将基于每日日期的两个数据框合并到一个数据框中以获取面板数据
Combine two data frames based on daily dates in one data frame for panel data
我有以下面板数据作为数据框（称为 returns），其中包含从 2018 年 8 月 1 日到 2019 年 12 月 31 日对三种产品的每日观察以及相应的每日观察 return:
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"C", "C", "C", "C", "C", "C", "C", "C", "C"), Date = structure(c(17744,
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0.009888692, 0.007100221, -0.023176272, 0.00977905, 0.00335199,
0.013128798, -0.007416598, -0.005371949, 0.015471911, 0.001171712,
0.002339313, 0.004385363, 0.005523655, -0.006576608, 0.003155537,
0.00342107, 0.00948446, -0.011469463, 0.001709544, -0.016724006,
0.001623513, -0.004653649, 0.015391783, -0.009787643, 0.012054027,
-0.006147731, 0.008684403, 0.0116083, 0.005970491, -0.0022212,
-0.002334864, -0.003212548, 0.001961426, 0.006185922, 0.000162272,
0.013003945, 0.002452811, -0.024148212, 0.000709007, 0.001253167,
0.004021307, 0.012396408, -0.003165324, 0.004984868, 0.002456479,
-0.012883317, 0.010319898, -0.017422701, 0.022862134, 0.008051317,
0.014611797, 0.011835681, -0.021553118, -0.00157621, 0.007438873,
0.001512426, 0.007889997, -0.017946123, 0.005773688, 0.003917579,
0.001094178, -0.003338379, 0.011119308, NA, 0.001235776, 0.005850898,
-0.011045164, 0.011675648, -0.003999795, 0.012993319, -0.018810979,
0.001272197, -0.013546874, 0.021461647, 0.001627938, -0.005366457,
0.001054519, -0.010861897, -0.009366622, -0.032796993, 0.006259541,
0.025299861, 0.000161512, 0.006546141, 0.013229041, -0.002695348,
-0.003605136, 0.018470807, -0.011431692, 0.004892815, 0.00507788,
0.000574218, 0.003802385, 0.001506533, -0.00176651, 0.004410437,
0.010250161, 0.001075572, 0.007750791, -0.008723877, -0.009938546,
0.000310463, -0.023927217, -0.012158526, 5.31844e-05, -0.001277207,
-0.003894896, 0.006075806, 0.014506387, -0.018819607, 0.01798134,
0.008299236, 0.000467715, -0.003904323, -0.00161825, 0.008324277,
0.011026493, -0.007509551, 0.005971089, 0.001897096, -0.008539586,
0.001393944, 0.008220773, 0.003371478, -0.000765248, -0.001021242,
-0.011768167, 0.000413458, 0.003456017, -0.009000216, 0.004355046,
0.004233138, -0.008640145, -0.002966203, -0.002504698, -0.005553532,
0.000315176, -0.003841402, -0.004120885, 0.000105876, -0.016869028,
0.000484405, -0.004368114, -0.003627606, 5.42402e-05, -0.042774234,
0.001979359, -0.007713287, 0.027128113, -0.006281113, -0.006601423,
0.015151805, 0.01710193, 0.003905407, -0.006190625, -0.003984395,
-0.011993982, 0.000774593, -0.005601634, 0.022655758, -0.009341012,
-0.014761844, 0.016845178, 0.01349093, 0.012030866, 0.004793362,
0.003288779, 0.007281586, -0.002790283, 0.000790493, -0.014637154,
