Thanks for pointing me out "aggregate", that works fine!
There is one complication though: I have mixed types (numerical and character),
So the matrix is of the form:
A 1.0 200 ID1
A 3.0 800 ID1
A 2.0 200 ID1
B 0.5 20 ID2
B 0.9 50 ID2
C 5.0 70 ID1
One letter always has the same ID but one
Try something like this:
# Initial data frame
> DF
V1 V2 V3 V4
1 A 1.0 200 ID1
2 A 3.0 800 ID1
3 A 2.0 200 ID1
4 B 0.5 20 ID2
5 B 0.9 50 ID2
6 C 5.0 70 ID1
# Now do the aggregation to get the means
DF.1 <- aggregate(DF[, 2:3], list(V1 = DF$V1), mean)
> DF.1
V1 V2 V3
1 A 2.0
See below.
On 9/13/06, Emmanuel Levy <[EMAIL PROTECTED]> wrote:
> Thanks for pointing me out "aggregate", that works fine!
>
> There is one complication though: I have mixed types (numerical and
> character),
>
> So the matrix is of the form:
>
> A 1.0 200 ID1
> A 3.0 800 ID1
> A 2.0 200 ID1
> B
Thanks Gabor, that is much faster than using a loop!
I've got a last question:
Can you think of a fast way of keeping track of the number of
observations collapsed for each entry?
i.e. I'd like to end up with:
A 2.0 400 ID1 3 (3obs in the first matrix)
B 0.7 35 ID2 2 (2obs in the first matrix)
Emmanuel,
I wouldn't be surprised if Gabor comes up with something, but since
aggregate() can only return scalars, you can't do it in one step here.
There are possibilities using other functions such as split(), tapply()
or by(), but each has it own respective limitations requiring more than
one s
Here are three different ways to do it:
# base R
fb <- function(x)
c(V1 = x$V1[1], V4 = x$V4[1], V2.mean = mean(x$V2),
V3.mean = mean(x$V3), n = length(x$V1))
do.call(rbind, by(DF, DF[c(1,4)], fb))
# package doBy
library(doBy)
summaryBy(V2 + V3 ~ V1 + V4, DF, FUN = c(mean, length))[,-5]
(re)-Hello
I actually thought about another possibility with a "1" column, a sum
(instead of a mean), and a division of the columns for which I want
the mean:
> DF = data.frame( V1=c("A","A","A","B","B","C") , V2=c(1,3,2,0.5,0.9,5.0),
> V3=c(200,800,200,20,50,70), V4=c("ID1","ID1","ID1","ID2","ID
Good idea. You could write it compactly like this:
> transform(aggregate(cbind(DF[2:3], o = 1), DF[c(1,4)], sum, na.rm = TRUE),
+ V2 = V2/o, V3 = V3/o)
V1 V4 V2 V3 o
1 A ID1 2.0 400 3
2 B ID2 0.7 35 2
3 C ID3 5.0 70 1
On 9/15/06, Emmanuel Levy <[EMAIL PROTECTED]> wrote:
> (re)-Hello
