Rank entities by achievement gap within peer group — gap_percentile

Calculates percentile rank of achievement gaps. By default, smaller gaps receive higher percentile ranks (better equity = higher rank). This enables questions like "Which DFG A districts have the smallest Black-White achievement gaps?"

Usage

gap_percentile_rank(
  df,
  gap_col,
  peer_type = "statewide",
  year_col = "end_year",
  smaller_is_better = TRUE
)

Arguments

df: Output of calculate_subgroup_gap() or any dataframe with a gap column
gap_col: Character. The column containing gap values. Default "metric_gap".
peer_type: Character. Peer group type. See define_peer_group().
year_col: Character. Year column name. Default "end_year".
smaller_is_better: Logical. If TRUE (default), smaller gaps get higher percentile ranks. Set to FALSE if larger gaps are preferred.

Value

df with added gap percentile columns

Examples

if (FALSE) { # \dontrun{
grate %>%
  calculate_subgroup_gap("grad_rate", "white", "black") %>%
  gap_percentile_rank(gap_col = "grad_rate_gap", peer_type = "dfg")
} # }