Find the Most Common Value in Your Population

w_modus() finds the mode (most frequently occurring value) of your data using survey weights for population-representative results. The mode tells you which category or value is the most common in your population. This is especially useful for categorical variables (e.g., the most common education level, the most frequent employment status).

Unlike mean and median, the mode works with both numeric and categorical data.

Usage

w_modus(data, ..., weights = NULL, na.rm = TRUE)

Arguments

data: Your survey data (a data frame or tibble)
...: The variables you want to analyze. Works best with categorical or discrete numeric variables. You can list multiple variables or use helpers like starts_with("trust")
weights: Survey weights to make results representative of your population. Without weights, the mode is simply the most frequent value in your sample.
na.rm: Remove missing values before calculating? (Default: TRUE)

Value

Population-weighted mode(s) with sample size information, including the most common value (by weighted frequency), effective sample size (effective N), and the number of valid observations used.

Details

Understanding the Results

Weighted Mode: The value that occurs most frequently in the weighted population. For weighted data, the mode is the value whose observations have the largest total weight.
Effective N: How many independent observations your weighted data represents.
N: The actual number of observations used.

If multiple values share the highest weighted frequency (ties), the first value encountered is returned.

When to Use This

Use w_modus() when:

You want to find the most common response (e.g., most popular education level)
You are working with categorical or ordinal data
You want the "typical" value for discrete data where mean is not meaningful
You need SPSS-compatible weighted mode values

Formula

The weighted mode is the value \(x_k\) that maximizes the total weight:

\(\text{Mode}_w = \arg\max_{x_k} \sum_{i: x_i = x_k} w_i\)

In other words, sum the weights for each unique value and pick the value with the largest total weight.

References

IBM Corp. (2023). IBM SPSS Statistics 29 Algorithms. IBM Corporation.

Examples

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

# Basic weighted mode (most frequent value)
survey_data %>% w_modus(gender, weights = sampling_weight)
#> 
#> Weighted Mode Statistics
#> ------------------------
#> # A tibble: 1 × 3
#>   Variable weighted_mode effective_n
#>   <chr>    <fct>               <dbl>
#> 1 gender   Female              2469.
#> 

# Multiple variables (works best with categorical/discrete data)
survey_data %>% w_modus(gender, region, weights = sampling_weight)
#> 
#> Weighted Mode Statistics
#> ------------------------
#> # A tibble: 2 × 3
#>   Variable weighted_mode effective_n
#>   <chr>    <chr>               <dbl>
#> 1 gender   Female              2469.
#> 2 region   West                2469.
#> 

# Grouped data
survey_data %>% group_by(region) %>% w_modus(gender, weights = sampling_weight)
#> 
#> Weighted Mode Statistics
#> ------------------------
#> 
#> Group: region = East
#> # A tibble: 1 × 2
#>   weighted_mode effective_n
#>   <fct>               <dbl>
#> 1 Female                477
#> 
#> Group: region = West
#> # A tibble: 1 × 2
#>   weighted_mode effective_n
#>   <fct>               <dbl>
#> 1 Female              1993.
#> 

# In summarise context
survey_data %>% summarise(mode_gender = w_modus(gender, weights = sampling_weight))
#> # A tibble: 1 × 1
#>   mode_gender
#>   <fct>      
#> 1 Female     

# Unweighted (for comparison)
survey_data %>% w_modus(gender)
#> 
#> Mode Statistics
#> ---------------
#> # A tibble: 1 × 3
#>   Variable mode       n
#>   <chr>    <chr>  <dbl>
#> 1 gender   Female  2500
#>