Classes

Introduction#

The class of a data-object determines which functions will process its contents. The class-attribute is a character vector, and objects can have zero, one or more classes. If there is no class-attribute, there will still be an implicit class determined by an object’s mode. The class can be inspected with the function class and it can be set or modified by the class<- function. The S3 class system was established early in S’s history. The more complex S4 class system was established later

Remarks#

There are several functions for inspecting the “type” of an object. The most useful such function is class, although sometimes it is necessary to examine the mode of an object. Since we are discussing “types”, one might think that typeof would be useful, but generally the result from mode will be more useful, because objects with no explicit “class”-attribute will have function dispatch determined by the “implicit class” determined by their mode.

Vectors

Inspect classes

Every object in R is assigned a class. You can use class() to find the object’s class and str() to see its structure, including the classes it contains. For example:

class(iris)
[1] "data.frame"

str(iris)
'data.frame':    150 obs. of  5 variables:
 $ Sepal.Length: num  5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
 $ Sepal.Width : num  3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
 $ Petal.Length: num  1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
 $ Petal.Width : num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
 $ Species     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 ...

class(iris$Species)
[1] "factor"

We see that iris has the class data.frame and using str() allows us to examine the data inside. The variable Species in the iris data frame is of class factor, in contrast to the other variables which are of class numeric. The str() function also provides the length of the variables and shows the first couple of observations, while the class() function only provides the object’s class.

Vectors and lists

Data in R are stored in vectors. A typical vector is a sequence of values all having the same storage mode (e.g., characters vectors, numeric vectors). See ?atomic for details on the atomic implicit classes and their corresponding storage modes: "logical", "integer", "numeric" (synonym "double"), "complex", "character" and "raw". Many classes are simply an atomic vector with a class attribute on top:

x <- 1826
class(x) <- "Date"
x 
# [1] "1975-01-01"
 x <- as.Date("1970-01-01")
 class(x)
#[1] "Date"
 is(x,"Date")
#[1] TRUE
 is(x,"integer")
#[1] FALSE
 is(x,"numeric")
#[1] FALSE
  mode(x)
#[1] "numeric"

Lists are a special type of vector where each element can be anything, even another list, hence the R term for lists: “recursive vectors”:

mylist <- list( A = c(5,6,7,8), B = letters[1:10], CC = list( 5, "Z") )

Lists have two very important uses:

• Since functions can only return a single value, it is common to return complicated results in a list:

  f <- function(x) list(xplus = x + 10, xsq = x^2)
  
  f(7)
  # $xplus
  # [1] 17
  # 
  # $xsq
  # [1] 49

• Lists are also the underlying fundamental class for data frames. Under the hood, a data frame is a list of vectors all having the same length:

  L <- list(x = 1:2, y = c("A","B"))
  DF <- data.frame(L)
  DF
  #   x y
  # 1 1 A
  # 2 2 B
  is.list(DF)
  # [1] TRUE

The other class of recursive vectors is R expressions, which are “language”- objects