Why R Language is Important For Data Science ????

Introduction to R Language and Data science with R Language :

The R language is very useful in the field of statistical computation and data science. It offers various techniques like clustering, time-series analysis and classification technique, nonlinear/linear modelling, and classical statistical tests. Also, this language is very adaptable and extensible. Along with these, it supports many graphical techniques too…

Features of R language:

  • Multiple calculations can be done with vectors

R is a vector language. Unlike other languages, R can do many things at once. You can add functions to a single vector without putting it in a loop. This feature of R makes it more powerful and faster than the other languages.

  • You can run your code without any compiler

As R is an interpreted language, you can run your code without any compilers. In other programming languages like Java or C, a compiler is required to make out the instructions from your code before running it. However, R directly interprets the code into a full-fledged program. This makes development of the code easier.

  • Statistical language

R was designed for statistical studies, and proved excellent in its field of work. However, as the power of R is being realized, it is finding use in a variety of other places, starting from financial studies to genetics and biology and medicine. This is because R is a Turing-complete language, which means that any task can be programmed in R.

  • Data science support

R provides support functions for data science applications. Some of them are charts, graphs, data interface, statistical functions, etc. All these functions are specifically used for data science applications and statistical analysis.

Following are some of the highlights which show why R is important for data science:

  • Data analysis software: R is s data analysis software. It is used by data scientists for statistical analysis, predictive modeling and visualization.
  • Statistical analysis environment: R provides a complete environment for statistical analysis. It is easy to implement statistical methods in R. Most of the new research in statistical analysis and modeling is done using R. So, the new techniques are first available only in R.
  • Open source: R is open source technology, so it is very easy to integrate with other applications.
  • Community support: R has the community support of leading statisticians, data scientists from different parts of the world and is growing rapidly.

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