Mirrors the React webapp's "Full Result (JSON)" export (`exportResultJson`, `packages/core/src/jsonExport.ts`) and Python's `RegionResult.to_json()` byte-for-byte.
Usage
to_result_json(result, path)
# S4 method for class 'RegionResult'
to_result_json(result, path)Details
Schema (key order PINNED): “` "schemaVersion": "1", "model": "<model id>", "setNames": "A": "...", ... , "universeSize": <int>, "regions": [ "label", "sets": [...], "depth": <int>, "exclusiveCount": <int>, "inclusiveCount": <int>, "exclusiveItems": [...] , ... ], "setSizes": "A": <int>, ... , "statistics": [ "a", "b", "jaccard", "dice", "overlapCoeff", "intersection", "union", "expected", "foldEnrichment", "pValue", "fdr", "bonferroni", "pTwoSided", "significant": "***" | "**" | "*" | "ns" , ... ] “`
`regions` covers all `2^n - 1` non-empty subsets, sorted by depth ascending then label ascending (ASCII); `exclusiveItems` preserves the dataset item order. `statistics` is sorted by p-value ascending, with `significant` rendered as the FDR star label. Every number is emitted through a shared number-rendering rule (fixed 6-decimal, trailing zeros stripped, never scientific) so the bytes match the webapp and Python exports. No trailing newline is written.
Examples
ds <- methods::new("VennDataset",
set_names = c("A", "B"),
items = list(A = c("x", "y"), B = c("y", "z")),
item_order = c("x", "y", "z"),
universe_size = 10L, source_path = NULL, format = "csv")
result <- analyze(ds)
to_result_json(result, tempfile(fileext = ".json"))
# \donttest{
result <- analyze(load_sample("dataset_real_cancer_drivers_4"))
to_result_json(result, tempfile(fileext = ".json"))
# }