我有以下数据框：
X.RIC Date Time Type Price Volume VolumeSEK
64 ALFAs.BCO 11.09.2018 25209.89 Trade 233.3000 158 36861.400
65 ALFAs.BCO 11.09.2018 25224.86 Trade 233.3000 1 233.300
66 ALFAs.BCO 11.09.2018 25248.27 Trade 233.4000 127 29641.800
67 ALFAs.BCO 11.09.2018 25633.92 Trade 233.1000 17 3962.700
68 ALFAs.BCO 11.09.2018 26107.33 Trade 232.9951 153 35648.250
69 ALFAs.BCO 11.09.2018 26389.27 Trade 232.8000 68 15830.400
70 ALFAs.BCO 11.09.2018 26405.14 Trade 232.8000 127 29565.600
71 ALFAs.BCO 11.09.2018 26554.28 Trade 232.8568 86 20025.685
72 ALFAs.BCO 11.09.2018 26652.19 Trade 232.4000 118 27423.200
73 ALFAs.BCO 11.09.2018 26654.21 Trade 232.7000 23 5352.100
74 ALFAs.BCO 11.09.2018 26666.24 Trade 232.7000 9 2094.300
75 ALFAs.BCO 11.09.2018 26978.54 Trade 232.4796 12 2789.755
76 ALFAs.BCO 11.09.2018 26983.92 Trade 232.5000 190 44175.000
77 ALFAs.BCO 11.09.2018 27108.03 Trade 232.5000 3 697.500
78 ALFAs.BCO 11.09.2018 27304.90 Trade 232.6674 274 63750.868
79 ALFAs.BCO 11.09.2018 27401.40 Trade 232.4000 591 137348.400
80 ALFAs.BCO 11.09.2018 27424.90 Trade 232.5468 79 18371.197
81 ALFAs.BCO 11.09.2018 27577.74 Trade 232.2000 190 44118.000
82 ALFAs.BCO 11.09.2018 27792.09 Trade 232.3000 218 50641.400
83 ALFAs.BCO 11.09.2018 27926.07 Trade 232.2699 82 19046.132
84 ALFAs.BCO 11.09.2018 27926.07 Trade 232.2699 78 18117.052
85 ALFAs.BCO 11.09.2018 27926.07 Trade 232.2699 25 5806.748
86 ALFAs.BCO 11.09.2018 27988.57 Trade 232.2500 1 232.250
87 ALFAs.BCO 11.09.2018 28150.02 Trade 232.7000 218 50728.600
88 ALFAs.BCO 11.09.2018 28171.02 Trade 232.7000 190 44213.000
89 ALFAs.BCO 11.09.2018 28198.79 Trade 232.8000 48 11174.400
90 ALFAs.BCO 11.09.2018 28210.15 Trade 232.8000 6 1396.800
91 ALFAs.BCO 11.09.2018 28226.06 Trade 232.5277 68 15811.884
92 ALFAs.BCO 11.09.2018 28226.07 Trade 232.5277 59 13719.134
93 ALFAs.BCO 11.09.2018 28226.07 Trade 232.5277 46 10696.274
具有几千行。每行代表一个事务，时间以秒为单位。我想添加一个行，用于标记在前60秒内是否出现了相同或相似数量的事务。我尝试使用duplicated()进行此操作，但是没有设法将其应用于滚动时间窗口。各种提示或反馈都受到高度赞赏。预先感谢！
其他：dput()是：
structure(list(X.RIC = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L), .Label = c("ALFAs.BCO", "INVEBs.BCO", "KINVBs.BCO",
"SEBAs.BCO", "SKABs.BCO", "SWEDAs.BCO"), class = "factor"), Date = structure(c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("11.09.2018",
"12.09.2018", "13.09.2018", "14.09.2018", "17.09.2018"), class = "factor"),
Time = c(25209.891063318, 25224.862743496, 25248.266841503,
25633.917236141, 26107.32746866, 26389.27354248, 26405.138667668,
26554.281062341, 26652.193081185, 26654.208526453, 26666.237043479,
26978.544043377, 26983.919406655, 27108.031169088, 27304.898035779,
27401.39801531, 27424.901479915, 27577.738223786, 27792.092294417,
27926.067647984, 27926.071789697, 27926.071789697, 27988.567139221,
28150.023490037, 28171.018701776, 28198.786920791, 28210.153751339,
28226.061861331, 28226.065520147, 28226.069560003, 28263.30156887,
28349.625180899, 28349.633532835, 28349.633532835, 28403.852984163,
28403.853357068, 28403.853357068, 28593.924744396, 28593.924744396,
