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Analysis-ready social science measures

compile_acs_data.Rd

Construct measures frequently used in social sciences research, leveraging tidycensus::get_acs() to acquire raw estimates from the Census Bureau API.

Usage

compile_acs_data(
  tables = NULL,
  indicators = NULL,
  years = c(2022),
  geography = "county",
  states = NULL,
  counties = NULL,
  spatial = FALSE,
  ...
)

Arguments

tables

A character vector of table names to include (e.g., c("race", "snap")). Use list_tables() to see available tables. When NULL (default) and indicators is also NULL, all tables are included.

indicators

A character vector of indicator names to include (e.g., c("snap_received_percent")). Each indicator's parent table is automatically included.

years

A numeric vector of four-digit years for which to pull five-year American Community Survey estimates.

geography

A geography type that is accepted by tidycensus::get_acs(), e.g., "tract", "county", "state", among others. Geographies below the tract level are not supported.

states

A vector of one or more state names, abbreviations, or codes as accepted by tidycensus::get_acs().

counties

A vector of five-digit county FIPS codes. If specified, this parameter will override the states parameter. If NULL, all counties in the the state(s) specified in the states parameter will be included.

spatial

Boolean. Return a simple features (sf), spatially-enabled dataframe?

...

Deprecated arguments. If variables is passed, a deprecation warning is issued and the value is ignored.

Value

A dataframe containing the requested variables, their MOEs, a series of derived variables, such as percentages, and the year of the data. Returned data are formatted wide. A codebook generated with generate_codebook() is attached and can be accessed via compile_acs_data() %>% attr("codebook").

See also

tidycensus::get_acs(), which this function wraps.

Examples

if (FALSE) { # \dontrun{
## Pull all tables (default, backward-compatible)
df = compile_acs_data(years = c(2022), geography = "county", states = "NJ")

## Pull specific tables
df = compile_acs_data(tables = c("race", "snap"), years = 2022,
                      geography = "county", states = "NJ")

## Pull by indicator name (returns the full parent table)
df = compile_acs_data(indicators = c("snap_received_percent"),
                      years = 2022, geography = "county", states = "NJ")
  } # }