# 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.

##### Usage

```
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)

##### Arguments

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

- levels
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*. Note that this set can be specified as smaller than`x`

`sort(unique(x))`

.- labels
*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.- exclude
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`

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

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

- …
(in

`ordered(.)`

): any of the above, apart from`ordered`

itself.- ifany
only add an

`NA`

level if it is used, i.e. if`any(is.na(x))`

.

##### Details

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
`NA`

s 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 `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 `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.

##### Value

`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>)`

.

##### Note

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.)

##### Warning

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.

##### References

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.

##### Examples

`library(base)`

```
# NOT RUN {
(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))
# }
# NOT RUN {
## suppose you want "NA" as a level, and to allow missing values.
(x <- factor(c(1, 2, NA), exclude = NULL))
is.na(x)[2] <- TRUE
x # [1] 1 <NA> <NA>
is.na(x)
# [1] FALSE TRUE FALSE
## 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))
table(addNA(Month, ifany = TRUE))
# }
```

*Documentation reproduced from package base, version 3.5.3, License: Part of R 3.5.3*

### Community examples

**xiaowyang@gmail.com**at Dec 3, 2018 base v3.5.1

> travel_time <- c("Dec", "Jan", "Feb", "Mar", "Apr", "May", "Jun") > duration (variable, levels=c(0, 1, 2, 3, 4, 5, 6) [1] Dec Jan Feb Mar Apr May Jun Levels: 1, 1, 2, 3, 4, 5, 6

**xiaowyang@gmail.com**at Dec 3, 2018 base v3.5.1

> travel_time <- c("Dec", "Jan", "Feb", "Mar", "Apr", "May", "Jun") > duration (variable, levels=c(0, 1, 2, 3, 4, 5, 6) [1] Dec Jan Feb Mar Apr May Jun Levels: 1, 1, 2, 3, 4, 5, 6

**xiaowyang@gmail.com**at Dec 3, 2018 base v3.5.1

> travel_time <- c("Dec", "Jan", "Feb", "Mar", "Apr", "May", "Jun") > duration (variable, levels=c(0, 1, 2, 3, 4, 5, 6) [1] Dec Jan Feb Mar Apr May Jun Levels: 1, 1, 2, 3, 4, 5, 6

**xiaowyang@gmail.com**at Dec 3, 2018 base v3.5.1

**mark@niemannross.com**at Nov 5, 2018 base v3.5.1

Example for [LinkedIn Learning : R for Data Science, Lunchbreak Lessons](https://linkedin-learning.pxf.io/rweekly_factor) ```r # Factors are lists of unique values, Stored as integers I.am.a.vector <- c("blue","black","green","white","black","blue","blue") # color of cars passing my window I.am.a.factor <- factor(I.am.a.vector) I.am.a.vector # notice the repeat of blue and black levels(I.am.a.factor) # no repeat! Efficient storage. levels(I.am.a.factor) <- c("negro","azul","verde","blanco") I.am.a.factor # now in spanish table(I.am.a.factor) # count frequency of values. table is a collection of factors nlevels(I.am.a.factor) # # of unique values barplot(table(I.am.a.factor)) # extra credit levels(ordered(I.am.a.factor)) sum(table(factor(I.am.a.vector,exclude = "blue"))) # count all cars except blue cars ```

**gvalentini4@gmail.com**at Oct 13, 2018 base v3.5.1

Use function factor() to create an ordinal categorical variable: ```r weather <- c("cold", "cool", "warm", "hot") f_weather <- factor(weather, ordered = TRUE, levels = c("cold", "cool", "warm", "hot")) f_weather ```

**dcp17kshitizgoel@imt.ac.in**at Dec 24, 2017 base v3.4.3

**dcp17kshitizgoel@imt.ac.in**at Dec 24, 2017 base v3.4.3

**dcp17kshitizgoel@imt.ac.in**at Dec 24, 2017 base v3.4.3

**dcp17kshitizgoel@imt.ac.in**at Dec 24, 2017 base v3.4.3

**dcp17kshitizgoel@imt.ac.in**at Dec 24, 2017 base v3.4.3

**dcp17kshitizgoel@imt.ac.in**at Dec 24, 2017 base v3.4.3

**dcp17kshitizgoel@imt.ac.in**at Dec 24, 2017 base v3.4.3

**dcp17kshitizgoel@imt.ac.in**at Dec 24, 2017 base v3.4.3

**dcp17kshitizgoel@imt.ac.in**at Dec 24, 2017 base v3.4.3

**dcp17kshitizgoel@imt.ac.in**at Dec 24, 2017 base v3.4.3

**dcp17kshitizgoel@imt.ac.in**at Dec 24, 2017 base v3.4.3

**dcp17kshitizgoel@imt.ac.in**at Dec 24, 2017 base v3.4.3

**maxinehe93@gmail.com**at Oct 24, 2017 base v3.4.1

Set levels: == ``` > test <- c("small", "big") > factor(test, levels=c("small", "big"), ordered=TRUE) [1] small big Levels: small < big ```