1 Codebook — Group 4 — Youth risk + world wealth/health

Spine: What do teenagers’ bodies and habits look like, which habits travel together, and where is life long?

1.0.1 Panel A

yrbss Quants: height, weight, activity days, sleep

n = 13,583 rows, 8 columns.

Column Inferred type Notes
age Quant-looking — check levels; may be Qual 7 unique, 77 NA
gender Qual 2 unique, 12 NA
grade Qual 5 unique, 79 NA
height Quant 34 unique, 1004 NA
weight Quant 239 unique, 1004 NA
physically_active_7d Quant-looking — check levels; may be Qual 8 unique, 273 NA
strength_training_7d Quant-looking — check levels; may be Qual 8 unique, 1176 NA
school_night_hours_sleep Qual 7 unique, 1248 NA

1.0.2 Panel B

yrbss Quals: helmet, texting, TV, race, gender, grade

n = 13,583 rows, 8 columns.

Column Inferred type Notes
age Quant-looking — check levels; may be Qual 7 unique, 77 NA
gender Qual 2 unique, 12 NA
grade Qual 5 unique, 79 NA
hispanic Qual 2 unique, 231 NA
race Qual 5 unique, 2805 NA
helmet_12m Qual 6 unique, 311 NA
text_while_driving_30d Qual 8 unique, 918 NA
hours_tv_per_school_day Qual 7 unique, 338 NA

1.0.3 Panel C

gapminder 2007 — ask a different question than Group 3 (e.g. gdpPercap vs lifeExp, or a symbol map of pop)

n = 142 rows, 6 columns.

Column Inferred type Notes
country Qual 142 unique, 0 NA
continent Qual 5 unique, 0 NA
year Quant-looking — check levels; may be Qual 1 unique, 0 NA
lifeExp Quant 142 unique, 0 NA
pop Quant 142 unique, 0 NA
gdpPercap Quant 142 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.

Back to top