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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."

adoption

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."

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

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?.

> 2 + 3 * 5      # Note the order of operations. > log (10)       # Natural logarithm with base e=2.718282 > 4^2            # 4 raised to the second power > 3/2            # Division > sqrt (16)      # Square root > abs (3-7)      # Absolute value of 3-7 > pi             # The mysterious number > exp(2)         # exponential function > 15 %/% 4       # This is the integer divide operation > # This is a comment line

x<- log(2.843432) *pi

R is a software package especially suitable for data analysis and graphical representation. Functions and results of analysis are all stored as objects, allowing easy function modification and model building. R provides the language, tool, and environment in one convenient package. It is very flexible and highly customizable. Excellent graphical tools make R an ideal environment for EDA (Exploratory Data Analysis). Since most high level functions are written in R language itself, you can learn the language by studying the function code.

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.

Why is R named R?

The name is partly based on the (first) names of the first two R authors (Robert Gentleman and Ross Ihaka), and partly a play on the name of the Bell Labs language `S'

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.

Many basic functions that are lazy in Haskell, for example, are strict in R, including data type constructors.

Lazy evaluation is a distinctive feature of R that has the potential for reducing unnecessary work performed by a computation

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 an object oriented programming language. This means that virtually everything can be stored as an R object. Each object has a class. This class describes what the object contains and what each function does with it. For instance, plot(x) produces different outputs depending on whether x is a regression object or a vecto

The assignment symbol is "<-". Alternatively, the classical "=" symbol can be used.

Arguments are passed to functions inside round brackets (parentheses). One can easily combine functions. For instance you can directly type

The symbol "#" comments to the end of the line:

Commands are normally separated by a newline. If you want to put more than one statement on a line, you can use the ";" delimiter.

You can also have one statement on multiple lines. R is case sensitive: a and A are two different objects. Traditionally underscores "_" are not used in names. It is often better to use dots ".". One should avoid using an underscore as the first character of an object name.

Here are some things editors do to keep this book internally consistent. If you have something to contribute, go ahead and make your contribution. Other editors can touch up your edits afterwards so that they conform to the guidelines.

The local manual of style WB:LMOS for the R programming book, including a brief explanation of why we do it that way, is: Examples use "source" tags : <source lang="rsplus"> a <- 1:10 ; mean(a) </source>. That makes them look pretty to our readers. The name of packages are in bold  : '''Hmisc'''. Name of functions are in "code" tags: <code>lm()</code>. Page titles -- the part after "R Programming/" -- are in sentence case, like "R Programming/Working with data frames". We couldn't decide between sentence case and title case, so I flipped a coin. Every page has <noinclude>{{R Programming/Navigation}}</noinclude> at the top and {{R Programming/Navbar|Mathematics|Probability Distributions}} at the bottom. That makes it easier to navigate from one page to another online.

5 Arrays and matrices

10 Writing your own functions

11 Statistical models in R

R is an integrated suite of software facilities for data manipulation, calculation and graphical display.