# xtabs {stats}

### Description

Create a contingency table (optionally a sparse matrix) from cross-classifying factors, usually contained in a data frame, using a formula interface.

### Usage

xtabs(formula = ~., data = parent.frame(), subset, sparse = FALSE, na.action, exclude = c(NA, NaN), drop.unused.levels = FALSE)

### Arguments

- formula
- a formula object with the cross-classifying variables (separated by
`+`

) on the right hand side (or an object which can be coerced to a formula). Interactions are not allowed. On the left hand side, one may optionally give a vector or a matrix of counts; in the latter case, the columns are interpreted as corresponding to the levels of a variable. This is useful if the data have already been tabulated, see the examples below. - data
- an optional matrix or data frame (or similar: see
`model.frame`

) containing the variables in the formula`formula`

. By default the variables are taken from`environment(formula)`

. - subset
- an optional vector specifying a subset of observations to be used.
- sparse
- logical specifying if the result should be a
*sparse*matrix, i.e., inheriting from`sparseMatrix`

Only works for two factors (since there are no higher-order sparse array classes yet). - na.action
- a function which indicates what should happen when the data contain
`NA`

s. - exclude
- a vector of values to be excluded when forming the set of levels of the classifying factors.
- drop.unused.levels
- a logical indicating whether to drop unused levels in the classifying factors. If this is
`FALSE`

and there are unused levels, the table will contain zero marginals, and a subsequent chi-squared test for independence of the factors will not work.

### Details

There is a `summary`

method for contingency table objects created by `table`

or `xtabs(*, sparse = FALSE)`

, 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).

If a left hand side is given in `formula`

, its entries are simply summed over the cells corresponding to the right hand side; this also works if the lhs does not give counts. For variables in `formula`

which are factors, `exclude`

must be specified explicitly; the default exclusions will not be used.

### Values

By default, when `sparse = FALSE`

, a contingency table in array representation of S3 class `c("xtabs", "table")`

, with a `"call"`

attribute storing the matched call.

When `sparse = TRUE`

, a sparse numeric matrix, specifically an object of S4 class `dgTMatrix`

from package Matrix.

### See Also

`table`

for traditional cross-tabulation, and `as.data.frame.table`

which is the inverse operation of `xtabs`

(see the `DF`

example below).

`sparseMatrix`

on sparse matrices in package Matrix.

### Examples

## 'esoph' has the frequencies of cases and controls for all levels of ## the variables 'agegp', 'alcgp', and 'tobgp'. xtabs(cbind(ncases, ncontrols) ~ ., data = esoph) ## Output is not really helpful ... flat tables are better: ftable(xtabs(cbind(ncases, ncontrols) ~ ., data = esoph)) ## In particular if we have fewer factors ... ftable(xtabs(cbind(ncases, ncontrols) ~ agegp, data = esoph)) ## This is already a contingency table in array form. DF <- as.data.frame(UCBAdmissions) ## Now 'DF' is a data frame with a grid of the factors and the counts ## in variable 'Freq'. DF ## Nice for taking margins ... xtabs(Freq ~ Gender + Admit, DF) ## And for testing independence ... summary(xtabs(Freq ~ ., DF)) ## Create a nice display for the warp break data. warpbreaks$replicate <- rep(1:9, len = 54) ftable(xtabs(breaks ~ wool + tension + replicate, data = warpbreaks)) ### ---- Sparse Examples ---- if(require("Matrix")) { ## similar to "nlme"s 'ergoStool' : d.ergo <- data.frame(Type = paste0("T", rep(1:4, 9*4)), Subj = gl(9, 4, 36*4)) print(xtabs(~ Type + Subj, data = d.ergo)) # 4 replicates each set.seed(15) # a subset of cases: print(xtabs(~ Type + Subj, data = d.ergo[sample(36, 10), ], sparse = TRUE)) ## Hypothetical two level setup: inner <- factor(sample(letters[1:25], 100, replace = TRUE)) inout <- factor(sample(LETTERS[1:5], 25, replace = TRUE)) fr <- data.frame(inner = inner, outer = inout[as.integer(inner)]) print(xtabs(~ inner + outer, fr, sparse = TRUE)) }

Documentation reproduced from R 3.0.2. License: GPL-2.