如何仅考虑 r 中交易日期之前的最后 6 个月计算每个帐户的网络度量？
How can I calculate network measures for each account only considering the last 6 month period prior to the transaction date in r?
这是我的交易数据：
data:
id from to date amount
<int> <fctr> <fctr> <date> <dbl>
19521 6644 6934 2005-01-01 700.0
19524 6753 8456 2005-01-01 600.0
19523 9242 9333 2005-01-01 1000.0
… … … … …
1056317 7819 7454 2010-12-31 60.2
1056318 6164 7497 2010-12-31 107.5
1056319 7533 7492 2010-12-31 164.1
之前问过，需要在指定的时间间隔内形成网络，然后对网络进行网络措施。
现在，我需要更进一步。
我想做的事情如下：
- 例如让我们考虑
row 1 。在 row 1 中，交易日期是 "2005-01-01"，因此我们需要提取 date_trx 位于 "2004-07-05"-"2005-01-01" 的数据（这是日期 [=19 之前的最后 6 个月） =]) 然后在这个提取的数据上形成网络来计算网络的度量，例如网络中帐户的度中心性，介数中心性。最后，新列的第一行 degree_centrality 和 betweenness_centrality 将分别包含帐户的度中心性 6644 和帐户的介数中心性 6644。
- 同样，在
row 2中，交易日期又是"2005-01-01"，所以我们需要提取date_trx位于"2004-07-05"-"2005-01-01"的数据（这是最近6个月日期 "2005-01-01") 之前的时间段，然后在此提取的数据上形成网络，以计算网络的度量，例如网络中账户的度中心性、介数中心性。最后，新列的第二行 degree_centrality 和 betweenness_centrality 将分别包含帐户的度中心性 6753 和帐户的介数中心性 6753。
- 所有数据行都是这样。
PS:
- Let's consider
row 1, row 2 and row 3. Since the dates are the same, the networks that will be created by data whose dates lie in the range "2004-07-05"-"2005-01-01" will be exactly the same for all the three rows. However, since network measures such as degree centr. or betweenness centr. will be added to the data according to the accounts in from column, these network measures for 6644, 6753 and 9242 will more likely be different from each other.
- Also, the boundaries of date intervals taken into account when we extract data between a date range to form a network. For example, the very first date in my data is
"2005-01-01"( and there are 4 transactions made in that date in fact). But, let's say there is only one observation(transaction) on this very first date "2005-01-01" and there's not any observation before this date, now we want to create a network of data ranging between"2004-07-05"-"2005-01-01". Since as I said the boundaries also count, the only observation we can take -in this specified date range- to form the network is the one with date "2005-01-01". So, since a transaction consists of two accounts, thus only these two accounts(nodes) will be present in the network. So, at the end of the day, one observation is sufficient to build a network and calculate network metrics on it.
数据样本的dput()输出：
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14274, 14275, 14275, 14275, 14275, 14280, 14285, 14286, 14286,
14288, 14291, 14299, 14302, 14303, 14303, 14308, 14312, 14313,
14316, 14316, 14317, 14334, 14334, 14334, 14339, 14342, 14347,
14364, 14364, 14364, 14369, 14377, 14377, 14377, 14391, 14395,
14395, 14395, 14395, 14395, 14395, 14400, 14402, 14403, 14406,
14408, 14425, 14425, 14425, 14425, 14425, 14430, 14433, 14436,
14437, 14438, 14440, 14443, 14445, 14456, 14456, 14456, 14456,
14458, 14461, 14461, 14463, 14463, 14464, 14466, 14469, 14484,
