Getting Started with hexmakR • hexmakR

Installation

Install hexmakR from CRAN:

install.packages("hexmakR")

Or install the development version from GitHub:

# install.packages("pak")
pak::pak("cttir/hexmakR")

Your first hex sticker in 3 lines

library(hexmakR)

hex_sticker("mypackage", icon = "atom", theme = "stats")

That’s it. The default settings produce a dark-themed sticker with the stats color palette and an atom icon.

Saving to a file

Pass filename to write a transparent PNG directly:

hex_sticker(
  "mypackage",
  icon     = "dna",
  theme    = "genomics",
  filename = "man/figures/logo.png"
)

The file will have the correct hexb.in aspect ratio (width / height = √3 / 2) and a transparent background, ready for use in your README or pkgdown site.

Exploring themes

names(hexmakr_themes())

Preview a theme interactively:

hexmakr_preview_theme("genomics", mode = "dark")
hexmakr_preview_theme("genomics", mode = "light")

Exploring icons

# All categories
names(hexmakr_icons())

# Icons in a specific category
hexmakr_icons("biology")

Customizing colors

Override any theme color individually:

hex_sticker(
  "mypackage",
  theme        = "stats",
  mode         = "dark",
  bg           = "#0D1117",
  accent       = "#58A6FF",
  text_color   = "#FFFFFF",
  border_color = "#58A6FF",
  sub_color    = "#8B949E"
)

Choosing a font

hex_sticker(
  "mypackage",
  font_family = "serif",
  font_bold   = TRUE,
  font_italic = TRUE,
  font_size   = 14
)

Available font shorthands: "mono", "courier", "consolas", "source_code", "fira_code", "serif", "times", "palatino", "sans", "helvetica", "roboto", "open_sans", "lato".

Next steps

For full parameter reference: vignette("programmatic-usage")
To use the interactive designer: vignette("shiny-app") or just run hexmakr_app()

Use of LLM tools

Portions of this package were prepared with assistance from large language model tooling for narrowly defined, non-authorial tasks: copyediting, prose smoothing, Markdown/LaTeX formatting, scaffolding of boilerplate files (CI configs, build scripts), code refactoring. The tools used were Chat AI, the LLM service of KISSKI (GWDG), and a self-hosted Mistral Small (24B, Apache-2.0) run locally via Ollama and the ollamar R package — local inference only, with no data sent to third parties for the self-hosted model.

All scientific claims, methodological choices, analyses, interpretations, and conclusions are the author’s own. No LLM-generated text was incorporated without review and revision, and every reference was verified against its DOI, arXiv ID, or ISBN.