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Compute Socioeconomic and Epidemiological Indicators

Source: R/sus_socio_compute_indicators.R
sus_socio_compute_indicators.Rd

Computes a set of standardised indicators from the columns already present in a climasus_df object (typically the output of sus_census_join()). Indicators span demographics, socioeconomic vulnerability, mortality, morbidity, maternal-child health, and health-resource availability. Column names from the census aggregation step (e.g. V003_sum) can be mapped to the formula-space names expected by each indicator via col_mapping.

Usage

sus_socio_compute_indicators(
  df,
  indicators = NULL,
  col_mapping = list(),
  confidence_level = 0.95,
  add_ci = TRUE,
  lang = "pt",
  verbose = TRUE
)

Arguments

df

A climasus_df object at stage >= "spatial". Typically the output of sus_census_join().

indicators

Character vector of indicator IDs to compute. If NULL (default), all indicators whose required columns are present in df are computed automatically. Use sus_socio_list_indicators() to inspect the full catalogue.

col_mapping

Named list mapping formula-space variable names to actual column names in df. For example:

list(pop_young = "pop_0_14_sum", total_hh = "V003_sum")

Only mappings that override the defaults need to be specified.

confidence_level

Numeric. Confidence level for Poisson / Binomial intervals. Default 0.95.

add_ci

Logical. If TRUE (default), adds *_low and *_high columns for indicators that support uncertainty quantification (Poisson for rates, Wilson score for proportions).

lang

Language for messages: "pt" (default), "en", "es".

verbose

Logical. Print progress messages. Default TRUE.

Value

The input climasus_df with additional columns prefixed ind_:

  • ind_<id> — computed value

  • ind_<id>_low / ind_<id>_high — confidence-interval bounds (only for Poisson/Binomial indicators when add_ci = TRUE)

sus_meta is updated to type = "indicators" with a history entry.

Details

Formula evaluation: each indicator formula is evaluated using rlang::eval_tidy() against a named-list environment built from the resolved column values, so no package-level NSE column names are needed.

Missing columns: if a requested indicator lacks required columns (after applying col_mapping), a warning is emitted and the indicator is skipped. The function never aborts due to missing columns.

Confidence intervals:

  • Poisson (Garwood exact): for event-count / population-at-risk rates (mortality, incidence, hospitalisation).

  • Binomial (Wilson score): for proportions (%, coverage indices).

  • None: for ratios with complex numerators or direct index values.

See also

sus_census_join(), sus_socio_list_indicators()

Examples

if (FALSE) { # \dontrun{
df_ind <- sus_socio_compute_indicators(
  df_census,
  indicators = c("dependency_ratio", "water_connection_rate"),
  col_mapping = list(
    pop_young   = "pop_0_14_sum",
    pop_elderly = "pop_65_plus_sum",
    pop_working = "pop_15_64_sum",
    hh_water    = "V111_sum",
    total_hh    = "V003_sum"
  )
)
} # }