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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 Tutorials even a year old may be out of date.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.
No installations needed; runs in your web browser.
R for Data Science by
Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle--transform your datasets into a form convenient for analysis Program--learn powerful R tools for solving data problems with greater clarity and ease Explore--examine your data, generate hypotheses, and quickly test them Model--provide a low-dimensional summary that captures true "signals" in your dataset Communicate--learn R Markdown for integrating prose, code, and results
See also R for Data Science: Exercise Solutions by Jeffrey B. Arnold
Many people will ultimately learn both. But, they are similar enough that you do not want to learn them at the same time--it can get confusing to switch back and forth. Knowing either one will help you learn the other. So, just pick one and get started!
If you already know another statistical software or programming language, you might try these first.
