union/merge overlapping time-ranges

Question

I know the following problam can be solved using Bioconductor's IRanges-package, using reduce. But since that function only accepts numeric input, and I am working with data.table anyway, I am wondering is the following van be achieved using data.tables'foverlaps().

Sample data

    structure(list(group = c("A", "A", "A", "A", "B", "B", "B", "B"
), subgroup = c(1, 1, 2, 2, 1, 1, 2, 2), start = structure(c(1514793600, 
1514795400, 1514794200, 1514798100, 1514815200, 1514817000, 1514815800, 
1514818800), class = c("POSIXct", "POSIXt"), tzone = "UTC"), 
    end = structure(c(1514794500, 1514797200, 1514794800, 1514799000, 
    1514816100, 1514818800, 1514817600, 1514820600), class = c("POSIXct", 
    "POSIXt"), tzone = "UTC")), row.names = c(NA, -8L), class = c("tbl_df", 
"tbl", "data.frame"))

#    group subgroup               start                 end
# 1:     A        1 2018-01-01 08:00:00 2018-01-01 08:15:00
# 2:     A        1 2018-01-01 08:30:00 2018-01-01 09:00:00
# 3:     A        2 2018-01-01 08:10:00 2018-01-01 08:20:00
# 4:     A        2 2018-01-01 09:15:00 2018-01-01 09:30:00
# 5:     B        1 2018-01-01 14:00:00 2018-01-01 14:15:00
# 6:     B        1 2018-01-01 14:30:00 2018-01-01 15:00:00
# 7:     B        2 2018-01-01 14:10:00 2018-01-01 14:40:00
# 8:     B        2 2018-01-01 15:00:00 2018-01-01 15:30:00

Question

What I would like to achieve, is to join/merge events (by group) when:

1. a range (start - end) overlaps (or partially overlaps) another range
2. the start of a range is the end of another range

Subgroups can be ignored

As mentioned above, I'm know this can be done using biocondustor's IRanges reduce, but I wonder if the same can be achieved using data.table. I can't shake the feeling that foverlaps should be able to tackle my problem, but I cannot figure out how...

Since I'm an intermediate R-user, but pretty much a novice in data.table, it's hard for me to 'read' some solutions already provided on stackoverflow. So I'm not sure if a similar quenstion has already been asked and answered (if so, please be gentle ;-) )

Desired output

structure(list(group = c("A", "A", "A", "B"), start = structure(c(1514793600, 
1514795400, 1514798100, 1514815200), class = c("POSIXct", "POSIXt"
), tzone = "UTC"), end = structure(c(1514794800, 1514797200, 
1514799000, 1514820600), class = c("POSIXct", "POSIXt"), tzone = "UTC")), row.names = c(NA, 
-4L), class = c("tbl_df", "tbl", "data.frame"))

#    group               start                 end
# 1:     A 2018-01-01 08:00:00 2018-01-01 08:20:00
# 2:     A 2018-01-01 08:30:00 2018-01-01 09:00:00
# 3:     A 2018-01-01 09:15:00 2018-01-01 09:30:00
# 4:     B 2018-01-01 14:00:00 2018-01-01 15:30:00

Answer 1

If you arrange on group and start (in that order) and unselect the indx column, this solution posted by David Arenburg works perfectly: How to flatten/merge overlapping time periods in R

library(dplyr)

df1 %>% 
group_by(group) %>%
  arrange(group, start) %>% 
  mutate(indx = c(0, cumsum(as.numeric(lead(start)) >
                              cummax(as.numeric(end)))[-n()])) %>%
  group_by(group, indx) %>%
  summarise(start = first(start), end = last(end)) %>% 
  select(-indx)

 group start               end                
  <chr> <dttm>              <dttm>             
1 A     2018-01-01 08:00:00 2018-01-01 08:20:00
2 A     2018-01-01 08:30:00 2018-01-01 09:00:00
3 A     2018-01-01 09:15:00 2018-01-01 09:30:00
4 B     2018-01-01 14:00:00 2018-01-01 15:30:00