Description
Construct synthetic two-state category values with controlled reversal or retention and explicit ties and competition-rank changes.
A chart dataset contains values plus the labels, grouping, totals, or missing-value conventions needed to interpret them. Separating the numeric construction from plotting makes conservation rules and deliberate edge cases visible before a chart is rendered.
Construct synthetic two-state category values with controlled reversal or retention and explicit ties and competition-rank changes.
When to use this tool
Use this generator to create reproducible slopegraph rank change dataset cases from the supplied model or bank. Inspect the returned evidence before using cases in a test or exercise.
How to use the tool and how it works
- Supply labels, spacing, mode in the field shapes below. Set seed when you need an explicit replay.
- Run the tool and correct any input error. For generated controls, distinguish a successful fixture containing an invalid case from failure to generate the fixture.
- Read rows. Use the conventions below and keep answer-bearing fields separate when presenting an exercise.
Shuffle the labels into an initial order and assign spaced numeric values. Keep or reverse that order for the second measurement, then rank larger values first and return afterRank minus beforeRank.1
| Input field | Meaning and units | Accepted input |
|---|---|---|
| labels | Labels | Required. Array of Text; minimum UTF-16 length 1; maximum UTF-16 length 64.; minimum items 2; maximum items 16. |
| spacing | Spacing | Required. Safe integer; minimum 0; maximum 1000000. |
| mode | Mode | Required. Text; one of "reverse", "retain". |
| seed | Optional numeric seed; takes precedence over context randomness. Without either, use the fixed tool.io default seed. | Optional. Safe integer. |
| Output field | Interpretation | Returned shape |
|---|---|---|
| rows | Rows | Required. Array of Object; no unknown keys.; minimum items 2; maximum items 16. |
| rows[].label | Caller-facing text label; not a globally unique identifier. | Required. Text; minimum UTF-16 length 1; maximum UTF-16 length 64. |
| rows[].before | Before | Required. Safe integer. |
| rows[].after | After | Required. Safe integer. |
| rows[].beforeRank | Before Rank | Required. Safe integer. |
| rows[].afterRank | After Rank | Required. Safe integer. |
| rows[].rankChange | Rank Change | Required. Safe integer. |
| randomness | Replay metadata: source is seed or context; seed is the normalized 32-bit seed or null for a context source. | Required. Object; no unknown keys. |
| randomness.seed | Optional numeric replay seed; normalization and precedence are explained below. | Required. Safe integer. or Null. |
| randomness.source | Source | Required. Text; one of "context", "seed". |
An explicit safe-integer seed takes precedence over injected context randomness and is normalized to an unsigned 32-bit integer. With no seed and no context source, replay uses the fixed tool.io default seed. A context source must return finite values from zero inclusive to one exclusive; its advancing state must be retained separately. randomness records the source and normalized seed, or null for context. These generators are not cryptographic random sources.
{
"input": {
"labels": [
"a",
"b",
"c"
],
"spacing": 2,
"mode": "reverse",
"seed": 42
},
"output": {
"rows": [
{
"label": "a",
"before": 4,
"after": 4,
"beforeRank": 2,
"afterRank": 2,
"rankChange": 0
},
{
"label": "b",
"before": 6,
"after": 2,
"beforeRank": 1,
"afterRank": 3,
"rankChange": 2
},
{
"label": "c",
"before": 2,
"after": 6,
"beforeRank": 3,
"afterRank": 1,
"rankChange": -2
}
],
"randomness": {
"seed": 42,
"source": "seed"
}
}
}Limitations and assumptions
- A positive rankChange means a worse numerical rank. The construction gives synthetic exact ordering, not observed change or independent random measurements; plotting remains the consumer's responsibility.
- A replay requires the same complete input and implementation. Randomized construction does not guarantee uniform coverage of every feasible output; finite samples can repeat unless a distinct-selection rule is explicitly stated.
- The field tables state structural bounds; cross-field constraints and bounded search or output budgets also apply. Unknown keys are rejected where the object contract disallows them. Input errors are distinct from null fields and deliberate invalid controls in a successful output.
Alternative or Complementary methods
For a supplied example, inspect the returned construction or delegated check directly. To evaluate a consumer, also test cases chosen for its own boundaries; a generated finite corpus does not establish exhaustive coverage.
References
Similar or alternative tools
- Random Waterfall Change Dataset Generator
Generate bounded signed changes with cumulative opening/closing reconciliation and explicit subtotal bars.
- Random Heatmap Block Structure Dataset Generator
Construct labeled heatmap blocks with bounded integer noise and optional mirrored symmetry, retaining the planted block mask.