Find events between time frames using data.table R

Question

I have a data.table of huts that a group if hikers are visiting.

library(data.table)
dt <- data.table(time = as.POSIXct(as.Date(10:35, origin = "2020-01-01")), 
           hut= c(1, NA, NA, 8, 1, 1, NA, NA, NA, 1, NA, NA, NA,
                     4, NA, NA, 4, NA, 5, NA, NA, 4, NA, 4, NA, 1))

The pattern is that they will move from a hut (eg hut 1) out in the wild (hut = NA) and come back within 2-5 days. This is an event. Sometime they will go to a new hut (eg hut 4) - this is not an event. The problem is that some times they will accidentially be in a hut inside an event (as in row 4). So this is still an event. The output should look like this, but I have no idea how to get that. The real data is billions of rows, so it should also be efficient, hence the data.table:

dt[, event:= c(NA, 1,1,1, NA, NA, 2,2,2, 
               NA, NA,NA, NA, NA, 
               3,3, NA, 4,4,4,4,NA, 5, NA,NA, NA)]

dt
                   time   hut event
 1: 2020-01-11 01:00:00     1    NA
 2: 2020-01-12 01:00:00    NA     1
 3: 2020-01-13 01:00:00    NA     1
 4: 2020-01-14 01:00:00     8     1
 5: 2020-01-15 01:00:00     1    NA
 6: 2020-01-16 01:00:00     1    NA
 7: 2020-01-17 01:00:00    NA     2
 8: 2020-01-18 01:00:00    NA     2
 9: 2020-01-19 01:00:00    NA     2
10: 2020-01-20 01:00:00     1    NA
11: 2020-01-21 01:00:00    NA    NA
12: 2020-01-22 01:00:00    NA    NA
13: 2020-01-23 01:00:00    NA    NA
14: 2020-01-24 01:00:00     4    NA
15: 2020-01-25 01:00:00    NA     3
16: 2020-01-26 01:00:00    NA     3
17: 2020-01-27 01:00:00     4    NA
18: 2020-01-28 01:00:00    NA     4
19: 2020-01-29 01:00:00     5     4
20: 2020-01-30 01:00:00    NA     4
21: 2020-01-31 01:00:00    NA     4
22: 2020-02-01 01:00:00     4    NA
23: 2020-02-02 01:00:00    NA     5
24: 2020-02-03 01:00:00     4    NA
25: 2020-02-04 01:00:00    NA    NA
26: 2020-02-05 01:00:00     1    NA

Answer 1

Here is another option using non-equi join:

dt[, rn := .I]
visits <- dt[!is.na(hut)]
visits[, c("start", "end") := .(time + 2L, time + 5L)]
rows <- visits[visits, on=.(hut, time>=start, time<=end), mult="first", nomatch=0L,
    .(hut, i.time, x.time, i.rn, x.rn)]

dt[rows, on=.(rn>i.rn, rn<x.rn), event := 1L]
dt[, ri := rleid(event)][!is.na(event), event := rleid(ri)]

dt[rn %in% unique(c(rows$i.rn, rows$x.rn)), event := NA_integer_]

dt[, c("ri", "rn") := NULL][]

output:

          time hut event
 1: 2020-01-11   1    NA
 2: 2020-01-12  NA     1
 3: 2020-01-13  NA     1
 4: 2020-01-14   8     1
 5: 2020-01-15   1    NA
 6: 2020-01-16   1    NA
 7: 2020-01-17  NA     2
 8: 2020-01-18  NA     2
 9: 2020-01-19  NA     2
10: 2020-01-20   1    NA
11: 2020-01-21  NA    NA
12: 2020-01-22  NA    NA
13: 2020-01-23  NA    NA
14: 2020-01-24   4    NA
15: 2020-01-25  NA     3
16: 2020-01-26  NA     3
17: 2020-01-27   4    NA
18: 2020-01-28  NA     4
19: 2020-01-29   5     4
20: 2020-01-30  NA     4
21: 2020-01-31  NA     4
22: 2020-02-01   4    NA
23: 2020-02-02  NA     5
24: 2020-02-03   4    NA
25: 2020-02-04  NA    NA
26: 2020-02-05   1    NA
          time hut event

Alternatively, using a rolling join instead of the non-equi join above:

is <- 2L
intvl <- 5L - is
dt[, c("rn", "oned") := .(.I, time + is)]
rows <- dt[dt[!is.na(hut)], on=.(hut, time=oned), roll=-intvl, nomatch=0L,
    .(hut, i.rn, x.rn)]
#the rest of the code from the non-equi join above is needed here as well

data:

library(data.table)
dt <- data.table(time = as.Date(10:35, origin = "2020-01-01"), 
    hut= c(1, NA, NA, 8, 1, 1, NA, NA, NA, 1, NA, NA, NA,
        4, NA, NA, 4, NA, 5, NA, NA, 4, NA, 4, NA, 1))

Answer 2

Ok, it's not obvious, but let's try ..

library(data.table)
dt <- data.table(time = as.POSIXct(as.Date(10:35, origin = "2020-01-01")), 
                 hut= c(1, NA, NA, 8, 1, 1, NA, NA, NA, 1, NA, NA, NA,
                        4, NA, NA, 4, NA, 5, NA, NA, 4, NA, 4, NA, 1))

library(dplyr)
dt[, last.hut1 := lag(hut, n = 1, order_by = time)]
dt[, last.hut2 := lag(hut, n = 2, order_by = time)]
dt[, last.hut3 := lag(hut, n = 3, order_by = time)]
dt[, last.hut4 := lag(hut, n = 4, order_by = time)]
dt[, last.hut5 := lag(hut, n = 5, order_by = time)]
dt[, next.hut1 := lead(hut, n = 1, order_by = time)]
dt[, next.hut2 := lead(hut, n = 2, order_by = time)]
dt[, next.hut3 := lead(hut, n = 3, order_by = time)]
dt[, next.hut4 := lead(hut, n = 4, order_by = time)]
dt[, next.hut5 := lead(hut, n = 5, order_by = time)]

dt[, end.event := case_when((hut == last.hut2 | hut ==  last.hut3 | hut ==  last.hut4 | hut ==  last.hut5) 
                            & (last.hut1 != hut | is.na(last.hut1)) ~ 1, 
                            TRUE ~ 0)]
dt[, start.event := case_when((hut == next.hut2 | hut ==  next.hut3 | hut ==  next.hut4 | hut ==  next.hut5) 
                            & (next.hut1 != hut | is.na(next.hut1)) ~ 1, 
                            TRUE ~ 0)]


dt[, start.event2 := cumsum(start.event)]
dt[, end.event2 := cumsum(end.event)]

dt[, event := case_when((start.event2 > end.event2) & (start.event == 0) & (end.event == 0) ~ start.event2, 
                       TRUE ~ NA_real_)]

dt[ ,c("last.hut1", "last.hut2", "last.hut3", "last.hut4", "last.hut5", 
       "next.hut1", "next.hut2", "next.hut3", "next.hut4", "next.hut5", 
       "start.event", "start.event2", "end.event", "end.event2") := .(NULL, NULL, NULL, NULL, NULL, 
                                                              NULL, NULL, NULL, NULL, NULL, 
                                                              NULL, NULL, NULL, NULL)]