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Just got the email-more software is good news!
Revolution R Enterprise 6.0 for 32-bit and 64-bit Windows and 64-bit Red Hat Enterprise Linux (RHEL 5.x and RHEL 6.x) features an updated release of the RevoScaleR package that provides fast, scalable data management and data analysis: the same code scales from data frames to local, high-performance .xdf files to data distributed across a Windows HPC Server cluster or IBM Platform Computing LSF cluster. RevoScaleR also allows distribution of the execution of essentially any R function across cores and nodes, delivering the results back to the user.
Detailed information on what’s new in 6.0 and known issues:
and from the manual-lots of function goodies for Big Data
- IBM Platform LSF Cluster support [Linux only]. The new RevoScaleR function, RxLsfCluster, allows you to create a distributed compute context for the Platform LSF workload manager.
- Azure Burst support added for Microsoft HPC Server [Windows only]. The new RevoScaleR function, RxAzureBurst, allows you to create a distributed compute context to have computations performed in the cloud using Azure Burst
- The rxExec function allows distributed execution of essentially any R function across cores and nodes, delivering the results back to the user.
- functions RxLocalParallel and RxLocalSeq allow you to create compute context objects for local parallel and local sequential computation, respectively.
- RxForeachDoPar allows you to create a compute context using the currently registered foreach parallel backend (doParallel, doSNOW, doMC, etc.). To execute rxExec calls, simply register the parallel backend as usual, then set your compute context as follows: rxSetComputeContext(RxForeachDoPar())
- rxSetComputeContext and rxGetComputeContext simplify management of compute contexts.
- rxGlm, provides a fast, scalable, distributable implementation of generalized linear models. This expands the list of full-featured high performance analytics functions already available: summary statistics (rxSummary), cubes and cross tabs (rxCube,rxCrossTabs), linear models (rxLinMod), covariance and correlation matrices (rxCovCor),
binomial logistic regression (rxLogit), and k-means clustering (rxKmeans)example: a Tweedie family with 1 million observations and 78 estimated coefficients (categorical data)
took 17 seconds with rxGlm compared with 377 seconds for glm on a quadcore laptop
and easier working with R’s big brother SAS language
RevoScaleR high-performance analysis functions will now conveniently work directly with a variety of external data sources (delimited and fixed format text files, SAS files, SPSS files, and ODBC data connections). New functions are provided to create data source objects to represent these data sources (RxTextData, RxOdbcData, RxSasData, and RxSpssData), which in turn can be specified for the ‘data’ argument for these RevoScaleR analysis functions: rxHistogram, rxSummary, rxCube, rxCrossTabs, rxLinMod, rxCovCor, rxLogit, and rxGlm.
you can analyze a SAS file directly as follows:
# Create a SAS data source with information about variables and # rows to read in each chunk
sasDataFile <- file.path(rxGetOption(“sampleDataDir”),”claims.sas7bdat”)
sasDS <- RxSasData(sasDataFile, stringsAsFactors = TRUE,colClasses = c(RowNum = “integer”),rowsPerRead = 50)
# Compute and draw a histogram directly from the SAS file
rxHistogram( ~cost|type, data = sasDS)
# Compute summary statistics
rxSummary(~., data = sasDS)
# Estimate a linear model
linModObj <- rxLinMod(cost~age + car_age + type, data = sasDS)
# Import a subset into a data frame for further inspection
subData <- rxImport(inData = sasDS, rowSelection = cost > 400,
varsToKeep = c(“cost”, “age”, “type”))
The installation instructions and instructions for getting started with Revolution R Enterprise & RevoDeployR for Windows: http://www.revolutionanalytics.com/downloads/instructions/windows.php
I was searching for a Linux install of Revolution’s latest enterprise version, but it seems version 4 will be available on Red Hat Enterprise Linux only by Decemebr 2010. Also even though Revolution once opted for co branding with Canonical’s Karmic Koala, they seem to have ignored Ubuntu from the Enterprise version of Revolution R.
|Base R||Revolution R Community||Revolution R Enterprise
|Target Use||Open Source||Product Evaluation & Simple Prototyping||Business, Research & Academics|
|100% Compatible with R language||X||X||X|
|Certified for Stability||X||X|
|Getting Started Guide||X||X|
|Performance & Scalability|
|Analyze larger data sets with 64-bit RAM||X||X|
|Optimized for Multi-processor workstations||X||X|
|Multi-threaded Math libraries||X||X|
|Parallel Programming (Single Workstation)||X||X|
|“Big Data” Analysis|
|Terabyte-Class File Structures||X|
|Specialized “Big Data” Algorithms||X|
|Integrated Web Services|
|Scalable Web Services Platform||X*|
|Comprehensive Data Analysis GUI||X*|
|Online Support||Mailing List||Forum||X|
|Support for Base & Recommended R Packages||X||X||X|
|Authorized Training & Consulting||X|
|Mac OS X||X||X|
|Red Hat Enterprise Linux||X|
and though the page on RED HAT’s Partner page for Revolution seems old/not so updated
, I was still curious to see what the buzz about Red Hat is all about.
