Frequency table including zeros for unused values, on a data.table

Question

I have a data set that is as follows:

library(data.table)

test <- data.table(structure(list(Issue.Date = structure(c(16041, 16056, 16042,15990, 15996, 16         


        

Answer 1

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.

Answer 2

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