Learn R Programming Offline & become a job-ready R programmer. R Language was designed by statisticians. R Language is specialized for statistical computing, statistical data analytics Machine learning, and deep learning, Web applications using RShinyand. As technology is improving, the data companies or research institutions collect has become more and more complex, and R has been adopted by many as the language of choice to analyze data.

R is great for machine learning, data visualization and analysis, and some areas of scientific computing. This app contains all the major topics of R Programming with Excellent Code Examples and Projects.


Why you should Learn R Programming?

R Programming in Open Source:
R is free, open-source software. It's plug and play, install R once, and start having fun with it. What more? You can even modify the code and add your own innovations to it. R language has no license restrictions as it is issued under the GNU.

R is Cross-Platform Compatible:
One of the biggest advantages of R is that you can run R on several operating systems and varied Software/Hardware.

Large Community:
Let’s say you are working on a financial project to find out how many of the credit card transactions are fraudulent and reach a roadblock while building the classification model. Thankfully, R boasts of a huge community to tap into whenever you need help. So, you can always seek help from people who have worked on similar projects.

Interactive Web apps:
Ever wondered if there is a tool that helps in creating stunning web applications directly from your data analysis software?
R provides a package called shiny, just for that. With the help of shiny, you can create interactive web pages and impressive dashboard designs directly from your R Console.

High Paying Jobs:
n a Survey done by Dice Tech of over 18,000 technology professionals, the highest-paid IT skill was R programming. R language skills attract median salaries in excess of $120,000.

With R language as a skill-set, one can find jobs such as:
1- Data Analyst
2- Data Scientist
3- Quantitative Analyst
4- Financial Analyst

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