as.data.frame is a S3 with .data.table method and is definitely faster than
data.frame(). But it still does copy(.). data.frame(.) would also convert
strings to factors by default (if stringsAsFactors=TRUE).
The most efficient way to convert data.table to data.frame would be to do
things by reference (in place). The code is already available in as.data.frame,
just remove the copy(.):
# convert data.table to data.frame by reference
setDF <- function(x) {
if (!is.data.table(x))
stop("x must be a data.table")
setattr(x, "row.names", .set_row_names(nrow(x)))
setattr(x, "class", "data.frame")
setattr(x, "sorted", NULL)
setattr(x, ".internal.selfref", NULL)
}
Now you’ve a function that’ll convert a data.table to data.frame by reference.
require(data.table)
dat <- data.table(x=1:5, y=6:10)
setDF(dat) # dat is now a data.frame
Probably we should export this function as well, like setDT so that users can
switch between the two as they desire without hitting performance?
Arun
From: Chris Neff [email protected]
Reply: Chris Neff [email protected]
Date: April 7, 2014 at 5:32:47 PM
To: [email protected]
[email protected]
Subject: [datatable-help] Is there any overhead to converting back and forth
from a data.table to a data.frame?
I prefer data.tables for all the code processing I do. But others on my team
using my functions aren't comfortable with data.tables, so most of the
libraries I write end with
return(data.frame(DT))
Is there any copying or other overhead happening there? Since it inherits from
data.frame, I think the answer is no.
Now, if I have a function that does such a return, but I wrap that itself in a
data.table call:
data.table(func_that_returns_df())
Is there any inefficiency there? Is there a difference between data.table()
and as.data.table() here?
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