Frequency table including zeros for unused values, on a data.table
To directly get the counts for each group:
setkey(test, Month, Complaint)
# may need to also add allow.cartesian, depending on actual data
test[CJ(Month, Complaint, unique = TRUE), .N, by = .EACHI]
# Month Complaint N
# 1: 2013-10 A 0
# 2: 2013-10 B 0
# 3: 2013-10 C 5
# 4: 2013-10 D 4
# 5: 2013-10 E 0
# 6: 2013-11 A 1
# 7: 2013-11 B 0
# 8: 2013-11 C 4
# 9: 2013-11 D 0
#10: 2013-11 E 0
#11: 2013-12 A 1
#12: 2013-12 B 0
#13: 2013-12 C 0
#14: 2013-12 D 2
#15: 2013-12 E 0
#16: 2014-01 A 0
#17: 2014-01 B 0
#18: 2014-01 C 1
#19: 2014-01 D 0
#20: 2014-01 E 1
#21: 2014-02 A 2
#22: 2014-02 B 0
#23: 2014-02 C 6
#24: 2014-02 D 2
#25: 2014-02 E 0
#26: 2014-03 A 1
#27: 2014-03 B 2
#28: 2014-03 C 3
#29: 2014-03 D 0
#30: 2014-03 E 0
# Month Complaint N
See first revision of the answer if you want to have the counts in the full data.table instead of summarizing.
It sounds like you perhaps need to use expand.grid to "fill in" your data.table:
EG <- data.table(expand.grid(Complaint = unique(test$Complaint),
Month = unique(test$Month)),
key = "Complaint,Month")
Then, you can merge:
setkey(test, Complaint, Month)
Full <- merge(test, EG, all.y = TRUE)
And count like this:
Full[ , list(sum(!is.na(Issue.Date))), by = "Month,Complaint"]
# Month Complaint V1
# 1: 2013-11 A 1
# 2: 2013-12 A 1
# 3: 2014-02 A 2
# 4: 2014-03 A 1
# 5: 2013-10 A 0
# 6: 2014-01 A 0
# 7: 2013-11 B 0
# 8: 2013-12 B 0
# ::: S N I P :::
# 24: 2014-01 D 0
# 25: 2013-11 E 0
# 26: 2013-12 E 0
# 27: 2014-02 E 0
# 28: 2014-03 E 0
# 29: 2013-10 E 0
# 30: 2014-01 E 1
# Month Complaint V1
Alternatively, just use table (???)
data.table(table(test[, c("Month", "Complaint")]))
# Month Complaint N
# 1: 2013-10 A 0
# 2: 2013-11 A 1
# 3: 2013-12 A 1
# 4: 2014-01 A 0
# 5: 2014-02 A 2
# 6: 2014-03 A 1
# 7: 2013-10 B 0
# ::: S N I P :::
# 28: 2014-01 E 1
# 29: 2014-02 E 0
# 30: 2014-03 E 0
# Month Complaint N