R Programming: The Present

R Programming: The Present

In my previous blog I had discussed the past of R programming language, now taking one step forward, this blog introduces you to the present status of R programming language.

So, at the end of reading this blog you will understand that where R language and Analytics is being used today.

Why R Programming is Essential?

R Programming and Analytics is widely used in various fields now because of generation of data in massive volume. Analytics is in enormous demand as:

“You may have Data without Information but you cannot have Information without Data”

 R in different fields

R in different fields

Analytics is being introduced to every field to make sense out of Data, to get knowledge from Data, to understand value of customers, for customer acquisition, to predict upcoming business, etc.

Globally, millions of Researchers, Data Analysts, Data Professionals and Data Scientists use R for Data Analysis, Predictive Modelling, Machine Learning and Graphical Analysis.

Package Bank of R

Useful Packages in R base

Useful Packages in R base

Even if you just look at the standard R distribution (the base and recommended packages), R can do almost everything you require for Statistical Analysis, Data Manipulation and Visualization. And there’s 4000+ packages on CRAN and on other repositories.

The above image shows you the list of very useful packages that are used by Data analyst and Data Scientists worldwide. R gives you a broad variety of predictive modelling techniques, statistical methods, machine learning methods and is highly extensible.

You can simply download and use 4000 plus available methods and packages free of charge.

Datasets in R

R provides you number of Datasets that can be used in Analytics and these datasets are built in and are available in packages. Various datasets have been designed respective of type of Analytics that could be:

  • Time Series Data
  • Numeric Data
  • Categorical Data
  • Character Data
  • Small Data
  • Large Data

You can explore all the datasets by downloading R base in your system. So now you have an idea about the presence of R programming and Analytics in different fields and in my next blog I will explore the future of R Programming language and Analytics.

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Neha is an enthusiastic person for her work. She is eager to explore Analytics, predictive modelling and have zeal to experiment with various technologies. Apart from writing blogs, she has a keen interest to explore Historical places. She comes up with innovative ideas and love to challenge her potential as she focus on self-improvement. She loves to visit new places with friends in hunt of delicious food.

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