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R & Open Source

Updated: Jul 13, 2020

The right tool for the right job. There is something immensely satisfiyng about knowing that you are not limited by your tools but by your current level.

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In order to be able to be at the forefront of technology, the right tools are needed.

Open source is not only charity and good intentions. It is good business and professionals all over the world are using it to push the boundaries of what can be achieved. 

R is a free software environment for mathmatical/statistical computing and graphics. The R community has evolved significantly due to the R Core team and the  Public Benefit Corporation (PBC) RStudio. 

The evolution in R has made it incredible for data science. Ivin suggest that this evolution has made it perfect for finance and economics. You can solve complex problems with functions, you can make robust unit test, make intuitive inline documentation that is exported, automize and schedule your algorithms.

The core R functionality is extremely robust when it comes to data handling and open source packages managed by CRAN, a R repository for packages, has made data manipulation systemized with packages as tidy and dplyr.

Since, a graphical layer has been added to the R environment through the packages system. 

A wide range of packages for machine learning and high level statistics are also available. This means that you can rather easily get going with high level analysis.

You have the option to use packages or you can get inspiration and code it yourself. The incredible thing is that you have a tool that is strong enough to do all the analysis you should expect from a modern finance department.

You have professors on top tier universities and business schools that have shown how to create machine learning algorithms with R.

You have world class professionals that have created graphical solutions that looks like something created in Adobe Photoshop

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