0.005279047, -0.003390472, 0.006394605, -0.003756578, 0.003381555,
-0.023530497, -0.001152738, -0.00501061, -0.00853711, -0.001560063,
0.008937304, 0.010007782, 0.006109221, -0.00152381, 0.004943916,
0.007182038, 0.00937214, -0.002615357, -0.003372684, -0.009565789,
-0.003466208, -0.002317498, -0.003874472, 0.007348708, -0.006313856,
-0.00373274, -0.012039273, -0.001136149, -0.003796512, 0.001520335,
-0.011459255, -0.010038694, -0.016432295, 0.001182732, -0.016286358,
-0.015742, 0.003654826, 0, 0.010483967, -0.004019298, -0.01174816,
-0.004492554, -0.006570866, -0.025455161, -0.010622577, 0.007235614,
-0.014093763, 0.008138833, 0.002130834, 0.017721983, 0.010815413,
-0.004145943, -0.001247142, 0.016502025, -0.003278691, -0.011560822,
-0.018441428, 0.007585371, -0.000419903, -0.012679798, 0.003820849,
-0.005097717, -0.021959978, 0.01082496, -0.004748552, 0.012043156,
-0.00772536, -0.001293382, 0.015835983, -0.007244864, 0.015701574,
0.004564134, -0.009149195, -0.007380107, 0.033237751, 0.004914015,
0.011960275, 0.016161086, 0.003028339, -0.000864304, -0.009120584,
0.00174368, 0.004779501, 0.001299545, 0.015890392, -0.009417878,
0, 0.006003449, 0.010208511, -0.008073126, 0.00595493, 0.011804522,
0.002093365, -0.001622718, -0.015548595, -0.015375329, 0.006261761,
0.007875688, 0.009860392, 0.008143367, 0.006467282, 0.005224042,
0.00838161, -0.009183534, 0.002003607, -0.022263619, -0.002048761,
0.004909993, -0.00122524, -0.001226743, 0.008150004, 0.013303966,
-0.00200441, 0.011966637, -0.00516797, 0.002786071, 0.001985309,
-0.006366914, 0.000798085, 0.00636185, 0.009860068, 0.000784621,
0, 0.001958864, -0.004707737, 0, -0.004509128, -0.006595242,
0.005773212, -0.005773212, -0.018364456, 0.012141661, -0.006680609
)), row.names = c(NA, -743L), class = "data.frame")
现在我有了另一个包含两列的数据框。在一栏中有每日无风险利率，在一栏中显示日期。
数据帧（称为RF）如下：
structure(list(Date = structure(c(17744, 17745, 17746, 17749,
17750, 17751, 17752, 17753, 17756, 17757, 17758, 17759, 17760,
17763, 17764, 17765, 17766, 17767, 17770, 17771, 17772, 17773,
17774, 17777, 17778, 17779, 17780, 17781, 17784, 17785, 17786,
17787, 17788, 17791, 17792, 17793, 17794, 17795, 17798, 17799,
17800, 17801, 17802, 17805, 17806, 17807, 17808, 17809, 17812,
17813, 17814, 17815, 17816, 17819, 17820, 17821, 17822, 17823,
17826, 17827, 17828, 17829, 17830, 17833, 17834, 17835, 17836,
17837, 17840, 17841, 17842, 17843, 17844, 17847, 17848, 17849,
17850, 17851, 17854, 17855, 17856, 17857, 17858, 17861, 17862,
17863, 17864, 17865, 17868, 17869, 17870, 17871, 17872, 17875,
17876, 17877, 17878, 17879, 17882, 17883, 17884, 17885, 17886,
17889, 17890, 17891, 17892, 17893, 17896, 17897, 17898, 17899,
