轻拍（）？等长？
tapply ()? Equal lengths?
下午好，
我正在尝试应用 tapply 函数，以便通过以下数据集的不同治疗组（'Placebo' 组和 'Active' 组）获取平均值：
> str(dat_long)
'data.frame': 1500 obs. of 7 variables:
$ subject.id: num 1 1 1 2 2 2 3 3 3 4 ...
$ treatment : Factor w/ 2 levels "Placebo","Active": 1 1 1 1 1 1 1 1 1 1 ...
$ sex : num 1 1 1 1 1 1 1 1 1 1 ...
$ age : num 58.1 58.1 58.1 54.8 54.8 ...
$ miss_pat : chr "---" "---" "---" "--X" ...
$ times : num 1 2 3 1 2 3 1 2 3 1 ...
$ scores : num 13.62 7.25 20.45 33.34 20.9 ..
我正在处理的数据集格式是“长”格式。我创建了以下列表对象
flst <- list(times, treatment)
通过收集不同的时间点和接受的治疗，其中我 运行 tapply() 函数。
(tN <-
tapply(scores, flst,
FUN = function(x) length(x[!is.na(x)])))
我不明白为什么我一直在找回同样的错误
Error in tapply(scores, flst, FUN = function(x) length(x[!is.na(x)])) :
arguments must have the same length
我试图寻找解决方案（例如，作为因子变量进行隐藏等），但其中 none 似乎适合我的情况。有人可能知道我要解决的问题吗？
为了以防万一，为了应对 NA 观察，我应该在代码中输入什么以及在哪里输入？
非常感谢关注
P.S。以防万一我在这里报告每个参数 lengths
> length(flst)
[1] 2
> length(scores)
[1] 1500
好的。
感谢到目前为止的回答。我将尝试提供更多细节，只是为了让情况更容易理解。
这是我正在处理的原始数据集，采用宽格式。
> dat_wide
subject.id treatment measure1 measure2 measure3 sex age miss_pat
1 1 1 13.61906 7.249175 20.44918 1 58.12831 ---
2 2 1 33.33751 20.896999 NA 1 54.77794 --X
3 3 1 NA 20.869364 26.12443 1 59.55550 X--
4 4 1 37.75143 26.253406 29.03896 1 59.37837 ---
5 5 1 28.21168 14.652736 23.65234 1 63.34226 ---
6 6 1 32.40856 18.702288 NA 0 55.36262 --X
7 7 1 31.63005 18.682889 NA 1 59.02119 --X
8 8 1 NA 18.244421 22.02274 0 57.57435 X--
9 9 1 NA 27.351230 36.86480 1 52.08140 X--
10 10 1 28.10867 16.769758 23.67930 1 62.93165 ---
11 11 1 33.55693 18.499841 NA 0 53.46754 --X
12 12 1 22.88379 14.061066 30.02255 1 57.49554 ---
13 13 1 30.99073 15.350301 20.69450 1 57.92600 ---
14 14 1 26.35118 15.195037 26.83573 0 58.42347 ---
15 15 1 20.80077 16.112581 34.78064 1 60.22746 ---
16 16 1 34.64003 NA 32.96944 1 56.28172 -X-
17 17 1 26.70361 23.846794 31.24356 0 57.93085 ---
18 18 1 25.61433 19.779788 25.75783 1 59.35028 ---
19 19 1 36.51201 24.594663 NA 0 59.32188 --X
20 20 1 33.18090 31.846358 34.21801 0 58.16217 ---
21 21 1 NA 23.813420 28.49156 1 58.39313 X--
22 22 1 28.20934 12.516007 NA 0 53.24436 --X
23 23 1 30.64196 NA 35.01832 1 59.12392 -X-
24 24 1 NA 26.596392 NA 1 56.22792 X-X
25 25 1 NA 13.380347 27.72617 1 59.75437 X--
26 26 1 27.95115 14.652209 27.44068 1 64.86220 ---
27 27 1 31.01423 18.881365 30.15189 0 61.84734 ---
28 28 1 41.28848 32.966200 NA 0 60.00848 --X
29 29 1 23.90214 16.894180 30.08366 0 55.09433 ---
30 30 1 27.91571 21.604654 29.97184 0 57.16355 ---
31 31 1 25.48024 14.512467 26.89893 1 52.28091 ---
32 32 1 NA 20.627647 24.94113 1 59.14089 X--
33 33 1 NA NA 30.30841 0 63.53035 XX-
34 34 1 NA 17.220079 27.11605 0 54.70157 X--
35 35 1 NA 22.904332 30.54679 0 62.82148 X--
36 36 1 24.71682 11.343220 20.16185 0 55.81194 ---
37 37 1 21.54434 15.245485 27.42116 0 56.77520 ---
38 38 1 31.00812 21.882236 31.04285 1 53.60319 ---
39 39 1 NA 29.445792 NA 0 62.88837 X-X
40 40 1 NA 21.325835 27.29858 0 59.92034 X--
41 41 1 NA 23.330492 31.01700 1 57.10396 X--
42 42 1 22.13132 17.385187 NA 1 49.80586 --X
43 43 1 NA 17.181349 24.70715 1 56.92603 X--
44 44 1 24.57225 NA 32.11101 1 52.17824 -X-
45 45 1 28.79543 15.302079 24.03278 1 63.82524 ---
46 46 1 25.36835 18.152139 23.56443 1 56.89171 ---
47 47 1 NA 21.594259 NA 1 56.04408 X-X
48 48 1 26.62196 22.544339 27.04008 1 62.31757 ---
49 49 1 24.22001 6.924847 19.89172 0 58.69229 ---
50 50 1 34.14105 22.283254 34.41771 1 57.73471 ---
51 51 1 18.90136 13.449724 28.69964 0 63.48494 ---
52 52 1 33.01857 20.880303 31.32787 1 60.22318 ---
53 53 1 31.06980 23.687328 32.66611 1 54.40638 ---
