• Getting help with R: Instructions for how to get documentation in R. 
• RStudio support document: You can find FAQs and how-to articles in this site. 
• RDocumentation in DataCamp: With RDocumentation, a searchable encyclopedia for R packages, users can find the package of their interest on CRAN, Bioconductor, and GitHub by looking for its name, functions, or keywords.  

An R cheat sheet is a collection of R functions in a package or for doing a task. You can use it to quick find a function that you need while coding with R. 

• RStudio cheat sheets: The RStudio cheat sheets depository contains reference cards on a wide variety of topics for R. You can also use this template to create your own cheat sheet and submit to RStudio. 
• DataCamp cheat sheets: The DataCamp cheatsheets depository maintains reference cards for a variety of specific topics in R and Python. 
• Other cheat sheets not included in RStudio or DataCamp
  • Base Graphics: Click here for a cheat sheet about R base graphics. 
  • ggplot2 quick reference: Click here for a reference guide to ggplot2 organized by topic types. 
  • ​My Data Cleaning in R: My Data Cleaning in R lists functions frequently used by journalists in R, with a special focus on data wrangling and visualization.  

• R for Data Science by Hadley Wickham and Garrett Grolemund: This book teaches absolute beginners of R how to turn raw data into analyses and graphs. It introduces the readers to R, RStudio, and a collection of packages designed to make data science fast, fluent, and fun.     
• R Programming for Data Science by Roger Peng: This book introduces its readers to the fundamentals of R, such as how to manipulate datasets, how to write functions, and how to debug and optimize code.     
• Cookbook for R by Winston Chang: The Cookbook for R contains solutions to common tasks and problems in analyzing data in R. Each chapter addresses a specific problem type, making it fast and easy to locate information. 
• R Graphics Cookbook by Winston Chang: The R Graphics Cookbook is a practical guide for generating high-quality graphics. Users can find answers to their problems with ease thanks to the book’s problem-solution centered layout .  
• R for Everyone by Jared P. Lander: This book is written for people new to statistical programming and modeling. With extensive hands-on practice, this guide focuses on the R functionality which non-specialists need to accomplish their data tasks.  
• RMarkdown: The Definitive Guide by Yihui Xie, J. J. Allaire, Garrett Grolemund: A comprehensive reference book for RMarkdown, written by the author of the RMarkdown package.  
• Reproducible Research with R and RStudio by Christopher Gandrud: Bringing together computational research tools in one accessible source, Reproducible Research with R and RStudio teaches its readers how to create dynamic and highly reproducible research. Suitable for researchers in any quantitative empirical discipline, the book presents a range of practical tools for data collection, data analysis, and the presentation of results. 
• Reproducible Medical Research with R by Peter D.R. Higgins: An R book for people who conduct medical research and want to make their research shareable and reproducible. The first few chapters of the book talk about basic knowledge for starting to use R for data analysis and follow by topics specifically for people who do medical/clinical research. Later, it covers data visualization, modeling, regression and RShiny. 
• Happy Git and GitHub for the useR by Jennifer Bryan: A book to guide R users to use Git to do version control of their code and to share on GitHub.  

And many more on JHU libraries website or online! Use the Sheridan Libraries online catalogue to find resources for learning R!  

