Check frequency of data.table value in other data.table

Question

 library(data.table)
 DT1 <- data.table(num = 1:6, group = c(\"A\", \"B\", \"B\", \"B\", \"A\", \"C\"))
 DT2 <- data.table(group = c(\"A\", \"B\", \"C\"))
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Answer 1

This is how I would do it: first count the number of times each group appears in DT1, then simply join DT2 and DT1.

require(data.table)
DT1 <- data.table(num = 1:6, group = c("A", "B", "B", "B", "A", "C"))
DT2 <- data.table(group = c("A", "B", "C"))

#solution:
DT1[,num_counts:=.N,by=group] #the number of entries in this group, just count the other column
setkey(DT1, group)
setkey(DT2, group)
DT2 = DT1[DT2,mult="last"][,list(group, popular = (num_counts >= 2))]

#> DT2
#   group popular
#1:     A    TRUE
#2:     B    TRUE
#3:     C   FALSE

Answer 2

I'd just do it this way:

## 1.9.4+
setkey(DT1, group)
DT1[J(DT2$group), list(popular = .N >= 2L), by = .EACHI]
#    group popular
# 1:     A    TRUE
# 2:     B    TRUE
# 3:     C   FALSE
# 4:     D   FALSE ## on the updated example

data.table's join syntax is quite powerful, in that, while joining, you can also aggregate / select / update columns in j. Here we perform a join. For each row in DT2$group, on the corresponding matching rows in DT1, we compute the j-expression .N >= 2L; by specifying by = .EACHI (please check 1.9.4 NEWS), we compute the j-expression each time.

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In 1.9.4, .() has been introduced as an alias in all i, j and by. So you could also do:

DT1[.(DT2$group), .(popular = .N >= 2L), by = .EACHI]

When you're joining by a single character column, you can drop the .() / J() syntax altogether (for convenience). So this can be also written as:

DT1[DT2$group, .(popular = .N >= 2L), by = .EACHI]