我有一个 data.table，其中包含多个站点超过 15 天的温度和其他天气信息的观测值。这dput是针对两个站点的所有观察结果。
library(data.table)
structure(list(site = c("100", "100", "100", "100", "100", "100",
"100", "100", "100", "100", "100", "100", "100", "100", "100"
), precursor_date = structure(c(15203, 15202, 15201, 15200, 15199,
15198, 15197, 15196, 15195, 15194, 15193, 15192, 15191, 15190,
15189), class = "Date"), lat = c(46.864, 46.864, 46.864, 46.864,
46.864, 46.864, 46.864, 46.864, 46.864, 46.864, 46.864, 46.864,
46.864, 46.864, 46.864), lon = c(-67.998, -67.998, -67.998, -67.998,
-67.998, -67.998, -67.998, -67.998, -67.998, -67.998, -67.998,
-67.998, -67.998, -67.998, -67.998), origDate = structure(c(15204,
15204, 15204, 15204, 15204, 15204, 15204, 15204, 15204, 15204,
15204, 15204, 15204, 15204, 15204), class = "Date"), last = c(2011,
2011, 2011, 2011, 2011, 2011, 2011, 2011, 2011, 2011, 2011, 2011,
2011, 2011, 2011), begin = c(2011, 2011, 2011, 2011, 2011, 2011,
2011, 2011, 2011, 2011, 2011, 2011, 2011, 2011, 2011), precursor_day_labl = structure(1:15, .Label = c("obd_p1",
"obd_p2", "obd_p3", "obd_p4", "obd_p5", "obd_p6", "obd_p7", "obd_p8",
"obd_p9", "obd_p10", "obd_p11", "obd_p12", "obd_p13", "obd_p14",
"obd_p15"), class = "factor"), year = c(2011, 2011, 2011, 2011,
2011, 2011, 2011, 2011, 2011, 2011, 2011, 2011, 2011, 2011, 2011
), yday = c(229, 228, 227, 226, 225, 224, 223, 222, 221, 220,
219, 218, 217, 216, 215), dayl = c(50112, 50457.6015625, 50457.6015625,
50803.19921875, 50803.19921875, 51148.80078125, 51148.80078125,
51494.3984375, 51494.3984375, 51840, 51840, 52185.6015625, 52185.6015625,
52531.19921875, 52531.19921875), prcp = c(0, 17, 5, 4, 6, 6,
13, 8, 0, 16, 14, 6, 0, 0, 7), srad = c(403.200012207031, 176,
249.600006103516, 288, 297.600006103516, 268.799987792969, 179.199996948242,
192, 406.399993896484, 208, 227.199996948242, 307.200012207031,
371.200012207031, 304, 182.399993896484), swe = c(0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), tmax = c(22.5, 20.5, 24.5,
26.5, 25, 22.5, 20.5, 21, 24, 23.5, 25, 28, 24, 23.5, 22), tmin = c(10.5,
14.5, 15, 14.5, 12.5, 12, 14, 14, 11, 15.5, 16, 14, 12.5, 14.5,
15), vp = c(1280, 1640, 1720, 1640, 1440, 1400, 1600, 1600, 1320,
1760, 1800, 1600, 1440, 1640, 1720), satv = c(19.99250234375,
17.77504867875, 22.44414580875, 25.14497571375, 23.09540625,
19.99250234375, 17.77504867875, 18.30827693, 21.80845952, 21.18811306125,
23.09540625, 27.34314816, 21.80845952, 21.18811306125, 19.41676944
), r_hum = c(64.024001497749, 92.2641636397099, 76.6346830330004,
65.2217770527891, 62.3500614976193, 70.026251638163, 90.0138181850829,
87.3921672758967, 60.526971141151, 83.0654431053979, 77.9375768720241,
