Generate large enough data:

require(data.table) ## 1.9.3
set.seed(1L)
DT = data.table(ID = sample(1e3, 1e8, TRUE), GROUP = sample(letters, 1e8, TRUE))

Benchmarks:

All benchmarks are minimum of three consecutive runs.

data.table - method 1

system.time(ans1 <- DT[, list(N=length(unique(GROUP))), by=ID])
#   user  system elapsed 
#  8.677   1.939  10.864 

data.table method 2

system.time(ans2 <- unique(DT)[, .N, by=ID])
#   user  system elapsed 
#  7.054   0.948   8.181 

identical(ans1, ans2) # [1] TRUE

Convert to data.frame now:

require(dplyr) ## latest commit from github
setDF(DT)

dplyr - generate groups first:

system.time(DT_g <- DT %>% group_by(ID))
#   user  system elapsed 
#  7.688   1.369   9.686 

gc() ## needed this for measuring timing correctly. group_by seems to take quite a bit of memory.

dplyr - method 1:

system.time(ans3 <- DT_g %>% summarise(N = n_distinct(GROUP)))
#   user  system elapsed 
# 16.170   0.050  16.618 

dplyr - method 2:

system.time(ans4 <- DT_g %>% summarise(N = length(unique(GROUP))))
#   user  system elapsed 
#  7.108   2.421   9.705 

identical(ans3, ans4) # [1] TRUE

identical(setDF(setorder(ans1)), as.data.frame(ans3)) # [1] TRUE

n_distinct() seems slower than length(unique(.)) here.. not sure why.. especially when ?n_distinct says:

This is a faster and more concise equivalent of length(unique(x))