Track achievement gap trends over time — gap

Combines gap calculation with trend tracking to show how achievement gaps have changed over time for specific entities. Answers questions like "How has Newark's Black-White gap changed from 2015 to 2023?"

Usage

gap_trajectory(
  df,
  metric_col,
  subgroup_a,
  subgroup_b,
  year_col = "end_year",
  entity_cols = "district_id"
)

Arguments

df: Dataframe with subgroups and metrics over multiple years
metric_col: Character. The metric to track.
subgroup_a: Character. Reference subgroup.
subgroup_b: Character. Comparison subgroup.
year_col: Character. Year column. Default "end_year".
entity_cols: Character vector. Columns identifying entity to track.

Value

df with gap values and trend columns:

{metric}_gap_yoy_change: Year-over-year change in gap
{metric}_gap_cumulative_change: Change from baseline year
{metric}_gap_baseline: Gap value in first year

Examples

if (FALSE) { # \dontrun{
# Track Newark's Black-White grad rate gap over time
grate %>%
  filter(district_id == "3570") %>%
  gap_trajectory(
    metric_col = "grad_rate",
    subgroup_a = "white",
    subgroup_b = "black"
  )
} # }