所以我有一些观察数据，包括每次观察记录的时间和日期。它基本上是一个不完整的日期和时间戳列表。
例如： （数据组成）
data=as.POSIXlt(c("2014-10-24 11:09",
"2014-10-24 11:32",
"2014-10-24 11:34",
"2014-10-24 14:09",
"2014-10-24 14:32",
"2014-10-24 14:34",
"2014-10-24 20:09",
"2014-10-24 21:32",
"2014-10-24 21:34",
"2014-10-24 23:01",
"2014-10-24 23:05",
"2014-10-24 23:58",
"2014-10-25 02:13",
"2014-10-25 02:32",
"2014-10-25 05:26",
"2014-10-25 05:46",
"2014-10-25 18:39",
"2014-10-25 22:49",
"2014-10-25 22:55",
"2014-10-26 01:43",
"2014-10-26 01:56",
"2014-10-26 09:15",
"2014-10-26 10:17",
"2014-10-26 10:34",
"2014-10-26 10:36",
"2014-10-26 11:32",
"2014-10-26 14:05",
"2014-10-26 14:09",
"2014-10-26 17:01",
"2014-10-26 20:41"))
我制作了另一个序列，其中包括整个学习期（在本例中为2014-10-20 00:00至2014-10-27 23:59），时间步长为30分钟：
start = "2014-10-20 00:00"
end = "2014-10-27 23:59"
timestep = 1800 #1800 sec = 30 min
timeseq = seq(from = as.POSIXlt(start), to = as.POSIXlt(end), by = timestep)
现在我想要一个带有序列的新数据框和另一列，在这30分钟内发生'数据'观察量。
结果看起来像是：
2014-10-20 00:00 0
2014-10-20 00:30 0
....
2014-10-24 11:00 1
2014-10-24 11:30 2
....
2014-10-26 14:00 2
希望这是有道理的！
编辑：原始数据超过75.000条记录，但前三天的情况如下：
Date and Time (UTC)
29-09-14 11:11
29-09-14 11:11
20-10-14 16:43
20-10-14 16:43
20-10-14 16:44
20-10-14 17:16
20-10-14 17:16
20-10-14 17:16
20-10-14 17:16
20-10-14 17:16
24-10-14 14:47
24-10-14 14:52
24-10-14 14:56
24-10-14 14:58
24-10-14 15:39
24-10-14 16:03
24-10-14 16:19
24-10-14 16:43
24-10-14 16:44
24-10-14 16:55
24-10-14 16:58
24-10-14 18:12
24-10-14 18:29
24-10-14 18:42
24-10-14 18:43
24-10-14 19:49
24-10-14 20:03
24-10-14 20:08
24-10-14 21:24
24-10-14 21:25
24-10-14 21:34
24-10-14 21:35
24-10-14 21:45
24-10-14 21:55
24-10-14 21:57
24-10-14 22:01
24-10-14 22:02
24-10-14 22:07
24-10-14 22:08
24-10-14 22:09
24-10-14 22:15
24-10-14 22:16
24-10-14 22:18
24-10-14 22:23
24-10-14 22:33
24-10-14 22:34
24-10-14 22:40
24-10-14 22:41
25-10-14 07:54
25-10-14 07:57
25-10-14 07:58
25-10-14 08:05
25-10-14 08:07
25-10-14 08:08
25-10-14 08:21
25-10-14 08:26
25-10-14 11:33
25-10-14 11:35
25-10-14 11:45
25-10-14 11:56
25-10-14 12:01
25-10-14 12:07
25-10-14 12:08
25-10-14 12:11
25-10-14 12:13
25-10-14 12:15
25-10-14 12:17
25-10-14 12:18
25-10-14 12:24
25-10-14 12:32
25-10-14 12:43
25-10-14 12:50
25-10-14 12:52
25-10-14 12:53
25-10-14 12:56
25-10-14 12:58
25-10-14 13:07
25-10-14 13:08
25-10-14 13:10
25-10-14 13:26
25-10-14 13:28
25-10-14 13:30
25-10-14 13:32
25-10-14 13:35
25-10-14 13:36
25-10-14 13:41
25-10-14 13:54
25-10-14 14:13
25-10-14 14:30
25-10-14 14:32
25-10-14 14:33
25-10-14 14:34
25-10-14 14:35
25-10-14 14:37
25-10-14 14:54
25-10-14 14:57
25-10-14 15:00
25-10-14 15:45
25-10-14 15:49
25-10-14 15:54
25-10-14 15:59
25-10-14 16:02
25-10-14 16:04
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25-10-14 16:34
25-10-14 16:35
25-10-14 16:37
25-10-14 16:38
25-10-14 16:41
25-10-14 16:42
25-10-14 16:43
25-10-14 16:44
25-10-14 16:46
25-10-14 16:48
25-10-14 16:52
25-10-14 16:54
25-10-14 16:56
25-10-14 16:57
25-10-14 16:59
25-10-14 17:01
25-10-14 17:03
25-10-14 17:04
25-10-14 17:08
25-10-14 17:24
25-10-14 17:25
25-10-14 17:27
25-10-14 17:29
