Hadley,
there was a whole discussion about subsetting and subassigning data frames (and
general efficiency issues) some time ago (I can't find it in a hurry but others
might) -- just look at the `[.data.frame` code to see why it's so slow. It
would need to be pushed into C code to allow certain optimizations, but it's a
quite complex code so I don't think there were volunteers. So the advice is
don't do it ;). Treating DFs as lists is always faster since you get to the
fast internal code.
Cheers,
S
On Dec 28, 2011, at 10:37 AM, Hadley Wickham wrote:
> Hi all,
>
> There seems to be rather a large speed disparity in subsetting when
> working with a whole data frame vs. working with just columns
> individually:
>
> df <- as.data.frame(replicate(10, runif(1e5)))
> ord <- order(df[[1]])
>
> system.time(df[ord, ])
> # user system elapsed
> # 0.043 0.007 0.059
> system.time(lapply(df, function(x) x[ord]))
> # user system elapsed
> # 0.022 0.008 0.029
>
> What's going on?
>
> I realise this isn't quite a fair example because the second case
> makes a list not a data frame, but I thought it would be quick
> operation to turn a list into a data frame if you don't do any
> checking:
>
> list_to_df <- function(list) {
> n <- length(list[[1]])
> structure(list,
> class = "data.frame",
> row.names = c(NA, -n))
> }
> system.time(list_to_df(lapply(df, function(x) x[ord])))
> # user system elapsed
> # 0.031 0.017 0.048
>
> So I guess this is slow because it has to make a copy of the whole
> data frame to modify the structure. But couldn't [.data.frame avoid
> that?
>
> Hadley
>
>
> --
> Assistant Professor / Dobelman Family Junior Chair
> Department of Statistics / Rice University
> http://had.co.nz/
>
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>
>
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