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What Can Be Done With R?


R is designed for data mining and analysis. It helps you get real value from your data. It provides a huge set of tools for data analysts, including things like basic maths and statistics, statistical modeling and tests, probability distributions, missing values, signal processing, big data analytics, simulation, machine learning, and optimization. R is great for creating data structures that suit your unique problems.


R is more than just a statistics tool. It has some seriously powerful graphing and charting engines to bring your data to life. Facebook uses R to visualize data in order to get a feel for what it means, and you can do the same. You can create static or dynamic graphs for different devices and formats for whatever problem or dataset you have.


R works in the way that problem solvers think. Large data and statistical problems can quickly get bloated and over-complicated in other popular programming languages. R works in an intuitive way that makes these problems easier to solve, giving you better results faster.


R is interactive. Rather than just sequentially running algorithms or statistical operations, you can interact with an R program as it is running, allowing you to dive deeper and get a better understanding of your data. R helps with suggestions, gives options for how to change inputs, and lets you manipulate and interact with graphics as you are building them.


R can be used for data mining and machine learning. In fact, R is the language of choice for the world‘s best academics in this field. There are plenty of world-class data mining and machine learning functions available for free developed by leading computer science researchers. You can take advantage of these tools to solve your own data mining problems.

Benefits of R


R is the go-to statistical computing and graphics language for top developers and researchers. This means most developers and even students will have experience with R, resulting in a great pool of talent for you to tap into. The amazing package ecosystem surrounding R is also a huge bonus for developers. They can spend less time solving common problems and more time focusing on your unique needs.


R‘s open source nature and popularity with academics means it is always at the cutting edge of technology. All new research is done in R, so you have access to the latest techniques and technologies in data mining and statistics that might take years to make their way into commercial software. If your developers can handle it, you can be years ahead of your competitors.


R is open source so you can use it how you feel fit for free – forever. All of the programming libraries are published for free too, so there won‘t be any hidden costs down the road.

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