12666727b9
Reputation: 1139
tapply ()? Equal lengths?
Good afternoon, I'm trying applying the tapply function in order to obtain means reading through different treatment group (the 'Placebo' one and the 'Active' one) of the following dataset:
> 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 ..
The format of dataset I'm working on, is the "long" one. I've created the following list object
flst <- list(times, treatment)
by gathering the different time-poimt and the received treatments, where I've run the tapply() function.
(tN <-
tapply(scores, flst,
FUN = function(x) length(x[!is.na(x)])))
I cannot figure out why I'm perpetuating in getting back the same error
Error in tapply(scores, flst, FUN = function(x) length(x[!is.na(x)])) :
arguments must have the same length
I've tried to seeking for a solution (e.g coverting as a factor variables and so on), but none of them seemed suit my case. Anyone may have an idea the problem I'm going throgh?
Just in case, on purpose to cope with NA observation, what and where am I supposed to enter in the code?
Thanks a lot for paying attention
P.S. Just in case I report here following each of these arguments lengths
> length(flst)
[1] 2
> length(scores)
[1] 1500
Upvotes: -1
Views: 199
Answers (1)
12666727b9
Reputation: 1139
Ok. Thanks for whom answers so far. I'm going to try to provide more details just for getting the situation more understandable.
This is the original dataset I was working on, that is in the wide format.
> 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--
After further changes, I've converted it in the long format by using the function gather of the tidyr library and the I've ordered each observation so that the subject columns got ordered for each of the three measurements time-points.
> 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
I'm going to build a sort of table where both the sample size (tN), the mean (tMn) and the median (tMd) are reported. On this purpose I was going using the following code
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
I think nonìw to have the possible doubts cleared. Please, just let me know how to cope with this alternatively.
Thanks
Upvotes: 0
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