R in a Nutshell

Introduction to Data Structures

In R, you can construct more complicated data structures than just vectors. An array is a multidimensional vector. Vectors and arrays are stored the same way internally, but an array may be displayed differently and accessed differently. An array object is just a vector that’s associated with a dimension attribute. Here’s a simple example.

First, let’s define an array explicitly:

> a <- array(c(1,2,3,4,5,6,7,8,9,10,11,12),dim=c(3,4))

Here is what the array looks like:

> a
     [,1] [,2] [,3] [,4]
[1,]    1    4    7   10
[2,]    2    5    8   11
[3,]    3    6    9   12

And here is how you reference one cell:

> a[2,2]
[1] 5

Now, let’s define a vector with the same contents:

> v <- c(1,2,3,4,5,6,7,8,9,10,11,12)
> v
 [1]  1  2  3  4  5  6  7  8  9 10 11 12

A matrix is just a two-dimensional array:

> m <- matrix(data=c(1,2,3,4,5,6,7,8,9,10,11,12),nrow=3,ncol=4)
> m
     [,1] [,2] [,3] [,4]
[1,]    1    4    7   10
[2,]    2    5    8   11
[3,]    3    6    9   12

Arrays can have more than two dimensions. For example:

> w <- array(c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18),dim=c(3,3,2))
> w
, , 1

     [,1] [,2] [,3]
[1,]    1    4    7
[2,]    2    5    8
[3,]    3    6    9

, , 2

     [,1] [,2] [,3]
[1,]   10   13   16
[2,]   11   14   17
[3,]   12   15   18

> w[1,1,1]
[1] 1

R uses very clean syntax for referring to part of an array. You specify separate indices for each dimension, separated by commas:

> a[1,2]
[1] 4
> a[1:2,1:2]
     [,1] [,2]
[1,]    1    4
[2,]    2    5

To get all rows (or columns) from a dimension, simply omit the indices:

> # first row only > a[1,] [1] 1 4 7 10 > # first column only > a[,1] [1] ...