我有一个带有以下列的R data.table
Date Time X Y
5/9/2016 12:00:00 AM 30 125
5/9/2016 12:30:00 AM 27 127
5/9/2016 1:00:00 AM 30 133
5/9/2016 1:30:00 AM 25 115
5/9/2016 2:00:00 AM 26 116
5/9/2016 3:00:00 AM 34 128
5/9/2016 3:30:00 AM 34 120
5/9/2016 4:00:00 AM 26 133
5/9/2016 5:00:00 AM 35 119
5/9/2016 5:30:00 AM 35 126
5/9/2016 6:00:00 AM 35 118
5/9/2016 6:30:00 AM 28 120
5/9/2016 7:00:00 AM 29 123
5/9/2016 7:30:00 AM 30 125
5/9/2016 8:30:00 AM 35 116
5/9/2016 9:00:00 AM 32 123
5/9/2016 9:30:00 AM 33 135
5/9/2016 10:00:00 AM 32 127
5/9/2016 10:30:00 AM 26 122
5/9/2016 11:00:00 AM 28 122
5/9/2016 11:30:00 AM 31 129
5/9/2016 12:00:00 PM 29 127
5/9/2016 12:30:00 PM 26 120
5/9/2016 1:00:00 PM 32 126
5/9/2016 1:30:00 PM 26 117
5/9/2016 2:30:00 PM 32 133
5/9/2016 3:00:00 PM 31 120
5/9/2016 3:30:00 PM 35 129
5/9/2016 4:00:00 PM 30 121
5/9/2016 4:30:00 PM 35 132
5/9/2016 5:00:00 PM 27 129
5/9/2016 5:30:00 PM 27 122
5/9/2016 6:00:00 PM 28 121
5/9/2016 6:30:00 PM 35 127
5/9/2016 7:00:00 PM 34 132
5/9/2016 7:30:00 PM 25 133
5/9/2016 8:00:00 PM 34 119
5/9/2016 8:30:00 PM 33 125
5/9/2016 9:00:00 PM 29 120
5/9/2016 9:30:00 PM 34 125
5/9/2016 10:00:00 PM 31 118
5/9/2016 10:30:00 PM 33 118
5/9/2016 11:00:00 PM 28 115
5/9/2016 11:30:00 PM 34 126
5/10/2016 12:00:00 AM 34 131
5/10/2016 12:30:00 AM 25 130
5/10/2016 1:00:00 AM 28 129
5/10/2016 1:30:00 AM 26 132
我想要输出如下 -
Date Time X Y Rowcount@6hours
5/9/2016 12:00:00 AM 30 125 0
5/9/2016 12:30:00 AM 27 127 1
5/9/2016 1:00:00 AM 30 133 2
5/9/2016 1:30:00 AM 25 115 3
5/9/2016 2:00:00 AM 26 116 4
5/9/2016 3:00:00 AM 34 128 5
5/9/2016 3:30:00 AM 34 120 6
5/9/2016 4:00:00 AM 26 133 7
5/9/2016 5:00:00 AM 35 119 8
5/9/2016 5:30:00 AM 35 126 9
5/9/2016 6:00:00 AM 35 118 10
5/9/2016 6:30:00 AM 28 120 10
5/9/2016 7:00:00 AM 29 123 10
5/9/2016 7:30:00 AM 30 125 10
5/9/2016 8:30:00 AM 35 116 9
5/9/2016 9:00:00 AM 32 123 10
5/9/2016 9:30:00 AM 33 135 10
5/9/2016 10:00:00 AM 32 127 10
5/9/2016 10:30:00 AM 26 122 10
5/9/2016 11:00:00 AM 28 122 11
5/9/2016 11:30:00 AM 31 129 11
5/9/2016 12:00:00 PM 29 127 11
5/9/2016 12:30:00 PM 26 120 11
5/9/2016 1:00:00 PM 32 126 11
5/9/2016 1:30:00 PM 26 117 11
5/9/2016 2:30:00 PM 32 133 11
5/9/2016 3:00:00 PM 31 120 11
5/9/2016 3:30:00 PM 35 129 11
5/9/2016 4:00:00 PM 30 121 11