14487, 14487, 14487, 14487, 14487, 14487, 14492, 14492, 14498,
14500, 14500, 14508, 14513, 14514, 14517, 14517, 14517, 14517,
14517, 14522, 14522, 14527, 14530, 14537, 14540, 14548, 14548,
14548, 14553, 14556, 14560, 14561, 14563, 14578, 14578, 14578,
14578, 14583, 14584, 14585, 14586, 14591, 14607, 14609, 14609,
14609, 14609, 14609, 14609, 14611, 14612, 14614, 14614, 14615,
14616, 14616, 14618, 14620, 14622, 14622, 14624, 14626, 14628,
14629, 14631, 14633, 14637, 14640, 14640, 14640, 14640, 14645,
14646, 14646, 14648, 14652, 14653, 14668, 14668, 14668, 14668,
14670, 14673, 14673, 14676, 14679, 14680, 14681, 14681, 14685,
14686, 14699, 14699, 14699, 14699, 14703, 14704, 14707, 14711,
14712, 14712, 14727, 14729, 14729, 14729, 14734, 14737, 14738,
14742, 14760, 14760, 14760, 14760, 14760, 14760, 14760, 14763,
14765, 14769, 14773, 14773, 14773, 14774, 14777, 14784, 14790,
14790, 14790, 14790, 14795, 14803, 14821, 14821, 14821, 14821,
14824, 14826, 14826, 14828, 14829, 14829, 14830, 14831, 14834,
14840, 14850, 14852, 14852, 14852, 14852, 14852, 14852, 14856,
14856, 14857, 14865, 14882, 14882, 14882, 14882, 14882, 14882,
14887, 14889, 14889, 14890, 14895, 14910, 14913, 14913, 14913,
14918, 14918, 14919, 14922, 14926, 14927, 14943, 14943, 14943,
14946, 14948, 14949, 14951, 14954, 14956, 14967, 14974, 14974
), class = "Date"), amount = c(24.4, 7618, 21971, 5245, 2921,
8000, 169.2, 71.5, 14.6, 4214, 14.6, 13920, 14.6, 24640, 1600,
261.1, 16400, 3500, 2700, 19882, 182, 14.6, 16927, 25653, 3059,
2880, 9658, 4500, 12480, 14.6, 1000, 3679, 34430, 12600, 14.6,
19.2, 4900, 826, 3679, 2100, 38000, 79, 11400, 21495, 3679, 200,
14.6, 100.6, 3679, 5300, 108.9, 3679, 2696, 7500, 171.6, 14.6,
99.2, 2452, 3679, 3218, 700, 69.7, 14.6, 91.5, 2452, 3679, 2900,
17572, 14.6, 14.6, 90.5, 2452, 49752, 3679, 1900, 14.6, 870,
85.2, 2452, 3679, 1600, 540, 14.6, 14.6, 79, 210, 2452, 28400,
720, 180, 420, 44289, 489, 3679, 840, 2900, 150, 870, 420, 14.6,
152.9, 64.1, 41464, 2452, 205, 3679, 2166, 100, 14.6, 14.6, 59.7,
6588, 2452, 3679, 65, 200, 14.6, 65.7, 2452, 3679, 15792, 2000,
2200, 99.7, 14.6, 140.3, 67.6, 2452, 5600, 873, 3679, 660, 36258,
1900, 2002, 263, 14.6, 63.3, 2452, 14607, 3679, 1400, 7680, 360,
14.6, 14.6, 65.5, 2452, 344, 3679, 990, 1000, 19652, 1080, 14.6,
72.8, 22991, 2452, 1140, 9226, 3679, 2400, 1410, 18300, 14.6,
14.6, 75.7, 2452, 3679, 1700, 14.6, 14.6, 65.4, 78.8, 2452, 6846,
270, 3679, 6339, 3000, 14.6, 14.6, 358.3, 102.3, 14.6, 73.2,
3869, 2452, 3679, 2800, 14.6, 14.6, 72.4, 228.2, 2452, 3679,
3700, 180, 6900, 350, 14.6, 14.6, 59.2, 23131, 2452, 2880, 1200,
630, 3679, 2400, 1680, 17100, 720, 390, 2520, 450, 1920, 840,
137.9, 14.6, 14.6, 55.4, 2452, 5800, 1700, 24977, 3679, 570,
630, 11700, 14.6, 58.3, 2452, 1347, 1455, 4600, 3679, 3300, 14.6,
14.6, 51.9, 2452, 2038, 3679, 14.6, 49.6, 14.6, 2452, 9252, 3294,
3679, 1170, 70.5, 70.1, 91.3, 14.6, 14.6, 54.1, 2452, 46181,
