table
Cross Tabulation and Table Creation
table
uses the cross-classifying factors to build a contingency
table of the counts at each combination of factor levels.
- Keywords
- category
Usage
table(..., exclude = if (useNA == "no") c(NA, NaN), useNA = c("no", "ifany", "always"), dnn = list.names(...), deparse.level = 1)
as.table(x, ...)
is.table(x)
"as.data.frame"(x, row.names = NULL, ..., responseName = "Freq", stringsAsFactors = TRUE, sep = "", base = list(LETTERS))
Arguments
- ...
- one or more objects which can be interpreted as factors
(including character strings), or a list (or data frame) whose
components can be so interpreted. (For
as.table
, arguments passed to specific methods; foras.data.frame
, unused.) - exclude
- levels to remove for all factors in
...
. If set toNULL
, it impliesuseNA = "always"
. See Details for its interpretation for non-factor arguments. - useNA
- whether to include
NA
values in the table. See Details. - dnn
- the names to be given to the dimensions in the result (the dimnames names).
- deparse.level
- controls how the default
dnn
is constructed. See Details. - x
- an arbitrary R object, or an object inheriting from class
"table"
for theas.data.frame
method. Note thatas.data.frame.table(x, *)
may be called explicitly for non-tablex
for reshapingarray
s. - row.names
- a character vector giving the row names for the data frame.
- responseName
- The name to be used for the column of table entries, usually counts.
- stringsAsFactors
- logical: should the classifying factors be returned as factors (the default) or character vectors?
- sep, base
- passed to
provideDimnames
.
Details
If the argument dnn
is not supplied, the internal function
list.names
is called to compute the dimname names. If the
arguments in ...
are named, those names are used. For the
remaining arguments, deparse.level = 0
gives an empty name,
deparse.level = 1
uses the supplied argument if it is a symbol,
and deparse.level = 2
will deparse the argument.
Only when exclude
is specified and non-NULL (i.e., not by
default), will table
potentially drop levels of factor
arguments.
useNA
controls if the table includes counts of NA
values: the allowed values correspond to never, only if the count is
positive and even for zero counts. This is overridden by specifying
exclude = NULL
. Note that levels specified in exclude
are mapped to NA
and so included in NA
counts.
Both exclude
and useNA
operate on an "all or none"
basis. If you want to control the dimensions of a multiway table
separately, modify each argument using factor
or
addNA
.
It is best to supply factors rather than rely on coercion. In
particular, exclude
will be used in coercion to a factor, and
so values (not levels) which appear in exclude
before coercion
will be mapped to NA
rather than be discarded.
The summary
method for class "table"
(used for objects
created by table
or xtabs
) which gives basic
information and performs a chi-squared test for independence of
factors (note that the function chisq.test
currently
only handles 2-d tables).
Value
table()
returns a contingency table, an object of
class "table"
, an array of integer values.
Note that unlike S the result is always an array, a 1D array if one
factor is given.as.table
and is.table
coerce to and test for contingency
table, respectively.The as.data.frame
method for objects inheriting from class
"table"
can be used to convert the array-based representation
of a contingency table to a data frame containing the classifying
factors and the corresponding entries (the latter as component
named by responseName
). This is the inverse of xtabs
.
References
Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) The New S Language. Wadsworth & Brooks/Cole.
See Also
tabulate
is the underlying function and allows finer
control.
Use ftable
for printing (and more) of
multidimensional tables. margin.table
,
prop.table
, addmargins
.
xtabs
for cross tabulation of data frames with a
formula interface.
Examples
library(base)
require(stats) # for rpois and xtabs
## Simple frequency distribution
table(rpois(100, 5))
## Check the design:
with(warpbreaks, table(wool, tension))
table(state.division, state.region)
# simple two-way contingency table
with(airquality, table(cut(Temp, quantile(Temp)), Month))
a <- letters[1:3]
table(a, sample(a)) # dnn is c("a", "")
table(a, sample(a), deparse.level = 0) # dnn is c("", "")
table(a, sample(a), deparse.level = 2) # dnn is c("a", "sample(a)")
## xtabs() <-> as.data.frame.table() :
UCBAdmissions ## already a contingency table
DF <- as.data.frame(UCBAdmissions)
class(tab <- xtabs(Freq ~ ., DF)) # xtabs & table
## tab *is* "the same" as the original table:
all(tab == UCBAdmissions)
all.equal(dimnames(tab), dimnames(UCBAdmissions))
a <- rep(c(NA, 1/0:3), 10)
table(a)
table(a, exclude = NULL)
b <- factor(rep(c("A","B","C"), 10))
table(b)
table(b, exclude = "B")
d <- factor(rep(c("A","B","C"), 10), levels = c("A","B","C","D","E"))
table(d, exclude = "B")
print(table(b, d), zero.print = ".")
## NA counting:
is.na(d) <- 3:4
d. <- addNA(d)
d.[1:7]
table(d.) # ", exclude = NULL" is not needed
## i.e., if you want to count the NA's of 'd', use
table(d, useNA = "ifany")
## Two-way tables with NA counts. The 3rd variant is absurd, but shows
## something that cannot be done using exclude or useNA.
with(airquality,
table(OzHi = Ozone > 80, Month, useNA = "ifany"))
with(airquality,
table(OzHi = Ozone > 80, Month, useNA = "always"))
with(airquality,
table(OzHi = Ozone > 80, addNA(Month)))
Community examples
```r table(ChickWeight$Diet) table(ChickWeight$Diet, dnn = "Observations per diet") with(ChickWeight, table(weight, Diet, dnn = c("cweight", "cDiet"))) ``` video tutorial at [LinkedIn Learning](http://niemannross.com/link/table)
```{r} table(mtcars$cyl, mtcars$gear) ```