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Why R language is used in Big Data

R is a free software environment and interpreted programming language, that is, it executes instructions directly, without prior compilation of the program to instructions in machine language. The term environment, in R, refers to a fully planned and consistent system, rather than an accumulation of specific and inflexible tools, as is often the case in other data analysis software. Are you dreaming to become to certified Pro Python Developer, then stop just dreaming, get your Python certification course from India��s Leading Python training institute?

This environment is commonly used for statistical and graphical computing, since it has a wide variety of statistical techniques (linear and nonlinear models, classical statistical tests, time series analysis, classification, grouping, etc.) and graphs. It works on UNIX platforms and similar systems (including FreeBSD and Linux), Windows and MacOS.

R Features 

Effective management and storage of data.

A set of operators for performing calculations with matrices.

A large collection of tools for data analysis.

Graphical utilities for data visualization.

A well-developed programming language that includes conditional jumps, loops, recursive functions, utilities for data input and output, etc.

It has a LaTeX-based documentation format, which is used to provide complete documentation in both physical and digital formats.

Using R in Big Data

In the field of Big Data, it is used for the manipulation, processing and graphic display of data. R allows us to:

Create high quality data visualizations. 

Create dashboards to visualize and analyze data. 

Create automatic reports. 

Have statistical analysis tools to deepen the knowledge of the data.

R is more than just a programming language . The user does not program properly, but uses R interactively: he tries, makes mistakes and tries again. Only when the cycle ends and the result is satisfactory does it produce a final result that is generally not a program, but a report.

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