I would like to take the unique rows from a data.table, given a subset of columns and a condition in i. What is the best way of going about it? ("Best" in terms of computing speed and short or readable syntax)
set.seed(1)
jk <- data.table(c1 = sample(letters,60,replace = TRUE),
c2 = sample(c(TRUE,FALSE),60, replace = TRUE),
c3 = sample(letters,60, replace = TRUE),
c4 = sample.int(10,60, replace = TRUE)
)
Say I'd like to find the unique combinations of c1 and c2 where c4 is 10. I can think of a couple of ways to do it but am not sure what is optimal. Whether the columns to extract are keyed or not may also be important.
## works but gives an extra column
jk[c4 >= 10, TRUE, keyby = list(c1,c2)]
## this removes extra column
jk[c4 >= 10, TRUE, keyby = list(c1,c2)][,V1 := NULL]
## this seems like it could work
## but no j-expression with a keyby throws an error
jk[c4 >= 10, , keyby = list(c1,c2)]
## using unique with .SD
jk[c4 >= 10, unique(.SD), .SDcols = c("c1","c2")]
The most straightforward, to me at least, would be either unique(jk[c4 >= 10, list(c1, c2)]) as suggested by @Justin, or unique(jk[c4 >= 10, c("c1", "c2")]). The latter of these is the quickest of the four suggestions so far, at least on my laptop:
microbenchmark(
a=jk[c4 >= 10, list(c1,c2), keyby = list(c1,c2)][,c("c1","c2")],
b=jk[c4 >= 10, unique(.SD), .SDcols = c("c1","c2")],
c=unique(jk[c4>=10,list(c1,c2)]),
d=unique(jk[c4>=10,c("c1","c2")])
)
Unit: microseconds
expr min lq median uq max neval
a 1378.742 1456.676 1494.9380 1531.1395 2515.796 100
b 906.404 943.072 963.7790 997.4930 3805.846 100
c 1167.125 1201.988 1232.3500 1272.2250 2077.047 100
d 627.768 653.314 669.8625 683.8045 739.808 100