58.5155736507555, 66.0294230630738, 77.4018901663935, 88.5832221119478
)), class = c("data.table", "data.frame"), row.names = c(NA,
-15L), .internal.selfref = <pointer: 0x000001b632fd1ef0>)
我想获取从开始日开始按时间向后移动的 15 天的每个 #-day 间隔的天气数据（prcp, tmax, tmin, r_hum）的平均值，我origDate在DT. 进入每个站点各自 15 天窗口的日期在precursor_date. 将只有一个 2 天平均值、一个 3 天平均值、一个 4 天平均值等，并且这些将基于紧接在origDate. 例如，如果开始日期是 2011-08-18，我想要 8-18（08-17 和 08-16）、3 天（08-17、08-16、08-15）之前 2 天的平均值等，直到最大的窗口，15 天（08-17 到 08-03）。我不需要在 15 天窗口中可能的每个小间隔平均值。就在前面的那个origDate。
为了了解我想要的子集，在 中dplyr，我可以使用
df %>% group_by(site) %>% slice_head(n= x)
# A tibble: 2,556 x 19
# Groups: site [1,278]
site precursor_date lat lon origDate last begin precursor_day_l~ year yday dayl prcp srad swe tmax
<chr> <date> <dbl> <dbl> <date> <dbl> <dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 100 2011-08-17 46.9 -68.0 2011-08-18 2011 2011 obd_p1 2011 229 50112 0 403. 0 22.5
2 100 2011-08-16 46.9 -68.0 2011-08-18 2011 2011 obd_p2 2011 228 50458. 17 176 0 20.5
3 101 2011-08-11 44.3 -72.7 2011-08-12 2011 2011 obd_p1 2011 223 50458. 8 272 0 26
4 101 2011-08-10 44.3 -72.7 2011-08-12 2011 2011 obd_p2 2011 222 50803. 25 253. 0 26.5
5 102 2011-08-21 46.5 -68.0 2011-08-22 2011 2011 obd_p1 2011 233 49421. 0 378. 0 27
6 102 2011-08-20 46.5 -68.0 2011-08-22 2011 2011 obd_p2 2011 232 49421. 1 397. 0 28
其中 x 是我想在获得平均值之前从每个组中提取子集的天数。但如果我使用
df %>% group_by(site) %>% slice_head(n= x) %>% mean(prcp)
我收到一个错误，我不知道为什么。错误是
Warning message:
In mean.default(., "prcp") :
argument is not numeric or logical: returning NA
虽然我不知道为什么会发生该错误，但我更倾向于让子集在data.table. 我想要子集均值的列是 prcp、tmax、tmin 和 r_hum。我最终会创建 60 个新列，每个天气变量 15 个。并且很多列都会有 NA 或其他东西，因为 DT 在列中有日常观察。为了了解输出可能是什么样子，这里有一个模型。它不必看起来像这样，只要我在 DT 中将每个天气变量和时间窗口的方法与适当的站点对齐即可。
site precursor_date lat lon origDate ... prcp2dmean prcp3dmean prcp4dmean ... tmax2dmean tmax3dmean ...
100 2011-08-17 46.864 -67.998 2011-08-18 ... 1.2 1.4 1.4 ... 25 24 ...
100 2011-08-16 46.864 -67.998 2011-08-18 ... 1.2 1.4 1.4 ... 25 24 ...
100 2011-08-15 46.864 -67.998 2011-08-18 ... NA 1.4 1.4 ... NA 24 ...
100 2011-08-14 46.864 -67.998 2011-08-18 ... NA NA 1.4 ... NA NA ...
100 2011-08-13 46.864 -67.998 2011-08-18 ... NA NA NA ... NA NA ...
100 2011-08-12 46.864 -67.998 2011-08-18 ... NA NA NA ... NA NA ...
100 2011-08-11 46.864 -67.998 2011-08-18 ... NA NA NA ... NA NA ...
100 2011-08-10 46.864 -67.998 2011-08-18 ... NA NA NA ... NA NA ...
100 2011-08-09 46.864 -67.998 2011-08-18 ... NA NA NA ... NA NA ...
100 2011-08-08 46.864 -67.998 2011-08-18 ... NA NA NA ... NA NA ...
100 2011-08-07 46.864 -67.998 2011-08-18 ... NA NA NA ... NA NA ...