28593.924744396, 28593.932109258, 28686.948138522, 28754.584046006,
28754.584046006, 28766.164856168, 28817.79249469, 28817.79249469,
29118.669834707, 29125.870160126, 29180.787060028, 29252.170090921,
29359.898647305, 29425.88512985, 29425.889294498, 29425.893298658,
29425.893298658, 29725.892421023, 29725.892421023, 29725.895962942,
29725.900418295), Type = structure(c(1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = "Trade", class = "factor"),
Price = c(233.3, 233.3, 233.4, 233.1, 232.9951, 232.8, 232.8,
232.8568, 232.4, 232.7, 232.7, 232.4796, 232.5, 232.5, 232.6674,
232.4, 232.5468, 232.2, 232.3, 232.2699, 232.2699, 232.2699,
232.25, 232.7, 232.7, 232.8, 232.8, 232.5277, 232.5277, 232.5277,
232.7, 232.7, 232.7, 232.7, 232.8, 232.8, 232.8, 232.811,
232.811, 232.811, 232.811, 232.8, 232.834, 232.834, 232.9,
233.0378, 233.0378, 233.1, 233.0451, 233.15, 233.1, 233.3,
233.135, 233.135, 233.135, 233.135, 233.2561, 233.2561, 233.2561,
233.2561), Volume = c(158L, 1L, 127L, 17L, 153L, 68L, 127L,
86L, 118L, 23L, 9L, 12L, 190L, 3L, 274L, 591L, 79L, 190L,
218L, 82L, 78L, 25L, 1L, 218L, 190L, 48L, 6L, 68L, 59L, 46L,
5L, 27L, 21L, 6L, 12L, 11L, 5L, 40L, 37L, 16L, 12L, 4L, 25L,
17L, 190L, 14L, 9L, 480L, 57L, 354L, 249L, 190L, 120L, 63L,
61L, 34L, 80L, 66L, 29L, 16L), VolumeSEK = c(36861.4, 233.3,
29641.8, 3962.7, 35648.2503, 15830.4, 29565.6, 20025.6848,
27423.2, 5352.1, 2094.3, 2789.7552, 44175, 697.5, 63750.8676,
137348.4, 18371.1972, 44118, 50641.4, 19046.1318, 18117.0522,
5806.7475, 232.25, 50728.6, 44213, 11174.4, 1396.8, 15811.8836,
13719.1343, 10696.2742, 1163.5, 6282.9, 4886.7, 1396.2, 2793.6,
2560.8, 1164, 9312.44, 8614.007, 3724.976, 2793.732, 931.2,
5820.85, 3958.178, 44251, 3262.5292, 2097.3402, 111888, 13283.5707,
82535.1, 58041.9, 44327, 27976.2, 14687.505, 14221.235, 7926.59,
18660.488, 15394.9026, 6764.4269, 3732.0976)), .Names = c("X.RIC",
"Date", "Time", "Type", "Price", "Volume", "VolumeSEK"), row.names = 64:123, class = "data.frame")
答案 0 :(得分：0)
此处的解决方案可以通过复杂的自连接表示。我们假设如果两个卷之间的距离在5之内，并且如果前一行在后一行的60秒之内并且在它之后，则这两个卷是相似的。 dat是问题中显示的输入数据帧。
library(sqldf)
sqldf("select a.*, coalesce(sum(abs(a.Volume - b.Volume) < 5) > 0, 0) SameVol
from dat a
left join dat b on a.Date = b.Date and
b.Time between a.Time - 60 and a.Time and
b.rowid < a.rowid
group by a.rowid")
给予：
X.RIC Date Time Type Price Volume VolumeSEK SameVol
1 ALFAs.BCO 11.09.2018 25209.89 Trade 233.3000 158 36861.400 0
2 ALFAs.BCO 11.09.2018 25224.86 Trade 233.3000 1 233.300 0
3 ALFAs.BCO 11.09.2018 25248.27 Trade 233.4000 127 29641.800 0
4 ALFAs.BCO 11.09.2018 25633.92 Trade 233.1000 17 3962.700 0
5 ALFAs.BCO 11.09.2018 26107.33 Trade 232.9951 153 35648.250 0
6 ALFAs.BCO 11.09.2018 26389.27 Trade 232.8000 68 15830.400 0
7 ALFAs.BCO 11.09.2018 26405.14 Trade 232.8000 127 29565.600 0
8 ALFAs.BCO 11.09.2018 26554.28 Trade 232.8568 86 20025.685 0
9 ALFAs.BCO 11.09.2018 26652.19 Trade 232.4000 118 27423.200 0
10 ALFAs.BCO 11.09.2018 26654.21 Trade 232.7000 23 5352.100 0
11 ALFAs.BCO 11.09.2018 26666.24 Trade 232.7000 9 2094.300 0
