used to set ad prices, find new drugs more quickly or fine-tune financial models

Google, Pfizer, Merck, Bank of America, the InterContinental Hotels Group and Shell use it.

R You Ready for R?

R helps people deal with large volumes of data in a wide variety of industries

the software is open source, meaning people can pick it up for free and make their own changes to the code

R es utilizado por el 47 por ciento de los profesionales de extracción de datos.

la capacidad para crear rápidamente exhibiciones visuales dinámicas de los datos, se ha tornado valiosa en el mundo empresarial a medida que las empresas comienzan a afrontar el desafío de los Grandes datos.

La diferencia está en la visualización

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.

R-based application to predict pipeline cracking for an international oil and gas company R-based application to optimize product recipe for a multinational food and beverage conglomerate Implementing a hedge fund's backtesting system in R  

We both had an interest in statistical computing and saw a common need for a better software environment in our Macintosh teaching laboratory. We saw no suitable commercial environment and we began to experiment to see what might be involved in developing one ourselves.

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