What Is R In Programming

The questions which come to almost every R aspirant before they begin to learn the R programming language are – What is R in programming and why it is important to learn R? What are the different applications of R Programming language? Here, in this blog, we are going to answer all your questions related to the R language. And, I promise once you go through this blog, you will not find any kind of confusion related to R Programming. So, let’s quickly start.

What is R In Programming?

On the basis of the first letter of first name of the two R developers (Robert Gentleman and Ross Ihaka) this programming language was named R. R is a programming language and an analytics tool. It was developed by Ross Ihaka and Robert Gentleman and in 1993 at the University of Auckland, New Zealand. It is widely accepted by Statisticians, Software Programmers, Data Miners, and Data Scientists. It is one of the best and the most popular tools utilized in Business Analytics and Data Analytics. It has several applications in areas like academics, healthcare, finance, consulting, media, and many more. Its wide applicability in Machine Learning, Statistical tests, and Data Visualization has given acceleration to the demand for certified trained experts in R.    

The R Software environment

R is an open suite of software tools for calculation, data manipulation, and graphical display. It covers:

  • A powerful data storage and handling facility,
  • The best option for operators for computations on arrays, in particular matrices,
  • For data analysis, a comprehensive, consistent, open collection of standard tools.
  • Graphical means for data analysis and display either on hardcopy or on-screen.
  • A well-developed, manageable, and powerful programming language covers loops, conditionals, user-defined recursive functions, and input and output facilities.

R, like S, is based on a true programming language, allowing users to extend its capabilities by specifying new features. Most of the system is written in the R version of the S programming language, making it simple for users to understand the algorithmic decisions. C, C++, and Fortran code can be connected and called at run time for computing tasks. Advanced users can explicitly modify R properties using C code.

Features Of R Programming

For data science, R is a commonly used programming language. R has several modern features that have improved it to become one of the most demanding programming languages. This programming language has several features that have served in its overall success. Below we’ll take a look at some of R’s unique features:

  • It is an open-source programming language
  • Strong Graphical Capabilities
  • Highly Active Community
  • Distributed Computing
  • A Wide Selection of Packages
  • Interfacing with Databases
  • Comprehensive Environment
  • Can Perform Complex Statistical Calculations
  • Running Code Without a Compiler
  • It has Cross-platform Support is an important feature

Why is it important to learn R?

Data science is giving a new shape to the companies and help run their businesses. Without a doubt, if there is no Artificial Intelligence and machines, then the company will fail. Now the big question that arises is which tool/language to use? They are several tools available in the market that can perform data analysis. Learning a new language requires some time investment. Below, we have given two pictures that show the interest R by region and Interest by time. No matter what language you choose or what tool you use, it always takes time to get familiar with it. If you want to give the best insight from the data, you must give some time learning the proper tool, which is R.

Interest Over Time

Interest By Region

Applications of R Programming

Some of the major applications of R Language in the area of Data Science are:

  • Finance

Data Science is most extensively utilized in the financial industry. The most common tool for this purpose is R because it gives an excellent analytical suite to carry out all the important financial tasks.

With the help of R, financial companies can do downside risk analysis, adjust risk performance, and use visualizations like density plots, candlestick charts, drawdown plots, etc.

  • Banking

Like financial institutes, banking industries also use R for credit risk modeling and other risk analytics forms. Banking industries heavily use the Mortgage Haircut Model to take over the property in case of loan delinquencies. Mortgage Haircut Modelling includes sales price deals and sales price volatility. For these purposes, R is usually used alongside exclusive tools like SAS(Statistical Analysis System).

R is also utilized in connection with Hadoop to help the study of customer segmentation, customer quality, and retention.

  • Social Media

Social media platforms like Facebook and Twitter are also using R for the study of behavior and sensibility. They will improve and enhance their support to users. Based on the user’s context and the mood and tone of their recent posts and content viewed. The advertisements displayed to the user adjusted according to user feelings and history. R analyzes total traffic, the time they spend, and content, all to enhance user experience.

Facebook: R is used in Facebook to forecast interactions between colleagues and update its social network database.

Twitter: R is used by Twitter for semantic clustering. They use it to conceive data.

  • Healthcare

Bioinformatics, Genetics,  Epidemiology, Drug Discovery are some healthcare fields that make heavy usage of R. Using R, these organizations can crunch and process data, giving a necessary backdrop for more analysis and data processing. Like drug discovery, R is most broadly utilized for conducting preclinical trials and drug-safety data analyses. It also gives a suite for doing exploratory vivid visualization and data analysis to its users.

  • E-Commerce

E-commerce is another most important sector that utilizes Data Science. R is one of the common tools that are utilized in e-commerce. Since these businesses deal with different kinds of data, be it structured and unstructured. R proves to be an effective option for these industries by changing data sources like databases (SQL & NoSQL) and spreadsheets. E-commerce businesses use R to analyze cross-selling products to their clients. 

  • Manufacturing

Manufacturing companies like Modelez, Ford, and John Deere also use R to interpret customer opinions. With the help of this, they can optimize their product based on trending customer interests and meet their increasing volume to changing market demand. These companies also use R to reduce their production costs and increase profits.

Conclusion

R is a comprehensive tool to examine and study data. Detailed analyses like clustering, data reduction, and correlation are done with R. 

Several brands are using R to create vehicles, checking user experience, weather forecast, etc. The R language value is growing day by day, and soon, many other sectors will be using R for more reliable results. Hope all your doubts are cleared through this blog regarding what is R in programming. Moreover, if you need R programming assignment help, Our team of experts can help you with that at a very affordable price.

Frequently Asked Questions

Should I choose R?

Data scientists can use two excellent tools: R and Python. You may not have time to learn them both, but R is the best option for you if you are new in data science. You need to have a basic understanding of statistics in order to learn R. 

Is R extensively used?

As compared to SAS and SPSS (Software package for statistical analysis), R is considered the most common analytic tool in the world. Further, its estimates of users range from 250k to over 2 million. Now, R is easily a hands-down winner if you take a look at popularity.

Is it difficult to learn?

R has a name for being difficult to get. It’s because it is completely different from other analytics software. Some is an inevitable byproduct of its greatest power and flexibility. And, as with any software, some are because of design choices that could have been better.

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