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Tidy CAASPP assessment data

Source: R/tidy_assessment.R
tidy_assess.Rd

Converts processed CAASPP assessment data from wide format to tidy (long) format. Pivots performance metrics into separate rows for easier analysis.

Usage

tidy_assess(processed_data)

Arguments

processed_data

Processed assessment data from process_assess()

Value

A tibble in tidy format with columns:

  • end_year: School year end

  • cds_code: 14-digit CDS identifier

  • county_code, district_code, school_code: CDS components

  • agg_level: Aggregation level (S/D/C/T)

  • grade: Grade level (03-11 or 13)

  • subject: Assessment subject (ELA/Math)

  • metric_type: Type of metric (mean_scale_score, pct_exceeded, etc.)

  • metric_value: Value of the metric

Details

Tidy Format Structure:

The tidy format converts performance metrics from separate columns into rows, making it easier to:

  • Filter and plot by metric type

  • Compare metrics across years

  • Calculate differences between metrics

  • Join with other datasets

Metric Types:

  • mean_scale_score: Average scale score for the test

  • pct_exceeded: Percentage of students who exceeded standard

  • pct_met: Percentage of students who met standard

  • pct_met_and_above: Percentage who met or exceeded standard

  • pct_nearly_met: Percentage who nearly met standard

  • pct_not_met: Percentage who did not meet standard

  • n_tested: Number of students tested

  • n_exceeded: Number of students who exceeded standard

  • n_met: Number of students who met standard

  • n_met_and_above: Number who met or exceeded standard

  • n_nearly_met: Number of students who nearly met standard

  • n_not_met: Number of students who did not meet standard

Examples

if (FALSE) { # \dontrun{
library(dplyr)

# Process and tidy assessment data
raw <- get_raw_assess(2023)
processed <- process_assess(raw$test_data, 2023)
tidy <- tidy_assess(processed)

# Filter to state-level proficiency rates
state_proficiency <- tidy %>%
  filter(agg_level == "T",
         metric_type %in% c("pct_met_and_above", "pct_exceeded"))

# Compare ELA vs Math performance
subject_comparison <- tidy %>%
  filter(agg_level == "T",
         grade == "11",
         metric_type == "pct_met_and_above") %>%
  select(grade, subject, metric_value)
} # }