awesome-awesomeness/html/rlearningresources.html

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<pre><code>&lt;div&gt;
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<p><br></p>
<blockquote>
<p>The <code>Awesome R Learning Resources</code> repository is meant to
help users from all skill levels and backgrounds deepen their
understanding of <code>R</code>, which is a programming language and
environment for statistical computing and graphics.</p>
</blockquote>
<p><br></p>
<blockquote>
<p>The <code>R</code> <code>Discord</code> server is a friendly and
dedicated community for <code>R</code> enthusiasts, programmers,
statisticians, data scientists, and students. Whether you are looking to
connect with fellow useRs, have awesome data viz to share, or just
needed help with your stats assignment, you are at the right place!</p>
</blockquote>
<div data-align="center">
<pre><code>To join the R Discord server, please click the discoRd badge below. &lt;br&gt;</code></pre>
<p><a href="https://discord.gg/6fcReuUHgg">
<img alt="Discord" src="https://img.shields.io/discord/676433858782298142?label=discoRd%20server&logo=R&logoColor=blue"></a>
<br></p>
</div>
<p><br></p>
<h2 id="contents"><strong>Contents</strong></h2>
<ul>
<li><a href="#topic-areas">Topic Areas</a></li>
<li><a href="#blogs">Blogs</a></li>
<li><a href="#books">Books</a></li>
<li><a href="#communities-of-practice">Communities of Practice</a></li>
<li><a href="#podcasts">Podcasts</a></li>
<li><a href="#youtube">YouTube</a></li>
</ul>
<h2 id="topic-areas">Topic Areas</h2>
<h3 id="comprehensive-r-tutorials">Comprehensive R Tutorials</h3>
<ul>
<li><a href="https://data-flair.training/blogs/r-tutorials-home/">Data
Flair</a> - The tutorials are grouped by skill level (beginner,
intermediate, expert).</li>
<li><a
href="https://colab.research.google.com/drive/1dLsdGbkvgn1JbWgsy9Z-pFmPd_2MG4Xu?usp=sharing#scrollTo=vGnW7giO9AeD">Intro
to R course by Fabio Votta - part 1</a> - A fun introduction to R
programming grouped into categories (operators, objects, functions,
exercises, and data frames).</li>
<li><a
href="https://colab.research.google.com/drive/14CRElnKewnp5MnlxhqVu6OOcIXd-Bkaj?usp=sharing">Intro
to R course by Fabio Votta - part 2</a> - A fun introduction to R
programming grouped into categories (data manipulation and cleaning
featuring the janitor, tidyr, and dplyr packages).</li>
<li><a href="https://jmbuhr.de/dataIntro20/">Introduction to Data
Analysis with R</a> - This is a lecture series with videos, scripts and
exercises introducing R and the tidyverse as well as statistical
concepts.</li>
<li><a href="https://r-coder.com">R CODER</a> - The tutorials are
grouped into categories (introduction, data structures, data wrangling,
programming, import &amp; export, graphics) that cover in-depth all the
basic needs for someone starting learning the R programming
language.</li>
<li><a href="https://www.tutorialspoint.com/r/index.htm">Tutorials
Point</a> - The tutorials are grouped into categories (R tutorial, R
Data Interfaces, R Charts &amp; Graphs, R Statistics Examples, R Useful
Resources) that cover in-depth all the basic needs for someone starting
learning the R programming language.</li>
</ul>
<h3 id="functions">Functions</h3>
<ul>
<li><a
href="https://www.stat.berkeley.edu/~statcur/Workshop2/Presentations/functions.pdf">stat.berkeley
- Introduction to Functions</a> - An introduction to functions in the R
language by the organizers of Integrating Computing into the Statistics
Curricula (U.C. Berkeley).</li>
</ul>
<h3 id="generative-art">Generative Art</h3>
<ul>
<li><a href="https://www.williamrchase.com/work/art/">12 Months of
aRt</a> - 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.</li>
