如何根据 r 中的唯一日期间隔形成多个子数据帧答案

【问题标题】：How to form multiple sub-dataframes based on unique date intervals in r如何根据 r 中的唯一日期间隔形成多个子数据帧
【发布时间】：2021-01-01 15:36:33
【问题描述】：

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

对于date 列中的每个唯一日期，我想确定一个日期间隔，即该指定唯一日期之前的最后 6 个月。例如，要确定日期 "2005-01-01" 之前的最后 6 个月期间，我从 "2005-01-01" 中减去 180 天以得到 "2004-07-05"。因此，间隔 "2004-07-05"-"2005-01-01" 是日期 "2005-01-01" 之前的最后 6 个月期间。因此，我相应地创建了一个新列date_minus_180，如下所示：

id          from    to          date        date_minus_180    amount  
<int>       <fctr>  <fctr>      <date>      <date>            <dbl>
19521       6644    6934        2005-01-01  2004-07-05        700.0
19522       9843    9115        2005-01-01  2004-07-05        900.0
19523       9242    9333        2005-01-01  2004-07-05        1000.0
19524       6753    8456        2005-01-01  2004-07-05        600.0
19525       7075    6510        2005-01-02  2004-07-06        400.0
19526       8685    7207        2005-01-02  2004-07-06        1100.0
19527       5513    6046        2005-01-03  2004-07-07        600.0
19528       6340    7047        2005-01-03  2004-07-07        1100.0
19529       6042    6213        2005-01-03  2004-07-07        200.0
19530       5587    9493        2005-01-03  2004-07-07        800.0
...

现在我想做的是通过根据每个唯一的日期间隔拆分数据来获取子数据帧。也就是说，考虑到第一个日期间隔"2004-07-05"-"2005-01-01"，我们将有一个包含观察值的子数据框，其中date 列中的日期在此日期间隔的范围内。由于我的数据中的日期是升序的，因此第一个日期是"2005-01-01"。因此，第一个子数据框将包含前 4 个观测值，因为这些观测值的 date 列中的日期 "2005-01-01" 在间隔 "2004-07-05"-"2005-01-01" 的范围内。同样，考虑到第二个日期间隔"2004-07-06"-"2005-01-02"，我们将有一个包含观察值的子数据框，其中date 列中的日期在此日期间隔的范围内。因此，第二个子数据框将包含前 6 个观测值，因为日期 "2005-01-01" 和 "2005-01-02" 在间隔 "2004-07-06"-"2005-01-02" 的范围内。那么，以这种方式继续，我怎样才能根据这些指定的日期间隔形成多个子数据帧？

让我们再次考虑区间"2004-07-05"-"2005-01-01"。对于这个特定的区间，我们可以将数据子集如下：

data[data$date >= "2004-07-05" & data$date <= "2005-01-01",]

给出输出：

id          from    to          date        date_minus_180    amount  
<int>       <fctr>  <fctr>      <date>      <date>            <dbl>
19521       6644    6934        2005-01-01  2004-07-05        700.0
19522       9843    9115        2005-01-01  2004-07-05        900.0
19523       9242    9333        2005-01-01  2004-07-05        1000.0
19524       6753    8456        2005-01-01  2004-07-05        600.0

