Running R and RStudio Server on Red Hat Linux RHEL #rstats

Installing R

(OR sudo rpm -ivh )


  • sudo yum install R


  • sudo R

(and to paste in Linux Window- just use Shift + Insert)

To Install RStudio (from


  •  sudo yum install --nogpgcheck rstudio-server-0.97.320-i686.rpm

OR 64-bit

  •  sudo yum install --nogpgcheck rstudio-server-0.97.320-x86_64.rpm


  • sudo rstudio-server verify-installation

Changing Firewalls in your RHEL

-Change to Root

  • sudo bash 

-Change directory

  • cd etc/sysconfig

-Read Iptables ( or firewalls file)

  • vi iptables

( to quite vi , press escape, then colon :  then q )

-Change Iptables to open port 8787

  • /sbin/iptables -A INPUT -p tcp --dport 8787 -j ACCEPT

Add new user name (here newuser1)

  • sudo useradd newuser1

Change password in new user name

  • sudo passwd newuser1

Now just login to IPADDRESS:8787 with user name and password above

(credit- IBM SmartCloud Support ,, Rstudio help, David Walker, ,Michael Grieb)


Revolution R Enterprise 6.0 launched!

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: rxHistogramrxSummary, 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:

Amazon Ec2 goes Red Hat

message from Amazing Amazon’s cloud team- this will also help for #rstats users given that revolution Analytics full versions on RHEL.


on-demand instances of Amazon EC2 running Red Hat Enterprise Linux (RHEL) for as little as $0.145 per instance hour. The offering combines the cost-effectiveness, scalability and flexibility of running in Amazon EC2 with the proven reliability of Red Hat Enterprise Linux.

Highlights of the offering include:

  • Support is included through subscription to AWS Premium Support with back-line support by Red Hat
  • Ongoing maintenance, including security patches and bug fixes, via update repositories available in all Amazon EC2 regions
  • Amazon EC2 running RHEL currently supports RHEL 5.5, RHEL 5.6, RHEL 6.0 and RHEL 6.1 in both 32 bit and 64 bit formats, and is available in all Regions.
  • Customers who already own Red Hat licenses will continue to be able to use those licenses at no additional charge.
  • Like all services offered by AWS, Amazon EC2 running Red Hat Enterprise Linux offers a low-cost, pay-as-you-go model with no long-term commitments and no minimum fees.

For more information, please visit the Amazon EC2 Red Hat Enterprise Linux page.

which is

Amazon EC2 Running Red Hat Enterprise Linux

Amazon EC2 running Red Hat Enterprise Linux provides a dependable platform to deploy a broad range of applications. By running RHEL on EC2, you can leverage the cost effectiveness, scalability and flexibility of Amazon EC2, the proven reliability of Red Hat Enterprise Linux, and AWS premium support with back-line support from Red Hat.. Red Hat Enterprise Linux on EC2 is available in versions 5.5, 5.6, 6.0, and 6.1, both in 32-bit and 64-bit architectures.

Amazon EC2 running Red Hat Enterprise Linux provides seamless integration with existing Amazon EC2 features including Amazon Elastic Block Store (EBS), Amazon CloudWatch, Elastic-Load Balancing, and Elastic IPs. Red Hat Enterprise Linux instances are available in multiple Availability Zones in all Regions.

Sign Up


Pay only for what you use with no long-term commitments and no minimum fee.

On-Demand Instances

On-Demand Instances let you pay for compute capacity by the hour with no long-term commitments.

