如何计算具有滞后的两列的时间差

Question

我目前正面临纽约市一名司机的出租车行程数据集。 我得到了司机 ID 以及每次旅行的接送日期和时间以及还车日期和时间。 现在我想计算上次行程的下车时间和新行程的接车时间之间的等待时间。 因此，我必须计算两列之间的时间差和一个 Lag（因为还车时间是指上次行程和到下一次行程（下一列）的接送时间）按司机 ID 分组（以确保我没有计算时间差在两个不同司机的行程之间）。

一个可能的数据集如下所示：

hack_license = c("303F79923DA5DA7A10DF15E2D91CDCF7","697ABFCDF7E7C77A01183C857132F2A4","697ABFCDF7E7C77A01183C857132F2A4","697ABFCDF7E7C77A01183C857132F2A4","ABE23CA71E2DE84972281BA1C70B6EBB","ABE23CA71E2DE84972281BA1C70B6EBB","BA83D7C383EAA4F9D78A1A8B83CB3E92","BA83D7C383EAA4F9D78A1A8B83CB3E92","D476A1872F1F6594BD638C274483ED06","D476A1872F1F6594BD638C274483ED06")

pickup_datetime = c("2013-12-31 23:01:07","2013-12-31 23:04:00","2013-12-31 23:31:00","2013-12-31 23:40:00","2013-12-31 23:16:39","2013-12-31 23:24:05","2013-12-31 23:09:10","2013-12-31 23:26:26","2013-12-31 23:13:00","2013-12-31 23:22:00")

dropoff_datetime = c("2013-12-31 23:20:33","2013-12-31 23:28:00","2013-12-31 23:33:00","2013-12-31 23:48:00","2013-12-31 23:22:29","2013-12-31 23:28:37","23:21:24","2013-12-31 23:36:54","2013-12-31 23:20:00","2013-12-31 23:27:00")

data <- data.frame(hack_license,pickup_datetime,dropoff_datetime)

我试图像这样使用 dplyr 和 lubridate，但它不起作用。

data %>%
group_by(data$hack_license) %>%
  group_by(hack_license) %>%
  mutate(waiting_time_in_secs = difftime(pickup_datetime,                                       
lag(dropoff_datetime), units = 'secs'))

也许你们中的一些人可以在这里帮助我。 会很好！

Answer 1

您可以为上车和下车创建一个日期时间列，并为每个hack_license计算当前上车时间和上次下车时间之间的时间差。

library(dplyr)
library(lubridate)

data <- data %>%
          mutate(pickup_datetime = ymd_hms(pickup_datetime), 
                 dropoff_datetime = ymd_hms(dropoff_datetime)) %>%
           group_by(hack_license) %>%
           mutate(waiting_time_in_secs = as.numeric(difftime(pickup_datetime, 
                                lag(dropoff_datetime), units = 'secs')))
data
#   hack_license                     pickup_datetime     dropoff_datetime    waiting_time_in_secs
#   <chr>                            <dttm>              <dttm>                             <dbl>
# 1 303F79923DA5DA7A10DF15E2D91CDCF7 2013-12-31 23:01:07 2013-12-31 23:20:33                   NA
# 2 697ABFCDF7E7C77A01183C857132F2A4 2013-12-31 23:04:00 2013-12-31 23:28:00                   NA
# 3 697ABFCDF7E7C77A01183C857132F2A4 2013-12-31 23:31:00 2013-12-31 23:33:00                  180
# 4 697ABFCDF7E7C77A01183C857132F2A4 2013-12-31 23:40:00 2013-12-31 23:48:00                  420
# 5 ABE23CA71E2DE84972281BA1C70B6EBB 2013-12-31 23:16:39 2013-12-31 23:22:29                   NA
# 6 ABE23CA71E2DE84972281BA1C70B6EBB 2013-12-31 23:24:05 2013-12-31 23:28:37                   96
# 7 BA83D7C383EAA4F9D78A1A8B83CB3E92 2013-12-31 23:09:10 2013-12-31 23:21:24                   NA
# 8 BA83D7C383EAA4F9D78A1A8B83CB3E92 2013-12-31 23:26:26 2013-12-31 23:36:54                  302
# 9 D476A1872F1F6594BD638C274483ED06 2013-12-31 23:13:00 2013-12-31 23:20:00                   NA
#10 D476A1872F1F6594BD638C274483ED06 2013-12-31 23:22:00 2013-12-31 23:27:00                  120