// Survey scoring: normalize raw responses, roll up per question, and flag
// low-confidence questions. Demonstrates avg_by, agg with min/max, a derived
// spread column, and assert_fails on an out-of-bounds index (negative path).

cell Survey {
    on responses() {
        return list(
            map("q", "ux",    "score", 4, "respondent", "r1"),
            map("q", "ux",    "score", 5, "respondent", "r2"),
            map("q", "ux",    "score", 2, "respondent", "r3"),
            map("q", "perf",  "score", 3, "respondent", "r1"),
            map("q", "perf",  "score", 3, "respondent", "r2"),
            map("q", "perf",  "score", 4, "respondent", "r3"),
            map("q", "price", "score", 1, "respondent", "r1"),
            map("q", "price", "score", 5, "respondent", "r2")
        )
    }

    // Per question: avg, min, max, and spread = max - min.
    on by_question() {
        let agged = responses() |> agg("q", "score:avg", "score:min", "score:max")
        return agged |> map(r => r |> with("spread", r.score_max - r.score_min))
            |> sort_by("spread", "desc")
    }

    // Questions where opinions diverge a lot (spread >= 3).
    on divisive() { return by_question() |> filter_by("spread", ">=", 3) |> pluck("q") }

    on overall_avg() { return responses() |> avg_by("score") }

    on run() {
        for r in by_question() { print("{r.q}: avg {r.score_avg}, spread {r.spread}") }
        return divisive()
    }
}

cell test SurveyTests {
    rules {
        // price spread = 5 - 1 = 4 (most divisive)
        assert by_question()[0].q == "price"
        assert by_question()[0].spread == 4
        // perf spread = 4 - 3 = 1
        assert (by_question() |> filter_by("q", "=", "perf"))[0].spread == 1
        // divisive (spread>=3): price(4), ux(5-2=3) -> 2 questions
        assert len(divisive()) == 2
        // indexing past the end of a 3-question result is a runtime error
        assert_fails by_question()[9].q
    }
}