Summing rows after first occurance of a certain number - r

Question

I would like to to get the sum of the rows after first occurrence of a certain number. In this case it is '10' for instance.
I though If we can know the row number after first occurrence and the ending row number of that group and we can sum in between them.
I can get the first occurrence of '10' each group but I don't know how can get the sum of the rows.
df <- data.frame(gr=rep(c(1,2),c(7,9)),
y_value=c(c(0,0,10,8,8,6,0),c(0,0,10,10,5,4,2,0,0)))
> df
gr y_value
1 1 0
2 1 0
3 1 10
4 1 8
5 1 8
6 1 6
7 1 0
8 2 0
9 2 0
10 2 10
11 2 10
12 2 5
13 2 4
14 2 2
15 2 0
16 2 0
My initial attempt is below which is not working for some reason even for grouping part:(!
library(dplyr)
df%>%
group_by(gr)%>%
mutate(check1=any(y_value==10),row_sum=which(y_value == 10)[1])
Expected output
> df
gr y_value sum_rows_range
1 1 0 22/4
2 1 0 22/4
3 1 10 22/4
4 1 8 22/4
5 1 8 22/4
6 1 6 22/4
7 1 0 22/4
8 2 0 21/6
9 2 0 21/6
10 2 10 21/6
11 2 10 21/6
12 2 5 21/6
13 2 4 21/6
14 2 2 21/6
15 2 0 21/6
16 2 0 21/6

Answer 1

A dplyr solution:
library(dplyr)
df %>%
group_by(gr) %>%
slice(if(any(y_value == 10)) (which.max(y_value == 10)+1):n() else row_number()) %>%
summarize(sum = sum(y_value),
rows = n()) %>%
inner_join(df)
Notes:
The main idea is to slice on the rows after the first 10 occurs. any(y_value == 10)) and else row_number() are just to take care of the case where there are no 10's in y_value.
Reading the documentation for ?which.max, you will notice that when it is applied to a logical vector, in this case y_value == 10, "with both FALSE and TRUE values, which.min(x) and which.max(x) return the index of the first FALSE or TRUE, respectively, as FALSE < TRUE."
In other words, which.max(y_value == 10) will give the index of the first occurrence of 10. By adding 1 to it, I can start sliceing from the value right after the first occurrence of 10.
Result:
# A tibble: 16 × 4
gr sum rows y_value
<dbl> <dbl> <int> <dbl>
1 1 22 4 0
2 1 22 4 0
3 1 22 4 10
4 1 22 4 8
5 1 22 4 8
6 1 22 4 6
7 1 22 4 0
8 2 21 6 0
9 2 21 6 0
10 2 21 6 10
11 2 21 6 10
12 2 21 6 5
13 2 21 6 4
14 2 21 6 2
15 2 21 6 0
16 2 21 6 0

Answer 2

It's a bit convoluted, and I'm not positive it's what you're looking for, but it does match your output.
df %>%
group_by(gr) %>%
mutate(is_ten = cumsum(y_value == 10)) %>%
filter(is_ten > 0) %>%
filter(!(y_value == 10 & is_ten == 1)) %>%
group_by(gr) %>%
summarize(sum_rows_range = paste(sum(y_value), n(), sep = "/")) %>%
right_join(df)
# A tibble: 16 x 3
gr sum_rows_range y_value
<dbl> <chr> <dbl>
1 1 22/4 0
2 1 22/4 0
3 1 22/4 10
4 1 22/4 8
5 1 22/4 8
6 1 22/4 6
7 1 22/4 0
8 2 21/6 0
9 2 21/6 0
10 2 21/6 10
11 2 21/6 10
12 2 21/6 5
13 2 21/6 4
14 2 21/6 2
15 2 21/6 0
16 2 21/6 0