25-10-14 17:34
25-10-14 17:35
25-10-14 17:36
25-10-14 17:37
25-10-14 17:41
25-10-14 17:46
25-10-14 17:51
25-10-14 17:58
25-10-14 18:00
25-10-14 18:01
25-10-14 18:03
25-10-14 18:04
25-10-14 18:13
25-10-14 18:15
25-10-14 18:16
25-10-14 18:18
25-10-14 18:19
25-10-14 18:34
25-10-14 18:41
25-10-14 18:42
25-10-14 18:43
25-10-14 18:44
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25-10-14 19:30
25-10-14 19:32
25-10-14 19:38
25-10-14 19:41
25-10-14 19:54
25-10-14 20:00
25-10-14 20:01
25-10-14 20:08
25-10-14 20:15
25-10-14 20:18
25-10-14 20:19
25-10-14 20:22
25-10-14 20:29
25-10-14 20:43
25-10-14 21:02
25-10-14 21:06
25-10-14 21:11
25-10-14 21:19
25-10-14 21:22
25-10-14 21:24
25-10-14 21:26
25-10-14 21:28
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25-10-14 21:55
25-10-14 21:56
25-10-14 21:58
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25-10-14 23:39
25-10-14 23:40
25-10-14 23:41
25-10-14 23:43
25-10-14 23:44
25-10-14 23:48
25-10-14 23:54
25-10-14 23:57
25-10-14 23:59
26-10-14 00:01
26-10-14 00:02
26-10-14 00:03
26-10-14 00:07
26-10-14 00:09
26-10-14 00:12
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26-10-14 00:43
26-10-14 00:48
26-10-14 00:50
26-10-14 00:59
26-10-14 01:00
26-10-14 01:03
26-10-14 01:04
26-10-14 01:07
26-10-14 01:13
26-10-14 01:25
26-10-14 01:37
26-10-14 01:46
26-10-14 01:54
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这是在上面提到的'data'DF中，但在实际代码中它被称为'receiver'。其余的都是一样的。 我如何使用下面答案中的代码。
timestep = 1800 #sec
start = "2014-10-21 00:00"
end = "2015-10-21 23:59"
#this is the DF that contains two columns needed: date and time
receiver = R125926
timeseq = seq(from = as.POSIXct(start), to = as.POSIXct(end), by = timestep)
#The origal date and time where in two different columns, so I have to combine them back into one.
receiver$date2 = as.POSIXct(paste(receiver$date, receiver$time), format="%Y-%m-%d %H:%M:%S")
#isolate the date/time data and put it in a new DF (since the rest from the receiver data doesn't have to be used). This is how the data snippet above looks like.
dateseq = receiver$date2
dt.timeseq = data.table(timeseq)
dtreceiver = data.table(dateseq)[, data := dateseq - as.numeric(dateseq) %% (3600/(timestep))][,list(count = .N), by=dateseq]
setkey(dt.timeseq, timeseq)
setkey(dtreceiver, dateseq)
new_dt = dtreceiver[dt.timeseq]
new_dt[is.na(count), count := 0]
答案 0 :(得分：2)
这样的事可能会有所帮助：
library(data.table)
#convert to data.tables
dt <- data.table(timeseq)
#floor the times down to 30min chunks and count occurances
dtdata <- data.table(data)[, data := data - as.numeric(data) %% (3600/(60/30))][,
list(count = .N), by=data]
#set the correct keys
setkey(dt, timeseq)
setkey(dtdata, data)
#merge and set NAs (unmatched) to zero
new_dt <- dtdata[dt]
new_dt[is.na(count), count := 0]
输出：
> new_dt
data count
1: 2014-10-20 00:00:00 0
2: 2014-10-20 00:30:00 0
3: 2014-10-20 01:00:00 0
4: 2014-10-20 01:30:00 0
5: 2014-10-20 02:00:00 0
---
390: 2014-10-27 21:30:00 0
391: 2014-10-27 22:00:00 0
392: 2014-10-27 22:30:00 0
393: 2014-10-27 23:00:00 0
394: 2014-10-27 23:30:00 0
这只是一些行，需要填充的行被填入。