100 2011-08-06 46.864 -67.998 2011-08-18 ... NA NA NA ... NA NA ...
100 2011-08-05 46.864 -67.998 2011-08-18 ... NA NA NA ... NA NA ...
100 2011-08-04 46.864 -67.998 2011-08-18 ... NA NA NA ... NA NA ...
100 2011-08-03 46.864 -67.998 2011-08-18 ... NA NA NA ... NA NA ...
10 2011-08-17 46.864 -67.998 2011-08-18 ... 1.2 1.4 1.4 ... 25 24 ...
10 2011-08-16 46.864 -67.998 2011-08-18 ... 1.2 1.4 1.4 ... 25 24 ...
10 2011-08-15 46.864 -67.998 2011-08-18 ... NA 1.4 1.4 ... NA 24 ...
10 2011-08-14 46.864 -67.998 2011-08-18 ... NA NA 1.4 ... NA NA ...
10 2011-08-13 46.864 -67.998 2011-08-18 ... NA NA NA ... NA NA ...
10 2011-08-12 46.864 -67.998 2011-08-18 ... NA NA NA ... NA NA ...
10 2011-08-11 46.864 -67.998 2011-08-18 ... NA NA NA ... NA NA ...
10 2011-08-10 46.864 -67.998 2011-08-18 ... NA NA NA ... NA NA ...
10 2011-08-09 46.864 -67.998 2011-08-18 ... NA NA NA ... NA NA ...
10 2011-08-08 46.864 -67.998 2011-08-18 ... NA NA NA ... NA NA ...
10 2011-08-07 46.864 -67.998 2011-08-18 ... NA NA NA ... NA NA ...
10 2011-08-06 46.864 -67.998 2011-08-18 ... NA NA NA ... NA NA ...
10 2011-08-05 46.864 -67.998 2011-08-18 ... NA NA NA ... NA NA ...
10 2011-08-04 46.864 -67.998 2011-08-18 ... NA NA NA ... NA NA ...
10 2011-08-03 46.864 -67.998 2011-08-18 ... NA NA NA ... NA NA ...
在我的 DT 我试过
pi_df5[, pi_df5 %>% slice_head(n=2) %>% mean(prcp), by = site]
但这不起作用。
这是使用 {data.table} 的方法frollmean。请注意，这种方法会产生大量您可能不需要的信息。例如，prcp2显示每天的两天平均值，它可以计算该平均值而不显示NAs。如果我理解正确的话，您只需要最后一天计算的两天平均值。另一方面，15 天平均值只能在最后一天计算一次，而在示例输出中，15 天平均值显示在每一行中。
注意：本文使用的早期版本eval(parse())是不必要的，因为frollmean可以将data.frames和 向量作为 x和n参数，正如 @jangorecki 在评论中正确指出的那样。
抱歉，大部分reprex都是原始数据：
library(data.table)
# original data
dat <-
structure(list(site = c("100", "100", "100", "100", "100", "100", "100", "100",
"100", "100", "100", "100", "100", "100", "100"),
precursor_date = structure(c(15203, 15202, 15201, 15200, 15199,
15198, 15197, 15196, 15195, 15194, 15193, 15192,
15191, 15190, 15189), class = "Date"),
lat = c(46.864, 46.864, 46.864, 46.864, 46.864, 46.864, 46.864,
46.864, 46.864, 46.864, 46.864, 46.864,46.864, 46.864,
46.864),
lon = c(-67.998, -67.998, -67.998, -67.998, -67.998, -67.998,
-67.998, -67.998, -67.998, -67.998, -67.998, -67.998,
-67.998, -67.998, -67.998),
origDate = structure(c(15204, 15204, 15204, 15204, 15204, 15204,
15204, 15204, 15204, 15204, 15204, 15204,
15204, 15204, 15204),class = "Date"),
last = c(2011, 2011, 2011, 2011, 2011, 2011, 2011, 2011, 2011,
2011, 2011, 2011, 2011, 2011, 2011),
