Adam,
because I did not have time to entirely test
Do you (or does your company) have an automated test suite in place?
R 2.10.0 is nearly two years old, and R 2.12.0 is nearly one.
Matthew
AdamMarczak adam.marc...@gmail.com wrote in message
news:1314385041626-3771731.p...@n4.nabble.com...
Thank you all for suggestions, they were great and informative.
I will surely use data.tables in future when our server will be upgraded for
now this is solution that I used. This solution performs exactly same task
and produces exact same results at ddply.
s - split(past,
On 08/26/2011 09:14 AM, AdamMarczak wrote:
Thank you all for suggestions, they were great and informative.
I will surely use data.tables in future when our server will be upgraded for
now this is solution that I used. This solution performs exactly same task
and produces exact same results
No, it's not much faster. I'd say it's faster about 10-15% in my case.
I dont want neither plyr or data.table package because our software on the
server does not support R version over 2.10 and both of them have
dependency for R = 2.12. Also I do not want to use old archives because I
did not
Hi Adam,
I don't think there is a faster alternative to plyr, without doing it in
nested for loops, with a lot of book-keeping of variables (but if someone
here were to correct me, I'd be happy to know).
Two things to consider:
1) See if you can optimizing your function. (there is a lot of
Hi Adam,
A recent thread on R-help deals exactly with your problem. In one of the
responses I compare ddply to a number of alternative solutions (using
ave and data.table) [1]. The test in the e-mail shows that for large
amounts of unique categories, ddply is quite slow. Hadley (Wickham,
author
Thank you for suggestions,
apparently data.table is much quicker than ddply and it's fantastic to use.
I forgot to mention in my topic I'm looking for alternative in R 2.10
version as on my platform our server runs older version of software which
only support R up to version of R-2.10 (upgrade
z - ddply(past, c(GEO_CNTRY_NAME,PROD_SEG_NAME),
function(x) summary(lm(VAL~fy,x))$r.squared)
But when ave is not exactly doing what I need. Above code runs under a
minute for my data set where as ave runs over 8 mins.
It's hard to know without a reproducible example, but I doubt that
ddply
Hello everyone,
I was asked to repost this again, sorry for any inconvenience.
I'm looking replacement for ddply function from plyr package.
Function allows to apply function by category stored in any column/columns.
Regular loops or lapplys slow down greatly because my unique combination
count
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