Create a contingency table or other aggregation from cross-classifying variables in a CrunchDataset, expanding on the notation allowed in stats::xtabs() to tailor to the kinds of calculations available in crunch.

  weight = crunch::weight(data),
  useNA = c("no", "ifany", "always")



a stats::formula object that specifies that query to calculate. See Details for more information.


an object of class CrunchDataset


a CrunchVariable that has been designated as a potential weight variable for data, or NULL for unweighted results. Default is the currently applied weight().


whether to include missing values in tabular results. See base::table().


an object of class CrunchCube


There are 3 types of queries supported:

  • Crosstabs: Share the most in common with stats::xtabs(), are defined by a formula with only a right hand side, with each dimension specified on the right-hand side, separated by a +. A dimension are generally variables, but categorical array variables contribute 2 dimensions, “categories” and “subvariables”. If you just use the categorical array variable directly, the subvariables dimensions will be added first and the categories second, but you can choose their order by specifying both categories(var) and subvariables(var) (where var is a Categorical Array CrunchVariable).

  • Aggregations: An extension to 'Crosstabs' where you can select one or more measures by putting them in the left-hand side of the formula. Multiple measures can be placed in a list to calculate them together. The currently supported measures are mean(var), n() (the same as a crosstab), min(var), max(var), sd(var), sum(var) and median(var) (where var is a CrunchVariable).

  • Scorecards: When you want to compare multiple MR variables with the same subvariables, you can use a scorecard to create a tabulation where they are lined up. Scorecard queries cannot be combined with the other types. Use the scorecard(..., vars = NULL) (where ... is a set of MR variables or vars is a list of them).

See also


if (FALSE) {
# Crosstab of people by `age_cat`:
crtabs(~age_cat, ds)

# Aggregation of means of income by `age_cat`
crtabs(mean(income) ~ age_cat, ds)

# Scorecard of multiple MRs with aligned subvariables
crtabs(~scorecard(trust_mr, value_mr, quality_mr), ds)
# Can also pre-define the variables in a scorecard with
mr_list <- list(ds$trust_mr, ds$value_mr, ds$quality_mr)
crtabs(~scorecard(vars = mr_list), ds)

# Crosstab of people by `age_cat` and the reasons for enjoying a brand (cat array)
crtabs(~age_cat + enjoy_array, ds)

# Crosstab of people by `age_cat` and the `enjoy_array` (cat array)
# But manually choosing the order of the dimensions
crtabs(~subvariables(enjoy_array) + age_cat + categories(enjoy_array), ds)

# Aggregation of means & standard deviations of income by `age_cat`
crtabs(list(mean = mean(income), sd = sd(income)) ~ age_cat, ds)