R - Learning

The tutorials are grouped by skill level (beginner, intermediate, expert).

A fun introduction to R programming grouped into categories (operators, objects, functions, exercises, and data frames).

A fun introduction to R programming grouped into categories (data manipulation and cleaning featuring the janitor, tidyr, and dplyr packages).

This is a lecture series with videos, scripts and exercises introducing R and the tidyverse as well as statistical concepts.

The tutorials are grouped into categories (introduction, data structures, data wrangling, programming, import & export, graphics) that cover in-depth all the basic needs for someone starting learning the R programming language.

The tutorials are grouped into categories (R tutorial, R Data Interfaces, R Charts & Graphs, R Statistics Examples, R Useful Resources) that cover in-depth all the basic needs for someone starting learning the R programming language.

An introduction to functions in the R language by the organizers of Integrating Computing into the Statistics Curricula (U.C. Berkeley).

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.

Course notes from the Joining Data in R with dplyr course on DataCamp. Topics include mutating joins, filtering joins and set operations, assembling data, advanced joining. Author: William Surles.

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.

This article explains how to compute the main descriptive statistics in R and how to present them graphically. Author - Antoine Soetewey.

An article discussing the key mathematical topics to master to become a better data scientist. Author: Tirthajyoti Sarkar.

Author: NIST/SEMATECH.

/) - A tutorial for plotting a distribution of data. Author: Winston Chang.

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

Introductory tutorial to Shiny. Note, this tutorial is deprecated. Author: RStudio.

This tutorial is a hands-on activity complement to a set of presentation slides for learning how to build Shiny apps. Author: Dean Attali.

Detailed introductory video tutorial. Author: Garrett Grolemund.

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.

Various articles covering individual Shiny topics at a more advanced level. Author: RStudio.

Author: Henry Cann.

Author: Brecht Vermeire.

Author: Laura Ellis.

From the "math et al" YouTube channel.

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.

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

Author: Andrew Abela, Ph.D.

Author: Color-Hex.

Author: Alboukadel Kassambara.

The super fast color schemes generator! Create the perfect palette or get inspired by thousands of beautiful color schemes. Features include color picker, pick palette from photo, create a collage, make your own gradient palette, create a gradient, contrast checker, etc.

From Data to Viz leads you to the most appropriate graph for your data. Author: Yan Holtz.

Maintained by Daniel Emaasit.

How to modify components of a theme in ggplot2. Author: the developers of Tidyverse.

A detailed guide for the use of graphics within ggplot2. Author: Antoine Soetewey.

Showcase and gallery of the various interactive web visualizations you can build using R.

Comprehensive list of color palettes available in r. Author: Emil Hvitfeldt.

The Data Visualization Catalogue is a project developed by Severino Ribecca to create a library of different information visualization types.

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.

A collection of charts made with the R programming language. Author: Yan Holtz.

Author: Laura Ellis.

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.

Guide, reference and cheatsheet on web scraping using rvest, httr and Rselenium. Author: yifyan et al.

Author: Suzan Baert.

Author: Suzan Baert.

Author: Suzan Baert.

Author: Suzan Baert.

A detailed comparison of R packages data.table and dplyr. Author: Atrebas.

A quick introduction to data.table. The main objective is to present the data.table syntax, showing how to perform basic, but essential, data wrangling tasks. Author: Atrebas.

Author: Colin Fay.

This vignette compares stringr functions to their base R equivalents to help users transitioning from using base R to stringr. Author: Sara Stoudt.

Making a great reprex is both an art and a science and this webinar will cover both aspects. A reprex makes a conversation about code more efficient and pleasant for all. This comes up whenever you ask someone for help, report a bug in software, or propose a new feature. The reprex package (https://reprex.Tidyverse.org) makes it especially easy to prepare R code as a reprex, in order to share on sites such as [https://community.rstudio.com](https://community.rstu…

Dedicated discoRd server with the following topic-based channels: R-Main for more general discussions, R-Share for showing off your data visuals, General R Help for asking questions and sharing learning resources, and Topical Help/Discussion for issues dealing with statistics, dbi, tidymodels, shiny, natural-science, social-science, bayesians, gis, and finance.

A Reddit subreddit focused on implementing the R programming language for statistics and data science.

A Reddit subreddit focused on using R for statistical computing.

A detailed comparison of base R and tidyverse. Author: Hugo Tavares.

Find code for dozens of data tasks in this searchable cheat sheet of R data.table and Tidyverse code. Author: Sharon Machlis.

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.

Tony ElHabr is passionate mostly about energy markets and sports analytics. His blog provides detailed tutorials, project explanations, and presentations.

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.

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.

A blog on all things R and Data Science by Martin Chan. Topics covered include comparing dplyr and data.table, Shiny apps, ggplot, data cleaning, using RStudio, interviews with other R users/data scientists, and web scraping.

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.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 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.

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.

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.

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.

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

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. Exercise Solutions Author: Hadley Wickham.

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 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.

The core content of the course focuses on data acquisition and wrangling, exploratory data analysis, data visualization, inference, modelling, and effective communication of results.