5/9/2016 4:30:00 PM 35 132 11
5/9/2016 5:00:00 PM 27 129 11
5/9/2016 5:30:00 PM 27 122 11
5/9/2016 6:00:00 PM 28 121 11
5/9/2016 6:30:00 PM 35 127 11
5/9/2016 7:00:00 PM 34 132 11
5/9/2016 7:30:00 PM 25 133 11
5/9/2016 8:00:00 PM 34 119 11
5/9/2016 8:30:00 PM 33 125 11
5/9/2016 9:00:00 PM 29 120 12
5/9/2016 9:30:00 PM 34 125 12
5/9/2016 10:00:00 PM 31 118 12
5/9/2016 10:30:00 PM 33 118 12
5/9/2016 11:00:00 PM 28 115 12
5/9/2016 11:30:00 PM 34 126 12
5/10/2016 12:00:00 AM 34 131 12
5/10/2016 12:30:00 AM 25 130 12
5/10/2016 1:00:00 AM 28 129 12
5/10/2016 1:30:00 AM 26 132 12
所以我的目标是计算我在过去n小时内获得的行数（例如n = 6）并将其附加到每一行（我想使用该值来确定width参数rollmean函数）。这里的数据粒度是30分钟，但它可以更改为秒级别（也可能是非均匀的！）并且可能缺少行
我尝试使用window.zoo和xts，但无法找到解决方案。我在另一个链接中发布了相同的问题，并且该响应在一天内有效，但不是在几天内Count number of rows between two timestamps in R dataframe
答案 0 :(得分：4)
使用：
# convert to a 'data.table' (if necessary)
# add a datetime & rowid variable
DT <- as.data.table(df)[, `:=` (DateTime = as.POSIXct(paste(Date, Time), format = '%m/%d/%Y %I:%M:%S%p'),
rid = .I)][]
# calculate the count with a rolling join
DT[, count6hr := DT[.(DateTime = DateTime - 6*60*60, rid = rid)
, on = 'DateTime'
, roll = -Inf
, mult = 'last'
, i.rid - rid]][]
给出：
Date Time X Y DateTime rid count6hr 1: 5/9/2016 12:00:00AM 30 125 2016-05-09 00:00:00 1 0 2: 5/9/2016 12:30:00AM 27 127 2016-05-09 00:30:00 2 1 3: 5/9/2016 1:00:00AM 30 133 2016-05-09 01:00:00 3 2 4: 5/9/2016 1:30:00AM 25 115 2016-05-09 01:30:00 4 3 5: 5/9/2016 2:00:00AM 26 116 2016-05-09 02:00:00 5 4 6: 5/9/2016 3:00:00AM 34 128 2016-05-09 03:00:00 6 5 7: 5/9/2016 3:30:00AM 34 120 2016-05-09 03:30:00 7 6 8: 5/9/2016 4:00:00AM 26 133 2016-05-09 04:00:00 8 7 9: 5/9/2016 5:00:00AM 35 119 2016-05-09 05:00:00 9 8 10: 5/9/2016 5:30:00AM 35 126 2016-05-09 05:30:00 10 9 11: 5/9/2016 6:00:00AM 35 118 2016-05-09 06:00:00 11 10 12: 5/9/2016 6:30:00AM 28 120 2016-05-09 06:30:00 12 10 13: 5/9/2016 7:00:00AM 29 123 2016-05-09 07:00:00 13 10 14: 5/9/2016 7:30:00AM 30 125 2016-05-09 07:30:00 14 10 15: 5/9/2016 8:30:00AM 35 116 2016-05-09 08:30:00 15 9 16: 5/9/2016 9:00:00AM 32 123 2016-05-09 09:00:00 16 10 17: 5/9/2016 9:30:00AM 33 135 2016-05-09 09:30:00 17 10 18: 5/9/2016 10:00:00AM 32 127 2016-05-09 10:00:00 18 10 19: 5/9/2016 10:30:00AM 26 122 2016-05-09 