[R] pre-allocation not always a timesaver

[Ross Boylan]

The R Inferno advises that if you are building up results in pieces it's
best to pre-allocate the result object and fill it in.  In some testing,
I see a benefit with this strategy for regular variables.  However, when
the results are held by a class, the opposite seems to be the case.

Comments?  Explanations?

Possibly for classes any update causes the entire object to be
replaced--perhaps to trigger the validation machinery?--and so
preallocation simply means on average a bigger object is being
manipulated.

Here is some test code, with CPU seconds given in the comments.  I tried
everything twice in case there was some "first-time" overhead such as
growing total memory in the image.  When the 2 times differed noticeably
I reported both values.

# class definitions
refbase <- setRefClass("refBase", fields = list(dispatch="ANY", myx="ANY"),
                       methods = list( initialize = function(x0=NULL, ...) {
                           usingMethods("foo")
                           dispatch <<- foo
                           myx <<- x0                                                                                                                                                                                                                                   
                       }
# some irrelevant methods edited out
                       ))

myclass <- setClass("simple", representation=list(myx="ANY"))

### Method 1: regular variables
pre <- function(n, j=1000) {
    x <- array(dim=(c(j, n)))
    for (i in 1:n) {
        x[,i] <- rnorm(j)
    }
    x
}
system.time(pre(1000)) #0.3s                                                                                                                                                                                                                                                   

nopre <- function(n, j=1000) {
    x <- numeric(0)
    for (i in 1:n)
        x <- c(x, rnorm(j))
    x
}

system.time(nopre(1000))  # 2.0s, 2.7s                                                                                                                                                                                                                                         

# Method 2: with ref class                                                                                                                                                                                                                                                               
pre2 <- function(n, j=1000) {
    a <- refbase(x0=numeric(0))
    a$myx <- array(dim=c(j, n))
    for (i in 1:n) {
        a$myx[,i] <- rnorm(j)
    }
    a$myx
}
system.time(pre2(1000)) # 4.0 s                                                                                                                                                                                                                                                

nopre2 <- function(n, j=1000) {
    a <- refbase(x0=numeric(0))
    for (i in 1:n)
        a$myx <- c(a$myx, rnorm(j))
    a$myx
}
system.time(nopre2(1000)) # 2.9s, 4.3                                                                                                                                                                                                                                          

# Method 3: with regular class                                                                                                                                                                                                                                                           
pre3 <- function(n, j=1000) {
    a <- myclass()
    a at myx <- array(dim=c(j, n))
    for (i in 1:n) {
        a at myx[,i] <- rnorm(j)
    }
    a at myx
}
system.time(pre3(1000)) # 7.3 s                                                                                                                                                                                                                                                

nopre3 <- function(n, j=1000) {
    a <- myclass(myx=numeric(0))
    for (i in 1:n)
        a at myx <- c(a at myx, rnorm(j))
    a at myx
}
system.time(nopre3(1000))  # 4.2s