7 π R and RStudio
Skill Level: β Beginner β β β Intermediate β β β β Advanced
7.1 Introduction to R
7.1.0.1 Programming with R - Course β
An online beginner course, developed by Software Carpentry, introducing the basics of R programming through data analysis
7.1.0.2 R for Reproducible Scientific Analysis - Course β
An online beginner course, developed by Software Carpentry, introducing the basics of R programming through analysis of the gapminder data
7.1.0.3 Base R Cheat Sheet from Posit - Cheatsheet β
A quick reference guide (downloadable pdf) of basic syntax and functionality in R
7.1.0.4 R for Data Science: Analysis and Visualization - Video β
An introducty LinkedIn Learning course (2h 46m) on the basics of getting started with R for data visualization, wrangling, and analysis
7.1.0.5 Quick-R - Reference β
Site containing a broad overview of R resources for learning R created by a statistical consultant and research methodologist
7.1.0.6 Introduction to R and RStudio - Video β
A recorded workshop on workflow basics, scripts, and projects from the University of Michigan Biostatistics department
7.1.0.7 R Essential Training: Wrangling and Visualizing Data - Video β
An introductory LinkedIn Learning course (4h 18m) on the essential tools for importing visualizing, and wrangling data in R
7.1.0.8 Big Book of R - Reference β β
A listing of over 300 Reference books on the R programming language
7.1.0.9 Tidy design principles - Book β β
Still under development, this book identifies challenges and patterns to help you write better R code. There is also a related Tidy Design Princiles blog/newsletter that the author is maintaining during the writing process
7.1.0.10 The tidyverse style guide - Book β β
A site that site describes the style used throughout the tidyverse, written by Hadley Wickham of Posit
7.1.0.11 23 RStudio Tips, Tricks, and Shortcuts - Blog β β
A blog post covering some of the best features of RStudio with a list of tips, tricks, and shortcuts
7.1.0.12 What They Forgot to Teach You About R - Book β β
This book focuses on content intrinsically related to the infrastructure surrounding data analysis in R, but does not delve into the data analysis itself
7.1.0.13 R for the Rest of Us: A Statistics-Free Introduction - Book β β
A crash course in R, a quick tour of the RStudio programming environment, and a collection of real-word applications that you can put to use right away
7.2 R and Data Science
7.2.0.1 R for Data Science (2e) - Textbook β β
FREE 2nd edition of βR for Data Scienceβ which provides practical skills for data science, including data importation, data structuring, data transformation, and data visualization
7.2.0.2 Statistical Inference via Data Science: A ModernDive into R and the Tidyverse - Textbook β
The companion website to Chester Ismay and Albert Y. Kimβs introductory statistics textbook, which walks students through the entire data analysis pipeline using R and tidyverse
7.2.0.3 R Screencasts - Video β β
Live data analysis screencasts from a top Data Scientist, David Robinson
7.2.0.4 Tools for Analyzing R Code the Tidy Way - Article β
An article on two packages, matahari and tidycode, written to analyze R code in a tidy manner
7.2.0.5 Tidy Modeling with R - Textbook β β
A guide to using a collection of software in the R programming language for model building called tidymodels
7.3 R Markdown
7.3.0.1 R Markdown Cookbook - Textbook β
This book is broken down into small βrecipesβ that aim to demonstrate a single R Markdown concept at a time.
7.3.0.2 R Markdown: The Definitive Guide - Reference β
This book is a definitive reference guide to the R Markdown language
7.3.0.3 R Markdown Tips, Tricks, and Shortcuts - Blog β
A blog post with some helpful tips and tricks for working with R Markdown in RStudio
7.4 R Packages
7.4.0.1 Quick list of useful R packages - Reference β β
A categorized list of recommended free libraries of code written by the active user community
7.4.0.2 CRAN Task Views - Reference β β
Comprehensive R Archive Network (CRAN)βs listing of packages by topic area or βtaskβ, i.e. Bayesian, ClinicalTrials, MachineLearning, MissingData, etc
7.4.0.3 ggplot2 Cheatsheet by Posit - Cheatsheet β
A quick reference guide for basic syntax and functionality of the data visualization ggplot2 package, including a downloadable pdf
7.4.0.4 rmarkdown Cheatsheet by Posit - Cheatsheet β
A quick reference guide for basic syntax and functionality of the authoring framework rmarkdown, including a downloadable pdf
7.4.0.5 tidyverse Cheatsheet by Posit - Cheatsheet β
A quick reference guide for basic syntax and functionality of the data science packages that comprise the tidyverse, including a downloadable pdf
7.4.0.6 dplyr Cheatsheet by Posit - Cheatsheet β
A quick reference guide for basic syntax and functionality of the data manipulation dplyr package, including a downloadable pdf
7.4.0.7 tidyr Cheatsheet by Posit - Cheatsheet β
A quick reference guide for basic syntax and functionality of the data cleaning tidyr package, including a downloadable pdf
7.4.0.8 Using Python with RStudio and reticulate - Video β β