10:30:00 19 10 20: 5/9/2016 11:00:00AM 28 122 2016-05-09 11:00:00 20 11 21: 5/9/2016 11:30:00AM 31 129 2016-05-09 11:30:00 21 11 22: 5/9/2016 12:00:00PM 29 127 2016-05-09 12:00:00 22 11 23: 5/9/2016 12:30:00PM 26 120 2016-05-09 12:30:00 23 11 24: 5/9/2016 1:00:00PM 32 126 2016-05-09 13:00:00 24 11 25: 5/9/2016 1:30:00PM 26 117 2016-05-09 13:30:00 25 11 26: 5/9/2016 2:30:00PM 32 133 2016-05-09 14:30:00 26 11 27: 5/9/2016 3:00:00PM 31 120 2016-05-09 15:00:00 27 11 28: 5/9/2016 3:30:00PM 35 129 2016-05-09 15:30:00 28 11 29: 5/9/2016 4:00:00PM 30 121 2016-05-09 16:00:00 29 11 30: 5/9/2016 4:30:00PM 35 132 2016-05-09 16:30:00 30 11 31: 5/9/2016 5:00:00PM 27 129 2016-05-09 17:00:00 31 11 32: 5/9/2016 5:30:00PM 27 122 2016-05-09 17:30:00 32 11 33: 5/9/2016 6:00:00PM 28 121 2016-05-09 18:00:00 33 11 34: 5/9/2016 6:30:00PM 35 127 2016-05-09 18:30:00 34 11 35: 5/9/2016 7:00:00PM 34 132 2016-05-09 19:00:00 35 11 36: 5/9/2016 7:30:00PM 25 133 2016-05-09 19:30:00 36 11 37: 5/9/2016 8:00:00PM 34 119 2016-05-09 20:00:00 37 11 38: 5/9/2016 8:30:00PM 33 125 2016-05-09 20:30:00 38 12 39: 5/9/2016 9:00:00PM 29 120 2016-05-09 21:00:00 39 12 40: 5/9/2016 9:30:00PM 34 125 2016-05-09 21:30:00 40 12 41: 5/9/2016 10:00:00PM 31 118 2016-05-09 22:00:00 41 12 42: 5/9/2016 10:30:00PM 33 118 2016-05-09 22:30:00 42 12 43: 5/9/2016 11:00:00PM 28 115 2016-05-09 23:00:00 43 12 44: 5/9/2016 11:30:00PM 34 126 2016-05-09 23:30:00 44 12 45: 5/10/2016 12:00:00AM 34 131 2016-05-10 00:00:00 45 12 46: 5/10/2016 12:30:00AM 25 130 2016-05-10 00:30:00 46 12 47: 5/10/2016 1:00:00AM 28 129 2016-05-10 01:00:00 47 12 48: 5/10/2016 1:30:00AM 26 132 2016-05-10 01:30:00 48 12
答案 1 :(得分：2)
这也可以使用 non-equi join 来解决：
library(data.table)
as.data.table(df)[
, ts := lubridate::mdy_hms(paste(Date, Time))][
, cnt := .SD[.(t6 = ts - 6*60*60, t0 = ts),
on = .(ts >= t6, ts < t0), .N, by = .EACHI]$N][]
Date Time ampm X Y ts cnt 1: 5/9/2016 12:00:00 AM 30 125 2016-05-09 00:00:00 0 2: 5/9/2016 12:30:00 AM 27 127 2016-05-09 00:30:00 1 3: 5/9/2016 1:00:00 AM 30 133 2016-05-09 01:00:00 2 4: 5/9/2016 1:30:00 AM 25 115 2016-05-09 01:30:00 3 5: 5/9/2016 2:00:00 AM 26 116 2016-05-09 02:00:00 4 6: 5/9/2016 3:00:00 AM 34 128 2016-05-09 03:00:00 5 7: 5/9/2016 3:30:00 AM 34 120 2016-05-09 