c.1 indicators

pipelines/snt_dhs_indicators/utils/snt_dhs_careseeking_computation.r

@@ -0,0 +1,86 @@
+#' Compute and export careseeking indicators with confidence intervals.
+#'
+#' For each indicator, runs survey-weighted estimation by admin unit, formats
+#' CI/sample-average columns as percentages, exports per-indicator tables, and
+#' builds a consolidated sample-average summary table.
+#'
+#' @param kr_design_sampling Survey design object.
+#' @param indicator_names Character vector of careseeking indicator names.
+#' @param admin_name_col Admin grouping column name.
+#' @param admin_data Admin reference table for merges.
+#' @param output_data_path Directory to write output files.
+#' @param country_code Country code used in filenames.
+#' @param data_source DHS source code used in filenames.
+#' @param admin_level Admin level label used in filenames.
+#' @return List with `summary_table` and per-indicator `indicator_tables`.
+compute_careseeking_indicators <- function(
+    kr_design_sampling,
+    indicator_names,
+    admin_name_col,
+    admin_data,
+    output_data_path,
+    country_code,
+    data_source,
+    admin_level
+) {
+    summary_table <- data.table::copy(admin_data)
+    indicator_tables <- list()
+
+    for (indicator_name in indicator_names) {
+        table_content <- survey::svyby(
+            formula = as.formula(paste("~", indicator_name)),
+            by = reformulate(admin_name_col),
+            FUN = survey::svymean,
+            design = kr_design_sampling,
+            level = 0.95,
+            vartype = "ci",
+            na.rm = TRUE,
+            influence = TRUE
+        )
+
+        data.table::setDT(table_content)
+        lower_bound_col <- glue::glue("{toupper(indicator_name)}_CI_LOWER_BOUND")
+        upper_bound_col <- glue::glue("{toupper(indicator_name)}_CI_UPPER_BOUND")
+        sample_avg_col <- glue::glue("{toupper(indicator_name)}_SAMPLE_AVERAGE")
+
+        names(table_content)[names(table_content) == "ci_l"] <- lower_bound_col
+        names(table_content)[names(table_content) == "ci_u"] <- upper_bound_col
+        names(table_content)[names(table_content) == indicator_name] <- sample_avg_col
+
+        table_content[get(lower_bound_col) < 0, (lower_bound_col) := 0]
+        table_content[get(upper_bound_col) > 1, (upper_bound_col) := 1]
+        table_content[, (lower_bound_col) := get(lower_bound_col) * 100]
+        table_content[, (upper_bound_col) := get(upper_bound_col) * 100]
+        table_content[, (sample_avg_col) := get(sample_avg_col) * 100]
+
+        indicator_estimation_table <- table_content[
+            ,
+            .SD,
+            .SDcols = c(admin_name_col, grep("SAMPLE_AVERAGE", names(table_content), value = TRUE))
+        ]
+
+        table_content <- data.table::merge.data.table(admin_data, table_content, by = admin_name_col)
+        summary_table <- data.table::merge.data.table(summary_table, indicator_estimation_table, by = admin_name_col)
+
+        filename_without_extension <- glue::glue("{country_code}_{data_source}_{admin_level}_{toupper(indicator_name)}")
+        utils::write.csv(
+            table_content,
+            file = file.path(output_data_path, paste0(filename_without_extension, ".csv")),
+            row.names = FALSE
+        )
+        arrow::write_parquet(table_content, file.path(output_data_path, paste0(filename_without_extension, ".parquet")))
+
+        indicator_tables[[indicator_name]] <- table_content
+    }
+
+    names(summary_table) <- gsub("_SAMPLE_AVERAGE", "", names(summary_table))
+    summary_filename_without_extension <- glue::glue("{country_code}_{data_source}_{admin_level}_PCT_CARESEEKING_SAMPLE_AVERAGE")
+    utils::write.csv(
+        summary_table,
+        file = file.path(output_data_path, paste0(summary_filename_without_extension, ".csv")),
+        row.names = FALSE
+    )
+    arrow::write_parquet(summary_table, file.path(output_data_path, paste0(summary_filename_without_extension, ".parquet")))
+
+    list(summary_table = summary_table, indicator_tables = indicator_tables)
+}