Mutating column to dataframe using apply function by group - r

Question

I am trying to use the apply function to rows within a grouped dataframe to check for the existence of other rows within that group that match certain conditions dependent on each row. I am able to get this to work for one group but not for all.
For example, with no grouping:
library(dplyr)
id <- c(1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2)
station <- c(1, 2, 3, 3, 2, 2, 1, 1, 3, 2, 2)
timeslot <- c(13, 14, 20, 21, 24, 23, 8, 9, 10, 15, 16)
df <- data.frame(id, station, timeslot)
s <- 2
df <-
df %>%
filter(id == 1) %>%
arrange(id, timeslot) %>%
mutate(match = ifelse(station == s, apply(., 1, function(x) (any(as.numeric(x[3] + 1) == .$timeslot))), FALSE))
id station timeslot match
1 1 1 13 FALSE
2 1 2 14 FALSE
3 1 3 20 FALSE
4 1 3 21 FALSE
5 1 2 23 TRUE
6 1 2 24 FALSE
In the above code, for each station 2 row, I am trying to check all other rows to see if there exists a timeslot with a value of one greater (for any station). This works as expected.
Then, I go on to apply this to a grouped dataframe:
df <-
df %>%
group_by(id) %>%
arrange(id, timeslot) %>%
mutate(match = ifelse(station == s, apply(., 1, function(x) (any(as.numeric(x[3] + 1) == .$timeslot))), FALSE))
id station timeslot match
<int> <int> <int> <lgl>
1 1 1 13 FALSE
2 1 2 14 TRUE
3 1 3 20 FALSE
4 1 3 21 FALSE
5 1 2 23 TRUE
6 1 2 24 FALSE
7 2 1 8 FALSE
8 2 1 9 FALSE
9 2 3 10 FALSE
10 2 2 15 FALSE
11 2 2 16 TRUE
and get some unwanted results. It seems like it is not applied by group and I can't figure out how to fix this. How can I apply this function so that only the other rows within a group are checked? In reality, my dataset is much bigger and the conditions are more complex, so it is not running quickly either.
Thanks in advance
Edit: I should add that I have also tried a solution using the arrange() and lead() function but since some timeslot values are shared by many stations in my larger dataset I could not get this to work

Answer 1

This seems to work:
df %>%
group_by(id) %>%
arrange(id, timeslot) %>%
mutate(match = station == s & ((timeslot + 1) %in% timeslot))
# # A tibble: 11 x 4
# # Groups: id [2]
# id station timeslot match
# <dbl> <dbl> <dbl> <lgl>
# 1 1 1 13 FALSE
# 2 1 2 14 FALSE
# 3 1 3 20 FALSE
# 4 1 3 21 FALSE
# 5 1 2 23 TRUE
# 6 1 2 24 FALSE
# 7 2 1 8 FALSE
# 8 2 1 9 FALSE
# 9 2 3 10 FALSE
# 10 2 2 15 TRUE
# 11 2 2 16 FALSE

Answer 2

My sincere apologies if I understood the question wrong. This does what I understand from the question:
df$match = apply(df, 1, function(line) any(df$id == line[1] &
df$station == line[2] &
df$timeslot == line[3] + 1))
The result then is
id station timeslot match
1 1 1 13 FALSE
2 1 2 14 FALSE
3 1 3 20 TRUE
4 1 3 21 FALSE
5 1 2 24 FALSE
6 1 2 23 TRUE
7 2 1 8 TRUE
8 2 1 9 FALSE
9 2 3 10 FALSE
10 2 2 15 TRUE
11 2 2 16 FALSE