我正在尝试使用zoo:na.approx 按组插入一些值。数据帧需要以非 NA 值开始和结束。有没有办法删除它们但保留“内部”NA？我不能使用基于其他变量的过滤器，因为插值是按组执行的，并且缺失值因组而异。
这是我的代码示例：
library(zoo)
library(lubridate)
library(dplyr)
set.seed(471)
db <- rep(seq(ymd("2021-12-20"), ymd("2021-12-30"), by = "days"),4) %>% merge(seq(1,4,1)) %>%
mutate(z=rnorm(176))
db$z[db$z<0] <- NA
db %>% group_by(y) %>% mutate(aa=na.approx(z))
【问题讨论】：
将rule=2 参数添加到na.approx 以在每个组的开头和结尾推断NAs，以便它们不是NA
db %>%
group_by(y) %>%
mutate(aa=na.approx(z, rule = 2)) %>%
ungroup
或使用na.trim 删除每组开头和结尾的NA。
db %>%
group_by(y) %>%
group_modify(~ na.trim(.)) %>%
mutate(aa = na.approx(z)) %>%
ungroup
【讨论】：
我将重点关注每组的前/后 3 行：
db %>%
group_by(y) %>%
slice(c(1:3, n() - 2:0)) %>%
print(n=99)
# # A tibble: 24 x 3
# # Groups: y [4]
# x y z
# <date> <dbl> <dbl>
# 1 2021-12-20 1 NA
# 2 2021-12-21 1 0.605
# 3 2021-12-22 1 0.185
# 4 2021-12-28 1 0.805
# 5 2021-12-29 1 NA
# 6 2021-12-30 1 NA
# 7 2021-12-20 2 NA
# 8 2021-12-21 2 0.402
# 9 2021-12-22 2 NA
# 10 2021-12-28 2 NA
# 11 2021-12-29 2 0.163
# 12 2021-12-30 2 0.796
# 13 2021-12-20 3 1.00
# 14 2021-12-21 3 NA
# 15 2021-12-22 3 0.733
# 16 2021-12-28 3 0.00858
# 17 2021-12-29 3 NA
# 18 2021-12-30 3 0.179
# 19 2021-12-20 4 NA
# 20 2021-12-21 4 0.298
# 21 2021-12-22 4 NA
# 22 2021-12-28 4 0.355
# 23 2021-12-29 4 2.42
# 24 2021-12-30 4 NA
第 1 组和第 4 组在 NA 开始/结束，第 2 组在 NA 开始。
试试这个：
db %>%
group_by(y) %>%
filter(cumany(!is.na(z)) & rev(cumany(rev(!is.na(z))))) %>%
slice(c(1:3, n() - 2:0)) %>%
print(n=99)
# # A tibble: 24 x 3
# # Groups: y [4]
# x y z
# <date> <dbl> <dbl>
# 1 2021-12-21 1 0.605
# 2 2021-12-22 1 0.185
# 3 2021-12-23 1 NA
# 4 2021-12-26 1 0.871
# 5 2021-12-27 1 NA
# 6 2021-12-28 1 0.805
# 7 2021-12-21 2 0.402
# 8 2021-12-22 2 NA
# 9 2021-12-23 2 0.364
# 10 2021-12-28 2 NA
# 11 2021-12-29 2 0.163
# 12 2021-12-30 2 0.796
# 13 2021-12-20 3 1.00
# 14 2021-12-21 3 NA
# 15 2021-12-22 3 0.733
# 16 2021-12-28 3 0.00858
# 17 2021-12-29 3 NA
# 18 2021-12-30 3 0.179
# 19 2021-12-21 4 0.298
# 20 2021-12-22 4 NA
# 21 2021-12-23 4 0.660
# 22 2021-12-27 4 NA
# 23 2021-12-28 4 0.355
# 24 2021-12-29 4 2.42
【讨论】：
您可以先进行近似，然后删除NAs：
db %>%
group_by(y) %>%
mutate(output = zoo::na.approx(z, na.rm = FALSE))
输出：
# A tibble: 176 x 4
# Groups: y [4]
x y z test
<date> <dbl> <dbl> <dbl>
1 2021-12-20 1 NA NA
2 2021-12-21 1 0.605 0.605
3 2021-12-22 1 0.185 0.185
4 2021-12-23 1 NA 0.455
5 2021-12-24 1 0.725 0.725
6 2021-12-25 1 1.51 1.51
7 2021-12-26 1 NA 1.41
8 2021-12-27 1 1.31 1.31
9 2021-12-28 1 1.07 1.07
10 2021-12-29 1 1.14 1.14
您可以部分地看到，na.approx 中的 na.rm = FALSE 参数保留每个组的顶部和底部 NA，同时计算组内的近似值。然后您可以过滤数据以删除新创建的列中的NA：
db %>%
group_by(y) %>%
mutate(output = zoo::na.approx(z, na.rm = F)) %>%
ungroup() %>%
filter(!is.na(output))
【讨论】：
您可以使用imputeTS::na_kalman，它也可以推断。
r <- do.call(rbind, by(db, db$y, FUN=\(x) transform(x, aa=imputeTS::na_kalman(z))))
tail(r[r$y == 1, ])
# x y z aa
# 1.39 2021-12-25 1 0.020848035 0.020848035
# 1.40 2021-12-26 1 0.017171691 0.017171691
# 1.41 2021-12-27 1 0.007122718 0.007122718
# 1.42 2021-12-28 1 NA 0.392535303
# 1.43 2021-12-29 1 0.629796532 0.629796532
# 1.44 2021-12-30 1 NA 0.258814648
数据：
db <- structure(list(x = structure(c(18981, 18982, 18983, 18984, 18985,
18986, 18987, 18988, 18989, 18990, 18991, 18981, 18982, 18983,
18984, 18985, 18986, 18987, 18988, 18989, 18990, 18991, 18981,
18982, 18983, 18984, 18985, 18986, 18987, 18988, 18989, 18990,
18991, 18981, 18982, 18983, 18984, 18985, 18986, 18987, 18988,
18989, 18990, 18991, 18981, 18982, 18983, 18984, 18985, 18986,
18987, 18988, 18989, 18990, 18991, 18981, 18982, 18983, 18984,
18985, 18986, 18987, 18988, 18989, 18990, 18991, 18981, 18982,
18983, 18984, 18985, 18986, 18987, 18988, 18989, 18990, 18991,
