base (version 3.6.2)

factor: Factors


The function factor is used to encode a vector as a factor (the terms ‘category’ and ‘enumerated type’ are also used for factors). If argument ordered is TRUE, the factor levels are assumed to be ordered. For compatibility with S there is also a function ordered.

is.factor, is.ordered, as.factor and as.ordered are the membership and coercion functions for these classes.


factor(x = character(), levels, labels = levels,
       exclude = NA, ordered = is.ordered(x), nmax = NA)

ordered(x, …)

is.factor(x) is.ordered(x)

as.factor(x) as.ordered(x)

addNA(x, ifany = FALSE)



a vector of data, usually taking a small number of distinct values.


an optional vector of the unique values (as character strings) that x might have taken. The default is the unique set of values taken by as.character(x), sorted into increasing order of x. Note that this set can be specified as smaller than sort(unique(x)).


either an optional character vector of labels for the levels (in the same order as levels after removing those in exclude), or a character string of length 1. Duplicated values in labels can be used to map different values of x to the same factor level.


a vector of values to be excluded when forming the set of levels. This may be factor with the same level set as x or should be a character.


logical flag to determine if the levels should be regarded as ordered (in the order given).


an upper bound on the number of levels; see ‘Details’.

(in ordered(.)): any of the above, apart from ordered itself.


only add an NA level if it is used, i.e. if any(


factor returns an object of class "factor" which has a set of integer codes the length of x with a "levels" attribute of mode character and unique (!anyDuplicated(.)) entries. If argument ordered is true (or ordered() is used) the result has class c("ordered", "factor"). Undocumentedly for a long time, factor(x) loses all attributes(x) but "names", and resets "levels" and "class".

Applying factor to an ordered or unordered factor returns a factor (of the same type) with just the levels which occur: see also [.factor for a more transparent way to achieve this.

is.factor returns TRUE or FALSE depending on whether its argument is of type factor or not. Correspondingly, is.ordered returns TRUE when its argument is an ordered factor and FALSE otherwise.

as.factor coerces its argument to a factor. It is an abbreviated (sometimes faster) form of factor.

as.ordered(x) returns x if this is ordered, and ordered(x) otherwise.

addNA modifies a factor by turning NA into an extra level (so that NA values are counted in tables, for instance).

.valid.factor(object) checks the validity of a factor, currently only levels(object), and returns TRUE if it is valid, otherwise a string describing the validity problem. This function is used for validObject(<factor>).


The interpretation of a factor depends on both the codes and the "levels" attribute. Be careful only to compare factors with the same set of levels (in the same order). In particular, as.numeric applied to a factor is meaningless, and may happen by implicit coercion. To transform a factor f to approximately its original numeric values, as.numeric(levels(f))[f] is recommended and slightly more efficient than as.numeric(as.character(f)).

The levels of a factor are by default sorted, but the sort order may well depend on the locale at the time of creation, and should not be assumed to be ASCII.

There are some anomalies associated with factors that have NA as a level. It is suggested to use them sparingly, e.g., only for tabulation purposes.

Comparison operators and group generic methods

There are "factor" and "ordered" methods for the group generic Ops which provide methods for the Comparison operators, and for the min, max, and range generics in Summary of "ordered". (The rest of the groups and the Math group generate an error as they are not meaningful for factors.)

Only == and != can be used for factors: a factor can only be compared to another factor with an identical set of levels (not necessarily in the same ordering) or to a character vector. Ordered factors are compared in the same way, but the general dispatch mechanism precludes comparing ordered and unordered factors.

All the comparison operators are available for ordered factors. Collation is done by the levels of the operands: if both operands are ordered factors they must have the same level set.


The type of the vector x is not restricted; it only must have an as.character method and be sortable (by order).

Ordered factors differ from factors only in their class, but methods and the model-fitting functions treat the two classes quite differently.

The encoding of the vector happens as follows. First all the values in exclude are removed from levels. If x[i] equals levels[j], then the i-th element of the result is j. If no match is found for x[i] in levels (which will happen for excluded values) then the i-th element of the result is set to NA.

Normally the ‘levels’ used as an attribute of the result are the reduced set of levels after removing those in exclude, but this can be altered by supplying labels. This should either be a set of new labels for the levels, or a character string, in which case the levels are that character string with a sequence number appended.

factor(x, exclude = NULL) applied to a factor without NAs is a no-operation unless there are unused levels: in that case, a factor with the reduced level set is returned. If exclude is used, since R version 3.4.0, excluding non-existing character levels is equivalent to excluding nothing, and when exclude is a character vector, that is applied to the levels of x. Alternatively, exclude can be factor with the same level set as x and will exclude the levels present in exclude.

The codes of a factor may contain NA. For a numeric x, set exclude = NULL to make NA an extra level (prints as <NA>); by default, this is the last level.

If NA is a level, the way to set a code to be missing (as opposed to the code of the missing level) is to use on the left-hand-side of an assignment (as in[i] <- TRUE; indexing inside does not work). Under those circumstances missing values are currently printed as <NA>, i.e., identical to entries of level NA.

is.factor is generic: you can write methods to handle specific classes of objects, see InternalMethods.

Where levels is not supplied, unique is called. Since factors typically have quite a small number of levels, for large vectors x it is helpful to supply nmax as an upper bound on the number of unique values.


Chambers, J. M. and Hastie, T. J. (1992) Statistical Models in S. Wadsworth & Brooks/Cole.

See Also

[.factor for subsetting of factors.

gl for construction of balanced factors and C for factors with specified contrasts. levels and nlevels for accessing the levels, and unclass to get integer codes.


Run this code
(ff <- factor(substring("statistics", 1:10, 1:10), levels = letters))
as.integer(ff)      # the internal codes
(f. <- factor(ff))  # drops the levels that do not occur
ff[, drop = TRUE]   # the same, more transparently

factor(letters[1:20], labels = "letter")

class(ordered(4:1)) # "ordered", inheriting from "factor"
z <- factor(LETTERS[3:1], ordered = TRUE)
## and "relational" methods work:
stopifnot(sort(z)[c(1,3)] == range(z), min(z) < max(z))
# }
## suppose you want "NA" as a level, and to allow missing values.
(x <- factor(c(1, 2, NA), exclude = NULL))[2] <- TRUE
x  # [1] 1    <NA> <NA>

## More rational, since R 3.4.0 :
factor(c(1:2, NA), exclude =  "" ) # keeps <NA> , as
factor(c(1:2, NA), exclude = NULL) # always did
## exclude = <character>
z # ordered levels 'A < B < C'
factor(z, exclude = "C") # does exclude
factor(z, exclude = "B") # ditto

## Now, labels maybe duplicated:
## factor() with duplicated labels allowing to "merge levels"
x <- c("Man", "Male", "Man", "Lady", "Female")
## Map from 4 different values to only two levels:
(xf <- factor(x, levels = c("Male", "Man" , "Lady",   "Female"),
                 labels = c("Male", "Male", "Female", "Female")))
#> [1] Male   Male   Male   Female Female
#> Levels: Male Female

## Using addNA()
Month <- airquality$Month
table(addNA(Month, ifany = TRUE))
# }

Run the code above in your browser using DataLab