Results for “shiny”

Deploy ShinyProxy for hosting multiple containerized Shiny applications. Covers ShinyProxy Docker deployment, application.yml configuration, Shiny app Docker images, authentication, container backends, usage tracking, and scaling. Use when hosting multiple Shiny apps behind a single entry point, needing per-app authentication and access control, deploying Shiny apps as isolated Docker containers, or scaling beyond single-app deployment with usage analytics and audit logging.

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Deploy Shiny applications to shinyapps.io, Posit Connect, or Docker containers. Covers rsconnect configuration, manifest generation, Dockerfile creation, and deployment verification. Use when publishing a Shiny app for external or internal users, moving from local development to a hosted environment, containerizing a Shiny app for Kubernetes or Docker deployment, or setting up automated deployment pipelines.

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Design Shiny application UIs using bslib for theming, layout_columns for responsive grids, value boxes, cards, and custom CSS/SCSS. Covers page layouts, accessibility, and brand consistency. Use when building a new Shiny app UI from scratch, modernizing an existing app from fluidPage to bslib, applying brand theming, making a Shiny app responsive across screen sizes, or improving accessibility of a Shiny application.

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Build modern Shiny dashboards and applications using bslib (Bootstrap 5). Use when creating new Shiny apps, modernizing legacy apps (fluidPage, fluidRow/column, tabsetPanel, wellPanel, shinythemes), or working with bslib page layouts, grid systems, cards, value boxes, navigation, sidebars, filling layouts, theming, accordions, tooltips, popovers, toasts, or bslib inputs. Assumes familiarity with basic Shiny.

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Diagnose and fix performance problems in Shiny for Python apps — slow startup, sluggish interactions, blocking operations, and apps that struggle beyond a handful of concurrent users. Use when a Shiny for Python app is slow, hangs, recomputes too much, times out, or must support more users; when the user asks to "speed up", "optimize", or "make faster" a Python Shiny app; or when preparing a prototyped app for real-world traffic.

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Diagnose and fix performance problems in Shiny for R apps — slow startup, sluggish interactions, long-running blocking operations, and apps that struggle beyond a handful of concurrent users. Use when a Shiny app is slow, hangs, recomputes too much, times out, or needs to support more users; when the user asks to "speed up", "optimize", or "make faster" an existing Shiny app; or when preparing a prototyped app for real-world traffic.

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Build reusable Shiny modules with proper namespace isolation using NS(). Covers module UI/server pairs, reactive return values, inter-module communication, and nested module composition. Use when extracting a reusable component from a growing Shiny app, building a UI widget used in multiple places, encapsulating complex reactive logic behind a clean interface, or composing larger applications from smaller, testable units.

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Create professional package release blog posts following Tidyverse or Shiny blog conventions. Use when the user needs to: (1) Write a release announcement blog post for an R or Python package for tidyverse.org or shiny.posit.co, (2) Transform NEWS/changelog content into blog format, (3) Generate acknowledgments sections with contributor lists, (4) Format posts following specific blog platform requirements. Supports both Tidyverse (hugodown) and Shiny (Quarto) blog formats with automated contributor fetching and comprehensive style guidance.

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Profile and optimize Shiny application performance using profvis, bindCache, memoise, async/promises, debounce/throttle, and ExtendedTask for long-running computations. Use when the app feels slow or unresponsive during user interaction, when server resources are exhausted under concurrent load, when specific operations create bottlenecks, or when preparing an app for production deployment with many concurrent users.

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Configure reverse proxy patterns across multiple tools including Nginx, Traefik, and ShinyProxy. Covers WebSocket proxying, path-based and host-based routing, SSL termination, and Docker label auto-discovery. Use when routing multiple services behind a single entry point, proxying WebSocket connections (Shiny, Socket.IO), auto-discovering Docker services with Traefik labels, or adding SSL termination to services that don't handle TLS natively.

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Create and use brand.yml files for consistent branding across Shiny apps and Quarto documents. Covers: (1) Creating new _brand.yml files, (2) Applying to Shiny (R and Python), (3) Using in Quarto, (4) Modifying existing files, and (5) Troubleshooting. Includes complete specifications and integration guides.

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Advanced theming for Shiny apps using bslib and Bootstrap 5. Use when customizing app appearance with bs_theme(), Bootswatch themes, custom colors, typography, brand.yml integration, Bootstrap Sass variables, custom Sass/CSS rules, dark mode and color modes, dynamic theme switching, real-time theming, theme inspection, or making R plots match the app theme with thematic.

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Install and configure the putior R package for workflow visualization. Covers CRAN and GitHub installation, optional dependencies (mcptools, ellmer, shiny, shinyAce, logger, plumber2), and verification of the complete annotation-to-diagram pipeline. Use when setting up putior for the first time, preparing a machine for workflow visualization tasks, when a downstream skill requires putior to be installed, or restoring an environment after an R version upgrade or renv wipe.

