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 an association measure 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,
assoc_measure = "auto",
assoc_ci = FALSE,
correct = FALSE,
simulate_p = FALSE,
simulate_B = 2000,
digits = NULL,
styled = TRUE,
show_n = TRUE
)
# S3 method for class 'spicy_cross_table_list'
print(x, ...)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
FALSE(the default), weights are used as-is. IfTRUE, rescales weights so total weighted N matches raw N.- percent
One of
"none"(the default),"row","column". Unique abbreviations are accepted (e.g."n","r","c").- include_stats
Logical. If
TRUE(the default), computes Chi-squared and an association measure (seeassoc_measure).- assoc_measure
Character. Which association measure to report.
"auto"(default) selects Kendall's Tau-b when both variables are ordered factors and Cramer's V otherwise. Other choices:"cramer_v","phi","gamma","tau_b","tau_c","somers_d","lambda","none".- assoc_ci
Logical. If
TRUE, includes the 95 percent confidence interval of the association measure in the note. Defaults toFALSE.- correct
Logical. If
FALSE(the default), no continuity correction is applied. IfTRUE, applies Yates correction (only for 2x2 tables).- simulate_p
Logical. If
FALSE(the default), uses asymptotic p-values. IfTRUE, uses Monte Carlo simulation.- simulate_B
Integer. Number of replicates for Monte Carlo simulation. Defaults to
2000.- digits
Number of decimals. Defaults to
1for percentages,0for counts.- styled
Logical. If
TRUE(the default), returns aspicy_cross_tableobject (for formatted printing). IfFALSE, returns a plaindata.frame.- show_n
Logical. If
TRUE(the default), adds marginal N totals whenpercent != "none".- ...
Additional arguments passed to individual print methods.
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:
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(sochealth, smoking, education)
#> Crosstable: smoking x education (N)
#>
#> Values │ Lower secondary Upper secondary Tertiary │ Total
#> ─────────────┼───────────────────────────────────────────────────────────┼────────────
#> No │ 179 415 332 │ 926
#> Yes │ 78 112 59 │ 249
#> ─────────────┼───────────────────────────────────────────────────────────┼────────────
#> Total │ 257 527 391 │ 1175
#>
#> Chi-2(2) = 21.6, p < 0.001
#> Cramer's V = 0.14
# Column percentages
cross_tab(sochealth, smoking, education, percent = "column")
#> Crosstable: smoking x education (Column %)
#>
#> Values │ Lower secondary Upper secondary Tertiary │ Total
#> ─────────────┼───────────────────────────────────────────────────────────┼────────────
#> No │ 69.6 78.7 84.9 │ 78.8
#> Yes │ 30.4 21.3 15.1 │ 21.2
#> ─────────────┼───────────────────────────────────────────────────────────┼────────────
#> Total │ 100.0 100.0 100.0 │ 100.0
#> N │ 257 527 391 │ 1175
#>
#> Chi-2(2) = 21.6, p < 0.001
#> Cramer's V = 0.14
# Weighted (rescaled)
cross_tab(sochealth, smoking, education, weights = weight, rescale = TRUE)
#> Crosstable: smoking x education (N)
#>
#> Values │ Lower secondary Upper secondary Tertiary │ Total
#> ─────────────┼───────────────────────────────────────────────────────────┼────────────
#> No │ 176 419 325 │ 921
#> Yes │ 79 115 60 │ 254
#> ─────────────┼───────────────────────────────────────────────────────────┼────────────
#> Total │ 255 534 385 │ 1175
#>
#> Chi-2(2) = 21.4, p < 0.001
#> Cramer's V = 0.13
#> Weight: weight (rescaled)
# Grouped by sex
cross_tab(sochealth, smoking, education, by = sex)
#> Crosstable: smoking x education (N) | sex = Female
#>
#> Values │ Lower secondary Upper secondary Tertiary │ Total
#> ─────────────┼───────────────────────────────────────────────────────────┼────────────
#> No │ 95 220 160 │ 475
#> Yes │ 38 62 31 │ 131
#> ─────────────┼───────────────────────────────────────────────────────────┼────────────
#> Total │ 133 282 191 │ 606
#>
#> Chi-2(2) = 7.1, p = 0.029
#> Cramer's V = 0.11
#>
#> Crosstable: smoking x education (N) | sex = Male
#>
#> Values │ Lower secondary Upper secondary Tertiary │ Total
#> ─────────────┼───────────────────────────────────────────────────────────┼────────────
#> No │ 84 195 172 │ 451
#> Yes │ 40 50 28 │ 118
#> ─────────────┼───────────────────────────────────────────────────────────┼────────────
#> Total │ 124 245 200 │ 569
#>
#> Chi-2(2) = 15.6, p < 0.001
#> Cramer's V = 0.17
# Grouped by combination of variables
cross_tab(sochealth, smoking, education, by = interaction(sex, age_group))
#> Crosstable: smoking x education (N) | sex x age_group = Female.25-34
