Henry Cann.
• Getting latitude & longitude for any address - Author: Brecht Vermeire.
• Map Plots Created With R And Ggmap - Author: Laura Ellis.
• Plot Spatial Data / Shapefiles in R - From the "math et al" YouTube channel.

Shiny

• Awesome R Shiny - A curated list of resources for R Shiny. Author: Rob Gilmore.
• Building Shiny Applications with R Tutorial (Deprecated) - Introductory tutorial to Shiny. Note, this tutorial is deprecated. Author: RStudio.
• Building Shiny apps - an interactive tutorial - This tutorial is a hands-on activity complement to a set of presentation slides for learning how to build Shiny apps. Author: Dean Attali.
• How to Start with Shiny - Detailed introductory video tutorial. Author: Garrett Grolemund.
• Learn Shiny - The video and written tutorials on this page are primarily designed for users who are new to Shiny and want a guided introduction. Author: RStudio.
• Shiny Articles - Various articles covering individual Shiny topics at a more advanced level. Author: RStudio.

Viz

• Awesome ggplot2 - A curated list of awesome ggplot2 tutorials, packages etc. Author: Erik Gahner Larsen.
• Chart Suggestions — A thought-starter on choosing the way to show your data. Author: Andrew Abela, Ph.D.
• Color Hex Color Codes - Author: Color-Hex.
• Combine Multiple GGPlots into a Figure - Author: Alboukadel Kassambara.
• From Data to Viz - From Data to Viz leads you to the most appropriate graph for your data. Author: Yan Holtz.
• ggplot2 extensions - gallery - Maintained by Daniel Emaasit.
• ggplot2 - Modify components of a theme - How to modify components of a theme in ggplot2. Author: the developers of Tidyverse.
• Graphics in R with ggplot2 - A detailed guide for the use of graphics within ggplot2. Author: Antoine Soetewey.
• r-color-palettes - Comprehensive list of color palettes available in r. Author: Emil Hvitfeldt.
• The Data Visualization Catalogue - The Data Visualization Catalogue is a project developed by Severino Ribecca to create a library of different information visualization types.
• The Graphic Continuum - The Graphic Continuum shows the many different types of visualizations available to us when we encode and present data. Authors: Jonathan Schwabish, and Severino Ribecca.
• The R Graph Gallery - A collection of charts made with the R programming language. Author: Yan Holtz.
• Time Based Heatmaps in R - Author: Laura Ellis.
• Top 50 ggplot2 Visualizations - The Master List (With Full R Code) - This tutorial helps you choose the right type of chart for your specific objectives and how to implement it in R using ggplot2. Author: Selva Prabhakaran.

Wrangling

• Data manipulation in r using data frames - an extensive article of basics - Author: dataENQ.
• Data Wrangling Part 1: Basic to Advanced Ways to Select Columns - Author: Suzan Baert.
• Data Wrangling Part 2: Transforming your columns into the right shape - Author: Suzan Baert.
• Data Wrangling Part 3: Basic and more advanced ways to filter rows - Author: Suzan Baert.
• Data Wrangling Part 4: Summarizing and slicing your data - Author: Suzan Baert.

Uncategorized

• A Data.Table and Dplyr Tour - A detailed comparison of R packages data.table and dplyr. Author: Atrebas.
• Fakir - Create Fake Data in R for Tutorials - Author: Colin Fay.
• Subreddit - r/Rlanguage - R Programming Language - A Reddit subreddit focused on implementing the R programming language for statistics and data science.
• Subreddit - r/programming - The R Project for Statistical Computing - A Reddit subreddit focused on using R for statistical computing.
• Syntax equivalents: base R vs Tidyverse - A detailed comparison of base R and tidyverse. Author: Hugo Tavares.

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.
• Stats and R - Through his blog, Antoine Soetewey (PhD in statistics) aims at helping academics and professionals working with data to grasp important statistical concepts, and shows how to apply them in R.

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.
• Engineering Production-Grade Shiny Apps - This book covers the process of building a Shiny application that will later be sent to production. Authors: Colin Fay, Sébastien Rochette, Vincent Guyader, Cervan Girard.
• 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. This book complements the Shiny 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, visualize 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 - Topics include modeling, creating functions, dashboards, and forecasting.
• 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 - Topics include regular expressions, data types, Shiny, and gganimate.
• Data Analysis and Visualization Using R - Topics for the online course Data Analysis and Visualization Using R.
• Data Science with Tom - Topics include time series, analyzing word relationships with ggraph and tidytext, and tidymodels.
• David Jablonski - The UC Berkeley R Bootcamp playlists include videos on R basics, handling data, performing calculations, programming, graphics, workflows, and statistics.
• 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 detailed TidyTuesday screencast annotations.
• Dragonfly Statistics - 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 - Topics include predictive text modeling, impute missing data, tidymodels, sentiment analysis, multinomial classification, principal component analysis, data preprocessing and resampling, and multinomial classification.
• Numyard - Topics include working with dataframes, for loops, basic math, vectors, lists, creating functions, data types, and random sampling.
• 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 - The R Programming for Beginners playlist includes videos on data science, charting, data visualization, algorithms, business analytics, regression, random forest, SVM, clustering, time series, modeling, and analytical techniques.
• Statistics Globe - 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 - 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 - 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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