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
Events in the field of data that impacted us in 2011
1) Oracle unveiled plans for R Enterprise. This is one of the strongest statements of its focus on in-database analytics. Oracle also unveiled plans for a Public Cloud
2) SAS Institute released version 9.3 , a major analytics software in industry use.
3) IBM acquired many companies in analytics and high tech. Again.However the expected benefits from Cognos-SPSS integration are yet to show a spectacular change in market share.
2011 Selected acquisitions
4) SAP promised a lot with SAP HANA- again no major oohs and ahs in terms of market share fluctuations within analytics.
5) Amazon continued to lower prices of cloud computing and offer more options.
6) Google continues to dilly -dally with its analytics and cloud based APIs. I do not expect all the APIs in the Google APIs suit to survive and be viable in the enterprise software space. This includes Google Cloud Storage, Cloud SQL, Prediction API at https://code.google.com/apis/console/b/0/ Some of the location based , translation based APIs may have interesting spin offs that may be very very commercially lucrative.
7) Microsoft -did- hmm- I forgot. Except for its investment in Revolution Analytics round 1 many seasons ago- very little excitement has come from MS plans in data mining- The plugins for cloud based data mining from Excel remain promising yet , while Azure remains a stealth mode starter.
8) Revolution Analytics promised us a GUI and didnt deliver (till yet 🙂 ) . But it did reveal a much better Enterprise software Revolution R 5.0 is one of the strongest enterprise software in the R /Stat Computing space and R’s memory handling problem is now an issue of perception than actual stuff thanks to newer advances in how it is used.
9) More conferences, more books and more news on analytics startups in 2011. Big Data analytics remained a strong buzzword. Expect more from this space including creative uses of Hadoop based infrastructure.
10) Data privacy issues continue to hamper and impede effective analytics usage. So does rational and balanced regulation in some of the most advanced economies. We expect more regulation and better guidelines in 2012.
Revolution Analytics Webinar-
Big Data Starts with R
Traditional IT infrastructure is simply unable to meet
the demands of the new “Big Data Analytics” landscape. Many enterprises are turning to the “R” statistical programming language and Hadoop (both open source projects) as a potential solution. This webinar will introduce the statistical capabilities of R within the Hadoop ecosystem. We’ll cover:
- An introduction to new packages developed by Revolution Analytics to facilitate interaction with the data stores HDFS and HBase so that they can be leveraged from the R environment
- An overview of how to write Map Reduce jobs in R using Hadoop
- Special considerations that need to be made when working with R and Hadoop.
We’ll also provide additional resources that are available to people interested in integrating R and Hadoop.
|Wed, Dec 14th
11:00AM – 11:30AM PT
|Revolution R Enterprise – 100% R and MoreR users already know why the R language is the lingua franca of statisticians today: because it’s the most powerful statistical language in the world. Revolution Analytics builds on the power of open source R, and adds performance, productivity and integration features to create Revolution R Enterprise. In this webinar, author and blogger David Smith will introduce the additional capabilities of Revolution R Enterprise.
So I got the email from Revolution R. Version 5.0 is ready for download, and unlike half hearted attempts by many software companies they make it easy for the academics and researchers to get their free copy. Free as in speech and free as in beer.
1) R ‘s memory problem is now an issue of marketing and branding. Revolution Analytics has definitely bridged this gap technically beautifully and I quote from their documentation-
The primary advantage 64-bit architectures bring to R is an increase in the amount of memory available to a given R process.
The first benefit of that increase is an increase in the size of data objects you can create. For example, on most 32-bit versions of R, the largest data object you can create is roughly 3GB; attempts to create 4GB objects result in errors with the message “cannot allocate vector of length xxxx.”
On 64-bit versions of R, you can generally create larger data objects, up to R’s current hard limit of 231 1 elements in a vector (about 2 billion elements). The functions memory.size and memory.limit help you manage the memory used byWindows versions of R.
In 64-bit Revolution R Enterprise, R sets the memory limit by default to the amount of physical RAM minus half a gigabyte, so that, for example, on a machine with 8GB of RAM, the default memory limit is 7.5GB:
2) The User Interface is best shown as below or at https://docs.google.com/presentation/pub?id=1V_G7r0aBR3I5SktSOenhnhuqkHThne6fMxly_-4i8Ag&start=false&loop=false&delayms=3000
-(but I am still hoping for the GUI ,Revolution Analytics promised us for Christmas)
3) The partnership with Microsoft HPC is quite awesome given Microsoft’s track record in enterprise software penetration
but I am also interested in knowing more about the Oracle version of R and what it will do there.
All you contest junkies, R lovers and general change the world people, here’s a new contest to use R in a business application
REVOLUTION ANALYTICS LAUNCHES “APPLICATIONS OF R IN BUSINESS” CONTEST
$20,000 in Prizes for Users Solving Business Problems with R
PALO ALTO, Calif. – September 1, 2011 – Revolution Analytics, the leading commercial provider of R software, services and support, today announced the launch of its “Applications of R in Business” contest to demonstrate real-world uses of applying R to business problems. The competition is open to all R users worldwide and submissions will be accepted through October 31. The Grand Prize winner for the best application using R or Revolution R will receive $10,000.
The bonus-prize winner for the best application using features unique to Revolution R Enterprise – such as itsbig-data analytics capabilities or its Web Services API for R – will receive $5,000. A panel of independent judges drawn from the R and business community will select the grand and bonus prize winners. Revolution Analytics will present five honorable mention prize winners each with $1,000.
“We’ve designed this contest to highlight the most interesting use cases of applying R and Revolution R to solving key business problems, such as Big Data,” said Jeff Erhardt, COO of Revolution Analytics. “The ability to process higher-volume datasets will continue to be a critical need and we encourage the submission of applications using large datasets. Our goal is to grow the collection of online materials describing how to use R for business applications so our customers can better leverage Big Analytics to meet their analytical and organizational needs.”
To enter Revolution Analytics’ “Applications of R in Business” competition Continue reading “Use R for Business- Competition worth $ 20,000 #rstats”
Here is a contest based community called CrowdANALYTIX.com which is quite nice and offers you free Revolution R for the statistical and analytical contests based there (a bit like Kaggle.com http://www.kaggle.com/). There are only 3 contests right now and that too low volume but I guess that number should increase. Also they seem to have a consulting arm.
Latest Analytics website- welcome! http://www.crowdanalytix.com/contests
David Smith of Revo presents a nice webinar on the capabilities and abilities of Revolution R- if you are R curious and wonder how the commercial version has matured- you may want to take a look.
click below to view an executive Webinar
Revolution R Enterprise—presented by author and blogger David Smith:
Revolution R: 100% R and More
This Webinar covers how R users can upgrade to:
- Multi-processor speed improvements and parallel processing
- Productivity and debugging with an integrated development environment (IDE) for the R language
- “Big Data” analysis, with out-of-memory storage of multi-gigabyte data sets
- Web Services for R, to integrate R computations and graphics into 3rd-Party applications like Excel and BI Dashboards
- Expert technical support and consulting services for R
This webinar will be of value to current R users who want to learn more about the additional capabilities of Revolution R Enterprise to enhance the productivity, ease of use, and enterprise readiness of open source R. R users in academia will also find this webinar valuable: we will explain how all members of the academic community can obtain Revolution R Enterprise free of charge.
contact -1-855-GET-REVO or via online form.
email@example.com | (650) 330-0553 | Twitter @RevolutionR