Request.id Pickup.point Driver.id Status Request.timestamp Drop.timestamp
1 619 Airport 1 Trip Completed 11/7/2016 11:51 11/7/2016 13:00
2 867 Airport 1 Trip Completed 11/7/2016 17:57 11/7/2016 18:47
3 1807 City 1 Trip Completed 12/7/2016 9:17 12/7/2016 9:58
4 2532 Airport 1 Trip Completed 12/7/2016 21:08 12/7/2016 22:03
5 3112 City 1 Trip Completed 13-07-2016 08:33:16 13-07-2016 09:25:47
6 3879 Airport 1 Trip Completed 13-07-2016 21:57:28 13-07-2016 22:28:59
7 4270 Airport 1 Trip Completed 14-07-2016 06:15:32 14-07-2016 07:13:15
8 5510 Airport 1 Trip Completed 15-07-2016 05:11:52 15-07-2016 06:07:52
9 6248 City 1 Trip Completed 15-07-2016 17:57:27 15-07-2016 18:50:51
10 267 City 2 Trip Completed 11/7/2016 6:46 11/7/2016 7:25
11 1467 Airport 2 Trip Completed 12/7/2016 5:08 12/7/2016 6:02
12 1983 City 2 Trip Completed 12/7/2016 12:30 12/7/2016 12:57
13 2784 Airport 2 Trip Completed 13-07-2016 04:49:20 13-07-2016 05:23:03
14 3075 City 2 Trip Completed 13-07-2016 08:02:53 13-07-2016 09:16:19
15 3379 City 2 Trip Completed 13-07-2016 14:23:02 13-07-2016 15:35:18
16 3482 Airport 2 Trip Completed 13-07-2016 17:23:18 13-07-2016 18:20:51
17 4652 City 2 Trip Completed 14-07-2016 12:01:02 14-07-2016 12:36:46
18 5335 Airport 2 Trip Completed 14-07-2016 22:24:13 14-07-2016 23:18:52
19 535 Airport 3 Trip Completed 11/7/2016 10:00 11/7/2016 10:31
20 960 Airport 3 Trip Completed 11/7/2016 18:45 11/7/2016 19:23
21 1934 Airport 3 Trip Completed 12/7/2016 11:17 12/7/2016 12:23
22 2083 Airport 3 Trip Completed 12/7/2016 15:46 12/7/2016 16:40
23 2211 Airport 3 Trip Completed 12/7/2016 18:00 12/7/2016 18:28
24 3096 Airport 3 Trip Completed 13-07-2016 08:17:29 13-07-2016 09:22:37
25 3881 Airport 3 Trip Completed 13-07-2016 21:54:18 13-07-2016 22:51:23
26 5254 City 3 Trip Completed 14-07-2016 21:23:03 14-07-2016 22:25:19
27 5434 City 3 Trip Completed 15-07-2016 02:41:38 15-07-2016 03:24:43
28 5916 City 3 Trip Completed 15-07-2016 10:00:43 15-07-2016 10:53:06
29 669 City 4 Trip Completed 11/7/2016 13:08 11/7/2016 13:49
30 1567 Airport 4 Trip Completed 12/7/2016 6:21 12/7/2016 7:10
在上面给出的数据集中，列Request.timestamp和Drop.timestamp包含不同格式的日期值。我们如何在两列中以相同的格式转换日期？我们如何分别提取日期和时间？
答案 0 :(得分：1)
要转换两种格式的时间，我们需要确定要使用的格式。我使用了lubridate包，因为它比一些标准的R日期格式更容易使用。
rawData <- "Request.id|Pickup.point|Driver.id|Status |Request.timestamp |Drop.timestamp
619 | Airport | 1 |Trip Completed| 11/7/2016 11:51| 11/7/2016 13:00
867 | Airport | 1 |Trip Completed| 11/7/2016 17:57| 11/7/2016 18:47
1807 | City | 1 |Trip Completed| 12/7/2016 9:17| 12/7/2016 9:58
