R中的Cbind投掷错误

Question

I have a dataframe of dimensions [1] 1 11 and [1] 3 29 . I am trying to cbind these two dataframes such that dataframe having one row will be copied thrice to the resultant dataframe.

When I use cbind, it works sometimes but throws error as

Error in data.frame(..., check.names = FALSE) : arguments imply differing number of rows: 1, 3

> dim(a)
[1]  1 11
> dim (b)
[1]  3 29
> cbind(a,b)
Error in data.frame(..., check.names = FALSE) : 
arguments imply differing number of rows: 1, 3

However if I try subsetting, it works.

> cbind(a[1:10],b)  #Works Fine
> cbind(a[1:11],b) #Throws Error

Note: It works sometimes, but doesn't work if I run the code again.

Thanks

Answer 1

Please refer [cbind] documentation

If there are several matrix arguments, they must all have the same number of columns (or rows) and this will be the number of columns (or rows) of the result.

The resultant row will be equal to the row number of a and b.

Answer 2

If you make them data tables then you do not have the error anymore.

cbind(iris[1:10, 1], iris[ 1:11,2:3]) # error

cbind(iris[1:10, 1], iris[ 1:11,2]) # warning message, missing values are copied
cbind(iris[1:10, 1], iris[ 1:11,2:3] %>% as.data.table()) # same
cbind(iris[1:10, 1] %>% as.data.table(), iris[ 1:11,2:3] )# same
      . Sepal.Width Petal.Length
 1: 5.1         3.5          1.4
 2: 4.9         3.0          1.4
 3: 4.7         3.2          1.3
 4: 4.6         3.1          1.5
 5: 5.0         3.6          1.4
 6: 5.4         3.9          1.7
 7: 4.6         3.4          1.4
 8: 5.0         3.4          1.5
 9: 4.4         2.9          1.4
10: 4.9         3.1          1.5
11: 5.1         3.7          1.5


# you can also make the extra values as zero or NA etc
temp <- iris[1:10,1] %>% as.data.table()
temp <- temp[match(rownames(iris[ 1:11,2:3]), rownames(temp[1:10, 1]))] 
# temp[is.na(temp)] <- ""
cbind(temp %>% as.data.table(), iris[ 1:11,2:3] )

      . Sepal.Width Petal.Length
 1: 5.1         3.5          1.4
 2: 4.9         3.0          1.4
 3: 4.7         3.2          1.3
 4: 4.6         3.1          1.5
 5: 5.0         3.6          1.4
 6: 5.4         3.9          1.7
 7: 4.6         3.4          1.4
 8: 5.0         3.4          1.5
 9: 4.4         2.9          1.4
10: 4.9         3.1          1.5
11:  NA         3.7          1.5