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It's available as an Oracle 11g database option and costs $23,000 per processor license.

It's not surprising that Oracle has moved to support R, which "has become a real ubiquitous force in advanced analytics," said Forrester Research analyst James Kobielus. "It's everywhere. Enterprise adoption of it has been growing steadily. When we ask our customers what they're using for statistical modeling they'll say SAS or [IBM's] SPSS, but they increasingly say R in the same breath."

It's not surprising that Oracle has moved to support R, which "has become a real ubiquitous force in advanced analytics," said Forrester Research analyst James Kobielus. "It's everywhere. Enterprise adoption of it has been growing steadily. When we ask our customers what they're using for statistical modeling they'll say SAS or [IBM's] SPSS, but they increasingly say R in the same breath."

What makes R particularly powerful is that statisticians and statistically minded people around the world have contributed packages to the R Group and maintain a very active news group offering suggestions and help. The growing collection of packages and the ease with which they interact with each other and the core R is perhaps the greatest advantage of R

Using R in 12 simple steps for personality research (These steps are not meant to limit what can be done with R, but merely to describe how to do the analysis for the most basic of research projects and to give a first experience with R). Install R on your computer or go to a machine that has it. Download the psych package as well as other recommended packages from CRAN using the install.packages function, or using the package installer in the GUI. To get packages recommended for a particular research field, use the ctv package to install a particular task view. Note, these first two steps need to be done only once! Activate the psych package or other desired packages using e.g., library(psych). This needs to be done every time you start R. Or, it is possible to modify the startup parameters for R so that certain libraries are loaded automatically. Enter your data using a text editor and save as a text file (perhaps comma delimited if using a spreadsheet program such as Excel or OpenOffice) Read the data file or copy and paste from the clipboard (using, e.g., read.clipboard). Find basic descriptive statistics (e.g., means, standard deviations, minimum and maxima) using describe. Prepare a simple descriptive graph (e.g, a box plot) of your variables. Find the correlation matrix to give an overview of relationships (if the number is not too great, a scatter plot matrix or SPLOM plot is very useful, this can be done with pairs.panels. If you have an experimental variable, do the appropriate multiple regression using stan- dardized or at least zero centered scores. If you want to do a factor analysis or principal components analysis, use the factanal or fa and principal function To score items and create a scale and find various reliability estimates, use score.items and perhaps omega. Graph the results.

Project Goals The broad goals of the Bioconductor project are: To provide widespread access to a broad range of powerful statistical and graphical methods for the analysis of genomic data. To facilitate the inclusion of biological metadata in the analysis of genomic data, e.g. literature data from PubMed, annotation data from Entrez genes. To provide a common software platform that enables the rapid development and deployment of extensible, scalable, and interoperable software. To further scientific understanding by producing high-quality documentation and reproducible research. To train researchers on computational and statistical methods for the analysis of genomic data