Region:US – N. VirginiaUS – N. CaliforniaEU – IrelandAPAC – SingaporeAPAC – Tokyo
Standard Instances Red Hat Enterprise Linux
Small (Default) $0.145 per hour
Large $0.40 per hour
Extra Large $0.74 per hour
Micro Instances Red Hat Enterprise Linux
Micro $0.08 per hour
High-Memory Instances Red Hat Enterprise Linux
Extra Large $0.56 per hour
Double Extra Large $1.06 per hour
Quadruple Extra Large $2.10 per hour
High-CPU Instances Red Hat Enterprise Linux
Medium $0.23 per hour
Extra Large $0.78 per hour
Cluster Compute Instances Red Hat Enterprise Linux
Quadruple Extra Large $1.70 per hour
Cluster GPU Instances Red Hat Enterprise Linux
Quadruple Extra Large $2.20 per hour

Pricing is per instance-hour consumed for each instance type. Partial instance-hours consumed are billed as full hours.

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Available Instance Types

Standard Instances

Instances of this family are well suited for most applications.

Small Instance – default*

1.7 GB memory
1 EC2 Compute Unit (1 virtual core with 1 EC2 Compute Unit)
160 GB instance storage
32-bit platform
I/O Performance: Moderate
API name: m1.small

Large Instance

7.5 GB memory
4 EC2 Compute Units (2 virtual cores with 2 EC2 Compute Units each)
850 GB instance storage
64-bit platform
I/O Performance: High
API name: m1.large

Extra Large Instance

15 GB memory
8 EC2 Compute Units (4 virtual cores with 2 EC2 Compute Units each)
1,690 GB instance storage
64-bit platform
I/O Performance: High
API name: m1.xlarge

Micro Instances

Instances of this family provide a small amount of consistent CPU resources and allow you to burst CPU capacity when additional cycles are available. They are well suited for lower throughput applications and web sites that consume significant compute cycles periodically.

Micro Instance

613 MB memory
Up to 2 EC2 Compute Units (for short periodic bursts)
EBS storage only
32-bit or 64-bit platform
I/O Performance: Low
API name: t1.micro

High-Memory Instances

Instances of this family offer large memory sizes for high throughput applications, including database and memory caching applications.

High-Memory Extra Large Instance

17.1 GB of memory
6.5 EC2 Compute Units (2 virtual cores with 3.25 EC2 Compute Units each)
420 GB of instance storage
64-bit platform
I/O Performance: Moderate
API name: m2.xlarge

High-Memory Double Extra Large Instance

34.2 GB of memory
13 EC2 Compute Units (4 virtual cores with 3.25 EC2 Compute Units each)
850 GB of instance storage
64-bit platform
I/O Performance: High
API name: m2.2xlarge

High-Memory Quadruple Extra Large Instance

68.4 GB of memory
26 EC2 Compute Units (8 virtual cores with 3.25 EC2 Compute Units each)
1690 GB of instance storage
64-bit platform
I/O Performance: High
API name: m2.4xlarge

High-CPU Instances

Instances of this family have proportionally more CPU resources than memory (RAM) and are well suited for compute-intensive applications.

High-CPU Medium Instance

1.7 GB of memory
5 EC2 Compute Units (2 virtual cores with 2.5 EC2 Compute Units each)
350 GB of instance storage
32-bit platform
I/O Performance: Moderate
API name: c1.medium

High-CPU Extra Large Instance

7 GB of memory
20 EC2 Compute Units (8 virtual cores with 2.5 EC2 Compute Units each)
1690 GB of instance storage
64-bit platform
I/O Performance: High
API name: c1.xlarge

Cluster Compute Instances

Instances of this family provide proportionally high CPU resources with increased network performance and are well suited for High Performance Compute (HPC) applications and other demanding network-bound applications. Learn more about use of this instance type for HPC applications.

Cluster Compute Quadruple Extra Large Instance

23 GB of memory
33.5 EC2 Compute Units (2 x Intel Xeon X5570, quad-core “Nehalem” architecture)
1690 GB of instance storage
64-bit platform
I/O Performance: Very High (10 Gigabit Ethernet)
API name: cc1.4xlarge

Cluster GPU Instances

Instances of this family provide general-purpose graphics processing units (GPUs) with proportionally high CPU and increased network performance for applications benefitting from highly parallelized processing, including HPC, rendering and media processing applications. While Cluster Compute Instances provide the ability to create clusters of instances connected by a low latency, high throughput network, Cluster GPU Instances provide an additional option for applications that can benefit from the efficiency gains of the parallel computing power of GPUs over what can be achieved with traditional processors. Learn more about use of this instance type for HPC applications.

