Computes a two-way cross-tabulation with optional weights, grouping (including combinations of multiple variables), percentage displays, and inferential statistics.
cross_tab() produces weighted or unweighted contingency tables with
row or column percentages, optional grouping via by, and associated
Chi-squared tests with Cramer's V and diagnostic information.
Both x and y variables are required. For one-way frequency tables,
use freq() instead.
Usage
cross_tab(
data,
x,
y = NULL,
by = NULL,
weights = NULL,
rescale = FALSE,
percent = c("none", "column", "row"),
include_stats = TRUE,
correct = FALSE,
simulate_p = FALSE,
simulate_B = 2000,
digits = NULL,
styled = TRUE,
show_n = TRUE
)Arguments
- data
A data frame. Alternatively, a vector when using the vector-based interface.
- x
Row variable (unquoted).
- y
Column variable (unquoted). Mandatory; for one-way tables, use
freq().- by
Optional grouping variable or expression. Can be a single variable or a combination of multiple variables (e.g.
interaction(vs, am)).- weights
Optional numeric weights.
- rescale
Logical. If TRUE, rescales weights so total weighted N matches raw N (default FALSE).
- percent
One of
"none","row","column".- include_stats
Logical; compute Chi-squared and Cramer's V (default TRUE).
- correct
Logical; apply Yates continuity correction to the Chi-squared test. Only applicable to 2x2 tables. Default is FALSE.
- simulate_p
Logical; use Monte Carlo p-value simulation (default FALSE).
- simulate_B
Integer; number of replicates for Monte Carlo (default 2000).
- digits
Number of decimals (default 1 for percentages, 0 for counts).
- styled
Logical; if TRUE, returns a "spicy_cross_table" object (for printing).
- show_n
Logical; if TRUE, adds marginal N totals when percent != "none".
Value
A data.frame, list of data.frames, or spicy_cross_table object.
When by is used, returns a spicy_cross_table_list.
Global Options
The function recognizes the following global options that modify its default behavior:
The function recognizes the following global options that modify its default behavior:
options(spicy.percent = "column")Sets the default percentage mode for all calls tocross_tab(). Valid values are"none","row", and"column". Equivalent to settingpercent = "column"(or another choice) in each call.options(spicy.simulate_p = TRUE)Enables Monte Carlo simulation for all Chi-squared tests by default. Equivalent to settingsimulate_p = TRUEin every call.options(spicy.rescale = TRUE)Automatically rescales weights so that total weighted N equals the raw N. Equivalent to settingrescale = TRUEin each call.
These options are convenient for users who wish to enforce consistent behavior
across multiple calls to cross_tab() and other spicy table functions.
They can be disabled or reset by setting them to NULL:
options(spicy.percent = NULL, spicy.simulate_p = NULL, spicy.rescale = NULL).
Example:
Examples
# Basic crosstab
cross_tab(mtcars, cyl, gear)
#> Crosstable: cyl x gear (N)
#>
#> Values │ 3 4 5 │ Total
#> ─────────────┼──────────────────────────┼────────────
#> 4 │ 1 8 2 │ 11
#> 6 │ 2 4 1 │ 7
#> 8 │ 12 0 2 │ 14
#> ─────────────┼──────────────────────────┼────────────
#> Total │ 15 12 5 │ 32
#>
#> Chi-2: 18.0 (df = 4), p = 0.001
#> Cramer's V: 0.53
#> Warning: 6 expected cells < 5 (66.7%). Minimum expected = 1.09. Consider `simulate_p = TRUE` or set globally via `options(spicy.simulate_p = TRUE)`.
# Weighted (rescaled)
cross_tab(mtcars, cyl, gear, weights = mtcars$mpg, rescale = TRUE)
#> Crosstable: cyl x gear (N)
#>
#> Values │ 3 4 5 │ Total
#> ─────────────┼──────────────────────────┼────────────
#> 4 │ 1 11 3 │ 15
#> 6 │ 2 4 1 │ 7
#> 8 │ 9 0 2 │ 11
#> ─────────────┼──────────────────────────┼────────────
#> Total │ 12 15 6 │ 32
#>
#> Chi-2: 17.7 (df = 4), p = 0.001
#> Cramer's V: 0.53
#> Warning: 7 expected cells < 5 (77.8%). Minimum expected = 1.14. Consider `simulate_p = TRUE` or set globally via `options(spicy.simulate_p = TRUE)`.
#> Weight: mpg (rescaled)
# Grouped
cross_tab(mtcars, cyl, gear, by = am)
#> Crosstable: cyl x gear (N) | am = 0
#>
#> Values │ 3 4 5 │ Total
#> ─────────────┼─────────────────────────┼────────────
#> 4 │ 1 2 0 │ 3
#> 6 │ 2 2 0 │ 4
#> 8 │ 12 0 0 │ 12
#> ─────────────┼─────────────────────────┼────────────
#> Total │ 15 4 0 │ 19
#>
#> Chi-2: NA (df = 4), p = NA
#> Cramer's V: NA
#> Warning: 8 expected cells < 5 (88.9%). 5 expected cells < 1. Minimum expected = 0. Consider `simulate_p = TRUE` or set globally via `options(spicy.simulate_p = TRUE)`.
