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Generate cross-domain quiz pairs comparing acute and chronic risks

Source: R/combined_quiz.R
combined_quiz_pairs.Rd

Creates candidate question pairs mixing acute risks from common_risks() (micromorts per event) with chronic risks from chronic_risks() (microlives per day), converting both to a common microlife scale so they can be directly compared. Each pair pits one acute activity against one chronic lifestyle factor and asks which has a larger total impact over time_period_days.

Usage

combined_quiz_pairs(n = 10, time_period_days = 365, seed = NULL)

Arguments

n

Integer. Number of pairs to generate. Default 10.

time_period_days

Numeric. Time period for chronic risk accumulation. Default 365 (1 year). For chronic risks, the cumulative impact is abs(microlives_per_day) × time_period_days.

seed

Integer or NULL. Random seed for reproducibility. Default NULL (random each call).

Value

A tibble with columns:

  • activity_a, type_a ("acute"), value_a, unit_a ("micromorts"), category_a, period_a

  • factor_b, type_b ("chronic"), value_b, unit_b ("microlives/day"), category_b, direction_b

  • common_unit ("microlives"), common_value_a, common_value_b

  • correct_answer ("a" or "b" — whichever has higher impact in common unit)

  • ratio (larger / smaller common value)

  • explanation describing both values and the conversion

Details

The conversion from micromorts to microlives uses the factor 0.7 (1 micromort ≈ 0.7 microlives at age 40). Chronic impact over the time period is abs(microlives_per_day) × time_period_days.

Examples

pairs <- combined_quiz_pairs(n = 5, seed = 42)
pairs[, c("activity_a", "factor_b", "correct_answer", "ratio")]
#> # A tibble: 5 × 4
#>   activity_a               factor_b                 correct_answer ratio
#>   <chr>                    <chr>                    <chr>          <dbl>
#> 1 Spanish flu infection    Smoking 10 cigarettes    a               1.15
#> 2 Base jumping (per jump)  Glass of wine daily      b               1.21
#> 3 Living (one day, age 90) Glass of wine daily      b               1.13
#> 4 Matterhorn ascent        Average cancer diagnosis b               1.10
#> 5 Living (one day, age 90) 2-3 cups coffee (men)    b               1.13

# One year time period (default)
pairs_year <- combined_quiz_pairs(n = 10, time_period_days = 365, seed = 1)