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

R is similar to other programming languages, like C, Java and Perl

. I.B.M., Hewlett-Packard and Dell make billions of dollars a year selling servers that run the open-source Linux operating system

Web sites are displayed using an open-source application called Apache,

the end results of all this technology via the Firefox Web

can help illuminate data trends more clearly than is possible by entering information into rows and columns

What makes R so useful

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

advanced algorithms, colored and textured graphs and mining techniques to dig deeper into databases.

Packages written for R add

the professors considered their coding efforts more of an academic game than anything else.

the notion of devising something like R sprang up during a hallway conversation.

They both wanted technology better suited for their statistics students

R immediately gained a following with people who saw the possibilities in customizing the free software.

The preceding scatterplot demonstrates that these data may not be linear.

In this tutorial, we will explore the a linear, quadratic, and cubic model.

To center a variable, simply subtract its mean from each data point and save the result into a new R variable, as demonstrated below.

Once the input variable has been centered, the higher order terms can be created.

we can simply multiply our centered term from step one and save the result into a new R variable, as demonstrated below.

Now we have all of the pieces necessary to assemble our linear and curvilinear models.

In this case, the quadratic and cubic terms are not statistically significant themselves nor are their models statistically significant beyond the linear model.

it can be helpful to summarize and compare our potential models using the summary(MODEL) and anova(MODEL1, MODEL2,… MODELi) functions.

The regression topics covered in these tutorials can be mixed and matched to create exceedingly complex models.

The good news is that more complex models can be created using the same techniques covered here. The basic principles remain the same.

se trata de software libre

Porque científicos, ingenieros, estadísticos que no son expertos en programación pueden emplearlo rápidamente

R se utiliza en

empresas muy diversas.

el lenguaje de programación R

R es tan utilizado

permite hacer análisis muy complejos a los estadísticos sin que conozcan en profundidad los sistemas de computación.

la mayoría de los sitios Web se basan en el software libre Apache y cada vez hay más confianza en MySQL, la base de dados libre.

El New York Times publicó una nota recientemente sobre sobre el lenguaje de programación R, destacando el hecho de que se trata de software libre, siendo los analistas de datos los más cautivados

La operación de procesar datos es utilizada tanto para fijar precios, perfeccionar modelos financieros o encontrar nuevas medicinas, es así que R se utiliza en Pfizer, Merck, Google, el InterContinental Hotels Group, Bank of America o Shell, empresas muy diversas.

¿Y por qué R es tan utilizado? Porque científicos, ingenieros, estadísticos que no son expertos en programación pueden emplearlo rápidamente.

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.

R is also used by Google for data analysis.

There are also around 5000 extension packages for R dedicated to performing specialised tasks.

People chimed in with the unique ways they’re using the technology

talked about its passion

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

for tweaking

Speaking of R, Mr. Chambers said, “It’s way beyond anything we could have imagined at Bell Labs.”

Were Pfizer to use R in clinical studies

“It’s very hard to displace the industry standard in those types of cases,” said Max Kuhn, associate director of nonclinical statistics at Pfizer.

Revolution offers ways to speed up the software on certain applications and to run it on large computers

Revolution provides support services to customers like Pfizer and Bank of America

R is a full-fledged programming language with a radically different approach to processing large and complex datasets

R community develops and releases new software continuously

R also offers benefits to companies trying to reduce their software expenditures with enterprise software vendors such as SAS and SPSS.

R is especially useful for quickly and easily generating charts and graphics

R allows

to create high-quality charts and graphs

R allowed them to effectively communicate their analytic results in ways that were easily understood by non-technical audiences

At this point, the future of predictive analytics is pointing directly to R, something we are calling the "perfect storm of predictive analytics."

The company says R users will be able to process, visualize and model terabyte-class data sets in a fraction of the time of legacy products, without the need for expensive or specialized hardware.

e processing of large d

multi-core processing of lar

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?.