• R-bloggers: is a collection of blogs about R (in English). Updated daily, it connects R bloggers with users in a virtual global community.   
• Simply Statistics: is a blog where three biostatistics professors, Jeff Leek, Roger Peng, and Rafa Irizarry, post their ideas, share inspiring articles, and participate in scientific discussions.  
• Where to get help with R?: A blog post about resources to get help with R: In the blog “Where to get help with R?” Maëlle Salmon outlines the steps for solving R-related problems. Besides providing the readers with online resources, Salmon teaches them how to identify the appropriate online forum and community to ask questions.  
• The R Graph Gallery: A collection of data visualization work: The R Graph Gallery contains over 400 charts with their reproducible code and explanations.  
• A good primer on creating graphs by DataCamp: For a quick primer on how to create graphs in base R, click here.  
• Anatomy of ggplot2: Slides for introduction to ggplot2: Psychology of Data Visualization is a course investigating the contributing factors of successful visualization. In the Introduction to ggplot2, students learn how to produce effective visualization with a graphic package of R, ggplot2.   
• Reproducible Research in R by NCEAS: The Introduction to Open Data Science teaches a wide audience how to conduct open and reproducible research. It covers the topics of coding, collaborating, and publishing with R. 
• Youtube channel by Roger Peng: Find tutorial videos and discussions of the latest topics in data science on the Youtube channel of Roger Peng, professor of biostatistics at the Johns Hopkins Bloomberg School of Public Health. 
• Google’s R style guide: Want to make your code easy to read, share, and verify? Follow this Google’s Style Guide for R.  
• ROpenSci: R for Open Science is an organization to prompt open science to R users. It provides an online platform for researchers to share data and reusable software written in R. It features an ecosystem of open source tools and reviewed software that is developed by the community. 
• The R-Podcast: A podcast providing tips and valuable information for R users. 

• JHU Data Services provides a series of R workshops regularly. Check out the schedule. 
• Cloud Base Data Science by Jeff Leek (SPH): Cloud Based Data Science is a free online course offered by Johns Hopkins University. This course helps people with no background or limited resources transition into data science.  
• R Programming course on Coursera by JHU: This course is created by several Biostatistics Department faculty, Roger Peng, Jeff Leek and Brian Caffo. This course is one of their 10 Data Science Specialization courses. You can access it for free with your JHED. 
• Swirl: Interactive R learning package: Swirl is a free software package for beginners of R. The Swirl learning environment is totally self-paced, which means users proceed through the lessons (usually 10-20 minutes long) at their own speed. With Swirl, users learn the basics of R in an interactive environment, where they receive immediate feedback at each step of the lesson. 
• Introduction to R by John Muschelli (SPH):  Introduction to R  is a 5-day online course, which consists of 2 or 3 hour-long modules each day. Students learn how to analyze data through data input/output, data management, data manipulation, and data visualization. 
• DataQuest:  Paid/Free data science courses: DataQuest offers free and paid online courses for programmers at different levels. Users can also follow the curated sequence of classes which is tailored to meet different learning goals. 
• Linkedin Learning: Access via my.jh.edu/Education: JHU employees can now access more than 13,000 LinkedIn Learning courses covering topics from data science to management skills. Sign up for free with your JHED account today! 
• Flowingdata: Data visualization tutorials: Brought to you by data visualization expert Nathan Yau, Flowingdata offers a combination of free and paid resources for data visualization. 
• Accessing and Analyzing Census Data Using R by the University of Michigan: Three workshops ran by the University of Michigan about analyzing census data using R. Topics include: Accessing and Analyzing US Census Data in R, Spatial Analysis of US Census Data in R, and Analyzing US Census Microdata in R. Here are workshop materials on GitHub and recordings. 
• DataCamp tutorials: Online tutorials created by DataCamp. It covers several programming languages, mainly R and Python. There are also some openly available courses about R and Python. 
• RStudio's webinars: Available on RStudio's official website. A collection of various topics, from beginners to advanced levels, about using RStudio and packages. There are also live webinars available. 
• RStudio Education: A comprehensive collection of R learning resources, grouped by your skill levels. In addition, there are teaching materials if you plan to teach R. 
• A list of R learning resources by Lucy D'Agostino McGowan. 

• userR! conference: An annual R conference for R users and developers. It is supported by the R Foundation. You can view their conference page this year, 2022, here or past conference recordings here.  
• RStudio: The rstudio::conf is an annual conference for R and RStudio users. It is sponsored by RStudio. You can find the event website for this year, 2022, here. You can also access recordings from past conferences on their RStudio Webinars website (go to the sidebar on the left choose "Conferences by year").  
• R/Medicine: An annual conference for R users in medical fields. It is sponsored by the Linux Foundation. This year's conference information can be found here. Access 2021's conference recordings here. 
• R-Ladies Baltimore: This is a meetup group in Baltimore and you don't have to be a lady to attend their events! Here are a list of their events, recordings and materials from their past events.