1) It is slower with bigger datasets than SPSS language and SAS language .If you use bigger datasets, then you should either consider more hardware , or try and wait for some of the ODBC connect packages.
2) It needs more time to learn than SAS language .Much more time to learn how to do much more.
3) R programmers are lesser paid than SAS programmers.They prefer it that way.It equates the satisfaction of creating a package in development with a world wide community with the satisfaction of using a package and earning much more money per hour.
4) It forces you to learn the exact details of what you are doing due to its object oriented structure. Thus you either get no answer or get an exact answer. Your customer pays you by the hour not by the correct answers.
5) You can not push a couple of buttons or refer to a list of top ten most commonly used commands to finish the project.
6) It is free. And open for all. It is socialism expressed in code. Some of the packages are built by university professors. It is free.Free is bad. Who pays for the mortgage of the software programmers if all softwares were free ? Who pays for the Friday picnics. Who pays for the Good Night cruises?
7) It is free. Your organization will not commend you for saving them money- they will question why you did not recommend this before. And why did you approve all those packages that expire in 2011.R is fReeeeee. Customers feel good while spending money.The more software budgets you approve the more your salary is. R thReatens all that.
8) It is impossible to install a package you do not need or want. There is no one calling you on the phone to consider one more package or solution. R can make you lonely.
9) R uses mostly Command line. Command line is from the Seventies. Or the Eighties. The GUI’s RCmdr and Rattle are there but still…..
10) R forces you to learn new stuff by the month. You prefer to only earn by the month. Till the day your job got offshored…
Ajay- The above post was reprinted by personal request. It was written on Jan 2009- and may not be truly valid now. It is meant to be taken in good humor-not so seriously.
“As a high tech company, SAS depends on a strong educational system for its long-term success,” said SAS CEO Jim Goodnight. “Beyond that, STEM education – developing skills for a knowledge economy – is critical to American competitiveness. Without emphasis on STEM, we sacrifice innovation and export our knowledge jobs to other countries.”
Goodnight and SAS have been active in education for years. The SAS co-founder and his wife, Ann Goodnight, launched college prep school Cary Academy in 1996, and the SAS inSchool program has developed educational software for schools since the mid-1990s. In 2008, Jim Goodnight made SAS Curriculum Pathways available free to all U.S. educators. The web-based service provides content in English, mathematics, social studies, science and Spanish.
SAS is the only Triangle-based company among the Change the Equation corporate partners, but the group includes several other companies with a significant Raleigh-Durham presence: chief among them IBM (NYSE: IBM), GlaxoSmithKline (NYSE: GSK), and Cisco Systems (Nasdaq: CSCO).
Here is the brand new release from Jaspersoft at a groovy price of 9000$. Somebody stop these guys!
It’s a great company to watch for buyouts as well- given their expertise in REPORTING and clientele- especially for anyone looking to im prove thier standing in both open source world and reporting software branding.
Webinar: Introducing JasperReports Server Professional
Thursday October 14
In this live webinar, learn how a new solution from Jaspersoft combines the world’s favorite reporting server with powerful, mature report server functionality—for about 80% less.
The World’s Most Powerful and Affordable Reporting Server
Limited Time Introductory Offer: Starting from $9,000 (restrictions apply)
JasperReports Server is the recommended product for organizations requiring an affordable reporting solution for interactive, operational, and production-based reporting. Deployed as a standalone reporting server or integrated inside another application, JasperReports Server is a flexible, powerful, interactive reporting environment for small or large enterprises.
Powered by the world’s most popular reporting tools in JasperReports and iReport, developers and users can take advantage of more interactivity, security, and scheduling of their reports.
Key Benefits:
Affordable: Unlimited reports for unlimited users starting at $9,000
Powerful: Report scheduling and distribution to 1,000s of users on a single server
Flexible: Web service architecture simplifies application integration
One more addition to the GPU stack that adds up power when combined with CPU and GPUs. For numeric computing, it may be essential to have GPU- CPU mixed software as almost all hardware people now have offered GPU-CPU products. Maybe software companies can get inspired for new kind of GPU-CPU blade server software again.
But for “true” supercomputing applications, the SL390s G7 is the go-to server. Like its sibling, the SL390s comes with Xeon 5600 processors, but the option to pair the CPUs with up to three on-board NVIDIA “Fermi” 20-series GPUs puts a lot more floating point performance into this design. Customers can choose from either the M2050 or M2070 Tesla GPU modules, the only difference being the amount of graphics memory — 3 GB of GDDR5 for the M2050 versus 6 GB for the M2070. Each GPU module is served by its own PCIe Gen2 x16 channel in order to maximize bandwidth to the graphics chips. At the maximum configuration with all three Fermi GPUs and two Westmere CPUs, a single server delivers on the order of 1 teraflop of double precision performance. “So this is very much a server that has been designed for HPC,” said Turkel.
With GPUs on board, the SL390s fill out a 2U half-width tray, so up to four of these can be packed into a 4U SL6500 chassis. A CPU-only version is also available and takes up just half the space (half-width 1U), enabling twice as many Xeons to occupy the same chassis. This configuration will likely be the server of choice for the majority of HPC setups, given that GPGPU deployment is really just getting started. Pricing on the CPU-only model starts at $2,259.
And
, the ProLiant SL390s G7, provides more raw FLOPS per square inch than any server HP has delivered to date, and is the basis for the 2.4 petaflop TSUBAME 2.0 supercomputer currently being deployed at the Tokyo Institute of Technology.
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 http://www.revolutionanalytics.com/why-revolution-r/benchmarks.php) –
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 ./install.py 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.
Log into the operating system as root.
su –
Change directory to the directory containing the downloaded distribution for RevoDeployR and RServe.
Unzip the contents of the RevoDeployR tar file. At prompt, type:
tar -xzf deployrRedHat.tar.gz
Change directories. At the prompt, type:
cd installFiles
Launch the automated installation script and follow the on-screen prompts. At the prompt, type:
./installRedHat.sh 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.
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.
Revolution Analytics has created its own tests to simulate common real-world computations. Their descriptions are explained below.
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))
LibreOffice is a new fork from OpenOffice– Basically people who want to ensure OpenOffice remains free. It basically consists of efforts from everybody except Apple, Microsoft and Oracle (http://www.documentfoundation.org/supporters/) and it’s a new kind of workable office productivity suite-determined to remain free. I have used it- a bit shaky- but I really liked the new design and willingly will test it (and auto submit bugs) . It would be interesting to see the reaction of enterprise vendors like SAS, IBM,Dell, HP (and Lenovo) and etc -as their support would be critical to both Unbreakable Oracle Linux and Unshakable LibreOffice.
DECISION STATS 