10  Writing your own functions ¶

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As we have seen informally along the way, the R language allows the user to create objects of mode function. These are true R functions that are stored in a special internal form and may be used in further expressions and so on. In the process, the language gains enormously in power, convenience and elegance, and learning to write useful functions is one of the main ways to make your use of R comfortable and productive.

It should be emphasized that most of the functions supplied as part of the R system, such as mean(), var(), postscript() and so on, are themselves written in R and thus do not differ materially from user written functions.

A function is defined by an assignment of the form

> name <- function(arg_1, arg_2, ...) expression

The expression is an R expression, (usually a grouped expression), that uses the arguments, arg_i, to calculate a value. The value of the expression is the value returned for the function.

A call to the function then usually takes the form name(expr_1,expr_2, ...) and may occur anywhere a function call is legitimate.

10.1 Simple examples ¶

As a first example, consider a function to calculate the two sample t-statistic, showing “all the steps”. This is an artificial example, of course, since there are other, simpler ways of achieving the same end.

The function is defined as follows:

> twosam <- function(y1, y2) {
    n1  <- length(y1); n2  <- length(y2)
    yb1 <- mean(y1);   yb2 <- mean(y2)
    s1  <- var(y1);    s2  <- var(y2)
    s <- ((n1-1)*s1 + (n2-1)*s2)/(n1+n2-2)
    tst <- (yb1 - yb2)/sqrt(s*(1/n1 + 1/n2))
    tst
  }

With this function defined, you could perform two sample t-tests using a call such as

> tstat <- twosam(data$male, data$female); tstat

As a second example, consider a function to emulate directly the MATLAB backslash command, which returns the coefficients of the orthogonal projection of the vector y onto the column space of the matrix, X. (This is ordinarily called the least squares estimate of the regression coefficients.) This would ordinarily be done with the qr() function; however this is sometimes a bit tricky to use directly and it pays to have a simple function such as the following to use it safely.

Thus given a n by 1 vector y and an n by p matrix X then X \ y is defined as (X’X)^{-}X’y, where (X’X)^{-} is a generalized inverse of X'X.

> bslash <- function(X, y) {
  X <- qr(X)
  qr.coef(X, y)
}

After this object is created it may be used in statements such as

> regcoeff <- bslash(Xmat, yvar)

and so on.

The classical R function lsfit() does this job quite well, and more²¹. It in turn uses the functions qr() and qr.coef() in the slightly counterintuitive way above to do this part of the calculation. Hence there is probably some value in having just this part isolated in a simple to use function if it is going to be in frequent use. If so, we may wish to make it a matrix binary operator for even more convenient use.

10.2 Defining new binary operators ¶

Had we given the bslash() function a different name, namely one of the form

%anything%

it could have been used as a binary operator in expressions rather than in function form. Suppose, for example, we choose ! for the internal character. The function definition would then start as

> "%!%" <- function(X, y) { ... }

(Note the use of quote marks.) The function could then be used as X %!% y. (The backslash symbol itself is not a convenient choice as it presents special problems in this context.)

The matrix multiplication operator, %*%, and the outer product matrix operator %o% are other examples of binary operators defined in this way.

10.3 Named arguments and defaults ¶

As first noted in Generating regular sequences, if arguments to called functions are given in the “name=object” form, they may be given in any order. Furthermore the argument sequence may begin in the unnamed, positional form, and specify named arguments after the positional arguments.

Thus if there is a function fun1 defined by

> fun1 <- function(data, data.frame, graph, limit) {
    [function body omitted]
  }

then the function may be invoked in several ways, for example

> ans <- fun1(d, df, TRUE, 20)
> ans <- fun1(d, df, graph=TRUE, limit=20)
> ans <- fun1(data=d, limit=20, graph=TRUE, data.frame=df)

are all equivalent.

In many cases arguments can be given commonly appropriate default values, in which case they may be omitted altogether from the call when the defaults are appropriate. For example, if fun1 were defined as

> fun1 <- function(data, data.frame, graph=TRUE, limit=20) { ... }

it could be called as

> ans <- fun1(d, df)

which is now equivalent to the three cases above, or as

> ans <- fun1(d, df, limit=10)

which changes one of the defaults.