2532 | Airport | 1 |Trip Completed| 12/7/2016 21:08| 12/7/2016 22:03
3112 | City | 1 |Trip Completed|13-07-2016 08:33:16|13-07-2016 09:25:47
3879 | Airport | 1 |Trip Completed|13-07-2016 21:57:28|13-07-2016 22:28:59
4270 | Airport | 1 |Trip Completed|14-07-2016 06:15:32|14-07-2016 07:13:15
5510 | Airport | 1 |Trip Completed|15-07-2016 05:11:52|15-07-2016 06:07:52
6248 | City | 1 |Trip Completed|15-07-2016 17:57:27|15-07-2016 18:50:51
267 | City | 2 |Trip Completed| 11/7/2016 6:46| 11/7/2016 7:25
1467 | Airport | 2 |Trip Completed| 12/7/2016 5:08| 12/7/2016 6:02
1983 | City | 2 |Trip Completed| 12/7/2016 12:30| 12/7/2016 12:57
2784 | Airport | 2 |Trip Completed|13-07-2016 04:49:20|13-07-2016 05:23:03
3075 | City | 2 |Trip Completed|13-07-2016 08:02:53|13-07-2016 09:16:19
3379 | City | 2 |Trip Completed|13-07-2016 14:23:02|13-07-2016 15:35:18
3482 | Airport | 2 |Trip Completed|13-07-2016 17:23:18|13-07-2016 18:20:51
4652 | City | 2 |Trip Completed|14-07-2016 12:01:02|14-07-2016 12:36:46
5335 | Airport | 2 |Trip Completed|14-07-2016 22:24:13|14-07-2016 23:18:52
535 | Airport | 3 |Trip Completed| 11/7/2016 10:00| 11/7/2016 10:31
960 | Airport | 3 |Trip Completed| 11/7/2016 18:45| 11/7/2016 19:23
1934 | Airport | 3 |Trip Completed| 12/7/2016 11:17| 12/7/2016 12:23
2083 | Airport | 3 |Trip Completed| 12/7/2016 15:46| 12/7/2016 16:40
2211 | Airport | 3 |Trip Completed| 12/7/2016 18:00| 12/7/2016 18:28
3096 | Airport | 3 |Trip Completed|13-07-2016 08:17:29|13-07-2016 09:22:37
3881 | Airport | 3 |Trip Completed|13-07-2016 21:54:18|13-07-2016 22:51:23
5254 | City | 3 |Trip Completed|14-07-2016 21:23:03|14-07-2016 22:25:19
5434 | City | 3 |Trip Completed|15-07-2016 02:41:38|15-07-2016 03:24:43
5916 | City | 3 |Trip Completed|15-07-2016 10:00:43|15-07-2016 10:53:06
669 | City | 4 |Trip Completed| 11/7/2016 13:08| 11/7/2016 13:49
1567 | Airport | 4 |Trip Completed| 12/7/2016 6:21| 12/7/2016 7:10"
library(lubridate)
data <- read.csv(text=rawData,header=TRUE,
sep="|",
stringsAsFactors=FALSE)
convertTime <- function(aVector){
unlist(lapply(aVector,function(x){
ifelse(grepl("/",x),
mdy_hm(x),
dmy_hms(x))
}))
}
requestTime <- convertTime(data$Request.timestamp)
dropTime <- convertTime(data$Drop.timestamp)
as_datetime(requestTime)
...和输出：
> as_datetime(requestTime)
[1] "2016-11-07 11:51:00 UTC" "2016-11-07 17:57:00 UTC" "2016-12-07 09:17:00 UTC"
[4] "2016-12-07 21:08:00 UTC" "2016-07-13 08:33:16 UTC" "2016-07-13 21:57:28 UTC"
[7] "2016-07-14 06:15:32 UTC" "2016-07-15 05:11:52 UTC" "2016-07-15 17:57:27 UTC"