15586, 74529, 3679, 189.5, 318.1, 14.6, 55.2, 14.6, 2452, 6446,
1500, 6300, 3679, 8600, 300, 6500, 2600, 185.2, 14.6, 64.1, 11200,
3041, 2452, 2895, 14037, 3103, 1200, 3679, 870, 14.6, 57.9, 93,
109.1, 67.1, 14.6, 2452, 390, 13200, 7746, 3679, 2300, 2960,
18292, 14.6, 136.9, 7500, 14.6, 65, 2452, 2974, 331, 3679, 1300,
14600, 14.6, 14.6, 72.4, 2452, 1100, 10315, 3679, 39864, 94.1,
101.2, 14.6, 72.4, 2452, 1469, 27132, 4700, 3679, 1000, 101.8,
58.4, 76, 14.6, 14.6, 61.6, 38200, 570, 2400, 2452, 600, 400,
100, 2412, 4485, 1320, 3679, 2500, 600, 810, 1800, 2400, 330,
480, 14.6, 114, 14.6, 55.9, 2452, 500, 4356, 7045, 11889, 3679,
146.1, 14.6, 288.5, 56.3, 1300, 3, 2452, 7156, 24784, 24423,
3679, 822, 810, 20800, 2.2, 1800, 14.6, 63, 1050, 2452, 15976,
4755, 11100, 3679, 180, 14.6, 166.6, 59.9, 2452, 5411, 13405,
3679, 183.9, 14.6, 35.4, 10.8, 14.6, 14.6, 66.7, 1800, 2452,
2277, 511, 800, 3679, 65, 5040, 450, 14.6, 14.6, 93.5, 71.5,
2452, 3679, 14.6, 14.6, 14.6, 75.4, 270, 14, 2452, 4622, 145,
24324, 1523, 372, 3679, 1400, 3100, 14.6, 14.6, 14.6, 77.9, 14.6,
77.1, 14989, 6500, 2452, 3679, 247.3, 14.6, 14.6, 14.6, 190.6,
58.7, 2452, 1398, 4268, 3597, 3679, 1800, 14.6, 14.6, 63.1, 2452,
12400, 5106, 4633, 3679, 510, 14.6, 204.7, 67.1, 4500, 2452,
5800, 1275, 6460, 3679, 6406, 78.7, 52.6)), row.names = c(NA,
-478L), class = "data.frame")
EDIT:
According to @pseudospin 's solution, the following code:
library(data.table)
setDT(dt)
measure_graph <- function(dt) {
network <- graph_from_data_frame(dt[,.(from, to)], directed=TRUE)
accounts <- dt[date == max(date), from]
list(
account = accounts,
degree = degree(network, mode = "all")[accounts],
in_degree = degree(network, mode = "in")[accounts],
out_degree = degree(network, mode = "out")[accounts]
)
}
dt[, measure_graph(dt[(date >= end_date - 180) & (date <= end_date)]), .(end_date = date)]
使用上面提供的数据应用时 returns 此输出：
end_date account degree in_degree out_degree
<date> <fctr> <dbl> <dbl> <dbl>
2005-05-31 5370 1 0 1
2005-08-05 5370 2 0 2
2005-09-12 5370 3 0 3
2005-10-05 8605 NA NA NA
2005-11-12 5370 5 1 4
2005-11-26 6390 NA NA NA
2005-11-30 5370 6 2 4
2006-01-31 5370 7 2 5
2006-03-31 8934 NA NA NA
2006-04-11 5370 6 2 4
...
在这里，为什么帐户 8605,6390,8934 没有 degree=1, in_degree=0 和 out_degree=1 而有 NA？此外，有些值是正确的，有些则不正确。例如，考虑帐户 5370.
的日期 "2010-12-11"
按此日期和帐户过滤输出结果：
end_date account degree in_degree out_degree
<date> <fctr> <dbl> <dbl> <dbl>
2010-12-11 5370 1 0 1
但是，当我根据日期 "2010-12-11" 和帐户 5370 之前的最后 6 个月期间（即 "2010-06-14"-"2010-12-11"）筛选上面提供的示例数据时，通过
data[(date <="2010-12-11" & date>=as.Date(as.numeric(as.Date("2010-12-11"))-180, origin="1970-01-01")) & (from==5370 | to==5370)]
它给出：
id from to date amount
<int> <fctr> <fctr> <date> <dbl>
890395 5939 5370 2010-06-14 65.0
891820 6592 5370 2010-06-17 5040.0
894176 5370 6352 2010-06-24 450.0
901068 5765 5370 2010-06-30 14.6
902416 5370 6251 2010-06-30 14.6
904059 5370 5722 2010-06-30 93.5
904624 5898 5370 2010-06-30 71.5
...
