What is R language used for? What does R stands for in R programming?

R Language Introduction

R Language: So let’s, 1st know the definition or purpose of programming language R. This language was developed by Mr. Ross Ihaka and Mr. Robert Gentleman, who was associated with the Department of Statistics within UoA (University of Auckland). The initial release of R version in August 1993 which allowed statisticians’ students and others users to learn with programming skills which ultimately help in analyzing statistical on complex data analysis and graphical representation. Both the developer kept this language name on the first letter of their name “R”, R-oss and R-obert.

r language

What is the importance of R language?

R language includes functioning to support various statistical methods for example

1. Linear Modeling Method

2. Non-Linear Modeling Method

3. Classifications Method

4. Classical Statistics Method

5. Clustering Method and much more

This language is more popular among the student’s cause of its robust features and due to its open source software version. It is free to download in terms of Free Software Foundation (FSF) as a general public license. This language support and can run on all type of popular operating systems Windows, Mac OS & Linux.

R environment

R allowed users to use their own syntax, functions, and code. In the R environment allows users to combine their individual needs, which may include adding different data set files into one file of the document. Its also allows users to pull out a single variable from the data set and by running a regression, single function/code can be used multiple time in the R environment.

Present world, the demand of the R language has gradually extended from students to business setups, institutes are also giving training to their students of data analysts, students of who trained on R more favorable in continue using this language rather than pick up a new software or tool with which they have not taken any experienced.

R has built with the standard command-line interface. Users control this to read data and load it to the language interface, specify commands and achieve desired results. R environments can be used after thru commands for Arithmetical Operators, that includes +, -, / and *, to more complex functions that act upon the Linear Regressions and more advanced calculations. The looping functions are also trendy in the R programming environment.

All the above functions/command allow users to repeat the same action without any hassles and in a smoother manner. With the loop function, users can pull out the samples from a bigger data set basis the timeline set by the user to a specific task.

Is R programming useful?

Like every language R also have few Advantage and Disadvantage in it.

The Advantage of R Language

  • Open source language and free to download,
  • Offers complex data analytics capabilities
  • An active group of communities and users online for support.
  • 25 years old quite mature language.
  • Multiple add-on packages for enhancing the basic functionality
  • users to graphical data representation,
  • Integration with external database systems,
  • Geographically mapping of data and
  • It allows the user to use advanced level statistical functions.
  • Simplification of coding experience with R Studio

It holds a much bigger library of statistical packages – Making specialized statistical job. R has multiple packages with a wide range of statistical tasks by using the CRAN task view.

In the Toto, we can say R packages also cover all from Psychometrics to the Genetics to Finance.

The Disadvantage of the R language

  • Utilizes only single-threaded processing system in basic open source version
  • Utilization of one CPU at a time – Slow analyses delivery for a larger data sets
  • Memory-based application.
  • All data objects stored only in computers RAM during a given session which is also limit the size of data R is able to work on at one time.

The final word on R

Nowadays various software service providers have also added their support for R language into their offerings, allowing R to gain stronger footprints in the modern Data Analyst structure and big data dominion. The service providers those offering support to R functions included big giants like IBM, Microsoft, Oracle, SAS Institute, TIBCO, and Tableau. Others small software players have also included R language integration in between their analytics software and the R programming language. As there are R packages available for successful open-source huge data programs, having Hadoop and Spark.

It is Gudtoknow that now R language has the ability to perform all statistical task. R is almost unique among all other programming languages available for Data Analyst it is meant for all level of Data Miners.

WE CAN ALSO SAY DATA IS DEEPLY INSERTED IN BLOOD OF THIS LANGUAGE

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