#>
#> Crosstable: cyl x gear (N) | am = 1
#>
#> Values │ 3 4 5 │ Total
#> ─────────────┼────────────────────────┼────────────
#> 4 │ 0 6 2 │ 8
#> 6 │ 0 2 1 │ 3
#> 8 │ 0 0 2 │ 2
#> ─────────────┼────────────────────────┼────────────
#> Total │ 0 8 5 │ 13
#>
#> Chi-2: NA (df = 4), p = NA
#> Cramer's V: NA
#> Warning: 9 expected cells < 5 (100%). 4 expected cells < 1. Minimum expected = 0. Consider `simulate_p = TRUE` or set globally via `options(spicy.simulate_p = TRUE)`.
# Grouped by an interaction
cross_tab(mtcars, cyl, gear, by = interaction(vs, am))
#> Crosstable: cyl x gear (N) | vs x am = 0.0
#>
#> Values │ 3 4 5 │ Total
#> ─────────────┼─────────────────────────┼────────────
#> 4 │ 0 0 0 │ 0
#> 6 │ 0 0 0 │ 0
#> 8 │ 12 0 0 │ 12
#> ─────────────┼─────────────────────────┼────────────
#> Total │ 12 0 0 │ 12
#>
#> Chi-2 and Cramer's V not computed: insufficient data (only one non-empty row/column).
#>
#> Crosstable: cyl x gear (N) | vs x am = 1.0
#>
#> Values │ 3 4 5 │ Total
#> ─────────────┼────────────────────────┼────────────
#> 4 │ 1 2 0 │ 3
#> 6 │ 2 2 0 │ 4
#> 8 │ 0 0 0 │ 0
#> ─────────────┼────────────────────────┼────────────
#> Total │ 3 4 0 │ 7
#>
#> Chi-2: NA (df = 4), p = NA
#> Cramer's V: NA
#> Warning: 9 expected cells < 5 (100%). 5 expected cells < 1. Minimum expected = 0. Consider `simulate_p = TRUE` or set globally via `options(spicy.simulate_p = TRUE)`.
#>
#> Crosstable: cyl x gear (N) | vs x am = 0.1
#>
#> Values │ 3 4 5 │ Total
#> ─────────────┼────────────────────────┼────────────
#> 4 │ 0 0 1 │ 1
#> 6 │ 0 2 1 │ 3
#> 8 │ 0 0 2 │ 2
#> ─────────────┼────────────────────────┼────────────
#> Total │ 0 2 4 │ 6
#>
#> Chi-2: NA (df = 4), p = NA
#> Cramer's V: NA
#> Warning: 9 expected cells < 5 (100%). 6 expected cells < 1. Minimum expected = 0. Consider `simulate_p = TRUE` or set globally via `options(spicy.simulate_p = TRUE)`.
#>
#> Crosstable: cyl x gear (N) | vs x am = 1.1
#>
#> Values │ 3 4 5 │ Total
#> ─────────────┼────────────────────────┼────────────
#> 4 │ 0 6 1 │ 7
#> 6 │ 0 0 0 │ 0
#> 8 │ 0 0 0 │ 0
#> ─────────────┼────────────────────────┼────────────
#> Total │ 0 6 1 │ 7
#>
#> Chi-2 and Cramer's V not computed: insufficient data (only one non-empty row/column).
# Set default percent mode globally
options(spicy.percent = "column")
# Now this will display column percentages by default
cross_tab(mtcars, cyl, gear)
#> Crosstable: cyl x gear (Column %)
#>
#> Values │ 3 4 5 │ Total
#> ─────────────┼────────────────────────────────────┼────────────
#> 4 │ 6.7 66.7 40.0 │ 34.4
#> 6 │ 13.3 33.3 20.0 │ 21.9
#> 8 │ 80.0 0.0 40.0 │ 43.8
#> ─────────────┼────────────────────────────────────┼────────────
#> Total │ 100.0 100.0 100.0 │ 100.0
#> N │ 15 12 5 │ 32
#>
#> Chi-2: 18.0 (df = 4), p = 0.001
#> Cramer's V: 0.53
#> Warning: 6 expected cells < 5 (66.7%). Minimum expected = 1.09. Consider `simulate_p = TRUE` or set globally via `options(spicy.simulate_p = TRUE)`.
# Reset to default behavior
options(spicy.percent = NULL)
# 2x2 table with Yates correction
cross_tab(mtcars, vs, am, correct = TRUE)
#> Crosstable: vs x am (N)
#>
#> Values │ 0 1 │ Total
#> ─────────────┼──────────────────┼────────────
#> 0 │ 12 6 │ 18
#> 1 │ 7 7 │ 14
#> ─────────────┼──────────────────┼────────────
#> Total │ 19 13 │ 32
#>
#> Chi-2: 0.3 (df = 1), p = 0.556
#> Cramer's V: 0.10
#> Yates continuity correction applied.