Aggregate ACS data to custom geographies

Aggregate tract-level ACS data to user-defined custom geographies by properly handling different variable types (counts, percentages, medians, etc.) and recalculating all error measures appropriately.

Usage

calculate_custom_geographies(
  .data,
  group_id,
  spatial = FALSE,
  weight_variable = "total_population_universe"
)

Arguments

.data

A dataframe returned from compile_acs_data() at the tract level. Must have a codebook attribute attached.

group_id

Character. The name of a column in .data that contains the custom geography identifiers to aggregate to.

spatial

Logical. If TRUE, dissolve tract geometries to create custom geography boundaries using sf::st_union(). Default is FALSE.

weight_variable

Character. The variable name to use for population-weighted averages of non-aggregatable variables. Default is "total_population_universe".

Value

A dataframe aggregated to custom geographies with recalculated estimates, MOEs, SEs, and CVs. A modified codebook is attached as an attribute.

Examples

if (FALSE) { # \dontrun{
# First, create tract-level data
tract_data = compile_acs_data(
  years = 2022,
  geography = "tract",
  states = "DC"
)

# Add a custom geography column (e.g., from a crosswalk)
tract_data_with_neighborhoods = tract_data %>%
  dplyr::left_join(neighborhood_crosswalk, by = "GEOID")

# Aggregate to custom geographies
neighborhood_data = calculate_custom_geographies(
  .data = tract_data_with_neighborhoods,
  group_id = "neighborhood_id",
  spatial = TRUE
)
} # }