17900, 17903, 17904, 17905, 17906, 17907, 17910, 17911, 17912,
17913, 17914, 17917, 17918, 17919, 17920, 17921, 17924, 17925,
17926, 17927, 17928, 17931, 17932, 17933, 17934, 17935, 17938,
17939, 17940, 17941, 17942, 17945, 17946, 17947, 17948, 17949,
17952, 17953, 17954, 17955, 17956, 17959, 17960, 17961, 17962,
17963, 17966, 17967, 17968, 17969, 17970, 17973, 17974, 17975,
17976, 17977, 17980, 17981, 17982, 17983, 17984, 17987, 17988,
17989, 17990, 17991, 17994, 17995, 17996, 17997, 17998, 18001,
18002, 18003, 18004, 18005, 18008, 18009, 18010, 18011, 18012,
18015, 18016, 18017, 18018, 18019, 18022, 18023, 18024, 18025,
18026, 18029, 18030, 18031, 18032, 18033, 18036, 18037, 18038,
18039, 18040, 18043, 18044, 18045, 18046, 18047, 18050, 18051,
18052, 18053, 18054, 18057, 18058, 18059, 18060, 18061, 18064,
18065, 18066, 18067, 18068, 18071, 18072, 18073, 18074, 18075,
18078, 18079, 18080, 18081, 18082, 18085, 18086, 18087, 18088,
18089, 18092, 18093, 18094, 18095, 18096, 18099, 18100, 18101,
18102, 18103, 18106, 18107, 18108, 18109, 18110, 18113, 18114,
18115, 18116, 18117, 18120, 18121, 18122, 18123, 18124, 18127,
18128, 18129, 18130, 18131, 18134, 18135, 18136, 18137, 18138,
18141, 18142, 18143, 18144, 18145, 18148, 18149, 18150, 18151,
18152, 18155, 18156, 18157, 18158, 18159, 18162, 18163, 18164,
18165, 18166, 18169, 18170, 18171, 18172, 18173, 18176, 18177,
18178, 18179, 18180, 18183, 18184, 18185, 18186, 18187, 18190,
18191, 18192, 18193, 18194, 18197, 18198, 18199, 18200, 18201,
18204, 18205, 18206, 18207, 18208, 18211, 18212, 18213, 18214,
18215, 18218, 18219, 18220, 18221, 18222, 18225, 18226, 18227,
18228, 18229, 18232, 18233, 18234, 18235, 18236, 18239, 18240,
18241, 18242, 18243, 18246, 18247, 18248, 18249, 18250, 18253,
18254, 18255, 18256, 18257, 18260, 18261), class = "Date"), RF = c(0.01,
0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
0.01, 0.01, 0.01, 0.01, 0.01, 0.01)), row.names = c(NA, -370L
), class = "data.frame")
谁能帮我用代码来执行以下操作：
我希望 RF 数据框中的无风险利率 (RF) 位于 Return 数据框中。因此，对于 Return 数据框中的每个日期，我希望从 RF 数据框中获得相应的无风险利率。由于 RF 数据框在 2018-08-01 到 2019-12-31 之间的每日日期比 Return 数据框多，我在如何匹配这两个数据框方面遇到了困难。
您可以通过 Date 使用 left_join 加入两个数据帧。您可以使用以下代码：
library(dplyr)
df <- left_join(returns, rf, by = "Date")
输出 head(df) 如下所示：
Product_Name Date Return RF
1 A 2018-08-01 NA 0.01
2 A 2018-08-02 -0.021053409 0.01
3 A 2018-08-03 0.005850216 0.01
4 A 2018-08-06 -0.005968756 0.01
5 A 2018-08-07 0.012370563 0.01
6 A 2018-08-08 0.000760790 0.01
除了@PaulS 在评论中建议的 right_join 之外，另一种方法是将 RF 数据框中的无风险利率 (RF) 置于 Return 数据框中就是用full_join。 full_join 如果你想检查哪些数据在一个数据框中可用但在另一个数据框中不可用，对于相同的 Date.