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Creates sashimi-style plots showing RNA-seq read coverage and splice junction counts using ggsashimi (general-purpose, condition-grouped overlays), rmats2sashimiplot (rMATS-output-aware), MAJIQ-VOILA (LSV posteriors interactive HTML), leafviz (leafcutter clusters Shiny), Jutils (tool-agnostic heatmaps and sashimi for rMATS/leafcutter/MntJULiP/MAJIQ output), or pyGenomeTracks (multi-track publication figures). Tool choice depends on the upstream differential-splicing tool's output format and the publication vs interactive use case. Use when visualizing specific splicing events, validating differential splicing calls, or producing publication-quality figures.

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A soft highlight sweeps across muted text — a quiet premium shimmer for one short label ("new", "pro", a CTA word). Keep the base text readable; one or two per view. Pure CSS; solid under reduced-motion.

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InterCellar is implemented as an R/Bioconductor Package containing a Shiny app that allows users to interactively analyze cell-cell communication from scRNA-seq data. Starting from precomputed ligand-receptor interactions, InterCellar provides filtering options, annotations and multiple visualizations to explore clusters, genes and functions. Finally, based on functional annotation from Gene Ontology and pathway databases, InterCellar implements data-driven analyses to investigate cell-cell comm

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This R package supports interactive visualization of multi-channel images and segmentation masks generated by imaging mass cytometry and other highly multiplexed imaging techniques using shiny. The cytoviewer interface is divided into image-level (Composite and Channels) and cell-level visualization (Masks). It allows users to overlay individual images with segmentation masks, integrates well with SingleCellExperiment and SpatialExperiment objects for metadata visualization and supports image do

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Data quality assessment is an integral part of preparatory data analysis to ensure sound biological information retrieval. We present here the MatrixQCvis package, which provides shiny-based interactive visualization of data quality metrics at the per-sample and per-feature level. It is broadly applicable to quantitative omics data types that come in matrix-like format (features x samples). It enables the detection of low-quality samples, drifts, outliers and batch effects in data sets. Visualiz

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Help users write correct R code for async, parallel, and distributed computing using mirai. Use when users need to run R code asynchronously or in parallel, write mirai code with correct dependency passing, set up parallel workers, convert from future or parallel, use mirai_map, integrate with Shiny or promises, or configure cluster/HPC computing.

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Create and assign PBR materials in Blender via Principled BSDF — metals, glass, plastic, fabric, skin, organics. Covers physically-based material recipes with real-world values, Coat layer (varnish/car paint), Sheen (cloth), Subsurface scattering (skin/wax), Transmission (glass), and procedural patterns (wood grain, marble, fabric weave). Use whenever the user asks to "make it look like X material", "give it a metallic finish", "apply a wood texture", "make this glass / plastic / brushed steel / leather / skin", or any look-development request. Make sure to use this skill even if the user does not say "material" — also covers "make it shiny", "matte finish", "looks like copper", "rough surface". Works with any geometry; pairs with blender-lighting (materials only look right under proper lighting).

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Configure Nginx as a web server and reverse proxy. Covers static file serving, reverse proxy to upstream services, SSL/TLS termination with Let's Encrypt, location blocks, load balancing, rate limiting, and security headers. Use when serving static files in production, reverse proxying to backend services (Node.js, Python, R/Shiny), terminating SSL/TLS, load balancing across instances, or adding rate limiting and security headers to harden an endpoint.

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Create a Dockerfile for R projects using rocker base images. Covers system dependency installation, R package installation, renv integration, and optimized layer ordering for fast rebuilds. Use when containerizing an R application or analysis, creating reproducible R environments, deploying R-based services (Shiny, Plumber, MCP server), or setting up consistent development environments across machines.

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Scaffold a new puzzle type across all 10+ pipeline integration points in jigsawR. Creates the core puzzle module, wires it into the unified pipeline (generation, positioning, rendering, adjacency), adds ggpuzzle geom/stat layers, updates DESCRIPTION and config.yml, extends the Shiny app, and creates a comprehensive test suite. Use when adding a completely new puzzle type to the package or following the 10-point integration checklist to ensure nothing is missed end-to-end.

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Creates sashimi-style plots showing RNA-seq read coverage and splice junction counts using ggsashimi (general-purpose, condition-grouped overlays), rmats2sashimiplot (rMATS-output-aware), MAJIQ-VOILA (LSV posteriors interactive HTML), leafviz (leafcutter clusters Shiny), Jutils (tool-agnostic heatmaps and sashimi for rMATS/leafcutter/SUPPA2/MAJIQ output), or pyGenomeTracks (multi-track publication figures). Tool choice depends on the upstream differential-splicing tool's output format and the publication vs interactive use case. Use when visualizing specific splicing events, validating differential splicing calls, or producing publication-quality figures.

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