那么，如何一次性对所有区间的数据进行子集化？

数据样本：

structure(list(id = c(18529L, 13742L, 9913L, 956L, 2557L, 1602L, 
18669L, 35900L, 48667L, 51341L, 53713L, 60126L, 60545L, 65113L, 
66783L, 83324L, 87614L, 88898L, 89874L, 94765L, 100277L, 101587L, 
103444L, 108414L, 113319L, 121516L, 126607L, 130170L, 131771L, 
135002L, 149431L, 157403L, 157645L, 158831L, 162597L, 162680L, 
163901L, 165044L, 167082L, 168562L, 168940L, 172578L, 173031L, 
173267L, 177507L, 179167L, 182612L, 183499L, 188171L, 189625L, 
193940L, 198764L, 199342L, 200134L, 203328L, 203763L, 204733L, 
205651L, 209672L, 210242L, 210979L, 214532L, 214741L, 215738L, 
216709L, 220828L, 222140L, 222905L, 226133L, 226527L, 227160L, 
228193L, 231782L, 232454L, 233774L, 237836L, 237837L, 238860L, 
240223L, 245032L, 246673L, 247561L, 251611L, 251696L, 252663L, 
254410L, 255126L, 255230L, 258484L, 258485L, 259309L, 259910L, 
260542L, 262091L, 264462L, 264887L, 264888L, 266125L, 268574L, 
272959L), from = c("5370", "5370", "5370", "8605", "5370", "6390", 
"5370", "5370", "8934", "5370", "5635", "6046", "5680", "8026", 
"9037", "5370", "7816", "8046", "5492", "8756", "5370", "9254", 
"5370", "5370", "7078", "6615", "5370", "9817", "8228", "8822", 
"5735", "7058", "5370", "8667", "9315", "6053", "7990", "8247", 
"8165", "5656", "9261", "5929", "8251", "5370", "6725", "5370", 
"6004", "7022", "7442", "5370", "8679", "6491", "7078", "5370", 
"5370", "5370", "5658", "5370", "9296", "8386", "5370", "5370", 
"5370", "9535", "5370", "7541", "5370", "9621", "5370", "7158", 
"8240", "5370", "5370", "8025", "5370", "5370", "5370", "6989", 
"5370", "7059", "5370", "5370", "5370", "9121", "5608", "5370", 
"5370", "7551", "5370", "5370", "5370", "5370", "9163", "9362", 
"6072", "5370", "5370", "5370", "5370", "5370"), to = c("9356", 
"5605", "8567", "5370", "5636", "5370", "8933", "8483", "5370", 
"7626", "5370", "5370", "5370", "5370", "5370", "9676", "5370", 
"5370", "5370", "5370", "9105", "5370", "9772", "6979", "5370", 
"5370", "7564", "5370", "5370", "5370", "5370", "5370", "8744", 
"5370", "5370", "5370", "5370", "5370", "5370", "5370", "5370", 
"5370", "5370", "7318", "5370", "8433", "5370", "5370", "5370", 
"7122", "5370", "5370", "5370", "8566", "6728", "9689", "5370", 
"8342", "5370", "5370", "5614", "5596", "5953", "5370", "7336", 
"5370", "7247", "5370", "7291", "5370", "5370", "6282", "7236", 
"5370", "8866", "8613", "9247", "5370", "6767", "5370", "9273", 
"7320", "9533", "5370", "5370", "8930", "9343", "5370", "9499", 
"7693", "7830", "5392", "5370", "5370", "5370", "7497", "8516", 
"9023", "7310", "8939"), date = structure(c(12934, 13000, 13038, 
13061, 13099, 13113, 13117, 13179, 13238, 13249, 13268, 13296, 
13299, 13309, 13314, 13391, 13400, 13404, 13409, 13428, 13452, 
13452, 13460, 13482, 13493, 13518, 13526, 13537, 13542, 13544, 
13596, 13616, 13617, 13626, 13633, 13633, 13639, 13642, 13646, 
13656, 13660, 13664, 13667, 13669, 13677, 13686, 13694, 13694, 
13707, 13716, 13725, 13738, 13739, 13746, 13756, 13756, 13756, 
13761, 13769, 13770, 13776, 13786, 13786, 13786, 13791, 13799, 
13806, 13813, 13817, 13817, 13817, 13822, 13829, 13830, 13836, 
13847, 13847, 13847, 13852, 13860, 13866, 13871, 13878, 13878, 
13878, 13882, 13883, 13883, 13887, 13887, 13888, 13889, 13890, 
13891, 13895, 13896, 13896, 13899, 13905, 13909), 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)), row.names = c(NA, -100L), class = "data.frame")

【问题讨论】：

标签： r date datetime split

【解决方案1】：

我们可以使用Map：

data$date_minus_180 <- data$date - 180

result <- Map(function(x, y) data[data$date >=y & data$date <= x,], 
                             data$date, data$date_minus_180)

与lapply 类似，不需要date_minus_180 列。

result <- lapply(data$date, function(x) 
                 data[data$date >= (x-180) & data$date <= x,])

【讨论】：

这些函数在大型数据集上表现不佳。有没有办法通过data.table解决这个问题？
您想将数据拆分为多个数据帧，我不确定data.table 是否会有所帮助。 ://