Create a cross-tabulation of two categorical variables

Creates a cross-tabulation of two categorical variables with row or column percentages, row and column totals, and optional hypothesis tests. Prints a formatted table to the console (similar to Stata's tab command) with the column variable name displayed as a spanning header above its levels.

Usage

tab(
  data,
  variable1,
  variable2,
  show = c("row", "col", "n"),
  test = c("both", "chi", "exact", "none"),
  totals = TRUE,
  dp = 1
)

Arguments

data: The data frame or tibble.
variable1: The row variable (first categorical variable).
variable2: The column variable (second categorical variable).
show: What to display in each cell: "row" (default) shows n (row%), "col" shows n (col%), "n" shows counts only.
test: Hypothesis test(s) to report: "both" (default) runs both chi-squared and Fisher's exact; "chi" for chi-squared only; "exact" for Fisher's exact only (falls back to simulated p-value for large tables); "none" to suppress tests.
totals: If TRUE (default), includes a Total column (row totals) and a Total row (column totals). Set to FALSE to suppress both.
dp: Number of decimal places for percentages. Default is 1.

Value

A wide-format tibble (invisibly) with:

First column: levels of variable1 (plus "Total" if totals = TRUE).
For each level of variable2: {level}_n (integer count) and, when show != "n", {level}_pct (numeric percentage).
total_n: row totals (when totals = TRUE).
p_chi, p_fisher: p-values on the first row, NA elsewhere (when test = "both"; individual tests add test, statistic, p_value instead).

Examples

example_data <- dplyr::tibble(
  group1 = sample(c("a", "b", "c"), 100, replace = TRUE),
  group2 = sample(c("male", "female"), 100, replace = TRUE)
)
tab(example_data, group1, group2)
#>         |         group2          |       
#> group1  |   female   |    male    | Total 
#> --------+------------+------------+-------
#> a       | 11 (36.7%) | 19 (63.3%) |  30   
#> b       | 16 (53.3%) | 14 (46.7%) |  30   
#> c       | 18 (45.0%) | 22 (55.0%) |  40   
#> --------+------------+------------+-------
#> Total   | 45 (45.0%) | 55 (55.0%) |  100  
#> 
#> Chi-squared: X²(2) = 1.684, p = 0.431
#> Fisher's Exact: p = 0.449
tab(example_data, group1, group2, show = "col")
#>         |          group2           |       
#> group1  |   female    |    male     | Total 
#> --------+-------------+-------------+-------
#> a       | 11 (24.4%)  | 19 (34.5%)  |  30   
#> b       | 16 (35.6%)  | 14 (25.5%)  |  30   
#> c       | 18 (40.0%)  | 22 (40.0%)  |  40   
#> --------+-------------+-------------+-------
#> Total   | 45 (100.0%) | 55 (100.0%) |  100  
#> 
#> Chi-squared: X²(2) = 1.684, p = 0.431
#> Fisher's Exact: p = 0.449
tab(example_data, group1, group2, test = "none")
#>         |         group2          |       
#> group1  |   female   |    male    | Total 
#> --------+------------+------------+-------
#> a       | 11 (36.7%) | 19 (63.3%) |  30   
#> b       | 16 (53.3%) | 14 (46.7%) |  30   
#> c       | 18 (45.0%) | 22 (55.0%) |  40   
#> --------+------------+------------+-------
#> Total   | 45 (45.0%) | 55 (55.0%) |  100  
result <- tab(example_data, group1, group2)
#>         |         group2          |       
#> group1  |   female   |    male    | Total 
#> --------+------------+------------+-------
#> a       | 11 (36.7%) | 19 (63.3%) |  30   
#> b       | 16 (53.3%) | 14 (46.7%) |  30   
#> c       | 18 (45.0%) | 22 (55.0%) |  40   
#> --------+------------+------------+-------
#> Total   | 45 (45.0%) | 55 (55.0%) |  100  
#> 
#> Chi-squared: X²(2) = 1.684, p = 0.431
#> Fisher's Exact: p = 0.449