</ul>
<h3 id="joining-data">Joining Data</h3>
<ul>
<li><a href="https://rpubs.com/williamsurles/293454">Joining Data in R
with dplyr</a> - 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.</li>
</ul>
<h3 id="math">Math</h3>
<ul>
<li><a href="https://rcompanion.org/handbook/C_02.html">Descriptive
Statistics</a> - 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.</li>
<li><a
href="https://statsandr.com/blog/descriptive-statistics-in-r/">Descriptive
statistics in R</a> - This article explains how to compute the main
descriptive statistics in R and how to present them graphically. Author
- Antoine Soetewey.</li>
<li><a
href="https://medium.com/s/story/essential-math-for-data-science-why-and-how-e88271367fbd">Essential
Math for Data Science</a> - An article discussing the key mathematical
topics to master to become a better data scientist. Author: Tirthajyoti
Sarkar.</li>
<li><a
href="https://www.itl.nist.gov/div898/handbook/eda/section3/eda366.htm">Gallery
of Statistical Distributions</a> - Author: NIST/SEMATECH.</li>
<li><a
href="http://www.cookbook-r.com/Graphs/Plotting_distributions_(ggplot2)/">Plotting
distributions (ggplot2)</a> - A tutorial for plotting a distribution of
data. Author: Winston Chang.</li>
</ul>
<h3 id="shiny">Shiny</h3>
<ul>
<li><a href="https://github.com/grabear/awesome-rshiny">Awesome R
Shiny</a> - A curated list of resources for R Shiny. Author: Rob
Gilmore.</li>
<li><a href="https://rstudio.github.io/shiny/tutorial/#">Building Shiny
Applications with R Tutorial (Deprecated)</a> - Introductory tutorial to
Shiny. Note, this tutorial is deprecated. Author: RStudio.</li>
<li><a
href="https://deanattali.com/blog/building-shiny-apps-tutorial/">Building
Shiny apps - an interactive tutorial</a> - This tutorial is a hands-on
activity complement to a set of <a
href="https://docs.google.com/presentation/d/1dXhqqsD7dPOOdcC5Y7RW--dEU7UfU52qlb0YD3kKeLw/edit">presentation
slides</a> for learning how to build Shiny apps. Author: Dean
Attali.</li>
<li><a
href="https://vimeo.com/rstudioinc/review/131218530/212d8a5a7a">How to
Start with Shiny</a> - Detailed introductory video tutorial. Author:
Garrett Grolemund.</li>
<li><a href="https://shiny.rstudio.com/tutorial/">Learn Shiny</a> - 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.</li>
<li><a href="https://shiny.rstudio.com/articles/">Shiny Articles</a> -
Various articles covering individual Shiny topics at a more advanced
level. Author: RStudio.</li>
</ul>
<h3 id="spatial">Spatial</h3>
<ul>
<li><a
href="https://rstudio-pubs-static.s3.amazonaws.com/324400_69a673183ba449e9af4011b1eeb456b9.html">An
Introduction to Choropleth maps in R</a> - Author: Henry Cann.</li>
<li><a
href="https://discourse.looker.com/t/get-latitude-longitude-for-any-location-through-google-sheets-and-plot-these-in-looker/5402">Getting
latitude &amp; longitude for any address</a> - Author: Brecht
Vermeire.</li>
<li><a href="https://www.littlemissdata.com/blog/maps">Map Plots Created
With R And Ggmap</a> - Author: Laura Ellis.</li>
<li><a href="https://www.youtube.com/watch?v=uZtto0cYjZM">Plot Spatial
Data / Shapefiles in R</a> - From the “math et al” YouTube channel.</li>
</ul>
<h3 id="viz">Viz</h3>
<ul>
<li><a
href="https://cedricscherer.netlify.app/2019/08/05/a-ggplot2-tutorial-for-beautiful-plotting-in-r/">A
ggplot2 Tutorial for Beautiful Plotting in R</a> - 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.</li>
<li><a href="https://www.aiseka.com/">AISEKA</a> - Discover the best
Color Palette &amp; Color Tools. Author: meetqy.</li>
<li><a href="https://github.com/erikgahner/awesome-ggplot2">Awesome
ggplot2</a> - A curated list of awesome ggplot2 tutorials, packages etc.
