Everything About “S- Now Called R” Programming

Everything About “S- Now Called R” Programming


S programming language is a statistical programming language created by Bell Laboratories’ John Chambers, Rick Becker, and Allan Wilks. According to John Chambers, the language’s goal is to quickly and accurately convert ideas into software.

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Old “S”

S was created to provide a different, more engaging approach. Interactive graphics devices and offering available documentation for the functions were early design considerations that still hold today. The first functioning version of S ran on the GCOS operating system, was produced in 1976. The letter ‘S’ was picked as the name since it was a standard letter in the submissions and was compatible with other computer languages created by the same school. The initial version of S was released outside of Bell Laboratories in 1980, and source versions were made available in 1981.

New “S”

S and the grammar of the language had undergone several changes by 1988. The New S Language was released to bring new features such as switching from macros to functions and passing functions to other functions. Many more improvements to the S language were made to broaden the concept of “objects” and improve the consistency of the syntax. The new S programming language is known as the R language.

R Programming Language

The R Development Core Team is currently working on R, founded by Ross Ihaka and Robert Gentleman at the University of Auckland in New Zealand. R is a statistical analysis, visual representation, and reporting programming language and software environment.

The R programming language implements the S programming language with lexical scoping semantics. R is free to download and use under the GNU General Public License, and pre-compiled binary versions are available for Linux, Windows, and Mac. The initial letter of the first names of the two R authors (Robert Gentleman and Ross Ihaka) inspired the name R, which was partly a play on the name of the Bell Labs Language S.

R Environment

R is a software package that includes tools for data processing, calculation, and graphical display. It consists of:

  • A well-developed, simple, and effective programming language that includes conditionals, loops, user-defined recursive functions.
  • Input and output facilities.
  • A suite of operators for calculations on arrays, in particular matrices.
  • An extensive, coherent, integrated collection of intermediate tools for data analysis.
  • Graphical facilities for data analysis and display either on-screen or on hardcopy.

The Audience of R Programming Language

R is a programming language aimed at software developers, statisticians, and data miners. It is, on the other hand, a frequently used programming language. It is used by academics, scientists, and researchers to examine experiment outcomes. Furthermore, it is used by businesses of all sizes and across all industries to derive insights from the growing amount of everyday data they collect.

Let us discuss some markets that focus on R programming language:


The R programming language is frequently used in academia and research. R is taught in statistical computing classes at Cornell University, for example. Many universities, including the University of California, teach students statistics and data analysis by introducing them to R.


Companies that deal with financial services are fintech companies. Because money and statistics go hand-in-hand, R is used at many of these types of businesses. Credit risk models and other sorts of risk assessments are created using the R language by banks. Other applications are fraud detection, loan analysis, variability modelling, customer assessment, and loan stress test simulations.


The R programming language is used by the National Weather Service to forecast weather and predict calamities. They also make weather forecast pictures using R’s visualisation tools. The FDA also uses r to evaluate medications, conduct pre-clinical trials, and forecast likely reactions to the food goods it regulates.

Healthcare System:

In genetics, bioinformatics, drug discovery, and epidemiology, R is widely utilised. R is used in drug research, for example, to crunch data from pre-clinical studies and determine the safety of a medicine. Further, it is used in epidemiology to forecast how a disease would spread in a pandemic.


R is used in retail and e-commerce to measure risk and develop marketing strategies. R’s machine learning skills promote cross-selling and propose better-related goods at checkout to increase profitability and sales. R is also utilised for sales modelling and targeted advertising in the retail industry. For data analytics, both Amazon and Flipkart employ the R programming language.


Many businesses use the R programming language to evaluate client feedback to enhance their goods. Ford Motor Company uses r to assess customer feedback on its automobiles and improve their design. Based on crop productivity and other data, John Deere utilises R to determine how many spare parts and goods they need to make.

