Dear Jason,
Try this:

# First case
N<-100000
X<-rnorm(N)

# Groups
g<-rep(1:(N/n),each=10)

# The result
tapply(X,g,mean)


For the second case, just change each=10 by each=100 and run again the code
above.

HTH,

Jorge


On Tue, Mar 24, 2009 at 10:52 PM, Jason Rupert <jasonkrupert@yahoo.com>wrote:

>
> Ugh...This should be very simple, but evidently I am not searching for the
> proper term.
>
> Given the below:
> val_size<-100000
> x_vals<-rnorm(val_size)
>
> I would like to group them according to the following
> x_vals_mean_tmp[1]<-mean(x_vals[1:10])
> x_vals_mean_tmp[2]<-mean(x_vals[11:20])
> ...
> x_vals_mean_tmp[n]<-mean(x_vals[99991:100000])
>
> Then,
> I would like to group them according to the following
> x_vals_mean_tmp[1]<-mean(x_vals[1:100])
> x_vals_mean_tmp[2]<-mean(x_vals[101:200])
> ...
> x_vals_mean_tmp[m]<-mean(x_vals[99901:100000])
> etc.
>
> I'm pretty sure I can come up with a loop to do this, but wondering if
> there is something that will allow me to break up the x_vals vector
> according to a certain step size.  I looked at split and cut, but those did
> not appear to accomplish what is needed.
>
> Thanks again.
>
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>

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