This tutorial walks through the steps to enable data scientists to use RStudio and the reticulate package to call their Python code from Shiny apps, R Markdown notebooks, and Plumber REST APIs
7.4.0.9 htmlwidget- Documentation β β
A package for creating JavaScript data visualizations in R
7.4.0.10 reprex - Documentation β β
An R package for preparing reproducible examples of R code
7.5 Write Your Own R Package
7.5.0.1 Reproducible research with R, RStudio and GitLab - Documentation β
A manual describing easy reproducibility and collaboration with R and Git
7.5.0.2 Instructions for Creating Your Own R Package - Article β β
An article with a step-by-step guide to creating your own R package
7.5.0.3 Writing R Packages in RStudio - Tutorial β β
A tutorial walking through the set up of a basic R package that can be installed from GitHub
7.5.0.4 Making Your First R Package - Blog β β
A blog entry that walks through the steps of organizing code in a package with consistent documentation
7.5.0.5 The Package: learning how to build an R package - Blog β β
A blog entry from R-Bloggers that walks through the process of setting up an R package
7.5.0.6 Writing an R package from scratch - Blog β
A blog entry from Hilary Parker of Etsy on setting up your first R package from scratch
7.5.0.7 CRAN documentation on Creating R packages - Reference β β
Original documentation on how to create an R package from the Comprehensive R Archive Network (CRAN)
7.5.0.8 How to write your own R package and publish it on CRAN - Tutorial β β
A blog tutorial on writing and publishing R packages
7.5.0.9 R Packages (2e) - Textbook β
Learn how to create a package, the fundamental unit of shareable, reusable, and reproducible R code from Hadley Wickham and Jennifer Bryan of Posit
7.6 Visualization in R
7.6.0.1 The R Graph Gallery - Reference β
A collection of Rβs available chart types listed by purpose, and including guidance on required packages and syntax
7.6.0.2 Data Visualization in R With ggplot2 - Video β
A video course on how to create great looking, insightful data visualizations using the R package, ggplot2
7.6.0.3 Color in Data Vis - Blog β β
A feed of blog entries on the selection of beautiful, legible colors in data visualization, charts, and maps
7.6.0.4 Exciting Data Visualizations with ggplot2 Extensions - Video β β
A (1h 28m) presentation, by CΓ©dric Scherer, diving into the world of data visualization and the art of creating engaging and effective data graphics with ggplot2
7.6.0.5 Psychology of Data Visualization - Course β
An online self-paced course that examines a variety of issues related to data visualization from a largely psychological perspective, including an overview of ggplot2
7.6.0.6 Modern Data Visualization with R - Book β
A helpful and user-friendly book that walks through creating the most popular visualizations - from quick and dirty plots to publication-ready graphs
7.7 Data Wrangling in R
7.7.0.1 tidyexplain - Tutorial β
Animations of tidyverse verbs using R, the tidyverse, and gganimate
7.7.0.2 Data Wrangling with R - Textbook β
A text for all levels of R programmers, covering the various data wrangling packages: dplyr, tidyr, httr, stringr, lubridate, readr, rvest, magrittr, xlsx, readxl, and others
7.7.0.3 Data Wrangling in R - Video β
A (2h 51m) LinkedIn Learning course that provides an overview of data wrangling techniques for cleaning and transforming data
7.7.0.4 Introduction to the Tidyverse - Video β
A recorded workshop on the tidyverse and visualizing data in ggplot2 from the University of Michigan Biostatistics department
7.7.0.5 Learning the R Tidyverse - Video β
A (3h 50m) LinkedIn Learning course walking through the tidyverse approach to data science
7.7.0.6 The Quartz Guide to Bad Data - Reference β
This guide presents thorough descriptions and suggested solutions to many of the kinds of problems that you will encounter when working with data
7.7.0.7 Visualizing {dplyr}βs mutate(), summarize(), group_by(), and ungroup() with animations - Article β
Visually explore how {dplyr}βs more complex core functions work together to wrangle data
7.7.0.8 Introduction to R with Tidyverse - Course β
This course is a gentle introduction to the RStudio interface and provides the basics of the R coding language and syntax. This course is ideal for beginners with little or no prior coding experience
7.8 Programming in R
7.8.0.1 Introduction to Programming in R - Video β
A recorded workshop on functions, vectors, and iteration in R from the University of Michigan Biostatistics department
7.8.0.2 Efficient R Programming - Textbook β β
A textbook for all skill levels on making R code faster, more efficient, and scalable
7.8.0.3 Hands-On Programming with R - Textbook β
This book will teach you how to program in R, with hands-on examples.
7.8.0.4 Advanced R - Textbook β β β
An advanced textbook on metaprogramming or the idea that code is data that can be inspected and modified programmatically
7.8.0.5 R Programming for Data Science - Textbook β β
This book is about the fundamentals of R programming. With the fundamentals provided in this book, you will have a solid foundation on which to build your data science toolbox.