Creating a dataframe grouping observations according to labels - r

Question

I have an x vector with categorical variables and a y vector of numerical variables, both of the same length.
I need to create a data-frame in which all numerical observations in y are separated into groups by a categorical label in x so the end result would look something like:
x obs1 obs2 obs3
a 1 3 5
b 6 7 8
c 3 4 6
Now both aggregate and tapply require a FUN specification but I don't want to do operations on the variables.
x= {random sampling from letters of the alphabet}
y= {random numbers}

Answer 1

Remember, everything is a function in R. So things like c() are just function calls.
x <- rep(letters[1:3], each=3)
y <- c(1, 3, 5, 6, 7, 8, 3, 4, 6)
foo <- tapply(y, x, c)
# > foo
# $a
# [1] 1 3 5
# $b
# [1] 6 7 8
# $c
# [1] 3 4 6
Then you can use this silly pattern to get the data.frame you're looking for:
do.call(rbind, foo)
# [,1] [,2] [,3]
# a 1 3 5
# b 6 7 8
# c 3 4 6

Answer 2

I am not clear about something from your example: is it possible for there to be different numbers of y-values for each category in x? For example, would you consider basic data like this:
> x <- c(rep(c("a", "b", "c"), 3), "c", "c")
> y <- sample(1:20, 11)
> df <- data.frame(x, y)
> df
x y
1 a 16
2 b 4
3 c 9
4 a 2
5 b 12
6 c 17
7 a 7
8 b 10
9 c 11
10 c 1
11 c 8
Here there are more values for category c. This is not entirely what you are looking for, but it might be a start:
> library(reshape2)
> dcast(df, x ~ y)
Using y as value column: use value.var to override.
x 1 2 4 7 8 9 10 11 12 16 17
1 a NA 2 NA 7 NA NA NA NA NA 16 NA
2 b NA NA 4 NA NA NA 10 NA 12 NA NA
3 c 1 NA NA NA 8 9 NA 11 NA NA 17
The values for each of the categories appear on the right rows... the NAs are a nuisance though. How would you want the data to appear in this case? Something like
1 a 2 7 16
2 b 4 10 12
3 c 1 8 9 11 17
This will not work, of course, because each row must have the same number of columns, so you would end up with NAs for the last two elements in the top two rows.
However, I suspect that a list would probably be the best solution in this case anyway, in which case, consider this:
> dl <- split(y, x)
> dl[["a"]]
[1] 16 2 7
> dl$b
[1] 4 12 10
> dl[["c"]]
[1] 9 17 11 1 8
You can then operate on the elements of this list. As with all things R, there are a variety of ways to do this. For example, to get the output as a list:
> lapply(dl, sum)
$a
[1] 25
$b
[1] 26
$c
[1] 46
Or with output as a vector
> sapply(dl, sum)
a b c
25 26 46
Or, alternatively, to get the output as a data frame:
> library(plyr)
> ldply(dl, sum)
.id V1
1 a 25
2 b 26
3 c 46
These mechanisms afford a far greater degree of generality than functions like rowSum() since you can apply essentially arbirary functions to each of the elements in the original list.