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The R Project for Statistical Computing is a free, open source statistical computing language that is popular among researchers in many fields.
The learning curve for true understanding is steep, but specific tasks (e.g., importing files from other statistical software) are often quite easy because of packages that can be created by anybody. R undergoes rapid development and improvement. Tutorials even a year old may be out of date.
Note that RStudio is currently in the process of changing names to Posit. Various tutorials may reference one or the other, but both are acceptable at this time.
See the slides from our Getting Started with R (pdf) workshop for an overview and recommendations.
RStudio = Posit (the company is changing names)
Video walk-through specifically for Windows or Mac (OpenIntro)
Learn R in 39 minutes (39 min, Equitable Equations) - Good overview of getting started and key functions in R, but goes quickly and may use terminology some are unfamiliar with.
R for Data Science by
Use R to turn data into insight, knowledge, and understanding. With this practical book, aspiring data scientists will learn how to do data science with R and RStudio, along with the tidyverseâ??a collection of R packages designed to work together to make data science fast, fluent, and fun. Even if you have no programming experience, this updated edition will have you doing data science quickly. You'll learn how to import, transform, and visualize your data and communicate the results. And you'll get a complete, big-picture understanding of the data science cycle and the basic tools you need to manage the details. Updated for the latest tidyverse features and best practices, new chapters show you how to get data from spreadsheets, databases, and websites. Exercises help you practice what you've learned along the way. You'll understand how to: Visualize: Create plots for data exploration and communication of results Transform: Discover variable types and the tools to work with them Import: Get data into R and in a form convenient for analysis Program: Learn R tools for solving data problems with greater clarity and ease Communicate: Integrate prose, code, and results with Quarto
This book is one of the best resources and good to either read or refer to. However, non-programmers who want help with the very first steps should look elsewhere.