Data Journalism:

To convey a storey, data journalists use data. They are journalists and data scientists who use public data to gain insights into our world and how we live. It might be data from local government and police sources to tell a crime storey, financial data to demonstrate the status of a country’s economy or any other type of data that reveals an intriguing pattern in how our world works. R is a popular programming language among data journalists because it unearths these insights and creates visually attractive graphics that illustrate the storey.

Features of R Programming Language

R has some unique characteristics that make it highly potent. The vector notation is likely the most important. We can conduct a sophisticated operation on a set of values with just one instruction with these vectors. R programming has the following characteristics:

  • It is a well-developed programming language that is easy and effective.
  • It has a set of tools for data analysis that are consistent and integrated.
  • It is a data analysis software.
  • It’s a well-designed, simple, and powerful language with user-defined, looping, conditional, and numerous I/O tools.
  • R has a set of operators for various calculations on arrays, lists, and vectors.
  • It has a lot of graphical approaches that may be customised.
  • It allows for efficient data handling and storage.
  • It is a powerful, open-source, and highly expandable software.
  • R is an interpretive programming language.
  • It enables us to use vectors to conduct many calculations.

Why Use the R Programming Language?

R is unique in that it is not a general-purpose programming language, and it does not aim to be all things to all people. It excels at a few tasks, notably statistical analysis and data visualisation. While data analysis and machine learning tools are available for Python, R has built-in statistical features. No third-party libraries are required for the fundamental data analysis you can accomplish with the language.

Cleaning, feature engineering, feature selection, and import are critical tasks in data science. The data scientist’s responsibility is to understand the data, manipulate it, and expose the best strategy.

R is an excellent tool for implementing machine learning algorithms. R interacts with other languages and may invoke Python, Java, and C++. R also has access to the world of big data. Different databases, like Spark or Hadoop, can be connected to R.

Applications of R

  • Google
  • Facebook
  • Twitter
  • Sunlight Foundation
  • RealClimate
  • NDAA
  • FDA
  • ANZ

Pros and Cons of R

Like every other programming language, R also has some advantages and disadvantages that come with it. Because R is an ever-evolving language, many disadvantages will gradually fade away with future versions.


Platform Independent Language:

R is a platform-agnostic or cross-platform programming language, which means that its code runs on any operating system. R allows programmers to create software for various platforms by writing only one programme. R is elementary to install and use. It can be used on Windows, Linux, and Mac.

Open-Source Language:

An open-source language can be used without a licence or payment. R is a free and open-source programming language. By optimising our packages, creating new ones, and addressing difficulties, we may contribute to the development of R.

Machine Learning Operations:

We can use R to do machine learning tasks like classification and regression. R includes several tools and functionalities for creating artificial neural networks for this purpose—the top data scientists in the world use R.

Quality plotting and Graphing:

R makes excellent charting and graphing a breeze. Visually pleasing and aesthetic graphs are promoted by R libraries such as ggplot2 and plotly, which distinguish R from other computer languages.

Support for Data Wrangling:

We can perform data wrangling with R. R includes programmes like dplyr and readr that can turn unstructured data into a structured format.


R is mainly known as a statistical programming language. For this reason, R is more widely used in the development of statistical tools than other programming languages.

An Array of Packages:

R has a large number of packages. The CRAN repository for R has over 10,000 packages and is constantly developing. Packages for data science and machine learning operations are available in R.

Continuously Evolving:

R is a dynamic programming language that is constantly changing. When anything evolves, it changes or develops with time, such as our musical and clothing preferences, which vary as we grow older. R is cutting-edge software that receives upgrades anytime new features are released.


Data Handling:

Objects in R are kept in physical memory, and it differs from other programming languages such as Python. When compared to Python, R uses more RAM. It necessitates storing all data in a single location, which is the memory. It is not the most remarkable technique for dealing with Big Data.

Low Security:

R is insecure in many ways. It is a required component of most programming languages, including Python. As a result, R has a lot of limitations because it can’t be incorporated into a web application.

