Research Guides: Research Software: R

What is R?

R is a free, open-source programming language primarily used for statistical computing and data visualization. R is a popular and powerful tool for researchers in any field working with data, as it allows users to perform complex data analysis including data cleaning, data manipulation using a wide range of statistical techniques. R is also well-liked for its ability to create publication-quality data visualizations. Numerous user-developed packages are available for use with R; these packages add specialized statistical methods and graphical features.

Because R is a scripted language, researchers can more easily ensure that their analyses are reproducible.

R is made available through CRAN (the Comprehensive R Archive Network) and is freely available for Windows, MacOS, and Linux.

There are several apps and environments available for coding in R. A popular application for working in R is RStudio, which is available from Posit.

Downloading R/RStudio

To install R on your computer:

Download R and RStudio from Posit.co. Make sure to follow both Step 1 (Install R), then Step 2 (Install RStudio). RStudio is an integrated development environment (IDE) for R which provides users some additional features for programming in R with an easy-to-navigate graphical user interface.
After installing R and RStudio, open the RStudio app on your desktop.
It is recommended that Windows users also install RTools (you can do this later).

You can also use R on the cloud through your browser, at posit.cloud. With the free version, there are some limits on memory, computation hours per month, etc.

Finding and Getting Help Using R at GW

R is free to install on Windows, Mac, and Linux computers, and R is also available for students, staff, faculty, and affiliates within GW's Virtual Computer Lab (VCL), provided by GW Information Technology.

GW Libraries offers workshops to help you get started learning coding, including R and Python. We also offer the following types of individual consultations:

Coding Consultations with a software developer librarian skilled in R or Python programming.
Data consultations with graduate students in Academic Commons who are skilled in statistics and statistical analysis using R, Python, SAS, SPSS, STATA, and Excel.

R for Beginners

Data Carpentry and Software Carpentry online workshop materials provide an excellent way to learn R in a free, self-paced fashion. Recommended Carpentries materials for beginners to learn R include the following:

Data Analysis and Visualisation in R for Ecologists
In this workshop, you will learn R basics, how to read a CSV-formatted data file into R, how to work with factors for categorical data, how to work with date data, how to subset data using the dplyr package, and how to create data visualizations using the ggplot2 package. (Episode 6, on SQL, is not essential for learning R.) You do not need to be an ecologist to benefit from this workshop!
Data Analysis and Visualization with R for Social Scientists
In this workshop, you will learn R basics, working with missing data, how to read a CSV-formatted data file into R, how to subset data using the dplyr package, how to pivot data using the tidyr package, how to export data, and how to create data visualizations using the ggplot2 package. You do not need to be a social scientist to benefit from this workshop!

GW's very own Professor John Paul Helveston has created excellent resources for learning R:

R for Analytics Primer
This site provides a foundation in using the R programming language for data analytics. It assumes no prior coding experience.
John Paul Helveston's "Programming for Analytics" course in R programming
This course teaches the fundamentals of programming (operators, data types, objects, functions, conditionals, loops, strings, testing, and debugging) as well as techniques for working with data sets in R and Python (file input/output, data structures, data wrangling, data visualization, external packages, and reproducible reporting).

If you prefer learning from an e-book, a detailed, yet excellent introduction to R for data science is the following:

R for Data Science (2nd edition)
This book by Hadley Wickham et al. will teach you the most important tools in R that will allow you to do data science efficiently and reproducibly. You will learn to use R to import, tidy, transform, model, and visualize data, as well as how to use the Quarto authoring framework to reproducibly communicate your data work.

Advanced R

Advanced R (2nd edition)
As its name suggests, this online book is more advanced, but the reader will gain a deeper understanding of how the R language really works.

R for Data Visualization

ggplot2: Elegant Graphics for Data Analysis (3rd edition)
The primary focus of this book by Hadley Wickham is explaining the Grammar of Graphics that ggplot2 uses. Although its focus is on the details of the underlying theory, the book contains many reusable R code examples.
R graphics cookbook (2nd edition)
This free online book by Winston Chang provides more than 150 recipes to help you quickly generate high-quality graphs, mostly using the ggplot2 and dplyr R packages.
R Graph Gallery
With over 400 examples organized into nearly 50 chart types, this collection displays charts along with their code so that you can reproduce them and adapt to your own work.
Data Visualization: A Practical Introduction
This online book by Kieran Healy is a hands-on introduction to the principles and practice of looking at and presenting data using R and ggplot.

R for Statistics

Elementary Statistics with R
This site teaches basic statistical concepts and also implements them in R.
UCLA Data Analysis Examples
Examples illustrating the application of different statistical analysis techniques using R, as well as Stata, SAS, SPSS, and Mplus. Includes many different types of regression, power analyses, and more.

