What is R Language and Why You Should Use it in 2023

R is a programming language for statistical computing and graphics1. Data miners, bioinformaticians, and statisticians often use it for data analysis and visualization. It is designed as a statistical platform for data cleaning, analysis, manipulation, and representation.

R language allows integration with the procedures written in the C, C++, Python, .Net, or FORTRAN languages for efficiency.

R language is freely available under the GNU General Public License, and pre-compiled binary versions are provided for different operating systems like Windows, Linux, and macOS.

R is free software distributed under a GNU-style copyleft and an official part of the GNU project called GNU S.

R compiles and runs on various UNIX platforms, Windows, and macOS.

R provides a wide variety of statistical (linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, clustering, etc.) and graphical techniques and is highly extensible.

Way Back then, R was not a popular programming choice, but it has gained tremendous applications and traction. According to the 2017 Burtch Works Survey, out of all surveyed data scientists, 40% prefer R, 34% prefer SAS, and 26% prefer Python.

History of R

The S language is often the driver programming language for research in statistical methodology, and R gives an Open Source route to participation in that activity.

R is implementing the S programming language combined with lexical scoping semantics inspired by Scheme.

Ross Ihaka and Robert Gentleman created the R language at the University of Auckland, New Zealand. It was developed by the R Development Core Team (of which Chambers was a member as of August 2018).

Why Learn R

R has consistently been at or near the top of data science languages; some scientists consider it a must-have language used in the field. 

If you’re going to be a severe player in data science, you need to have R as part of your toolkit.

Regarding the Data Science language, we always have one question. Which is to learn Python or R?

Python is exceptionally robust in Machine Learning and data-centric apps.

R is powerful in data analysis and scientific research.

My first impression of R was that it was just software for statistical computing.

Good thing I was wrong! R has enough provisions to implement machine learning algorithms quickly and straightforwardly.

Each language has its strengths and weaknesses against the others. Sometimes, you have to learn both languages as a Data Scientist.

Advantages of learning R

R Language has the following advantages.

  1. It has a Documented process.
  2. It has an extensive toolset.
  3. It has a huge community.
  4. It is open source and has free licensing fees.
  5. The style of coding is relatively easy.
  6. Availability of instant access to over 7800 packages customized for various computation tasks.
  7. The community support is overwhelming. There are numerous forums to help you out.
  8. Get high-performance computing experience ( require packages)
  9. One of the highly sought-after skills by analytics and data science companies.

First, there is a clear explanation of the process, and things are documented. Second, there are also numerous tools for data wrangling and visualization. Third, R has a huge community of like-minded peers. Last but not least, R is open-source and free.

The R Environment

R is the integrated suite of software facilities for data calculation, manipulation, and graphical display. It includes the following.

  1. It is an effective data handling and storage facility,
  2. It is a suite of operators for calculation on arrays, in particular matrices,
  3. It is an extensive, coherent, integrated collection of intermediate tools for data analysis.
  4. It is a graphical data analysis facility and displays on-screen or hardcopy.
  5. It is a well-developed, simple, and effective programming language with loops, conditionals, user-defined, recursive, and input and output facilities.

The term “environment” is intended to generalize it as a thoroughly planned and coherent system rather than an incremental accretion of concrete and inflexible tools, as is frequently the case with other data analysis software.

That is it for the R Programming Language.

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