1032335 6700 5370 2010-11-30 67.1
1033712 5370 5827 2010-12-03 4500.0
1035096 5370 5852 2010-12-05 2452.0
1036537 7078 5370 2010-12-06 5800.0
1038682 7349 5370 2010-12-08 1275.0
1042439 5370 6710 2010-12-11 6460.0
(63 rows in total)
所以，根据这个，实际上 5370 应该有 degree=63，如果我们只按 to==5370 过滤，我们会看到它应该有 in_degree=37 和类似地，如果我们只按 from==5370 过滤，我们会看到它应该有 out_degree=26，对于指定的日期范围 "2010-06-14"-"2010-12-11"，而代码将其计算为 1,0 和 1 分别。
为什么代码对一些人给出正确的结果，但对另一些人给出错误的结果？
我想你只要按照你描述的去做就行了。我会这样安排：
library(data.table)
setDT(dt)
measure_graph <- function(dt) {
network <- graph_from_data_frame(dt[,.(from, to)], directed=TRUE)
accounts <- dt[date == max(date), from]
list(
account = accounts,
closeness = closeness(network, normalized = TRUE)[accounts],
betweenness = betweenness(network, normalized = TRUE)[accounts]
)
}
dt[, measure_graph(dt[(date >= end_date - 180) & (date <= end_date)]), .(end_date = date)]
这只会为每个日期创建一次图表，并为该日期的所有 from 帐户提取您想要的统计信息。
这是我的交易数据：
data:
id from to date amount
<int> <fctr> <fctr> <date> <dbl>
19521 6644 6934 2005-01-01 700.0
19524 6753 8456 2005-01-01 600.0
19523 9242 9333 2005-01-01 1000.0
… … … … …
1056317 7819 7454 2010-12-31 60.2
1056318 6164 7497 2010-12-31 107.5
1056319 7533 7492 2010-12-31 164.1
之前问过
我想做的事情如下：
- 例如让我们考虑
row 1。在row 1中，交易日期是"2005-01-01"，因此我们需要提取date_trx位于"2004-07-05"-"2005-01-01"的数据（这是日期 [=19 之前的最后 6 个月） =]) 然后在这个提取的数据上形成网络来计算网络的度量，例如网络中帐户的度中心性，介数中心性。最后，新列的第一行degree_centrality和betweenness_centrality将分别包含帐户的度中心性6644和帐户的介数中心性6644。 - 同样，在
row 2中，交易日期又是"2005-01-01"，所以我们需要提取date_trx位于"2004-07-05"-"2005-01-01"的数据（这是最近6个月日期"2005-01-01") 之前的时间段，然后在此提取的数据上形成网络，以计算网络的度量，例如网络中账户的度中心性、介数中心性。最后，新列的第二行degree_centrality和betweenness_centrality将分别包含帐户的度中心性6753和帐户的介数中心性6753。 - 所有数据行都是这样。
PS:
- Let's consider
row 1,row 2androw 3. Since the dates are the same, the networks that will be created by data whose dates lie in the range"2004-07-05"-"2005-01-01"will be exactly the same for all the three rows. However, since network measures such asdegree centr.orbetweenness centr.will be added to the data according to the accounts infromcolumn, these network measures for6644,6753and9242will more likely be different from each other.- Also, the boundaries of date intervals taken into account when we extract data between a date range to form a network. For example, the very first date in my data is
"2005-01-01"( and there are 4 transactions made in that date in fact). But, let's say there is only one observation(transaction) on this very first date"2005-01-01"and there's not any observation before this date, now we want to create a network of data ranging between"2004-07-05"-"2005-01-01". Since as I said the boundaries also count, the only observation we can take -in this specified date range- to form the network is the one with date"2005-01-01". So, since a transaction consists of two accounts, thus only these two accounts(nodes) will be present in the network. So, at the end of the day, one observation is sufficient to build a network and calculate network metrics on it.
