The data.table
package also has unique
and duplicated
methods of it's own with some additional features.
Both the unique.data.table
and the duplicated.data.table
methods have an additional by
argument which allows you to pass a character
or integer
vector of column names or their locations respectively
library(data.table)
DT <- data.table(id = c(1,1,1,2,2,2),
val = c(10,20,30,10,20,30))
unique(DT, by = "id")
# id val
# 1: 1 10
# 2: 2 10
duplicated(DT, by = "id")
# [1] FALSE TRUE TRUE FALSE TRUE TRUE
Another important feature of these methods is a huge performance gain for larger data sets
library(microbenchmark)
library(data.table)
set.seed(123)
DF <- as.data.frame(matrix(sample(1e8, 1e5, replace = TRUE), ncol = 10))
DT <- copy(DF)
setDT(DT)
microbenchmark(unique(DF), unique(DT))
# Unit: microseconds
# expr min lq mean median uq max neval cld
# unique(DF) 44708.230 48981.8445 53062.536 51573.276 52844.591 107032.18 100 b
# unique(DT) 746.855 776.6145 2201.657 864.932 919.489 55986.88 100 a
microbenchmark(duplicated(DF), duplicated(DT))
# Unit: microseconds
# expr min lq mean median uq max neval cld
# duplicated(DF) 43786.662 44418.8005 46684.0602 44925.0230 46802.398 109550.170 100 b
# duplicated(DT) 551.982 558.2215 851.0246 639.9795 663.658 5805.243 100 a