Author: Erik Gahner Larsen.</li>
<li><a
href="https://extremepresentation.typepad.com/files/choosing-a-good-chart-09.pdf">Chart
Suggestions — A thought-starter on choosing the way to show your
data</a> - Author: Andrew Abela, Ph.D.</li>
<li><a href="https://www.color-hex.com/">Color Hex Color Codes</a> -
Author: Color-Hex.</li>
<li><a
href="https://www.datanovia.com/en/lessons/combine-multiple-ggplots-into-a-figure/">Combine
Multiple GGPlots into a Figure</a> - Author: Alboukadel Kassambara.</li>
<li><a href="https://coolors.co/">Coolors</a> - 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.</li>
<li><a href="https://www.data-to-viz.com/">From Data to Viz</a> - From
Data to Viz leads you to the most appropriate graph for your data.
Author: Yan Holtz.</li>
<li><a href="https://exts.ggplot2.tidyverse.org/gallery/">ggplot2
extensions - gallery</a> - Maintained by Daniel Emaasit.</li>
<li><a href="https://ggplot2.tidyverse.org/reference/theme.html">ggplot2
- Modify components of a theme</a> - How to modify components of a theme
in ggplot2. Author: the developers of Tidyverse.</li>
<li><a
href="https://www.statsandr.com/blog/graphics-in-r-with-ggplot2/">Graphics
in R with ggplot2</a> - A detailed guide for the use of graphics within
ggplot2. Author: Antoine Soetewey.</li>
<li><a href="https://www.htmlwidgets.org/">htmlwidgets for R</a> -
Showcase and gallery of the various interactive web visualizations you
can build using R.</li>
<li><a
href="https://github.com/EmilHvitfeldt/r-color-palettes">r-color-palettes</a>
- Comprehensive list of color palettes available in r. Author: Emil
Hvitfeldt.</li>
<li><a href="https://datavizcatalogue.com/index.html">The Data
Visualization Catalogue</a> - The Data Visualization Catalogue is a
project developed by Severino Ribecca to create a library of different
information visualization types.</li>
<li><a
href="https://www.informationisbeautifulawards.com/showcase/611-the-graphic-continuum">The
Graphic Continuum</a> - 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.</li>
<li><a href="https://www.r-graph-gallery.com/">The R Graph Gallery</a> -
A collection of charts made with the R programming language. Author: Yan
Holtz.</li>
<li><a href="https://www.littlemissdata.com/blog/heatmaps">Time Based
Heatmaps in R</a> - Author: Laura Ellis.</li>
<li><a
href="http://r-statistics.co/Top50-Ggplot2-Visualizations-MasterList-R-Code.html">Top
50 ggplot2 Visualizations - The Master List (With Full R Code)</a> -
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.</li>
</ul>
<h3 id="web-scraping">Web Scraping</h3>
<ul>
<li><a href="https://github.com/yusuzech/r-web-scraping-cheat-sheet">Web
Scraping Reference: Cheat Sheet for Web Scraping using R</a> - Guide,
reference and cheatsheet on web scraping using rvest, httr and
Rselenium. Author: <a href="https://github.com/yusuzech">yifyan et
al.</a></li>
</ul>
<h3 id="wrangling">Wrangling</h3>
<ul>
<li><a href="https://suzan.rbind.io/2018/01/dplyr-tutorial-1/">Data
Wrangling Part 1: Basic to Advanced Ways to Select Columns</a> - Author:
Suzan Baert.</li>
<li><a href="https://suzan.rbind.io/2018/02/dplyr-tutorial-2/">Data
Wrangling Part 2: Transforming your columns into the right shape</a> -
Author: Suzan Baert.</li>
<li><a href="https://suzan.rbind.io/2018/02/dplyr-tutorial-3/">Data
Wrangling Part 3: Basic and more advanced ways to filter rows</a> -
Author: Suzan Baert.</li>
<li><a href="https://suzan.rbind.io/2018/04/dplyr-tutorial-4/">Data
Wrangling Part 4: Summarizing and slicing your data</a> - Author: Suzan
Baert.</li>
</ul>
<h3 id="uncategorized">Uncategorized</h3>
<ul>
<li><a
href="https://atrebas.github.io/post/2019-03-03-datatable-dplyr/#reshape-data">Data.Table
and Dplyr Tour</a> - A detailed comparison of R packages data.table and
dplyr. Author: Atrebas.</li>
<li><a
href="https://atrebas.github.io/post/2020-06-17-datatable-introduction/">data.table:
A gentle introduction</a> - 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.</li>
<li><a href="https://thinkr-open.github.io/fakir/">Fakir - Create Fake
Data in R for Tutorials</a> - Author: Colin Fay.</li>
<li><a href="https://stringr.tidyverse.org/articles/from-base.html">From
base R to stringr</a> - This vignette compares stringr functions to
their base R equivalents to help users transitioning from using base R