18981, 18982, 18983, 18984, 18985, 18986, 18987, 18988, 18989,
18990, 18991, 18981, 18982, 18983, 18984, 18985, 18986, 18987,
18988, 18989, 18990, 18991, 18981, 18982, 18983, 18984, 18985,
18986, 18987, 18988, 18989, 18990, 18991, 18981, 18982, 18983,
18984, 18985, 18986, 18987, 18988, 18989, 18990, 18991, 18981,
18982, 18983, 18984, 18985, 18986, 18987, 18988, 18989, 18990,
18991, 18981, 18982, 18983, 18984, 18985, 18986, 18987, 18988,
18989, 18990, 18991, 18981, 18982, 18983, 18984, 18985, 18986,
18987, 18988, 18989, 18990, 18991, 18981, 18982, 18983, 18984,
18985, 18986, 18987, 18988, 18989, 18990, 18991, 18981, 18982,
18983, 18984, 18985, 18986, 18987, 18988, 18989, 18990, 18991
), class = "Date"), y = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4,
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4), z = c(0.305344789017667,
0.256644623614096, NA, 1.31852719135355, 0.115506505762677, 0.732802091953865,
NA, 0.239925107412262, 0.685318244939073, 0.691973256906341,
1.32378575746467, NA, 0.384693043255873, 1.45895509632899, NA,
0.0599714441492927, NA, NA, NA, NA, NA, 0.71683339822062, NA,
3.27310516365819, 1.69204573033578, NA, 0.14017486940184, NA,
1.16261380170504, NA, NA, NA, 1.68438289810619, NA, NA, 1.31386940315565,
0.594623922245712, NA, 0.0208480351055444, 0.0171716909393243,
0.00712271758331095, NA, 0.629796532479193, NA, 0.244580018794366,
NA, 0.820911116824006, NA, NA, 0.557088403848106, 0.0130780982496676,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1.28902764727033,
0.242390057597798, NA, 1.75609046517858, 0.921685169855448, 0.240269454747801,
NA, 0.133290865347424, 0.760944667549314, NA, 2.10865624982592,
0.201965354187563, NA, 0.372617511993437, 0.40925122336274, 0.598185767876918,
NA, NA, 1.51486434937749, NA, 0.365799492559624, 1.93980359376164,
NA, NA, NA, 1.39839171014837, NA, NA, 1.131273582479, 1.35134680218024,
NA, 1.02956577738351, 0.271873664141861, 0.777813782525466, NA,
NA, 0.286721974151372, 0.0305405702707527, NA, NA, 0.922064532313788,
NA, 0.211308210750866, NA, NA, 0.416086290075234, 0.744175318362445,
1.05570394997758, NA, 2.10096763825364, NA, NA, 0.945801512771798,
1.64923864766573, NA, 0.0338301608791077, 1.93867810865554, 0.611903344641826,
NA, NA, NA, 0.664664842786913, 0.992532329760494, 0.106067365628389,
NA, NA, 0.253237072580547, 1.39727781231248, 0.750659506338532,
NA, NA, 0.531677176826455, NA, 0.334496935245917, NA, 0.237217689673067,
NA, 0.729615340974382, 0.418007005399876, NA, NA, NA, 0.575142620388619,
2.27297683347494, NA, 1.0088509112411, NA, NA, NA, 1.07213691727514,
NA, 0.950964366873889, NA, NA, 1.37008596018781, NA, 0.581570283604887,
0.903895963902468, NA, 0.170520505104898, 0.664123540127705,
1.20066990898952, NA, 0.243496848502427, 0.679868588335254, NA,
2.09127742408436, 0.77948087799739, NA, 0.658167166169738, NA,
2.15919199233993, NA, 0.778191585042783)), row.names = c(NA,
-176L), class = "data.frame")
【讨论】：
另一种可能的解决方案：
library(zoo)
library(lubridate)
library(dplyr)
set.seed(471)
db <- rep(seq(ymd("2021-12-20"), ymd("2021-12-30"), by = "days"),4) %>% merge(seq(1,4,1)) %>%
mutate(z=rnorm(176))
db$z[db$z<0] <- NA
db %>%
group_by(y) %>%
mutate(aux = data.table::rleid(z)) %>%
filter(!((aux == 1 | aux == max(aux)) & is.na(z))) %>%
ungroup %>% select(-aux) %>% mutate(aa=na.approx(z))
#> # A tibble: 170 × 4
#> x y z aa
#> <date> <dbl> <dbl> <dbl>
#> 1 2021-12-21 1 0.605 0.605
#> 2 2021-12-22 1 0.185 0.185
#> 3 2021-12-23 1 NA 0.455
#> 4 2021-12-24 1 0.725 0.725
#> 5 2021-12-25 1 1.51 1.51
#> 6 2021-12-26 1 NA 1.41
#> 7 2021-12-27 1 1.31 1.31
#> 8 2021-12-28 1 1.07 1.07
#> 9 2021-12-29 1 1.14 1.14
#> 10 2021-12-30 1 NA 0.585
#> # … with 160 more rows
【讨论】：