#>
#> Values │ Lower secondary Upper secondary Tertiary │ Total
#> ─────────────┼───────────────────────────────────────────────────────────┼────────────
#> No │ 23 49 29 │ 101
#> Yes │ 9 9 7 │ 25
#> ─────────────┼───────────────────────────────────────────────────────────┼────────────
#> Total │ 32 58 36 │ 126
#>
#> Chi-2(2) = 2.1, p = 0.356
#> Cramer's V = 0.13
#>
#> Crosstable: smoking x education (N) | sex x age_group = Male.25-34
#>
#> Values │ Lower secondary Upper secondary Tertiary │ Total
#> ─────────────┼───────────────────────────────────────────────────────────┼────────────
#> No │ 9 42 32 │ 83
#> Yes │ 11 11 4 │ 26
#> ─────────────┼───────────────────────────────────────────────────────────┼────────────
#> Total │ 20 53 36 │ 109
#>
#> Chi-2(2) = 14.2, p < 0.001
#> Cramer's V = 0.36
#>
#> Crosstable: smoking x education (N) | sex x age_group = Female.35-49
#>
#> Values │ Lower secondary Upper secondary Tertiary │ Total
#> ─────────────┼───────────────────────────────────────────────────────────┼────────────
#> No │ 24 73 48 │ 145
#> Yes │ 10 20 8 │ 38
#> ─────────────┼───────────────────────────────────────────────────────────┼────────────
#> Total │ 34 93 56 │ 183
#>
#> Chi-2(2) = 3.0, p = 0.223
#> Cramer's V = 0.13
#>
#> Crosstable: smoking x education (N) | sex x age_group = Male.35-49
#>
#> Values │ Lower secondary Upper secondary Tertiary │ Total
#> ─────────────┼───────────────────────────────────────────────────────────┼────────────
#> No │ 33 59 60 │ 152
#> Yes │ 14 17 7 │ 38
#> ─────────────┼───────────────────────────────────────────────────────────┼────────────
#> Total │ 47 76 67 │ 190
#>
#> Chi-2(2) = 6.9, p = 0.032
#> Cramer's V = 0.19
#>
#> Crosstable: smoking x education (N) | sex x age_group = Female.50-64
#>
#> Values │ Lower secondary Upper secondary Tertiary │ Total
#> ─────────────┼───────────────────────────────────────────────────────────┼────────────
#> No │ 28 63 45 │ 136
#> Yes │ 8 16 6 │ 30
#> ─────────────┼───────────────────────────────────────────────────────────┼────────────
#> Total │ 36 79 51 │ 166
#>
#> Chi-2(2) = 2.0, p = 0.360
#> Cramer's V = 0.11
#>
#> Crosstable: smoking x education (N) | sex x age_group = Male.50-64
#>
#> Values │ Lower secondary Upper secondary Tertiary │ Total
#> ─────────────┼───────────────────────────────────────────────────────────┼────────────
#> No │ 28 58 42 │ 128
#> Yes │ 8 13 5 │ 26
#> ─────────────┼───────────────────────────────────────────────────────────┼────────────
#> Total │ 36 71 47 │ 154
#>
#> Chi-2(2) = 2.1, p = 0.343
#> Cramer's V = 0.12
#>
#> Crosstable: smoking x education (N) | sex x age_group = Female.65-75
#>
#> Values │ Lower secondary Upper secondary Tertiary │ Total
#> ─────────────┼───────────────────────────────────────────────────────────┼────────────
#> No │ 20 35 38 │ 93
#> Yes │ 11 17 10 │ 38
#> ─────────────┼───────────────────────────────────────────────────────────┼────────────
#> Total │ 31 52 48 │ 131
#>
#> Chi-2(2) = 2.5, p = 0.282
#> Cramer's V = 0.14
#>
#> Crosstable: smoking x education (N) | sex x age_group = Male.65-75
#>
#> Values │ Lower secondary Upper secondary Tertiary │ Total
#> ─────────────┼───────────────────────────────────────────────────────────┼────────────
#> No │ 14 36 38 │ 88
#> Yes │ 7 9 12 │ 28
#> ─────────────┼───────────────────────────────────────────────────────────┼────────────
#> Total │ 21 45 50 │ 116
#>
#> Chi-2(2) = 1.4, p = 0.499
#> Cramer's V = 0.11
# Ordinal variables: auto-selects Kendall's Tau-b
cross_tab(sochealth, education, self_rated_health)
#> Crosstable: education x self_rated_health (N)
#>
#> Values │ Poor Fair Good Very good │ Total
#> ──────────────────────┼─────────────────────────────────────────────────┼────────────
#> Lower secondary │ 28 86 102 44 │ 260
#> Upper secondary │ 28 118 263 118 │ 527
#> Tertiary │ 5 62 193 133 │ 393
#> ──────────────────────┼─────────────────────────────────────────────────┼────────────
#> Total │ 61 266 558 295 │ 1180
#>
#> Chi-2(6) = 73.2, p < 0.001
#> Kendall's Tau-b = 0.20
# 2x2 table with Yates correction
cross_tab(sochealth, smoking, physical_activity, correct = TRUE)
#> Crosstable: smoking x physical_activity (N)
#>
#> Values │ No Yes │ Total
#> ─────────────┼────────────────────┼────────────
#> No │ 505 421 │ 926
#> Yes │ 134 115 │ 249
#> ─────────────┼────────────────────┼────────────
#> Total │ 639 536 │ 1175
#>
#> Chi-2(1) = 0.0, p = 0.896
#> Cramer's V = 0.01
#> Yates continuity correction applied.