table uses the cross-classifying factors to build a contingency
table of the counts at each combination of factor levels.
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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
levels to remove for all factors in
whether to include
the names to be given to the dimensions in the result (the dimnames names).
controls how the default
an arbitrary R object, or an object inheriting from class
a character vector giving the row names for the data frame.
The name to be used for the column of table entries, usually counts.
logical: should the classifying factors be returned as factors (the default) or character vectors?
If the argument
dnn is not supplied, the internal function
list.names is called to compute the ‘dimname names’. If the
... are named, those names are used. For the
deparse.level = 0 gives an empty name,
deparse.level = 1 uses the supplied argument if it is a symbol,
deparse.level = 2 will deparse the argument.
exclude is specified and non-NULL (i.e., not by
table potentially drop levels of factor
useNA controls if the table includes counts of
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
are mapped to
NA and so included in
useNA operate on an "all or none"
basis. If you want to control the dimensions of a multiway table
separately, modify each argument using
It is best to supply factors rather than rely on coercion. In
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.
summary method for class
"table" (used for objects
xtabs) which gives basic
information and performs a chi-squared test for independence of
factors (note that the function
only handles 2-d tables).
table() returns a contingency table, an object of
"table", an array of integer values.
Note that unlike S the result is always an array, a 1D array if one
factor is given.
is.table coerce to and test for contingency
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
responseName). This is the inverse of
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
ftable for printing (and more) of
xtabs for cross tabulation of data frames with a
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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)))