Cluster GPU Quadruple Extra Large Instance

22 GB of memory
33.5 EC2 Compute Units (2 x Intel Xeon X5570, quad-core “Nehalem” architecture)
2 x NVIDIA Tesla “Fermi” M2050 GPUs
1690 GB of instance storage
64-bit platform
I/O Performance: Very High (10 Gigabit Ethernet)
API name: cg1.4xlarge


Getting Started

To get started using Red Hat Enterprise Linux on Amazon EC2, perform the following steps:

  • Open and log into the AWS Management Console
  • Click on Launch Instance from the EC2 Dashboard
  • Select the Red Hat Enterprise Linux AMI from the QuickStart tab
  • Specify additional details of your instance and click Launch
  • Additional details can be found on each AMI’s Catalog Entry page

The AWS Management Console is an easy tool to start and manage your instances. If you are looking for more details on launching an instance, a quick video tutorial on how to use Amazon EC2 with the AWS Management Console can be found here .
A full list of Red Hat Enterprise Linux AMIs can be found in the AWS AMI Catalog.

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All customers running Red Hat Enterprise Linux on EC2 will receive access to repository updates from Red Hat. Moreover, AWS Premium support customers can contact AWS to get access to a support structure from both Amazon and Red Hat.

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About Red Hat

Red Hat, the world’s leading open source solutions provider, is headquartered in Raleigh, NC with over 50 satellite offices spanning the globe. Red Hat provides high-quality, low-cost technology with its operating system platform, Red Hat Enterprise Linux, together with applications, management and Services Oriented Architecture (SOA) solutions, including the JBoss Enterprise Middleware Suite. Red Hat also offers support, training and consulting services to its customers worldwide.


also from Revolution Analytics- in case you want to #rstats in the cloud and thus kill all that talk of RAM dependency, slow R than other softwares (just increase the RAM above in the instances to keep it simple)

,or Revolution not being open enough


Revolution Analytics uses an Open-Core Licensing model. We provide open- source R bundled with proprietary modules from Revolution Analytics that provide additional functionality for our users. Open-source R is distributed under the GNU Public License (version 2), and we make our software available under a commercial license.

Revolution Analytics respects the importance of open source licenses and has contributed code to the open source R project and will continue to do so. We have carefully reviewed our compliance with GPLv2 and have worked with Mark Radcliffe of DLA Piper, the outside General Legal Counsel of the Open Source Initiative, to ensure that we fully comply with the obligations of the GPLv2.

For our Revolution R distribution, we may make some minor modifications to the R sources (the ChangeLog file lists all changes made). You can download these modified sources of open-source R under the terms of the GPLv2, using either the links below or those in the email sent to you when you download a specific version of Revolution R.

Download GPL Sources

Product Version Platform Modified R Sources
Revolution R Community 3.2 Windows R 2.10.1
Revolution R Community 3.2 MacOS R 2.10.1
Revolution R Enterprise 3.1.1 RHEL R 2.9.2
Revolution R Enterprise 4.0 Windows R 2.11.1
Revolution R Enterprise 4.0.1 RHEL R 2.11.1
Revolution R Enterprise 4.1.0 Windows R 2.11.1
Revolution R Enterprise 4.2 Windows R 2.11.1
Revolution R Enterprise 4.2 RHEL R 2.11.1
Revolution R Enterprise 4.3 Windows & RHEL R 2.12.2




Revolution R Enterprise 4.2

Revo R gets more and more yum yum-

he following new features:

  • Direct import of SAS data sets into the native, efficient XDF file format
  • Direct import of fixed-format text data files into XDF file format
  • New commands to read subsets of rows and variables from XDF files in memory;
  • Many enhancements to the R Productivity Environment (RPE) for Windows
  • Expanded and updated user documentation
  • Added support on Linux for the big-data statistics package RevoScaleR
  • Added support on Windows for Web Services integration of predictive analytics with RevoDeployR.