54 54 1 31.98485 20.204848 26.00955 0 62.71068 ---
55 55 1 30.50251 19.632418 19.03141 1 59.27135 ---
56 56 1 NA 24.607656 30.37130 1 59.40362 X--
57 57 1 33.55517 22.213067 27.59768 0 57.04789 ---
58 58 1 24.19856 10.095334 21.76153 0 54.56281 ---
59 59 1 29.62803 15.703452 26.99926 0 56.66213 ---
60 60 1 34.01922 18.515003 NA 0 52.76635 --X
61 61 1 29.86470 17.587567 26.02713 1 61.06847 ---
62 62 1 25.61215 20.746458 25.80680 1 64.25965 ---
63 63 1 27.60233 16.047261 27.63316 1 60.87826 ---
64 64 1 24.18180 15.007888 NA 1 62.95863 --X
65 65 1 30.34542 24.986253 32.33394 0 52.55152 ---
66 66 1 21.53614 15.999301 27.95432 1 57.96632 ---
67 67 1 NA 27.856354 NA 1 61.76696 X-X
68 68 1 22.09629 15.252170 28.43995 0 64.99414 ---
69 69 1 25.80952 18.565819 27.77111 0 61.26983 ---
70 70 1 32.99136 22.610543 NA 0 57.79422 --X
71 71 1 NA 20.701995 23.77574 0 59.83622 X--
72 72 1 25.53110 18.466437 29.50384 1 60.38576 ---
73 73 1 32.21151 21.740227 24.21088 1 56.07185 ---
74 74 1 21.28635 9.444369 18.28459 0 50.20183 ---
75 75 1 NA 25.000759 33.57668 1 50.22598 X--
76 76 1 26.12087 12.385796 18.91420 1 53.40782 ---
77 77 1 NA 22.767420 28.75717 1 48.27044 X--
78 78 1 24.64162 21.218430 NA 1 55.94655 --X
79 79 1 27.07266 15.348420 24.72210 0 53.69175 ---
80 80 1 32.75587 28.013513 NA 0 58.04689 --X
81 81 1 29.30842 17.072955 24.29882 1 61.06444 ---
82 82 1 NA 18.382335 28.69544 0 58.54447 X--
83 83 1 31.78905 24.603102 33.91789 0 56.24937 ---
84 84 1 27.99531 19.921259 25.14260 1 58.49543 ---
85 85 1 26.34945 17.718344 33.91154 1 65.19380 ---
86 86 1 31.28590 18.000405 29.33850 1 64.79698 ---
87 87 1 19.46943 12.058767 24.16417 0 61.99136 ---
88 88 1 33.05861 27.022073 35.98174 1 57.79309 ---
89 89 1 25.82099 14.353354 22.29782 1 56.99255 ---
90 90 1 NA 18.394579 25.89987 0 52.29756 X--
91 91 1 NA 17.594238 NA 0 57.60890 X-X
92 92 1 36.62178 25.121140 34.90862 1 59.53585 ---
93 93 1 31.58383 16.929367 18.06532 1 56.49771 ---
94 94 1 32.79600 25.161476 29.57509 1 58.27547 ---
95 95 1 32.57059 23.029130 NA 1 58.93147 --X
96 96 1 32.44934 12.892612 NA 1 58.54679 --X
97 97 1 28.83562 15.361608 21.96002 0 52.01731 ---
98 98 1 28.49706 22.351217 NA 1 58.02899 --X
99 99 1 25.28790 14.283020 NA 0 53.90112 --X
100 100 1 NA 20.031777 29.93904 0 59.34664 X--
101 101 1 30.86933 20.853957 23.67089 1 60.41797 ---
102 102 1 NA NA 25.25865 1 58.51619 XX-
103 103 1 32.60158 23.890740 27.78209 1 52.93076 ---
104 104 1 29.64438 26.275628 32.96612 0 57.67552 ---
105 105 1 19.47877 10.237089 14.53323 1 62.56323 ---
106 106 1 28.50985 24.172877 NA 1 56.20708 --X
107 107 1 NA 21.111304 27.44881 1 55.47168 X--
108 108 1 26.90495 15.140661 26.54333 0 59.25477 ---
109 109 1 NA 28.740031 NA 0 53.61378 X-X
110 110 1 27.55861 21.708753 30.61987 0 62.90328 ---
111 111 1 NA 26.379662 NA 1 55.03740 X-X
112 112 1 22.88802 13.638065 25.35349 0 57.83266 ---
113 113 1 32.92606 24.907022 27.52318 0 62.04887 ---
114 114 1 NA 23.853791 34.18453 1 60.97984 X--
115 115 1 NA 32.755543 34.09703 1 60.03185 X--
116 116 1 17.04010 10.772446 22.38210 1 56.68107 ---
117 117 1 20.80304 16.040884 19.17766 0 60.20649 ---
118 118 1 NA 15.337750 28.42040 0 58.86986 X--
119 119 1 26.22063 9.210329 16.85208 1 61.22018 ---
120 120 1 39.27848 21.273545 29.96761 0 57.69893 ---
121 121 1 NA 20.177623 27.03394 1 58.71332 X--
122 122 1 NA 19.084165 26.29572 1 61.85389 X--
123 123 1 25.33397 22.082934 35.30603 0 55.82552 ---
124 124 1 27.30526 22.526807 33.81731 1 58.29532 ---
125 125 1 NA 26.020347 35.15877 1 66.13519 X--
进一步更改后，我使用 tidyr 库的函数 gather 将其转换为长格式，并且我对每个观察进行了排序，以便为三个测量时间中的每一个排序主题列-点.