It is important to note that defaults may be arbitrary expressions, even involving other arguments to the same function; they are not restricted to be constants as in our simple example here.

10.4 The ...’ argument ¶

Another frequent requirement is to allow one function to pass on argument settings to another. For example many graphics functions use the function par() and functions like plot() allow the user to pass on graphical parameters to par() to control the graphical output. (See Permanent changes: The par() function, for more details on the par() function.) This can be done by including an extra argument, literally ...’, of the function, which may then be passed on. An outline example is given below.

fun1 <- function(data, data.frame, graph=TRUE, limit=20, ...) {
  [omitted statements]
  if (graph)
    par(pch="*", ...)
  [more omissions]
}

Less frequently, a function will need to refer to components of ...‘. The expression list(...) evaluates all such arguments and returns them in a named list, while ..1, ..2, etc. evaluate them one at a time, with ..n’ returning the n-th unmatched argument.

10.5 Assignments within functions ¶

Note that any ordinary assignments done within the function are local and temporary and are lost after exit from the function. Thus the assignment X <- qr(X) does not affect the value of the argument in the calling program.

To understand completely the rules governing the scope of R assignments the reader needs to be familiar with the notion of an evaluation frame. This is a somewhat advanced, though hardly difficult, topic and is not covered further here.

If global and permanent assignments are intended within a function, then either the ‘superassignment’ operator, <<- or the function assign() can be used. See the help document for details.

10.6 More advanced examples ¶

10.6.1 Efficiency factors in block designs ¶

As a more complete, if a little pedestrian, example of a function, consider finding the efficiency factors for a block design. (Some aspects of this problem have already been discussed in Index matrices.)

A block design is defined by two factors, say blocks (b levels) and varieties (v levels). If R and K are the v by v and b by b replications and block size matrices, respectively, and N is the b by v incidence matrix, then the efficiency factors are defined as the eigenvalues of the matrix E = I_v - R^{-1/2}N’K^{-1}NR^{-1/2} = I_v - A’A, where A = K^{-1/2}NR^{-1/2}. One way to write the function is given below.

> bdeff <- function(blocks, varieties) {
    blocks <- as.factor(blocks)             # minor safety move
    b <- length(levels(blocks))
    varieties <- as.factor(varieties)       # minor safety move
    v <- length(levels(varieties))
    K <- as.vector(table(blocks))           # remove dim attr
    R <- as.vector(table(varieties))        # remove dim attr
    N <- table(blocks, varieties)
    A <- 1/sqrt(K) * N * rep(1/sqrt(R), rep(b, v))
    sv <- svd(A)
    list(eff=1 - sv$d^2, blockcv=sv$u, varietycv=sv$v)
}

It is numerically slightly better to work with the singular value decomposition on this occasion rather than the eigenvalue routines.

The result of the function is a list giving not only the efficiency factors as the first component, but also the block and variety canonical contrasts, since sometimes these give additional useful qualitative information.

10.6.2 Dropping all names in a printed array ¶

For printing purposes with large matrices or arrays, it is often useful to print them in close block form without the array names or numbers. Removing the dimnames attribute will not achieve this effect, but rather the array must be given a dimnames attribute consisting of empty strings. For example to print a matrix, X

> temp <- X
> dimnames(temp) <- list(rep("", nrow(X)), rep("", ncol(X)))
> temp; rm(temp)

This can be much more conveniently done using a function, no.dimnames(), shown below, as a “wrap around” to achieve the same result. It also illustrates how some effective and useful user functions can be quite short.

no.dimnames <- function(a) {
  ## Remove all dimension names from an array for compact printing.
  d <- list()
  l <- 0
  for(i in dim(a)) {
    d[[l <- l + 1]] <- rep("", i)
  }
  dimnames(a) <- d
  a
}

With this function defined, an array may be printed in close format using

> no.dimnames(X)

This is particularly useful for large integer arrays, where patterns are the real interest rather than the values.

10.6.3 Recursive numerical integration ¶

Functions may be recursive, and may themselves define functions within themselves. Note, however, that such functions, or indeed variables, are not inherited by called functions in higher evaluation frames as they would be if they were on the search path.

