Inf and lazy evaluation

The only place I know lazy evaluation really is visible and widely
used is in the passing of function arguments. It's what allows magic
like

zz <- 1:5
plot(zz)

to know your variable was called "zz."

This is actually *not* lazy evaluation. You could achieve the same effect just by passing the defining expression along with zz. Real lazy evaluation is seen in the arguments to switch() (and "if" once you realize that it is actually a function too) -- only the chosen expression is evaluated. Also, in function definitions, we have default arguments only sometimes evaluated, or not evaluated until some other computation has taken place; e.g., take a look at

args(hist.default)

function (x, breaks = "Sturges", freq = NULL, probability = !freq, 
    include.lowest = TRUE, right = TRUE, density = NULL, angle = 45, 
    col = NULL, border = NULL, main = paste("Histogram of", xname), 
    xlim = range(breaks), ylim = NULL, xlab = xname, ylab, axes = TRUE, 
    plot = TRUE, labels = FALSE, nclass = NULL, warn.unused = TRUE, 
    ...) 
NULL

and notice that you won't actually ever be setting xlim=range("Sturges"), which would result in an error. What will in fact happen is that you first get the conversion

            breaks <- switch(breaks, sturges = nclass.Sturges(x), 
                `freedman-diaconis` = , fd = nclass.FD(x), scott = nclass.scott(x), 
                stop("unknown 'breaks' algorithm"))

(note, in passing, that lazy evaluation prevents the stop() from kicking in, if a known algorithm is given). Then later on, we have

        breaks <- pretty(range(x), n = breaks, min.n = 1)

so by the time we need the xlim, breaks will be a numeric vector which you _can_ take the range of.

Peter Dalgaard, Professor
Center for Statistics, Copenhagen Business School
Solbjerg Plads 3, 2000 Frederiksberg, Denmark
Phone: (+45)38153501
Email: pd.mes at cbs.dk  Priv: PDalgd at gmail.com