> dat_long
subject.id treatment sex age miss_pat times scores
1 1 Placebo 1 58.12831 --- 1 13.619060
501 1 Placebo 1 58.12831 --- 2 7.249175
1001 1 Placebo 1 58.12831 --- 3 20.449176
2 2 Placebo 1 54.77794 --X 1 33.337510
502 2 Placebo 1 54.77794 --X 2 20.896999
1002 2 Placebo 1 54.77794 --X 3 NA
3 3 Placebo 1 59.55550 X-- 1 NA
503 3 Placebo 1 59.55550 X-- 2 20.869364
1003 3 Placebo 1 59.55550 X-- 3 26.124432
4 4 Placebo 1 59.37837 --- 1 37.751430
504 4 Placebo 1 59.37837 --- 2 26.253406
1004 4 Placebo 1 59.37837 --- 3 29.038963
5 5 Placebo 1 63.34226 --- 1 28.211679
505 5 Placebo 1 63.34226 --- 2 14.652736
1005 5 Placebo 1 63.34226 --- 3 23.652342
6 6 Placebo 0 55.36262 --X 1 32.408561
506 6 Placebo 0 55.36262 --X 2 18.702288
1006 6 Placebo 0 55.36262 --X 3 NA
7 7 Placebo 1 59.02119 --X 1 31.630050
507 7 Placebo 1 59.02119 --X 2 18.682889
1007 7 Placebo 1 59.02119 --X 3 NA
8 8 Placebo 0 57.57435 X-- 1 NA
508 8 Placebo 0 57.57435 X-- 2 18.244421
1008 8 Placebo 0 57.57435 X-- 3 22.022740
9 9 Placebo 1 52.08140 X-- 1 NA
509 9 Placebo 1 52.08140 X-- 2 27.351230
1009 9 Placebo 1 52.08140 X-- 3 36.864796
10 10 Placebo 1 62.93165 --- 1 28.108672
510 10 Placebo 1 62.93165 --- 2 16.769758
1010 10 Placebo 1 62.93165 --- 3 23.679297
11 11 Placebo 0 53.46754 --X 1 33.556930
511 11 Placebo 0 53.46754 --X 2 18.499841
1011 11 Placebo 0 53.46754 --X 3 NA
12 12 Placebo 1 57.49554 --- 1 22.883795
512 12 Placebo 1 57.49554 --- 2 14.061066
1012 12 Placebo 1 57.49554 --- 3 30.022550
13 13 Placebo 1 57.92600 --- 1 30.990728
513 13 Placebo 1 57.92600 --- 2 15.350301
1013 13 Placebo 1 57.92600 --- 3 20.694504
14 14 Placebo 0 58.42347 --- 1 26.351176
514 14 Placebo 0 58.42347 --- 2 15.195037
1014 14 Placebo 0 58.42347 --- 3 26.835728
15 15 Placebo 1 60.22746 --- 1 20.800769
515 15 Placebo 1 60.22746 --- 2 16.112581
1015 15 Placebo 1 60.22746 --- 3 34.780644
16 16 Placebo 1 56.28172 -X- 1 34.640032
516 16 Placebo 1 56.28172 -X- 2 NA
1016 16 Placebo 1 56.28172 -X- 3 32.969443
17 17 Placebo 0 57.93085 --- 1 26.703609
517 17 Placebo 0 57.93085 --- 2 23.846794
1017 17 Placebo 0 57.93085 --- 3 31.243559
18 18 Placebo 1 59.35028 --- 1 25.614333
518 18 Placebo 1 59.35028 --- 2 19.779788
1018 18 Placebo 1 59.35028 --- 3 25.757833
19 19 Placebo 0 59.32188 --X 1 36.512015
519 19 Placebo 0 59.32188 --X 2 24.594663
1019 19 Placebo 0 59.32188 --X 3 NA
20 20 Placebo 0 58.16217 --- 1 33.180896
520 20 Placebo 0 58.16217 --- 2 31.846358
1020 20 Placebo 0 58.16217 --- 3 34.218013
21 21 Placebo 1 58.39313 X-- 1 NA
521 21 Placebo 1 58.39313 X-- 2 23.813420
1021 21 Placebo 1 58.39313 X-- 3 28.491556
22 22 Placebo 0 53.24436 --X 1 28.209340
522 22 Placebo 0 53.24436 --X 2 12.516007
1022 22 Placebo 0 53.24436 --X 3 NA
23 23 Placebo 1 59.12392 -X- 1 30.641962
523 23 Placebo 1 59.12392 -X- 2 NA
1023 23 Placebo 1 59.12392 -X- 3 35.018324
24 24 Placebo 1 56.22792 X-X 1 NA
524 24 Placebo 1 56.22792 X-X 2 26.596392
1024 24 Placebo 1 56.22792 X-X 3 NA
25 25 Placebo 1 59.75437 X-- 1 NA
525 25 Placebo 1 59.75437 X-- 2 13.380347
1025 25 Placebo 1 59.75437 X-- 3 27.726174
26 26 Placebo 1 64.86220 --- 1 27.951153
526 26 Placebo 1 64.86220 --- 2 14.652209
1026 26 Placebo 1 64.86220 --- 3 27.440682
