Togaware DATA MINING
Desktop Survival Guide
by Graham Williams
Google

Methods

Sometimes you may want to know how a function is implemented. R is also an object oriented functional language and so what method is called depends on the type of the argument. This can be illustrated with the mean function. We can try to determine its implementation:

> mean
function (x, ...)
UseMethod("mean")
<environment: namespace:base>

The UseMethod part indicates that mean is a generic function, possibly with many different implementations. So to find out what function our call might actually be dispatched to we can use the methods function:

> methods(mean)
[1] mean.data.frame mean.Date       mean.default    mean.difftime  
[5] mean.POSIXct    mean.POSIXlt

In there we see mean.default which will be dispatched to if no other methods are appropriate. So we might be interested in its definition:

> mean.default
function (x, trim = 0, na.rm = FALSE, ...)
{
    if (!is.numeric(x) && !is.complex(x) && !is.logical(x)) {
        warning("argument is not numeric or logical: returning NA")
        return(as.numeric(NA))
    }
    if (na.rm)
        x <- x[!is.na(x)]
    trim <- trim[1]
    n <- length(x)
[...]
    if (is.integer(x))
        sum(as.numeric(x))/n
    else sum(x)/n
}
<environment: namespace:base>

We might also be interested in the mean.data.frame function which will be dispatched to if the argument is a data.frame:

> mean.data.frame
function (x, ...) 
sapply(x, mean, ...)
<environment: namespace:base>

So this function does no more than to apply the mean function to the columns of the data frame, but is quite useful.

Note that the function getAnywhere may also be useful in obtaining the definition of objects.

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