And one of the answers is Red Hat is now a 7.8 Billion Dollar Company.
Red Hat Reports Second Quarter Results
- Revenue of $220 million, up 20% from the prior year
- GAAP operating income up 24%, non-GAAP operating income up 25% from the prior year
- Deferred revenue of $650 million, up 12% from the prior year
RALEIGH, NC – Sept 22, 2010 – Red Hat, Inc. (NYSE: RHT), the world’s leading provider of open source solutions, today announced financial results for its fiscal year 2011 second quarter ended August 31, 2010.
Total revenue for the quarter was $219.8 million, an increase of 20% from the year ago quarter. Subscription revenue for the quarter was $186.2 million, up 19% year-over-year.
and the stock goes zoom 48 % up for the year
(Note to Google- please put the URL shortener on Google Finance as well)
From the monthly newsletter- which I consider quite useful for keeping updated on application of R
Every month, we’ll bring you the latest news about Revolution’s products and events in this section. Follow us on Twitter at @RevolutionR for up-to-the-minute news and updates from Revolution Analytics!
Revolution R Enterprise 4.0 for Windows now available. Based on the latest R 2.11.1 and including the RevoScaleR package for big-data analysis in R, Revolution R Enterprise is now available for download for Windows 32-bit and 64-bit systems. Click here to subscribe, or available free to academia.
New! Integrate R with web applications, BI dashboards and more with web services. RevoDeployR is a new Web Services framework that integrates dynamic R-based computations into applications for business users. It will be available September 30 with Revolution R Enterprise Server on RHEL 5. Click here to learn more.
Free Webinar, September 22: In a joint webinar from Revolution Analytics and Jaspersoft, learn how to use RevoDeployR to integrate advanced analytics on-demand in applications, BI dashboards, and on the web. Register here.
Revolution in the News: SearchBusinessAnalytics.com previews the forthcoming Revolution R GUI; Channel Register introduces RevoDeployR, while IT Business Edge shows off the Web Services architecture; and ReadWriteWeb.com looks at how RevoScaleR tackles the Big Data explosion.
Inside-R: A new site for the R Community. At www.inside-R.org you’ll find the latest information about R from around the Web, searchable R documentation and packages, hints and tips about R, and more. You can even add a “Download R” badge to your own web-page to help spread the word about R.
R News, Tips and Tricks from the Revolutions blog
The Revolutions blog brings you daily news and tips about R, statistics and open source. Here are some highlights from Revolutions from the past month.
R’s key role in the oil spill response: Read how NIST’s Division Chief of Statistical Engineering used R to provide critical analysis in real time to the Secretaries of Energy and the Interior, and helped coordinate the government’s response.
Animating data with R and Google Earth: Learn how to use R to create animated visualizations of geographical data with Google Earth, such as this video showing how tuna migrations intersect with the location of the Gulf oil spill.
Are baseball games getting longer? Or is it just Red Sox games? Ryan Elmore uses nonparametric regression in R to find out.
Keynote presentations from useR! 2010: the worldwide R user’s conference was a great success, and there’s a wealth of useful tips and information in the presentations. Video of the keynote presentations are available too: check out in particular Frank Harrell’s talk Information Allergy, and Friedrich Leisch’s talk on reproducible statistical research.
Looking for more R tips and tricks? Check out the monthly round-ups at the Revolutions blog.
Every month, we’ll highlight some upcoming events from R Community Calendar.
September 23: The San Diego R User Group has a meetup on BioConductor and microarray data analysis.
September 28: The Sydney Users of R Forum has a meetup on building world-class predictive models in R (with dinner to follow).
September 28: The Los Angeles R User Group presents an introduction to statistical finance with R.
September 28: The Seattle R User Group meets to discuss, “What are you doing with R?”
September 29: The Raleigh-Durham-Chapel Hill R Users Group has its first meeting.
October 7: The NYC R User Group features a presentation by Prof. Andrew Gelman.
subscribe here for Revo’s Monthly newsletter-