Aggregate paired ACS estimate and MOE columns by group.

Usage

acs_aggregate(
  data,
  group_var,
  value_cols,
  moe_cols,
  cov_strategy = c("zero", "supplied", "constant"),
  cov_value = 0,
  conf = 0.9
)

Arguments

data: A data frame containing ACS estimate and MOE columns.
group_var: Name of the grouping column, supplied as a single string.
value_cols: Character vector of estimate column names to aggregate.
moe_cols: Character vector of MOE column names paired with value_cols.
cov_strategy: Covariance strategy: "zero", "supplied", or "constant".
cov_value: For "constant", a scalar correlation. For "supplied", a named list of full covariance matrices keyed by estimate column.
conf: Confidence level associated with input and output MOEs.

Value

A data frame with one row per group and aggregated estimate/MOE columns.

Details

cov_strategy = "constant" interprets cov_value as a correlation, not a covariance. This differs from scalar cov arguments in core propagation functions, where a scalar means an off-diagonal covariance on the standard-error scale.

Output rows are ordered by first appearance of each group level in data, not alphabetically.

Examples

tracts <- data.frame(
  region = c("north", "north", "south", "south"),
  pop = c(1000, 1200, 900, 1100),
  pop_moe = c(120, 140, 100, 130)
)
acs_aggregate(tracts, "region", "pop", "pop_moe")
#>   region  pop  pop_moe
#> 1  north 2200 184.3909
#> 2  south 2000 164.0122
acs_aggregate(tracts, "region", "pop", "pop_moe",
              cov_strategy = "constant", cov_value = 0.25)
#>   region  pop  pop_moe
#> 1  north 2200 205.9126
#> 2  south 2000 182.7567