27 27 Placebo 0 61.84734 --- 1 31.014231
527 27 Placebo 0 61.84734 --- 2 18.881365
1027 27 Placebo 0 61.84734 --- 3 30.151889
28 28 Placebo 0 60.00848 --X 1 41.288482
528 28 Placebo 0 60.00848 --X 2 32.966200
1028 28 Placebo 0 60.00848 --X 3 NA
29 29 Placebo 0 55.09433 --- 1 23.902144
529 29 Placebo 0 55.09433 --- 2 16.894180
1029 29 Placebo 0 55.09433 --- 3 30.083663
30 30 Placebo 0 57.16355 --- 1 27.915708
530 30 Placebo 0 57.16355 --- 2 21.604654
1030 30 Placebo 0 57.16355 --- 3 29.971840
31 31 Placebo 1 52.28091 --- 1 25.480237
531 31 Placebo 1 52.28091 --- 2 14.512467
1031 31 Placebo 1 52.28091 --- 3 26.898932
32 32 Placebo 1 59.14089 X-- 1 NA
532 32 Placebo 1 59.14089 X-- 2 20.627647
1032 32 Placebo 1 59.14089 X-- 3 24.941132
33 33 Placebo 0 63.53035 XX- 1 NA
533 33 Placebo 0 63.53035 XX- 2 NA
1033 33 Placebo 0 63.53035 XX- 3 30.308410
34 34 Placebo 0 54.70157 X-- 1 NA
534 34 Placebo 0 54.70157 X-- 2 17.220079
1034 34 Placebo 0 54.70157 X-- 3 27.116048
35 35 Placebo 0 62.82148 X-- 1 NA
535 35 Placebo 0 62.82148 X-- 2 22.904332
1035 35 Placebo 0 62.82148 X-- 3 30.546789
36 36 Placebo 0 55.81194 --- 1 24.716824
536 36 Placebo 0 55.81194 --- 2 11.343220
1036 36 Placebo 0 55.81194 --- 3 20.161854
37 37 Placebo 0 56.77520 --- 1 21.544340
537 37 Placebo 0 56.77520 --- 2 15.245485
1037 37 Placebo 0 56.77520 --- 3 27.421159
38 38 Placebo 1 53.60319 --- 1 31.008123
538 38 Placebo 1 53.60319 --- 2 21.882236
1038 38 Placebo 1 53.60319 --- 3 31.042851
39 39 Placebo 0 62.88837 X-X 1 NA
539 39 Placebo 0 62.88837 X-X 2 29.445792
1039 39 Placebo 0 62.88837 X-X 3 NA
40 40 Placebo 0 59.92034 X-- 1 NA
540 40 Placebo 0 59.92034 X-- 2 21.325835
1040 40 Placebo 0 59.92034 X-- 3 27.298584
41 41 Placebo 1 57.10396 X-- 1 NA
541 41 Placebo 1 57.10396 X-- 2 23.330492
1041 41 Placebo 1 57.10396 X-- 3 31.017001
42 42 Placebo 1 49.80586 --X 1 22.131323
542 42 Placebo 1 49.80586 --X 2 17.385187
1042 42 Placebo 1 49.80586 --X 3 NA
43 43 Placebo 1 56.92603 X-- 1 NA
543 43 Placebo 1 56.92603 X-- 2 17.181349
1043 43 Placebo 1 56.92603 X-- 3 24.707149
44 44 Placebo 1 52.17824 -X- 1 24.572247
544 44 Placebo 1 52.17824 -X- 2 NA
1044 44 Placebo 1 52.17824 -X- 3 32.111015
45 45 Placebo 1 63.82524 --- 1 28.795431
545 45 Placebo 1 63.82524 --- 2 15.302079
1045 45 Placebo 1 63.82524 --- 3 24.032779
46 46 Placebo 1 56.89171 --- 1 25.368353
546 46 Placebo 1 56.89171 --- 2 18.152139
1046 46 Placebo 1 56.89171 --- 3 23.564433
47 47 Placebo 1 56.04408 X-X 1 NA
547 47 Placebo 1 56.04408 X-X 2 21.594259
1047 47 Placebo 1 56.04408 X-X 3 NA
48 48 Placebo 1 62.31757 --- 1 26.621959
我将构建一种 table，其中报告了样本量 (tN)、平均值 (tMn) 和中值 (tMd)。为此，我打算使用以下代码
attach(dat_long)
flst <- list(times, treatment)
(tN <-
tapply(visual, flst,
FUN = function(x) length(x[!is.na(x)])))
(tMn <- tapply(scores, flst, FUN = mean))
(tMd <- tapply(scores, flst, FUN = median))
colnames(res <- cbind(tN, tMn, tMd))
nms1 <- rep(c("P", "A"), 3)
nms2 <- rep(c("n", "Mean", "Mdn"), rep(2, 3)) #n = numerosità
colnames(res) <- paste(nms1, nms2, sep = ":")
res
我认为不会消除可能的疑虑。请让我知道如何处理这个问题。
谢谢
下午好， 我正在尝试应用 tapply 函数，以便通过以下数据集的不同治疗组（'Placebo' 组和 'Active' 组）获取平均值：
> str(dat_long)
'data.frame': 1500 obs. of 7 variables:
$ subject.id: num 1 1 1 2 2 2 3 3 3 4 ...
