G Sunil

R is a programming language and software environment to perform statistical analysis, graphics representation, and reporting. R Programming was developed by Robert Gentleman and Ross Ihaka at the University of Auckland, New Zealand, and is currently developed by the Core Team of R Development. R Programming can be available under the GNU General Public License for free of cost, and pre-compiled binary versions are provided for various operating systems like Linux, Windows, and Mac. This language was known as R, based on the first letter of the first name of the two R developers, i.e.; Ross Ihaka and Robert Gentleman, and partly a play on the name of the Bell Labs Language S.


R Programming is known for Statistical inference, Data analysis, Machine learning algorithms and also used as a programming language additionally a software environment for statistical analysis, graphics representation, and reporting. The R Programming language is an interpreted programming language that allows looping and branching also as modular programming using functions. R language allows integration with the techniques written within the C++, C, Python, Dotnet, or FORTRAN languages for performance.


Whenever we tend to find out something new, we’re curious to grasp its features. The identical condition is applicable while learning programming in R. We’re very eager to know about its features. R provides a good style of features that makes it stand alone from the group. R programming is very easy to use, and hence it is universally acceptable.


Features of R Programming:

  1. Cross-Platform Compatibility

R supports cross-platform compatibility. We can run it on any software environment on any OS. Additionally, It can be run on any hardware configuration without any additional hardware requirements.

Possesses a Number of Graphical Libraries

The most essential feature of R that sets it except for other programming languages of Data Science is its hige collection of graphical libraries like ggplot2, plotly, etc. that are capable of quality visualizations.

  1. Machine Learning with R

R had variety of packages for different machine learning algorithms. This might be declared as not helpful and, therefore, the MLR package which stands for Machine Learning in R has become highly popular. This package is helpful for all machine learning algorithms and provides other tools that help with machine learning as well.

  1. Comprehensive Language

R could be a comprehensive programming language, meaning that it provides services for statistical modeling in addition to software development. R is that the primary language for Data Science additionally ad for developing web applications through its robust package RShiny. R is called object-oriented programming language which consists of objects as references.

  1. Can Handle all kinds of Data

R provides an excellent data handling and storage facility. In conjunction with data platforms like Hadoop, R facilitates the handling of structured and unstructured data that provides and imparts a comprehensive data capability. Furthermore, R provides various data operation and data modeling facilities that are often a consequence of active interaction with the storage facility. It also provides extensions for SQL and Massive Data.

  1. R has an lively Community

R is continuously evolving and emerging. The contribution is fuelled by the largely growing number of users who are using R on a routine. As mentioned above, R is an open-source library that is supported and maintained by an oversized user-base. A part from this, R has an engaging community which conducts seminars, boot camps and other training sessions of R.

  1. Facilities for Various Industries

Almost every industry that produces use of information, utilizes the R language. While only the tutorial areas made use of R within the past, it’s now being heavily used in industries that need to mine insights from the information.

  1. Provides a Wide Array of Packages

R is most generally used thanks to its wide availability of libraries. R has CRAN, which may be a repository holding more than 10,0000 packages. These packages appeal to every functionality and different fields that cater to data. Based on user requirements these packages provide different features to their users.

  1. No Need for a Compiler

R language is interpreted rather than compiled. The R code is interpreted each line at a time and it gets converted into machine level calls. It helps running an R script much less time-consuming. Hence it doesn’t need any compiler to run the code.

  1. Performs Fast Calculations

While using R, you’ll perform a good kind of complex operations on vectors, arrays, data frames and other data objects of varying sizes. Addition to all these operations operate at a lightning speed. It also provides various suites of operators to perform these miscellaneous calculations.

  1. Integration with Other Technologies

R is integrated with a variety of various technologies, frameworks, software packages, and programming languages. It will be paired with Hadoop to use its distributed computing ability. It can even be integrated with programs in other programming languages like C, C++, Java, Python, and FORTRAN.


  1. Open-source

R is an open-source programming language. It means that it is free of cost and requires no license. Furthermore, Anyone can contribute towards the development of R, customize its packages and add more features.


Careers in R Programming:

Careers in R programming are most related to data science and business analytics fields. It has a huge demand for the latest technologies. Data analysts, Data scientists, Statisticians are famous job roles for professional R programmers. There are so many entry-level R programmers and experienced R programmers in the data science domain. Data science projects related to companies pay off well for R programmers. They are a good fit in statistical analysis job roles such as research-oriented industry.

R Programming Language Job Positions:

As a fresher R programmer one can choose his career based on their interest. Always doubt which field is good to go. Some of the careers in R Programming is listed below:

  • R programmer
  • Data Scientist
  • Data Analyst
  • Data Architect
  • Data Visualization Analyst
  • Geo Statisticians
  • Database Administrator
  • Quantitative Analysis with R

R Programmer– 

The role of R programmers is to use their problem solving skills and create tools that can be helpful in the process of data analysis. R programming makes use of data structures such as matrices, vectors and data framers which helps in development

Data Scientist– 

A data scientist has to design and implement layouts for large data sets,data mining, and research purposes. It is the most favourite one among R programmers.



Data Analyst

A data analyst must be a mixture of technical and analytical knowledge. A data analyst must be good in who is well-versed in finding, retrieving, wrangling, and delivering insights from data. Adding to that, a DataAnalyst must have in-depth information regarding a database system and machine learning.

Data Architect– 

Data architects integrate and maintain their data sources. So many tech companies these days are in need of skilled data architects. Data architects are in line with the latest technologies like Spark to stay relevant. Data architects must be aware of data warehousing solutions, database architecture, ETL and BI tools, data modeling, and system development.

Data Visualization Analyst– 

A data visualization analyst is always ready to create and distribute interactive dashboards which contain all information to make major business decisions. Data Visualization analysts provide a graphical report that helps the business in predictions and makes the right decisions that will help the business grow.

Database Administrator– 

A database administrator always confirms that a database is available for all users that takes a particular set of data from the administrator ensures that a database is available to all users that require a particular set of data. Database administrators ensure that all data has a recovery or backup mechanism. Additionally, They need to take care of data modeling, data security, design and should be properly versed with the business they are involved in.

Quantitative Analysis with R

Variety domains like finance, telecom, hospitality, loans etc. Risk-balance can be ensured from quantitative analysis and help us in finding new opportunities and evaluate all asset prices using mathematical models.




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