我想将脉率与左撇子还是右撇子相比较。问题在于，在我的数据中，左手只有18个人，右手是218个人。在尝试创建数据帧时，这是有问题的，因为两列的长度都不相同。该错误显示为：
Error in data.frame(..., check.names = FALSE) : arguments imply differing number of rows: 18, 218.
有没有办法解决这个问题？
table(pulse)
pulse
35 40 48 50 54 55 56 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 78 79
1 1 2 2 1 1 1 1 12 1 4 1 9 6 6 1 16 1 13 2 14 1 5 5 13 4 3
80 81 83 84 85 86 87 88 89 90 92 96 97 98 100 104
18 1 4 5 4 3 2 4 1 8 6 3 1 1 2 2
table(whnd)
whnd
Left Right
18 218
left= collect[which(whnd=="Left"),]
left
left
sex wrspan nwspan whnd fold pulse clap exer smoke height units age
2 2 19.5 20.5 1 3 104 1 2 4 177.80 1 17.583
18 2 19.4 19.2 1 3 74 3 3 2 182.88 1 18.333
51 2 22.0 21.5 1 3 55 1 1 2 200.00 2 18.500
60 2 20.6 21.0 1 1 NA 1 1 3 175.26 1 18.417
64 1 18.7 18.0 1 1 NA 1 2 2 170.00 2 19.833
81 2 19.5 19.5 1 3 66 1 3 2 NA NA 16.750
90 1 18.0 17.7 1 3 92 1 3 2 NA NA 17.583
118 2 23.0 22.0 1 1 83 1 3 1 193.04 1 18.917
119 1 18.5 18.0 1 1 100 2 3 2 171.00 2 18.917
124 2 19.8 20.0 1 1 59 3 1 2 180.00 2 17.417
134 1 15.4 16.4 1 1 80 1 1 3 160.02 1 18.500
137 NA 19.8 19.0 1 1 73 2 1 2 172.00 2 21.500
145 1 20.0 19.5 1 3 68 2 1 2 172.00 2 19.167
162 2 18.1 18.2 1 2 NA 3 3 2 168.00 2 21.167
172 2 20.5 19.5 1 1 80 3 3 3 182.88 1 18.667
176 1 19.0 18.5 1 1 104 1 1 2 170.00 2 17.250
209 2 17.5 17.0 1 1 97 2 2 2 165.00 2 19.500
212 1 17.5 17.5 1 3 83 2 3 2 163.00 2 17.250
right= collect[which(whnd=="Right"),]
right
sex wrspan nwspan whnd fold pulse clap exer smoke height units age
1 1 18.5 18.0 2 3 92 1 3 2 173.00 2 18.250
3 2 18.0 13.3 2 1 87 2 2 3 NA NA 16.917
4 2 18.8 18.9 2 3 NA 2 2 2 160.00 2 20.333
5 2 20.0 20.0 2 2 35 3 3 2 165.00 2 23.667
6 1 18.0 17.7 2 1 64 3 3 2 172.72 1 21.000
7 2 17.7 17.7 2 1 83 3 1 2 182.88 1 18.833
8 1 17.0 17.3 2 3 74 3 1 2 157.00 2 35.833
9 2 20.0 19.5 2 3 72 3 3 2 175.00 2 19.000
10 2 18.5 18.5 2 3 90 3 3 2 167.00 2 22.333
11 1 17.0 17.2 2 1 80 3 1 2 156.20 1 28.500
12 2 21.0 21.0 2 3 68 1 1 2 NA NA 18.250
13 1 16.0 16.0 2 1 NA 3 3 2 155.00 2 18.750
14 1 19.5 20.2 2 1 66 2 3 2 155.00 2 17.500
15 2 16.0 15.5 2 3 60 3 3 2 NA NA 17.167
16 1 17.5 17.0 2 3 NA 3 1 2 156.00 2 17.167
17 1 18.0 18.0 2 1 89 2 1 2 157.00 2 19.333
19 2 20.5 20.5 2 1 NA 1 3 2 190.50 1 19.750
20 2 21.0 20.9 2 3 78 3 1 2 177.00 2 17.917
21 2 21.5 22.0 2 3 72 1 1 2 190.50 1 17.917
22 2 20.1 20.7 2 1 72 3 1 2 180.34 1 18.167
23 2 18.5 18.0 2 1 64 3 1 2 180.34 1 17.833
24 2 21.5 21.2 2 3 62 3 3 2 184.00 2 18.250
25 1 17.0 17.5 2 3 64 1 3 2 NA NA 19.167
26 2 18.5 18.5 2 2 90 2 3 2 NA NA 17.583
27 2 21.0 20.7 2 3 90 3 3 2 172.72 1 17.500
28 2 20.8 21.4 2 3 62 2 1 2 175.26 1 18.083
29 2 17.8 17.8 2 1 76 2 1 2 NA NA 21.917
30 2 19.5 19.5 2 1 79 3 3 2 167.00 2 19.250
31 1 18.5 18.0 2 3 76 3 2 3 NA NA 41.583
32 2 18.8 18.2 2 1 78 3 1 2 180.00 2 17.500
33 1 17.1 17.5 2 3 72 3 1 1 166.40 1 39.750
34 2 20.1 20.0 2 3 70 3 3 2 180.00 2 17.167
