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. Give a column name (unquoted or as a string), an expression such as sampling_weight * 2, or a numeric vector with one weight per row.

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). With FALSE, missing values of either variable form their own "NA" row or column.

digits

Decimal places for percentages (Default: 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).

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 rounded weighted cell counts (see below). They follow Haberman's formula (as chisq.test()$stdres) and are not compared with SPSS output.

Weighted Tables

As SPSS CROSSTABS does by default (/COUNT ROUND CELL), each weighted cell count is rounded to a whole number first; the row and column totals are sums of the rounded cells and all percentages come from the rounded counts. The table total can therefore differ by a few cases from the rounded sum of weights shown as "N (valid)" (SPSS: 2518 vs. 2516 for education x employment in survey_data).

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)
#> 
#> Crosstabulation: Gender x Region (East/West)
#> --------------------------------------------
#> - Row variable: gender
#> - Column variable: region
#> - Percentages: Row percentages
#> - N (valid): 2500
#> 
#> +---------+-------+-------+--------+
#> |         |   Region (East/West)   |
#> | Gender  |  East |  West |  Total |
#> +---------+-------+-------+--------+
#> | Male    |   238 |   956 |   1194 |
#> |   row % | 19.9% | 80.1% | 100.0% |
#> +---------+-------+-------+--------+
#> | Female  |   247 |  1059 |   1306 |
#> |   row % | 18.9% | 81.1% | 100.0% |
#> +=========+=======+=======+========+
#> | Total   |   485 |  2015 |   2500 |
#> |   row % | 19.4% | 80.6% | 100.0% |
#> +---------+-------+-------+--------+

# With weights and all percentages
survey_data %>% crosstab(gender, education,
                         weights = sampling_weight,
                         percentages = "all")
#> 
#> Crosstabulation: Gender x Highest educational attainment
#> --------------------------------------------------------
#> - Row variable: gender
#> - Column variable: education
#> - Percentages: All percentages (row, col, total)
#> - Weights variable: sampling_weight
#> - N (valid): 2516 (weighted)
#> 
#> +-----------+-----------+--------------+-----------+------------+--------+
#> |           |               Highest educational attainment               |
#> |           |     Basic | Intermediate |  Academic |            |        |
#> | Gender    | Secondary |    Secondary | Secondary | University |  Total |
#> +-----------+-----------+--------------+-----------+------------+--------+
#> | Male      |       402 |          291 |       326 |        176 |   1195 |
#> |   row %   |     33.6% |        24.4% |     27.3% |      14.7% | 100.0% |
#> |   col %   |     47.3% |        45.4% |     50.8% |      45.7% |  47.5% |
#> |   total % |     16.0% |        11.6% |     13.0% |       7.0% |  47.5% |
#> +-----------+-----------+--------------+-----------+------------+--------+
#> | Female    |       447 |          350 |       316 |        209 |   1322 |
#> |   row %   |     33.8% |        26.5% |     23.9% |      15.8% | 100.0% |
#> |   col %   |     52.7% |        54.6% |     49.2% |      54.3% |  52.5% |
#> |   total % |     17.8% |        13.9% |     12.6% |       8.3% |  52.5% |
#> +===========+===========+==============+===========+============+========+
#> | Total     |       849 |          641 |       642 |        385 |   2517 |
#> |   row %   |     33.7% |        25.5% |     25.5% |      15.3% | 100.0% |
#> |   col %   |    100.0% |       100.0% |    100.0% |     100.0% | 100.0% |
#> |   total % |     33.7% |        25.5% |     25.5% |      15.3% | 100.0% |
#> +-----------+-----------+--------------+-----------+------------+--------+

