| table | R Documentation |
table uses the cross-classifying factors to build a contingency
table of the counts at each combination of factor levels.
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)
## S3 method for class 'table'
as.data.frame(x, row.names = NULL, ...,
responseName = "Freq", stringsAsFactors = TRUE,
sep = "", base = list(LETTERS))
... |
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 |
exclude |
levels to remove for all factors in |
useNA |
whether to include |
dnn |
the names to be given to the dimensions in the result (the dimnames names). |
deparse.level |
controls how the default |
x |
an arbitrary R object, or an object inheriting from class
|
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 |
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 (i.e., not by default) and
non-empty, will table potentially drop levels of factor
arguments.
useNA controls if the table includes counts of NA
values: the allowed values correspond to never ("no"), only if the count is
positive ("ifany") and even for zero counts ("always").
Note the somewhat “pathological” case of two different kinds of
NAs which are treated differently, depending on both
useNA and exclude, see d.patho in the
‘Examples:’ below.
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.
Non-factor arguments a are coerced via factor(a,
exclude=exclude). Since R 3.4.0, care is taken not to
count the excluded values (where they were included in the NA
count, previously).
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).
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.
Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) The New S Language. Wadsworth & Brooks/Cole.
tabulate is the underlying function and allows finer
control.
Use ftable for printing (and more) of
multidimensional tables. margin.table,
prop.table, addmargins.
addNA for constructing factors with NA as
a level.
xtabs for cross tabulation of data frames with a
formula interface.
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) # does not report NA's
table(a, exclude = NULL) # reports NA's
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")
## "pathological" case:
d.patho <- addNA(c(1,NA,1:2,1:3))[-7]; is.na(d.patho) <- 3:4
d.patho
## just 3 consecutive NA's ? --- well, have *two* kinds of NAs here :
as.integer(d.patho) # 1 4 NA NA 1 2
##
## In R >= 3.4.0, table() allows to differentiate:
table(d.patho) # counts the "unusual" NA
table(d.patho, useNA = "ifany") # counts all three
table(d.patho, exclude = NULL) # (ditto)
table(d.patho, exclude = NA) # counts none
## 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)))
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