我有以下数据，但我以某种方式“随机”具有我想估算的NA值。
例如，在这种情况下，我认为仅取NA值之后的结果与之前的结果之间的平均值即可。
date eur_usd
2008-01-18 - 1.4616
2008-01-21 - NA
2008-01-22 - 1.4609
将NA的值插入为(1.4616 + 14609) / 2 = 1.46125
如果您对估算FX数据有更好的建议，那也将是很好的选择。
数据：
df <- structure(list(WEEK = structure(c(1199660400, 1199746800, 1199833200,
1199919600, 1200006000, 1200265200, 1200351600, 1200438000, 1200524400,
1200610800, 1200870000, 1200956400, 1201042800, 1201129200, 1201215600,
1201474800, 1201561200, 1201647600, 1201734000, 1201820400, 1202079600,
1202166000, 1202252400, 1202338800, 1202425200, 1202684400, 1202770800,
1202857200, 1202943600, 1203030000, 1203289200, 1203375600, 1203462000,
1203548400, 1203634800, 1203894000, 1203980400, 1204066800, 1204153200,
1204239600, 1204498800, 1204585200, 1204671600, 1204758000, 1204844400,
1205103600, 1205190000, 1205276400, 1205362800, 1205449200, 1205708400,
1205794800, 1205881200, 1205967600, 1206054000, 1206313200, 1206399600,
1206486000, 1206572400, 1206658800, 1206914400, 1207000800, 1207087200,
1207173600, 1207260000, 1207519200, 1207605600, 1207692000, 1207778400,
1207864800, 1208124000, 1208210400, 1208296800, 1208383200, 1208469600,
1208728800, 1208815200, 1208901600, 1208988000, 1209074400, 1209333600,
1209420000, 1209506400, 1209592800, 1209679200, 1209938400, 1210024800,
1210111200, 1210197600, 1210284000, 1210543200, 1210629600, 1210716000,
1210802400, 1210888800, 1211148000, 1211234400, 1211320800, 1211407200,
1211493600), class = c("POSIXct", "POSIXt"), tzone = ""), USD_EUR = c(1.4684,
1.4717, 1.4663, 1.474, 1.4787, 1.4877, 1.4842, 1.4643, 1.469,
1.4616, NA, 1.4609, 1.4574, 1.4737, 1.4673, 1.4789, 1.4767, 1.4782,
1.4841, 1.4851, 1.4832, 1.4638, 1.4647, 1.4495, 1.4505, 1.4506,
1.4592, 1.4569, 1.4635, 1.4674, NA, 1.4742, 1.4663, 1.4813, 1.4825,
1.4822, 1.4889, 1.5132, 1.5172, 1.5187, 1.5195, 1.521, 1.5282,
1.5356, 1.5333, 1.5369, 1.5329, 1.5495, 1.5584, 1.5604, 1.5765,
1.5784, 1.5642, 1.5417, 1.5447, 1.5394, 1.5595, 1.5761, 1.5798,
1.5759, 1.5805, 1.5615, 1.5618, 1.5667, 1.5735, 1.5713, 1.5705,
1.5804, 1.5744, 1.5796, 1.5827, 1.5801, 1.5978, 1.5924, 1.5742,
1.5881, 1.601, 1.5898, 1.5668, 1.5634, 1.5648, 1.5604, 1.5568,
1.5458, 1.5431, 1.5491, 1.5548, 1.537, 1.5409, 1.5447, 1.5536,
1.5509, 1.5467, 1.5492, 1.5591, 1.5516, 1.5667, 1.5772, 1.5703,
1.5784)), row.names = c(NA, 100L), class = "data.frame")
答案 0 :(得分：2)
使用dplyr软件包
library(dplyr)
df$USD_EUR <- ifelse(is.na(df$USD_EUR), (lag(df$USD_EUR)+ lead(df$USD_EUR))/2,df$USD_EUR)
答案 1 :(得分：1)
这是一种方法
df <- data.frame(df)
n <- nrow(df)
df$nextp <- c(df$USD_EUR[2:n],NA)
df$previous <- c(NA, df$USD_EUR[1:n-1])
