R is a programming language meant for statistical computing and data science.
- Build and debug modern web and cloud applications. Code is free and available on your favorite platform - Linux, Mac OSX, and Windows. RStudio and Visual Studio Code are primarily classified as 'Integrated Development Environment' and 'Text Editor' tools respectively.
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Using Rtools40 on Windows. Starting with R 4.0.0 (released April 2020), R for Windows uses a brand new toolchain bundle called rtools40. This version of Rtools upgrades the mingw-w64 gcc toolchains to version 8.3.0, and introduces a new build system based on msys2, which makes easier to build and maintain R itself as well as the system libraries needed by R packages on Windows.
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R programming is a software supported by R foundation for statistical computing and non-profit making organization.
Being a statistical software package, it has increased in popularity among data scientists and data miners who use R for data mining surveys and data analysis. Its source code was primarily written in C, Fortran and R languages.
R programming is freely available for the public under a GNU license. R has pre-compiled binary versions for common operating systems.
R can be run in the command line for terminal nerds and graphical user interfaces in integrated development environments. Today, I am going to list down 11 best R programming IDE. Have a look at them below!
1.RStudio
RStudio was developed by RStudio Inc. founded by JJ Allaire.
RStudio is available in two formats, the one run locally as a desktop application known as RStudio Desktop and RStudio Server which allows access to RStudio through a web browser while running remotely on Linux server.
RStudio is available under a free GNU AGPL v3 license, an open-source license that guarantees freedom for sharing the code.
RStudio is available in the pre-packaged distribution for Windows, Linux, and macOS, while RStudio Server can run on Ubuntu, Debian, Linux, SLES, OpenSUSE and CentOS.
2.R Tools for Visual Studio
Visual Studio being a powerful IDE for coding has brought along amazing experience for R programmers.
You can now enjoy features of IDE even when writing R programs with the newly released RTVS a product released by Microsoft under free and open-source MIT license.
3.Rattle
Rattle is a popular graphical user interface for data mining in R programming language. It presents visual data summaries and the statistical data and it can model and transform data in supervised and unsupervised machine learning models.
The beauty of Rattle is its main feature which captures GUI interactions in R scripts executable in R independently.
It can be useful as a learning tool to develop R skills and later fine-tune models in Rattle to R for more powerful data modeling options
4.StatET for R
StatET is an eclipse based IDE for R programming.
It provides a set of unmatched tools for R code writing and package building. Features include integrated R console, Object browser, and R help, and its support for multiple local and remote installations.
StatET is a plugin for Eclipse IDE, therefore it can be combined with a range of other tools on top of Eclipse platform.
StatET is an open-source software that runs on most operating systems.
5.ESS
ESS stands for Emacs Speaks Statistics, an add-on package for GNU Emacs. ESS is designed to support scripts and interactions with statistical analysis programs like R, S-Plus, SAS, Stata, and OpenBUGS-JAGS.
ESS is beneficial when used by professionals who analyze text-based scripts in different operating systems. It provides a more sophisticated graphical user interface.
Aside from supporting different statistical programs, ESS provides keybindings, abbreviations, code formatting, syntax highlighting, commenting, script submitting and displaying results. ESS is freely available under the GNU license.
6.Tinn-R
Tinn-R is a text editor or word processor, ASCII & UNICODE for Windows operating system, with integration with R.
It has user interface characteristics and at the same time an IDE characteristic. Its sole purpose is to facilitate learning R and provide an environment for statistical computing.
7. R AnalyticalFlow
R AnalyticalFlow is a data analysis integrated development environment for R statistical computing.
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It has an intuitive user interface with advanced R features for R experts, which allows for the sharing of analysis processes amongst several users of different R proficiency levels. It works on Windows, Linux and Mac operating systems.
It is available for free under the GNU license.
8. Radiant
Radiant is an open-source and platform-independent browser-based interface that is used for business analysis in R programming. It is based on the Shiny package with the option to run locally or on a server. Radiant was developed by Vincent Nijs.
9.RBox
RBox is an integrated R package for the Atom editor.
This package has a collection of several packages for running R on Atom editor. Its features include the ability to use Hydrogen to execute a line, a selection and a block of code at a time.
It has access to several terminals and has useful snippets for running R. RBox is available under MIT license.
10. NVim-R
NVim-R is a plugin for Vim to work with R programming.
It improves Vim's support for editing R code. It has Omni completion for objects and function arguments for R.
It has the ability to see R documentation in Vim's buffer and it is highly customizable.
11. r4intelliJ
r4intelliJ provides R language support for IntelliJ IDEA.
This integration helps IntelliJ IDEA offer support for R statistical computing.
IntelliJ IDEA is one of the best IDE aims to bring onboard one of the best statistical computing languages for data mining and modeling.
conclusion
R programming is one of the popular statistical and data mining language available and it is open-source, it makes sense to you as well choose an open-source IDE.
RStudio is available under a free GNU AGPL v3 license, an open-source license that guarantees freedom for sharing the code.
RStudio is available in the pre-packaged distribution for Windows, Linux, and macOS, while RStudio Server can run on Ubuntu, Debian, Linux, SLES, OpenSUSE and CentOS.
2.R Tools for Visual Studio
Visual Studio being a powerful IDE for coding has brought along amazing experience for R programmers.
You can now enjoy features of IDE even when writing R programs with the newly released RTVS a product released by Microsoft under free and open-source MIT license.
