R is one of the most commonly used programming languages.  It is an open-source programming language. Ross Ihaka and Robert Gentleman are the two names that are credited with the development of this programming language in 1993.

R has a vast catalog of statistical as well as the graphical methods.

Some of the more important aspects of this programming language include machine learning algorithms, time series, linear regression, and statistical inference.

Mostly the R libraries are composed in R, however, in case of the onerous computational task, usage of C, C++, as well as Fortran codes, is made preferably though not necessarily.

Though not a part of the academics, R programming language is used by a lot of big business which includes Uber and Facebook.

The major reason for having this language developed was to lay greater emphasis on delivering a comprehensive and more user-friendly way of performing statistics, data analysis as well as graphical modules.

Earlier, R was employed only for research as well as academic purposes but now it finds most use in the enterprises.

At present, R is a single programming language that lets the statisticians do even the most complex and intricate analyses quickly. In this article you will get informed about Basic Know How About R Programming.

Data Analysis With R

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There is a vast array of steps that are followed for data analysis using R.

Firstly programming takes places as R is one of the clearest and at the same time one of the most accessible programming tools.

It is followed by transforming where R is composed of several libraries that are specifically designed for data science.

Investigating the data in addition to refining the hypothesis as well as simultaneously analyzing; this process is known as discovering.

After that modeling takes place where you are provided with a range of tools to capture the model for the data you have. Last but not least is communicating the results where codes, graphs, and outputs, are integrated into a report having R Markdown.

Programming features of R

R Programming Features

There are a plethora of programming features that R has. Distributed Computing and R packages are the two standard programming features.

Distributed computing is an open source as well as a high-performance platform for the R language.

It ideally splits the tasks between multiple processing nodes to cut down on the execution time as well as analyze massive datasets.

R packages, however, are a lovely collection of diverse R functions as well as compiled code plus sample data.

During installation, R installs the set of packages by default.

In addition to this, it has the data inputs that include the data type, importing data as well as keyboard typing. Apart from this, it offers Data Management including data variables as well as operators. It is owing to these features that it is a popular choice among businesses.

Importance of R programming

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The primary reason that makes R programming the right choice for businesses is its open source nature. It offers visualization and a variety of other paramount tools that the earlier programming languages fail to provide. In addition to this companies make use of R programming as a significant platform and therefore hire users who are trained in R.

R is an interpreted language which implies that you can run the Code without the need of any compiler.

R interprets the Code for you making the development of the code comparatively easier.

Being a vector language, anyone can add functions to the single Vector without having to put in a loop. This implies that R is a comparatively faster language.

You can use it in biology and genetics in addition to statistics. In today’s scenario, R has turned out to be the leading tools for not only machine learning but also statistics as well as data analysis.

Using this programming language, you can create objects, functions as well as packages. There is no need for you to buy a license to use it.

Pros and Cons of R programming

R language comes with its own set of advantages as well as disadvantages; you must have thorough know-how about them both. Here we are going to highlight the Pros and Cons of R.

Benefits of R Programming

Many benefits of R programming can be highlighted without going into in-depth analysis.

R is one of the most comprehensive statistical analysis packages because the latest technology, as well as the ideas, usually make their first appearance in R.

Being open-source software, anyone can not only use it but at the same time also change it.

This gives one the freedom to use it anywhere irrespective of time and geography.

Also, one can sell it if one follows the conditions of the license. Not only is it excellent for the  GNU/Linux but also for Microsoft Windows.

Being a cross-platform R runs on various operating systems. In R, there is no restriction as to who can make provision of bug fixes, code enhancements or new packages. Anyone can do the same efficiently.

Limitations of R Programming

Every bed of roses comes with thorns. Similarly, there is a specific limitation of R programming as well.

Firstly, the quality of a few packages in R is comparatively less than being perfect. Being a software Application, it takes a lot of time for developing, and it can be a cumbersome task to memorize the entire thing. These are the few negatives of R programming that one can undoubtedly ignore.

Features that make R programming different

R Ranking

Open Source Programming Language

R is an open source programming language that means it is available free.

The programming codes are capable of being used across all the platforms.

These platforms include Linux, Windows as well as Mac. As far as subscription costs or say where license management is concerned, there is no limitation, implying that people who are fond of programming can use it without restrictions.

You also get completely free access to the R programming libraries. However, there are certain commercial libraries that are solely meant for those enterprises that deal with data in terabytes.

Complete Statistical Analysis Kit

R programming language offers the complete standard data analysis tools so that you can have access to the information in the varied formats.

This implies that you can make use of the data for diverse purposes like the merges, transformations as well as aggregations.

There are tools not only for convenience but also for the modern statistical models. These include Regression, ANOVA, GLM as well as Tree.

This implies that you can not only extract but at the same time also merge the required information which skips the need for copying.

There are also tools that help in data visualization to create graphs and bar charts, multi-panel lattice charts and scatter plots in addition to new custom-designed graphics.

It also offers unparalleled charting as well as graphics that are greatly influenced by data visualization experts.

It offers you the loyal user base as statisticians, scientists as well as engineers and those who lack the proper computer programming knowledge, are able to use it without any issue.

All in all, this is the Basic Know How About R Programming that will surely make you eager to have a professional knowledge of the same for which you can join a lot of online institutions.