Compare Two Categories: See How They Relate

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
)

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)

Value

A cross-tabulation table showing the relationship between two variables

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")

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

Examples

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

# Basic crosstab
survey_data %>% crosstab(gender, region)
#> Crosstab: gender x region
#>   2 x 2 table, N = 2500
#>   Note: no significance test included - use chi_square() for a test of independence.
#> Use summary() for detailed output.

# With weights and all percentages
survey_data %>% crosstab(gender, education,
                         weights = sampling_weight,
                         percentages = "all")
#> Crosstab: gender x education [Weighted]
#>   2 x 4 table, N = 2516
#>   Note: no significance test included - use chi_square() for a test of independence.
#> Use summary() for detailed output.

# Grouped analysis
survey_data %>%
  group_by(employment) %>%
  crosstab(gender, region, weights = sampling_weight)
#> Crosstab: gender x region [Weighted]
#>   [employment = Student] 2 x 2 table, N = 80
#>   [employment = Employed] 2 x 2 table, N = 1603
#>   [employment = Unemployed] 2 x 2 table, N = 184
#>   [employment = Retired] 2 x 2 table, N = 534
#>   [employment = Other] 2 x 2 table, N = 115
#>   Note: no significance test included - use chi_square() for a test of independence.
#> Use summary() for detailed output.

# Column percentages only
survey_data %>% crosstab(education, employment, percentages = "col")
#> Crosstab: education x employment
#>   4 x 5 table, N = 2500
#>   Note: no significance test included - use chi_square() for a test of independence.
#> Use summary() for detailed output.