to stringr. Author: Sara Stoudt.</li>
<li><a href="https://www.youtube.com/watch?v=5gqksthQ0cM">Help me help
you: creating reproducible examples</a> - 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://github.com, or
https://stackoverflow.com. Author: Jenny Bryan.</li>
<li><a href="https://discord.gg/88uG5UVyE2">R - discoRd server</a> -
Dedicated discoRd server with the following topic-based channels:
<code>R-Main</code> for more general discussions, <code>R-Share</code>
for showing off your data visuals, <code>General R Help</code> for
asking questions and sharing learning resources, and
<code>Topical Help/Discussion</code> for issues dealing with statistics,
dbi, tidymodels, shiny, natural-science, social-science, bayesians, gis,
and finance.</li>
<li><a href="https://www.reddit.com/r/Rlanguage/new/">Subreddit -
r/Rlanguage - R Programming Language</a> - A Reddit subreddit focused on
implementing the R programming language for statistics and data
science.</li>
<li><a href="https://www.reddit.com/r/rprogramming/">Subreddit -
r/programming - The R Project for Statistical Computing</a> - A Reddit
subreddit focused on using R for statistical computing.</li>
<li><a
href="https://tavareshugo.github.io/data_carpentry_extras/base-r_tidyverse_equivalents/base-r_tidyverse_equivalents.html">Syntax
equivalents: base R vs Tidyverse</a> - A detailed comparison of base R
and tidyverse. Author: Hugo Tavares.</li>
<li><a
href="https://www.infoworld.com/article/3575086/the-ultimate-r-datatable-cheat-sheet.html">The
ultimate R data.table cheat sheet</a> - Find code for dozens of data
tasks in this searchable cheat sheet of R data.table and Tidyverse code.
Author: Sharon Machlis.</li>
</ul>
<h2 id="blogs">Blogs</h2>
<ul>
<li><a href="https://www.alexcookson.com/">Alex Cookson</a> - 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.</li>
<li><a href="https://www.avery-robbins.com">Avery Robbins</a> - 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.</li>
<li><a href="https://tonyelhabr.rbind.io/">Tony ElHabr</a> - Tony ElHabr
is passionate mostly about energy markets and sports analytics. His blog
provides detailed tutorials, project explanations, and
presentations.</li>
<li><a href="https://cedricscherer.netlify.app/">Cédric Scherer</a> -
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.</li>
<li><a href="https://www.data-imaginist.com/">Data Imaginist</a> -
Thomas Lin Pedersen is a data scientist turned software engineer who
focuses on improving researchers’ interactions with the data they
produce.</li>
<li><a href="http://www.rebeccabarter.com/blog/">Data meets
Narrative</a> - 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.</li>
<li><a href="https://johnmackintosh.net/">HighlandR</a> - 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.</li>
<li><a href="https://juliasilge.com/blog/">Julia Silge</a> - 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.</li>
<li><a href="https://martinctc.github.io/blog/">Musings on R</a> - 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.</li>
<li><a href="https://rweekly.org/about">rweekly</a> - 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.</li>
<li><a href="https://www.r-bloggers.com/">r-bloggers</a> -
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.</li>
<li><a href="https://ryo-n7.github.io/">Ryo Nakagawara</a> - 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.</li>
<li><a href="https://statisticsglobe.com/">Statistics Globe</a> -
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.</li>
<li><a href="https://www.statsandr.com/">Stats and R</a> - 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.</li>
</ul>
<h2 id="books">Books</h2>
<ul>
<li><a href="https://dereksonderegger.github.io/570L/">A Sufficient
Introduction to R</a> - 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.</li>
<li><a
href="http://faculty.marshall.usc.edu/gareth-james/ISL/ISLR%20Seventh%20Printing.pdf">An
Introduction to Statistical Learning</a> - This book provides an
introduction to statistical learning methods. Authors: Gareth James,
Daniela Witten, Trevor Hastie and Robert Tibshirani.</li>
<li><a href="https://adv-r.hadley.nz/introduction.html">Advanced R</a> -
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. <a
