**R** has wide options for holding data, such as * scalars, vectors, matrices, arrays, data frames,* and

*. Letâ€™s look at each structure in this post.*

**lists**### Scalars

Scalars are one-element vectors. These are used to hold constants.

**Example**

a <- 1 b < "Phone" c <- TRUE

### Vectors

Vectors are one-dimensional arrays that hold numbers, characters, or logical data. The combine function `c()`

is used to form a vector. Vectors can hold only one data type you can mix numbers with characters. Let’s look at some example

**Numeric vector**

a <- c(2,10,-5,15)

**Character vector**

b <- c("Male", "Female", "Neutral")

**Logical vector**

c <- c(TRUE, FALSE, FALSE, TRUE)

To refer an elements of a vector you can use square brackets. For example,

a<-c(2,4,6,8,10,12,14,16,18,20) > a[6] [1] 12 > a[3:6] [1] 6 8 10 12 > a[c(1,7)] [1] 2 14

**Matrices**

A matrix is a two-dimensional array where each element has the same data type. Matrices are created with the `matrix`

function. The syntax for matric function is

a <- matrix(vector, nrow=number_of_rows, ncol=number_of_columns, byrow=logical_value, dimnames=list( char_vector_rownames, char_vector_colnames))

where `vector`

contains the elements for the matrix, `nrow`

and `ncol`

specify the row and column dimensions, and `dimnames`

contains optional row and column labels stored in character vectors. The option `byrow`

indicates whether the matrix should be filled in by row ( `byrow=TRUE`

) or by column ( `byrow=FALSE`

). The default is by column. The following listing demonstrates the matrix function.

Let’s see some examples for matrices now

**Creating a 5×2 matrix**

> a<-matrix(1:10, nrow=5,ncol=2) > a [,1] [,2] [1,] 1 6 [2,] 2 7 [3,] 3 8 [4,] 4 9 [5,] 5 10

Let’s create a `2x2`

matrix with row and column label

> cells <- c(2,8,12,16) > r <- c("A1","A2") > c <- c("X1","X2") > b<-matrix(cells,nrow = 2, ncol = 2, byrow = TRUE,dimnames = list(r,c)) > b X1 X2 A1 2 8 A2 12 16

In the above example, a matrix was created `byrow = TRUE`

, try the same argument with `FALSE`

and see the difference.

### Subscripts in matrix

You can also subscript matrix using square brackets

> x<-matrix(11:20, nrow=2) > x [,1] [,2] [,3] [,4] [,5] [1,] 11 13 15 17 19 [2,] 12 14 16 18 20 > m <-x[,3] > m [1] 15 16 > n <-x[1,4] > n [1] 17 > o <-x[2,c(3,4,5)] > o [1] 16 18 20

First, we created a `2x5`

matrix, then we subscript the matrix with square brackets mentioning the column number and row number.

### Arrays

Arrays are similar to matrices, the difference is this can have more than two dimensions. If we create an array of dimension (2, 3, 4) then it creates 4 rectangular matrices each with 2 rows and 3 columns. Arrays can store only data type. This can be created with `array`

function. The syntax for the function is

`array<-array(vector, dimentions, dimnames)`

Here `vector`

contains the data for the array, `dimensions`

is the numeric vector giving maximal index for each dimension and `dimnames`

is an optional list of dimension labels. This is useful in programming new statistical methods.

Let’s see this with the following examples,

> column <- c("COL1","COL2","COL3") > row <- c("ROW1","ROW2","ROW3") > matrix <- c("Matrix1","Matrix2") > a <- array(1:24,c(3,3,2),dimnames = list(column,row,matrix)) > a , , Matrix1 ROW1 ROW2 ROW3 COL1 1 4 7 COL2 2 5 8 COL3 3 6 9 , , Matrix2 ROW1 ROW2 ROW3 COL1 10 13 16 COL2 11 14 17 COL3 12 15 18

Keep reading about data structures. Data structures in R – Part 2