Plot QCI score distributions by sex

Creates density plots comparing Male and Female QCI score distributions. Supports comparing across two time points.

Usage

plot_qci_distribution(
  data,
  score_col = "qci_score",
  years = NULL,
  sex = c("Male", "Female"),
  age = "Age-standardized",
  alpha = 0.5,
  colors = c(Male = "skyblue4", Female = "firebrick")
)

Arguments

data

A data.frame with QCI results. Can be wide format (from pipeline output) or long format.

score_col

Character. Column name containing the score to plot. Default "qci_score".

years

Integer vector of 1 or 2 years to compare. Default NULL (all years overlaid).

sex

Character vector of sex categories. Default c("Male", "Female").

age

Character. Age group. Default "Age-standardized".

alpha

Numeric. Transparency for density fills. Default 0.5.

colors

Named character vector. Default c(Male = "skyblue4", Female = "firebrick").

Value

A ggplot2 object.

Examples

data(sample_gbd)
result <- qci_pipeline(sample_gbd)
#> ℹ Cleaning and reshaping data...
#> ✔ Cleaned data: 9 locations, 3 years.
#> ℹ Computing epidemiological ratios...
#> ℹ Running PCA...
#> ℹ PCA done for "Both / Age-standardized": 74.1% variance explained (n=27).
#> ℹ PCA done for "Female / Age-standardized": 75.7% variance explained (n=27).
#> ℹ PCA done for "Male / Age-standardized": 73.2% variance explained (n=27).
#> ℹ Creating long format output...
#> ✔ QCI pipeline complete.
plot_qci_distribution(result$wide, years = c(1990, 2019))