Compare Discipline Across Years — compare_discipline_across

Compares discipline metrics across multiple years, calculating year-over-year changes and identifying long-term trends.

Usage

compare_discipline_across_years(df_list, metrics = NULL)

Arguments

df_list: A named list of data frames from different years. Each element should be named by its end_year (e.g., list("2022" = df_2022, "2024" = df_2024)). Data frames should be from fetch_disciplinary_removals, fetch_violence_vandalism_hib, or similar.
metrics: Character vector of metrics to compare. If NULL (default), attempts to auto-detect numeric metric columns. Metrics should match column names in the data (e.g., "discipline_rate" if calculated).

Value

Data frame with year-over-year comparisons including:

year - Year identifier
location_id - Combined location identifier
metric_name - Name of the metric
metric_value - Value of the metric in this year
year_over_year_change - Change from previous year
year_over_year_pct_change - Percentage change from previous year
multi_year_trend - Trend classification: "increasing", "decreasing", "stable", or "insufficient_data"

Examples

if (FALSE) { # \dontrun{
# Fetch data for multiple years
disc_2022 <- fetch_disciplinary_removals(2022)
disc_2023 <- fetch_disciplinary_removals(2023)
disc_2024 <- fetch_disciplinary_removals(2024)

# Combine into named list
df_list <- list(
  "2022" = disc_2022,
  "2023" = disc_2023,
  "2024" = disc_2024
)

# Compare trends
trends <- compare_discipline_across_years(df_list)

# View schools with increasing discipline rates
trends %>%
  dplyr::filter(metric_name == "discipline_rate", multi_year_trend == "increasing")
} # }