Plot QCI trends over time — plot_qci

Creates line plots of QCI over years, stratified by sex. When multiple locations are present, automatically facets by location for readability.

Usage

plot_qci_trend(
  data,
  locations = NULL,
  sex = c("Male", "Female", "Both"),
  age = "Age-standardized",
  colors = c(Male = "skyblue4", Female = "firebrick", Both = "grey30"),
  facet_by = NULL,
  free_y = FALSE
)

Arguments

data: A data.frame with columns: year, qci_score, sex_name, location_name, age_name.
locations: Character vector of location names. Default NULL (all).
sex: Character vector of sex categories to include. Default c("Male", "Female", "Both").
age: Character. Default "Age-standardized".
colors: Named character vector. Default c(Male = "skyblue4", Female = "firebrick", Both = "grey30").
facet_by: Character. Column to facet by. When NULL (default), auto-facets by location_name if more than one location is present. Set to FALSE to disable auto-faceting.
free_y: Logical. Free y-axis scales in facets. Default FALSE.

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_trend(result$wide)