I'd like to thank Cam O'Neill and Daniel Cohen (no relation) for writing up the brief descriptions of most of these resources.

Free digital copy of a book covering a variety of topics demonstrating useful, time-saving skills for R data science. The website starts with visualization and transformation for data that has already been imported and tidied, then moves on to programming tools. Also covers modeling challenges. Each chapter begins with a discussion of the big picture for each set of tools and provides exercises to reinforce what the chapter has taught.

Free digital copy of a book for data scientists using R. Book does not require pre-existing knowledge (e.g., in algebra, calculus, or programming). Gives readers the ability to explore their first dataset in R, introducing important topics such as ggplot2 package for visualizing data, skills to model data using basic and multiple regression functions, and statistical inference (i.e. variability, confidence intervals, hypothesis tests). The website goes through “data stories” that use data and thoughtful visualization to answer engaging questions such as the relationship between per capita income and crime in Chicago neighborhoods. Another main goal of the website is for users to understand the importance of reproducible research (i.e., being able to pass one’s data sets and analysis code to someone else and have them find the same conclusion).

This is the website for the 2nd edition of “Advanced R”, which is intended for R users to advance their programming skills and further their knowledge of the R language. This website is designed for programmers who use other languages in order to help them understand the intricacies of R. As such, to best use this website, readers should have already written a good amount of code in R. The website encourages “meta-techniques” such as reading source code to see things that are worth incorporating into one’s own code/seeing what makes high-quality R code and adopting a scientific mindset to help understand what to do if something doesn’t work when using R (i.e. developing a hypothesis, running experiments, recording them , and showing others what you tried). The website also covers three main resources for when you’re stuck in R: RStudio Community, StackOverflow, and R-help mailing list.

Website for a free book (with associated code, data) that covers material for an introductory statistics class. The course was created for undergraduate psychology students learning to use R statistical software. The book introduces readers to manipulating data and writing scripts in R. Covers descriptive statistics, graphing, probability theory, t-tests, ANOVAs, regression, and Bayesian statistics while explaining how to perform related analyses in R.

Website that offers basic information about R such as how to install the software, import data, how to enter inputs into the console, assign variables, etc. The bottom of the first page has links to other core R information including basic data types, vectors, matrices, and data frames.

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Link to the adventr package - has multiple interactive R tutorials alongside chapters of Field’s textbook Adventure in Statistics: the reality enigma. This page also provides a summary of the contents of adventr, and instructs readers how to install adventr packge, R and RStudio. In addition, Fields describes different ways to work with and customize Rstudio.

Link to online version of by Nathaniel D. Phillips’ book which introduces readers to basic analytical tools in R, such as coding, data wrangling, plotting, and statistical inference. This book begins with instructions on how to install R, its packages, write code, and “debug” problems when your code doesn’t run. Phillips moves onto describing the how to create vectors, matrices, dataframes, and plots in R and then explains more advanced operations such as regressions, ANOVAs, and loops.

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This article provides the historical background of R, such as the “Intentional ambiguity” in defining R, which allowed it to fill a void through its flexibility and ability to program whatever a user needed. Discusses how R allows users to develop new things (i.e. packages) on the software, and reviews developments in R that have made it easier to use (i.e. tidyverse and ggplot2 packages) and the author’s argument for why to use R (reproducibility, graphics, R packages and community, and RStudio).

Link to GitHub page for “sharing knowledge and material for teaching R courses!” The site contains courses and a description of “The Open-R Project!” for teaching R based courses

GitHub page with resources on how to teach R. The “docs” folder has slides designed to show instructors how to create fun R courses. More specifically, the slides have general information about R, tips for teaching R (such as going straight to practical skills rather than technical details), cool, funny, and unexpected examples for use of R, and links with more teaching resources. The other folders contain links to more resources, cool and unorthodox examples in R, and administrative information.

This page describes the plan for Danielle Navarro’s past workshop (which was tailored for beginners and old school programmers in R).  Below are links for slides and corresponding GitHub content for the workshop. The slides instruct readers how to install R, RStudio, and tidyverse, use important functions such as “pipe”, group, summarize, filter, and mutate, visualize data, and gather and spread data. The GitHub link contains files such as datasets, outputs, and scripts for students to practice and learn with. 

Resources including a link to the Github page from Cambridge University that the workshop was based upon. The Cambridge Basic R Course link has an introduction to R, tutorials on data structures, analysis, plotting, statistics, data manipulation, and plotting along with further resources at the bottom of the bage. The GitHub page itself contains a wide area of files consisting of the zip folder for the Basic R course and other R scripts, data files, and solutions relating to the course.

This cheatsheet has useful information for data wrangling such as how to tidy, reshape, summarize, and combine data and make new variables.

This page has three links to different R workshops on data wrangling, visualization, and tidyverse. The data wrangling Github describes prerequisites for the workshop, important final steps, and an outline for the workshop online with numerous folders containing the workshop content in data sets and scripts. The visualization Github is structured in the same way and the tidyverse Github is structured similarly.

This page titled “A Gentle Introduction to tidymodels” by Edgar Ruiz has handy visual models on how to use tidyverse along with comprehensive written information. As such, the written portion lays out functions from tidyverse packages and goes through the steps for operations such as how to load tidyverse, transform data, preprocess data, and validate models. 

This page introduces readers to the “purrr” function in R, which is an alternative solution to loops that allows users to read and clean data, run models, and make tables and plots. Beck explains the nature of nested data frames, describes how to perform some purrr operations such as the map function, unnest function, and then goes into specifics on how to create plots and predict values.

This page has links to download various cheatsheets that focus on writing and debugging R code, importing, transforming, and visualizing data, and applying functions through packages such as “purrr”.

by LITTLE MISS DATA - Laura Ellis. This article highlights the vast number of R packages and introduces useful resources for installing R. The author provides a flow chart as a basic troubleshooting guide for installing packages.

This page has links to various articles about R such as a list of machine learning packages, how to predict baseball game attendance with R, and how to quickly categorize messy data. Other articles on this page compare R to Excel for data analysis, walk through how to visualize data with the “ggplot2” function, and show readers how to work with CSV files in R. 

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