df$meanprevnext <- apply(df, 1 , function(x) mean(c(as.numeric(x["previous"]), as.numeric(x["nextp"]))))
df$result <- ifelse(is.na(df$USD_EUR), df$meanprevnext, df$USD_EUR)
WEEK USD_EUR previous nextp meanprevnext result
1 2008-01-07 1.4684 NA 1.4717 NA 1.46840
2 2008-01-08 1.4717 1.4684 1.4663 1.46735 1.47170
3 2008-01-09 1.4663 1.4717 1.4740 1.47285 1.46630
4 2008-01-10 1.4740 1.4663 1.4787 1.47250 1.47400
5 2008-01-11 1.4787 1.4740 1.4877 1.48085 1.47870
6 2008-01-14 1.4877 1.4787 1.4842 1.48145 1.48770
7 2008-01-15 1.4842 1.4877 1.4643 1.47600 1.48420
8 2008-01-16 1.4643 1.4842 1.4690 1.47660 1.46430
9 2008-01-17 1.4690 1.4643 1.4616 1.46295 1.46900
10 2008-01-18 1.4616 1.4690 NA NA 1.46160
11 2008-01-21 NA 1.4616 1.4609 1.46125 1.46125
12 2008-01-22 1.4609 NA 1.4574 NA 1.46090
13 2008-01-23 1.4574 1.4609 1.4737 1.46730 1.45740
14 2008-01-24 1.4737 1.4574 1.4673 1.46235 1.47370
15 2008-01-25 1.4673 1.4737 1.4789 1.47630 1.46730
16 2008-01-28 1.4789 1.4673 1.4767 1.47200 1.47890
17 2008-01-29 1.4767 1.4789 1.4782 1.47855 1.47670
18 2008-01-30 1.4782 1.4767 1.4841 1.48040 1.47820
19 2008-01-31 1.4841 1.4782 1.4851 1.48165 1.48410
20 2008-02-01 1.4851 1.4841 1.4832 1.48365 1.48510
21 2008-02-04 1.4832 1.4851 1.4638 1.47445 1.48320
22 2008-02-05 1.4638 1.4832 1.4647 1.47395 1.46380
23 2008-02-06 1.4647 1.4638 1.4495 1.45665 1.46470
24 2008-02-07 1.4495 1.4647 1.4505 1.45760 1.44950
25 2008-02-08 1.4505 1.4495 1.4506 1.45005 1.45050
26 2008-02-11 1.4506 1.4505 1.4592 1.45485 1.45060
27 2008-02-12 1.4592 1.4506 1.4569 1.45375 1.45920
28 2008-02-13 1.4569 1.4592 1.4635 1.46135 1.45690
29 2008-02-14 1.4635 1.4569 1.4674 1.46215 1.46350
30 2008-02-15 1.4674 1.4635 NA NA 1.46740
31 2008-02-18 NA 1.4674 1.4742 1.47080 1.47080
32 2008-02-19 1.4742 NA 1.4663 NA 1.47420
33 2008-02-20 1.4663 1.4742 1.4813 1.47775 1.46630
34 2008-02-21 1.4813 1.4663 1.4825 1.47440 1.48130
35 2008-02-22 1.4825 1.4813 1.4822 1.48175 1.48250
36 2008-02-25 1.4822 1.4825 1.4889 1.48570 1.48220
37 2008-02-26 1.4889 1.4822 1.5132 1.49770 1.48890
38 2008-02-27 1.5132 1.4889 1.5172 1.50305 1.51320
39 2008-02-28 1.5172 1.5132 1.5187 1.51595 1.51720
40 2008-02-29 1.5187 1.5172 1.5195 1.51835 1.51870
41 2008-03-03 1.5195 1.5187 1.5210 1.51985 1.51950
42 2008-03-04 1.5210 1.5195 1.5282 1.52385 1.52100
43 2008-03-05 1.5282 1.5210 1.5356 1.52830 1.52820
44 2008-03-06 1.5356 1.5282 1.5333 1.53075 1.53560
45 2008-03-07 1.5333 1.5356 1.5369 1.53625 1.53330
46 2008-03-10 1.5369 1.5333 1.5329 1.53310 1.53690
47 2008-03-11 1.5329 1.5369 1.5495 1.54320 1.53290
48 2008-03-12 1.5495 1.5329 1.5584 1.54565 1.54950
49 2008-03-13 1.5584 1.5495 1.5604 1.55495 1.55840