$ treatment : Factor w/ 2 levels "Placebo","Active": 1 1 1 1 1 1 1 1 1 1 ...
$ sex : num 1 1 1 1 1 1 1 1 1 1 ...
$ age : num 58.1 58.1 58.1 54.8 54.8 ...
$ miss_pat : chr "---" "---" "---" "--X" ...
$ times : num 1 2 3 1 2 3 1 2 3 1 ...
$ scores : num 13.62 7.25 20.45 33.34 20.9 ..
我正在处理的数据集格式是“长”格式。我创建了以下列表对象
flst <- list(times, treatment)
通过收集不同的时间点和接受的治疗，其中我 运行 tapply() 函数。
(tN <-
tapply(scores, flst,
FUN = function(x) length(x[!is.na(x)])))
我不明白为什么我一直在找回同样的错误
Error in tapply(scores, flst, FUN = function(x) length(x[!is.na(x)])) :
arguments must have the same length
我试图寻找解决方案（例如，作为因子变量进行隐藏等），但其中 none 似乎适合我的情况。有人可能知道我要解决的问题吗？
为了以防万一，为了应对 NA 观察，我应该在代码中输入什么以及在哪里输入？
非常感谢关注
P.S。以防万一我在这里报告每个参数 lengths
> length(flst)
[1] 2
> length(scores)
[1] 1500
好的。 感谢到目前为止的回答。我将尝试提供更多细节，只是为了让情况更容易理解。
这是我正在处理的原始数据集，采用宽格式。
> dat_wide
subject.id treatment measure1 measure2 measure3 sex age miss_pat
1 1 1 13.61906 7.249175 20.44918 1 58.12831 ---
2 2 1 33.33751 20.896999 NA 1 54.77794 --X
3 3 1 NA 20.869364 26.12443 1 59.55550 X--
4 4 1 37.75143 26.253406 29.03896 1 59.37837 ---
5 5 1 28.21168 14.652736 23.65234 1 63.34226 ---
6 6 1 32.40856 18.702288 NA 0 55.36262 --X
7 7 1 31.63005 18.682889 NA 1 59.02119 --X
8 8 1 NA 18.244421 22.02274 0 57.57435 X--
9 9 1 NA 27.351230 36.86480 1 52.08140 X--
10 10 1 28.10867 16.769758 23.67930 1 62.93165 ---
11 11 1 33.55693 18.499841 NA 0 53.46754 --X
12 12 1 22.88379 14.061066 30.02255 1 57.49554 ---
13 13 1 30.99073 15.350301 20.69450 1 57.92600 ---
14 14 1 26.35118 15.195037 26.83573 0 58.42347 ---
15 15 1 20.80077 16.112581 34.78064 1 60.22746 ---
16 16 1 34.64003 NA 32.96944 1 56.28172 -X-
17 17 1 26.70361 23.846794 31.24356 0 57.93085 ---
18 18 1 25.61433 19.779788 25.75783 1 59.35028 ---
19 19 1 36.51201 24.594663 NA 0 59.32188 --X
20 20 1 33.18090 31.846358 34.21801 0 58.16217 ---
21 21 1 NA 23.813420 28.49156 1 58.39313 X--
22 22 1 28.20934 12.516007 NA 0 53.24436 --X
23 23 1 30.64196 NA 35.01832 1 59.12392 -X-
24 24 1 NA 26.596392 NA 1 56.22792 X-X
25 25 1 NA 13.380347 27.72617 1 59.75437 X--
26 26 1 27.95115 14.652209 27.44068 1 64.86220 ---
27 27 1 31.01423 18.881365 30.15189 0 61.84734 ---
28 28 1 41.28848 32.966200 NA 0 60.00848 --X
29 29 1 23.90214 16.894180 30.08366 0 55.09433 ---
30 30 1 27.91571 21.604654 29.97184 0 57.16355 ---
31 31 1 25.48024 14.512467 26.89893 1 52.28091 ---
32 32 1 NA 20.627647 24.94113 1 59.14089 X--
33 33 1 NA NA 30.30841 0 63.53035 XX-
34 34 1 NA 17.220079 27.11605 0 54.70157 X--
35 35 1 NA 22.904332 30.54679 0 62.82148 X--
36 36 1 24.71682 11.343220 20.16185 0 55.81194 ---
37 37 1 21.54434 15.245485 27.42116 0 56.77520 ---
38 38 1 31.00812 21.882236 31.04285 1 53.60319 ---
39 39 1 NA 29.445792 NA 0 62.88837 X-X
40 40 1 NA 21.325835 27.29858 0 59.92034 X--
41 41 1 NA 23.330492 31.01700 1 57.10396 X--
42 42 1 22.13132 17.385187 NA 1 49.80586 --X
43 43 1 NA 17.181349 24.70715 1 56.92603 X--
44 44 1 24.57225 NA 32.11101 1 52.17824 -X-
45 45 1 28.79543 15.302079 24.03278 1 63.82524 ---
46 46 1 25.36835 18.152139 23.56443 1 56.89171 ---
47 47 1 NA 21.594259 NA 1 56.04408 X-X
48 48 1 26.62196 22.544339 27.04008 1 62.31757 ---
49 49 1 24.22001 6.924847 19.89172 0 58.69229 ---
50 50 1 34.14105 22.283254 34.41771 1 57.73471 ---
51 51 1 18.90136 13.449724 28.69964 0 63.48494 ---
52 52 1 33.01857 20.880303 31.32787 1 60.22318 ---
53 53 1 31.06980 23.687328 32.66611 1 54.40638 ---
54 54 1 31.98485 20.204848 26.00955 0 62.71068 ---
55 55 1 30.50251 19.632418 19.03141 1 59.27135 ---
