R is a programming language that came into existence in 1995. It was created by Ross Ihaka and Robert Gentleman at the Auckland University, New Zealand. This is an open source language that is extensively used by statisticians and data miners to develop statistical applications. R is a tool that is used for statistical analysis, data analysis and machine learning.

As machines are used a lot for generating data, R is gaining popularity as well. The latest TIOBE ranking shows that R has made a jump of one position from previous year’s ranking, and is on 14th position among the top programming languages in the world, and the only language for statistics and data analysis.

There are various reasons, why it is the preferred language of the data professionals. You need not to purchase any license for using R software, it is available free of charge on the internet. Moreover, it is platform independent which allows the you to use it in any operating system like Windows, Linux, Mac OS etc. You can create objects, functions and packages using R. R being an open source platform allows you to look at the source code, add new features, fix bugs, integrate with other programming languages such as C, C++, and most importantly interact with data sources and statistical packages such as SAS, and SPSS.

In this article, we will throw light on why R is a useful and important in different domains. So, let us begin our discussion on the importance of R programming language.

Why R is important?

R is very important when it comes to statistical analysis and data science. Many of you might think of R as a statistical package, but it is not. R is a programming language. Although the difference between a language and package is subtle, but this subtle difference has a tremendous impact. A package has certain limitations, and you can perform only some limited operations in a package. For instance, in MS Word, you can write text, or create charts, insert images and other shapes etc. On the other hand, a language is limitless, you can do anything with a language. You can even create a package with the help of a language.

This language is the preferred language of statisticians and data analysts and it is used extensively for data analysis because of its interactive nature. Data analysis is basically an interactive process, which means that what is the next step that you are going to take is determined by what you see at one stage.

It is obvious that if you have to do the analysis of your data, you would like to present your data in a natural form. For that, you need to use data structures, and R includes an impressive mechanism to create data structures. Also, the capability of producing graphs easily makes R distinct.

Graph is an integral part of data analysis. Any data analysis report is considered ineffective if it lacks graphical representation of that analysis because it makes visualization of data easy. We humans tend to grasp images more easily as compared to numbers, so graphs are indeed important in data analysis. With R, you can easily generate graphs like bar graphs, pie graphs, scatter plots, box plots, histograms etc from the data available.

Another important thing in R is the capability to handle those values which are missing in the data. If you deal with a concrete data in the real world, it is likely to have some missing values. Practically, every data has some missing values. In R, there are multiple functions having the arguments which control the way in which the missing values are supposed to be handled.

The availability of R packages makes it stand differently from the other languages. There are thousands of packages available which perform all sorts of exceptional tasks, and R package system also allows you to add your own functionality, which is somewhat identical to the central part of R.

The strength of the programming language majorly lies in its community. The stronger and the larger a programming language community is, the more help and support you would likely to get. In programming world, support is very crucial, and the role of developer community is to provide help to the other developers and receive inputs from them as well. In addition, the larger the developer community is, the more will be the developers developing some useful tools to make the development easier in particular programming language. Fortunately, R has a very strong and dedicated community for consistently making improvements in the domain of Data Analysis.

Career Prospects

In the recent job market, data science is gaining a remarkable traction because machine learning, big data, data mining and data analytics have become fairly relevant in the mainstream information technology industry. There is a vicious scarcity of skilled data scientists and professionals that has resulted in an unforeseen inflation in the demand of trained data professionals. If you are interested in making huge money, then data science is the field that can help you achieve massive numbers in terms of US dollars. As per the website PayScale.com, the data scientist jobs are highly paid jobs in the United States. The average annual remuneration of a data scientist is $91,588. An experienced data scientist having an extensive industry experience gets an annual package up to $148,000, which clearly indicates that data scientists are top ranked in terms of compensation in the entire IT industry.

Future Scope

With more and more companies focusing on Data, a significant growth has been noticed in R programming as well. According to google trends the percentage of people interested in learning R (in 2018) has increased by 53% as compared to the year 2017. So, future of R programming language is bright due to the fact that large business organizations prefer open source platforms, tools and technologies for the analysis of the massive critical data. However, R programming has been successful in making an impact in the minds of big organizations. For example, huge companies like Google, Accenture, Genpact, MuSigma, Facebook and many others have started adopting the R platform.

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