Cross Tabulation

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, na.rm = FALSE, addNA = FALSE,
      exclude = if(!addNA) c(NA, NaN), drop.unused.levels = FALSE)

## S3 method for class 'xtabs'
print(x, na.print = "", ...)

Arguments

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.

In R versions before 3.4.0, e.g., when na.action = na.pass, sometimes zeroes (0) were returned instead of NAs.

In R versions before 4.4.0, when !addNA as by default, the default na.action was na.omit, effectively treating missing counts as zero.

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.

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))

## with NA's
DN <- DF; DN[cbind(6:9, c(1:2,4,1))] <- NA
DN # 'Freq' is missing only for (Rejected, Female, B)
(xtNA <- xtabs(Freq ~ Gender + Admit, DN))     # NA prints 'invisibly'
print(xtNA, na.print = "NA")                   # show NA's better
xtabs(Freq ~ Gender + Admit, DN, na.rm = TRUE) # ignore missing Freq
## Use addNA = TRUE to tabulate missing factor levels:
xtabs(Freq ~ Gender + Admit, DN, addNA = TRUE)
xtabs(Freq ~ Gender + Admit, DN, addNA = TRUE, na.rm = TRUE)
## na.action = na.omit removes all rows with NAs right from the start:
xtabs(Freq ~ Gender + Admit, DN, na.action = na.omit)

## Create a nice display for the warp break data.
warpbreaks$replicate <- rep_len(1:9, 54)
ftable(xtabs(breaks ~ wool + tension + replicate, data = warpbreaks))

### ---- Sparse Examples ----

if(require("Matrix")) withAutoprint({
 ## similar to "nlme"s  'ergoStool' :
 d.ergo <- data.frame(Type = paste0("T", rep(1:4, 9*4)),
                      Subj = gl(9, 4, 36*4))
 xtabs(~ Type + Subj, data = d.ergo) # 4 replicates each
 set.seed(15) # a subset of cases:
 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)])
 xtabs(~ inner + outer, fr, sparse = TRUE)
})