begin = c(2011, 2011, 2011, 2011, 2011, 2011,2011, 2011, 2011,
2011, 2011, 2011, 2011, 2011, 2011),
precursor_day_labl = structure(1:15,
.Label = c("obd_p1", "obd_p2", "obd_p3", "obd_p4", "obd_p5",
"obd_p6", "obd_p7", "obd_p8", "obd_p9", "obd_p10",
"obd_p11", "obd_p12", "obd_p13", "obd_p14", "obd_p15"),
class = "factor"),
year = c(2011, 2011, 2011, 2011, 2011, 2011,2011, 2011, 2011,
2011, 2011, 2011, 2011, 2011, 2011),
yday = c(229, 228, 227, 226, 225, 224, 223, 222, 221, 220, 219,
218, 217, 216, 215),
dayl = c(50112, 50457.6015625, 50457.6015625, 50803.19921875,
50803.19921875, 51148.80078125, 51148.80078125,
51494.3984375, 51494.3984375, 51840, 51840, 52185.6015625,
52185.6015625, 52531.19921875, 52531.19921875),
prcp = c(0, 17, 5, 4, 6, 6, 13, 8, 0, 16, 14, 6, 0, 0, 7),
srad = c(403.200012207031, 176, 249.600006103516, 288,
297.600006103516, 268.799987792969, 179.199996948242,
192, 406.399993896484, 208, 227.199996948242, 307.200012207031,
371.200012207031, 304, 182.399993896484),
swe = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0),
tmax = c(22.5, 20.5, 24.5, 26.5, 25, 22.5, 20.5, 21, 24, 23.5,
25, 28, 24, 23.5, 22),
tmin = c(10.5, 14.5, 15, 14.5, 12.5, 12, 14, 14, 11, 15.5, 16,
14, 12.5, 14.5, 15),
vp = c(1280, 1640, 1720, 1640, 1440, 1400, 1600, 1600, 1320, 1760,
1800, 1600, 1440, 1640, 1720),
satv = c(19.99250234375, 17.77504867875, 22.44414580875,
25.14497571375, 23.09540625,19.99250234375,
17.77504867875, 18.30827693, 21.80845952, 21.18811306125,
23.09540625, 27.34314816, 21.80845952, 21.18811306125,
19.41676944),
r_hum = c(64.024001497749, 92.2641636397099, 76.6346830330004,
65.2217770527891, 62.3500614976193, 70.026251638163,
90.0138181850829, 87.3921672758967, 60.526971141151,
83.0654431053979, 77.9375768720241, 58.5155736507555,
66.0294230630738, 77.4018901663935, 88.5832221119478)
), class = c("data.table", "data.frame"), row.names = c(NA, -15L))
# the actual calculation starts here -------------------------------
mean_vars <- c("prcp", "tmax", "tmin", "r_hum")
setDT(dat)[,
c(paste0(rep(mean_vars, each = 14) , 2:15)) :=
frollmean(.SD, 2:15, align = 'left'),
by = site,
.SDcols = mean_vars][]
#> site precursor_date lat lon origDate last begin precursor_day_labl
#> 1: 100 2011-08-17 46.864 -67.998 2011-08-18 2011 2011 obd_p1
#> 2: 100 2011-08-16 46.864 -67.998 2011-08-18 2011 2011 obd_p2
#> 3: 100 2011-08-15 46.864 -67.998 2011-08-18 2011 2011 obd_p3
#> 4: 100 2011-08-14 46.864 -67.998 2011-08-18 2011 2011 obd_p4
