Apply several summary functions on several variables by group in one call
You can do it all in one step and get proper labeling:
> aggregate(. ~ id1+id2, data = x, FUN = function(x) c(mn = mean(x), n = length(x) ) )
# id1 id2 val1.mn val1.n val2.mn val2.n
# 1 a x 1.5 2.0 6.5 2.0
# 2 b x 2.0 2.0 8.0 2.0
# 3 a y 3.5 2.0 7.0 2.0
# 4 b y 3.0 2.0 6.0 2.0
This creates a dataframe with two id columns and two matrix columns:
str( aggregate(. ~ id1+id2, data = x, FUN = function(x) c(mn = mean(x), n = length(x) ) ) )
'data.frame': 4 obs. of 4 variables:
$ id1 : Factor w/ 2 levels "a","b": 1 2 1 2
$ id2 : Factor w/ 2 levels "x","y": 1 1 2 2
$ val1: num [1:4, 1:2] 1.5 2 3.5 3 2 2 2 2
..- attr(*, "dimnames")=List of 2
.. ..$ : NULL
.. ..$ : chr "mn" "n"
$ val2: num [1:4, 1:2] 6.5 8 7 6 2 2 2 2
..- attr(*, "dimnames")=List of 2
.. ..$ : NULL
.. ..$ : chr "mn" "n"
As pointed out by @lord.garbage below, this can be converted to a dataframe with "simple" columns by using do.call(data.frame, ...)
str( do.call(data.frame, aggregate(. ~ id1+id2, data = x, FUN = function(x) c(mn = mean(x), n = length(x) ) ) )
)
'data.frame': 4 obs. of 6 variables:
$ id1 : Factor w/ 2 levels "a","b": 1 2 1 2
$ id2 : Factor w/ 2 levels "x","y": 1 1 2 2
$ val1.mn: num 1.5 2 3.5 3
$ val1.n : num 2 2 2 2
$ val2.mn: num 6.5 8 7 6
$ val2.n : num 2 2 2 2
This is the syntax for multiple variables on the LHS:
aggregate(cbind(val1, val2) ~ id1 + id2, data = x, FUN = function(x) c(mn = mean(x), n = length(x) ) )
You could add a count column, aggregate with sum, then scale back to get the mean:
x$count <- 1
agg <- aggregate(. ~ id1 + id2, data = x,FUN = sum)
agg
# id1 id2 val1 val2 count
# 1 a x 3 13 2
# 2 b x 4 16 2
# 3 a y 7 14 2
# 4 b y 6 12 2
agg[c("val1", "val2")] <- agg[c("val1", "val2")] / agg$count
agg
# id1 id2 val1 val2 count
# 1 a x 1.5 6.5 2
# 2 b x 2.0 8.0 2
# 3 a y 3.5 7.0 2
# 4 b y 3.0 6.0 2
It has the advantage of preserving your column names and creating a single count column.
Using the dplyr package you could achieve this by using summarise_all. With this summarise-function you can apply other functions (in this case mean and n()) to each of the non-grouping columns:
x %>%
group_by(id1, id2) %>%
summarise_all(funs(mean, n()))
which gives:
id1 id2 val1_mean val2_mean val1_n val2_n
1 a x 1.5 6.5 2 2
2 a y 3.5 7.0 2 2
3 b x 2.0 8.0 2 2
4 b y 3.0 6.0 2 2
If you don't want to apply the function(s) to all non-grouping columns, you specify the columns to which they should be applied or by excluding the non-wanted with a minus using the summarise_at() function:
# inclusion
x %>%
group_by(id1, id2) %>%
summarise_at(vars(val1, val2), funs(mean, n()))
# exclusion
x %>%
group_by(id1, id2) %>%
summarise_at(vars(-val2), funs(mean, n()))
Given this in the question :
I could use the plyr package, but my data set is quite large and plyr is very slow (almost unusable) when the size of the dataset grows.
Then in data.table (1.9.4+) you could try :
> DT
id1 id2 val1 val2
1: a x 1 9
2: a x 2 4
3: a y 3 5
4: a y 4 9
5: b x 1 7
6: b y 4 4
7: b x 3 9
8: b y 2 8
> DT[ , .(mean(val1), mean(val2), .N), by = .(id1, id2)] # simplest
id1 id2 V1 V2 N
1: a x 1.5 6.5 2
2: a y 3.5 7.0 2
3: b x 2.0 8.0 2
4: b y 3.0 6.0 2
> DT[ , .(val1.m = mean(val1), val2.m = mean(val2), count = .N), by = .(id1, id2)] # named
id1 id2 val1.m val2.m count
1: a x 1.5 6.5 2
2: a y 3.5 7.0 2
3: b x 2.0 8.0 2
4: b y 3.0 6.0 2
> DT[ , c(lapply(.SD, mean), count = .N), by = .(id1, id2)] # mean over all columns
id1 id2 val1 val2 count
1: a x 1.5 6.5 2
2: a y 3.5 7.0 2
3: b x 2.0 8.0 2
4: b y 3.0 6.0 2
For timings comparing aggregate (used in question and all 3 other answers) to data.table see
this benchmark (the agg and agg.x cases).