本文へ移動
cccskills
無料GitHub で公開

build-shiny-module

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.

インストール方法を見る

含まれるファイル(1)

  • SKILL.md9.8 KB

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

Build Shiny Module

Create reusable Shiny UI/server module pairs with proper namespace isolation, reactive communication, and composability.

When to Use

  • Extracting a reusable component from a growing Shiny app
  • Building a UI widget that will be used in multiple places
  • Encapsulating complex reactive logic behind a clean interface
  • Composing larger applications from smaller, testable units

Inputs

  • Required: Module purpose and functionality description
  • Required: Input/output contract (what the module receives and returns)
  • Optional: Whether the module nests other modules (default: no)
  • Optional: Framework context (golem, rhino, or vanilla)

Procedure

Step 1: Define the Module Interface

Before writing code, define what the module accepts and returns:

Module: data_filter
Inputs: reactive dataset, column names to filter on
Outputs: reactive filtered dataset
UI: filter controls (selectInput, sliderInput, dateRangeInput)

Expected: Clear contract specifying reactive inputs, reactive outputs, and UI elements.

On failure: If the interface is unclear, the module is probably too broad. Split it into smaller modules with single responsibilities.

Step 2: Create the Module UI Function

#' Data Filter Module UI
#'
#' @param id Module namespace ID
#' @return A tagList of filter controls
#' @export
dataFilterUI <- function(id) {
  ns <- NS(id)
  tagList(
    selectInput(
      ns("column"),
      "Filter column",
      choices = NULL
    ),
    uiOutput(ns("filter_control")),
    actionButton(ns("apply"), "Apply Filter", class = "btn-primary")
  )
}

Key rules:

  • Function name follows <name>UI convention
  • First argument is always id
  • Create ns <- NS(id) at the top
  • Wrap every inputId and outputId with ns()
  • Return a tagList() to allow flexible placement

Expected: UI function that creates namespaced input/output elements.

On failure: If IDs collide when using the module twice, check that every ID is wrapped with ns(). Common miss: IDs inside renderUI() or uiOutput() — these need ns() too.

Step 3: Create the Module Server Function

#' Data Filter Module Server
#'
#' @param id Module namespace ID
#' @param data Reactive expression returning a data frame
#' @param columns Character vector of filterable column names
#' @return Reactive expression returning the filtered data frame
#' @export
dataFilterServer <- function(id, data, columns) {
  moduleServer(id, function(input, output, session) {
    ns <- session$ns

    # Update column choices when data changes
    observeEvent(data(), {
      available <- intersect(columns, names(data()))
      updateSelectInput(session, "column", choices = available)
    })

    # Dynamic filter control based on selected column
    output$filter_control <- renderUI({
      req(input$column)
      col_data <- data()[[input$column]]

      if (is.numeric(col_data)) {
        sliderInput(
          ns("value_range"),
          "Range",
          min = min(col_data, na.rm = TRUE),
          max = max(col_data, na.rm = TRUE),
          value = range(col_data, na.rm = TRUE)
        )
      } else {
        selectInput(
          ns("value_select"),
          "Values",
          choices = unique(col_data),
          multiple = TRUE,
          selected = unique(col_data)
        )
      }
    })

    # Return filtered data as a reactive
    filtered <- eventReactive(input$apply, {
      req(input$column)
      col <- input$column
      df <- data()

      if (is.numeric(df[[col]])) {
        req(input$value_range)
        df[df[[col]] >= input$value_range[1] &
           df[[col]] <= input$value_range[2], ]
      } else {
        req(input$value_select)
        df[df[[col]] %in% input$value_select, ]
      }
    }, ignoreNULL = FALSE)

    return(filtered)
  })
}

Key rules:

  • Function name follows <name>Server convention
  • First argument is always id
  • Additional arguments are reactive expressions or static values
  • Use moduleServer(id, function(input, output, session) { ... })
  • Use session$ns for dynamic UI created inside the server
  • Return reactive values explicitly

Expected: Server function that processes inputs and returns reactive output.

On failure: If reactive values don't update, check that inputs from dynamic UI use session$ns (not the outer ns). If the module returns NULL, ensure return() is the last expression inside moduleServer().

Step 4: Wire the Module into the Parent App

# In app_ui.R or ui
ui <- page_sidebar(
  title = "Analysis App",
  sidebar = sidebar(
    dataFilterUI("filter1")
  ),
  card(
    DT::dataTableOutput("table")
  )
)

# In app_server.R or server
server <- function(input, output, session) {
  # Raw data source
  raw_data <- reactive({ mtcars })

  # Call module — capture its return value
  filtered_data <- dataFilterServer(
    "filter1",
    data = raw_data,
    columns = c("cyl", "mpg", "hp", "wt")
  )

  # Use the module's returned reactive
  output$table <- DT::renderDataTable({
    filtered_data()
  })
}

Expected: Module appears in the UI and its returned reactive flows into downstream outputs.

On failure: If the module UI doesn't render, verify the id string matches between UI and server calls. If the returned reactive is NULL, check that the server function actually returns a value.

Sibling modules communicate through the parent, not directly with each other. Capture the first module's returned reactive and pass it as an argument into the second module's server call — filtered_data <- dataFilterServer("filter1", data = raw_data, columns = cols), then summaryServer("summary1", data = filtered_data). Pass the reactive itself, not filtered_data(): the receiving module has to re-run when the value changes, so it needs the reactive, not whatever value it happened to hold at the moment of the call. Reactive expressions are the most portable format for moving reactive information between modules — a module that takes a plain reactive argument works under any parent that can produce one.

