Here's a possible solution to avoid the loop
k <- as.matrix(expand.grid(1:ncol(x),1:ncol(x)))
a1 <- as.data.frame(matrix(sapply(1:nrow(k), function(n)
agree(x[,k[n,]])$value), nrow=ncol(x)))
colnames(a1) <- colnames(x)
rownames(a1) <- colnames(x)
> identical(a, a1)
[1] TRUE
Or if you want to avo
Hi Trying,
It would be helpful if you provided reproducible examples. It would
also be polite to sign a name so that we have something by which to
address you.
On Tue, Jul 12, 2011 at 8:00 AM, Trying To learn again
wrote:
> Hi all,
>
> I have this information on a file ht.txt, imagine it is a da
On 30.05.2010 19:23, Alan Lue wrote:
Is there a performance advantage to doing this, as opposed to growing
the vector within the loop? I suppose R could have to dynamically
reallocate memory at some point?
Right, but that takes time since memory management is always expensive
(and this way
Is there a performance advantage to doing this, as opposed to growing
the vector within the loop? I suppose R could have to dynamically
reallocate memory at some point?
Alan
2010/5/30 Uwe Ligges :
>
>
> On 26.05.2010 08:52, Alan Lue wrote:
>>
>> Come to think of it, we can't save the output of
On 26.05.2010 08:52, Alan Lue wrote:
Come to think of it, we can't save the output of each invocation and
concatenate it later, since we need the output as input for the next
iteration.
Yes, but you can do it a bit cleverer than before by initializing to the
fill length as in:
r.seq <- nu
Hi:
On Tue, May 25, 2010 at 11:43 PM, Alan Lue wrote:
> Since `for' loops are slow in R, and since `apply' functions are
> faster, I was wondering whether there were a way to use an apply
> function—or to otherwise avoid using a loop—when iterating over a
> statement that updates its input.
>
T
Come to think of it, we can't save the output of each invocation and
concatenate it later, since we need the output as input for the next
iteration.
Alan
On Tue, May 25, 2010 at 11:43 PM, Alan Lue wrote:
> Since `for' loops are slow in R, and since `apply' functions are
> faster, I was wonderin
thank you very much
--- On Sat, 10/17/09, Kenn Konstabel wrote:
> From: Kenn Konstabel
> Subject: Re: [R] avoiding loops in equation
Thank God for R-help mailing list. Thanks..
> To: "Julius Tesoro"
> Date: Saturday, October 17, 2009, 1:51 PM
> a3 <- sapply(acc,
bco.com
> -Original Message-
> From: r-help-boun...@r-project.org
> [mailto:r-help-boun...@r-project.org] On Behalf Of Martin Morgan
> Sent: Wednesday, September 02, 2009 9:17 AM
> To: Alexander Shenkin
> Cc: r-help@r-project.org; spec...@stat.berkeley.edu;
> cbe
Alexander Shenkin wrote:
> Though, from my limited understanding, the 'apply' family of functions
> are actually just loops. Please correct me if I'm wrong. So, while
> more readable (which is important), they're not necessarily more
> efficient than explicit 'for' loops.
Hi Allie -- This uses a
On Tue, 1 Sep 2009, dolar wrote:
Would like some tips on how to avoid loops as I know they are slow in R
If I understand your criterion (and calling your data.frame 'dat'):
criterion <- as.matrix(dist(dat$a)) <= 5 & outer(dat$a,dat$a,">=")
criterion %*% as.matrix(dat[, c("b","c")])
b
Another advantage of the apply family of functions is that
they determine the size and type of their output in an
efficient way, which is sometimes tricky when you write
the loop yourself.
- Phil Spector
Statistica
If you can do it- try a for loop and another solution to prove this to
yourself. A for loop can get a little unwieldy for a novice like me
to understand the code, but doable. The simpler the better, but they
are not terribly slow. I have run into a couple of situations where a
vectorized solutio
> Would like some tips on how to avoid loops as I know they are slow in R
They are not slow. They are slower than vectorised equivalents, but
not slower than apply and friends.
Hadley
--
http://had.co.nz/
__
R-help@r-project.org mailing list
https:/
Though, from my limited understanding, the 'apply' family of functions
are actually just loops. Please correct me if I'm wrong. So, while
more readable (which is important), they're not necessarily more
efficient than explicit 'for' loops.
allie
On 9/2/2009 3:13 AM, Phil Spector wrote:
> Here's
Here's one way (assuming your data frame is named dat):
with(dat,
data.frame(a,t(sapply(a,function(x){
apply(dat[a - x >= -5 & a - x <=
0,c('b','c')],2,sum)}
- Phil Spector
St
"DM" == David Masson <[EMAIL PROTECTED]>
on Wed, 05 Nov 2008 15:13:37 +0100
DM> I have a question concerning avoiding loops.
DM> I know the function "apply" and I have used it several times, but I feel
DM> blocked
DM> with this situation :
DM>
DM> E <- array(X, dim =
Ingmar Visser asks:
>
> Thanks for this.
> I was afraid someone was going to say this ...
> Does this mean the only way of getting this to run faster is by
> moving to C code?
Perhaps, but that's not all that difficult for this kind of operation,
surely? Even portability across platforms shoul
Thanks for this.
I was afraid someone was going to say this ...
Does this mean the only way of getting this to run faster is by
moving to C code?
The cases I'm thinking of applying this in have dimensions of A that
are much larger than
the example, eg n by n by T where n has a max of 10 or so b
If you have lots of memory there is an obvious strategy:
d12 <- prod(dim(A)[1:2])
A <- A * array(rep(B, each = d12), dim = dim(A))
I don't really see much wrong with the obvious for() loop, though:
for(b in 1:length(B)) A[,,b] <- A[,,b] * B[b]
Bill Venables
CSIRO Laboratories
PO Box 120, Cleve
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