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))
- 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.data.frame, arguments passed to specific methods.)
- levels to remove for all factors in
.... If set to
NULL, it implies
useNA = "always". See ‘Details’ for its interpretation for non-factor arguments.
- whether to include
NAvalues in the table. See ‘Details’.
- the names to be given to the dimensions in the result (the dimnames names).
- controls how the default
dnnis constructed. See ‘Details’.
- 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 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.
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
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.
summary method for class
"table" (used for objects created by
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.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
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.
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)))
Documentation reproduced from R 3.0.1. License: GPL-2.