Cole,
Bioconductor's high throughput sequencing infrastructure package IRanges
contains code that may be useful for speeding up base::rbind.data.frame.
I've extracted the salient bits from that rbind method, but left the
corner case handling code out. IRanges's rbind method took the approach
of treating a data set as a list of equal length columns, and so it
contains a number of lapplys and vector concatenation calls. Given that
base::rbind.data.frame sits at the core of many operations, I'm not sure
if patches would be accepted for it, but I could take a crack at it.
biocRBind <- function(..., deparse.level=1)
{
## Simplified version of IRanges's rbind method for DataFrame
## Removed all data checks, ignored row names
args <- list(...)
df <- args[[1L]]
cn <- colnames(df)
cl <- unlist(lapply(as.list(df, use.names = FALSE), class))
factors <- unlist(lapply(as.list(df, use.names = FALSE), is.factor))
cols <- lapply(seq_len(length(df)), function(i) {
cols <- lapply(args, `[[`, cn[i])
if (factors[i]) { # combine factor levels, coerce to character
levs <- unique(unlist(lapply(cols, levels), use.names=FALSE))
cols <- lapply(cols, as.character)
}
combined <- do.call(c, unname(cols))
if (factors[i])
combined <- factor(combined, levs)
as(combined, cl[i])
})
names(cols) <- colnames(df)
do.call(data.frame, cols)
}
# create list of data.frames
set.seed(123)
dat <- vector("list", 20000)
for(i in seq_along(dat)) {
size <- sample(1:30, 1)
dat[[i]] <- data.frame(id=rep(i, size), value=rnorm(size),
letter=sample(LETTERS, size, replace=TRUE), ind=sample(c(TRUE,FALSE),
size, replace=TRUE))
}
# sample runs
> system.time(do.call(biocRBind, dat))
user system elapsed
2.120 0.000 2.125
> system.time(do.call(biocRBind, dat))
user system elapsed
2.092 0.000 2.091
> system.time(do.call(biocRBind, dat))
user system elapsed
2.080 0.000 2.077
> sessionInfo()
R Under development (unstable) (2012-04-19 r59111)
Platform: x86_64-unknown-linux-gnu (64-bit)
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=C LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
loaded via a namespace (and not attached):
[1] tools_2.16.0
Cheers,
Patrick
On 4/19/2012 3:34 PM, Cole Beck wrote:
> It's normal for me to create a list of data.frames and then use
> do.call('rbind', list(...)) to create a single data.frame. However,
> I've noticed as the size of the list grows large, it is perhaps better
> to do this in chunks. As an example here's a list of 20,000 similar
> data.frames.
>
> # create list of data.frames
> dat <- vector("list", 20000)
> for(i in seq_along(dat)) {
> size <- sample(1:30, 1)
> dat[[i]] <- data.frame(id=rep(i, size), value=rnorm(size),
> letter=sample(LETTERS, size, replace=TRUE), ind=sample(c(TRUE,FALSE),
> size, replace=TRUE))
> }
> # combine into one data.frame, normal usage
> # system.time(do.call('rbind', dat)) # takes 2-3 minutes
> combine <- function(x, steps=NA, verbose=FALSE) {
> nr <- length(x)
> if(is.na(steps)) steps <- nr
> while(nr %% steps != 0) steps <- steps+1
> if(verbose) cat(sprintf("step size: %s\r\n", steps))
> dl <- vector("list", steps)
> for(i in seq(steps)) {
> ix <- seq(from=(i-1)*nr/steps+1, length.out=nr/steps)
> dl[[i]] <- do.call("rbind", x[ix])
> }
> do.call("rbind", dl)
> }
> # combine into one data.frame
> system.time(combine(dat, 100)) # takes 5-10 seconds
>
> I'm very surprised by this result. Does this improvement seem
> reasonable? I would think "do.call" could utilize something similar
> by default when the length of "args" is too high. Is using "do.call"
> not recommended in this scenario?
>
> Regards,
> Cole Beck
>
> ______________________________________________
> R-devel@r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-devel
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