href="https://advanced-r-solutions.rbind.io/">Exercise Solutions</a>
Author: Hadley Wickham.</li>
<li><a
href="https://cran.r-project.org/doc/manuals/r-release/R-intro.pdf">An
Introduction to R</a> - 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.</li>
<li><a href="https://intro2r.com/">An Introduction to R</a> - 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 &amp; David
Lusseau</li>
<li><a href="https://crumplab.github.io/statistics/">Answering Questions
with Data</a> - 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.</li>
<li><a href="https://datasciencebox.org/index.html">Data Science in a
Box</a> - The core content of the course focuses on data acquisition and
wrangling, exploratory data analysis, data visualization, inference,
modelling, and effective communication of results.</li>
<li><a href="https://datascienceineducation.com/">Data Science in
Education Using R</a> - 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.</li>
<li><a href="https://csgillespie.github.io/efficientR/">Efficient R
programming</a> - 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.</li>
<li><a href="https://engineering-shiny.org/">Engineering
Production-Grade Shiny Apps</a> - 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.</li>
<li><a href="https://bookdown.org/rdpeng/exdata/">Exploratory Data
Analysis with R</a> - 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.</li>
<li><a href="https://otexts.com/fpp3/">Forecasting: Principles and
Practice</a> - 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.</li>
<li><a href="https://geocompr.robinlovelace.net/">Geocomputation with
R</a> - 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.</li>
<li><a href="https://ggplot2-book.org/index.html">ggplot2: Elegant
Graphics for Data Analysis</a> - 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.</li>
<li><a href="https://happygitwithr.com/">Happy Git and GitHub for the
useR</a> - 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.</li>
<li><a href="https://rafalab.github.io/dsbook/">Introduction to Data
Science - Data Analysis and Prediction Algorithms with R</a> - 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
GitHub, and reproducible document preparation with knitr and R markdown.
Author: Professor Rafael A. Irizarry.</li>
<li><a
href="http://www.atmos.albany.edu/facstaff/timm/ATM315spring14/R/IPSUR.pdf">Introduction
to Probability and Statistics Using R</a> - 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 isinferential
statistics. This last part includes point and interval estimation,
hypothesis testing, and finishes with introductions to selected topics
in applied statistics. Author: G. Jay Kerns.</li>
<li><a href="https://rspatial.org/intr/index.html">Introduction to R
&amp; Spatial Data with Raster and Terra</a> - 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.</li>
<li><a href="https://book.javascript-for-r.com/">JavaScript for R</a> -
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.</li>
<li><a href="https://learningstatisticswithr.com/">Learning Statistics
with R</a> - 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.</li>
<li><a href="https://mastering-shiny.org/">Mastering Shiny</a> - 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 <a href="https://shiny.rstudio.com/">Shiny online documentation</a>
and is intended to help app authors develop a deeper understanding of
Shiny. Author: Hadley Wickham. <a
href="https://mastering-shiny-solutions.org/index.html">Mastering Shiny
Exercise solutions</a></li>
<li><a href="https://b-rodrigues.github.io/modern_R/">Modern R with the
tidyverse</a> - 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.</li>
<li><a href="http://www.modernstatisticswithr.com/">Modern Statistics
with R</a> - 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.</li>
<li><a
href="https://www.manning.com/books/practical-data-science-with-r-second-edition#toc">Practical
Data Science with R</a> - 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.</li>
<li><a
href="https://cran.r-project.org/doc/contrib/Faraway-PRA.pdf">Practical
Regression and Anova using R</a> - 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.</li>
<li><a
href="http://www.columbia.edu/~cjd11/charles_dimaggio/DIRE/resources/R/practicalsBookNoAns.pdf">Practicals
