I have a data.frame as simple as this one:
id group idu value
1 1 1_1 34
2 1 2_1 23
3 1 3_1 67
4 2 4_2 6
5 2 5_2 24
6 2 6_2 45
1 3 1_3 34
2 3 2_3 67
3 3 3_3 76
from where I want to retrieve a subset with the first entries of each group; something like:
id group idu value
1 1 1_1 34
4 2 4_2 6
1 3 1_3 34
id is not unique so the approach should not rely on it.
Can I achieve this avoiding loops?
data <- data.frame(
id = c(1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L),
group = rep(1:3, each = 3L),
idu = factor(c("1_1", "2_1", "3_1", "4_2", "5_2", "6_2", "1_3", "2_3", "3_3")),
value = c(34L, 23L, 67L, 6L, 24L, 45L, 34L, 67L, 76L)
)
Using Gavin's million row df:
DF3 <- data.frame(id = sample(1000, 1000000, replace = TRUE),
group = factor(rep(1:1000, each = 1000)),
value = runif(1000000))
DF3 <- within(DF3, idu <- factor(paste(id, group, sep = "_")))
I think the fastest way is to reorder the data frame and then use duplicated:
system.time({
DF4 <- DF3[order(DF3$group), ]
out2 <- DF4[!duplicated(DF4$group), ]
})
# user system elapsed
# 0.335 0.107 0.441
This compares to 7 seconds for Gavin's fastet lapply + split method on my computer.
Generally, when working with data frames, the fastest approach is usually to generate all the indices and then do a single subset.