[10] "2016-11-07 06:46:00 UTC" "2016-12-07 05:08:00 UTC" "2016-12-07 12:30:00 UTC"
[13] "2016-07-13 04:49:20 UTC" "2016-07-13 08:02:53 UTC" "2016-07-13 14:23:02 UTC"
[16] "2016-07-13 17:23:18 UTC" "2016-07-14 12:01:02 UTC" "2016-07-14 22:24:13 UTC"
[19] "2016-11-07 10:00:00 UTC" "2016-11-07 18:45:00 UTC" "2016-12-07 11:17:00 UTC"
[22] "2016-12-07 15:46:00 UTC" "2016-12-07 18:00:00 UTC" "2016-07-13 08:17:29 UTC"
[25] "2016-07-13 21:54:18 UTC" "2016-07-14 21:23:03 UTC" "2016-07-15 02:41:38 UTC"
[28] "2016-07-15 10:00:43 UTC" "2016-11-07 13:08:00 UTC" "2016-12-07 06:21:00 UTC"
>
答案 1 :(得分：0)
OP在数据框中以异构格式获得日期/时间。在这种情况下，lubridate非常方便。
library(lubridate)
df <- read.table(text = "Request.id Pickup.point Driver.id Status Request.timestamp Drop.timestamp
1 619 Airport 1 'Trip Completed' '11/7/2016 11:51' '11/7/2016 13:00'
2 867 Airport 1 'Trip Completed' '11/7/2016 17:57' '11/7/2016 18:47'
3 1807 City 1 'Trip Completed' '12/7/2016 9:17' '12/7/2016 9:58'
4 2532 Airport 1 'Trip Completed' '12/7/2016 21:08' '12/7/2016 22:03'
5 3112 City 1 'Trip Completed' '13-07-2016 08:33:16' '13-07-2016 09:25:47'
6 3879 Airport 1 'Trip Completed' '13-07-2016 21:57:28' '13-07-2016 22:28:59'
7 4270 Airport 1 'Trip Completed' '14-07-2016 06:15:32' '14-07-2016 07:13:15'
8 5510 Airport 1 'Trip Completed' '15-07-2016 05:11:52' '15-07-2016 06:07:52'
9 6248 City 1 'Trip Completed' '15-07-2016 17:57:27' '15-07-2016 18:50:51'", header = T, stringsAsFactors = F)
#Use parse_date_time to convert hetrogeneous date-time
df$Request.timestamp <- parse_date_time(df$Request.timestamp, c("dmY HM", "dmY HMS"))
df$Drop.timestamp <- parse_date_time(df$Drop.timestamp, c("dmY HM", "dmY HMS"))
df
Request.id Pickup.point Driver.id Status Request.timestamp Drop.timestamp
1 619 Airport 1 Trip Completed 2016-07-11 11:51:00 2016-07-11 13:00:00
2 867 Airport 1 Trip Completed 2016-07-11 17:57:00 2016-07-11 18:47:00
3 1807 City 1 Trip Completed 2016-07-12 09:17:00 2016-07-12 09:58:00
4 2532 Airport 1 Trip Completed 2016-07-12 21:08:00 2016-07-12 22:03:00
5 3112 City 1 Trip Completed 2016-07-13 08:33:16 2016-07-13 09:25:47
6 3879 Airport 1 Trip Completed 2016-07-13 21:57:28 2016-07-13 22:28:59
7 4270 Airport 1 Trip Completed 2016-07-14 06:15:32 2016-07-14 07:13:15
8 5510 Airport 1 Trip Completed 2016-07-15 05:11:52 2016-07-15 06:07:52
9 6248 City 1 Trip Completed 2016-07-15 17:57:27 2016-07-15 18:50:51
分隔日期和时间的附加代码：
df$Request.timestamp_date <- as.character(df$Request.timestamp, "%Y-%m-%d")
df$Request.timestamp_time <- as.character(df$Request.timestamp, "%H:%M:%S")