I've written a "dataframe" package that replaces existing methods for
data frame creation and subscripting with versions that use less
memory. For example, as.data.frame(a vector) makes 4 copies of the
data in R 2.9.2, and 1 copy with the package. There is a small speed
gain.
I and others have b
Timothée,
On Jul 4, 2011, at 2:47 AM, Timothée Carayol wrote:
> Hi --
>
> It's my first post on this list; as a relatively new user with little
> knowledge of R internals, I am a bit intimidated by the depth of some
> of the discussions here, so please spare me if I say something
> incredibly si
Hi --
It's my first post on this list; as a relatively new user with little
knowledge of R internals, I am a bit intimidated by the depth of some
of the discussions here, so please spare me if I say something
incredibly silly.
I feel that someone at this point should mention Matthew Dowle's
excel
thank you, simon. this was very interesting indeed. I also now
understand how far out of my depth I am here.
fortunately, as an end user, obviously, *I* now know how to avoid the
problem. I particularly like the as.list() transformation and back to
as.data.frame() to speed things up without los
Robert,
it's not the handling of row names per se that causes the slowdown, but my
point was that if what you need is just matrix-like structure with different
column types, you may want to use lists instead and for equal column types
you're better of with a matrix.
But to address your point,
Hi Simon,
On 03/07/11 05:30, Simon Urbanek wrote:
This is just a quick, incomplete response, but the main misconception is really
the use of data.frames. If you don't use the elaborate mechanics of data frames
that involve the management of row names, then they are definitely the wrong
tool
This is just a quick, incomplete response, but the main misconception is really
the use of data.frames. If you don't use the elaborate mechanics of data frames
that involve the management of row names, then they are definitely the wrong
tool to use, because most of the overhead is exactly to man
Dear R developers: R is supposed to be slow for iterative
calculations. actually, it isn't. matrix operations are fast. it is
data frame operations that are slow.
R <- 1000
C <- 1000
example <- function(m) {
cat("rows: "); cat(system.time( for (r in 1:R) m[r,20] <-
sqrt(abs(m[r,20])) + rnorm
