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Process raw CAASPP assessment data

Source: R/process_assessment.R
process_assess.Rd

Processes raw CAASPP research file data into a standardized schema. Cleans column names, standardizes data types, and validates data quality.

Usage

process_assess(raw_data, end_year)

Arguments

raw_data

Raw CAASPP data from get_raw_assess() or import_local_assess()

end_year

School year end (e.g., 2023 for 2022-23 school year)

Value

A tibble with processed assessment data including columns:

  • end_year: School year end (integer)

  • cds_code: 14-digit CDS identifier (character)

  • county_code: 2-digit county code (character)

  • district_code: 5-digit district code (character)

  • school_code: 7-digit school code (character)

  • agg_level: Aggregation level (S=School, D=District, C=County, T=State)

  • grade: Grade level (03, 04, 05, 06, 07, 08, 11, or 13 for all grades)

  • subject: Assessment subject (ELA or Mathematics)

  • student_group: Student group identifier

  • test_id: Test identifier code

  • mean_scale_score: Mean scale score (numeric)

  • pct_exceeded: Percentage standard exceeded (numeric)

  • pct_met: Percentage standard met (numeric)

  • pct_met_and_above: Percentage standard met and above (numeric)

  • pct_nearly_met: Percentage standard nearly met (numeric)

  • pct_not_met: Percentage standard not met (numeric)

  • n_tested: Number of students tested (integer)

  • n_exceeded: Number standard exceeded (integer)

  • n_met: Number standard met (integer)

  • n_met_and_above: Number standard met and above (integer)

  • n_nearly_met: Number standard nearly met (integer)

  • n_not_met: Number standard not met (integer)

Details

Data Processing Steps:

  1. Extract CDS code components (county, district, school)

  2. Determine aggregation level from school code (0000000 = district summary)

  3. Clean and standardize column names

  4. Convert data types (character to numeric where appropriate)

  5. Validate ranges (percentages 0-100, non-negative counts)

  6. Handle suppressed values (groups with < 11 students)

Data Quality Checks:

  • No Inf or NaN values in numeric columns

  • Percentages between 0 and 100

  • Counts are non-negative integers

  • At least state-level data exists

Examples

if (FALSE) { # \dontrun{
# Process raw assessment data
raw <- get_raw_assess(2024)
processed <- process_assess(raw$test_data, 2024)

# View processed data
head(processed)

# Filter to state-level 11th grade ELA results
library(dplyr)
state_11_ela <- processed %>%
  filter(agg_level == "T", grade == "11", subject == "ELA")
} # }