Revolution R Enterprise 4.2 is available immediately for 64-bit Red Hat Enterprise Linux systems and both 32-bit and 64-bit Windows systems. Pricing starts at $1,000 per single-user workstation

And its free for academic licenses- so come on guys it is worth  atleast one download, and test.


Choosing R for business – What to consider?

A composite of the GNU logo and the OSI logo, ...
Image via Wikipedia

Additional features in R over other analytical packages-

1) Source Code is given to ensure complete custom solution and embedding for a particular application. Open source code has an advantage that is extensively peer- reviewed in Journals and Scientific Literature.  This means bugs will found, shared and corrected transparently.

2) Wide literature of training material in the form of books is available for the R analytical platform.

3) Extensively the best data visualization tools in analytical software (apart from Tableau Software ‘s latest version). The extensive data visualization available in R is of the form a variety of customizable graphs, as well as animation. The principal reason third-party software initially started creating interfaces to R is because the graphical library of packages in R is more advanced as well as rapidly getting more features by the day.

4) Free in upfront license cost for academics and thus budget friendly for small and large analytical teams.

5) Flexible programming for your data environment. This includes having packages that ensure compatibility with Java, Python and C++.


6) Easy migration from other analytical platforms to R Platform. It is relatively easy for a non R platform user to migrate to R platform and there is no danger of vendor lock-in due to the GPL nature of source code and open community.

Statistics are numbers that tell (descriptive), advise ( prescriptive) or forecast (predictive). Analytics is a decision-making help tool. Analytics on which no decision is to be made or is being considered can be classified as purely statistical and non analytical. Thus ease of making a correct decision separates a good analytical platform from a not so good analytical platform. The distinction is likely to be disputed by people of either background- and business analysis requires more emphasis on how practical or actionable the results are and less emphasis on the statistical metrics in a particular data analysis task. I believe one clear reason between business analytics is different from statistical analysis is the cost of perfect information (data costs in real world) and the opportunity cost of delayed and distorted decision-making.

Specific to the following domains R has the following costs and benefits

  • Business Analytics
    • R is free per license and for download
    • It is one of the few analytical platforms that work on Mac OS
    • It’s results are credibly established in both journals like Journal of Statistical Software and in the work at LinkedIn, Google and Facebook’s analytical teams.
    • It has open source code for customization as per GPL
    • It also has a flexible option for commercial vendors like Revolution Analytics (who support 64 bit windows) as well as bigger datasets
    • It has interfaces from almost all other analytical software including SAS,SPSS, JMP, Oracle Data Mining, Rapid Miner. Existing license holders can thus invoke and use R from within these software
    • Huge library of packages for regression, time series, finance and modeling
    • High quality data visualization packages
    • Data Mining
      • R as a computing platform is better suited to the needs of data mining as it has a vast array of packages covering standard regression, decision trees, association rules, cluster analysis, machine learning, neural networks as well as exotic specialized algorithms like those based on chaos models.
      • Flexibility in tweaking a standard algorithm by seeing the source code
      • The RATTLE GUI remains the standard GUI for Data Miners using R. It was created and developed in Australia.
      • Business Dashboards and Reporting
      • Business Dashboards and Reporting are an essential piece of Business Intelligence and Decision making systems in organizations. R offers data visualization through GGPLOT, and GUI like Deducer and Red-R can help even non R users create a metrics dashboard
        • For online Dashboards- R has packages like RWeb, RServe and R Apache- which in combination with data visualization packages offer powerful dashboard capabilities.
        • R can be combined with MS Excel using the R Excel package – to enable R capabilities to be imported within Excel. Thus a MS Excel user with no knowledge of R can use the GUI within the R Excel plug-in to use powerful graphical and statistical capabilities.

