DS with R s1

R provides rich sets of tested packages and algorithms from the developer community. It have a wide range of library that can support your needs. It can deal with data sources with structural or unstructured format.

R language is widely used among statisticians and data miners for advanced data analysis. It has became the main player in the filed of data science nowaday.

R can address your problem if the below is one of your concern:

  • Plan to do machine learning and text mining
  • Limited fund to share visual report with a large users group
  • Predictive analysis on periodical basis
  • Attractive graphical summaries
  • Self develop data cleansing mechanism
  • Integrate special data task with in-house applications


Why R ? There are 5 reasons:

  • R is free. Multi-platforms and no hidden cost for business.
  • R is popular. The fastest growing computer language from 2014 to 2015 while others are stagnant. R packages repository that is comparable and even superior than the commercial software is growing exponentially.

          R language

  • R is powerful. It can be used in complex simulation under high perfomance computer clusters and supports multicore task distribution with high performance library.
  • R is flexible. Capabilities is ranging from complex or standard statistical practices, to baysian modelling, to GIS map building, to building interactive web applications, and to building interactive tests.
  • R is well-supported. One could find for the R questions much faster and more thorough than that typically commercial paid service.


Type of R Products

Features CRAN R Microsoft R Open Microsoft R Client Microsoft R Server
Big Data In-memory bound
Can only process datasets that fit into the available memory
In-memory bound
Can only process datasets that fit into the available memory
In-memory bound
Can process datasets that fit into the available memory
Operates on large volumes when connected to R Server
Disk scalability
Operates on bigger volumes & factors
Speed of
Single-threaded process Multi-threaded when MKL is installed for R open functions Multi-threaded with MKL for non-ScaleR functions
Up to 2 threads for ScaleR functions with a local compute context
Full parallel threading & processing
Open source Risk of deployment to open source Free for everyone

Commercial licenses

Part of SQL Server 2016 Enterprise


Contact us for more information.