R Data Types

R exposes a few atomic data types and many more derived data types.

Contents

  1. R Data Types
    1. Classes
    2. Numeric
    3. Complex
    4. Logical
    5. Character
    6. Vector
    7. Array
    8. List
    9. Matrix
    10. Factor

Classes

Objects in the R language belong to one or more classes. These are inspected using the class() function. 

> now <- Sys.time()
> class(now)
[1] "POSIXct" "POSIXt"

Numeric

There are principally two subtypes of numeric data:

Most implementations of algorithms and estimations implicitly use double precision, not arbitrary precision. Furthermore, implicitly casting between numeric data types is generally an acceptable side effect.

Data types can be explicitly cast into a numeric type using the as.integer(), as.double(), and as.numeric() functions. Similarly, use the is.integer(), is.double(), and is.numeric() functions to test for these specific data types. 

my.char <- 1.23

Complex

The complex data type stores a complex numeric value. 

my.char <- 9i + 3

Complex values are not numeric in R. Calling is.numeric() on a complex value will return FALSE.

Logical

The logical data type stores a Boolean value. These are represented as the keywords TRUE and FALSE. 

my.char <- TRUE

Character

The character data type stores a string value. 

my.char <- "R"

Vector

A vector is an ordered container whose members are of a uniform data type. 

my.vector <- c(1, 2, 3)

Array

An array is another ordered container whose members are of a uniform data type. 

my.array <- array(c(1, 2, 3, 4))

List

A list is an ordered container of any data types. 

my.list <- list("R", 1.23, TRUE)

Matrix

A matrix is a multi-dimensional container whose members are of a uniform data type. 

my.matrix <- matrix(c(1, 2, 3, 4), nrow=2, ncol=2)

Factor

A factor is a data type that represents categorical levels. For one, this is an optimization for storage; the vector of unique levels is stored, then all row's values are replaced with integers that refer to an index in that vector. 

my.factor <- as.factor(my.factor)

to inspect the levels of a factor, try the levels() function.

When regressing on a factor, dummy coding is automatically applied. To force a reference level, try: 

my.factor <- relevel(my.factor, ref="Label")

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