R for Authoring Documents and More

R Markdown and Quarto allow you to mix narrative and code to create reproducible documents, web sites, presentations, and more. R Markdown and Quarto can even include code from numerous languages beyond R, such as Python, C++, Julia and many more.

RMarkdown
This is the main RMarkdown site from posit.co with information on getting started, gallery (with code), and how-to articles.
R Markdown: The Definitive Guide
This online book by Yihui Xie et al. can be read as a textbook but can also serve as a reference guide for specific how-to topics in RMarkdown. It contains RMarkdown basics as well as sections on how to create documents, presentations, dashboards, websites, and more.
Quarto
Quarto is an open-source scientific and technical publishing system. It is a multi-language, next generation version of R Markdown, with many new new features and capabilities. It can also be used with Python Jupyter notebooks, with Julia, and with Observable JS.

RShiny - Create Interactive Data Visualizations

Shiny is a framework for creating web applications using R or Python code. It is designed primarily with data scientists in mind. You can create rShiny apps with no knowledge of HTML, CSS, or JavaScript.

Mastering Shiny: Build interactive Apps, Reports, and Dashboards
This comprehensive online book by Hadley Wickham teaches you how to create a Shiny app using R and RStudio. Includes examples you can use and adapt.
shiny.posit.co
Main site for all things Shiny. Contains tutorials, a gallery of examples (with code), and how-to articles.

R for various applications

Text mining with R, tutorials:

Text Mining Tutorials in R
A collection of text mining course materials and tutorials developed for humanists and social scientists interested to learn R.
Text Mining with R: A Tidy Approach
This book by Julia Silge and David Robinson serves as an introduction to text mining using the tidytext package and other tidy tools in R. Sections include sentiment analysis, word and document frequency, n-grams and correlations, topic modeling, and more.
The Programming Historian
A collection of novice-friendly, peer-reviewed tutorials to help humanists learn a wide range of digital tools, techniques, and workflows to facilitate research and teaching. Several of these tutorials use R. Materials available in English, Spanish, and French.

R for geospatial analysis, tutorials:

Data Carpentry Geospatial workshop
This Data Carpentry workshop, co-developed with the National Ecological Observatory Network (NEON), focuses on working with geospatial data - managing and understanding spatial data formats, understanding coordinate reference systems, and working with raster and vector data in R for analysis and visualization.
Spatial Data Science with R and “terra”
This online tutorial contains modules that teach spatial data analysis and modeling with R, including some case studies.
Spatial Data Science with Applications in R
This online book, by Edzer Pebesma and Roger Bivand, introduces the concepts behind spatial data, coordinate reference systems, spatial analysis, and introduces a number of R packages, including sf, stars, s2, and lwgeom, as well as a number of spatial tidyverse extensions, and a number of spatial analysis and visualisation packages that can be used with these packages.

R can even be used to create generative art:

Art from Code
This free online workshop by Danielle Navarro provides a hands-on introduction to generative art in R. You’ll learn artistic techniques that generative artists use regularly in their work including flow fields, iterative function systems, and more. You’ll also learn about R packages specialized for generative art.
Getting started with generative art in R
A useful article that highlights a few of the key R packages for creating generative art in R

Here is a great resource if you are looking for packages that might be useful in your analysis:

R-Universe
R-universe is a great place to start if you're looking to search for R packages relevant to a particular term. R-universe contains a searchable catalog of software, articles, and datasets. If you're looking to develop your own package, you can also use R-universe to develop and publish your package.

R Learning Resources and Communities

LinkedIn Learning
LinkedIn Learning offers video-based learning on a variety of coding topics, including R. GW users will be prompted to log in with GW credentials. As of January 2024, LinkedIn Learning offers 36 courses on R programming, consisting of over 1,200 videos.
Talk recordings and workshop materials from Posit conference 2023
Conference talk video recordings and online workshop from the 2023 Posit conference. There are recordings from 108 talks and materials from 18 workshops (scroll further down for workshops).
R for Data Science Slack community
The R4DS Online Learning Community is a diverse, friendly, and inclusive community of over 10,000 R learners at all skill levels working together to improve our skills.
R-Ladies DC Meetup
R-Ladies D.C. is the Washington, D.C. chapter of the global R-Ladies organization. The goal of the R-Ladies organization is to promote Gender Diversity in the R statistical computing community.
R-bloggers
An online community of bloggers contributing tutorials, learning materials, and R-related news for a variety of different disciplines.
R Cheatsheets
Cheatsheets for R and popular R libraries. In addition to cheatsheets created by Posit, this site also offers contributed cheatsheets, and cheatsheets translated into numerous languages.
O'Reilly Online Learning
Online collection of ebooks, videos, and learning paths including many about R programming. GW users will be prompted to log in using GW credentials in order to access this resource.