Additional factors to consider in your R installation-

There are some more choices awaiting you now-
1) Licensing Choices-Academic Version or Free Version or Enterprise Version of R

2) Operating System Choices-Which Operating System to choose from? Unix, Windows or Mac OS.

3) Operating system sub choice- 32- bit or 64 bit.

4) Hardware choices-Cost -benefit trade-offs for additional hardware for R. Choices between local ,cluster and cloud computing.

5) Interface choices-Command Line versus GUI? Which GUI to choose as the default start-up option?

6) Software component choice- Which packages to install? There are almost 3000 packages, some of them are complimentary, some are dependent on each other, and almost all are free.

7) Additional Software choices- Which additional software do you need to achieve maximum accuracy, robustness and speed of computing- and how to use existing legacy software and hardware for best complementary results with R.

1) Licensing Choices-
You can choose between two kinds of R installations – one is free and open source from The other R installation is commercial and is offered by many vendors including Revolution Analytics. However there are other commercial vendors too.

Commercial Vendors of R Language Products-
1) Revolution Analytics
2) XL Solutions-
3) Information Builder – Webfocus RStat -Rattle GUI
4) Blue Reference- Inference for R

  1. Choosing Operating System
      1. Windows


Windows remains the most widely used operating system on this planet. If you are experienced in Windows based computing and are active on analytical projects- it would not make sense for you to move to other operating systems. This is also based on the fact that compatibility problems are minimum for Microsoft Windows and the help is extensively documented. However there may be some R packages that would not function well under Windows- if that happens a multiple operating system is your next option.

        1. Enterprise R from Revolution Analytics- Enterprise R from Revolution Analytics has a complete R Development environment for Windows including the use of code snippets to make programming faster. Revolution is also expected to make a GUI available by 2011. Revolution Analytics claims several enhancements for it’s version of R including the use of optimized libraries for faster performance.
      1. MacOS


Reasons for choosing MacOS remains its considerable appeal in aesthetically designed software- but MacOS is not a standard Operating system for enterprise systems as well as statistical computing. However open source R claims to be quite optimized and it can be used for existing Mac users. However there seem to be no commercially available versions of R available as of now for this operating system.

      1. Linux


        1. Ubuntu
        2. Red Hat Enterprise Linux
        3. Other versions of Linux


Linux is considered a preferred operating system by R users due to it having the same open source credentials-much better fit for all R packages and it’s customizability for big data analytics.

Ubuntu Linux is recommended for people making the transition to Linux for the first time. Ubuntu Linux had an marketing agreement with revolution Analytics for an earlier version of Ubuntu- and many R packages can  installed in a straightforward way as Ubuntu/Debian packages are available. Red Hat Enterprise Linux is officially supported by Revolution Analytics for it’s enterprise module. Other versions of Linux popular are Open SUSE.

      1. Multiple operating systems-
        1. Virtualization vs Dual Boot-


You can also choose between having a VMware VM Player for a virtual partition on your computers that is dedicated to R based computing or having operating system choice at the startup or booting of your computer. A software program called wubi helps with the dual installation of Linux and Windows.

  1. 64 bit vs 32 bit – Given a choice between 32 bit versus 64 bit versions of the same operating system like Linux Ubuntu, the 64 bit version would speed up processing by an approximate factor of 2. However you need to check whether your current hardware can support 64 bit operating systems and if so- you may want to ask your Information Technology manager to upgrade atleast some operating systems in your analytics work environment to 64 bit operating systems.


  1. Hardware choices- At the time of writing this book, the dominant computing paradigm is workstation computing followed by server-client computing. However with the introduction of cloud computing, netbooks, tablet PCs, hardware choices are much more flexible in 2011 than just a couple of years back.

