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Find the Most Common Value in Your Population

Source: R/w_modus.R
w_modus.Rd

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. 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.

na.rm

Remove missing values before calculating? (Default: TRUE). With FALSE, the result for a variable that contains missing values is NA (as in base R).

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 / Missing: Valid and missing cases. With weights, both are sums of weights (displayed rounded), as SPSS reports them under WEIGHT BY; Kish's effective N is shown by summary().

If multiple values share the highest (weighted) frequency, the smallest value (for factors: the first level) is returned, as in SPSS; the print flags such results ("Multiple modes exist") and $results$n_modes holds the number of tied values.

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.

See also

w_median for the weighted middle value.

w_mean for weighted means.

frequency for complete frequency tables of categorical variables.

describe for comprehensive descriptive statistics including the mode.

Other weighted_statistics: w_iqr(), w_kurtosis(), w_mean(), w_median(), w_quantile(), w_range(), w_sd(), w_se(), w_skew(), w_var()

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
#> ------------------------
#> Weights: sampling_weight
#> 
#>   -------------------------------
#>   Variable  Mode       N  Missing
#>   -------------------------------
#>   gender    Female  2516        0
#>   -------------------------------

# Multiple variables (works best with categorical/discrete data)
survey_data %>% w_modus(gender, region, weights = sampling_weight)
#> 
#> Weighted Mode Statistics
#> ------------------------
#> Weights: sampling_weight
#> 
#>   -------------------------------
#>   Variable  Mode       N  Missing
#>   -------------------------------
#>   gender    Female  2516        0
#>   region    West    2516        0
#>   -------------------------------

# Grouped data
survey_data %>% group_by(region) %>% w_modus(gender, weights = sampling_weight)
#> 
#> Weighted Mode Statistics
#> ------------------------
#> Weights: sampling_weight
#> 
#> Group: region = East
#> --------------------
#> 
#>   ------------------------------
#>   Variable  Mode      N  Missing
#>   ------------------------------
#>   gender    Female  509        0
#>   ------------------------------
#> 
#> Group: region = West
#> --------------------
#> 
#>   -------------------------------
#>   Variable  Mode       N  Missing
#>   -------------------------------
#>   gender    Female  2007        0
#>   -------------------------------

# 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
#> ---------------
#> 
#>   -------------------------------
#>   Variable  Mode       N  Missing
#>   -------------------------------
#>   gender    Female  2500        0
#>   -------------------------------