Efficiently counting non-NA elements in data.table - r

Question

Sometimes I need to count the number of non-NA elements in one or another column in my data.table. What is the best data.table-tailored way to do so?
For concreteness, let's work with this:
DT <- data.table(id = sample(100, size = 1e6, replace = TRUE),
var = sample(c(1, 0, NA), size = 1e6, replace = TRUE), key = "id")
The first thing that comes to my mind works like this:
DT[!is.na(var), N := .N, by = id]
But this has the unfortunate shortcoming that N does not get assigned to any row where var is missing, i.e. DT[is.na(var), N] = NA.
So I work around this by appending:
DT[!is.na(var), N:= .N, by = id][ , N := max(N, na.rm = TRUE), by = id] #OPTION 1
However, I'm not sure this is the best approach; another option I thought of and one suggested by the analog to this question for data.frames would be:
DT[ , N := length(var[!is.na(var)]), by = id] # OPTION 2
and
DT[ , N := sum(!is.na(var)), by = id] # OPTION 3
Comparing computation time of these (average over 100 trials), the last seems to be the fastest:
OPTION 1 | OPTION 2 | OPTION 3
.075 | .065 | .043
Does anyone know a speedier way for data.table?

Answer 1

Yes the option 3rd seems to be the best one. I've added another one which is valid only if you consider to change the key of your data.table from id to var, but still option 3 is the fastest on your data.
library(microbenchmark)
library(data.table)
dt<-data.table(id=(1:100)[sample(10,size=1e6,replace=T)],var=c(1,0,NA)[sample(3,size=1e6,replace=T)],key=c("var"))
dt1 <- copy(dt)
dt2 <- copy(dt)
dt3 <- copy(dt)
dt4 <- copy(dt)
microbenchmark(times=10L,
dt1[!is.na(var),.N,by=id][,max(N,na.rm=T),by=id],
dt2[,length(var[!is.na(var)]),by=id],
dt3[,sum(!is.na(var)),by=id],
dt4[.(c(1,0)),.N,id,nomatch=0L])
# Unit: milliseconds
# expr min lq mean median uq max neval
# dt1[!is.na(var), .N, by = id][, max(N, na.rm = T), by = id] 95.14981 95.79291 105.18515 100.16742 112.02088 131.87403 10
# dt2[, length(var[!is.na(var)]), by = id] 83.17203 85.91365 88.54663 86.93693 89.56223 100.57788 10
# dt3[, sum(!is.na(var)), by = id] 45.99405 47.81774 50.65637 49.60966 51.77160 61.92701 10
# dt4[.(c(1, 0)), .N, id, nomatch = 0L] 78.50544 80.95087 89.09415 89.47084 96.22914 100.55434 10