来说，full_join 是非常合适的选项
# Your data frames are Return and RF.
glimpse(Return)
# Rows: 743
# Columns: 3
# $ Product_Name <chr> "A", "A", "A", "A", "A", "A", "A", "A", "A", "~
# $ Date <date> 2018-08-01, 2018-08-02, 2018-08-03, 2018-08-0~
# $ Return <dbl> NA, -0.021053409, 0.005850216, -0.005968756, 0~
glimpse(RF)
# Rows: 370
# Columns: 2
# $ Date <date> 2018-08-01, 2018-08-02, 2018-08-03, 2018-08-06, 2018-~
# $ RF <dbl> 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, ~
#Join Return with RF
library(dplyr)
joined <- Return %>% full_join(RF, by = "Date")
head(joined)
# Product_Name Date Return RF
# 1 A 2018-08-01 NA 0.01
# 2 A 2018-08-02 -0.021053409 0.01
# 3 A 2018-08-03 0.005850216 0.01
# 4 A 2018-08-06 -0.005968756 0.01
# 5 A 2018-08-07 0.012370563 0.01
# 6 A 2018-08-08 0.000760790 0.01
tail(joined)
# Product_Name Date Return RF
# 757 <NA> 2019-06-10 NA 0.01
# 758 <NA> 2019-08-15 NA 0.01
# 759 <NA> 2019-11-01 NA 0.01
# 760 <NA> 2019-12-24 NA 0.01
# 761 <NA> 2019-12-25 NA 0.01
# 762 <NA> 2019-12-26 NA 0.01
如果要获取所有 Return 数据不可用的行，可以使用 joined %>% filter(is.na(Return))，这将导致：
# Product_Name Date Return RF
# 1 A 2018-08-01 NA 0.01
# 2 B 2018-08-01 NA 0.01
# 3 C 2018-08-01 NA 0.01
# 4 <NA> 2018-08-15 NA 0.01
# 5 <NA> 2018-11-01 NA 0.01
# 6 <NA> 2018-11-29 NA 0.01
# 7 <NA> 2018-11-30 NA 0.01
# 8 <NA> 2018-12-24 NA 0.01
# 9 <NA> 2018-12-25 NA 0.01
# 10 <NA> 2018-12-26 NA 0.01
# 11 <NA> 2019-01-01 NA 0.01
# 12 <NA> 2019-04-19 NA 0.01
# 13 <NA> 2019-04-22 NA 0.01
# 14 <NA> 2019-05-01 NA 0.01
# 15 <NA> 2019-05-09 NA 0.01
# 16 <NA> 2019-05-30 NA 0.01
# 17 <NA> 2019-06-10 NA 0.01
# 18 <NA> 2019-08-15 NA 0.01
我有以下面板数据作为数据框（称为 returns），其中包含从 2018 年 8 月 1 日到 2019 年 12 月 31 日对三种产品的每日观察以及相应的每日观察 return:
structure(list(Product_Name = c("A", "A", "A", "A", "A", "A",
"A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A",
"A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A",
"A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A",
"A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A",
"A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A",
"A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A",
"A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A",
"A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A",
"A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A",
"A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A",
"A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A",
"A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A",
"A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A",
"A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A",
"A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A",
"A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A",
"A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A",
"A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A",
"A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A",
"A", "A", "A", "A", "A", "A", "B", "B", "B", "B", "B", "B", "B",
"B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B",
"B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B",
"B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B",
"B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B",
"B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B",
"B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B",
