Group, take duration and set condition within R (dplyr, r) - r

Question

I have a dataset, df: (the dataset contains over 4000 rows)
DATEB
9/9/2019 7:51:58 PM
9/9/2019 7:51:59 PM
9/9/2019 7:51:59 PM
9/9/2019 7:52:00 PM
9/9/2019 7:52:01 PM
9/9/2019 7:52:01 PM
9/9/2019 7:52:02 PM
9/9/2019 7:52:03 PM
9/9/2019 7:54:00 PM
9/9/2019 7:54:02 PM
9/10/2019 8:00:00PM
I am wanting to place these in separate groups, and take the duration, if the time between date-time exceeds 120 seconds.
Desired output:
Group Duration
a 5 sec
b 2 sec
c 0 sec
dput:
structure(list(DATEB = structure(c(2L, 3L, 3L, 4L, 5L, 5L, 6L,
7L, 8L, 9L, 1L), .Label = c(" 9/10/2019 8:00:00 PM", " 9/9/2019 7:51:58 PM",
" 9/9/2019 7:51:59 PM", " 9/9/2019 7:52:00 PM", " 9/9/2019 7:52:01 PM",
" 9/9/2019 7:52:02 PM", " 9/9/2019 7:52:03 PM", " 9/9/2019 7:54:00 PM",
" 9/9/2019 7:54:02 PM"), class = "factor")), class = "data.frame", row.names = c(NA,
-11L))
I have tried the code below, which works well, except I am wanting the 7:51:59 and 7:52:00 to be in the same group. The only time the duration should break and create a new group, is when the time in between datetimes exceed 120 secs.
df %>%
mutate(DATEB = lubridate::mdy_hms(DATEB),
temp = floor_date(DATEB, "120 secs")) %>%
group_by(temp) %>%
summarise(duration = difftime(max(DATEB), min(DATEB), units = "secs"))
Any suggestion is appreciated.

Answer 1

We can use cut here :
library(dplyr)
df %>%
mutate(DATEB = lubridate::mdy_hms(DATEB),
temp = cut(DATEB, breaks = "2 mins")) %>%
group_by(temp) %>%
summarise(duration = difftime(max(DATEB), min(DATEB), units = "secs"))
# A tibble: 3 x 2
# temp duration
# <fct> <drtn>
#1 2019-09-09 19:51:00 5 secs
#2 2019-09-09 19:53:00 2 secs
#3 2019-09-10 19:59:00 0 secs

Answer 2

The OP has asked for:
The only time the duration should break and create a new group, is
when the time in between datetimes exceed 120 secs.
The words "the time in between datetimes" suggest the OP is looking for a gap or pause. (Well, this is what I would look for if I've been given a vector of ordered date-times and been tasked to group the data.)
Unfortunately, the expected result and accepted answer do not correspond to this interpretation.
However, here is what I would do:
gap_threshold <- 10
df %>%
mutate(DATEB = lubridate::mdy_hms(DATEB),
gap = c(0, diff(DATEB))) %>%
group_by(grp = cumsum(gap > gap_threshold)) %>%
summarise(begin = min(DATEB), end = max(DATEB), duration = difftime(end, begin, units = "secs"))
# A tibble: 3 x 4
grp begin end duration
<int> <dttm> <dttm> <drtn>
1 0 2019-09-09 19:51:58 2019-09-09 19:52:03 5 secs
2 1 2019-09-09 19:54:00 2019-09-09 19:54:02 2 secs
3 2 2019-09-10 20:00:00 2019-09-10 20:00:00 0 secs