On occasion, as pointed out in an earlier posting, it is efficient to convert
to a matrix and when finished convert back to a data frame. The Hmisc
package's asNumericMatrix and matrix2dataFrame functions assist by
converting character variables to factors if needed, and by holding on to
original
On 02.07.2011 21:35, ivo welch wrote:
hi uwe---thanks for the clarification. of course, my example should always
be done in vectorized form. I only used it to show how iterative access
compares in the simplest possible fashion.<100 accesses per seconds is
REALLY slow, though.
I don't know R
hi uwe---thanks for the clarification. of course, my example should always
be done in vectorized form. I only used it to show how iterative access
compares in the simplest possible fashion. <100 accesses per seconds is
REALLY slow, though.
I don't know R internals and the learning curve would b
Some comments:
the comparison matrix rows vs. matrix columns is incorrect: Note that R
has lazy evaluation, hence you construct your matrix in the timing for
the rows and it is already constructed in the timing for the columns,
hence you want to use:
M <- matrix( rnorm(C*R), nrow=R )
D <-
This email is intended for R users that are not that familiar with R
internals and are searching google about how to speed up R.
Despite common misperception, R is not slow when it comes to iterative
access. R is fast when it comes to matrices. R is very slow when it
comes to iterative access in
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