w_quantile() calculates percentiles (quantiles) using survey weights
for population-representative results. Percentiles divide your data into
equal portions – for example, the 25th percentile is the value below which
25% of your population falls. This is essential for understanding the
distribution of variables like income, age, or satisfaction scores.
Usage
w_quantile(
data,
...,
weights = NULL,
probs = c(0, 0.25, 0.5, 0.75, 1),
na.rm = TRUE
)Arguments
- data
Your survey data (a data frame or tibble)
- ...
The numeric variables you want to analyze. You can list multiple variables or use helpers like
starts_with("income")- weights
Survey weights to make results representative of your population. Without weights, you get the simple sample quantiles.
- probs
Which percentiles to calculate, as proportions between 0 and 1. Default:
c(0, 0.25, 0.5, 0.75, 1)for the minimum, 25th percentile, median, 75th percentile, and maximum. Usec(0.1, 0.5, 0.9)for deciles, orc(0.25, 0.5, 0.75)for quartiles only.- na.rm
Remove missing values before calculating? (Default: TRUE)
Value
Population-weighted quantile(s) with sample size information, including the weighted percentile values, effective sample size (effective N), and the number of valid observations used.
Details
Understanding the Results
Percentile values: The data values at each requested percentile in the weighted population. For example, if the weighted 25th percentile of income is 35,000, then 25% of the population earns less than that.
Effective N: How many independent observations your weighted data represents.
N: The actual number of observations used.
Common percentiles and their meaning:
0% (minimum): The smallest observed value
25% (Q1): One quarter of the population falls below this value
50% (median): Half the population falls below this value
75% (Q3): Three quarters of the population falls below this value
100% (maximum): The largest observed value
When to Use This
Use w_quantile() when:
You want to know at what values the population splits into groups
You need to construct population-representative income brackets or age groups
You want to identify the median or quartiles with proper weighting
You need SPSS-compatible weighted percentile values
See also
quantile for the base R quantile function.
w_median for the weighted median (50th percentile).
w_iqr for the weighted interquartile range (Q3 - Q1).
describe for comprehensive descriptive statistics including quantiles.
Other weighted_statistics:
w_iqr(),
w_kurtosis(),
w_mean(),
w_median(),
w_modus(),
w_range(),
w_sd(),
w_se(),
w_skew(),
w_var()
Examples
# Load required packages and data
library(dplyr)
data(survey_data)
# Basic weighted quantiles (0%, 25%, 50%, 75%, 100%)
survey_data %>% w_quantile(age, weights = sampling_weight)
#>
#> Weighted Quantile Statistics
#> ----------------------------
#> Variable Quantile Value N Effective_N Weights
#> age Min 18 2500 2468.8 sampling_weight
#> age 25% 38 2500 2468.8 sampling_weight
#> age 50% 50 2500 2468.8 sampling_weight
#> age 75% 63 2500 2468.8 sampling_weight
#> age Max 95 2500 2468.8 sampling_weight
#> ----------------------------------------
# Custom quantiles
survey_data %>% w_quantile(income, weights = sampling_weight, probs = c(0.1, 0.5, 0.9))
#>
#> Weighted Quantile Statistics
#> ----------------------------
#> Variable Quantile Value N Effective_N Weights
#> income 10% 2100 2186 2158.9 sampling_weight
#> income 50% 3500 2186 2158.9 sampling_weight
#> income 90% 5700 2186 2158.9 sampling_weight
#> ----------------------------------------
# Multiple variables
survey_data %>% w_quantile(age, income, weights = sampling_weight)
#>
#> Weighted Quantile Statistics
#> ----------------------------
#> Variable Quantile Value N Effective_N Weights
#> age Min 18 2500 2468.8 sampling_weight
#> age 25% 38 2500 2468.8 sampling_weight
#> age 50% 50 2500 2468.8 sampling_weight
#> age 75% 63 2500 2468.8 sampling_weight
#> age Max 95 2500 2468.8 sampling_weight
#> income Min 800 2186 2158.9 sampling_weight
#> income 25% 2700 2186 2158.9 sampling_weight
#> income 50% 3500 2186 2158.9 sampling_weight
#> income 75% 4600 2186 2158.9 sampling_weight
#> income Max 8000 2186 2158.9 sampling_weight
#> ----------------------------------------
# Grouped data
survey_data %>% group_by(region) %>% w_quantile(age, weights = sampling_weight)
#>
#> Weighted Quantile Statistics
#> ----------------------------
#>
#> Group: region = East
#> ----------------------------------------
#> Variable Quantile Value N Effective_N Weights
#> age Min 18 485 477 sampling_weight
#> age 25% 40 485 477 sampling_weight
#> age 50% 53 485 477 sampling_weight
#> age 75% 64 485 477 sampling_weight
#> age Max 95 485 477 sampling_weight
#> ----------------------------------------
#>
#> Group: region = West
#> ----------------------------------------
#> Variable Quantile Value N Effective_N Weights
#> age Min 18 2015 1993.1 sampling_weight
#> age 25% 38 2015 1993.1 sampling_weight
#> age 50% 49 2015 1993.1 sampling_weight
#> age 75% 62 2015 1993.1 sampling_weight
#> age Max 95 2015 1993.1 sampling_weight
#> ----------------------------------------
# Unweighted (for comparison)
survey_data %>% w_quantile(age)
#>
#> Quantile Statistics
#> -------------------
#> Variable Quantile Value N
#> age Min 18 2500
#> age 25% 38 2500
#> age 50% 50 2500
#> age 75% 62 2500
#> age Max 95 2500
#> ----------------------------------------