#> 5: 100 2011-08-13 46.864 -67.998 2011-08-18 2011 2011 obd_p5
#> 6: 100 2011-08-12 46.864 -67.998 2011-08-18 2011 2011 obd_p6
#> 7: 100 2011-08-11 46.864 -67.998 2011-08-18 2011 2011 obd_p7
#> 8: 100 2011-08-10 46.864 -67.998 2011-08-18 2011 2011 obd_p8
#> 9: 100 2011-08-09 46.864 -67.998 2011-08-18 2011 2011 obd_p9
#> 10: 100 2011-08-08 46.864 -67.998 2011-08-18 2011 2011 obd_p10
#> 11: 100 2011-08-07 46.864 -67.998 2011-08-18 2011 2011 obd_p11
#> 12: 100 2011-08-06 46.864 -67.998 2011-08-18 2011 2011 obd_p12
#> 13: 100 2011-08-05 46.864 -67.998 2011-08-18 2011 2011 obd_p13
#> 14: 100 2011-08-04 46.864 -67.998 2011-08-18 2011 2011 obd_p14
#> 15: 100 2011-08-03 46.864 -67.998 2011-08-18 2011 2011 obd_p15
#> year yday dayl prcp srad swe tmax tmin vp satv r_hum prcp2
#> 1: 2011 229 50112.0 0 403.2 0 22.5 10.5 1280 19.99250 64.02400 8.5
#> 2: 2011 228 50457.6 17 176.0 0 20.5 14.5 1640 17.77505 92.26416 11.0
#> 3: 2011 227 50457.6 5 249.6 0 24.5 15.0 1720 22.44415 76.63468 4.5
#> 4: 2011 226 50803.2 4 288.0 0 26.5 14.5 1640 25.14498 65.22178 5.0
#> 5: 2011 225 50803.2 6 297.6 0 25.0 12.5 1440 23.09541 62.35006 6.0
#> 6: 2011 224 51148.8 6 268.8 0 22.5 12.0 1400 19.99250 70.02625 9.5
#> 7: 2011 223 51148.8 13 179.2 0 20.5 14.0 1600 17.77505 90.01382 10.5
#> 8: 2011 222 51494.4 8 192.0 0 21.0 14.0 1600 18.30828 87.39217 4.0
#> 9: 2011 221 51494.4 0 406.4 0 24.0 11.0 1320 21.80846 60.52697 8.0
#> 10: 2011 220 51840.0 16 208.0 0 23.5 15.5 1760 21.18811 83.06544 15.0
#> 11: 2011 219 51840.0 14 227.2 0 25.0 16.0 1800 23.09541 77.93758 10.0
#> 12: 2011 218 52185.6 6 307.2 0 28.0 14.0 1600 27.34315 58.51557 3.0
#> 13: 2011 217 52185.6 0 371.2 0 24.0 12.5 1440 21.80846 66.02942 0.0
#> 14: 2011 216 52531.2 0 304.0 0 23.5 14.5 1640 21.18811 77.40189 3.5
#> 15: 2011 215 52531.2 7 182.4 0 22.0 15.0 1720 19.41677 88.58322 NA
#> prcp3 prcp4 prcp5 prcp6 prcp7 prcp8 prcp9 prcp10 prcp11
#> 1: 7.333333 6.50 6.4 6.333333 7.285714 7.375 6.555556 7.5 8.090909
#> 2: 8.666667 8.00 7.6 8.500000 8.428571 7.375 8.333333 8.9 8.636364
#> 3: 5.000000 5.25 6.8 7.000000 6.000000 7.250 8.000000 7.8 7.090909
#> 4: 5.333333 7.25 7.4 6.166667 7.571429 8.375 8.111111 7.3 6.636364
#> 5: 8.333333 8.25 6.6 8.166667 9.000000 8.625 7.666667 6.9 6.909091
#> 6: 9.000000 6.75 8.6 9.500000 9.000000 7.875 7.000000 7.0 NA
#> 7: 7.000000 9.25 10.2 9.500000 8.142857 7.125 7.111111 NA NA
#> 8: 8.000000 9.50 8.8 7.333333 6.285714 6.375 NA NA NA
