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Open-source software is free for anyone to use and modify

an improve

R is similar to other programming languages

R is particularly useful because it contains a number of built-in mechanisms for organizing data, running calculations on the information and creating graphical representations of data sets.

For statisticians

R is also the name of a popular programming language used by a growing number of data analysts inside corporations and academia

statisticians, engineers and scientists can improve the software’s code or write variations for specific tasks

software’s

Packages written for R add advanced algorithms, colored and textured graphs and mining techniques to dig deeper into databases

While it is difficult to calculate exactly how many people use R, those most familiar with the software estimate that close to 250,000 people work with it regularly

“R is a real demonstration of the power of collaboration, and I don’t think you could construct something like this any other way,” Mr. Ihaka said. “We could have chosen to be commercial, and we would have sold five copies of the software.”

his section presumes the reader has some familiarity with statistical methodology, in particular with regression analysis and the analysis of variance. Later we make some rather more ambitious presumptions, namely that something is known about generalized linear models and nonlinear regression. The requirements for fitting statistical models are sufficiently well defined to make it possible to construct general tools that apply in a broad spectrum of problems. R provides an interlocking suite of facilities that make fitting statistical models very simple. As we mention in the introduction, the basic output is minimal, and one needs to ask for the details by calling extractor functions.

When you use the R program it issues a prompt when it expects input commands. The default prompt is ‘>’, which on UNIX might be the same as the shell prompt, and so it may appear that nothing is happening. However, as we shall see, it is easy to change to a different R prompt if you wish. We will assume that the UNIX shell prompt is ‘$’.

R can be regarded as an implementation of the S language which was developed at Bell Laboratories by Rick Becker, John Chambers and Allan Wilks, and also forms the basis of the S-Plus systems. The evolution of the S language is characterized by four books by John Chambers and coauthors. For R, the basic reference is The New S Language: A Programming Environment for Data Analysis and Graphics by Richard A. Becker, John M. Chambers and Allan R. Wilks. The new features of the 1991 release of S are covered in Statistical Models in S edited by John M. Chambers and Trevor J. Hastie. The formal methods and classes of the methods package are based on those described in Programming with Data by John M. Chambers. See References, for precise references. There are now a number of books which describe how to use R for data analysis and statistics, and documentation for S/S-Plus can typically be used with R, keeping the differences between the S implementations in mind. See What documentation exists for R?.

Many people view the end results of all this technology via the Firefox Web browser, also open-source software.

R is also the name of a popular programming language used by a growing number of data analysts inside corporations and academia. It is becoming their lingua franca partly because data mining has entered a golden age

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

R has also quickly found a following because statisticians, engineers and scientists without computer programming skills find it easy to use.

Many people view the end results of all this technology via the Firefox Web browser, also open-source software.

Most Web sites are displayed using an open-source application called Apache, and companies increasingly rely on the open-source MySQL database to store their critical information. Many people view the end results of all this technology via the Firefox Web browser, also open-source software.

For statisticians

R is particularly useful because it contains a number of built-in mechanisms for organizing data, running calculations on the information and creating graphical representations of data sets.

What makes R so useful

is that statisticians, engineers and scientists can improve the software’s code or write variations for specific tasks. Packages written for R add advanced algorithms, colored and textured graphs and mining techniques to dig deeper into databases.

The financial services community has demonstrated a particular affinity for R; dozens of packages exist for derivatives analysis alone.

al margen de los datos estructurados que podemos extraer: usuario, conexiones con otros usuarios, fecha y hora de publicación

con el lenguaje R y las funciones especializadas podemos trasladar las informaciones

Una vez que hemos obtenido los datos en bruto

en tablas normalizadas que nos permitirán realizar una exploración analítica de los datos y su representación gráfica

el aspecto más interesante es poder extraer información significativa del propio texto

Asimismo, tenemos la posibilidad de realizar análisis, en el texto

, de actitudes positivas o negativas hacia un determinado acontecimiento, producto o servicio

Just one simple line of [R] code that would work just as well for a 100-by-100 loss triangle as it would for a 10-by-10 triangle. No hidden cells in the spreadsheet, no risk of spreadsheet error. It's a little bit of code you could look at in one screen, it's replicable ... and this does all the work that a spreadsheet would do. 

He uses the Allstate Claim Prediction Challenge data (from a recent Kaggle competition) to fit a Tweedie model to 13 million records of claim data. (The Tweedie distribution is often used to model insurance claims, where many claims are exactly zero, and non-zero claims follow a continuous Gamma-like distribution.) Using the forthcoming rxGLM function, he fit the model to this large data set in just over two minutes (140.22 seconds) using a single quad-core PC.

Manage variables in active data set > Convert

numeric

Statistics > Summaries > Numerical summaries

I’ve heard that Revolution R isn’t always the easiest software to use and that the experience isn’t exactly seamless, but it can be argued that in the cutting-edge field of big data analytics a challenging environment is to be expected. If Revolution Analytics can address some of these useability challenges, it may find its pie growing even faster than it is now. Regardless, I anticipate that Revolution Analytics will continue its fast growth (already its headcount is doubling year-over-year). Furthermore, I anticipate that in-database analytics (an area where R really shines) will become the de-facto approach to big data analytics and that companies that take full advantage of that trend will reap benefits.