mariposa (version 0.7.3)

crosstab: Compare Two Categories: See How They Relate

Description

crosstab() shows you how two categorical variables relate to each other. It creates a table that reveals patterns - like whether education level differs by region, or if gender influences product preferences.

Think of it as a two-way frequency table that shows:

• How many people fall into each combination of categories

• What percentage each cell represents

• Whether there are patterns or associations

Usage

crosstab(
  data,
  row,
  col,
  weights = NULL,
  percentages = c("row", "none", "col", "total", "all"),
  na.rm = TRUE,
  digits = 1
)

Value

A cross-tabulation table showing the relationship between two variables. The result also carries the expected cell counts ($expected) and adjusted standardized residuals ($adj_residuals); display the residuals with summary(result, residuals = TRUE).

Arguments

data

Your survey data (a data frame or tibble)

row

The variable for table rows (e.g., education, age_group)

col

The variable for table columns (e.g., region, gender)

weights

Optional survey weights for population-representative results

percentages

Which percentages to show:

• "row" (default): Percentages across each row (adds to 100% horizontally)

• "col": Percentages down each column (adds to 100% vertically)

• "total": Percentage of the entire table

• "all": Show all three types

• "none": Just counts, no percentages

na.rm

Remove missing values before calculating? (Default: TRUE)

digits

Decimal places for percentages (Default: 1)

Details

Understanding the Results

The crosstab table shows:

• Cell counts: Number of people in each combination

• Row %: Distribution within each row (e.g., "Among those with high school education, X% live in the East")

• Column %: Distribution within each column (e.g., "Among those in the East, X% have high school education")

• Total %: Percentage of the entire sample (e.g., "X% of all respondents have high school education AND live in the East")

• Adjusted residuals (via summary(result, residuals = TRUE), SPSS /CELLS=ASRESID): which cells deviate from independence. After a significant chi_square test, cells with |adj. residual| > 2 are the ones driving the association. For weighted tables the residuals are computed on the unrounded weighted cell counts; an SPSS v29 reference run for the residuals is pending, so they are currently verified against the Haberman formula (chisq.test()$stdres) rather than SPSS output.

When to Use This

Use crosstab when you want to:

• See if two categorical variables are related

• Compare distributions across groups

• Find patterns in survey responses

• Create demographic breakdowns

Choosing Percentages

• Row %: Use when your row variable is the grouping factor (e.g., "How does region vary BY education level?")

• Column %: Use when your column variable is the grouping factor (e.g., "How does education vary BY region?")

• Total %: Use to understand the overall sample composition

Tips for Success

• Start with row or column percentages, not both at once

• Use chi-squared test to check if the relationship is statistically significant

• Watch for small cell counts (< 5) which may be unreliable

• Consider combining sparse categories if many cells are empty

See Also

table for base R contingency tables.

frequency for single-variable frequency tables.

chi_square for testing if the cross-tabulated variables are related.

Other descriptive: describe(), frequency(), multiple_response(), normality_test()

Examples

# Load required packages and data
library(dplyr)
data(survey_data)

# Basic crosstab
survey_data %>% crosstab(gender, region)

# With weights and all percentages
survey_data %>% crosstab(gender, education,
                         weights = sampling_weight,
                         percentages = "all")

# Grouped analysis
survey_data %>%
  group_by(employment) %>%
  crosstab(gender, region, weights = sampling_weight)

# Column percentages only
survey_data %>% crosstab(education, employment, percentages = "col")