12 ALFAs.BCO 11.09.2018 26978.54 Trade 232.4796 12 2789.755 0
13 ALFAs.BCO 11.09.2018 26983.92 Trade 232.5000 190 44175.000 0
14 ALFAs.BCO 11.09.2018 27108.03 Trade 232.5000 3 697.500 0
15 ALFAs.BCO 11.09.2018 27304.90 Trade 232.6674 274 63750.868 0
16 ALFAs.BCO 11.09.2018 27401.40 Trade 232.4000 591 137348.400 0
17 ALFAs.BCO 11.09.2018 27424.90 Trade 232.5468 79 18371.197 0
18 ALFAs.BCO 11.09.2018 27577.74 Trade 232.2000 190 44118.000 0
19 ALFAs.BCO 11.09.2018 27792.09 Trade 232.3000 218 50641.400 0
20 ALFAs.BCO 11.09.2018 27926.07 Trade 232.2699 82 19046.132 0
21 ALFAs.BCO 11.09.2018 27926.07 Trade 232.2699 78 18117.052 1
22 ALFAs.BCO 11.09.2018 27926.07 Trade 232.2699 25 5806.748 0
23 ALFAs.BCO 11.09.2018 27988.57 Trade 232.2500 1 232.250 0
24 ALFAs.BCO 11.09.2018 28150.02 Trade 232.7000 218 50728.600 0
25 ALFAs.BCO 11.09.2018 28171.02 Trade 232.7000 190 44213.000 0
26 ALFAs.BCO 11.09.2018 28198.79 Trade 232.8000 48 11174.400 0
27 ALFAs.BCO 11.09.2018 28210.15 Trade 232.8000 6 1396.800 0
28 ALFAs.BCO 11.09.2018 28226.06 Trade 232.5277 68 15811.884 0
29 ALFAs.BCO 11.09.2018 28226.07 Trade 232.5277 59 13719.134 0
30 ALFAs.BCO 11.09.2018 28226.07 Trade 232.5277 46 10696.274 1
31 ALFAs.BCO 11.09.2018 28263.30 Trade 232.7000 5 1163.500 1
32 ALFAs.BCO 11.09.2018 28349.63 Trade 232.7000 27 6282.900 0
33 ALFAs.BCO 11.09.2018 28349.63 Trade 232.7000 21 4886.700 0
34 ALFAs.BCO 11.09.2018 28349.63 Trade 232.7000 6 1396.200 0
35 ALFAs.BCO 11.09.2018 28403.85 Trade 232.8000 12 2793.600 0
36 ALFAs.BCO 11.09.2018 28403.85 Trade 232.8000 11 2560.800 1
37 ALFAs.BCO 11.09.2018 28403.85 Trade 232.8000 5 1164.000 1
38 ALFAs.BCO 11.09.2018 28593.92 Trade 232.8110 40 9312.440 0
39 ALFAs.BCO 11.09.2018 28593.92 Trade 232.8110 37 8614.007 1
40 ALFAs.BCO 11.09.2018 28593.92 Trade 232.8110 16 3724.976 0
41 ALFAs.BCO 11.09.2018 28593.93 Trade 232.8110 12 2793.732 1
42 ALFAs.BCO 11.09.2018 28686.95 Trade 232.8000 4 931.200 0
43 ALFAs.BCO 11.09.2018 28754.58 Trade 232.8340 25 5820.850 0
44 ALFAs.BCO 11.09.2018 28754.58 Trade 232.8340 17 3958.178 0
45 ALFAs.BCO 11.09.2018 28766.16 Trade 232.9000 190 44251.000 0
46 ALFAs.BCO 11.09.2018 28817.79 Trade 233.0378 14 3262.529 0
47 ALFAs.BCO 11.09.2018 28817.79 Trade 233.0378 9 2097.340 0
48 ALFAs.BCO 11.09.2018 29118.67 Trade 233.1000 480 111888.000 0
49 ALFAs.BCO 11.09.2018 29125.87 Trade 233.0451 57 13283.571 0
50 ALFAs.BCO 11.09.2018 29180.79 Trade 233.1500 354 82535.100 0
51 ALFAs.BCO 11.09.2018 29252.17 Trade 233.1000 249 58041.900 0
52 ALFAs.BCO 11.09.2018 29359.90 Trade 233.3000 190 44327.000 0
53 ALFAs.BCO 11.09.2018 29425.89 Trade 233.1350 120 27976.200 0
54 ALFAs.BCO 11.09.2018 29425.89 Trade 233.1350 63 14687.505 0
55 ALFAs.BCO 11.09.2018 29425.89 Trade 233.1350 61 14221.235 1
56 ALFAs.BCO 11.09.2018 29425.89 Trade 233.1350 34 7926.590 0
57 ALFAs.BCO 11.09.2018 29725.89 Trade 233.2561 80 18660.488 0
58 ALFAs.BCO 11.09.2018 29725.89 Trade 233.2561 66 15394.903 0
59 ALFAs.BCO 11.09.2018 29725.90 Trade 233.2561 29 6764.427 0
60 ALFAs.BCO 11.09.2018 29725.90 Trade 233.2561 16 3732.098 0