Learn R Programming. R was designed by statisticians and was specialized for statistical computing, and thus is known as the lingua franca of statistics. As technology improves, 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.


Top Reasons to Learn R Programming in 2019

R Programming in Open Source
R is a free, open source software. Its 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. R will run seamlessly irrespective of whether you are working on a Linux based, Mac or a Windows system.

Huge 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 which 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 17,000 technology professionals, the highest-paid IT skill was R programming. R language skills attract median salaries in excess of $110,000.

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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