Aline Santos 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.
>
con.data <- 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 ")
df <- read.table(con.data,header=TRUE)
close(con.data)
aggregate(df[,!names(df) %in% c("sample","replicate")
],by=list(sample=df$sample), FUN=mean)
best regards
Berend
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