More on the Vector/Matrix Distinction

At the beginning of the chapter, I said that a matrix is just a vector but with two additional attributes: the number of rows and the number of columns. Here, we’ll take a closer look at the vector nature of matrices. Consider this example:

> z <- matrix(1:8,nrow=4)
> z
     [,1] [,2]
[1,]    1    5
[2,]    2    6
[3,]    3    7
[4,]    4    8

As z is still a vector, we can query its length:

> length(z)
[1] 8

But as a matrix, z is a bit more than a vector:

> class(z)
[1] "matrix"
> attributes(z)
$dim
[1] 4 2

In other words, there actually is a matrix class, in the object-oriented programming sense. As noted in Chapter 1, most of R consists of S3 classes, whose components are denoted by dollar signs. The matrix class has one attribute, named dim, which is a vector containing the numbers of rows and columns in the matrix. Classes will be covered in detail in Chapter 9.

You can also obtain dim via the dim() function:

> dim(z)
[1] 4 2

The numbers of rows and columns are obtainable individually via the nrow() and ncol() functions:

> nrow(z)
[1] 4
> ncol(z)
[1] 2

These just piggyback on dim(), as you can see by inspecting the code. Recall once again that objects can be printed in interactive mode by simply typing their names:

> nrow
function (x)
dim(x)[1]

These functions are useful when you are writing a general-purpose library function whose argument is a matrix. By being able to determine the number of rows and columns in your code, you alleviate the caller of the burden of supplying that information as two additional arguments. This is one of the benefits of object-oriented programming.