What is R Programming?


Present-day developers are spoilt for choices when it comes to using a programming language. Though Java sat at the top of the programming languages pyramid in terms of popularity for a long time, its position is being challenged by languages like Rust, Python, Typescript, and more. The uses of programming languages have expanded far beyond software development to areas like data science, machine learning, embedded systems, virtual reality, cloud computing, and mobile application development. If you are interested in data analysis or a data science career, then you must have surely heard about R programming. 

R, which was initially designed in 1993, traditionally used to be a popular statistical programming language. However, due to the advancements in the field of data science and artificial intelligence, R has become a language of choice for applications like data mining, statistical computing, machine learning, linear modeling, clustering, and many more. In fact, a number of professionals are pursuing R certification to establish their careers in data science. 

If you too are an aspiring data scientist, you should know more about the R programming language. Read on to know why learning R would benefit you in the long run. 

An Introduction to R Programming

R is an open-source, platform-independent programming language that is quite powerful and highly extensible. Developers can easily create functions, objects, and packages in R, and it can be applied to all operating systems. It is not only a statistical package; one can also integrate R with other languages like C and C++.


Apart from including statistical features like static graphics, basic plots, and probability distribution, R also features over 14,000 add-on packages and storing a series of complex data-analysis steps, allowing you to reuse the analysis work on similar data more easily. The language offers sophisticated data analytics capabilities and has an active community where developers can turn for support. Currently, there is an R group on LinkedIn with over 95,000 members and over 400 R Meetup groups where you can discuss all your queries related to R.

R Package

R package basically refers to the collection of data sets and functions built by the community. In other words, it is a unit of sharable code that improves the existing base R functionalities or adds new ones to increase the power of R. You can find a number of R packages in repositories like Bioconductor, CRAN, and GitHub. You can install an R package from any of these repositories and use it in your application. For example, if you are working with data frames, dplyr and data.table are the useful R packages. Similarly, ggplot2, esquisse, and ggvis are the commonly used R packages for the purpose of data visualization. 


The RStudio IDE is a set of integrated tools that helps users become more productive with R. In other words, it is a user interface that simplifies coding in the R language. It involves a console, syntax-highlighting editor that supports direct code execution, including a number of robust tools for viewing history, plotting, debugging, and managing your workspace. Since R has a syntax quite different from other programming languages and it can be tough to learn, RStudio allows users to build and edit programs in R with ease. RStudio can autocomplete certain functions or names, have seamless integration with some very useful packages, and is a strong text editor. 

Benefits of Learning R

Most data scientists and data analysts use R and Python throughout the various processes involved in data science. By using an open-source platform like R, users can find existing solutions to problems they may face. The work that you perform on R can also be shared and the document can be exported in a variety of formats like Word, PDF beamer, HTML, presentation, etc. 

Here are the top reasons why learning R must be your priority for a bright career in data science.

  • R language has over 3 million users in the US alone. It is used in various professions like scientific research, software development, business analytics, statistical reporting, and more. 
  • Some of the renowned firms using R language are Thomas Cook, Pfizer, Google, Flipkart, Ford Motors, and LinkedIn. So, if you are seeking a job in any of these top companies, then learning R will open your door of opportunities. 
  • Unlike Matlab or SAS, users can install, clone, update, modify, use, or redistribute R for free. All these capabilities help companies save a lot of money. 
  • As most companies now deal with terabytes and more data sizes, R proves to be the best platform for resource-intensive simulations and works well on high-performance computer clusters.     
  • Though R has a little downside in the learning curve, it has the best mix of desirable attributes like low cost, good growth, high data science for business capability, and a massive ecosystem of powerful R libraries. 

Knowing about all these benefits should encourage you to learn R. When it comes to data science, you may be confused whether to learn R or Python. Well, both the languages have their strengths and top features, and selecting one depends entirely on your business needs and the application you are trying to develop. R has a wide range of benefits and learning it is an obvious choice for data scientist aspirants.   


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