#> 9: 10.000000 9.00 7.2 6.000000 6.142857 NA NA NA NA
#> 10: 12.000000 9.00 7.2 7.166667 NA NA NA NA NA
#> 11: 6.666667 5.00 5.4 NA NA NA NA NA NA
#> 12: 2.000000 3.25 NA NA NA NA NA NA NA
#> 13: 2.333333 NA NA NA NA NA NA NA NA
#> 14: NA NA NA NA NA NA NA NA NA
#> 15: NA NA NA NA NA NA NA NA NA
#> prcp12 prcp13 prcp14 prcp15 tmax2 tmax3 tmax4 tmax5 tmax6
#> 1: 7.916667 7.307692 6.785714 6.8 21.50 22.50000 23.500 23.8 23.58333
#> 2: 7.916667 7.307692 7.285714 NA 22.50 23.83333 24.125 23.8 23.25000
#> 3: 6.500000 6.538462 NA NA 25.50 25.33333 24.625 23.8 23.33333
#> 4: 6.666667 NA NA NA 25.75 24.66667 23.625 23.1 23.25000
#> 5: NA NA NA NA 23.75 22.66667 22.250 22.6 22.75000
#> 6: NA NA NA NA 21.50 21.33333 22.000 22.3 22.75000
#> 7: NA NA NA NA 20.75 21.83333 22.250 22.8 23.66667
#> 8: NA NA NA NA 22.50 22.83333 23.375 24.3 24.25000
#> 9: NA NA NA NA 23.75 24.16667 25.125 24.9 24.66667
#> 10: NA NA NA NA 24.25 25.50000 25.125 24.8 24.33333
#> 11: NA NA NA NA 26.50 25.66667 25.125 24.5 NA
#> 12: NA NA NA NA 26.00 25.16667 24.375 NA NA
#> 13: NA NA NA NA 23.75 23.16667 NA NA NA
#> 14: NA NA NA NA 22.75 NA NA NA NA
#> 15: NA NA NA NA NA NA NA NA NA
#> tmax7 tmax8 tmax9 tmax10 tmax11 tmax12 tmax13 tmax14
#> 1: 23.14286 22.8750 23.00000 23.05 23.22727 23.62500 23.65385 23.64286
#> 2: 22.92857 23.0625 23.11111 23.30 23.72727 23.75000 23.73077 23.60714
#> 3: 23.42857 23.4375 23.61111 24.05 24.04545 24.00000 23.84615 NA
#> 4: 23.28571 23.5000 24.00000 24.00 23.95455 23.79167 NA NA
#> 5: 23.07143 23.6875 23.72222 23.70 23.54545 NA NA NA
#> 6: 23.50000 23.5625 23.55556 23.40 NA NA NA NA
#> 7: 23.71429 23.6875 23.50000 NA NA NA NA NA
#> 8: 24.14286 23.8750 NA NA NA NA NA NA
#> 9: 24.28571 NA NA NA NA NA NA NA
#> 10: NA NA NA NA NA NA NA NA
#> 11: NA NA NA NA NA NA NA NA
#> 12: NA NA NA NA NA NA NA NA
#> 13: NA NA NA NA NA NA NA NA
#> 14: NA NA NA NA NA NA NA NA
#> 15: NA NA NA NA NA NA NA NA
#> tmax15 tmin2 tmin3 tmin4 tmin5 tmin6 tmin7 tmin8 tmin9
#> 1: 23.53333 12.50 13.33333 13.625 13.4 13.16667 13.28571 13.3750 13.11111
#> 2: NA 14.75 14.66667 14.125 13.7 13.75000 13.78571 13.4375 13.66667
#> 3: NA 14.75 14.00000 13.500 13.6 13.66667 13.28571 13.5625 13.83333
#> 4: NA 13.50 13.00000 13.250 13.4 13.00000 13.35714 13.6875 13.72222
#> 5: NA 12.25 12.83333 13.125 12.7 13.16667 13.57143 13.6250 13.50000
#> 6: NA 13.00 13.33333 12.750 13.3 13.75000 13.78571 13.6250 13.72222
#> 7: NA 14.00 13.00000 13.625 14.1 14.08333 13.85714 13.9375 14.05556