"B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B",
"B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B",
"B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B",
"B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B",
"B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B",
"B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B",
"B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B",
"B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B",
"B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B",
"B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B",
"B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B",
"B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B",
"B", "B", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C",
"C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C",
"C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C",
"C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C",
"C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C",
"C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C",
"C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C",
"C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C",
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"C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C",
"C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C",
"C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C",
"C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C",
"C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C",
"C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C",
"C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C",
"C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C",
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"C", "C", "C", "C", "C", "C", "C", "C", "C"), Date = structure(c(17744,
17745, 17746, 17749, 17750, 17751, 17752, 17753, 17756, 17757,
17759, 17760, 17774, 17777, 17778, 17779, 17780, 17781, 17784,
17785, 17786, 17787, 17788, 17791, 17792, 17793, 17794, 17795,
17798, 17799, 17800, 17801, 17802, 17805, 17806, 17807, 17808,
17809, 17812, 17813, 17814, 17815, 17816, 17819, 17820, 17821,
17822, 17823, 17826, 17827, 17828, 17829, 17830, 17833, 17834,
17835, 17837, 17840, 17841, 17842, 17843, 17844, 17847, 17848,
17849, 17850, 17851, 17854, 17855, 17856, 17857, 17858, 17861,
17862, 17863, 17872, 17875, 17876, 17877, 17878, 17879, 17882,
17883, 17884, 17885, 17886, 17892, 17893, 17896, 17898, 17899,
17900, 17903, 17904, 17905, 17906, 17907, 17910, 17911, 17912,
17913, 17914, 17917, 17918, 17919, 17920, 17921, 17925, 17926,
17927, 17928, 17931, 17932, 17933, 17934, 17935, 17938, 17939,
17940, 17941, 17942, 17945, 17946, 17947, 17948, 17949, 17952,
17953, 17963, 17966, 17967, 17968, 17969, 17970, 17973, 17974,
17975, 17976, 17977, 17980, 17981, 17982, 17983, 17984, 17987,
17988, 17989, 17990, 17991, 17994, 17995, 17996, 17997, 17998,
18001, 18002, 18003, 18004, 18009, 18010, 18011, 18012, 18015,
18016, 18018, 18019, 18022, 18023, 18024, 18026, 18029, 18030,
18031, 18032, 18033, 18036, 18037, 18038, 18039, 18040, 18043,
18044, 18045, 18086, 18087, 18088, 18089, 18092, 18093, 18094,
18095, 18096, 18099, 18100, 18101, 18102, 18103, 18106, 18107,
18108, 18109, 18110, 18113, 18114, 18115, 18116, 18117, 18120,
18121, 18122, 18124, 18127, 18128, 18129, 18130, 18131, 18134,