Step 5: Compose Nested Modules (Optional)

For modules that contain other modules:

analysisUI <- function(id) {
  ns <- NS(id)
  tagList(
    dataFilterUI(ns("filter")),
    plotOutput(ns("plot"))
  )
}

analysisServer <- function(id, data) {
  moduleServer(id, function(input, output, session) {
    # Call inner module with namespaced ID
    filtered <- dataFilterServer("filter", data = data, columns = names(data()))

    output$plot <- renderPlot({
      req(filtered())
      plot(filtered())
    })

    return(filtered)
  })
}

Key rule: In the UI, nest with ns("inner_id"). In the server, call with just "inner_id" — moduleServer handles the namespace chaining.

Expected: Inner module renders correctly within the outer module's namespace.

On failure: If the inner module's UI doesn't appear, you likely forgot ns() around the inner module's ID in the outer UI function. If server communication breaks, check that the inner module ID matches (no ns() in the server call).

Step 6: Test the Module in Isolation

# Quick test app for the module
if (interactive()) {
  shiny::shinyApp(
    ui = fluidPage(
      dataFilterUI("test"),
      DT::dataTableOutput("result")
    ),
    server = function(input, output, session) {
      data <- reactive(iris)
      filtered <- dataFilterServer("test", data, names(iris))
      output$result <- DT::renderDataTable(filtered())
    }
  )
}

Expected: Module works correctly in the minimal test app.

On failure: If the module fails in isolation but works in the full app (or vice versa), check for implicit dependencies on global variables or parent session state.

Validation

  • Module UI function accepts id as first argument and uses NS(id)
  • Every input/output ID in the UI is wrapped with ns()
  • Module server uses moduleServer(id, function(input, output, session) { ... })
  • Dynamic UI in server uses session$ns for IDs
  • Module can be instantiated multiple times without ID collisions
  • Reactive return values are accessible to the parent app
  • Module works in a minimal standalone test app

Common Pitfalls

  • Forgetting ns() in renderUI(): Dynamic UI created inside the server must use session$ns — the outer ns is not available inside moduleServer().
  • Passing non-reactive data: Module arguments that change over time must be reactive expressions. Pass reactive(data) not data.
  • ID mismatch: The id string in the UI call must exactly match the id in the server call.
  • Not returning reactives: If the module computes something the parent needs, it must return() a reactive. Forgetting this is a silent bug.
  • Namespace in nested modules: In UI: ns("inner_id"). In server: just "inner_id". Mixing these up causes namespace double-wrapping or missing prefixes.

Related Skills

  • scaffold-shiny-app — set up the app structure before adding modules
  • test-shiny-app — test modules with testServer() unit tests
  • design-shiny-ui — bslib layout and theming for module UIs
  • optimize-shiny-performance — cache and async patterns within modules

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

同じリポジトリのスキル

概要と使いどころ

Execute structural metamorphosis using strangler fig migration, chrysalis phases, and interface preservation. Covers transformation planning, parallel running, progressive cutover, rollback design, and post-metamorphosis stabilization for system architecture evolution. Use when assess-form has classified the system as READY for transformation, when migrating from monolith to microservices, when replacing a core subsystem while dependents continue operating, or when any architectural change must be gradual rather than big-bang.

日本語の概要は準備中です。原文の説明を表示しています。

pjt222/agent-almanac372026年10月10日 更新

adaptic

無料

Master skill composing the 5-step synoptic cycle for panoramic synthesis across multiple domains. Orchestrates meditate, expand-awareness, observe, awareness, integrate-gestalt, and express-insight into a coherent process that produces unified understanding rather than sequential compromise. Use when a problem genuinely spans 3+ domains and the interactions between domains matter more than depth in any one, when sequential analysis feels like compromise rather than integration, or before major architectural decisions affecting multiple stakeholders.

日本語の概要は準備中です。原文の説明を表示しています。

pjt222/agent-almanac372026年10月10日 更新

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.

日本語の概要は準備中です。原文の説明を表示しています。

pjt222/agent-almanac372026年10月10日 更新

Add Rcpp or RcppArmadillo integration to an R package for high-performance C++ code. Covers setup, writing C++ functions, RcppExports generation, testing compiled code, and debugging. Use when an R function is too slow and profiling confirms a bottleneck, when you need to interface with existing C/C++ libraries, or when implementing algorithms (loops, recursion, linear algebra) that benefit from compiled code.

日本語の概要は準備中です。原文の説明を表示しています。

pjt222/agent-almanac372026年10月10日 更新

aikido

無料

Practice aikido as a defensive martial art emphasizing harmony, redirection, and controlled resolution. Covers centering and grounding, ukemi (safe falling and rolling), core techniques (irimi-nage, shiho-nage, kote-gaeshi, ikkyo), blending and tai sabaki (body movement), randori (multiple attacker practice), weapons awareness, and applying principles off the mat. Use when learning a defensive martial art that prioritizes de-escalation, developing the ability to redirect aggression without causing unnecessary harm, building safe falling skills, or cultivating calm centeredness under physical pressure.

日本語の概要は準備中です。原文の説明を表示しています。

pjt222/agent-almanac372026年10月10日 更新

Analyze an arbitrary codebase to identify functions, APIs, and data sources suitable for exposure as MCP tools, producing a tool specification document. Use when planning an MCP server for an existing project, auditing a codebase before wrapping it as an AI-accessible tool surface, comparing what a codebase can do versus what is already exposed via MCP, or generating a tool spec to hand off to scaffold-mcp-server.

日本語の概要は準備中です。原文の説明を表示しています。

pjt222/agent-almanac372026年10月10日 更新

pjt222 のスキルをすべて見る

このスキルの問題を報告する