r - Group By then Aggregate -

i working several large data frames , need sort data first , last entry boat , net. data frame looks this:

boat     net     datetime dawn     71      2014-07-10 10:10 dawn     71      2014-07-15 11:10 whip     71      2014-07-17 08:10 whip     71      2014-07-29 12:36 dawn     71      2014-08-24 14:53 whip     71      2014-09-02 11:17 whip     73      2014-09-14 16:24 whip     71      2014-09-15 18:16 whip     73      2014-09-17 20:25 

i need dataframe include first , last entry each net boat. data should looks this:

boat     net     datetime dawn     71      2014-07-10 10:10 whip     71      2014-07-17 08:10 dawn     71      2014-08-24 14:53 whip     73      2014-09-14 16:24 whip     71      2014-09-15 18:16 whip     73      2014-09-17 20:25 

i tried couple of different things , got close not quite there.

head <- aggregate(df, = list(df$net), fun = head, n = 1) tail <- aggregate(df, = list(df$net), fun = tail, n = 1) final <- rbind(head, tail) 

this worked not take account same net number on different boats, tried group boat got same result:

head <- df %>% group_by(boat) %>% aggregate(df, = list(df$net), fun = head, n = 1) %>% ungroup 

both of these functions returned following data: (the first , last entry net number only)

boat     net     datetime dawn     71      2014-07-10 10:10 whip     73      2014-09-14 16:24 whip     71      2014-09-15 18:16 whip     73      2014-09-17 20:25 

i think close can't quite there, appreciated.

for aggregate approach, can want providing both df$boat , df$net aggregate:

head <- aggregate(df, = list(df$boat, df$net), fun = head, n = 1) tail <- aggregate(df, = list(df$boat, df$net), fun = tail, n = 1) final <- rbind(head, tail) 

since try using dplyr's group_by, here's dplyr alternative, uses slice group:

final <- df %>%   group_by(boat, net) %>%   slice(c(1, n())) %>%   ungroup() 

(note group_by , aggregate don't special in combination- group_by works other dplyr functions slice, summarize, or mutate).