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Calculate Population-Representative Averages

Source: R/w_mean.R
w_mean.Rd

w_mean() calculates averages that accurately represent your population by using survey weights. This ensures that groups who were over- or under-sampled contribute appropriately to the final average.

Usage

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

Arguments

data

Your survey data (a data frame or tibble)

...

The numeric variables you want to average. You can list multiple variables or use helpers like starts_with("income")

weights

Survey weights to make the average representative of your population. Without weights, you get the simple sample average.

na.rm

Remove missing values before calculating? (Default: TRUE)

Value

Population-weighted average(s) with sample size information

Details

When to Use This

Use w_mean() when your survey uses sampling weights and you need population-representative averages. Weights correct for:

  • Oversampling of certain groups (weights < 1)

  • Undersampling of other groups (weights > 1)

  • Non-response patterns

  • Complex survey designs

Understanding the Results

  • Weighted Mean: The population-representative average

  • Effective N: How many independent observations your weighted data represents

  • N: Actual number of observations used

Formula

\(\bar{x}_w = \frac{\sum w_i \cdot x_i}{\sum w_i}\)

The effective sample size is: \(n_{eff} = (\sum w_i)^2 / \sum w_i^2\)

References

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

See also

weighted.mean for the base R weighted mean function.

w_sd for weighted standard deviation.

w_median for weighted median.

describe for comprehensive descriptive statistics.

Other weighted_statistics: w_iqr(), w_kurtosis(), w_median(), w_modus(), 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 usage
survey_data %>% w_mean(age, weights = sampling_weight)
#> 
#> Weighted Mean Statistics
#> ------------------------
#> 
#> --- age ---
#>  Variable weighted_mean Effective_N
#>       age        50.514      2468.8
#> 

# Multiple variables
survey_data %>% w_mean(age, income, life_satisfaction, weights = sampling_weight)
#> 
#> Weighted Mean Statistics
#> ------------------------
#> 
#> --- age ---
#>  Variable weighted_mean Effective_N
#>       age        50.514      2468.8
#> 
#> --- income ---
#>  Variable weighted_mean Effective_N
#>    income      3743.099      2158.9
#> 
#> --- life_satisfaction ---
#>           Variable weighted_mean Effective_N
#>  life_satisfaction         3.625      2390.9
#> 

# Grouped data
survey_data %>% group_by(region) %>% w_mean(age, weights = sampling_weight)
#> 
#> Weighted Mean Statistics
#> ------------------------
#> 
#> Group: region = East
#> Warning: Unknown or uninitialised column: `Variable`.
#> 
#> Group: region = West
#> Warning: Unknown or uninitialised column: `Variable`.
#> 

# In summarise context
survey_data %>% summarise(mean_age = w_mean(age, weights = sampling_weight))
#> # A tibble: 1 × 1
#>   mean_age
#>      <dbl>
#> 1     50.5

# Unweighted (for comparison)
survey_data %>% w_mean(age)
#> 
#> Mean Statistics
#> ---------------
#> 
#> --- age ---
#>  Variable  mean    N
#>       age 50.55 2500
#>