What is R Programming | Installation guide

R is an open source scripting language for predictive analytics, statistical analysis, graphical representation, and reporting. R language was created first in 1993 by Robert Gentleman and Ross Ihaka at the University Of Auckland, New Zealand as an implementation of the S programming language. R Language is commonly used for statistical computing where there is a heavy data roles such as data mining and statistics, etc. It is a GNU project, which is similar to the S language and environment, which was developed at Bell Laboratories.

R Programming language includes vector, scalars, matrices, list and data frames. It also includes genetic functions that support linear modeling, non-linear modeling, classification, clustering and more. It is remained popular in academic due to its robust features. It is freely available under GNU General Public License and runs on UNIX platforms and other systems including Linux, Windows, and macOS.

Features of R Programming:
• R provides a large and integrated collection of tools for data analysis.
• R is an interpreting language, can be rather slow, but can be integrated with other high efficient languages such as C, C++ etc.
• R is an effective language that includes conditions, loops, user-defined recursive functions.

Local Environment Setup

If you want to set up your environment for R, then you can follow the given steps given below:

Windows Installation:

You can download the Windows installer version of R from R-3.4.3 for Windows (32/ 64 bit) (https://cran.r-project.org/bin/windows/base/).
After downloading, you can just double click and run the installer accepting the default settings. After the installation, click on the R icon on a screen to do Programming.

Why should you learn R programming Language?

While R can seem overly complex at the start, for data enthusiastic who is looking for a programming language while R can be worth for your consideration. R has a fantastic mechanism for creating the data structure. If you are doing data analysis, you should to be able to put your data into a natural form. You don’t need to warp your data into a particular structure. Graphics should be central to data analysis. We don’t intuitively grasp numbers like we do pictures. It is easy to make graphs for exploring data. Real data has missing values. R has many functions that control, how missing values are to be handled.
Some employees in Facebook use R language to analyze user behavior, while more than 500 Google Employees are using R to make its advertising more effective. One of the biggest benefit to open source software is that upgrades are much more regular. According to 2014 survey, R is one of the most powerful and popular programming language used by data scientist.

Hopefully, this helps you to get an idea of Why R programming is vital for doing data analysis.

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