1 Codebook — Group 1 — Clinic bodies

Spine: Who is well in the clinic, who is labelled ill, and where does infant death still sit on the map?

1.0.1 Panel A

heartdisease clinic Quants + Sex/HeartDisease for grouping

n = 303 rows, 6 columns.

Column Inferred type Notes
Age Quant 41 unique, 0 NA
Sex Qual 2 unique, 0 NA
BP Quant 50 unique, 0 NA
Cholesterol Quant 152 unique, 0 NA
MaximumHR Quant 91 unique, 0 NA
HeartDisease Qual 2 unique, 0 NA

1.0.2 Panel B

same people, Quals only

n = 303 rows, 5 columns.

Column Inferred type Notes
Sex Qual 2 unique, 0 NA
ChestPain Qual 4 unique, 0 NA
BloodSugar Quant-looking — check levels; may be Qual 2 unique, 0 NA
ExerciseInducedAngina Qual 2 unique, 0 NA
HeartDisease Qual 2 unique, 0 NA

1.0.3 Panel C

openintro infmortrate — country infant mortality; join to rnaturalearth by country name

n = 222 rows, 2 columns.

Column Inferred type Notes
country Qual 222 unique, 0 NA
inf_mort_rate Quant 218 unique, 0 NA

1.1 Pair card (Day 1 brick)

Pair Legal question First mark Later test (kit first, campus later)
Quant + Qual (2 levels) Does amount differ by kind? Two violins Two-sample comparison
Quant + Qual (3+ levels) Do kinds have different amounts? Grouped violin ANOVA
Qual + Qual Are kinds entangled? Mosaic Chi-square / two proportions
One Qual vs “more than half” Is this share rare? Bar / waffle One proportion
Quant + Quant How do two amounts move? Scatter Not a Day-7 campus hunch unless they insist

1.2 Forbidden list

  • Do not histogram a yes/no, a 0/1, a chest-pain type, a grade, or a wealth-like code.
  • Do not average Sex, ChestPain, degree, helmet, immigrant, ethnicity.
  • Do not choropleth a count when a rate exists (infant mortality, under-5 mortality, lifeExp are rates or expectations — pop is a count).
  • Do not run ANOVA on Panel B.
  • Do not treat area in the Scottish file as a map.
  • One AI prompt per panel, after paper, and only for a geom the codebook allows.

1.3 Sources (cite on the Quarto site)

  • CardioDataSets (Cáceres Rossi): heartdisease_tbl_df, cardioRiskFactors_df, scottish_CVD_df, cardiac_failure_df
  • openintro: infmortrate, sowc_child_mortality, bdims, yrbss, gss2010
  • gapminder: gapminder
  • AER: GSS7402
  • Files pulled via Rdatasets (Arel-Bundock)

NHANES and NFHS-5 / malariaAtlas were the preferred A and India-C objects. They are not in this zip because they need extra packages or an API. Group 3 A is a body-measure stand-in. Group 5 C is Asia-only gapminder, not district NFHS.

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