R is an open source programming language and software environment for statistical computing and graphics. It consists of a language together with a run-time environment with a debugger, graphics, access to system functions, and scripting.
R is an implementation of the S programming language, developed by Bell Laboratories, adding lexical scoping semantics. R offers a wide variety of statistical and graphical techniques including time series analysis, linear and nonlinear modelling, classical statistical tests, classification, clustering, and more). Combined with a large collection of intermediate tools for data analysis, good data handling and storage, general matrix calculation toolbox, R offers a coherent and well developed system which is highly extensible.
Many statisticians and data scientists use R with the command line. However, the command line can be quite daunting to a beginner of R. Fortunately, there are many different graphical user interfaces available for R which help to flatten the learning curve. We’ve restricted this group test to software that’s released under an open source license, and offers Integrated Development Environment (IDEs) facilities. Software like Jupyter Notebook and Radiant interface with R, but they are not IDEs.
Read our interactive tutorial for data science using R and RStudio. No programming knowledge required.
To provide an insight into the quality of software available for Linux, we have compiled a list of 7 of the best graphical user interfaces for R. Hopefully, there will be something of interest for anyone who wants to quickly get to grips with this programming language and environment. We give our highest recommendation to RStudio.
Here’s our verdict for each application.
Now, let’s explore the 7 graphical user interfaces tools at hand. For each application we have compiled its own portal page, a full description with an in-depth analysis of its features, screenshots, together with links to relevant resources.
| Graphical User Interfaces for R | |
|---|---|
| RStudio | Professional software for R with a code editor, debugging & visualization tools |
| Rattle | R Analytic Tool To Learn Easily: Data Mining using R |
| StatET for R | Eclipse based IDE (integrated development environment) for R |
| RKWard | Easy to use and easily extensible IDE/GUI |
| JGR | Universal and unified graphical user interface for R |
| R Commander | A Basic-Statistics GUI for R |
| Deducer | Intuitive, cross-platform graphical data analysis system |
 Read our complete collection of recommended free and open source software. Our curated compilation covers all categories of software. The software collection forms part of our series of informative articles for Linux enthusiasts. There are hundreds of in-depth reviews, open source alternatives to proprietary software from large corporations like Google, Microsoft, Apple, Adobe, IBM, Cisco, Oracle, and Autodesk. There are also fun things to try, hardware, free programming books and tutorials, and much more. |
The TIOBE Programming Community index is an indicator of the popularity of programming languages. At the time of writing, R ranks 12th on that index. But this doesn’t reflect R’s popularity in data science.
Want to learn R? Check out these excellent open source R books.