![]() Institute for Digital Research and Education (IDRE) at UCLA has brought together guides and tutorials on various topics such as reading data to R, subsetting data, and step-by-step instructions to analyze major public-use survey data sets with R by Anthony Damico.įree Introduction to R Programming Online Course by DataCampĪn interactive course with a lot of exercises to help you master some basic concepts of R, including factors, lists and data frames. Other R resourcesĪ few recommended resources to get you started:Ī learning path charted to help you learn R step by step to build up your confidence and competency. You can also view lessons on more specific topics, such as data manipulation, data visualization, and switching from SAS/Excel to R. Tutorials and guides for beginners and beyond LinkedIn LearningĪccess LinkedIn Learning via the SFU Library - an excellent source of tutorials on R catering to users at different levels of expertise on a variety of topics.įor example, "Learning R" will help get you to up and running on R with a variety of elementary tasks such as modifying data, charts and graphs. Information to help you install R, customize it, and keep it up to date, maintained by UCLA's Institute for Digital Research & Education.Īnswers to questions about download, installation, and licence terms, covering Linux, Mac, Unix, and Windows. R Installing, Customizing, and Updating (UCLA) Note: You don’t need RStudio to do analysis with R, but you must have R before you can use RStudio. It is also open source and free to download. It provides a user-friendly interface for R with features to make working with R easier. Downloads and installationį Windows, Mac OS X, and Linux for free from CRAN (Comprehensive R Archive Network).Īn integrated development environment (IDE) for R. ![]() Want to know more about how R has been gaining popularity in academia and industry? Read this article The Popularity of Data Science Software by data scientist Robert Muenchen on comparison between R and other statistical packages. R has a steep learning curve and while it may take a while to become proficient, you can learn R incrementally. R is open source - a benefit for those who don’t have the budget for proprietary programs like SPSS. ![]() How does R compare to other statistical packages? You can install it on every computer you use. There is also a large and enthusiastic community eager to help you out. R's Help pages include extensive documentation. (And these packages are free too!) R has robust online documentation and an active user community Packages can help you solve your specific data issues more effectively - and there are currently thousands of R packages for download, with more new packages on the way to expand what you can do with R. Packages are collections of R functions, data and code written by a very activity community of R users. R connects you to a pipeline of new packages ![]() GUI(Graphical User Interface) statistical packages offer point-and-click operations that ease your learning, but they might also blind you to underlying mechanism of your analysis.īy contrast, R gives you the opportunity to code by hand, enabling you to understand the fundamentals of operations, which in turn facilitates your future learning. R is more hands-on and facilitates learning Not only do you make your own work more effective and efficient, but also make your data analysis replicable and transparent to other researchers and the public. With R, it is easier to document, reuse, and reproduce all the steps of your statistical analysis, compared to other statistical packages. It's been gaining popularity rapidly across a variety of disciplines.įor a fuller description of R, see What is R? from the R Project. R is a free, open-source programming language primarily for statistics and graphics.
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