Hardware costs are a significant cost to an analytics environment and are also  remarkably depreciated over a short period of time. You may thus examine your legacy hardware, and your future analytical computing needs- and accordingly decide between the various hardware options available for R.
Unlike other analytical software which can charge by number of processors, or server pricing being higher than workstation pricing and grid computing pricing extremely high if available- R is well suited for all kinds of hardware environment with flexible costs. Given the fact that R is memory intensive (it limits the size of data analyzed to the RAM size of the machine unless special formats and /or chunking is used)- it depends on size of datasets used and number of concurrent users analyzing the dataset. Thus the defining issue is not R but size of the data being analyzed.

    1. Local Computing- This is meant to denote when the software is installed locally. For big data the data to be analyzed would be stored in the form of databases.
      1. Server version- Revolution Analytics has differential pricing for server -client versions but for the open source version it is free and the same for Server or Workstation versions.
      2. Workstation
    2. Cloud Computing- Cloud computing is defined as the delivery of data, processing, systems via remote computers. It is similar to server-client computing but the remote server (also called cloud) has flexible computing in terms of number of processors, memory, and data storage. Cloud computing in the form of public cloud enables people to do analytical tasks on massive datasets without investing in permanent hardware or software as most public clouds are priced on pay per usage. The biggest cloud computing provider is Amazon and many other vendors provide services on top of it. Google is also coming for data storage in the form of clouds (Google Storage), as well as using machine learning in the form of API (Google Prediction API)
      1. Amazon
      2. Google
      3. Cluster-Grid Computing/Parallel processing- In order to build a cluster, you would need the RMpi and the SNOW packages, among other packages that help with parallel processing.
    3. How much resources
      1. RAM-Hard Disk-Processors- for workstation computing
      2. Instances or API calls for cloud computing
  1. Interface Choices
    1. Command Line
    2. GUI
    3. Web Interfaces
  2. Software Component Choices
    1. R dependencies
    2. Packages to install
    3. Recommended Packages
  3. Additional software choices
    1. Additional legacy software
    2. Optimizing your R based computing
    3. Code Editors
      1. Code Analyzers
      2. Libraries to speed up R

citation-  R Development Core Team (2010). R: A language and environment for statistical computing. R Foundation for Statistical Computing,Vienna, Austria. ISBN 3-900051-07-0, URL

(Note- this is a draft in progress)

So which software is the best analytical software? Sigh- It depends


Graph of typical Operating System placement on...
Image via Wikipedia


Here is the software matrix that I am trying to develop for analytical software- It should help as a tentative guide for software purchases- it’s independent so unbiased (hopefully)- and it will try and bring as much range or sensitivity as possible. The list (rather than matrix) is of the format-

Type 0f analysis-

  • Data Visualization (Reporting with Pivot Ability to aggregate, disaggregate)
  • Reporting without Pivot Ability
  • Regression -Logistic Regression for Propensity or Risk Models
  • Regression- Linear for Pricing Models
  • Hypothesis Testing
  • A/B Scenario Testing
  • Decision Trees (CART, CHAID)
  • Time Series Forecasting
  • Association Analysis
  • Factor Analysis
  • Survey (Questionnaires)
  • Clustering
  • Segmentation
  • Data Manipulation

Dataset Size-

  • small dataset (upto X mb)
  • big dataset (upto Y gb)
  • enterprise class production BigData datasets (no limit)

Pricing of Software that can be used-

Ease of using Software

  • GUI vs Non GUI
  • Software that require not much extensive training
  • Software that require extensive training

Installation, Customization, Maintainability (or Support) for Software

  • Installation Dependencies- Size- Hardware (costs and  efficiencies)
  • Customization provided for specific use
  • Support Channels (including approximate Turn Around Time)


