process_dhs_data: Process DHS Data: Merge RDS Files with Shapefiles and Extract...

In AgePopDenom: Model Fine-Scale Age-Structured Population Data using Open-Source Data

Process DHS Data: Merge RDS Files with Shapefiles and Extract Gamma Parameters

Description

This function processes DHS (Demographic and Health Survey) data by: 1. Reading RDS files and shapefiles for each country. 2. Merging demographic data with geographic information. 3. Cleaning and aggregating the data. 4. Extracting gamma parameters for age-related analysis.

Usage

process_dhs_data(
  rds_dir = here::here("01_data", "1a_survey_data", "raw", "pr_records"),
  shp_dir = here::here("01_data", "1a_survey_data", "raw", "shapefiles"),
  output_path = here::here("01_data", "1a_survey_data", "processed",
    "dhs_pr_records_combined.rds")
)

Arguments

rds_dir	Character. Path to the directory containing raw RDS files.
shp_dir	Character. Path to the directory containing shapefiles.
output_path	Character. Path to save the final processed dataset as an RDS file.

Details

The function loops through RDS files, processes each country's data by merging demographic information with shapefile data, and computes gamma parameters for age-related analysis. The progress is tracked and displayed for each country.

The function also filters out incomplete data (e.g., age values of '98') and handles labelled data using the 'haven::zap_labels' function.

The final output includes two datasets: 1. Outlier-free data. 2. Aggregated age parameter data.

Value

None. Saves the final combined dataset to the specified output path.

Examples

tf <- file.path(tempdir(), "test_env")
dir.create(tf, recursive = TRUE, showWarnings = FALSE)
tmp_rds_dir <- file.path(tf, "rds")
tmp_shp_dir <- file.path(tf, "shp")
tmp_output <- file.path(tf, "output.rds")

dir.create(tmp_rds_dir, recursive = TRUE, showWarnings = FALSE)
dir.create(tmp_shp_dir, recursive = TRUE, showWarnings = FALSE)

# Create fake DHS data
create_fake_dhs_data <- function(country_code) {
  set.seed(123) # For reproducibility
  n <- 100

  # Create labelled vectors
  hv007 <- haven::labelled(
    sample(c(2015, 2016), n, replace = TRUE),
    labels = c("2015" = 2015, "2016" = 2016)
  )

  hv001 <- haven::labelled(
    sample(1:20, n, replace = TRUE),
    labels = setNames(1:20, paste("Cluster", 1:20))
  )

  hv105 <- haven::labelled(
    sample(c(1:97, 98), n, replace = TRUE),
    labels = c(setNames(1:97, paste("Age", 1:97)), "Don't know" = 98)
  )

  # Combine into data frame
  data.frame(
    hv007 = hv007,
    hv001 = hv001,
    hv105 = hv105
  )
}

# Create fake shapefile data
# Create fake shapefile data with explicit CRS
create_fake_shapefile <- function(country_code) {
  set.seed(123)
  n_clusters <- 20

  # Create spatial data frame with explicit CRS
  sf_data <- sf::st_as_sf(
    dplyr::tibble(
      DHSCLUST = 1:n_clusters,
      URBAN_RURA = sample(c("R", "U"), n_clusters, replace = TRUE),
      LATNUM = runif(n_clusters, -10, 10),
    LONGNUM = runif(n_clusters, -10, 10)
  ),
    coords = c("LONGNUM", "LATNUM"),
    crs = 4326 # WGS84
  ) |>
    dplyr::mutate(
      LATNUM = runif(n_clusters, -10, 10),
      LONGNUM = runif(n_clusters, -10, 10)
    )
}

# Save test data for two countries
countries <- c("KE", "TZ")
for (country in countries) {
  saveRDS(
    create_fake_dhs_data(country),
    file = file.path(tmp_rds_dir, paste0(country, "HR71FL.rds"))
  )
  saveRDS(
    create_fake_shapefile(country),
    file = file.path(tmp_shp_dir, paste0(country, "HR7SHP.rds"))
  )
}

# Run the function
process_dhs_data(
  rds_dir = tmp_rds_dir,
  shp_dir = tmp_shp_dir,
  output_path = tmp_output
)

AgePopDenom documentation built on April 12, 2025, 1:41 a.m.