Error in data.frame(..., check.names = FALSE) : arguments imply differing number of rows: 18, 218

Question

I would like to compare pulse rate with whether people are left or right handed. The problem is that in my data only 18 people are left handed, where 218 are right handed. In trying to create a data frame, this is problematic, as both columns are not the same length. The error reads:

Error in data.frame(..., check.names = FALSE) : arguments imply differing number of rows: 18, 218.

Is there a way to fix this?

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

You can share data with `dput(head(df,n))`. It is more reproducible and makes the post less crowded. See [this](https://stackoverflow.com/questions/5963269/how-to-make-a-great-r-reproducible-example) for more guidance. — NelsonGon, Oct 20 '19 at 14:09
like this?? > dput(head(pulse)) c(92L, 104L, 87L, NA, 35L, 64L) > dput(head(whnd)) structure(c(2L, 1L, 2L, 2L, 2L, 2L), .Label = c("Left", "Right" ), class = "factor") @NelsonGon — Beth, Oct 20 '19 at 14:16
Please take a look at the answers there. You can comment here if they do not help. — NelsonGon, Oct 20 '19 at 14:17
I'm not sure why you would want to make two columns here. If you want to compare people who are left and right handed you would what handedess to be one column and pulse to be the other. Then you can make comparisons using whatever techniques you want. — Elin, Oct 20 '19 at 14:27
This question is in no way a duplicate of the linked question. You could say it is off topic because it is more about how to do data analysis than about programming. @NelsonGon — Elin, Oct 23 '19 at 12:45
@Elin Sorry, it's been days since I made this "flag". I normally retract my close votes if a user states that it is not what they need. In this case OP said nothing and someone with more "power" decided to close it. Sorry for the mistake(I did focus on the error). — NelsonGon, Oct 23 '19 at 13:14

score 2 · Answer 1 · answered Oct 20 '19 at 14:36

If you want to compare two groups the way to do it is to leave whnd as a column and then do bivariate analysis. There are many ways you can do this.

Let's use the am variable from mtcars as an example since it defines groups of two sizes. Let's get the mean of mpg. We will use the aggregate function from stats which is part of the base install of r.

aggregate(mpg~am, FUN= mean, data = mtcars)

  am      mpg
1  0 17.14737
2  1 24.39231

There are a lot of other ways to do this including using many different packages. Probably the tidyverse group_by and summarize would be a popular alternative.

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

@Beth If this answered your question you should accept it. If it is not an answer you should say so. See my comment on the actual question. — Elin, Oct 23 '19 at 12:46

Error in data.frame(..., check.names = FALSE) : arguments imply differing number of rows: 18, 218

1 Answers1