Filter Rows Between with Multiple Events per Subject - r

Question

I have a large data set and I'm trying to filter the days following a specific event for each subject. This issue is that the "event" of interest may happen multiple times for some subjects and for a few subjects the event doesn't happen at all (in which case they could just be removed from the summarized data).
Here is an example of the data and what I've tried:
library(tidyverse)
set.seed(355)
subject <- c(rep(LETTERS[1:4], each = 40), rep("E", times = 40))
event <- c(sample(0:1, size = length(subject)-40, replace = T, prob = c(0.95, 0.05)), rep(0, times = 40))
df <- data.frame(subject, event)
df %>%
filter(event == 1) %>%
count(subject, event, sort = T)
# A tibble: 4 x 3
subject event n
<fct> <dbl> <int>
1 D 1 3
2 A 1 2
3 B 1 2
4 C 1 2
So we see that subject D has had the event 3 times while subjects A, B, and C have had the event 2 times. Subject E has not had the event at all.
My next step was to create an "event" tag that identifies where each event happened and then produced an NA for all over rows. I also created an event sequence, which sequences along between events, because I thought it might be useful, but I didn't end up trying to use it.
df_cleaned <- df %>%
group_by(subject, event) %>%
mutate(event_seq = seq_along(event == 1),
event_detail = ifelse(event == 1, "event", NA)) %>%
as.data.frame()
I tried two different approaches using a filter() and between() to get each event and the 2 rows following each event. Both of these approaches create an error because of the multiple events within subject. I can't figure out a good workaround for it.
Approach 1:
df_cleaned %>%
group_by(subject) %>%
filter(., between(row_number(),
left = which(!is.na(event_detail)),
right = which(!is.na(event_detail)) + 1))
Approach 2:
df_cleaned %>%
group_by(subject) %>%
mutate(event_group = cumsum(!is.na(event_detail))) %>%
filter(., between(row_number(), left = which(event_detail == "event"), right = which(event_detail == "event") + 2))

Answer 1

If you want to get rows with 1 in event and the following two rows, you can do the following. Thanks to Ananda Mahto who is the author of splitstackshape package, we can handle this type of operation with getMyRows(), which returns a list. You can specify a range of rows in the function. Here I said 0:2. So I am asking R to take each row with 1 in event and the following two rows. I used bind_rows() to return a data frame. But if you need to work with a list, you do not have to do that.
install_github("mrdwab/SOfun")
library(SOfun)
library(dplyr)
ind <- which(x = df$event == 1)
bind_rows(getMyRows(data = df, pattern = ind, range = 0:2))
subject event
1 A 1
2 A 0
3 A 0
4 A 1
5 A 0
6 A 0
7 B 1
8 B 0
9 B 0
10 B 1
11 B 0
12 B 0
13 C 1
14 C 0
15 C 0
16 C 1
17 C 0
18 C 0
19 D 1
20 D 0
21 D 0
22 D 1
23 D 0
24 D 0
25 D 1
26 D 0
27 D 0

Answer 2

Here is a tidyverse approach which uses cumsum() to create groups of rows after (and including) an event and which picks the top 3 rows of each group:
df %>%
group_by(subject) %>%
mutate(event_group = cumsum(event == 1L)) %>%
group_by(event_group, add = TRUE) %>%
filter(event_group > 0 & row_number() <= 3L)
# A tibble: 27 x 3
# Groups: subject, event_group [9]
subject event event_group
<fct> <dbl> <int>
1 A 1 1
2 A 0 1
3 A 0 1
4 A 1 2
5 A 0 2
6 A 0 2
7 B 1 1
8 B 0 1
9 B 0 1
10 B 1 2
# … with 17 more rows
For testing an edge case, here is a modified data set where subject A starts with three subsequent events. Furthermore, I have added row numbers rn in order to check that the correct rows are picked:
df2 <- df %>%
mutate(event = ifelse(row_number() <= 2L, 1L, event),
rn = row_number())
Now we get
df2 %>%
group_by(subject) %>%
mutate(event_group = cumsum(event == 1L)) %>%
group_by(event_group, add = TRUE) %>%
filter(event_group > 0 & row_number() <= 3L)
# A tibble: 29 x 4
# Groups: subject, event_group [11]
subject event rn event_group
<fct> <dbl> <int> <int>
1 A 1 1 1
2 A 1 2 2
3 A 1 3 3
4 A 0 4 3
5 A 0 5 3
6 A 1 22 4
7 A 0 23 4
8 A 0 24 4
9 B 1 59 1
10 B 0 60 1
# … with 19 more rows
which is in line with my expectations for this edge case.

Answer 3

Here is a base R option which looks similar to #jazzurro's attempt. We get the row indices where event == 1, then select next two rows from each index, use unique so in case there are overlapping indices we select only the unique ones and subset it from the original df.
inds <- which(df$event == 1)
df[unique(c(sapply(inds, `+`, 0:2))), ]
# subject event
#3 A 1
#4 A 0
#5 A 0
#22 A 1
#23 A 0
#24 A 0
#59 B 1
#60 B 0
#61 B 0
#62 B 1
#63 B 0
#64 B 0
#....
Another option using dplyr, could be using lag
library(dplyr)
df %>%
group_by(subject) %>%
filter(event == 1 | lag(event) == 1 | lag(event, 2) == 1)