This book is primarily about learning to use R as a tool for data science in education. Authors: Ryan A. Estrellado, Emily A. Bovee, Jesse Mostipak, Joshua M. Rosenberg, and Isabella C. Velásquez.

Efficient R Programming is about increasing the amount of work you can do with R in a given amount of time. It’s about both computational and programmer efficiency. Authors: Colin Gillespie, Robin Lovelace.

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.

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.

This textbook is intended to provide a comprehensive introduction to forecasting methods and to present enough information about each method for readers to be able to use them sensibly. Authors: Rob J Hyndman and George Athanasopoulos.

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.

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.

Happy Git provides opinionated instructions on how to install Git and get it working smoothly with GitHub, in the shell and in the RStudio IDE, develop a few key workflows that cover your most common tasks, and integrate Git and GitHub into your daily work with R and R Markdown. Authors: Jenny Bryan, the STAT 545 TAs, Jim Hester.

This book started out as the class notes used in the HarvardX Data Science Series. It introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning. It also helps you develop skills such as R programming, data wrangling with dplyr, data visualization with ggplot2, algorithm building with caret, file organization with UNIX/Linux shell, version control with Git and Gi…

The book can be subdivided into three basic parts. The first part includes the introductions and elementary descriptive statistics; I want the students to be knee-deep in data right out of the gate. The second part is the study of probability, which begins at the basics of sets and the equally likely model, journeys past discrete/continuous random variables, and continues through to multivariate distributions. The chapter on sampling distributions paves the way to the third part, which isinfere…

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.

The ultimate aim of this work is to demonstrate to the reader the many great benefits one can reap by inviting JavaScript into their data science workflow. Author: John Coene.

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.

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. Mastering Shiny Exercise solutions

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.

From wrangling and exploring data to inference and predictive modelling. The book includes plenty of examples and more than 200 exercises with worked solutions. Author: Måns Thulin.

The intent of this book is to present data science from a pragmatic, practice-oriented viewpoint. The book concentrates on the process of data science, from the planning stages of a project, through the data collection and exploration, to the modeling, and finally to deployment and the sharing of results. Authors: Nina Zumel and John Mount.

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.

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.

The aim of this book is to provide an easily accessible introduction to R for the collection, study and presentation of different types of political data. Authors: Erik Gahner Larsen and Zoltán Fazekas.

This book is full of how-to recipes, each of which solves a specific problem. The recipe includes a quick introduction to the solution followed by a discussion that aims to unpack the solution and give you some insight into how it works. Authors: James (JD) Long and Paul Teetor.

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. Exercise Solutions Authors: Garrett Grolemund and Hadley Wickham.

In this book you will learn how to turn your code into packages that others can easily download and use. Author: Hadley Wickham.

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.

A tour of the R programming language that explores its different and essential concepts. This R DataFlair Tutorial Series is designed to help beginners to get started with R and experienced to brush up their R programming skills and gain perfection in the language.

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.

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.

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.

This book is for those who wish to learn about developing software in R. Author: Norman Matloff.

The aim of The Book of R: A First Course in Programming and Statistics is to provide a relatively gentle yet informative exposure to the statistical software environment R, alongside some common statistical analyses, so that readers may have a solid foundation from which to eventually become experts in their own right. Exercise solutions Author: Tilman M. Davies.

A book about trouble spots, oddities, traps, and glitches in R. Author: Patrick Burns.

An introduction to R written by the authors of the R language.

This book is a guide to using a new collection of software in the R programming language for model building.

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.

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.

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.

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.

The UC Berkeley R Bootcamp playlists include videos on R basics, handling data, performing calculations, programming, graphics, workflows, and statistics.

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.

Shiny, including several videos on debugging Shiny.

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.

Do More with R playlist includes tutorials on shiny, data.table, getting API data, using Git and Github with R, writing your own packages, run Python in R code, RStudio addins and keyboard shortcuts, dashboards and flexdashboards.

Topics include predictive text modeling, impute missing data, tidymodels, sentiment analysis, multinomial classification, principal component analysis, data preprocessing and resampling, and multinomial classification.

In-depth talks by different experts on a wide variety of topics.

Topics include descriptive statistics, ANOVA, bootstrapping, linear regression, bivariate analysis, and probability distributions.

Topics include working with dataframes, for loops, basic math, vectors, lists, creating functions, data types, and random sampling.

This channel provides teaching videos on data analysis and statistical analysis using R programming. The teaching videos include subjects like data cleaning, data manipulation, data visualization, statistical analysis, and machine learning and AI (artificial intelligence).

Topics include the paste function, the apply family of functions, while and for loops, conditional statements, visualization, removing NAs, and combining data.

The R playlist includes videos on manipulating data with dplyr, visualizing data with ggplot2 and ggThemeAssist, data types and structures, important base r functions, handling datetimes with lubridate, conquering factors with forcats, manipulating text with stringr.

The goals of the Shiny Developer Series are to showcase the innovative applications and packages in the ever-growing Shiny ecosystem, as well as the brilliant developers behind them!

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.

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.

Playlists on Efficient R Programming (e. g. running R code in parallel), Visualization, Regression Analyses.

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 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.