The example below shows a naive way of performing one-dimensional numerical integration. The integrand is evaluated at the end points of the range and in the middle. If the one-panel trapezium rule answer is close enough to the two panel, then the latter is returned as the value. Otherwise the same process is recursively applied to each panel. The result is an adaptive integration process that concentrates function evaluations in regions where the integrand is farthest from linear. There is, however, a heavy overhead, and the function is only competitive with other algorithms when the integrand is both smooth and very difficult to evaluate.

The example is also given partly as a little puzzle in R programming.

area <- function(f, a, b, eps = 1.0e-06, lim = 10) {
  fun1 <- function(f, a, b, fa, fb, a0, eps, lim, fun) {
    ## function ‘fun1’ is only visible inside ‘area’
    d <- (a + b)/2
    h <- (b - a)/4
    fd <- f(d)
    a1 <- h * (fa + fd)
    a2 <- h * (fd + fb)
    if(abs(a0 - a1 - a2) < eps || lim == 0)
      return(a1 + a2)
    else {
      return(fun(f, a, d, fa, fd, a1, eps, lim - 1, fun) +
             fun(f, d, b, fd, fb, a2, eps, lim - 1, fun))
    }
  }
  fa <- f(a)
  fb <- f(b)
  a0 <- ((fa + fb) * (b - a))/2
  fun1(f, a, b, fa, fb, a0, eps, lim, fun1)
}

10.7 Scope ¶

The discussion in this section is somewhat more technical than in other parts of this document. However, it details one of the major differences between S-PLUS and R.

The symbols which occur in the body of a function can be divided into three classes; formal parameters, local variables and free variables. The formal parameters of a function are those occurring in the argument list of the function. Their values are determined by the process of binding the actual function arguments to the formal parameters. Local variables are those whose values are determined by the evaluation of expressions in the body of the functions. Variables which are not formal parameters or local variables are called free variables. Free variables become local variables if they are assigned to. Consider the following function definition.

f <- function(x) {
  y <- 2*x
  print(x)
  print(y)
  print(z)
}

In this function, x is a formal parameter, y is a local variable and z is a free variable.

In R the free variable bindings are resolved by first looking in the environment in which the function was created. This is called lexical scope. First we define a function called cube.

cube <- function(n) {
  sq <- function() n*n
  n*sq()
}

The variable n in the function sq is not an argument to that function. Therefore it is a free variable and the scoping rules must be used to ascertain the value that is to be associated with it. Under static scope (S-PLUS) the value is that associated with a global variable named n. Under lexical scope (R) it is the parameter to the function cube since that is the active binding for the variable n at the time the function sq was defined. The difference between evaluation in R and evaluation in S-PLUS is that S-PLUS looks for a global variable called n while R first looks for a variable called n in the environment created when cube was invoked.

## first evaluation in S
S> cube(2)
Error in sq(): Object "n" not found
Dumped
S> n <- 3
S> cube(2)
[1] 18
## then the same function evaluated in R
R> cube(2)
[1] 8

Lexical scope can also be used to give functions mutable state. In the following example we show how R can be used to mimic a bank account. A functioning bank account needs to have a balance or total, a function for making withdrawals, a function for making deposits and a function for stating the current balance. We achieve this by creating the three functions within account and then returning a list containing them. When account is invoked it takes a numerical argument total and returns a list containing the three functions. Because these functions are defined in an environment which contains total, they will have access to its value.

The special assignment operator, <<-, is used to change the value associated with total. This operator looks back in enclosing environments for an environment that contains the symbol total and when it finds such an environment it replaces the value, in that environment, with the value of right hand side. If the global or top-level environment is reached without finding the symbol total then that variable is created and assigned to there. For most users <<- creates a global variable and assigns the value of the right hand side to it²². Only when <<- has been used in a function that was returned as the value of another function will the special behavior described here occur.

open.account <- function(total) {
  list(
    deposit = function(amount) {
      if(amount <= 0)
        stop("Deposits must be positive!\n")
      total <<- total + amount
      cat(amount, "deposited.  Your balance is", total, "\n\n")
    },
    withdraw = function(amount) {
      if(amount > total)
        stop("You don't have that much money!\n")
      total <<- total - amount
      cat(amount, "withdrawn.  Your balance is", total, "\n\n")
    },
    balance = function() {
      cat("Your balance is", total, "\n\n")
    }
  )
}

ross <- open.account(100)
robert <- open.account(200)

ross$withdraw(30)
ross$balance()
robert$balance()

ross$deposit(50)
ross$balance()
ross$withdraw(500)

10.8 Customizing the environment ¶

Users can customize their environment in several different ways. There is a site initialization file and every directory can have its own special initialization file. Finally, the special functions .First and .Last can be used.