3.Rattle
Rattle is a popular graphical user interface for data mining in R programming language. It presents visual data summaries and the statistical data and it can model and transform data in supervised and unsupervised machine learning models.
The beauty of Rattle is its main feature which captures GUI interactions in R scripts executable in R independently.
It can be useful as a learning tool to develop R skills and later fine-tune models in Rattle to R for more powerful data modeling options
4.StatET for R
StatET is an eclipse based IDE for R programming.
It provides a set of unmatched tools for R code writing and package building. Features include integrated R console, Object browser, and R help, and its support for multiple local and remote installations.
StatET is a plugin for Eclipse IDE, therefore it can be combined with a range of other tools on top of Eclipse platform.
StatET is an open-source software that runs on most operating systems.
5.ESS
ESS stands for Emacs Speaks Statistics, an add-on package for GNU Emacs. ESS is designed to support scripts and interactions with statistical analysis programs like R, S-Plus, SAS, Stata, and OpenBUGS-JAGS.
ESS is beneficial when used by professionals who analyze text-based scripts in different operating systems. It provides a more sophisticated graphical user interface.
Aside from supporting different statistical programs, ESS provides keybindings, abbreviations, code formatting, syntax highlighting, commenting, script submitting and displaying results. ESS is freely available under the GNU license.
6.Tinn-R
Tinn-R is a text editor or word processor, ASCII & UNICODE for Windows operating system, with integration with R.
It has user interface characteristics and at the same time an IDE characteristic. Its sole purpose is to facilitate learning R and provide an environment for statistical computing.
7. R AnalyticalFlow
R AnalyticalFlow is a data analysis integrated development environment for R statistical computing.
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It has an intuitive user interface with advanced R features for R experts, which allows for the sharing of analysis processes amongst several users of different R proficiency levels. It works on Windows, Linux and Mac operating systems.
It is available for free under the GNU license.
8. Radiant
Radiant is an open-source and platform-independent browser-based interface that is used for business analysis in R programming. It is based on the Shiny package with the option to run locally or on a server. Radiant was developed by Vincent Nijs.
9.RBox
RBox is an integrated R package for the Atom editor.
This package has a collection of several packages for running R on Atom editor. Its features include the ability to use Hydrogen to execute a line, a selection and a block of code at a time.
It has access to several terminals and has useful snippets for running R. RBox is available under MIT license.
10. NVim-R
NVim-R is a plugin for Vim to work with R programming.
It improves Vim's support for editing R code. It has Omni completion for objects and function arguments for R.
It has the ability to see R documentation in Vim's buffer and it is highly customizable.
11. r4intelliJ
r4intelliJ provides R language support for IntelliJ IDEA.
This integration helps IntelliJ IDEA offer support for R statistical computing.
IntelliJ IDEA is one of the best IDE aims to bring onboard one of the best statistical computing languages for data mining and modeling.
conclusion
R programming is one of the popular statistical and data mining language available and it is open-source, it makes sense to you as well choose an open-source IDE.
R is supported by numerous IDEs, some were purely designed for R environment like RStudio while others are universal editors and gains support for R through plugins.
Whatever the case, choose an IDE that will best meet your requirements and you are familiar with.
Starting with R 4.0.0 (released April 2020), R for Windows uses a brand new toolchain bundle called rtools40.
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This version of Rtools upgrades the mingw-w64 gcc toolchains to version 8.3.0, and introduces a new build system based on msys2, which makes easier to build and maintain R itself as well as the system libraries needed by R packages on Windows. For more information about the latter, follow the links at the bottom of this document.
This documentation is about rtools40, the current version used for R 4.0.0 and newer. For information about previous versions of Rtools that can be used with R 3.6.3 or older, please visit this page.
Installing Rtools40
Note that rtools40 is only needed build R packages with C/C++/Fortran code from source. By default, R for Windows installs the precompiled 'binary packages' from CRAN, for which you do not need rtools!
To use rtools40, download the installer from CRAN:
- On Windows 64-bit: rtools40-x86_64.exe (recommended: includes both i386 and x64 compilers)
- On Windows 32-bit: rtools40-i686.exe (i386 compilers only)
Note for RStudio users: please check you are using the latest version of RStudio (at least 1.2.5042) to work with rtools40.
Putting Rtools on the PATH
After installation is complete, you need to perform one more step to be able to compile R packages: you need to put the location of the Rtools make utilities (bash, make, etc) on the PATH. The easiest way to do so is create a text file .Renviron in your Documents folder which contains the following line:
You can do this with a text editor, or you can even do it from R like so:
Now restart R, and verify that make can be found, which should show the path to your Rtools installation.
If this works, you can try to install an R package from source:
If this succeeds, you're good to go! See the links below to learn more about rtools40 and the Windows build infrastructure.
Further Documentation
More documentation about using rtools40 for R users and package authors:
- Using pacman: the new rtools package manager to build and install C/C++ system libraries.
- Installing R packages: Some older R packages that need extra help to compile.
- FAQ: Common questions about Rtools40 and R on Windows.
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Advanced information about building R base and building system libraries:
- r-base: Scripts for building R for Windows using rtools40.
- rtools-packages: Toolchains and static libraries for rtools40 (GCC 8+)
- rtools-backports: Backported C/C++ libraries for the gcc-4.9.3 legacy toolchain (for R 3.3 - 3.6)
- rtools-installer: Builds the rtools40 installer bundle.
