R: All content tagged as R in NoSQL databases and polyglot persistence
In Academic torrents: Almost 1.7TB of research data available, I complained about the lack of interesting open data. Dan Goldin’s Visualizing RunKeeper data in R is a good example of what I mean. While learning R, he used his own data about his running results. That made it both interesting and fun.
What better way to celebrate running 1000 miles in 2013 than dumping the data into R and generating some visualizations? It’s also a step in my quest to replace Excel with R.
I hope no one will argue that this is a more exciting experience than learning a new technology while using the Enron email archive.
Original title and link: Visualizing RunKeeper data in R ( ©myNoSQL)
If you find a good way to put together two things that excel at what they are doing, you’ll most probably get a gold nugget. That’s what I feel when thinking about integrating R and Hadoop. Jeffrey Breen’s slides seem to agree:
Until yesterday I didn’t know there’s an attempt to implement the R language on the JVM. But there’s one: renjin. And it sounds like it needs some helping hands to accomplish its goal of reaching a 1.0 release in 2012.
In case you’d wonder why R on the JVM—same question have been asked so many times related to JRuby, Jython, etc—just think of:
- it would allow access to the tons of Java libraries
- it would integrate seamlessly with tools like Hadoop
If you are ready to start contributing head on to the Renjin’s plan of attack for 2012 page and learn where your help would be needed.
Original title and link: Call to Arms: Renjin, R Implementation on JVM Needs Contributions ( ©myNoSQL)
Revolution Analytics, the commercial provider of the leading statistics language for advanced analytics as showed also by this data analysis tools survey among data scientist has released Revolution R Enterprise 5.0 featuring:
- Distributed/Parallel Computing: Automatically distribute statistical analyses from a desktop across nodes of a cluster through Windows HPC server and distribute R function calls across nodes.
- Scalable Data Management: Increase flexibility in data analysis with new data import and cleaning/manipulation tools.
- Integration with Hadoop: Support MapReduce programming in R and integration with HDFS and HBASE with Cloudera Certified Technology
- Expanded Scalable Analytics Functionality: Apply new big data statistics algorithms including principal components analysis, factor analysis, contingency table analysis and more.
- Enhanced R Productivity Environment: Create and build R packages with expanded support features.
- Enhanced RevoDeployR server: Add multiple compute nodes to support more users, batch execution of large analysis jobs, and LDAP enterprise security support.
- Upgraded Open Source R: Revolution R 5.0 includes the fully-patched R 2.13.2, which features a new byte-compiler to improve performance of user-written functions and packages.
If you are not familiar with R, check this brief description of what is R and how can it help.
Original title and link: Revolution R Enterprise 5.0 Released ( ©myNoSQL)