56 56 1 NA 24.607656 30.37130 1 59.40362 X--
57 57 1 33.55517 22.213067 27.59768 0 57.04789 ---
58 58 1 24.19856 10.095334 21.76153 0 54.56281 ---
59 59 1 29.62803 15.703452 26.99926 0 56.66213 ---
60 60 1 34.01922 18.515003 NA 0 52.76635 --X
61 61 1 29.86470 17.587567 26.02713 1 61.06847 ---
62 62 1 25.61215 20.746458 25.80680 1 64.25965 ---
63 63 1 27.60233 16.047261 27.63316 1 60.87826 ---
64 64 1 24.18180 15.007888 NA 1 62.95863 --X
65 65 1 30.34542 24.986253 32.33394 0 52.55152 ---
66 66 1 21.53614 15.999301 27.95432 1 57.96632 ---
67 67 1 NA 27.856354 NA 1 61.76696 X-X
68 68 1 22.09629 15.252170 28.43995 0 64.99414 ---
69 69 1 25.80952 18.565819 27.77111 0 61.26983 ---
70 70 1 32.99136 22.610543 NA 0 57.79422 --X
71 71 1 NA 20.701995 23.77574 0 59.83622 X--
72 72 1 25.53110 18.466437 29.50384 1 60.38576 ---
73 73 1 32.21151 21.740227 24.21088 1 56.07185 ---
74 74 1 21.28635 9.444369 18.28459 0 50.20183 ---
75 75 1 NA 25.000759 33.57668 1 50.22598 X--
76 76 1 26.12087 12.385796 18.91420 1 53.40782 ---
77 77 1 NA 22.767420 28.75717 1 48.27044 X--
78 78 1 24.64162 21.218430 NA 1 55.94655 --X
79 79 1 27.07266 15.348420 24.72210 0 53.69175 ---
80 80 1 32.75587 28.013513 NA 0 58.04689 --X
81 81 1 29.30842 17.072955 24.29882 1 61.06444 ---
82 82 1 NA 18.382335 28.69544 0 58.54447 X--
83 83 1 31.78905 24.603102 33.91789 0 56.24937 ---
84 84 1 27.99531 19.921259 25.14260 1 58.49543 ---
85 85 1 26.34945 17.718344 33.91154 1 65.19380 ---
86 86 1 31.28590 18.000405 29.33850 1 64.79698 ---
87 87 1 19.46943 12.058767 24.16417 0 61.99136 ---
88 88 1 33.05861 27.022073 35.98174 1 57.79309 ---
89 89 1 25.82099 14.353354 22.29782 1 56.99255 ---
90 90 1 NA 18.394579 25.89987 0 52.29756 X--
91 91 1 NA 17.594238 NA 0 57.60890 X-X
92 92 1 36.62178 25.121140 34.90862 1 59.53585 ---
93 93 1 31.58383 16.929367 18.06532 1 56.49771 ---
94 94 1 32.79600 25.161476 29.57509 1 58.27547 ---
95 95 1 32.57059 23.029130 NA 1 58.93147 --X
96 96 1 32.44934 12.892612 NA 1 58.54679 --X
97 97 1 28.83562 15.361608 21.96002 0 52.01731 ---
98 98 1 28.49706 22.351217 NA 1 58.02899 --X
99 99 1 25.28790 14.283020 NA 0 53.90112 --X
100 100 1 NA 20.031777 29.93904 0 59.34664 X--
101 101 1 30.86933 20.853957 23.67089 1 60.41797 ---
102 102 1 NA NA 25.25865 1 58.51619 XX-
103 103 1 32.60158 23.890740 27.78209 1 52.93076 ---
104 104 1 29.64438 26.275628 32.96612 0 57.67552 ---
105 105 1 19.47877 10.237089 14.53323 1 62.56323 ---
106 106 1 28.50985 24.172877 NA 1 56.20708 --X
107 107 1 NA 21.111304 27.44881 1 55.47168 X--
108 108 1 26.90495 15.140661 26.54333 0 59.25477 ---
109 109 1 NA 28.740031 NA 0 53.61378 X-X
110 110 1 27.55861 21.708753 30.61987 0 62.90328 ---
111 111 1 NA 26.379662 NA 1 55.03740 X-X
112 112 1 22.88802 13.638065 25.35349 0 57.83266 ---
113 113 1 32.92606 24.907022 27.52318 0 62.04887 ---
114 114 1 NA 23.853791 34.18453 1 60.97984 X--
115 115 1 NA 32.755543 34.09703 1 60.03185 X--
116 116 1 17.04010 10.772446 22.38210 1 56.68107 ---
117 117 1 20.80304 16.040884 19.17766 0 60.20649 ---
118 118 1 NA 15.337750 28.42040 0 58.86986 X--
119 119 1 26.22063 9.210329 16.85208 1 61.22018 ---
120 120 1 39.27848 21.273545 29.96761 0 57.69893 ---
121 121 1 NA 20.177623 27.03394 1 58.71332 X--
122 122 1 NA 19.084165 26.29572 1 61.85389 X--
123 123 1 25.33397 22.082934 35.30603 0 55.82552 ---
124 124 1 27.30526 22.526807 33.81731 1 58.29532 ---
125 125 1 NA 26.020347 35.15877 1 66.13519 X--
进一步更改后，我使用 tidyr 库的函数 gather 将其转换为长格式，并且我对每个观察进行了排序，以便为三个测量时间中的每一个排序主题列-点.
