r - collapsing repeated observations counting sum of a given variable to a certain point -

id  bleed   episodes j1  0   0 ji  0   1 j1  0   1 ji  yes 0 j2  0   0 j2  0   1 j2  0   1 j2  0   1 j2  yes 0 j2  0   0 j3  0   1 j3  0   1 j3  0   0 j3  0   1 j3  yes 0 j3  0   0 

i want collapse data count number of episodes bleed occurs every individual this

id  episodes j1  2 j2  3 j3  3 

the observations made @ different times, didn’t include time variable, weekly

with sample input

dd <- structure(list(id = structure(c(1l, 1l, 1l, 1l, 2l, 2l, 2l, 2l,  2l, 2l, 3l, 3l, 3l, 3l, 3l, 3l), .label = c("j1", "j2", "j3"), class = "factor"),      bleed = structure(c(1l, 1l, 1l, 2l, 1l, 1l, 1l, 1l, 2l, 1l,      1l, 1l, 1l, 1l, 2l, 1l), .label = c("0", "yes"), class = "factor"),      episodes = c(0l, 1l, 1l, 0l, 0l, 1l, 1l, 1l, 0l, 0l, 1l,      1l, 0l, 1l, 0l, 0l)), .names = c("id", "bleed", "episodes" ), class = "data.frame", row.names = c(na, -16l)) 

you can accomplish task dplyr

library(dplyr)  dd %>% group_by(id) %>%      mutate(bleed_count=cumsum(bleed=="yes")) %>%     filter(bleed_count==0) %>%     summarize(episodes = sum(episodes)) 

use use cumsum() on boolean value track when bleed occurs. sum values before first bleed