Hi,
I use the code below to aggregate / cnt my test data. It works fine,
but the problem is with my real data (33'000 rows) where the function
is really slow (nothing happened in half an hour).
Does anybody know of other functions that I could use?
Thanks,
Hans-Peter
--
dat <- data.
Convert dat to a matrix and see if working with the
matrix instead of a data frame speeds things up
enough.
On 10/13/05, Hans-Peter <[EMAIL PROTECTED]> wrote:
> Hi,
>
> I use the code below to aggregate / cnt my test data. It works fine,
> but the problem is with my real data (33'000 rows) where t
Gabor Grothendieck wrote:
> Convert dat to a matrix and see if working with the
> matrix instead of a data frame speeds things up
> enough.
In the Hmisc package the asNumericMatrix and matrix2dataFrame functions
facilite this.
Also look at the summarize and mApply functions in Hmisc, which can b
Hi,
Yesterday, I have analysed data with 16 rows and 10 columns.
Aggregation would be impossible with a data frame format, but when converting
it to a matrix with *numeric* entries (check, if the variables are of class
numeric!) the computation needs only 7 seconds on a Pentium III. I´m sad
Here is the way that I would do it. Using 'lapply' to process the list and
create a matrix; take less than 1 second:
> dat <- data.frame(D=sample(32000:33000, 33000, T),
+ Fid=sample(1:10,33000,T), A=sample(1:5,33000,T))
> system.time({
+ result <- lapply(split(seq(nrow(dat)), dat$D), function(.d)
Many thanks for all your answers. Converting to a matrix didn't help,
I tried with Hmisc but didn't get anywhere (different summary
functions, multiple levels).
2005/10/14, jim holtman <[EMAIL PROTECTED]>:
> Here is the way that I would do it. Using 'lapply' to process the list and
> create a ma