Complicated Language:

R is difficult to learn, with a steep learning curve. It may be challenging to learn R if you have no prior knowledge or programming expertise.


Other programming languages, such as MATLAB and Python, are significantly slower than R. R packages are substantially slower than those written in other computer languages.

Weak Origin and Support:

R’s primary drawback is that it lacks dynamic or 3D graphics support. Its origin is the cause behind this. Its roots can be traced to the far earlier computer language “S.”

How to Learn the R Programming Language?

Learning R can be challenging, especially if you have no prior programming knowledge or are used to dealing with point-and-click statistical tools rather than a simple programming language. This learning path is aimed mainly at new R users, although it will also cover some of the language’s most recent improvements, which may interest more experienced R users.

Step 1: Why Learn?

At first, you should know why you want to learn the R programming language. R is quickly becoming the de facto language of data science. Although it has its roots in academia, you can now find it in a growing number of business contexts, where it competes with commercial software incumbents such as SAS, STATA, and SPSS. R’s popularity grows year after year, and in 2015, IEEE named R one of the top 10 languages of the year.

This poll indicates that the demand for people who know R is increasing and that studying R is a prudent career investment, as R is the highest-paying skill. With significant competitors like Oracle and Microsoft stepping up and adding R to their offerings, this trend is unlikely to slow down in the coming years.

However, money should not be the sole motivator, and R has a lot more to offer than just a steady income. You will become acquainted with a diverse and intriguing community by engaging with R. Daily; you will come across a diversified variety of examples and applications, which will keep things interesting and allow you to apply your knowledge to a wide range of challenges.

Step 2: The Set-Up

You must first download a copy of R to your local computer before working with it. R has been changing since its inception in 1993, and various versions have been produced.

Once R is installed, you should consider installing one of R’s integrated development environments. Two well-known development environments are RStudio and Architect. If you want a graphical user interface, R-commander is a good choice.

Step 3: Understanding the Syntax

Learning the grammar of a computer language like R is akin to learning a natural language like French or Spanish: through doing and practicing. The following online tutorials are one of the most acceptable ways to learn R by doing:

  1. DataCamp offers free R training and a follow-up course called Intermediate R Programming. These interactive courses teach you R programming and data science in the comfort of your browser, at your speed.
  2. A swirl is a software that contains offline interactive R coding assignments. There’s also an online version that you may use without downloading anything.
  3. Introduction to R Programming by Microsoft is available on edX.
  4. Johns Hopkins’ R Programming course on Coursera.

R vs Python

Since both languages are used for statistical data analysis, let us see the differences:

  1. R is a software environment for analyzing statistical data, graphical representation, reporting, and data modelling, whereas Python is a high-level interpretable programming language for general-purpose programming.
  2. Advanced approaches in R packages are convenient for statistical work. Everything from psychometrics to genetics to finance is covered in these programs. R and Python are equally good at spotting outliers in a data set. On the other hand, Python is superior for creating a web service that allows people to upload datasets and discover outliers.
  3. R provides built-in data analysis capabilities, whereas Python lacks many capabilities. In Python, these can be found in packages such as Numpy and Pandas.
  4. Data visualisation is an integral part of the analysis process. Data visualisation is made easier using R tools like ggplot2, ggvis, lattice, etc. On the other hand, Python is better for deep learning since Python libraries like Caffe, Keras, OpenNMS, and others make building a deep neural network quite simple.
  5. There are hundreds of packages and techniques to do necessary data science jobs in R. In contrast, Python has only a few primary data analysis and machine learning packages, such as viz, Sccikit learn, and Pandas.


R is available on various platforms, including Windows, Linux, and Mac OS X. In addition, the R programming language is the most up-to-date tool. I hope I have provided enough insights regarding the R programming language. If you are interested in statistics and data analysis, you must start learning the R programming language. For more, read our other blogs.

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