Say I have a df:
df <- data.frame(flag = c(rep(0, 20)),
include = c(rep(1, 20)))
df[c(4,8,16), ]$flag <- 1
df
flag include
1 0 1
2 0 1
3 0 1
4 1 1
5 0 1
6 0 1
7 0 1
8 1 1
9 0 1
10 0 1
11 0 1
12 0 1
13 0 1
14 0 1
15 0 1
16 1 1
17 0 1
18 0 1
19 0 1
20 0 1
What I wish to do is change the include flag to 0 if the row is within +/- two rows of a row where flag == 1. The result would look like:
flag include
1 0 1
2 0 0
3 0 0
4 1 1
5 0 0
6 0 0
7 0 0
8 1 1
9 0 0
10 0 0
11 0 1
12 0 1
13 0 1
14 0 0
15 0 0
16 1 1
17 0 0
18 0 0
19 0 1
20 0 1
I've thought of some 'innovative' (read: inefficient and over complicated) ways to do it but was thinking there must be a simple way I'm overlooking.
Would be nice if the answer was such that I could generalize this to +/- n rows, since I have a lot more data and would be looking to potentially search within +/- 10 rows...
Another option with data.table:
library(data.table)
n = 2
# find the row number where flag is one
flag_one = which(df$flag == 1)
# find the index where include needs to be updated
idx = setdiff(outer(flag_one, -n:n, "+"), flag_one)
# update include in place
setDT(df)[idx[idx >= 1 & idx <= nrow(df)], include := 0][]
# or as @Frank commented the last step with base R would be
# df$include[idx[idx >= 1 & idx <= nrow(df)]] = 0
# flag include
# 1: 0 1
# 2: 0 0
# 3: 0 0
# 4: 1 1
# 5: 0 0
# 6: 0 0
# 7: 0 0
# 8: 1 1
# 9: 0 0
#10: 0 0
#11: 0 1
#12: 0 1
#13: 0 1
#14: 0 0
#15: 0 0
#16: 1 1
#17: 0 0
#18: 0 0
#19: 0 1
#20: 0 1
Put in a function:
update_n <- function(df, n) {
flag_one = which(df$flag == 1)
idx = setdiff(outer(flag_one, -n:n, "+"), flag_one)
df$include[idx[idx >= 1 & idx <= nrow(df)]] = 0
df
}