18135, 18136, 18137, 18138, 18141, 18142, 18143, 18144, 18145,
18148, 18149, 18150, 18151, 18152, 18155, 18156, 18157, 18158,
18159, 18162, 18163, 18228, 18229, 18232, 18233, 18234, 18235,
18236, 18239, 18240, 18241, 18242, 18243, 18246, 18247, 18248,
18249, 18250, 18253, 18257, 18260, 18261, 17744, 17745, 17746,
17749, 17750, 17751, 17752, 17753, 17756, 17757, 17759, 17760,
17763, 17764, 17765, 17766, 17767, 17770, 17771, 17772, 17773,
17774, 17777, 17778, 17779, 17780, 17781, 17784, 17785, 17786,
17787, 17788, 17791, 17792, 17793, 17794, 17795, 17798, 17799,
17800, 17801, 17802, 17805, 17806, 17807, 17808, 17809, 17812,
17813, 17814, 17815, 17816, 17819, 17820, 17821, 17822, 17869,
17870, 17871, 17872, 17875, 17876, 17877, 17878, 17879, 17882,
17883, 17884, 17885, 17886, 17892, 17893, 17896, 17898, 17899,
17900, 17903, 17904, 17905, 17906, 17907, 17910, 17911, 17912,
17913, 17914, 17917, 17918, 17919, 17920, 17921, 17924, 17925,
17926, 17927, 17928, 17931, 17932, 17933, 17934, 17935, 17938,
17939, 17940, 17941, 17942, 17945, 17946, 17947, 17948, 17949,
17952, 17953, 17954, 17955, 17956, 17959, 17960, 17961, 17962,
17963, 17966, 17967, 17968, 17969, 17970, 17973, 17974, 17975,
17976, 17977, 17980, 17981, 17982, 17983, 17984, 17987, 18107,
18108, 18109, 18110, 18113, 18114, 18115, 18116, 18117, 18120,
18121, 18122, 18124, 18127, 18128, 18129, 18130, 18131, 18134,
18135, 18136, 18137, 18138, 18141, 18142, 18143, 18144, 18145,
18148, 18149, 18150, 18151, 18152, 18155, 18156, 18157, 18158,
18159, 18162, 18163, 18164, 18165, 18166, 18169, 18170, 18171,
18172, 18173, 18176, 18177, 18178, 18179, 18180, 18183, 18184,
18185, 18186, 18187, 18190, 18191, 18192, 18193, 18194, 18197,
18198, 18199, 18200, 18204, 18205, 18206, 18207, 18208, 18211,
18212, 18213, 18214, 18215, 18218, 18219, 18220, 18221, 18222,
18225, 18226, 18227, 18228, 18229, 18232, 18233, 18234, 18235,
18236, 18239, 18240, 18241, 18242, 18243, 18246, 18247, 18248,
18249, 18250, 18253, 18257, 18260, 18261, 17744, 17745, 17774,
17777, 17778, 17779, 17780, 17781, 17784, 17785, 17786, 17787,
17788, 17791, 17792, 17793, 17794, 17795, 17798, 17799, 17800,
17801, 17802, 17805, 17806, 17807, 17808, 17809, 17812, 17813,
17814, 17815, 17816, 17819, 17820, 17821, 17822, 17823, 17826,
17827, 17868, 17869, 17870, 17871, 17872, 17875, 17876, 17877,
17878, 17879, 17882, 17883, 17884, 17885, 17886, 17892, 17893,
17896, 17898, 17899, 17900, 17903, 17904, 17905, 17906, 17907,
17910, 17911, 17912, 17913, 17914, 17917, 17918, 17919, 17920,
17921, 17924, 17925, 17926, 17927, 17928, 17931, 17932, 17933,
17934, 17935, 17938, 17939, 17940, 17941, 17942, 17945, 17946,
17947, 17948, 17949, 17952, 17953, 17954, 17955, 17956, 17959,
17960, 17961, 17962, 17963, 17966, 17968, 17969, 17970, 17973,
17974, 17975, 17976, 17977, 17980, 17981, 17982, 17983, 17984,
17987, 17988, 17989, 17990, 17991, 17994, 17995, 17997, 17998,
18001, 18002, 18003, 18024, 18026, 18029, 18030, 18031, 18032,
18033, 18036, 18037, 18038, 18039, 18040, 18043, 18044, 18045,
18047, 18050, 18051, 18052, 18053, 18054, 18058, 18059, 18060,
18061, 18064, 18065, 18066, 18067, 18068, 18071, 18072, 18073,
18074, 18075, 18078, 18079, 18080, 18081, 18082, 18085, 18086,
18087, 18088, 18089, 18092, 18093, 18116, 18117, 18120, 18121,
18122, 18124, 18127, 18128, 18129, 18130, 18131, 18134, 18135,
