Skip to contents

Run the complete QCI pipeline

Source: R/pipeline.R
qci_pipeline.Rd

A convenience function that chains all processing steps: load (optional) -> clean -> compute ratios -> PCA -> merge with Rate data -> create long format output.

Usage

qci_pipeline(
  data,
  loc_types = NULL,
  keep_types = "Country",
  age_categories = "Age-standardized",
  exclude_location_ids = 533L,
  ratio_cols = c("MIR", "YLLtoYLD", "DALtoPER", "PERtoINC"),
  verbose = TRUE
)

Arguments

data

A data.frame of raw GBD data. If a character vector of file paths, qci_load() is called first.

loc_types

Location type metadata. NULL uses the bundled dataset.

keep_types

Character vector of location types. Default "Country".

age_categories

Character vector of age groups. Default "Age-standardized".

exclude_location_ids

Integer vector. Default 533.

ratio_cols

Character vector of ratio column names for PCA. Default c("MIR", "YLLtoYLD", "DALtoPER", "PERtoINC").

verbose

Logical. Print progress messages. Default TRUE.

Value

A list with elements:

wide

data.table in wide format. Contains Rate columns, Number columns (suffixed _Number), the 4 ratios, and qci_score.

long

data.table in long format with columns: location_id, location_name, year, sex_name, age_name, measure, value, upper, lower.

pca_details

data.frame of variance explained per subgroup.

Details

This is the primary user-facing function for computing QCI from raw GBD data.

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.
head(result$wide[, .(location_name, year, sex_name, qci_score)])
#> Key: <location_name, year, sex_name>
#>    location_name  year sex_name qci_score
#>           <char> <int>   <char>     <num>
#> 1:     Australia  1990     Both  99.96851
#> 2:     Australia  1990   Female  99.96648
#> 3:     Australia  1990     Male  99.94012
#> 4:     Australia  2005     Both 100.00000
#> 5:     Australia  2005   Female  99.91433
#> 6:     Australia  2005     Male  99.97438