---
title: "Custom styling and export"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Custom styling and export}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include = FALSE}
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)
```

# Custom styling and export

`vennDiagramLab` separates analysis from rendering. Once you have a
`RegionResult`, you can render it with custom names, custom colors,
post-process the SVG, embed it in a ggplot2 chain via `geom_venn()`, or
export to PNG / PDF.

```{r load}
library(vennDiagramLab)
result <- analyze(load_sample("dataset_real_cancer_drivers_4"))
```

## Custom names

Pass a per-letter mapping (`A`-`I`) to override the dataset's set names:

```{r names}
svg <- render_venn_svg(
    result,
    set_names = c(A = "Vogelstein\n(2013)",
                   B = "COSMIC CGC",
                   C = "OncoKB",
                   D = "IntOGen"),
    title = "Pan-source cancer driver agreement"
)
substr(svg, 1, 60)
```

## Custom colors

Pass a per-letter hex map. Each letter's color is applied to the matching
shape AND the legend bullet (and, where present, the Euler extra shape).

```{r colors}
svg <- render_venn_svg(
    result,
    colors = c(A = "#E69F00", B = "#56B4E9", C = "#009E73", D = "#CC79A7")
)
nchar(svg)
```

## Hide the count labels

```{r hide-counts}
svg_clean <- render_venn_svg(result, show_counts = FALSE)
nchar(svg_clean)
```

(`show_names = FALSE` does the analogous thing for set names.)

## Post-render SVG manipulation with xml2

The returned SVG is a plain string; parse it with `xml2` to make targeted
edits (e.g. set the page background or add a watermark):

```{r xml2, eval = NOT_CRAN && requireNamespace("xml2", quietly = TRUE)}
svg <- render_venn_svg(result)
doc <- xml2::read_xml(svg)
xml2::xml_attr(doc, "viewBox")
```

## Embed in a ggplot2 chain

`geom_venn()` returns a list of layers that draws the venn on a
unit-square coordinate system, ready to compose with titles, themes, and
other annotations.

```{r ggplot, eval = NOT_CRAN}
library(ggplot2)
ggplot() +
    geom_venn(data = result) +
    theme_void() +
    labs(title = "4 cancer-driver sources",
          subtitle = "Vogelstein, COSMIC CGC, OncoKB, IntOGen") +
    theme(plot.title = element_text(size = 14, face = "bold"),
          plot.subtitle = element_text(size = 10, colour = "grey40"))
```

## Multi-format export

`render_venn_svg()` returns a string. Convert it to PNG or PDF via the
`rsvg` package (already a hard import of `vennDiagramLab`):

```{r export, eval = NOT_CRAN}
svg <- render_venn_svg(result)
png_path <- tempfile(fileext = ".png")
rsvg::rsvg_png(charToRaw(svg), png_path, width = 1200)
file.size(png_path)

pdf_path <- tempfile(fileext = ".pdf")
rsvg::rsvg_pdf(charToRaw(svg), pdf_path)
file.size(pdf_path)
```

For a multi-page composite report (venn + upset + statistics + network + about), use `to_pdf_report()` — see `vignette("v07_pdf_reports")`.

## What's next

* `vignette("v01_quickstart")` — basic usage.
* `vignette("v04_upset_vs_venn_vs_network")` — alternative visualizations.
* `vignette("v07_pdf_reports")` — composite multi-page PDF reports.