and Exercises</a> - 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.</li>
<li><a href="http://qpolr.com/qpolr.pdf">Quantitative Politics with
R</a> - 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.</li>
<li><a href="https://rc2e.com/index.html">R Cookbook, 2nd Edition</a> -
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.</li>
<li><a href="https://r4ds.had.co.nz/">R for Data Science</a> - 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. <a
href="https://jrnold.github.io/r4ds-exercise-solutions/">Exercise
Solutions</a> Authors: Garrett Grolemund and Hadley Wickham.</li>
<li><a href="http://r-pkgs.had.co.nz/">R Packages</a> - In this book you
will learn how to turn your code into packages that others can easily
download and use. Author: Hadley Wickham.</li>
<li><a href="https://leanpub.com/rprogramming">R Programming for Data
Science</a> - 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.</li>
<li><a href="https://data-flair.training/blogs/r-tutorial/">R Tutorial –
Be a Data Science rock star with R</a> - 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.</li>
<li><a href="https://moderndive.com/">Statistical Inference via Data
Science</a> - 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.</li>
<li><a href="https://smltar.com/">Supervised Machine Learning for Text
Analysis in R</a> - 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.</li>
<li><a href="https://www.tidytextmining.com/">Text Mining with R</a> -
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.</li>
<li><a
href="http://diytranscriptomics.com/Reading/files/The%20Art%20of%20R%20Programming.pdf">The
Art of R Programming</a> - This book is for those who wish to learn
about developing software in R. Author: Norman Matloff.</li>
<li><a href="https://web.itu.edu.tr/~tokerem/The_Book_of_R.pdf">The Book
of R</a> - 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. <a
href="https://nostarch.com/bookofr">Exercise solutions</a> Author:
Tilman M. Davies.</li>
<li><a href="http://www.burns-stat.com/pages/Tutor/R_inferno.pdf">The R
Inferno</a> - A book about trouble spots, oddities, traps, and glitches
in R. Author: Patrick Burns.</li>
<li><a href="https://stat.ethz.ch/R-manual/R-patched/doc/html/">The R
Language</a> - An introduction to R written by the authors of the R
language.</li>
<li><a href="https://www.tmwr.org/">Tidy Modeling with R</a> - This book
is a guide to using a new collection of software in the R programming
language for model building.</li>
</ul>
<h2 id="communities-of-practice">Communities of Practice</h2>
<blockquote>
<p>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.</p>
</blockquote>
<ul>
<li><a
href="https://github.com/rfordatascience/tidytuesday">TidyTuesday</a> -
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.</li>
<li><a href="https://www.rfordatasci.com/">R for Data Science (R4DS)
Online Learning Community</a> - Founded by Jessie Mostipak (<span
class="citation" data-cites="kierisi">@kierisi</span>) 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.</li>
</ul>
<h2 id="podcasts">Podcasts</h2>
<ul>
<li><a href="http://nssdeviations.com/">Not so Standard Deviations</a> -
A data science podcast where Roger Peng and Hilary Parker talk about the
latest in data science and data analysis in academia and industry.</li>
<li><a href="https://r-podcast.org/">The R-Podcast</a> - Practical
advice on how to take advantage of R to accomplish innovative and robust
data analyses. Hosted by Eric Nantz.</li>
</ul>
<h2 id="youtube">YouTube</h2>
<ul>
<li><a
href="https://www.youtube.com/channel/UCnwYO3Sz_emBTC1sTZ6TlsQ">Andrew
Couch</a> - Topics include modeling, creating functions, dashboards, and
forecasting.</li>
<li><a href="https://www.youtube.com/user/benastenhaug/videos">Ben
Stenhaug</a> - Topics include saving and reading data, map functions in
purrr, t-tests, item response theory, and the basics of R and the
tidyverse.</li>
<li><a
href="https://www.youtube.com/playlist?list=PLd6eTXMmV3X-4-pHkZSJwHRACzSSyeT9T">Cédric
Scherer</a> - A collection of talks and seminars about R-related topics
such as ggplot2 or Shiny, and data visualization in general.</li>
<li><a
href="https://www.youtube.com/channel/UC-vtwz7ueU2dtnHk5e-WblA">Colin
Quirk</a> - Topics include regular expressions, data types, Shiny, and
gganimate.</li>
<li><a