50 2008-03-14 1.5604 1.5584 1.5765 1.56745 1.56040
51 2008-03-17 1.5765 1.5604 1.5784 1.56940 1.57650
52 2008-03-18 1.5784 1.5765 1.5642 1.57035 1.57840
53 2008-03-19 1.5642 1.5784 1.5417 1.56005 1.56420
54 2008-03-20 1.5417 1.5642 1.5447 1.55445 1.54170
55 2008-03-21 1.5447 1.5417 1.5394 1.54055 1.54470
56 2008-03-24 1.5394 1.5447 1.5595 1.55210 1.53940
57 2008-03-25 1.5595 1.5394 1.5761 1.55775 1.55950
58 2008-03-26 1.5761 1.5595 1.5798 1.56965 1.57610
59 2008-03-27 1.5798 1.5761 1.5759 1.57600 1.57980
60 2008-03-28 1.5759 1.5798 1.5805 1.58015 1.57590
61 2008-03-31 1.5805 1.5759 1.5615 1.56870 1.58050
62 2008-04-01 1.5615 1.5805 1.5618 1.57115 1.56150
63 2008-04-02 1.5618 1.5615 1.5667 1.56410 1.56180
64 2008-04-03 1.5667 1.5618 1.5735 1.56765 1.56670
65 2008-04-04 1.5735 1.5667 1.5713 1.56900 1.57350
66 2008-04-07 1.5713 1.5735 1.5705 1.57200 1.57130
67 2008-04-08 1.5705 1.5713 1.5804 1.57585 1.57050
68 2008-04-09 1.5804 1.5705 1.5744 1.57245 1.58040
69 2008-04-10 1.5744 1.5804 1.5796 1.58000 1.57440
70 2008-04-11 1.5796 1.5744 1.5827 1.57855 1.57960
71 2008-04-14 1.5827 1.5796 1.5801 1.57985 1.58270
72 2008-04-15 1.5801 1.5827 1.5978 1.59025 1.58010
73 2008-04-16 1.5978 1.5801 1.5924 1.58625 1.59780
74 2008-04-17 1.5924 1.5978 1.5742 1.58600 1.59240
75 2008-04-18 1.5742 1.5924 1.5881 1.59025 1.57420
76 2008-04-21 1.5881 1.5742 1.6010 1.58760 1.58810
77 2008-04-22 1.6010 1.5881 1.5898 1.58895 1.60100
78 2008-04-23 1.5898 1.6010 1.5668 1.58390 1.58980
79 2008-04-24 1.5668 1.5898 1.5634 1.57660 1.56680
80 2008-04-25 1.5634 1.5668 1.5648 1.56580 1.56340
81 2008-04-28 1.5648 1.5634 1.5604 1.56190 1.56480
82 2008-04-29 1.5604 1.5648 1.5568 1.56080 1.56040
83 2008-04-30 1.5568 1.5604 1.5458 1.55310 1.55680
84 2008-05-01 1.5458 1.5568 1.5431 1.54995 1.54580
85 2008-05-02 1.5431 1.5458 1.5491 1.54745 1.54310
86 2008-05-05 1.5491 1.5431 1.5548 1.54895 1.54910
87 2008-05-06 1.5548 1.5491 1.5370 1.54305 1.55480
88 2008-05-07 1.5370 1.5548 1.5409 1.54785 1.53700
89 2008-05-08 1.5409 1.5370 1.5447 1.54085 1.54090
90 2008-05-09 1.5447 1.5409 1.5536 1.54725 1.54470
91 2008-05-12 1.5536 1.5447 1.5509 1.54780 1.55360
92 2008-05-13 1.5509 1.5536 1.5467 1.55015 1.55090
93 2008-05-14 1.5467 1.5509 1.5492 1.55005 1.54670
94 2008-05-15 1.5492 1.5467 1.5591 1.55290 1.54920
95 2008-05-16 1.5591 1.5492 1.5516 1.55040 1.55910
96 2008-05-19 1.5516 1.5591 1.5667 1.56290 1.55160
97 2008-05-20 1.5667 1.5516 1.5772 1.56440 1.56670
98 2008-05-21 1.5772 1.5667 1.5703 1.56850 1.57720
99 2008-05-22 1.5703 1.5772 1.5784 1.57780 1.57030
100 2008-05-23 1.5784 1.5703 NA NA 1.57840