change value of row and subsequent rows depending on row number - r

Question

I have a dataframe where some rows have values as 0. I want to make a code that makes the next few rows as 0 too.
> head(df$n,n=20)
df$n
1 0
2 9009
3 0
4 0
5 0
6 0
7 0
8 5410
9 0
10 0
11 0
12 0
13 0
14 0
15 32
16 0
17 0
18 1054
19 0
20 0
I want to create a code that converts the next five rows with value 0 as 0.
basically row with 0 is 0 and the next five rows is also 0.
I tried
for(j in 1:nrow(indx)){
for(i in 1:4){
df$n[j+i]<-0
}
}
where indx is dataframe containing all the row number with 0 values.
This works but incorrectly.
How to I get my desired output?
> head(df$n,n=20)
df$n
1 0
2 0
3 0
4 0
5 0
6 0
7 0
8 5410
9 0
10 0
11 0
12 0
13 0
14 0
15 32
16 0
17 0
18 0
19 0
20 0
Edit: sorry for the unclear language. My aim is to convert 5 values after 0 to 0. since it is incorrect data.
Edit2: I think this code worked for me. its a little bit primitive.
for( i in 1:nrow(indx)){
u<-indx[i,]
df[u,]<-0
df[u+1,]<-0
df[u+2,]<-0
df[u+3,]<-0
df[u+4,]<-0
df[u+5,]<-0
}
however it introduces extra rows at end but it works.

Answer 1

If I understand correctly, you want to make sure any run of zeros is at least five rows long, unless it's at the end of the data. Here's a dplyr-based solution:
library(dplyr)
df %>%
group_by(zero_run = cumsum(n == 0 & lag(n, default = 1) != 0)) %>%
mutate(
zeros_consecutive = row_number(),
n_new = ifelse(zero_run == 0 | zeros_consecutive > 5, n, 0)
) %>%
ungroup()
# # A tibble: 20 × 4
# n zero_run zeros_consecutive n_new
# <dbl> <int> <int> <dbl>
# 1 0 1 1 0
# 2 9009 1 2 0
# 3 0 2 1 0
# 4 0 2 2 0
# 5 0 2 3 0
# 6 0 2 4 0
# 7 0 2 5 0
# 8 5410 2 6 5410
# 9 0 3 1 0
# 10 0 3 2 0
# 11 0 3 3 0
# 12 0 3 4 0
# 13 0 3 5 0
# 14 0 3 6 0
# 15 32 3 7 32
# 16 0 4 1 0
# 17 0 4 2 0
# 18 1054 4 3 0
# 19 0 5 1 0
# 20 0 5 2 0
I left in the helper columns to better demonstrate the approach, but you could remove these by using n = ifelse(...) instead of n_new = ifelse(...) and adding select(!zeros_run:zeros_consecutive).