Quickstart
In vennDiagramLab: Headless Venn Diagram Analysis and Rendering

knitr::opts_chunk$set(collapse = TRUE, comment = "#>")
# Skip evaluation of all chunks on CRAN's auto-check farm to fit the
# 10-minute build budget. Locally, on CI, and under devtools::check(),
# NOT_CRAN=true and all chunks evaluate normally. The vignette source
# (which CRAN users see in browseVignettes() / vignette()) is unchanged.
NOT_CRAN <- identical(tolower(Sys.getenv("NOT_CRAN")), "true")
knitr::opts_chunk$set(eval = NOT_CRAN)

Quickstart

Five steps to your first Venn diagram with vennDiagramLab.

1. Load the package

library(vennDiagramLab)

2. Pick a bundled sample

The package ships five sample datasets (3 biological, 2 mock).

list_samples()

3. Load it as a `VennDataset`

load_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 sizes

4. Analyze

analyze() 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 regions

5. Render

svg <- render_venn_svg(result)
nchar(svg)        # SVG length in bytes
substr(svg, 1, 80)

To save the SVG:

writeLines(svg, "cancer_drivers.svg")

What's next

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.