18136, 18137, 18138, 18141, 18142, 18143, 18144, 18145, 18148,
18179, 18180, 18183, 18184, 18185, 18186, 18187, 18190, 18191,
18192, 18193, 18194, 18197, 18198, 18199, 18200, 18204, 18205,
18206, 18207, 18208, 18211, 18212, 18213, 18214, 18215, 18218,
18219, 18220, 18221, 18222, 18225, 18226, 18248, 18249, 18250,
18253, 18257, 18260, 18261), class = "Date"), Return = c(NA,
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-0.004145943, -0.001247142, 0.016502025, -0.003278691, -0.011560822,
-0.018441428, 0.007585371, -0.000419903, -0.012679798, 0.003820849,
-0.005097717, -0.021959978, 0.01082496, -0.004748552, 0.012043156,
-0.00772536, -0.001293382, 0.015835983, -0.007244864, 0.015701574,
0.004564134, -0.009149195, -0.007380107, 0.033237751, 0.004914015,
0.011960275, 0.016161086, 0.003028339, -0.000864304, -0.009120584,
0.00174368, 0.004779501, 0.001299545, 0.015890392, -0.009417878,
0, 0.006003449, 0.010208511, -0.008073126, 0.00595493, 0.011804522,
0.002093365, -0.001622718, -0.015548595, -0.015375329, 0.006261761,
0.007875688, 0.009860392, 0.008143367, 0.006467282, 0.005224042,
0.00838161, -0.009183534, 0.002003607, -0.022263619, -0.002048761,
0.004909993, -0.00122524, -0.001226743, 0.008150004, 0.013303966,
-0.00200441, 0.011966637, -0.00516797, 0.002786071, 0.001985309,
-0.006366914, 0.000798085, 0.00636185, 0.009860068, 0.000784621,
0, 0.001958864, -0.004707737, 0, -0.004509128, -0.006595242,
0.005773212, -0.005773212, -0.018364456, 0.012141661, -0.006680609
)), row.names = c(NA, -743L), class = "data.frame")
现在我有了另一个包含两列的数据框。在一栏中有每日无风险利率，在一栏中显示日期。 数据帧（称为RF）如下：
structure(list(Date = structure(c(17744, 17745, 17746, 17749,
17750, 17751, 17752, 17753, 17756, 17757, 17758, 17759, 17760,
17763, 17764, 17765, 17766, 17767, 17770, 17771, 17772, 17773,
17774, 17777, 17778, 17779, 17780, 17781, 17784, 17785, 17786,
17787, 17788, 17791, 17792, 17793, 17794, 17795, 17798, 17799,
17800, 17801, 17802, 17805, 17806, 17807, 17808, 17809, 17812,
17813, 17814, 17815, 17816, 17819, 17820, 17821, 17822, 17823,
17826, 17827, 17828, 17829, 17830, 17833, 17834, 17835, 17836,
17837, 17840, 17841, 17842, 17843, 17844, 17847, 17848, 17849,
17850, 17851, 17854, 17855, 17856, 17857, 17858, 17861, 17862,
17863, 17864, 17865, 17868, 17869, 17870, 17871, 17872, 17875,
17876, 17877, 17878, 17879, 17882, 17883, 17884, 17885, 17886,
17889, 17890, 17891, 17892, 17893, 17896, 17897, 17898, 17899,
17900, 17903, 17904, 17905, 17906, 17907, 17910, 17911, 17912,
17913, 17914, 17917, 17918, 17919, 17920, 17921, 17924, 17925,
17926, 17927, 17928, 17931, 17932, 17933, 17934, 17935, 17938,
17939, 17940, 17941, 17942, 17945, 17946, 17947, 17948, 17949,
17952, 17953, 17954, 17955, 17956, 17959, 17960, 17961, 17962,
17963, 17966, 17967, 17968, 17969, 17970, 17973, 17974, 17975,
17976, 17977, 17980, 17981, 17982, 17983, 17984, 17987, 17988,
17989, 17990, 17991, 17994, 17995, 17996, 17997, 17998, 18001,
18002, 18003, 18004, 18005, 18008, 18009, 18010, 18011, 18012,
18015, 18016, 18017, 18018, 18019, 18022, 18023, 18024, 18025,
18026, 18029, 18030, 18031, 18032, 18033, 18036, 18037, 18038,
18039, 18040, 18043, 18044, 18045, 18046, 18047, 18050, 18051,
18052, 18053, 18054, 18057, 18058, 18059, 18060, 18061, 18064,
18065, 18066, 18067, 18068, 18071, 18072, 18073, 18074, 18075,
18078, 18079, 18080, 18081, 18082, 18085, 18086, 18087, 18088,