# Grouped analysis
survey_data %>%
  group_by(employment) %>%
  crosstab(gender, region, weights = sampling_weight)
#> 
#> Weighted Grouped Crosstabulation
#> --------------------------------
#> 
#> 
#> Group: employment = Student
#> ---------------------------
#> 
#> Crosstabulation: Gender x Region (East/West)
#> --------------------------------------------
#> - Row variable: gender
#> - Column variable: region
#> - Percentages: Row percentages
#> - Weights variable: sampling_weight
#> - N (valid): 80 (weighted)
#> 
#> +---------+-------+-------+--------+
#> |         |   Region (East/West)   |
#> | Gender  |  East |  West |  Total |
#> +---------+-------+-------+--------+
#> | Male    |     7 |    29 |     36 |
#> |   row % | 19.4% | 80.6% | 100.0% |
#> +---------+-------+-------+--------+
#> | Female  |     4 |    40 |     44 |
#> |   row % |  9.1% | 90.9% | 100.0% |
#> +=========+=======+=======+========+
#> | Total   |    11 |    69 |     80 |
#> |   row % | 13.8% | 86.2% | 100.0% |
#> +---------+-------+-------+--------+
#> 
#> 
#> Group: employment = Employed
#> ----------------------------
#> 
#> Crosstabulation: Gender x Region (East/West)
#> --------------------------------------------
#> - Row variable: gender
#> - Column variable: region
#> - Percentages: Row percentages
#> - Weights variable: sampling_weight
#> - N (valid): 1603 (weighted)
#> 
#> +---------+-------+-------+--------+
#> |         |   Region (East/West)   |
#> | Gender  |  East |  West |  Total |
#> +---------+-------+-------+--------+
#> | Male    |   149 |   599 |    748 |
#> |   row % | 19.9% | 80.1% | 100.0% |
#> +---------+-------+-------+--------+
#> | Female  |   172 |   683 |    855 |
#> |   row % | 20.1% | 79.9% | 100.0% |
#> +=========+=======+=======+========+
#> | Total   |   321 |  1282 |   1603 |
#> |   row % | 20.0% | 80.0% | 100.0% |
#> +---------+-------+-------+--------+
#> 
#> 
#> Group: employment = Unemployed
#> ------------------------------
#> 
#> Crosstabulation: Gender x Region (East/West)
#> --------------------------------------------
#> - Row variable: gender
#> - Column variable: region
#> - Percentages: Row percentages
#> - Weights variable: sampling_weight
#> - N (valid): 184 (weighted)
#> 
#> +---------+-------+-------+--------+
#> |         |   Region (East/West)   |
#> | Gender  |  East |  West |  Total |
#> +---------+-------+-------+--------+
#> | Male    |    17 |    66 |     83 |
#> |   row % | 20.5% | 79.5% | 100.0% |
#> +---------+-------+-------+--------+
#> | Female  |    16 |    85 |    101 |
#> |   row % | 15.8% | 84.2% | 100.0% |
#> +=========+=======+=======+========+
#> | Total   |    33 |   151 |    184 |
#> |   row % | 17.9% | 82.1% | 100.0% |
#> +---------+-------+-------+--------+
#> 
#> 
#> Group: employment = Retired
#> ---------------------------
#> 
#> Crosstabulation: Gender x Region (East/West)
#> --------------------------------------------
#> - Row variable: gender
#> - Column variable: region
#> - Percentages: Row percentages
#> - Weights variable: sampling_weight
#> - N (valid): 534 (weighted)
#> 
#> +---------+-------+-------+--------+
#> |         |   Region (East/West)   |
#> | Gender  |  East |  West |  Total |
#> +---------+-------+-------+--------+
#> | Male    |    61 |   199 |    260 |
#> |   row % | 23.5% | 76.5% | 100.0% |
#> +---------+-------+-------+--------+
#> | Female  |    61 |   212 |    273 |
#> |   row % | 22.3% | 77.7% | 100.0% |
#> +=========+=======+=======+========+
#> | Total   |   122 |   411 |    533 |
#> |   row % | 22.9% | 77.1% | 100.0% |
#> +---------+-------+-------+--------+
#> 
#> 
#> Group: employment = Other
#> -------------------------
#> 
#> Crosstabulation: Gender x Region (East/West)
#> --------------------------------------------
#> - Row variable: gender
#> - Column variable: region
#> - Percentages: Row percentages
#> - Weights variable: sampling_weight
#> - N (valid): 115 (weighted)
#> 
#> +---------+-------+-------+--------+
#> |         |   Region (East/West)   |
#> | Gender  |  East |  West |  Total |
#> +---------+-------+-------+--------+
#> | Male    |    15 |    53 |     68 |
#> |   row % | 22.1% | 77.9% | 100.0% |
#> +---------+-------+-------+--------+
#> | Female  |     6 |    41 |     47 |
#> |   row % | 12.8% | 87.2% | 100.0% |
#> +=========+=======+=======+========+
#> | Total   |    21 |    94 |    115 |
#> |   row % | 18.3% | 81.7% | 100.0% |
#> +---------+-------+-------+--------+
#> 

# Column percentages only
survey_data %>% crosstab(education, employment, percentages = "col")
#> 
#> Crosstabulation: Highest educational attainment x Employment status
#> -------------------------------------------------------------------
#> - Row variable: education
#> - Column variable: employment
#> - Percentages: Column percentages
#> - N (valid): 2500
#> 
#> +----------------+---------+----------+------------+---------+--------+--------+
#> | Highest        |                      Employment status                      |
#> | educational    |                                                             |
#> | attainment     | Student | Employed | Unemployed | Retired |  Other |  Total |
#> +----------------+---------+----------+------------+---------+--------+--------+
#> | Basic          |       0 |      571 |         65 |     171 |     34 |    841 |
#> | Secondary      |         |          |            |         |        |        |
#> |   col %        |    0.0% |    35.7% |      35.7% |   32.6% |  29.6% |  33.6% |
#> +----------------+---------+----------+------------+---------+--------+--------+
#> | Intermediate   |       0 |      412 |         51 |     137 |     29 |    629 |
#> | Secondary      |         |          |            |         |        |        |
#> |   col %        |    0.0% |    25.8% |      28.0% |   26.1% |  25.2% |  25.2% |
#> +----------------+---------+----------+------------+---------+--------+--------+
#> | Academic       |      44 |      366 |         44 |     145 |     32 |    631 |
#> | Secondary      |         |          |            |         |        |        |
#> |   col %        |   56.4% |    22.9% |      24.2% |   27.6% |  27.8% |  25.2% |
#> +----------------+---------+----------+------------+---------+--------+--------+
#> | University     |      34 |      251 |         22 |      72 |     20 |    399 |
#> |   col %        |   43.6% |    15.7% |      12.1% |   13.7% |  17.4% |  16.0% |
#> +================+=========+==========+============+=========+========+========+
#> | Total          |      78 |     1600 |        182 |     525 |    115 |   2500 |
#> |   col %        |  100.0% |   100.0% |     100.0% |  100.0% | 100.0% | 100.0% |
#> +----------------+---------+----------+------------+---------+--------+--------+