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Fetch Chronic Absenteeism Data (Cross-State Standard)

Source: R/fetch_absence.R
fetch_absence.Rd

Unified entry point for NJ chronic absenteeism data, matching the fetch_absence() naming convention used across all state packages. Wraps fetch_chronic_absenteeism() from the SPR database with optional tidy normalization.

Usage

fetch_absence(
  end_year,
  level = "school",
  type = "chronic",
  tidy = TRUE,
  use_cache = TRUE
)

Arguments

end_year

A school year (2017-2024). Year is the end of the academic year — e.g., the 2023-24 school year is end_year = 2024.

level

One of "school" or "district". Default "school" returns school-level data; "district" returns district and state-level data.

type

One of "chronic" (default), "by_grade", "days_absent", or "essa". Selects which underlying absenteeism function to call.

tidy

Logical; if TRUE (default), normalizes subgroup names to cross-state standards (e.g., "econ_disadv", "lep", "special_ed").

use_cache

Logical; if TRUE, uses session cache for faster repeat calls.

Value

A data frame with chronic absenteeism data. When tidy = TRUE, subgroup names are standardized:

  • total — total population

  • white, black, hispanic, asian — race/ethnicity

  • native_american, pacific_islander, multiracial

  • econ_disadv — economically disadvantaged

  • lep — limited English proficiency

  • special_ed — students with disabilities

  • male, female

Examples

if (FALSE) { # \dontrun{
# Get school-level chronic absenteeism with standard subgroup names
ca <- fetch_absence(2024)

# District-level data
ca_dist <- fetch_absence(2024, level = "district")

# Grade-level breakdown
ca_grade <- fetch_absence(2024, type = "by_grade")

# Days absent distribution
days <- fetch_absence(2024, type = "days_absent")

# ESSA chronic absenteeism (school-level only)
essa <- fetch_absence(2024, type = "essa")

# Without tidy normalization (original NJ subgroup names)
ca_raw <- fetch_absence(2024, tidy = FALSE)

# Cross-state filtering patterns
library(dplyr)
ca %>%
  filter(subgroup == "econ_disadv", is_district) %>%
  arrange(desc(chronically_absent_rate))
} # }