> dat_long
subject.id treatment sex age miss_pat times scores
1 1 Placebo 1 58.12831 --- 1 13.619060
501 1 Placebo 1 58.12831 --- 2 7.249175
1001 1 Placebo 1 58.12831 --- 3 20.449176
2 2 Placebo 1 54.77794 --X 1 33.337510
502 2 Placebo 1 54.77794 --X 2 20.896999
1002 2 Placebo 1 54.77794 --X 3 NA
3 3 Placebo 1 59.55550 X-- 1 NA
503 3 Placebo 1 59.55550 X-- 2 20.869364
1003 3 Placebo 1 59.55550 X-- 3 26.124432
4 4 Placebo 1 59.37837 --- 1 37.751430
504 4 Placebo 1 59.37837 --- 2 26.253406
1004 4 Placebo 1 59.37837 --- 3 29.038963
5 5 Placebo 1 63.34226 --- 1 28.211679
505 5 Placebo 1 63.34226 --- 2 14.652736
1005 5 Placebo 1 63.34226 --- 3 23.652342
6 6 Placebo 0 55.36262 --X 1 32.408561
506 6 Placebo 0 55.36262 --X 2 18.702288
1006 6 Placebo 0 55.36262 --X 3 NA
7 7 Placebo 1 59.02119 --X 1 31.630050
507 7 Placebo 1 59.02119 --X 2 18.682889
1007 7 Placebo 1 59.02119 --X 3 NA
8 8 Placebo 0 57.57435 X-- 1 NA
508 8 Placebo 0 57.57435 X-- 2 18.244421
1008 8 Placebo 0 57.57435 X-- 3 22.022740
9 9 Placebo 1 52.08140 X-- 1 NA
509 9 Placebo 1 52.08140 X-- 2 27.351230
1009 9 Placebo 1 52.08140 X-- 3 36.864796
10 10 Placebo 1 62.93165 --- 1 28.108672
510 10 Placebo 1 62.93165 --- 2 16.769758
1010 10 Placebo 1 62.93165 --- 3 23.679297
11 11 Placebo 0 53.46754 --X 1 33.556930
511 11 Placebo 0 53.46754 --X 2 18.499841
1011 11 Placebo 0 53.46754 --X 3 NA
12 12 Placebo 1 57.49554 --- 1 22.883795
512 12 Placebo 1 57.49554 --- 2 14.061066
1012 12 Placebo 1 57.49554 --- 3 30.022550
13 13 Placebo 1 57.92600 --- 1 30.990728
513 13 Placebo 1 57.92600 --- 2 15.350301
1013 13 Placebo 1 57.92600 --- 3 20.694504
14 14 Placebo 0 58.42347 --- 1 26.351176
514 14 Placebo 0 58.42347 --- 2 15.195037
1014 14 Placebo 0 58.42347 --- 3 26.835728
15 15 Placebo 1 60.22746 --- 1 20.800769
515 15 Placebo 1 60.22746 --- 2 16.112581
1015 15 Placebo 1 60.22746 --- 3 34.780644
16 16 Placebo 1 56.28172 -X- 1 34.640032
516 16 Placebo 1 56.28172 -X- 2 NA
1016 16 Placebo 1 56.28172 -X- 3 32.969443
17 17 Placebo 0 57.93085 --- 1 26.703609
517 17 Placebo 0 57.93085 --- 2 23.846794
1017 17 Placebo 0 57.93085 --- 3 31.243559
18 18 Placebo 1 59.35028 --- 1 25.614333
518 18 Placebo 1 59.35028 --- 2 19.779788
1018 18 Placebo 1 59.35028 --- 3 25.757833
19 19 Placebo 0 59.32188 --X 1 36.512015
519 19 Placebo 0 59.32188 --X 2 24.594663
1019 19 Placebo 0 59.32188 --X 3 NA
20 20 Placebo 0 58.16217 --- 1 33.180896
520 20 Placebo 0 58.16217 --- 2 31.846358
1020 20 Placebo 0 58.16217 --- 3 34.218013
21 21 Placebo 1 58.39313 X-- 1 NA
521 21 Placebo 1 58.39313 X-- 2 23.813420
1021 21 Placebo 1 58.39313 X-- 3 28.491556
22 22 Placebo 0 53.24436 --X 1 28.209340
522 22 Placebo 0 53.24436 --X 2 12.516007
1022 22 Placebo 0 53.24436 --X 3 NA
23 23 Placebo 1 59.12392 -X- 1 30.641962
523 23 Placebo 1 59.12392 -X- 2 NA
1023 23 Placebo 1 59.12392 -X- 3 35.018324
24 24 Placebo 1 56.22792 X-X 1 NA
524 24 Placebo 1 56.22792 X-X 2 26.596392
1024 24 Placebo 1 56.22792 X-X 3 NA
25 25 Placebo 1 59.75437 X-- 1 NA
525 25 Placebo 1 59.75437 X-- 2 13.380347
1025 25 Placebo 1 59.75437 X-- 3 27.726174
26 26 Placebo 1 64.86220 --- 1 27.951153
