best IDEs for R programming-UPDATED 2021

Read on to discover a list of 10 of the best IDE (Integrated development environment) for R programming and a brief description of each along with pros and cons, depending on your need and application to see which one suits your project. With any further delay, let’s dive into the content.

Best R IDEs in 2021

The list is randomly ordered, better to read the reviews first to get a closer insight about an IDE.

R studio

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Review

The most widely used and applied IDE for R programing- R Studio is open-source software that is enterprise-ready. It is extensively used in data sciences. It is easy to learn and what’s more, it is a complete package with proofing, plots, and debugging.

Pros

  1. Interactive user interface
  2. Easy to Debug
  3. Auto-complete feature is available
  4. Beginner-friendly environment for Package Development
  5. written codes and work done can be saved as a project.

Cons

Often RStudio crops up problems while showing all the fields in data.

Jupyter notebook

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Review

Jupyter provides an extendible environment for reproducible and interactive computing possibilities. It is also an open-source platform that allows the use of multiple languages.

Pros

  • Browser IDE interface
  • Autocomplete feature available
  • Kernels can be changed
  • Support for many languages, including R.

Cons

  • Often In-memory variables can be overwritten
  • Codes cannot be used as a programming asset, can’t import.
  • lack support for JSON

Vim

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Review

An extremely configurable text editor, which has programmable macros that are easy to understand and predict. It comes with many add-ons and plugins, which help in working in various environments like Python, Ruby/Rails, C, etc. Read also Vim vs Vi.

Pros

  • Support many languages.
  • Easily handles repetitive tasks.
  • Many Add-on, plugins available.

Cons

  • Steep learning curve.

RKWard

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Review

An easily extensive and easy to use IDE that for R programming RKWard combines the power of the R language along with statistical tools. Intentionally suitable for the R language, if you are an enthusiast, only work in R, possibly it is a great choice for you.

Pros

Easy to use with R.

Robust for statistical tasks.

Cons

Deploying apps is troublesome.

A little slow when working with big data.

StatET


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Review

An Eclipse-based IDE for R programming StatET comes with comprehensive tools that enable package building and R coding. It comes powered with an R Help System, Object Browser, and a fully-loaded R console. Plus, if you are eclipse users you will get double-advantages there as most of its features depend on that. 

Pros

  • Easier to utilize.
  • Runs multiple snippers simultaneously

Cons

  • However, it’s more favorable to eclipse users.

R Commander


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Review

The GUI (Graphical User Interface ) of R statistical software is R Commander. It is free to download and is used as an R package by many professional coders today. Although it is not the one we can say popular, yet the program is still a fair choice for R users. Essentially when you highly stick to statistical data.

Pros

  • The graphical user interface is good.
  • Best for startups.

Cons

  • Not popular, reason lack of features, language support.

Emacs +ESS

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Review

An add on package for GNU Emacs, ESS (Emacs Speaks Statistics) has been conceptualized to allow the editing of scripts and working with statistical data. It supports various Operational Systems like MS Office, Unix, Mac OS, and Linux. Also, it allows the use of various languages such as Stata, SAS, R, S-Plus, and Open BUGS.

Pros

Syntax Highlighting available.

Support for many files.

Lightweight.

The user interface can be customized

Cons

Outdated fashion means you will need to go through a hard process to understand how it works.

Architect


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Review

An IDE that has been designed specifically to fulfill the needs of Data Science -Architect allows for a wide variety of tasks to be performed under a single environment. You can use it for anything from analyzing data to computing reports. The architect is completely open-source and works on Linux, Windows, and Mac. It supports numerous languages like Python, R, Scala, Julia, C++, etc.

Pros

  • Open source and free.
  • Average performance.
  • Cross-platform supported.

Cons

Doesn’t have advanced features, and ease.

Displayr

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Review

This one is an online analysis and reporting tool, specially composed for market researchers who otherwise go through a difficult process to do statistical research. It makes it easier, faster and robust analysis is guaranteed. With Displayr data scientists can manage everything for analysis, visualization, reporting, and dashboarding with one app.

Pros

Depends

Cons

Depends

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