Tricks to manage the available memory in an R sess

Question

What tricks do people use to manage the available memory of an interactive R session? I use the functions below [based on postings by Petr Pikal and David Hinds to the r-help list in 2004] to list (and/or sort) the largest objects and to occassionally rm() some of them. But by far the most effective solution was ... to run under 64-bit Linux with ample memory.

Any other nice tricks folks want to share? One per post, please.

# improved list of objects
.ls.objects <- function (pos = 1, pattern, order.by,
                        decreasing=FALSE, head=FALSE, n=5) {
    napply <- function(names, fn) sapply(names, function(x)
                                         fn(get(x, pos = pos)))
    names <- ls(pos = pos, pattern = pattern)
    obj.class <- napply(names, function(x) as.character(class(x))[1])
    obj.mode <- napply(names, mode)
    obj.type <- ifelse(is.na(obj.class), obj.mode, obj.class)
    obj.size <- napply(names, object.size)
    obj.dim <- t(napply(names, function(x)
                        as.numeric(dim(x))[1:2]))
    vec <- is.na(obj.dim)[, 1] & (obj.type != "function")
    obj.dim[vec, 1] <- napply(names, length)[vec]
    out <- data.frame(obj.type, obj.size, obj.dim)
    names(out) <- c("Type", "Size", "Rows", "Columns")
    if (!missing(order.by))
        out <- out[order(out[[order.by]], decreasing=decreasing), ]
    if (head)
        out <- head(out, n)
    out
}
# shorthand
lsos <- function(..., n=10) {
    .ls.objects(..., order.by="Size", decreasing=TRUE, head=TRUE, n=n)
}

Answer 1

I make aggressive use of the subset parameter with selection of only the required variables when passing dataframes to the data= argument of regression functions. It does result in some errors if I forget to add variables to both the formula and the select= vector, but it still saves a lot of time due to decreased copying of objects and reduces the memory footprint significantly. Say I have 4 million records with 110 variables (and I do.) Example:

# library(rms); library(Hmisc) for the cph,and rcs functions
Mayo.PrCr.rbc.mdl <- 
cph(formula = Surv(surv.yr, death) ~ age + Sex + nsmkr + rcs(Mayo, 4) + 
                                     rcs(PrCr.rat, 3) +  rbc.cat * Sex, 
     data = subset(set1HLI,  gdlab2 & HIVfinal == "Negative", 
                           select = c("surv.yr", "death", "PrCr.rat", "Mayo", 
                                      "age", "Sex", "nsmkr", "rbc.cat")
   )            )

By way of setting context and the strategy: the gdlab2 variable is a logical vector that was constructed for subjects in a dataset that had all normal or almost normal values for a bunch of laboratory tests and HIVfinal was a character vector that summarized preliminary and confirmatory testing for HIV.