R How to calculate "task time" for business hours only

Question

Is there a way to calculate a "task time" for working hours only? Working hours 8 to 5, Monday through Friday. Example:

Using datediff():

expected result:

sample task times:

# df %>%
#   select(v_v_initiated,v_v_complete)
                  v_v_initiated                v_v_complete
1   2020-04-23 14:13:52.0000000 2020-04-23 16:04:28.0000000
2   2020-11-10 11:48:53.0000000 2020-11-10 13:12:31.0000000
3   2020-10-20 16:03:39.0000000 2020-10-20 16:25:16.0000000
4   2020-04-02 13:43:54.0000000 2020-04-02 14:14:45.0000000
5   2020-07-09 08:52:54.0000000 2020-07-23 09:18:29.0000000
6   2020-06-09 14:56:33.0000000 2020-06-10 07:44:17.0000000
7   2020-09-17 15:11:39.0000000 2020-09-17 15:13:41.0000000
8   2020-10-28 14:08:20.0000000 2020-10-28 14:07:35.0000000
9   2020-04-21 12:55:36.0000000 2020-04-27 12:56:17.0000000
10  2020-11-06 11:02:03.0000000 2020-11-06 11:02:30.0000000
11  2020-02-17 12:29:21.0000000 2020-02-18 12:52:23.0000000
12  2020-08-25 15:25:46.0000000 2020-08-26 10:18:26.0000000
13  2020-02-19 15:05:28.0000000 2020-02-20 09:43:48.0000000
14  2020-09-23 21:19:41.0000000 2020-09-24 14:52:21.0000000
15  2020-07-01 14:20:11.0000000 2020-07-01 14:20:59.0000000
16  2020-05-01 15:22:58.0000000 2020-05-01 16:32:35.0000000
17  2020-06-29 13:10:58.0000000 2020-06-30 13:53:29.0000000
18  2020-06-16 12:56:54.0000000 2020-06-16 14:27:15.0000000
19  2020-03-27 11:02:29.0000000 2020-03-30 15:18:51.0000000
20  2020-04-08 07:38:01.0000000 2020-04-08 07:52:35.0000000
21  2020-07-30 09:32:42.0000000 2020-07-30 10:32:28.0000000
22  2020-06-17 14:03:31.0000000 2020-07-10 15:38:03.0000000
23  2020-04-24 10:41:27.0000000 2020-04-29 13:07:05.0000000
24  2020-08-26 10:41:10.0000000 2020-08-26 12:55:23.0000000
25  2020-10-26 18:11:16.0000000 2020-10-27 16:10:39.0000000
26  2020-01-08 11:12:49.0000000 2020-01-09 09:18:37.0000000
27  2020-04-17 11:40:10.0000000 2020-04-17 15:51:21.0000000
28  2020-02-11 10:38:21.0000000 2020-02-11 10:33:54.0000000
29  2020-03-23 12:10:21.0000000 2020-03-23 12:33:06.0000000
30  2020-06-02 12:44:00.0000000 2020-06-03 08:28:05.0000000
31  2020-04-13 09:30:31.0000000 2020-04-13 13:16:55.0000000
32  2020-04-07 17:36:02.0000000 2020-04-07 17:36:44.0000000
33  2020-01-15 12:24:42.0000000 2020-01-15 12:25:00.0000000
34  2020-08-18 08:55:58.0000000 2020-08-18 09:02:34.0000000
35  2020-07-06 14:10:23.0000000 2020-07-07 10:28:05.0000000
36  2020-03-25 15:03:20.0000000 2020-03-31 14:17:43.0000000
37  2020-01-29 12:58:33.0000000 2020-02-14 09:53:06.0000000
38  2020-02-07 15:11:21.0000000 2020-02-10 09:13:53.0000000
39  2020-07-27 17:51:13.0000000 2020-07-29 11:52:51.0000000
40  2020-09-02 11:43:02.0000000 2020-09-02 13:10:46.0000000
41  2020-07-22 11:04:50.0000000 2020-07-22 11:12:34.0000000
42  2020-06-29 13:57:17.0000000 2020-06-30 07:34:55.0000000
43  2020-07-21 10:46:58.0000000 2020-07-21 16:15:59.0000000
44  2020-05-27 07:38:46.0000000 2020-05-27 07:51:24.0000000
45  2020-07-14 10:33:49.0000000 2020-07-14 11:38:28.0000000
46  2020-06-04 16:59:09.0000000 2020-06-09 10:49:20.0000000

Answer 1

You could adapt another function that calculates business hours for a time interval (such as this.

