Create a grouping column with cumsum on the 'trigger' and taking the lag, then do the difference between the first and last element and replace it as the last value per group
library(dplyr)
df1 %>%
group_by(grp = lag(cumsum(trigger), default = 0)) %>%
mutate(difference = replace(rep(NA, n()), n(),
values[n()] - values[1])) %>%
ungroup %>%
select(-grp)
-output
# A tibble: 7 × 3
trigger values difference
<int> <int> <int>
1 0 3 NA
2 0 NA NA
3 0 NA NA
4 1 5 2
5 0 4 NA
6 0 NA NA
7 1 10 6
For the second case, we may need a condition with if/else that checks the number of rows i.e. if the number of rows is greater than 1 only need the computation to replace
df2 %>%
group_by(grp = lag(cumsum(trigger), default = 0)) %>%
mutate(difference = if(n() > 1) replace(rep(NA, n()), n(),
values[n()] - values[1]) else NA) %>%
ungroup
-output
# A tibble: 7 × 4
trigger values grp difference
<int> <int> <dbl> <int>
1 0 3 0 NA
2 0 NA 0 NA
3 0 NA 0 NA
4 1 5 0 2
5 0 4 1 NA
6 1 5 1 1
7 0 10 2 NA
data
df1 <- structure(list(trigger = c(0L, 0L, 0L, 1L, 0L, 0L, 1L), values = c(3L,
NA, NA, 5L, 4L, NA, 10L)), class = "data.frame", row.names = c(NA,
-7L))
df2 <- structure(list(trigger = c(0L, 0L, 0L, 1L, 0L, 1L, 0L), values = c(3L,
NA, NA, 5L, 4L, 5L, 10L)), class = "data.frame", row.names = c(NA,
-7L))