526 26 Placebo 1 64.86220 --- 2 14.652209
1026 26 Placebo 1 64.86220 --- 3 27.440682
27 27 Placebo 0 61.84734 --- 1 31.014231
527 27 Placebo 0 61.84734 --- 2 18.881365
1027 27 Placebo 0 61.84734 --- 3 30.151889
28 28 Placebo 0 60.00848 --X 1 41.288482
528 28 Placebo 0 60.00848 --X 2 32.966200
1028 28 Placebo 0 60.00848 --X 3 NA
29 29 Placebo 0 55.09433 --- 1 23.902144
529 29 Placebo 0 55.09433 --- 2 16.894180
1029 29 Placebo 0 55.09433 --- 3 30.083663
30 30 Placebo 0 57.16355 --- 1 27.915708
530 30 Placebo 0 57.16355 --- 2 21.604654
1030 30 Placebo 0 57.16355 --- 3 29.971840
31 31 Placebo 1 52.28091 --- 1 25.480237
531 31 Placebo 1 52.28091 --- 2 14.512467
1031 31 Placebo 1 52.28091 --- 3 26.898932
32 32 Placebo 1 59.14089 X-- 1 NA
532 32 Placebo 1 59.14089 X-- 2 20.627647
1032 32 Placebo 1 59.14089 X-- 3 24.941132
33 33 Placebo 0 63.53035 XX- 1 NA
533 33 Placebo 0 63.53035 XX- 2 NA
1033 33 Placebo 0 63.53035 XX- 3 30.308410
34 34 Placebo 0 54.70157 X-- 1 NA
534 34 Placebo 0 54.70157 X-- 2 17.220079
1034 34 Placebo 0 54.70157 X-- 3 27.116048
35 35 Placebo 0 62.82148 X-- 1 NA
535 35 Placebo 0 62.82148 X-- 2 22.904332
1035 35 Placebo 0 62.82148 X-- 3 30.546789
36 36 Placebo 0 55.81194 --- 1 24.716824
536 36 Placebo 0 55.81194 --- 2 11.343220
1036 36 Placebo 0 55.81194 --- 3 20.161854
37 37 Placebo 0 56.77520 --- 1 21.544340
537 37 Placebo 0 56.77520 --- 2 15.245485
1037 37 Placebo 0 56.77520 --- 3 27.421159
38 38 Placebo 1 53.60319 --- 1 31.008123
538 38 Placebo 1 53.60319 --- 2 21.882236
1038 38 Placebo 1 53.60319 --- 3 31.042851
39 39 Placebo 0 62.88837 X-X 1 NA
539 39 Placebo 0 62.88837 X-X 2 29.445792
1039 39 Placebo 0 62.88837 X-X 3 NA
40 40 Placebo 0 59.92034 X-- 1 NA
540 40 Placebo 0 59.92034 X-- 2 21.325835
1040 40 Placebo 0 59.92034 X-- 3 27.298584
41 41 Placebo 1 57.10396 X-- 1 NA
541 41 Placebo 1 57.10396 X-- 2 23.330492
1041 41 Placebo 1 57.10396 X-- 3 31.017001
42 42 Placebo 1 49.80586 --X 1 22.131323
542 42 Placebo 1 49.80586 --X 2 17.385187
1042 42 Placebo 1 49.80586 --X 3 NA
43 43 Placebo 1 56.92603 X-- 1 NA
543 43 Placebo 1 56.92603 X-- 2 17.181349
1043 43 Placebo 1 56.92603 X-- 3 24.707149
44 44 Placebo 1 52.17824 -X- 1 24.572247
544 44 Placebo 1 52.17824 -X- 2 NA
1044 44 Placebo 1 52.17824 -X- 3 32.111015
45 45 Placebo 1 63.82524 --- 1 28.795431
545 45 Placebo 1 63.82524 --- 2 15.302079
1045 45 Placebo 1 63.82524 --- 3 24.032779
46 46 Placebo 1 56.89171 --- 1 25.368353
546 46 Placebo 1 56.89171 --- 2 18.152139
1046 46 Placebo 1 56.89171 --- 3 23.564433
47 47 Placebo 1 56.04408 X-X 1 NA
547 47 Placebo 1 56.04408 X-X 2 21.594259
1047 47 Placebo 1 56.04408 X-X 3 NA
48 48 Placebo 1 62.31757 --- 1 26.621959
我将构建一种 table，其中报告了样本量 (tN)、平均值 (tMn) 和中值 (tMd)。为此，我打算使用以下代码
attach(dat_long)
flst <- list(times, treatment)
(tN <-
tapply(visual, flst,
FUN = function(x) length(x[!is.na(x)])))
(tMn <- tapply(scores, flst, FUN = mean))
(tMd <- tapply(scores, flst, FUN = median))
colnames(res <- cbind(tN, tMn, tMd))
nms1 <- rep(c("P", "A"), 3)
nms2 <- rep(c("n", "Mean", "Mdn"), rep(2, 3)) #n = numerosità
colnames(res) <- paste(nms1, nms2, sep = ":")
res
我认为不会消除可能的疑虑。请让我知道如何处理这个问题。
谢谢