  • Software I have used personally
  • SAS (Base, Stat,Enterprise,Connect,ETS) WPS KXEN SPSS (Base,Trends),Revolution R,R,Rapid Miner,Knime,JMP,SQL SERVER,Rattle, R Commander,Deducer
  • Software I know by reputation- SAS Enterprise Miner etc etc

Are there any other parameters for judging software?  let me know at

Revolution R for Linux

Screenshot of the Redhat Enterprise Linux Desktop
Image via Wikipedia

New software just released from the guys in California (@RevolutionR) so if you are a Linux user and have academic credentials you can download it for free  (@Cmastication doesnt), you can test it to see what the big fuss is all about (also see –

Revolution Analytics has just released Revolution R Enterprise 4.0.1 for Red Hat Enterprise Linux, a significant step forward in enterprise data analytics. Revolution R Enterprise 4.0.1 is built on R 2.11.1, the latest release of the open-source environment for data analysis and graphics. Also available is the initial release of our deployment server solution, RevoDeployR 1.0, designed to help you deliver R analytics via the Web. And coming soon to Linux: RevoScaleR, a new package for fast and efficient multi-core processing of large data sets.

As a registered user of the Academic version of Revolution R Enterprise for Linux, you can take advantage of these improvements by downloading and installing Revolution R Enterprise 4.0.1 today. You can install Revolution R Enterprise 4.0.1 side-by-side with your existing Revolution R Enterprise installations; there is no need to uninstall previous versions.

Download Information

The following information is all you will need to download and install the Academic Edition.

Supported Platforms:

Revolution R Enterprise Academic edition and RevoDeployR are supported on Red Hat® Enterprise Linux® 5.4 or greater (64-bit processors).

Approximately 300MB free disk space is required for a full install of Revolution R Enterprise. We recommend at least 1GB of RAM to use Revolution R Enterprise.

For the full list of system requirements for RevoDeployR, refer to the RevoDeployR™ Installation Guide for Red Hat® Enterprise Linux®.

Download Links:

You will first need to download the Revolution R Enterprise installer.

Installation Instructions for Revolution R Enterprise Academic Edition

After downloading the installer, do the following to install the software:

  • Log in as root if you have not already.
  • Change directory to the directory containing the downloaded installer.
  • Unpack the installer using the following command:
    tar -xzf Revo-Ent-4.0.1-RHEL5-desktop.tar.gz
  • Change directory to the RevolutionR_4.0.1 directory created.
  • Run the installer by typing ./ and following the on-screen prompts.

Getting Started with the Revolution R Enterprise

After you have installed the software, launch Revolution R Enterprise by typing Revo64 at the shell prompt.

Documentation is available in the form of PDF documents installed as part of the Revolution R Enterprise distribution. Type Revo.home(“doc”) at the R prompt to locate the directory containing the manuals Getting Started with Revolution R (RevoMan.pdf) and the ParallelR User’s Guide(parRman.pdf).

Installation Instructions for RevoDeployR (and RServe)

After downloading the RevoDeployR distribution, use the following steps to install the software:

Note: These instructions are for an automatic install.  For more details or for manual install instructions, refer to RevoDeployR_Installation_Instructions_for_RedHat.pdf.

  1. Log into the operating system as root.
    su –
  2. Change directory to the directory containing the downloaded distribution for RevoDeployR and RServe.
  3. Unzip the contents of the RevoDeployR tar file. At prompt, type:
    tar -xzf deployrRedHat.tar.gz
  4. Change directories. At the prompt, type:
    cd installFiles
  5. Launch the automated installation script and follow the on-screen prompts. At the prompt, type:
    Note: Red Hat installs MySQL without a password.

Getting Started with RevoDeployR

After installing RevoDeployR, you will be directed to the RevoDeployR landing page. The landing page has links to documentation, the RevoDeployR management console, the API Explorer development tool, and sample code.


For help installing this Academic Edition, please email

Also interestingly some benchmarks on Revolution R vs R.

