The R language is an open source programming language for developing statistical analyses, predictive analytics models and data visualizations. Created in the 1990s, it was long used mainly for small-scale data analysis in academic circles. In recent years, vendors have picked up on R for its large user base and are now offering products that can scale up to meet business analytics needs.
The buzz: A February 2014 Gartner Magic Quadrant report on advanced analytics tools put R vendor Revolution Analytics in its "visionaries" category. R-based software is often cheaper than statistical analysis tools from the likes of SAS and IBM. And because so many universities use R to teach budding data scientists, its user base has come to be seen as a virtual talent pool by businesses. Another plus is an online library of ready-to-use code sets that can save data analysts programming time.
R programming language demands the right use case
R language well-suited to analytical data sampling and manipulations
Revolution brings R programming language to AWS, plus SAS on Hadoop
The reality: Commercial applications built on the R programming language are still in their infancy. It's desktop-based and processes data in memory, so it can handle only limited data volumes, and analyses can be slow. Though many vendors now offer workarounds to distribute jobs across multiple servers, using R is still coding-intensive. Businesses should have programmers who know R and make sure the tools they buy can handle their data sets before implementing anything -- or else their projects might become a statistic.
- A Guide to Predictive Analytics –TIBCO
- Deploying Predictive Analytics Models –DataRobot Singapore Pte Ltd
- Assessing the Impact of Predictive Analytics –Hewlett Packard Enterprise
- Predictive Analytics with Machine Learning –Estafet Ltd