Big Data analysis and R programming language

The one of the latest and most promising IT trends today is Big Data analysis. Big Data are datasets that grow so large that they become awkward to work with using on-hand database management tools. Difficulties include capture, storage, search, sharing, analytics, and visualizing.

SoftElegance is attended in tools and technologies to work with Big Data, such as Probase, Hadoop, R project, etc. Today we would like to introduce you R programming language, that provids a wide variety of statistical and graphical techniques, including linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, and others.

R is a programming language and software environment for statistical computing and graphics. The R language has become a de facto standard among statisticians for developing statistical software, and is widely used for statistical software development and data analysis.

This is cool example graphics created in R with 14 lines of code. The data for such graphics could be taken from big unstructured missives of data such as Big Data.

Big Data

Big Data plays big role in today’s businesses: Large organizations increasingly face the need to maintain massive amounts of structured and unstructured data — from transaction information in data warehouses to employee tweets, from supplier records to regulatory filings — to comply with government regulations. That need has been driven even more by recent court cases that have encouraged companies to keep large quantities of documents, email messages, and other electronic communications such as instant messaging and IP telephony that may be required for e-discovery if they face litigation.

R programming language

R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, …) and graphical techniques, and is highly extensible. The S language is often the vehicle of choice for research in statistical methodology, and R provides an Open Source route to participation in that activity.

R could be downloaded for various platforms such as Unix\Linux, Windows, MasOS and it’s a GNU project.

See the video Amanda Cox from The New York Times’ graphics department spoke to the New York R statistical programming meetup.

Conclusion

Waiting for April 13, 2011 for Release 2.13.0, and pure implementation for C# and in Visual Studio. At the moment it’s possible to write C code to manipulate R objects directly.