Check Details On What Is R In Programming

Want to know what is R in programming? If yes! Then you are in the right place. Just keep on reading the blog.

 

R programming language was released in 1995. The purpose of it is to allow statisticians and anyone with advanced programming abilities. It uses to do extensive data statistical analysis and show the results in a variety of graphic visuals. 

 

The “R” stands for the initial letter of the names of its two creators, Robert Gentleman and Ross Ihaka. Both of them were at the time affiliated with the University of Auckland. Linear and non-linear modeling, classifications, classical statistics, clustering, and other functions are available in the R programming language. 

 

Because of its powerful features and free to download in source code, it has proved popular in academic contexts. It may be compiled and run on UNIX platforms and other operating systems such as Windows, Linux, and macOS. 

 

The R programming language has steadily gained traction outside of academia. Many data analysts who learned it in college choose to stick with it rather than choosing a new technology with which they are unfamiliar.

 

Let go deeper into the detail of what is R in programming and what are its different applications.

 

What is R in programming?

 

Apart from being a programming language, it is also a free software developed in 1993 by Robert Gentleman and Ross Ihaka. The name R was derived from the name of its creator’s first name. It has a comprehensive catalogue of graphical and statistical methods. 

 

It has ML algorithms, time series, linear regression, statistical inference, etc. Most R libraries are composed in R. Many big organizations also use the R language. Examples of big companies include Google, Uber, Facebook, Airbnb, etc.

 

Data analysis with R programming language is performed in a series of steps:

 

  • Programming
  • Transforming
  • Discovering
  • Modeling 
  • Communicate 

 

We hope you are clear with what is R in programming. Let us now discuss some of its best applications.

 

Some Applications of R Programming

 

R is a prevalent data science programming language. R programming provides users with several libraries for data research. In addition to providing an environment for statistical design. Here are a few examples:

 

  • Ggplot2
  • Lubridate
  • Dplyr
  • Shiny
  • Knitr
  • RMarkdown
  • Janitor
  • RCrawler
  • Quanteda.dictionaries
  • Caret
  • Leaflet

 

R language can also help with data import and cleaning, as well as quantitative analysis.

Healthcare, Academics, government, finance, insurance, retail, energy, media, technology, manufacturing, and electronics are just a few of the areas where R programming language is used.

 

R Programming language: Pros and cons

 

Many R programmers like this language because it is free to use. Also, it has extensive data analytics abilities and has an active online users community. The language is pretty refined & best. Because it has been around for several decades and is still popular. Users can download add-on packages that extend the language’s core features. 

Also, users can view data, link to external databases, map data regionally, and execute complex statistical operations with these programs. There’s also RStudio, a popular UI for the R language that makes it easier to code.

 

When used to massive data sets, the R programming language has been criticized for being sluggish. Because R language employs single-threaded processing, an open-source version can only employ one CPU at a time. Today, big data analytics relies on parallel data processing. It uses hundreds of CPUs to handle massive data volumes swiftly. 

 

Is R programming difficult?

 

R used to be a tough programming language to learn. The language was difficult and unstructured compared to the other programming tools. Hadley Wickham created the tidyverse package collection to address this key issue. The game’s rules have been altered for the better. Manipulation of data was no longer difficult and uncomplicated. 

 

Creating a graph has become an easy task once you know what is R in programming. High-end machine learning systems are created using packages like TensorFlow and Keras. A package for performing Xgboost is also available in R. R can converse with other languages. 

 

You can call Java, Python, C++ in the R programming language. R can be connected with other databases like Hadoop or Spark. Eventually, R programming has developed and let the parallelizing process speed up the analysis. R language allows for utilizing only one CPU at a time. The parallel package enables users to complete tasks in various machine cores.

 

Let’s wrap it up!

 

In conclusion, the R programming language is a highly used statistics language. It is one of the best choices for data scientists. And is backed by a dynamic and proficient community of supporters. R language is used to do complex correlation, clustering, and data reduction analyses. 

 

R used to be a difficult programming language to master for newbies. Also, it was confusing and not as structured as the other programming tools. To overcome this significant issue, a collection of packages was developed by Hadley Wickham called tidyverse.

 

We hope you understand what is R in programming. We have covered everything related to the R programming language, from its history to its pros and cons.

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