# 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.