use the package 'data.table'
> x <- read.table(textConnection("sample replicate height weight age
+ A 1.00 12.0 0.64 6.00
+ A 2.00 12.2 0.38 6.00
+ A 3.00 12.4 0.49 6.00
+ B 1.00 12.7 0.65 4.00
+ B 2.00 12.8 0.78 5.00
+ C 1.00 11.9 0.45 6.00
+ C 2.00 11.84 0.44 2.00
+ C 3.00 11.43 0.32 3.00
+ C 4.00 10.24 0.84 4.00
+ D 1.00 14.2 0.54 2.00
+ D 2.00 15.67 0.67 7.00
+ D 3.00 15.11 0.81 7.00"), header = TRUE)
> closeAllConnections()
> require(data.table)
> x.dt <- data.table(x) # convert
> x.dt[, list(height = mean(height)
+ , weight = mean(weight)
+ , age = mean(age)
+ ), by = sample]
sample height weight age
[1,] A 12.20000 0.5033333 6.000000
[2,] B 12.75000 0.7150000 4.500000
[3,] C 11.35250 0.5125000 3.750000
[4,] D 14.99333 0.6733333 5.333333
>
On Fri, Mar 11, 2011 at 4:32 AM, Aline Santos <aline...@gmail.com> wrote:
> Hello R-helpers:
>
> I have data like this:
>
> sample replicate height weight age
> A 1.00 12.0 0.64 6.00
> A 2.00 12.2 0.38 6.00
> A 3.00 12.4 0.49 6.00
> B 1.00 12.7 0.65 4.00
> B 2.00 12.8 0.78 5.00
> C 1.00 11.9 0.45 6.00
> C 2.00 11.84 0.44 2.00
> C 3.00 11.43 0.32 3.00
> C 4.00 10.24 0.84 4.00
> D 1.00 14.2 0.54 2.00
> D 2.00 15.67 0.67 7.00
> D 3.00 15.11 0.81 7.00
>
> Now, how can I calculate the mean for each condition (heigth, weigth, age)
> in each sample, considering the samples have different number of replicates?
>
>
> The final matrix should look like:
>
> sample height weight age
> A 12.20 0.50 6.00
> B 12.75 0.72 4.50
> C 11.35 0.51 3.75
> D 14.99 0.67 5.33
>
> This is a simplified version of my dataset, which consist of 100 samples
> (unequally distributed in 530 replicates) for 600 different conditions.
>
> I appreciate all the help.
>
> A.S.
>
> [[alternative HTML version deleted]]
>
> ______________________________________________
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> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>
--
Jim Holtman
Data Munger Guru
What is the problem that you are trying to solve?
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