数据样本的dput()输出：
structure(list(id = c(18529L, 13742L, 9913L, 956L, 2557L, 1602L,
18669L, 35900L, 48667L, 51341L, 53713L, 60126L, 60545L, 65113L,
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EDIT:
According to @pseudospin 's solution, the following code:
library(data.table)
setDT(dt)
measure_graph <- function(dt) {
network <- graph_from_data_frame(dt[,.(from, to)], directed=TRUE)
accounts <- dt[date == max(date), from]
list(
account = accounts,
degree = degree(network, mode = "all")[accounts],
in_degree = degree(network, mode = "in")[accounts],
out_degree = degree(network, mode = "out")[accounts]
)
}
dt[, measure_graph(dt[(date >= end_date - 180) & (date <= end_date)]), .(end_date = date)]
使用上面提供的数据应用时 returns 此输出：
end_date account degree in_degree out_degree
<date> <fctr> <dbl> <dbl> <dbl>
2005-05-31 5370 1 0 1
2005-08-05 5370 2 0 2
2005-09-12 5370 3 0 3
2005-10-05 8605 NA NA NA
2005-11-12 5370 5 1 4
2005-11-26 6390 NA NA NA
2005-11-30 5370 6 2 4
2006-01-31 5370 7 2 5
2006-03-31 8934 NA NA NA
2006-04-11 5370 6 2 4
...
在这里，为什么帐户 8605,6390,8934 没有 degree=1, in_degree=0 和 out_degree=1 而有 NA？此外，有些值是正确的，有些则不正确。例如，考虑帐户 5370.
的日期 "2010-12-11"
按此日期和帐户过滤输出结果：
end_date account degree in_degree out_degree
<date> <fctr> <dbl> <dbl> <dbl>
2010-12-11 5370 1 0 1
但是，当我根据日期 "2010-12-11" 和帐户 5370 之前的最后 6 个月期间（即 "2010-06-14"-"2010-12-11"）筛选上面提供的示例数据时，通过
data[(date <="2010-12-11" & date>=as.Date(as.numeric(as.Date("2010-12-11"))-180, origin="1970-01-01")) & (from==5370 | to==5370)]
它给出：
id from to date amount
<int> <fctr> <fctr> <date> <dbl>
890395 5939 5370 2010-06-14 65.0
891820 6592 5370 2010-06-17 5040.0
894176 5370 6352 2010-06-24 450.0
901068 5765 5370 2010-06-30 14.6
902416 5370 6251 2010-06-30 14.6
904059 5370 5722 2010-06-30 93.5
904624 5898 5370 2010-06-30 71.5
...
1032335 6700 5370 2010-11-30 67.1
1033712 5370 5827 2010-12-03 4500.0
1035096 5370 5852 2010-12-05 2452.0
1036537 7078 5370 2010-12-06 5800.0
1038682 7349 5370 2010-12-08 1275.0
1042439 5370 6710 2010-12-11 6460.0
(63 rows in total)
所以，根据这个，实际上 5370 应该有 degree=63，如果我们只按 to==5370 过滤，我们会看到它应该有 in_degree=37 和类似地，如果我们只按 from==5370 过滤，我们会看到它应该有 out_degree=26，对于指定的日期范围 "2010-06-14"-"2010-12-11"，而代码将其计算为 1,0 和 1 分别。
为什么代码对一些人给出正确的结果，但对另一些人给出错误的结果？
我想你只要按照你描述的去做就行了。我会这样安排：
library(data.table)
setDT(dt)
measure_graph <- function(dt) {
network <- graph_from_data_frame(dt[,.(from, to)], directed=TRUE)
accounts <- dt[date == max(date), from]
list(
account = accounts,
closeness = closeness(network, normalized = TRUE)[accounts],
betweenness = betweenness(network, normalized = TRUE)[accounts]
)
}
dt[, measure_graph(dt[(date >= end_date - 180) & (date <= end_date)]), .(end_date = date)]
这只会为每个日期创建一次图表，并为该日期的所有 from 帐户提取您想要的统计信息。