03:30:00 6 8: 5/9/2016 4:00:00 AM 26 133 2016-05-09 04:00:00 7 9: 5/9/2016 5:00:00 AM 35 119 2016-05-09 05:00:00 8 10: 5/9/2016 5:30:00 AM 35 126 2016-05-09 05:30:00 9 11: 5/9/2016 6:00:00 AM 35 118 2016-05-09 06:00:00 10 12: 5/9/2016 6:30:00 AM 28 120 2016-05-09 06:30:00 10 13: 5/9/2016 7:00:00 AM 29 123 2016-05-09 07:00:00 10 14: 5/9/2016 7:30:00 AM 30 125 2016-05-09 07:30:00 10 15: 5/9/2016 8:30:00 AM 35 116 2016-05-09 08:30:00 9 16: 5/9/2016 9:00:00 AM 32 123 2016-05-09 09:00:00 10 17: 5/9/2016 9:30:00 AM 33 135 2016-05-09 09:30:00 10 18: 5/9/2016 10:00:00 AM 32 127 2016-05-09 10:00:00 10 19: 5/9/2016 10:30:00 AM 26 122 2016-05-09 10:30:00 10 20: 5/9/2016 11:00:00 AM 28 122 2016-05-09 11:00:00 11 21: 5/9/2016 11:30:00 AM 31 129 2016-05-09 11:30:00 11 22: 5/9/2016 12:00:00 PM 29 127 2016-05-09 12:00:00 11 23: 5/9/2016 12:30:00 PM 26 120 2016-05-09 12:30:00 11 24: 5/9/2016 1:00:00 PM 32 126 2016-05-09 13:00:00 11 25: 5/9/2016 1:30:00 PM 26 117 2016-05-09 13:30:00 11 26: 5/9/2016 2:30:00 PM 32 133 2016-05-09 14:30:00 11 27: 5/9/2016 3:00:00 PM 31 120 2016-05-09 15:00:00 11 28: 5/9/2016 3:30:00 PM 35 129 2016-05-09 15:30:00 11 29: 5/9/2016 4:00:00 PM 30 121 2016-05-09 16:00:00 11 30: 5/9/2016 4:30:00 PM 35 132 2016-05-09 16:30:00 11 31: 5/9/2016 5:00:00 PM 27 129 2016-05-09 17:00:00 11 32: 5/9/2016 5:30:00 PM 27 122 2016-05-09 17:30:00 11 33: 5/9/2016 6:00:00 PM 28 121 2016-05-09 18:00:00 11 34: 5/9/2016 6:30:00 PM 35 127 2016-05-09 18:30:00 11 35: 5/9/2016 7:00:00 PM 34 132 2016-05-09 19:00:00 11 36: 5/9/2016 7:30:00 PM 25 133 2016-05-09 19:30:00 11 37: 5/9/2016 8:00:00 PM 34 119 2016-05-09 20:00:00 11 38: 5/9/2016 8:30:00 PM 33 125 2016-05-09 20:30:00 12 39: 5/9/2016 9:00:00 PM 29 120 2016-05-09 21:00:00 12 40: 5/9/2016 9:30:00 PM 34 125 2016-05-09 21:30:00 12 41: 5/9/2016 10:00:00 PM 31 118 2016-05-09 22:00:00 12 42: 5/9/2016 10:30:00 PM 33 118 2016-05-09 22:30:00 12 43: 5/9/2016 11:00:00 PM 28 115 2016-05-09 23:00:00 12 44: 5/9/2016 11:30:00 PM 34 126 2016-05-09 23:30:00 12 45: 5/10/2016 12:00:00 AM 34 131 2016-05-10 00:00:00 12 46: 5/10/2016 12:30:00 AM 25 130 2016-05-10 00:30:00 12 47: 5/10/2016 1:00:00 AM 28 129 2016-05-10 01:00:00 12 48: 5/10/2016 1:30:00 AM 26 132 2016-05-10 01:30:00 12 Date Time ampm X Y ts cnt