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As an interpreted language, R has a native command line interface. Moreover, multiple third-party graphical user interfaces are available, such as RStudio—an integrated development environment—and Jupyter—a notebook interface. CRAN Task Views are documents that summarize R resources on CRAN in particular areas of application, helping your to navigate the maze of thousands of CRAN packages.
Packages
The R Foundation for Statistical Computing was founded in April 2003 to provide financial support. The R Consortium is a Linux Foundation project to develop R infrastructure. Available for installation are various integrated development environments (IDE). IDEs for R include R.app (OSX/macOS only), Rattle GUI, R Commander, RKWard, RStudio, and Tinn-R.
Choose your learning path
It can be particularly helpful to paste an error message into a search engine to find out whether others have solved a problem that you encountered. R has its own LaTeX-like documentation format, which is used to supply comprehensive documentation, both on-line in a number of formats and in hardcopy. If you have questions about R like how to download and install the software, or what the license terms are, please read our answers to frequently asked questions before you send an email.
Vignettes and Code Demonstrations: browseVignettes(), vignette() and demo()
R is a language and environment for statistical computing and graphics. It is a GNU project which is similar to the S language and environment which was developed at Bell Laboratories (formerly AT&T, now Lucent Technologies) by John Chambers and colleagues. There are some important differences, but much code written for S runs unaltered under R. The core R language is augmented by a large number of extension packages, containing reusable code, documentation, and sample data. We have created three tracks to help learners navigate the R ecosystem.
R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, …) and graphical techniques, and is highly extensible. The S language is often the vehicle of choice for research in statistical methodology, and R provides an Open Source route to participation in that activity. Packages add the capability to implement various statistical techniques such as linear, generalized linear and nonlinear modeling, classical statistical tests, spatial analysis, time-series analysis, and clustering. There are internet search sites that are specialized for R searches, including search.r-project.org (which is the site used by RSiteSearch) and Rseek.org. We prefer to think of it as an environment within which statistical techniques are implemented. There are about eight packages supplied with the R distribution and many more are available through the CRAN family of Internet sites covering a very wide range of modern statistics.
All these features help you think about problems as a data scientist, while supporting fluent interaction between your brain and the computer. Before posing a question on one of these lists, please read the R mailing list instructions and the posting guide. Before asking others for help, it’s generally a good idea for you to try to help yourself. R includes extensive facilities for accessing documentation and searching for help. There are also specialized search engines for accessing information about R on the internet, and general internet search engines can also prove useful (see below).
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One of R's strengths is the ease of creating new functions.[26] Objects in the function body remain local to the function, and any data type may be returned. R is a free software environment for statistical computing and graphics. It compiles and runs on a wide variety of UNIX platforms, Windows and MacOS. It is also possible to use a general search site like Google, by qualifying the search with “R” or the name of an R package (or both).
What else should I study if I am learning R?
Learn how to code and clean and manipulate data for analysis and visualization with the R programming language. The R language has built-in support for data modeling and graphics. The following example shows how R can generate and plot a linear model with residuals.
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Help operator in R provide access to the documentation pages for R functions, data sets, and other objects, both for packages in the standard R distribution and for contributed packages. To access documentation for the standard lm (linear model) function, for example, enter the command help(lm) or help("lm"), or ? We think R is a great place to start your data science journey because it is an environment designed for data science. R is not just a programming language, but it is also an interactive ecosystem including a runtime, libraries, development environments, and extensions.
The R Journal is an open access, academic journal which features short to medium-length articles on the use and development of R. It includes articles on packages, programming tips, CRAN news, and foundation news. The Bioconductor project provides packages for genomic data analysis, complementary DNA, microarray, and high-throughput sequencing methods. The R programming language may be one of the languages you ran into in your search. To help you decide, let’s take a look at why someone would want to learn R, what it is used for, and how easy it is to learn.
Vignettes may also be accessed from the CRAN page for the package (e.g. survival), if you wish to review the vignette for a package prior to installing and/or using it. One of R’s strengths is the ease with which well-designed publication-quality plots can be produced, including mathematical symbols and formulae where needed. Great care has been taken over the defaults for the minor design choices in graphics, but the user retains full control.
R, like S, is designed around a true computer language, and it allows users to add additional functionality by defining new functions. Much of the system is itself written in the R dialect of S, which makes it easy for users to follow the algorithmic choices made. For computationally-intensive tasks, C, C++ and Fortran code can be linked and called at run time. Advanced users can write C code to manipulate R objects directly.
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