Majority Minority Change

Use this skill to compare county demographics across two years and flag threshold crossings.

Requirements

• R with dplyr, readr, stringr, and jsonlite
• Two local CSV files with county identifiers and required race/population fields
• Bundled Census CSV snapshots for immediate use:
  • skills/majority-minority-change/data/county_race_acs5_2010.csv
  • skills/majority-minority-change/data/county_race_acs5_2020.csv
• No API key required

Required Input Columns

• Join keys available in both files:
  • county_fips (preferred), or
  • both state and county
• Metrics in both files:
  • total_population
  • non_hispanic_white

Standard Workflow

1. Confirm start/end files and year labels.
   • --state accepts full names or USPS abbreviations (for example, Georgia or GA).
   • Default bundled files:
     • start: skills/majority-minority-change/data/county_race_acs5_2010.csv
     • end: skills/majority-minority-change/data/county_race_acs5_2020.csv
2. Review skills/majority-minority-change/references/DATA_SCHEMA.md for exact derived/output field names before downstream filtering or selecting columns.
   • Expected output naming includes nonwhite_share_<year_label> and delta_nonwhite_share_pp.
3. Run script with optional state filter.
   • Example: Rscript skills/majority-minority-change/scripts/analyze_majority_minority_change.R --input-start skills/majority-minority-change/data/county_race_acs5_2010.csv --input-end skills/majority-minority-change/data/county_race_acs5_2020.csv --start-label 2010 --end-label 2020
4. Review counties with largest percentage-point shifts and threshold crossings.

Output

Return:

• non-white share in each year
• percentage-point change
• crossed_to_majority_minority
• crossed_out_of_majority_minority