R-25 Benchmarks

The simple R-benchmark-25.R test script is a quick-running survey of general R performance. The Community-developed test consists of three sets of small benchmarks, referred to in the script as Matrix Calculation, Matrix Functions, and Program Control.

R-25 Matrix Calculation R-25 Matrix Functions R-Matrix Program Control
R-25 Benchmarks Base R 2.9.2 Revolution R (1-core) Revolution R (4-core) Speedup (4 core)
Matrix Calculation 34 sec 6.6 sec 4.4 sec 7.7x
Matrix Functions 20 sec 4.4 sec 2.1 sec 9.5x
Program Control 4.7 sec 4 sec 4.2 sec Not Appreciable

Speedup = Slower time / Faster Time – 1   Test descriptions available at

Additional Benchmarks

Revolution Analytics has created its own tests to simulate common real-world computations.  Their descriptions are explained below.

Matrix Multiply Cholesky Factorization
Singular Value Decomposition Principal Component Analysis Linear Discriminant Analysis
Linear Algebra Computation Base R 2.9.2 Revolution R (1-core) Revolution R (4-core) Speedup (4 core)
Matrix Multiply 243 sec 22 sec 5.9 sec 41x
Cholesky Factorization 23 sec 3.8 sec 1.1 sec 21x
Singular Value Decomposition 62 sec 13 sec 4.9 sec 12.6x
Principal Components Analysis 237 sec 41 sec 15.6 sec 15.2x
Linear Discriminant Analysis 142 sec 49 sec 32.0 sec 4.4x

Speedup = Slower time / Faster Time – 1

Matrix Multiply

This routine creates a random uniform 10,000 x 5,000 matrix A, and then times the computation of the matrix product transpose(A) * A.

set.seed (1)
m <- 10000
n <-  5000
A <- matrix (runif (m*n),m,n)
system.time (B <- crossprod(A))

The system will respond with a message in this format:

User   system elapsed
37.22    0.40   9.68

The “elapsed” times indicate total wall-clock time to run the timed code.

The table above reflects the elapsed time for this and the other benchmark tests. The test system was an INTEL® Xeon® 8-core CPU (model X55600) at 2.5 GHz with 18 GB system RAM running Windows Server 2008 operating system. For the Revolution R benchmarks, the computations were limited to 1 core and 4 cores by calling setMKLthreads(1) and setMKLthreads(4) respectively. Note that Revolution R performs very well even in single-threaded tests: this is a result of the optimized algorithms in the Intel MKL library linked to Revolution R. The slight greater than linear speedup may be due to the greater total cache available to all CPU cores, or simply better OS CPU scheduling–no attempt was made to pin execution threads to physical cores. Consult Revolution R’s documentation to learn how to run benchmarks that use less cores than your hardware offers.

Cholesky Factorization

The Cholesky matrix factorization may be used to compute the solution of linear systems of equations with a symmetric positive definite coefficient matrix, to compute correlated sets of pseudo-random numbers, and other tasks. We re-use the matrix B computed in the example above:

system.time (C <- chol(B))

Singular Value Decomposition with Applications

The Singular Value Decomposition (SVD) is a numerically-stable and very useful matrix decompisition. The SVD is often used to compute Principal Components and Linear Discriminant Analysis.

# Singular Value Deomposition
m <- 10000
n <- 2000
A <- matrix (runif (m*n),m,n)
system.time (S <- svd (A,nu=0,nv=0))

# Principal Components Analysis
m <- 10000
n <- 2000
A <- matrix (runif (m*n),m,n)
system.time (P <- prcomp(A))

# Linear Discriminant Analysis
require (‘MASS’)
g <- 5
k <- round (m/2)
A <- data.frame (A, fac=sample (LETTERS[1:g],m,replace=TRUE))
train <- sample(1:m, k)
system.time (L <- lda(fac ~., data=A, prior=rep(1,g)/g, subset=train))