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)
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
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()
Examples
# Load required packages and data
library(dplyr)
data(survey_data)
# Basic crosstab
survey_data %>% crosstab(gender, region)
#>
#> Crosstabulation: gender × region
#> --------------------------------
#> - Row variable: gender
#> - Column variable: region
#> - Percentages: Row percentages
#> - N (valid): 2500
#>
#> +------------+--------+--------+--------+
#> | | region |
#> | 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 |
#> +------------+--------+--------+--------+
# With weights and all percentages
survey_data %>% crosstab(gender, education,
weights = sampling_weight,
percentages = "all")
#>
#> Crosstabulation: gender × education
#> -----------------------------------
#> - Row variable: gender
#> - Column variable: education
#> - Percentages: All percentages (row, col, total)
#> - Weights variable: sampling_weight
#> - N (valid): 2516 (weighted)
#>
#> +------------+----------------------+----------------------+----------------------+----------------------+----------------------+
#> | | education |
#> | gender | Basic Secondary | Intermediate Seco... | Academic Secondary | University | Total |
#> +------------+----------------------+----------------------+----------------------+----------------------+----------------------+
#> | Male | 402 | 291 | 326 | 176 | 1195 |
#> | row % | 33.6% | 24.3% | 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 | 1321 |
#> | row % | 33.8% | 26.5% | 23.9% | 15.8% | 100.0% |
#> | col % | 52.7% | 54.6% | 49.2% | 54.3% | 52.5% |
#> | total % | 17.7% | 13.9% | 12.5% | 8.3% | 52.5% |
#> +============+======================+======================+======================+======================+======================+
#> | Total | 848 | 641 | 642 | 385 | 2516 |
#> | col % | 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 × region
#> --------------------------------
#> - Row variable: gender
#> - Column variable: region
#> - Percentages: Row percentages
#> - Weights variable: sampling_weight
#> - N (valid): 80 (weighted)
#>
#> +------------+--------+--------+--------+
#> | | region |
#> | gender | East | West | Total |
#> +------------+--------+--------+--------+
#> | Male | 7 | 29 | 36 |
#> | row % | 20.0% | 80.0% | 100.0% |
#> +------------+--------+--------+--------+
#> | Female | 4 | 40 | 44 |
#> | row % | 10.2% | 89.8% | 100.0% |
#> +============+========+========+========+
#> | Total | 12 | 68 | 80 |
#> +------------+--------+--------+--------+
#>
#>
#> Group: employment = Employed
#> ----------------------------
#>
#> Crosstabulation: gender × region
#> --------------------------------
#> - Row variable: gender
#> - Column variable: region
#> - Percentages: Row percentages
#> - Weights variable: sampling_weight
#> - N (valid): 1603 (weighted)
#>
#> +------------+--------+--------+--------+
#> | | region |
#> | 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 | 320 | 1282 | 1603 |
#> +------------+--------+--------+--------+
#>
#>
#> Group: employment = Unemployed
#> ------------------------------
#>
#> Crosstabulation: gender × region
#> --------------------------------
#> - Row variable: gender
#> - Column variable: region
#> - Percentages: Row percentages
#> - Weights variable: sampling_weight
#> - N (valid): 184 (weighted)
#>
#> +------------+--------+--------+--------+
#> | | region |
#> | gender | East | West | Total |
#> +------------+--------+--------+--------+
#> | Male | 17 | 66 | 83 |
#> | row % | 20.8% | 79.2% | 100.0% |
#> +------------+--------+--------+--------+
#> | Female | 16 | 85 | 101 |
#> | row % | 15.5% | 84.5% | 100.0% |
#> +============+========+========+========+
#> | Total | 33 | 151 | 184 |
#> +------------+--------+--------+--------+
#>
#>
#> Group: employment = Retired
#> ---------------------------
#>
#> Crosstabulation: gender × region
#> --------------------------------
#> - Row variable: gender
#> - Column variable: region
#> - Percentages: Row percentages
#> - Weights variable: sampling_weight
#> - N (valid): 534 (weighted)
#>
#> +------------+--------+--------+--------+
#> | | region |
#> | gender | East | West | Total |
#> +------------+--------+--------+--------+
#> | Male | 61 | 199 | 260 |
#> | row % | 23.5% | 76.5% | 100.0% |
#> +------------+--------+--------+--------+
#> | Female | 61 | 212 | 273 |
#> | row % | 22.5% | 77.5% | 100.0% |
#> +============+========+========+========+
#> | Total | 123 | 411 | 534 |
#> +------------+--------+--------+--------+
#>
#>
#> Group: employment = Other
#> -------------------------
#>
#> Crosstabulation: gender × region
#> --------------------------------
#> - Row variable: gender
#> - Column variable: region
#> - Percentages: Row percentages
#> - Weights variable: sampling_weight
#> - N (valid): 115 (weighted)
#>
#> +------------+--------+--------+--------+
#> | | region |
#> | gender | East | West | Total |
#> +------------+--------+--------+--------+
#> | Male | 15 | 53 | 68 |
#> | row % | 22.2% | 77.8% | 100.0% |
#> +------------+--------+--------+--------+
#> | Female | 6 | 41 | 47 |
#> | row % | 13.1% | 86.9% | 100.0% |
#> +============+========+========+========+
#> | Total | 21 | 94 | 115 |
#> +------------+--------+--------+--------+
#>
# Column percentages only
survey_data %>% crosstab(education, employment, percentages = "col")
#>
#> Crosstabulation: education × employment
#> ---------------------------------------
#> - Row variable: education
#> - Column variable: employment
#> - Percentages: Column percentages
#> - N (valid): 2500
#>
#> +----------------------+------------+------------+------------+------------+------------+------------+
#> | | employment |
#> | education | Student | Employed | Unemployed | Retired | Other | Total |
#> +----------------------+------------+------------+------------+------------+------------+------------+
#> | Basic Secondary | 0 | 571 | 65 | 171 | 34 | 841 |
#> | col % | 0.0% | 35.7% | 35.7% | 32.6% | 29.6% | 33.6% |
#> +----------------------+------------+------------+------------+------------+------------+------------+
#> | Intermediate Seco... | 0 | 412 | 51 | 137 | 29 | 629 |
#> | col % | 0.0% | 25.8% | 28.0% | 26.1% | 25.2% | 25.2% |
#> +----------------------+------------+------------+------------+------------+------------+------------+
#> | Academic Secondary | 44 | 366 | 44 | 145 | 32 | 631 |
#> | 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 % | 3.1% | 64.0% | 7.3% | 21.0% | 4.6% | 100.0% |
#> +----------------------+------------+------------+------------+------------+------------+------------+