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 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 Analysis |
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 |
| Commercial Viability |
Open source | Risk of deployment to open source | Free for everyone |
Commercial licenses Part of SQL Server 2016 Enterprise |
Contact us for more information.
