R programming is a software environment and language for graphic tasks, data analysis and statistical computing. If you are interested in computer programming, R programming is a language in which you should learn and gain expertise. With R programming, you can easily work on various applications like data visualisation, Statistical Analysis, Scientific Research, Business Intelligence, Machine learning, Data Mining, and General Data Analysis.
One of the main advantages of IDE for R programming is its ease; you can easily produce well-designed plots with publication-level quality. R provides full user control but minor suggestions for better results. R is not limited to the statistical system; it is a complete environment that provides multiple software facilities and tools. It is a completely planned and logical system. The R language is open-source; users can easily access and modify it according to their requirements.
8 IDEs and editors for R programming:
There are many IDEs and editors available for R programming, but the most suitable for anyone are:
- RStudio:
Rstudio is an open-source, free IDE( integrated development environment) for the R language. The interface of RStudio is well-designed and organized for users to easily view all the data tables, graphs, R codes, and outputs simultaneously. Remember to install RStudio; you will have to install R first.
Why Rstudio:
- It is free
- Available for Mac OS, Linux, and Windows.
- High-quality graphs and data projection.
- Jupyter Notebook:
Jupyter Notebook is a web application that lets users create live codes in multiple languages. However, most users use Jupyter Notebook to write python codes, but Jupyter Notebook has a lot more to offer; Jupyter supports many other programming languages like R, Octave, Java, Matlab, scheme, and many others. Remember, Jupyter is not an interpreter or compiler but sends the codes to the actual compiler and gets results back. So to use Jupyter in your desired language, you will have to install the kernel of that language. For instance, if you want to run the script in R, you will have to install the R kernel on your device.
- Visual Studio:
Visual Studio is a popular powerful IDE code editor with useful operations like version control, debugging, and task running. A visual Studio is a tool for the developer looking for a quick code-build-debug. Now you can use R language in visual Studio by just using an easy plug-in. It will be free and open-source.
- Rattle:
Rattle is a very popular and useful graphical user interface for data mining in the R programming language. It presents statistical data and data visual data summaries. Rattle can transform data in supervised and unsupervised machine learning models.
- Radiant:
Radiant is an open-source interface used for business analysis in R programming. Radiant is one of the ideal options for developers because it is platform-independent and comes with a shiny user interface.
Why Radiant:
- Available for Windows, Linux, and MacOS.
- Quickly summarize and visualize and analyze data.
- Radiant analyze function with R codes.
- Recreate results and share them on different platforms.
- R AnalyticalFlow:
R AnalyticalFlow is an IDE(integrated development environment) for data analysis for R statistical computing. It is perfectly built IDE for R language; it comes with an intuitive user interface with multiple advanced R features for R experts. It is free and open-source, so anyone can easily access and use this IDE. R AnalyticalFLow allows you to share analysis processes among several users. R AnalyticalFlow works on Mac, Windows, and Linux.
- Tinn-R:
Tinn-R is a word processor and text editor, and it is UNICODE for the windows operating system, with compatibility with R. Tinn-R comes with a user interface and IDE characteristics. Tinn-R’s primary purpose is to facilitate R learning and provide a statistical computing environment.
- Sublime Text:
Sublime Text is a source code editor and shareware text easily available for Windows, Linux, and Mac. Sublime Text supports many different programming languages with customization options available for users. Users can choose themes and expand the functionality by using different plug-ins.
These are the IDEs and editors most suitable for R language because of their user interface and to execute R codes.
Conclusion:
The R language is a very popular and functional statistical and data mining language; it is open-source, which means it’s free to use. Many IDEs support the R language. In this article, we mentioned different IDE for R and editors that support R. Some of the IDEs were specifically designed for the R environment. Fortunately, most of the IDEs are open-source, free, and cross-platform, which makes them ideal options for developers. Now it is up to the users to choose which one they find suitable for your work.
