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Five steps to your first Venn diagram with vennDiagramLab.
library(vennDiagramLab)The package ships five sample datasets (3 biological, 2 mock).
list_samples()VennDatasetload_sample() returns an S4 VennDataset with deduplicated set members and first-seen item ordering (matching the web tool’s CSV semantics).
ds <- load_sample("dataset_real_cancer_drivers_4")
ds@set_names
vapply(ds@items, length, integer(1L)) # set sizesanalyze() resolves the model, enumerates regions, and returns a RegionResult. With model = "auto" (the default), it picks the canonical SVG model for the dataset’s set count.
result <- analyze(ds)
result@model
length(result@regions) # number of non-empty regionssvg <- render_venn_svg(result)
nchar(svg) # SVG length in bytes
substr(svg, 1, 80)To save the SVG:
writeLines(svg, "cancer_drivers.svg")vignette("v02_real_cancer_drivers") — full walkthrough with custom names, colors, and biological interpretation.vignette("v04_upset_vs_venn_vs_network") — choose the right visualization per set count.vignette("v05_statistics_deep_dive") — Jaccard, Dice, hypergeometric, BH-FDR with worked examples.vignette("v07_pdf_reports") — generate publication-ready multi-page PDFs.These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.