R: Aggregate on Group 1 and NOT Group 2

Question

I am trying to create two data sets, one which summarizes data by 2 groups which I have done using the following code:

x = rnorm(1:100)
g1 = sample(LETTERS[1:3], 100, replace = TRUE)
g2 = sample(LETTERS[24:26], 100, replace = TRUE)

aggregate(x, list(g1, g2), mean)

The second needs to summarize the data by the first group and NOT the second group.

If we consider the possible pairs from the previous example:

A - X    B - X    C - X
A - Y    B - Y    C - Y
A - Z    B - Z    C - Z

The second dataset should to summarize the data as the average of the outgroup.

A - not X
A - not Y
A - not Z etc. 

Is there a way to manipulate aggregate functions in R to achieve this? Or I also thought there could be dummy variable that could represent the data in this way, although I am unsure how it would look.

I have found this answer here: R using aggregate to find a function (mean) for "all other"

I think this indicates that a dummy variable for each pairing is necessary. However if there is anyone who can offer a better or more efficient way that would be appreciated, as there are many pairings in the true data set.

Thanks in advance

Answer 1

First let us generate the data reproducibly (using set.seed):

# same as question but added set.seed for reproducibility
set.seed(123)
x = rnorm(1:100)
g1 = sample(LETTERS[1:3], 100, replace = TRUE)
g2 = sample(LETTERS[24:26], 100, replace = TRUE)

Now we have two solutions both of which use aggregate:

1) ave

# x equals the sums over the groups and n equals the counts
ag = cbind(aggregate(x, list(g1, g2), sum),
            n = aggregate(x, list(g1, g2), length)[, 3])

ave.not <- function(x, g) ave(x, g, FUN = sum) - x
transform(ag, 
     x = NULL, # don't need x any more
     n = NULL, # don't need n any more
     mean = x/n, 
     mean.not = ave.not(x, Group.1) / ave.not(n, Group.1)
)

This gives:

  Group.1 Group.2       mean     mean.not
1       A       X  0.3155084 -0.091898832
2       B       X -0.1789730  0.332544353
3       C       X  0.1976471  0.014282465
4       A       Y -0.3644116  0.236706489
5       B       Y  0.2452157  0.099240545
6       C       Y -0.1630036  0.179833987
7       A       Z  0.1579046 -0.009670734
8       B       Z  0.4392794  0.033121335
9       C       Z  0.1620209  0.033714943

To double check the first value under mean and under mean.not:

> mean(x[g1 == "A" & g2 == "X"])
[1] 0.3155084
> mean(x[g1 == "A" & g2 != "X"])
[1] -0.09189883

2) sapply Here is a second approach which gives the same answer:

ag <- aggregate(list(mean = x), list(g1, g2), mean)
f <- function(i) mean(x[g1 == ag$Group.1[i] & g2 != ag$Group.2[i]]))
ag$mean.not = sapply(1:nrow(ag), f)
ag

REVISED Revised based on comments by poster, added a second approach and also made some minor improvements.