The first discrete data type examined is the integer type. Values are 32-bit integers. In most circumstances, a number must be explicitly cast as being an integer, as the default type in R is a double precision number. There are a variety of commands used to cast integers as well as allocate space for integers. The integer command takes a number for an argument and will return a vector of integers whose length is given by the argument:

> bubba <- integer(12)
> bubba
 [1] 0 0 0 0 0 0 0 0 0 0 0 0
> bubba[1]
[1] 0
> bubba[2]
[1] 0
> bubba[[4]]
[1] 0
>  b[4] <- 15
> b
 [1]  0  0  0 15  0  0  0  0  0  0  0  0

In the preceding example, a vector of twelve integers was defined. The default values are zero, and the individual entries in the vector are accessed using braces. The first entry in the vector has index 1, so in this example, bubba[1] refers to the initial entry in the vector. Note that there are two ways to access an element in the vector: single versus double braces. For a vector, the two methods are nearly the same, but when we explore the use of lists as opposed to vectors, the meaning will change. In short, the double braces return objects of the same type as the elements within the vector, and the single braces return values of the same type as the variable itself. For example, using single braces on a list will return a list, while double braces may return a vector.

A number can be cast as an integer using the as.integer command. A variable's type can be checked using the typeof command. The typeof command indicates how R stores the object and is different from the class command, which is an attribute that you can change or query:

> as.integer(13.2)
[1] 13
> thisNumber <- as.integer(8/3)
> typeof(thisNumber)
[1] "integer"

Note that a sequence of numbers can be automatically created using either the : operator or the seq command:

> 1:5
[1] 1 2 3 4 5
> myNum <- as.integer(1:5)


> myNum[1]
[1] 1
> myNum[3]
[1] 3

> seq(4,11,by=2)
[1]  4  6  8 10
> otherNums <- seq(4,11,by=2)


> otherNums[3]
[1] 8

A common task is to determine whether or not a variable is of a certain type. For integers, the is.integer command is used to determine whether or not a variable has an integer type:

> a <- 1.2
> typeof(a)
[1] "double"
> is.integer(a)
[1] FALSE

> a <- as.integer(1.2)
> typeof(a)
[1] "integer"
> is.integer(a)
[1] TRUE

Logical data consists of variables that are either true or false. The words TRUE and FALSE are used to designate the two possible values of a logical variable. (The TRUE value can also be abbreviated to T, and the FALSE value can be abbreviated to F.) The basic commands associated with logical variables are similar to the commands for integers discussed in the previous subsection. The logical command is used to allocate a vector of Boolean values. In the following example, a logical vector of length 10 is created. The default value is FALSE, and the Boolean not operator is used to flip the values to evaluate to TRUE:

> b <- logical(10)
> b
 [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
> b[3]
[1] FALSE
> !b
 [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
> !b[5]
[1] TRUE
> typeof(b)
[1] "logical"
> mode(b)
[1] "logical"
> storage.mode(b)
[1] "logical"
>  b[3] <- TRUE
> b
 [1] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE

To cast a value to a logical type, you can use the as.logical command. Note that zero is mapped to a value of FALSE and other numbers are mapped to a value of TRUE:

> a <- -1:1
> a
[1] -1  0  1
> as.logical(a)
[1]  TRUE FALSE  TRUE

To determine whether or not a value has a logical type, you use the is.logical command:

> b <- logical(4)
> b
[1] FALSE FALSE FALSE FALSE
> is.logical(b)
[1] TRUE

The standard operators for logical operations are available, and a list of some of the more common operations is given in Table 1. Note that there is a difference between operations such as & and &&. A single & is used to perform an and operation on each pairwise element of two vectors, while the double && returns a single logical result using only the first elements of the vectors:

> l1 <- c(TRUE,FALSE)
> l2 <- c(TRUE,TRUE)
> l1&l1
[1]  TRUE FALSE
> l1&&l1
[1] TRUE
> l1|l2
[1] TRUE TRUE
> l1||l2
[1] TRUE

The following table shows various logical operators and their description:

> a <- "hello"
> a
[1] "hello"
> b <- 'there'
> b
[1] "there"
> typeof(a)
[1] "character"

> many <- character(3)
> many
[1] "" "" ""
> many[2] <- "this is the second"
> many[3] <- 'yo, third!'
> many[1] <- "and the first"
> many
[1] "and the first"      "this is the second" "yo, third!"        

> a <- 3.0
> a
[1] 3
> b <- as.character(a)
> b
[1] "3"

> a <- as.character(4.5)
> a
[1] "4.5"
> is.character(a)
[1] TRUE

> lev <- factor(x=c("one","two","three","one"))
> lev
[1] one   two   three one  
Levels: one three two
> levels(lev)
[1] "one"   "three" "two"  
> sort(lev)
[1] one   one   two   three
Levels: one two three

>  lev <- factor(x=c("one","two","three","one"),levels=c("one","two","three"))
> lev
[1] one   two   three one  
Levels: one two three
> levels(lev)
[1] "one"   "two"   "three"
> sort(lev)
[1] one   one   two   three
Levels: one two three

The data types for continuous data types are given here. The double and complex data types are given. A list of the commands discussed here is given in Table 2 and Table 3.

> d <- double(8)
> d
[1] 0 0 0 0 0 0 0 0
> typeof(d)
[1] "double"
> d[3] <- 17
> d
[1]  0  0 17  0  0  0  0  0

> 1i
[1] 0+1i
> 1i*1i
[1] -1+0i
> z <- 3+2i
> z
[1] 3+2i
> z*z
[1] 5+12i
> Mod(z)
[1] 3.605551
> Re(z)
[1] 3
> Im(z)
[1] 2
> Arg(z)
[1] 0.5880026
> Conj(z)
[1] 3-2i

> z <- complex(3)
> z
[1] 0+0i 0+0i 0+0i
> typeof(z)
[1] "complex"
> z <- complex(real=c(1,2),imag=c(3,4))
> z
[1] 1+3i 2+4i
> Re(z)
[1] 1 2

There are two other common data types that occur that are important. We will discuss these two data types and provide a note about objects. The two data types are NA and NULL. These are brief comments, as these are recurring topics that we will revisit many times.

The first data type is a constant, NA. This is a type used to indicate a missing value. It is a constant in R, and a variable can be tested using the is.na command, as follows:

> n <- c(NA,2,3,NA,5)
> n
[1] NA  2  3 NA  5
> is.na(n)
[1]  TRUE FALSE FALSE  TRUE FALSE
> n[!is.na(n)]
[1] 2 3 5

Another special type is the NULL type. It has the same meaning as the null keyword in the C language. It is not an actual type but is used to determine whether or not an object exists:

> a <- NULL
> typeof(a)
[1] "NULL"

Finally, we'll quickly explore the term objects. The variables that we defined in all of the preceding examples are treated as objects within the R environment. When we start writing functions and creating classes, it will be important to realize that they are treated like variables. The names used to assign variables are just a shortcut for R to determine where an object is located.

For example, the complex command is used to allocate a vector of complex values. The command is defined to be a set of instructions, and there is an object called complex that points to those instructions:

> complex
function (length.out = 0L, real = numeric(), imaginary = numeric(),
    modulus = 1, argument = 0)
{
    if (missing(modulus) && missing(argument)) {
        .Internal(complex(length.out, real, imaginary))
    }
    else {
        n <- max(length.out, length(argument), length(modulus))
        rep_len(modulus, n) * exp((0+1i) * rep_len(argument,
            n))
    }
}
<bytecode: 0x2489c80>
<environment: namespace:base>

There is a difference between calling the complex()function and referring to the set of instructions located at complex.