href="https://www.youtube.com/channel/UClLf9MZuUy89IwGtRHC0RzQ">Data
Analysis and Visualization Using R</a> - Topics for the online course
Data Analysis and Visualization Using R.</li>
<li><a
href="https://www.youtube.com/channel/UCb5aI-GwJm3ZxlwtCsLu78Q">Data
Science with Tom</a> - Topics include time series, analyzing word
relationships with ggraph and tidytext, and tidymodels.</li>
<li><a
href="https://www.youtube.com/channel/UCzE7zgPikKvVUJPBKrndHMA">David
Jablonski</a> - The UC Berkeley R Bootcamp playlists include videos on R
basics, handling data, performing calculations, programming, graphics,
workflows, and statistics.</li>
<li><a
href="https://www.youtube.com/channel/UCeiiqmVK07qhY-wvg3IZiZQ">David
Robinson</a> - 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 <a
href="https://github.com/dgrtwo/data-screencasts/tree/master/screencast-annotations">here</a>
for detailed TidyTuesday screencast annotations.</li>
<li><a href="https://www.youtube.com/c/DAattali/videos">Dean Attali</a>
- Shiny, including several videos on debugging Shiny.</li>
<li><a
href="https://www.youtube.com/c/DragonflyStatistics/videoss">Dragonfly
Statistics</a> - 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.</li>
<li><a
href="https://www.youtube.com/playlist?list=PL7D2RMSmRO9JOvPC1gbA8Mc3azvSfm8Vv">IDG
TECHtalk</a> - 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.</li>
<li><a
href="https://www.youtube.com/channel/UCTTBgWyJl2HrrhQOOc710kA">Julia
Silge</a> - Topics include predictive text modeling, impute missing
data, tidymodels, sentiment analysis, multinomial classification,
principal component analysis, data preprocessing and resampling, and
multinomial classification.</li>
<li><a
href="https://www.youtube.com/channel/UC2-hKemnrmVCH_29duyJ26A/videos">Lander
Analytics</a> - In-depth talks by different experts on a wide variety of
topics.</li>
<li><a
href="https://www.youtube.com/c/marinstatlectures/featured">MarinStatsLectures</a>
- Topics include descriptive statistics, ANOVA, bootstrapping, linear
regression, bivariate analysis, and probability distributions.</li>
<li><a href="https://www.youtube.com/c/TheLearnR/videos">Numyard</a> -
Topics include working with dataframes, for loops, basic math, vectors,
lists, creating functions, data types, and random sampling.</li>
<li><a href="https://www.youtube.com/c/RProgramming101/featured">R
Programming 101</a> - 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).</li>
<li><a
href="https://www.youtube.com/channel/UC5ktyacv_aPSBmKB7uX5Piw/videos">Richard
Webster</a> - Topics include the paste function, the apply family of
functions, while and for loops, conditional statements, visualization,
removing NAs, and combining data.</li>
<li><a
href="https://www.youtube.com/playlist?list=PLOKCg4WX8ZG4nboHnOgA8PJxGWnO4csiZ">RichardOnData</a>
- 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.</li>
<li><a
href="https://www.youtube.com/c/ShinyDeveloperSeries/videos">Shiny
Developer Series</a> - 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!</li>
<li><a
href="https://www.youtube.com/playlist?list=PLEiEAq2VkUUKAw0aAJ1W4jpZ1q9LpX4yG">Simplilearn</a>
- 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.</li>
<li><a
href="https://www.youtube.com/channel/UCyHEww8_SCdxZvEnkCfi55w">Statistics
Globe</a> - 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.</li>
<li><a
href="https://www.youtube.com/c/StatistikinDD/featured">StatistikinDD</a>
- Playlists on Efficient R Programming (e. g. running R code in
parallel), Visualization, Regression Analyses.</li>
<li><a
href="https://www.youtube.com/playlist?list=PLblh5JKOoLUJJpBNfk8_YadPwDTO2SCbx">StatQuest
with Josh Starmer</a> - 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.</li>
<li><a
href="https://www.youtube.com/channel/UCP8l94xtoemCH_GxByvTuFQ">TidyX</a>
- 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.</li>
</ul>
<h2 id="contributing">Contributing</h2>
<ul>
<li>Your contributions are always welcome! Please visit our <a
href="https://github.com/iamericfletcher/r-learning-resources/blob/main/contributing.md">contributing.md</a>
to learn how to contribute to this list.</li>
</ul>
<p><a href="#contents">Back to Top</a></p>
<p><a
href="https://github.com/iamericfletcher/awesome-r-learning-resources">rlearningresources.md
Github</a></p>