factor is used to encode a vector as a factor (the terms ‘category’ and ‘enumerated type’ are also used for factors). If argument
TRUE, the factor levels are assumed to be ordered. For compatibility with S there is also a function
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 values that
xmight 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
- either an optional vector of labels for the levels (in the same order as
levelsafter removing those in
exclude), or a character string of length 1.
- a vector of values to be excluded when forming the set of levels. This should be of the same type as
x, and will be coerced if necessary.
- 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’.
ordered(.)): any of the above, apart from
- (only add an
NAlevel if it is used, i.e. if
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[j], then the
i-th element of the result is
j. If no match is found for
levels (which will happen for excluded values) then the
i-th element of the result is set to
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 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 it should also be a factor with the same level set as
x or a set of codes for the levels to be excluded.
The codes of a factor may contain
NA. For a numeric
exclude = NULL to make
NA an extra level (prints as
<NA>); by default, this is the last level.
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
is.na on the left-hand-side of an assignment (as in
is.na(f)[i] <- TRUE; indexing inside
is.na does not work). Under those circumstances missing values are currently printed as
<NA>, i.e., identical to entries of level
is.factor is generic: you can write methods to handle specific classes of objects, see InternalMethods.
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.
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
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.
addNA modifies a factor by turning
NA into an extra level (so that
NA values are counted in tables, for instance).
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
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
"ordered" methods for the group generic
Ops which provide methods for the Comparison operators, and for the
range generics in
"ordered". (The rest of the groups and the
Math group generate an error as they are not meaningful for factors.)
!= 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.
Chambers, J. M. and Hastie, T. J. (1992) Statistical Models in S. Wadsworth & Brooks/Cole.
In earlier versions of R, storing character data as a factor was more space efficient if there is even a small proportion of repeats. However, identical character strings now share storage, so the difference is small in most cases. (Integer values are stored in 4 bytes whereas each reference to a character string needs a pointer of 4 or 8 bytes.)
[.factor for subsetting of factors.
(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)) is.na(x) <- TRUE x #  1 <NA> <NA> is.na(x) #  FALSE TRUE FALSE ## Using addNA() Month <- airquality$Month table(addNA(Month)) table(addNA(Month, ifany = TRUE))
Documentation reproduced from R 3.0.2. License: GPL-2.