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R Programming Language - Introduction

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Data structures & algorithms (Beginner to Intermediate)
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Hello Ninja, Welcome back. Are you into Data science and the concepts of Big Data? Do you want to dig deeper into your technical roots? Are you willing to learn R Programming Language? If your answer to these questions is yes, then you’re at the right place. R being user-friendly makes an excellent choice of language for you to learn and to expand your skill set. 

r programming language

In this article, we look into the Introduction, history, features, uses, advantages, disadvantages, and applications of R. We will also explore the industry-based applications of R, so don’t worry and buckle up! We will go through everything. We got you covered.

What is R Programming?

It is a  Data science language used by most Data Mining experts. Just like Python, R is an interpreted computer programming language which means that the program is compiled line by line. 

It is a software environment for graphical representation, analysing of statistical information, Data Mining, reporting, Data modelling, Data Visualisation, Data Analytics, Data Science, etc. It is an optimal programming language choice for implementing Machine Learning algorithms.

R is a modern implementation of S combined with lexical scoping mechanics. S programming language was commercially centred, whereas R is an open-source programming language, user-friendly, and offers over 9000 packages that can be modified, created & used by the users. 

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History of R Programming

Ross Ihaka and Robert Gentleman developed R at the University of Aukland, Ireland. The name of its developers inspired the name of the programming language. The first-ever project in R dates back to 1992, after which initial and stable beta versions were released in 1995 and 2000, respectively. The latest version of R, version 4.2.2, was released in 2022. 

History of R

Features of R Programming Language

Here are some of the features of the R programming language:

  1. Supports functions of both procedural programming language and Object-oriented programming language.
  2. R can easily integrate several technologies, frameworks, and other programming languages.
  3. It is free and open source.
  4. R offers a wide range of packages that are available on online repositories like Github. 
  5. It offers good-quality graphics.
  6. Being an interpreted language, R is not dependent on compilers.
  7. R is supported on all Operating Systems and software environments.
  8. It is optimal for performing complex operations on vectors, arrays, etc.  

Why use R Programming?

R is a widely used programming language that Ross Ihaka and Robert Gentleman created. R can also act as a software environment used in graphics representation and statistical analysis. It is a simple and efficiently developed language that provides a vast collection of tools for data analysis. Below are some of the reasons that make R a useful programming language.

  1. R is very flexible in data handling. This means we can transform, manipulate and organize data according to our needs. We can do this by R's functions and methods, which help us achieve our end goal. For example,  we are given a dataset of information about school students. The dataset contains their names, class, address, marks, etc. So we can read the data using the ‘read.csv()’ function, explore the data using the ‘head()’ function, filter rows using the logical operators, and perform many more operations.
  2. R also has strong graphical abilities that help users create high-quality visuals for analyzing data, and plots are used for presenting it.
  3. Its high performance with data storage, handling facility, tools, etc., makes it a multipurpose language used in various fields such as data analysis, statistical analysis, and machine learning. R has various features and packages for artificial neural network development, and slo has various options, such as classification and regression.
  4. We can run R in combination with other programming languages as well. For example, R combined with C and C++ can give performance optimization. 
  5. R has more than 10,000 packages which are stored in the CRAN (Comprehensive R Archive Network) repository. This number is continuously increasing. R has a very large community of developers are users who contribute to its development. 

Applications of R Programming Language

Applications of R

Listed below are some of the industry-based applications of R:

1. Data Science

R provides its users with a statistical computing environment ideal for analysing statistical information and offers a wide range of libraries used in statistics.

2. Research  

R is used to perform complex calculations. It is used as a statistical research tool to perform techniques like linear and non-linear modelling, classical statistic tests, time series analysis, etc.

3. IT Industry 

R is a business intelligence tool in IT and product-based companies. For example - Infosys, Google, IBM, Microsoft, Paytm, etc.

4. Finance 

R plays a significant role in making commercial and finance-related decisions. R’s data visualisation and analysis tools are used to make candlestick charts, graphical studies, financial data mining, etc.

5. Healthcare 

R is used to make Data Processing and Data Analysis simple. It is used to analyse genetic sequences in fields like genetics and to research and test chemical reactions in areas like drug development.

6. Education

R programming language is a powerful tool for educational purposes in machine learning, data science, and artificial intelligence. It is also used in the fields of biology, genetics, and business intelligence.

7. Social Media

Many social media companies use R for different purposes. Like, it is used to predict user interactions after a thorough analysis. It can also be used for semantic clustering and data visualization.

Advantages of R  Programming Language

Some advantages of the R Programming Language are as follows - 

  • R lets you perform statistical analysis, Machine Learning, Data Science, etc. 
  • It is platform-independent and enables you to create & modify objects, functions, and packages.
  • You can integrate R with different databases.
  • It is a high-in-demand and high-paying skill.
  • Used in several industries like Data science, healthcare, finance, banking, manufacturing, etc.
  • It is an optimal language for Big Data and Machine Learning.

Disadvantages of R  Programming Language

Despite being a great language, R has its flaws. Here are some disadvantages of R - 

  • R can consume most of your system’s memory.
  • R programming language is comparatively slower than other programming languages like Python.
  • R is not a secure language.
  • R is comparatively a complicated language to learn.
  • R programming language doesn’t have a consistent package quality.

Comparison between R and Python

Below is the comparison table for R programming language and Python.

R Python
RosslHaka and Robert Gentleman developed the R programming language. Guido Van Rossum developed the Python programming language.
R is a programming language mainly used in data analysis. Python is a programming language widely used in deep learning, artificial learning, etc.
The statistical packages of R are highly powerful. The statistical packages of Python are less powerful compared to R.
There are a few IDEs for R, for example, StatET, RStudio. There are many IDEs for Python as compared to R, such as Sypder, Pycharm, Notebook, Jupyter, etc.
Complex mathematical problems can be simplified using R. We can use Python for building applications from scratch.
R is used by scientists and research and development teams who rely on data analysis. Python is more popular with a huge user base, including programmers and developers.

Frequently Asked Questions

What is the R programming language used for?

R programming language is used for doing statistical analysis. You can also use it to perform data manipulation and visualization. This is an efficiently developed language that is also used by scientists and research and development teams who rely on data analysis. It provides a vast collection of tools for data analysis.

Is R programming better than Python?

R programming language is comparatively better than Python if you wish to do statistical analysis. Also, R was found to be faster than Python when a speed benchmark test was performed. The statistical packages of R are highly powerful as compared to Python.

What is the full form of R programming language?

R is a widely used programming language that Ross Ihaka and Robert Gentleman created. R comes from the names of the developers who created the R programming language, i.e., Ross Ihaka and Robert Gentleman. Therefore there is no such full form for it.


R programming language is widely used in many areas like education, finance, social media, healthcare, and many more. And it is potent when it comes to data analysis, data handling, data visualization, semantic clustering, and various other vital concepts. In this article, we studied this powerful R programming language. We started with its history and a brief introduction. Then we explored its uses, advantages, disadvantages, and applications.

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