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Adjust ACS counts to match county totals

adjust_ref_counts_dataset.Rd

Adjust block group-level ACS counts (5-year) to match county-level 1-year ACS totals for weighting. Use when block group reference data is outdated or mismatched with county-level targets.

Usage

adjust_ref_counts_dataset(acs_counts, settings, study_unit = NULL)

Arguments

acs_counts

data.table. Required columns:

  • bg_geoid — block group GEOID

  • county_fips — county FIPS code

  • ref_count_h — household reference count

  • ref_count_p — person reference count Rows: one per block group. Keys: (bg_geoid). Modified by reference: no (returns copy).

settings

list. Must include:

  • acs_count_vars — ACS count variable names

  • pums_year — PUMS target year

  • pums_dataset — ACS dataset type

  • study_unit — study unit type Modified by reference: no.

study_unit

character. Study unit(s) to adjust: 'household', 'person', or both. Default: settings$study_unit.

Value

data.table. Adjusted ACS counts. Same schema as input. Columns:

  • bg_geoid

  • county_fips

  • ref_count_h

  • ref_count_p Rows: one per block group. Keys: (bg_geoid).

Details

  • Computes adjustment factors using county-level ACS for the target year.

  • Only reference counts are used for adjustment, not PUMS, to avoid circularity.

  • Handles both 'household' and 'person' units, merging and adjusting as needed.

  • Checks that adjusted totals match county-level ACS within tolerance; errors if not.

  • Cleans up temporary columns before returning a copy.

  • Returns a data.table copy; does not modify by reference.

Settings

  • acs_count_vars (direct): ACS count variable names. Default from config/settings.json.

  • pums_year (direct): PUMS target year. Default from config/settings.json.

  • pums_dataset (direct): ACS dataset type. Default from config/settings.json.

  • study_unit (direct): Study unit type. Default from config/settings.json.

See also

get_county_fips(), get_state_fips(), calc_sample_plan_counts()

Other weighting adjustment: adjust_pums_to_reference(), adjust_target_to_study_zones(), adjust_unrelated_per_wts(), adjust_unrelated_pums(), blend_initial_weights(), calc_alpha(), calc_initial_weights(), calc_uwe(), force_balance_pums_weights()

Examples

## Not run:
acs_counts <- data.table(bg_geoid = "12345", county_fips = "001", ref_count_h = 100, ref_count_p = 250)
settings <- list(acs_count_vars = c("ref_count_h", "ref_count_p"), 
                 pums_year = 2020, 
                 pums_dataset = "acs5", 
                 study_unit = "household")
adjusted <- adjust_ref_counts_dataset(acs_counts, settings, study_unit = c('household', 'person'))
#> Error in get("acs_count_vars", settings)[[sunit]]: subscript out of bounds
## End(Not run)