Will this do it for you:
> Bill <- 1:100 # test data
> # partition
> Bill.p <- split(Bill, rep(1:10, each=10))
> Bill.p
$`1`
[1] 1 2 3 4 5 6 7 8 9 10
$`2`
[1] 11 12 13 14 15 16 17 18 19 20
$`3`
[1] 21 22 23 24 25 26 27 28 29 30
$`4`
[1] 31 32 33 34 35 36 37 38 39 40
$`5`
[1] 41 42 43 44 45 46 47 48 49 50
$`6`
[1] 51 52 53 54 55 56 57 58 59 60
$`7`
[1] 61 62 63 64 65 66 67 68 69 70
$`8`
[1] 71 72 73 74 75 76 77 78 79 80
$`9`
[1] 81 82 83 84 85 86 87 88 89 90
$`10`
[1] 91 92 93 94 95 96 97 98 99 100
> sapply(Bill.p, mean)
1 2 3 4 5 6 7 8 9 10
5.5 15.5 25.5 35.5 45.5 55.5 65.5 75.5 85.5 95.5
>
>
On Tue, Jun 3, 2008 at 8:35 PM, William Pepe
wrote:
>
> I have a data set(Bill) of with 1 variable (var1), with 100 obs that are in
> ascending order. I want to sample every 10 observations and save them in 10
> different groups such as
>
> Group1 is obs 1-10
> Group 2 is obs-11-20
>
> and so on.
>
> First step is to subset them into the 10 groups, then calculate the mean of
> var1 for each of the 10 groups. Any help would be appreciated. Thanks.
>
> _________________________________________________________________
>
>
> sh_skydrive_062008
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>
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>
--
Jim Holtman
Cincinnati, OH
+1 513 646 9390
What is the problem you are trying to solve?
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