#> 8: NA 12.50 13.50000 14.125 14.1 13.83333 13.92857 14.0625 NA
#> 9: NA 13.25 14.16667 14.125 13.8 13.91667 14.07143 NA NA
#> 10: NA 15.75 15.16667 14.500 14.5 14.58333 NA NA NA
#> 11: NA 15.00 14.16667 14.250 14.4 NA NA NA NA
#> 12: NA 13.25 13.66667 14.000 NA NA NA NA NA
#> 13: NA 13.50 14.00000 NA NA NA NA NA NA
#> 14: NA 14.75 NA NA NA NA NA NA NA
#> 15: NA NA NA NA NA NA NA NA NA
#> tmin10 tmin11 tmin12 tmin13 tmin14 tmin15 r_hum2 r_hum3
#> 1: 13.35 13.59091 13.62500 13.53846 13.60714 13.7 78.14408 77.64095
#> 2: 13.90 13.90909 13.79167 13.84615 13.92857 NA 84.44942 78.04021
#> 3: 13.85 13.72727 13.79167 13.88462 NA NA 70.92823 68.06884
#> 4: 13.60 13.68182 13.79167 NA NA NA 63.78592 65.86603
#> 5: 13.60 13.72727 NA NA NA NA 66.18816 74.13004
#> 6: 13.85 NA NA NA NA NA 80.02003 82.47741
#> 7: NA NA NA NA NA NA 88.70299 79.31099
#> 8: NA NA NA NA NA NA 73.95957 76.99486
#> 9: NA NA NA NA NA NA 71.79621 73.84333
#> 10: NA NA NA NA NA NA 80.50151 73.17286
#> 11: NA NA NA NA NA NA 68.22658 67.49419
#> 12: NA NA NA NA NA NA 62.27250 67.31563
#> 13: NA NA NA NA NA NA 71.71566 77.33818
#> 14: NA NA NA NA NA NA 82.99256 NA
#> 15: NA NA NA NA NA NA NA NA
#> r_hum4 r_hum5 r_hum6 r_hum7 r_hum8 r_hum9 r_hum10 r_hum11
#> 1: 74.53616 72.09894 71.75349 74.36211 75.99087 74.27265 75.15193 75.40517
#> 2: 74.11767 73.29939 76.08513 77.70042 75.55374 76.38837 76.54329 74.90441
#> 3: 68.55819 72.84932 75.27313 73.16653 74.40390 74.79653 73.16843 72.51943
#> 4: 71.90298 75.00082 72.58851 74.08521 74.56676 72.78329 72.10791 72.58918
#> 5: 77.44557 74.06185 75.56245 75.90176 73.72848 72.87303 73.32592 74.71295
#> 6: 76.98980 78.20493 78.16037 75.35397 74.18840 74.54546 75.94923 NA
#> 7: 80.24960 79.78720 76.24193 74.78300 75.11036 76.60734 NA NA
#> 8: 77.23054 73.48755 72.24453 72.98129 74.93153 NA NA NA
#> 9: 70.01139 69.21500 70.57948 73.15144 NA NA NA NA
#> 10: 71.38700 72.58998 75.25552 NA NA NA NA NA
#> 11: 69.97112 73.69354 NA NA NA NA NA NA
#> 12: 72.63253 NA NA NA NA NA NA NA
#> 13: NA NA NA NA NA NA NA NA
#> 14: NA NA NA NA NA NA NA NA
#> 15: NA NA NA NA NA NA NA NA
#> r_hum12 r_hum13 r_hum14 r_hum15
#> 1: 73.99771 73.38476 73.67170 74.6658
#> 2: 74.16483 74.41383 75.42593 NA
#> 3: 72.92630 74.13068 NA NA
#> 4: 73.92201 NA NA NA
#> 5: NA NA NA NA
#> 6: NA NA NA NA
#> 7: NA NA NA NA
#> 8: NA NA NA NA
#> 9: NA NA NA NA
#> 10: NA NA NA NA
#> 11: NA NA NA NA
#> 12: NA NA NA NA
#> 13: NA NA NA NA
#> 14: NA NA NA NA
#> 15: NA NA NA NA
由reprex 包(v0.3.0)于 2020 年 11 月 10 日创建