I ran the following in R and received the same output for both matrix()
and as.matrix()
and now I am not sure what the difference between them is:
> a=c(1,2,3,4)
> a
[1] 1 2 3 4
> matrix(a)
[,1]
[1,] 1
[2,] 2
[3,] 3
[4,] 4
> as.matrix(a)
[,1]
[1,] 1
[2,] 2
[3,] 3
[4,] 4
matrix
constructs a matrix from its first argument, with a given number of rows and columns. If the supplied object isn't large enough for the desired output, matrix
will recycle its elements: for example, matrix(1:2), nrow=3, ncol=4)
. Conversely, if the object is too big, then the surplus elements will be dropped: for example, matrix(1:20, nrow=3, ncol=4)
.
as.matrix
converts its first argument into a matrix, the dimensions of which will be inferred from the input.
matrix
takes data
and further arguments nrow
and ncol
.
?matrix
If one of ‘nrow’ or ‘ncol’ is not given, an attempt is made to
infer it from the length of ‘data’ and the other parameter. If
neither is given, a one-column matrix is returned.
as.matrix
is a method with different behaviours for different types, but mainly to give back an n*m matrix from an n*m input.
?as.matrix
‘as.matrix’ is a generic function. The method for data frames
will return a character matrix if there is only atomic columns and
any non-(numeric/logical/complex) column, applying ‘as.vector’ to
factors and ‘format’ to other non-character columns. Otherwise,
the usual coercion hierarchy (logical < integer < double <
complex) will be used, e.g., all-logical data frames will be
coerced to a logical matrix, mixed logical-integer will give a
integer matrix, etc.
The difference between them comes primarily from the shape of the input, matrix
doesn't care about the shape, as.matrix
does and will maintain it (though the details depend on the actual methods for the input, and in your case a dimensionless vector corresponds to a single column matrix.) It doesn't matter if the input is raw, logical, integer, numeric, character, or complex, etc.
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