• A system for doing object oriented programming
• R was originally quite interesting because it is both interactive and has a system for object orientation.
  • Other languages which support OOP (C++, Java, Lisp, Python, Perl) generally speaking are not interactive languages

• In R much of the code for supporting classes/methods is written by John Chambers himself (the creator of the original S language) and documented in the book Programming with Data: A Guide to the S Language
• A natural extension of Chambers' idea of allowing someone to cross the user -> programmer spectrum
• Object oriented programming is a bit different in R than it is in most languages - even if you are familiar with the idea, you may want to pay attention to the details

S3 classes/methods - Included with version 3 of the S language. - Informal, a little kludgey - Sometimes called old-style classes/methods

S4 classes/methods - more formal and rigorous - Included with S-PLUS 6 and R 1.4.0 (December 2001) - Also called new-style classes/methods

• For now (and the forseeable future), S3 classes/methods and S4 classes/methods are separate systems (but they can be mixed to some degree).
• Each system can be used fairly independently of the other.
• Developers of new projects (you!) are encouraged to use the S4 style classes/methods.
  • Used extensively in the Bioconductor project
• But many developers still use S3 classes/methods because they are "quick and dirty" (and easier).
• In this lecture we will focus primarily on S4 classes/methods
• The code for implementing S4 classes/methods in R is in the methods package, which is usually loaded by default (but you can load it with library(methods) if for some reason it is not loaded)

• A class is a description of a thing. A class can be defined using setClass() in the methods package.
• An object is an instance of a class. Objects can be created using new().
• A method is a function that only operates on a certain class of objects.
• A generic function is an R function which dispatches methods. A generic function typically encapsulates a "generic" concept (e.g. plot, mean, predict, …)
  • The generic function does not actually do any computation.
• A method is the implementation of a generic function for an object of a particular class.

• The help files for the 'methods' package are extensive - do read them as they are the primary documentation
• You may want to start with ?Classes and ?Methods
• Check out ?setClass, ?setMethod, and ?setGeneric
• Some of it gets technical, but try your best for now-it will make sense in the future as you keep using it.
• Most of the documentation in the methods package is oriented towards developers/programmers as these are the primary people using classes/methods

All objects in R have a class which can be determined by the class function

class(1)

## [1] "numeric"

class(TRUE)

## [1] "logical"

class(rnorm(100))

## [1] "numeric"

class(NA)

## [1] "logical"

class("foo")

## [1] "character"

Data classes go beyond the atomic classes

x <- rnorm(100)
y <- x + rnorm(100)
fit <- lm(y ~ x)  ## linear regression model
class(fit)

## [1] "lm"

• S4 and S3 style generic functions look different but conceptually, they are the same (they play the same role).
• When you program you can write new methods for an existing generic OR create your own generics and associated methods.
• Of course, if a data type does not exist in R that matches your needs, you can always define a new class along with generics/methods that go with it.

The mean and print functions are generic

mean

## function (x, ...) 
## UseMethod("mean")
## <bytecode: 0x7fe7dc83eea0>
## <environment: namespace:base>

print

## function (x, ...) 
## UseMethod("print")
## <bytecode: 0x7fe7ddc57158>
## <environment: namespace:base>

The mean generic function has a number of methods associated with it.

methods("mean")

## [1] mean.Date     mean.default  mean.difftime mean.POSIXct  mean.POSIXlt 
## see '?methods' for accessing help and source code

The show function is from the methods package and is the S4 equivalent of print

library(methods)
show

## standardGeneric for "show" defined from package "methods"
## 
## function (object) 
## standardGeneric("show")
## <bytecode: 0x7fe7dea11bf8>
## <environment: 0x7fe7ddabaa78>
## Methods may be defined for arguments: object
## Use  showMethods("show")  for currently available ones.
## (This generic function excludes non-simple inheritance; see ?setIs)

The show function is usually not called directly (much like print) because objects are auto-printed.

showMethods("show")

## Function: show (package methods)
## object="ANY"
## object="C++Class"
## object="C++Function"
## object="C++Object"
## object="classGeneratorFunction"
## object="classRepresentation"
## object="envRefClass"
## object="externalRefMethod"
## object="function"
##     (inherited from: object="ANY")
## object="genericFunction"
## object="genericFunctionWithTrace"
## object="MethodDefinition"
## object="MethodDefinitionWithTrace"
## object="MethodSelectionReport"
## object="MethodWithNext"
## object="MethodWithNextWithTrace"
## object="Module"
## object="namedList"
## object="ObjectsWithPackage"
## object="oldClass"
## object="refClassRepresentation"
## object="refMethodDef"
## object="refObjectGenerator"
## object="signature"
## object="sourceEnvironment"
## object="standardGeneric"
##     (inherited from: object="genericFunction")
## object="traceable"

The first argument of a generic function is an object of a particular class (there may be other arguments)

1. The generic function checks the class of the object.
2. A search is done to see if there is an appropriate method for that class.
3. If there exists a method for that class, then that method is called on the object and we're done.
4. If a method for that class does not exist, a search is done to see if there is a default method for the generic. If a default exists, then the default method is called.
5. If a default method doesn't exist, then an error is thrown.

