# xtabs

0th

Percentile

##### Cross Tabulation

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

Keywords
category
##### 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 NAs.
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.

##### Value

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">https://CRAN.R-project.org/package=Matrix.

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">https://CRAN.R-project.org/package=Matrix.
library(stats) ## '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)) }<!-- % only if Matrix is available -->