?aggregate
> x <- read.table('clipboard')
> x
V1 V2 V3
1 1 100 150
2 1 45 32
3 1 99 100
4 2 150 33
5 2 22 87
6 2 71 31
7 1000 64 32
8 1 100 150
9 1 45 32
10 1 99 100
11 2 22 89
12 2 31 44
13 2 88 11
14 1200 64 32
15 1 100 150
16 1 45 32
17 1 99 100
18 2 150 33
19 2 22 87
20 2 71 31
21 1100 31 34
> aggregate(x[,2:3], list(x[,1]), mean)
Group.1 V2 V3
1 1 81.33333 94.00000
2 2 69.66667 49.55556
3 1000 64.00000 32.00000
4 1100 31.00000 34.00000
5 1200 64.00000 32.00000
>
On 2/17/06, Prasanna BALAPRAKASH <[EMAIL PROTECTED]> wrote:
>
> Dear Rs
>
> I have a single table with three columns in the following form:
>
> 1 100 150
> 1 45 32
> 1 99 100
> 2 150 33
> 2 22 87
> 2 71 31
> ....
> ....
> 1000 64 32
> 1 100 150
> 1 45 32
> 1 99 100
> 2 22 89
> 2 31 44
> 2 88 11
> ....
> ....
> 1200 64 32
> 1 100 150
> 1 45 32
> 1 99 100
> 2 150 33
> 2 22 87
> 2 71 31
> ...
> ...
> 1100 31 34
>
> Totally 1000+1200+1100 rows. Now, I need to group by first column
> and average then second and third column to get a table as follows as
> follows:
>
> 1 Avg. of all second col. values whose first col value is 1
> Avg. of
> all third col. values whose first col value is 1
> 2 Avg. of all second col. values whose first col value is 2
> Avg. of
> all third col. values whose first col value is 2
> ..
> ..
> 1200 Avg. of all second col. values whose first col value is 1200
> Avg. of all third col. values whose first col value is 1200
>
>
> Right now, I have a dirty implementation with a lot of "for" loops
> and "if" conditions. However, I am looking for some built in
> functions and lib. to make the code faster and easier.
>
>
> Thanks
> Prasanna
>
> ______________________________________________
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>
--
Jim Holtman
Cincinnati, OH
+1 513 646 9390
What the problem you are trying to solve?
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