18089, 18092, 18093, 18094, 18095, 18096, 18099, 18100, 18101,
18102, 18103, 18106, 18107, 18108, 18109, 18110, 18113, 18114,
18115, 18116, 18117, 18120, 18121, 18122, 18123, 18124, 18127,
18128, 18129, 18130, 18131, 18134, 18135, 18136, 18137, 18138,
18141, 18142, 18143, 18144, 18145, 18148, 18149, 18150, 18151,
18152, 18155, 18156, 18157, 18158, 18159, 18162, 18163, 18164,
18165, 18166, 18169, 18170, 18171, 18172, 18173, 18176, 18177,
18178, 18179, 18180, 18183, 18184, 18185, 18186, 18187, 18190,
18191, 18192, 18193, 18194, 18197, 18198, 18199, 18200, 18201,
18204, 18205, 18206, 18207, 18208, 18211, 18212, 18213, 18214,
18215, 18218, 18219, 18220, 18221, 18222, 18225, 18226, 18227,
18228, 18229, 18232, 18233, 18234, 18235, 18236, 18239, 18240,
18241, 18242, 18243, 18246, 18247, 18248, 18249, 18250, 18253,
18254, 18255, 18256, 18257, 18260, 18261), class = "Date"), RF = c(0.01,
0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
0.01, 0.01, 0.01, 0.01, 0.01, 0.01)), row.names = c(NA, -370L
), class = "data.frame")
谁能帮我用代码来执行以下操作：
我希望 RF 数据框中的无风险利率 (RF) 位于 Return 数据框中。因此，对于 Return 数据框中的每个日期，我希望从 RF 数据框中获得相应的无风险利率。由于 RF 数据框在 2018-08-01 到 2019-12-31 之间的每日日期比 Return 数据框多，我在如何匹配这两个数据框方面遇到了困难。
您可以通过 Date 使用 left_join 加入两个数据帧。您可以使用以下代码：
library(dplyr)
df <- left_join(returns, rf, by = "Date")
输出 head(df) 如下所示：
Product_Name Date Return RF
1 A 2018-08-01 NA 0.01
2 A 2018-08-02 -0.021053409 0.01
3 A 2018-08-03 0.005850216 0.01
4 A 2018-08-06 -0.005968756 0.01
5 A 2018-08-07 0.012370563 0.01
6 A 2018-08-08 0.000760790 0.01
除了@PaulS 在评论中建议的 right_join 之外，另一种方法是将 RF 数据框中的无风险利率 (RF) 置于 Return 数据框中就是用full_join。 full_join 如果你想检查哪些数据在一个数据框中可用但在另一个数据框中不可用，对于相同的 Date.
full_join 是非常合适的选项
# Your data frames are Return and RF.
glimpse(Return)
# Rows: 743
# Columns: 3
# $ Product_Name <chr> "A", "A", "A", "A", "A", "A", "A", "A", "A", "~
# $ Date <date> 2018-08-01, 2018-08-02, 2018-08-03, 2018-08-0~
# $ Return <dbl> NA, -0.021053409, 0.005850216, -0.005968756, 0~
glimpse(RF)
# Rows: 370
# Columns: 2
# $ Date <date> 2018-08-01, 2018-08-02, 2018-08-03, 2018-08-06, 2018-~
# $ RF <dbl> 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, ~
#Join Return with RF
library(dplyr)
joined <- Return %>% full_join(RF, by = "Date")
head(joined)
# Product_Name Date Return RF
# 1 A 2018-08-01 NA 0.01
# 2 A 2018-08-02 -0.021053409 0.01
# 3 A 2018-08-03 0.005850216 0.01
# 4 A 2018-08-06 -0.005968756 0.01
# 5 A 2018-08-07 0.012370563 0.01
# 6 A 2018-08-08 0.000760790 0.01
tail(joined)
# Product_Name Date Return RF
# 757 <NA> 2019-06-10 NA 0.01
# 758 <NA> 2019-08-15 NA 0.01
# 759 <NA> 2019-11-01 NA 0.01
# 760 <NA> 2019-12-24 NA 0.01
# 761 <NA> 2019-12-25 NA 0.01
# 762 <NA> 2019-12-26 NA 0.01
如果要获取所有 Return 数据不可用的行，可以使用 joined %>% filter(is.na(Return))，这将导致：
# Product_Name Date Return RF
# 1 A 2018-08-01 NA 0.01
# 2 B 2018-08-01 NA 0.01
# 3 C 2018-08-01 NA 0.01
# 4 <NA> 2018-08-15 NA 0.01
# 5 <NA> 2018-11-01 NA 0.01
# 6 <NA> 2018-11-29 NA 0.01
# 7 <NA> 2018-11-30 NA 0.01
# 8 <NA> 2018-12-24 NA 0.01
# 9 <NA> 2018-12-25 NA 0.01
# 10 <NA> 2018-12-26 NA 0.01
# 11 <NA> 2019-01-01 NA 0.01
# 12 <NA> 2019-04-19 NA 0.01
# 13 <NA> 2019-04-22 NA 0.01
# 14 <NA> 2019-05-01 NA 0.01
# 15 <NA> 2019-05-09 NA 0.01
# 16 <NA> 2019-05-30 NA 0.01
# 17 <NA> 2019-06-10 NA 0.01
# 18 <NA> 2019-08-15 NA 0.01