First, create a sequence of dates from start to end, and filter by only include weekdays.

Next, create time intervals using the business hours of interest (in this case, "08:00" to "17:00").

Determine how much of each day business hours overlap with your times. This way, if a time starts at "09:05", that time will be used for the start of the day, and not "08:00".

Finally, sum up the time intervals, and determine the number of business days (assuming a 9-hour day), and remainder hours and minutes.

If you want to apply this function to rows in a data frame, you could use mapply as in:

df$business_hours <- mapply(calc_bus_hours, df$start_date, df$end_date)

Hope this is helpful.

library(lubridate)
library(dplyr)

calc_bus_hours <- function(start, end) {
  my_dates <- seq.Date(as.Date(start), as.Date(end), by = "day")
  my_dates <- my_dates[!weekdays(my_dates) %in% c("Saturday", "Sunday")]
  
  my_intervals <- interval(ymd_hm(paste(my_dates, "08:00"), tz = "UTC"), ymd_hm(paste(my_dates, "17:00"), tz = "UTC"))
  
  int_start(my_intervals[1]) <- pmax(pmin(start, int_end(my_intervals[1])), int_start(my_intervals[1]))
  int_end(my_intervals[length(my_intervals)]) <- pmax(pmin(end, int_end(my_intervals[length(my_intervals)])), 
                                                      int_start(my_intervals[length(my_intervals)]))
  
  total_time <- sum(time_length(my_intervals, "minutes"))
  
  total_days <- total_time %/% (9 * 60)
  total_hours <- total_time %% (9 * 60) %/% 60
  total_minutes <- total_time - (total_days * 9 * 60) - (total_hours * 60)
  paste(total_days, "days,", total_hours, "hours,", total_minutes, "minutes")
}

calc_bus_hours(as.POSIXct("11/4/2020 9:05", format = "%m/%d/%Y %H:%M", tz = "UTC"), 
               as.POSIXct("11/9/2020 11:25", format = "%m/%d/%Y %H:%M", tz = "UTC"))

[1] "3 days, 2 hours, 20 minutes"

Edit: As mentioned by @DPH this is more complex with holidays and partial holidays.

You could create a data frame of holidays and indicate times open, allowing for partial holidays (e.g., Christmas Eve from 8:00 AM to Noon).

Here is a modified function that should give comparable results.

library(lubridate)
library(dplyr)

holiday_df <- data.frame(
  date = as.Date(c("2020-12-24", "2020-12-25", "2020-12-31", "2020-01-01")),
  start = c("08:00", "08:00", "08:00", "08:00"),
  end = c("12:00", "08:00", "08:00", "08:00")
)

calc_bus_hours <- function(start, end) {
  my_dates <- seq.Date(as.Date(start), as.Date(end), by = "day")
  
  my_dates_df <- data.frame(
    date = my_dates[!weekdays(my_dates) %in% c("Saturday", "Sunday")], 
    start = "08:00", 
    end = "17:00"
  )
  
  all_dates <- union_all(
    inner_join(my_dates_df["date"], holiday_df),
    anti_join(my_dates_df, holiday_df["date"])
  ) %>%
    arrange(date)
  
  my_intervals <- interval(ymd_hm(paste(all_dates$date, all_dates$start), tz = "UTC"), 
                           ymd_hm(paste(all_dates$date, all_dates$end), tz = "UTC"))
  
  int_start(my_intervals[1]) <- pmax(pmin(start, int_end(my_intervals[1])), int_start(my_intervals[1]))
  int_end(my_intervals[length(my_intervals)]) <- pmax(pmin(end, int_end(my_intervals[length(my_intervals)])), 
                                                      int_start(my_intervals[length(my_intervals)]))
  
  total_time <- sum(time_length(my_intervals, "minutes"))
  
  total_days <- total_time %/% (9 * 60)
  total_hours <- total_time %% (9 * 60) %/% 60
  total_minutes <- total_time - (total_days * 9 * 60) - (total_hours * 60)
  paste(total_days, "days,", total_hours, "hours,", total_minutes, "minutes")
}