The location of the site initialization file is taken from the value of the R_PROFILE environment variable. If that variable is unset, the file Rprofile.site in the R home subdirectory etc is used. This file should contain the commands that you want to execute every time R is started under your system. A second, personal, profile file named .Rprofile²³ can be placed in any directory. If R is invoked in that directory then that file will be sourced. This file gives individual users control over their workspace and allows for different startup procedures in different working directories. If no .Rprofile file is found in the startup directory, then R looks for a .Rprofile file in the user’s home directory and uses that (if it exists). If the environment variable R_PROFILE_USER is set, the file it points to is used instead of the .Rprofile files.

Any function named .First() in either of the two profile files or in the .RData image has a special status. It is automatically performed at the beginning of an R session and may be used to initialize the environment. For example, the definition in the example below alters the prompt to $ and sets up various other useful things that can then be taken for granted in the rest of the session.

Thus, the sequence in which files are executed is, Rprofile.site, the user profile, .RData and then .First(). A definition in later files will mask definitions in earlier files.

> .First <- function() {
  options(prompt="$ ", continue="+\t")  # $ is the prompt
  options(digits=5, length=999)         # custom numbers and printout
  x11()                                 # for graphics
  par(pch = "+")                        # plotting character
  source(file.path(Sys.getenv("HOME"), "R", "mystuff.R"))
                                        # my personal functions
  library(MASS)                         # attach a package
}

Similarly a function .Last(), if defined, is (normally) executed at the very end of the session. An example is given below.

> .Last <- function() {
  graphics.off()                        # a small safety measure.
  cat(paste(date(),"\nAdios\n"))        # Is it time for lunch?
}

10.9 Classes, generic functions and object orientation ¶

The class of an object determines how it will be treated by what are known as generic functions. Put the other way round, a generic function performs a task or action on its arguments specific to the class of the argument itself. If the argument lacks any class attribute, or has a class not catered for specifically by the generic function in question, there is always a default action provided.

An example makes things clearer. The class mechanism offers the user the facility of designing and writing generic functions for special purposes. Among the other generic functions are plot() for displaying objects graphically, summary() for summarizing analyses of various types, and anova() for comparing statistical models.

The number of generic functions that can treat a class in a specific way can be quite large. For example, the functions that can accommodate in some fashion objects of class "data.frame" include

[     [[<-    any    as.matrix
[<-   mean    plot   summary

A currently complete list can be got by using the methods() function:

> methods(class="data.frame")

Conversely the number of classes a generic function can handle can also be quite large. For example the plot() function has a default method and variants for objects of classes "data.frame", "density", "factor", and more. A complete list can be got again by using the methods() function:

> methods(plot)

For many generic functions the function body is quite short, for example

> coef
function (object, ...)
UseMethod("coef")

The presence of UseMethod indicates this is a generic function. To see what methods are available we can use methods()

> methods(coef)
[1] coef.aov*     coef.Arima*   coef.default* coef.listof*  coef.maov*   
[6] coef.nls*    
see '?methods' for accessing help and source code

In this example there are six methods, none of which can be seen by typing its name (as indicated by the asterisk). We can read these by either of

> getAnywhere("coef.aov")
A single object matching 'coef.aov' was found
It was found in the following places
  registered S3 method for coef from namespace stats
  namespace:stats
with value

function (object, complete = FALSE, ...)
{
    cf <- object$coefficients
    if (complete) 
        cf
    else cf[!is.na(cf)]
}

> getS3method("coef", "aov")
function (object, complete = FALSE, ...)
{
    cf <- object$coefficients
    if (complete) 
        cf
    else cf[!is.na(cf)]
}

A function named gen.cl will be invoked by the generic gen for class cl, so do not name functions in this style unless they are intended to be methods.

The reader is referred to the R Language Definition for a more complete discussion of this mechanism.