35 2 18.0 19.0 2 1 54 2 3 4 NA NA 17.750
36 2 22.2 21.0 2 1 66 3 1 3 190.00 2 18.000
37 1 16.0 16.5 2 1 NA 3 3 2 168.00 2 19.000
38 2 19.4 18.5 2 3 72 2 1 2 182.50 2 17.917
39 2 22.0 22.0 2 3 80 3 3 2 185.00 2 35.500
40 2 19.0 19.0 2 3 NA 2 1 3 171.00 2 19.917
41 1 17.5 16.0 2 1 NA 3 3 2 169.00 2 17.500
42 1 17.8 18.0 2 3 72 3 3 2 154.94 1 17.083
43 2 NA NA 2 3 60 NA 3 2 172.00 2 28.583
44 1 20.1 20.2 2 1 80 3 3 2 176.50 1 17.500
46 2 17.0 17.5 2 3 NA 2 1 2 180.34 1 18.500
47 2 23.2 22.7 2 1 84 1 1 4 180.00 2 18.917
48 2 22.5 23.0 2 3 96 3 2 2 170.00 2 19.417
49 1 18.0 17.6 2 3 60 3 3 3 168.00 2 18.417
50 1 18.0 17.9 2 3 50 1 2 2 165.00 2 30.750
52 2 20.5 20.0 2 1 68 3 1 2 190.00 2 17.500
53 2 17.0 18.0 2 1 78 1 3 2 170.18 1 18.333
54 2 20.5 19.5 2 1 56 3 1 2 179.00 2 17.417
55 2 22.5 22.5 2 3 65 3 1 4 182.00 2 20.000
56 2 18.5 18.5 2 1 NA 2 1 2 171.00 2 18.333
57 1 15.5 15.4 2 3 70 2 2 2 157.48 1 17.167
58 2 19.5 19.7 2 3 72 3 1 2 NA NA 17.417
59 2 19.5 19.0 2 1 62 3 1 2 177.80 1 17.667
61 2 22.8 23.2 2 3 66 2 1 2 187.00 2 20.333
62 1 18.5 18.2 2 3 72 2 1 2 167.64 1 17.333
63 1 19.6 19.7 2 1 70 3 1 2 178.00 2 17.500
65 1 17.3 18.0 2 1 64 2 1 2 164.00 2 18.583
66 2 19.5 19.8 2 2 NA 3 1 2 183.00 2 18.000
67 1 19.0 19.1 2 1 NA 2 1 2 172.00 2 30.667
68 1 18.5 18.0 2 3 64 3 1 2 NA NA 16.917
69 2 19.0 19.0 2 1 NA 3 3 2 180.00 2 19.917
70 2 21.0 19.5 2 1 80 1 2 NA NA NA 18.333
71 1 18.0 17.5 2 1 64 1 1 2 170.00 2 17.583
72 2 19.4 19.5 2 3 NA 3 1 1 176.00 2 17.833
73 1 17.0 16.6 2 3 68 3 3 2 171.00 2 17.667
74 1 16.5 17.0 2 1 40 1 1 2 167.64 1 17.417
75 1 15.6 15.8 2 3 88 1 3 2 165.00 2 17.750
76 1 17.5 17.5 2 2 68 3 1 1 170.00 2 20.667
77 1 17.0 17.6 2 1 76 3 3 2 165.00 2 23.583
78 1 18.6 18.0 2 1 NA 2 1 1 165.10 1 17.167
79 1 18.3 18.5 2 3 68 2 3 2 165.10 1 17.083
80 2 20.0 20.5 2 1 NA 3 1 2 185.42 1 18.750
82 2 19.2 18.9 2 3 76 3 1 2 176.50 1 20.167
83 1 17.5 17.5 2 3 98 1 1 2 NA NA 17.667
84 1 17.0 17.4 2 3 NA 2 3 2 NA NA 17.167
85 2 23.0 23.5 2 1 90 3 1 2 167.64 1 17.167
86 1 17.7 17.0 2 3 76 3 3 2 167.00 2 17.250
87 1 18.2 18.0 2 1 70 3 3 2 162.56 1 18.000
88 1 18.3 18.5 2 3 75 1 1 2 170.00 2 18.750
89 2 18.0 18.0 2 2 60 3 1 2 179.00 2 21.583
91 2 20.5 20.0 2 3 75 1 3 2 183.00 2 19.667
[ reached getOption("max.print") -- omitted 135 rows ]
hand=as.data.frame(cbind(left, right))
hand=as.data.frame(cbind(left, right))
Error in data.frame(..., check.names = FALSE) :
arguments imply differing number of rows: 18, 218
答案 0 :(得分：1)
如果要比较两个组，则将whnd留为一列，然后进行双变量分析。您可以通过多种方式执行此操作。
让我们以am中的mtcars变量为例，因为它定义了两个大小的组。让我们得到mpg的平均值。我们将使用aggregate中的stats函数，它是r基本安装的一部分。
aggregate(mpg~am, FUN= mean, data = mtcars)
am mpg
1 0 17.14737
2 1 24.39231
还有许多其他方法可以做到这一点，包括使用许多不同的软件包。整洁的group_by和summarize可能是一个受欢迎的选择。
library(dplyr)
mtcars %>% group_by(am) %>% summarize("Mean mpg"= mean(mpg))
# A tibble: 2 x 2
am `Mean mpg`
<dbl> <dbl>
1 0 17.1
2 1 24.4