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Analyze Course Access Equity

Source: R/fetch_course_enrollment.R
analyze_course_access_equity.Rd

Analyzes equity in course access across demographic subgroups over multiple years. Calculates access gaps, disparity indices, and trends over time to identify schools with large inequities in course offerings.

Usage

analyze_course_access_equity(df_list, subgroup_cols = 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 course enrollment fetch functions.

subgroup_cols

Character vector specifying which subgroup dimensions to analyze. If NULL (default), attempts to auto-detect subgroup columns. Common options: "subgroup" for demographic subgroups.

Value

Data frame with:

  • year - Year identifier

  • location_id - Combined location identifier

  • subgroup - Demographic subgroup (e.g., "black", "hispanic", "white")

  • access_rate - Course participation rate for this subgroup

  • total_population_rate - Rate for total population

  • access_gap_percentage - Difference from total population (percentage points)

  • disparity_index - Ratio of subgroup rate to total population rate (1.0 = parity, < 1.0 = under-representation, > 1.0 = over-representation)

  • trend - Trend classification: "improving", "widening", "stable", or "insufficient_data"

  • flag_large_gap - Logical flag if gap > 20 percentage points

Details

Equity analysis calculates:

  • Access Rate: Percentage of students in subgroup enrolled in courses

  • Access Gap: Difference between subgroup rate and total population rate

  • Disparity Index: Ratio of subgroup rate to total population rate (1.0 indicates equal access)

  • Trend: Direction of change over time based on linear regression

Schools with access gaps > 20 percentage points are flagged for attention.

Examples

if (FALSE) { # \dontrun{
# Fetch data for multiple years
math_2022 <- fetch_math_course_enrollment(2022)
math_2023 <- fetch_math_course_enrollment(2023)
math_2024 <- fetch_math_course_enrollment(2024)

# Combine into named list
df_list <- list(
  "2022" = math_2022,
  "2023" = math_2023,
  "2024" = math_2024
)

# Analyze equity across all subgroups
equity <- analyze_course_access_equity(df_list)

# View schools with large access gaps for Hispanic students
equity %>%
  dplyr::filter(subgroup == "hispanic", flag_large_gap == TRUE) %>%
  dplyr::select(year, school_name, subgroup, access_gap_percentage, disparity_index)
} # }