• You cannot just print the code for a method like other functions because the code for the method is usually hidden.
• If you want to see the code for an S3 method, you can use the function getS3method.
• The call is getS3method(<generic>, <class>)
• For S4 methods you can use the function getMethod
• The call is getMethod(<generic>, <signature>) (more details later)

What's happening here?

set.seed(2)
x <- rnorm(100)
mean(x)

## [1] -0.03069816

1. The class of x is "numeric"
2. But there is no mean method for "numeric" objects!
3. So we call the default function for mean.

head(getS3method("mean", "default"), 10)

##                                                                       
## 1  function (x, trim = 0, na.rm = FALSE, ...)                         
## 2  {                                                                  
## 3      if (!is.numeric(x) && !is.complex(x) && !is.logical(x)) {      
## 4          warning("argument is not numeric or logical: returning NA")
## 5          return(NA_real_)                                           
## 6      }                                                              
## 7      if (na.rm)                                                     
## 8          x <- x[!is.na(x)]                                          
## 9      if (!is.numeric(trim) || length(trim) != 1L)                   
## 10         stop("'trim' must be numeric of length one")

tail(getS3method("mean", "default"), 10)

##                                                                
## 15         if (anyNA(x))                                       
## 16             return(NA_real_)                                
## 17         if (trim >= 0.5)                                    
## 18             return(stats::median(x, na.rm = FALSE))         
## 19         lo <- floor(n * trim) + 1                           
## 20         hi <- n + 1 - lo                                    
## 21         x <- sort.int(x, partial = unique(c(lo, hi)))[lo:hi]
## 22     }                                                       
## 23     .Internal(mean(x))                                      
## 24 }

What happens here?

set.seed(3)
df <- data.frame(x = rnorm(100), y = 1:100)
sapply(df, mean)

##           x           y 
##  0.01103557 50.50000000

1. The class of df is "data.frame"; each column can be an object of a different class

2. We sapply over the columns and call the mean function

3. In each column, mean checks the class of the object and dispatches the appropriate method.

4. We have a numeric column and an integer column; mean calls the default method for both

• Some S3 methods are visible to the user (i.e. mean.default),

• Never call methods directly

• Use the generic function and let the method be dispatched automatically.

• With S4 methods you cannot call them directly at all

The plot function is generic and its behavior depends on the object being plotted.

set.seed(10)
x <- rnorm(100)
plot(x)

For time series objects, plot connects the dots

set.seed(10)
x <- rnorm(100)
x <- as.ts(x) ## Convert to a time series object 
plot(x)

If you write new methods for new classes, you'll probably end up writing methods for the following generics: - print/show - summary - plot

There are two ways that you can extend the R system via classes/methods - Write a method for a new class but for an existing generic function (i.e. like print) - Write new generic functions and new methods for those generics

Why would you want to create a new class? - To represent new types of data (e.g. gene expression, space-time, hierarchical, sparse matrices) - New concepts/ideas that haven't been thought of yet (e.g. a fitted point process model, mixed-effects model, a sparse matrix) - To abstract/hide implementation details from the user I say things are "new" meaning that R does not know about them (not that they are new to the statistical community).

A new class can be defined using the setClass function - At a minimum you need to specify the name of the class - You can also specify data elements that are called slots - You can then define methods for the class with the setMethod function - Information about a class definition can be obtained with the showClass function

Creating new classes/methods is usually not something done at the console; you likely want to save the code in a separate file

library(methods)
setClass("polygon",
         representation(x = "numeric",
                        y = "numeric"))

• The slots for this class are x and y

• The slots for an S4 object can be accessed with the @ operator.

A plot method can be created with the setMethod function.

• For setMethod you need to specify a generic function (plot), and a signature.

• A signature is a character vector indicating the classes of objects that are accepted by the method.

• In this case, the plot method will take one type of object, a polygon object.

Creating a plot method with setMethod.

setMethod("plot", "polygon",
          function(x, y, ...) {
                  plot(x@x, x@y, type = "n", ...)
                  xp <- c(x@x, x@x[1])
                  yp <- c(x@y, x@y[1])
                  lines(xp, yp)
      })

## [1] "plot"

• Notice that the slots of the polygon (the x- and y-coordinates) are accessed with the @ operator.

After calling setMethod the new plot method will be added to the list of methods for plot.

library(methods)
showMethods("plot")

## Function: plot (package graphics)
## x="ANY"
## x="polygon"

Notice that the signature for class polygon is listed. The method for ANY is the default method and it is what is called when now other signature matches

p <- new("polygon", x = c(1, 2, 3, 4), y = c(1, 2, 3, 1))
plot(p)

• Developing classes and associated methods is a powerful way to extend the functionality of R

• Classes define new data types

• Methods extend generic functions to specify the behavior of generic functions on new classes

• As new data types and concepts are created, classes/methods provide a way for you to develop an intuitive interface to those data/concepts for users

• The best way to learn this stuff is to look at examples

• There are quite a few examples on CRAN which use S4 classes/methods. You can usually tell if they use S4 classes/methods if the methods package is listed in the Depends: field

• Bioconductor (http://www.bioconductor.org) - a rich resource, even if you know nothing about bioinformatics

• Some packages on CRAN (as far as I know) - SparseM, gpclib, flexmix, its, lme4, orientlib, filehash

• The stats4 package (comes with R) has a bunch of classes/methods for doing maximum likelihood analysis.