R for Data Science

By Hari V.G.

R features for data science applications:

R is a very unique language and has some really interesting features which aren’t present in other languages. These features are very important for data science applications. Some of these features are explained below:

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 the 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 interfaces, statistical functions, etc. All these functions are specifically used for data science applications and statistical analysis.

Why is R important for data science professionals?

R is very important in data science because of its versatility in the field of statistics. R is usually used in the field of data science when the task requires a special analysis of data for standalone or distributed computing.

R is also perfect for exploration. It can be used in any kind of analysis work, as it has many tools and is also very extensible. Additionally, it is a perfect fit for big data solutions.

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.

So, most of the development of the R language is done by keeping data science and statistics in mind. As a result, R has become the default choice for data science applications and data science professionals.