การนำแอป Shiny ไปใช้งาน

ภาพรวมตัวเลือกการนำไปใช้งาน

# Summary of deployment options and their trade-offs:
# shinyapps.io: managed, auto-scaling, per-instance pricing
# Shiny Server: self-hosted, unlimited users, needs sysadmin
# Posit Connect: enterprise SSO, scheduling, REST APIs
# Docker: any cloud provider, reproducible environments

cat('Choose based on: scale, budget, infra control needs')

โครงสร้าง app.R สำหรับการนำไปใช้งาน

# Minimal app.R for deployment
library(shiny)
library(ggplot2)
library(dplyr)

# Load data relative to app directory
data <- readRDS('data/processed_data.rds')

ui <- fluidPage(
  titlePanel('My Deployed App'),
  sidebarLayout(
    sidebarPanel(selectInput('var', 'Variable:', choices = names(data))),
    mainPanel(plotOutput('main_plot'))
  )
)

server <- function(input, output, session) {
  output$main_plot <- renderPlot({
    ggplot(data, aes_string(x = input$var)) + geom_histogram()
  })
}

shinyApp(ui, server)

rsconnect::deployApp()

library(rsconnect)

# One-time setup: link your shinyapps.io account
# rsconnect::setAccountInfo(
#   name   = 'your_account_name',
#   token  = 'YOUR_TOKEN',
#   secret = 'YOUR_SECRET'
# )

# Deploy the app in the current directory
rsconnect::deployApp(
  appDir  = '.',          # or 'path/to/app'
  appName = 'my_analysis_app',
  forceUpdate = TRUE
)

การตั้งค่า shinyapps.io

library(rsconnect)

# Copy credentials from shinyapps.io -> Account -> Tokens
rsconnect::setAccountInfo(
  name   = 'your_username',
  token  = 'xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx',
  secret = 'yyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyy'
)

# Verify the connection
rsconnect::accountInfo()

# List currently deployed apps
rsconnect::applications()

Shiny Server โอเพนซอร์ส

# Install Shiny Server on Ubuntu:
# sudo apt-get install r-base
# sudo R -e 'install.packages(c("shiny", "rmarkdown"))'
# wget https://download3.rstudio.org/ubuntu-20.04/x86_64/shiny-server-1.5.21.1012-amd64.deb
# sudo dpkg -i shiny-server-1.5.21.1012-amd64.deb

# Deploy an app:
# sudo cp -r /path/to/myapp /srv/shiny-server/myapp
# Access at: http://your-server:3838/myapp

# Config file: /etc/shiny-server/shiny-server.conf
cat('Shiny Server config controls ports, auth, and app paths')

Docker ร่วมกับ rocker/shiny

# Dockerfile for a custom Shiny app
# FROM rocker/shiny:4.4.0
#
# # Install R package dependencies
# RUN R -e 'install.packages(c("ggplot2","dplyr","DT"))'
#
# # Copy app files
# COPY myapp/ /srv/shiny-server/myapp/
#
# EXPOSE 3838

# Build and run:
# docker build -t my-shiny-app .
# docker run -p 3838:3838 my-shiny-app

# Access at: http://localhost:3838/myapp

renv สำหรับการจัดการแพ็กเกจที่ทำซ้ำได้

library(renv)

# Initialise renv in the project (creates renv.lock)
renv::init()

# After installing/updating packages, record the state
renv::snapshot()

# On the server: restore exact versions from lockfile
renv::restore()

# Check lockfile for a specific package version
renv::dependencies()  # lists all used packages

ตัวแปรสภาพแวดล้อมสำหรับการกำหนดค่า

# In .Renviron (never commit this file):
# DB_HOST=prod-db.example.com
# DB_PASSWORD=secret123
# API_KEY=key_abc

# In app.R:
db_host <- Sys.getenv('DB_HOST',     unset = 'localhost')
db_pass <- Sys.getenv('DB_PASSWORD', unset = '')
api_key <- Sys.getenv('API_KEY',     unset = '')

if (api_key == '') {
  warning('API_KEY not set — some features disabled')
}

ภาพรวม Posit Connect

library(rsconnect)

# Configure connection to Posit Connect server
rsconnect::addServer(
  url  = 'https://connect.yourcompany.com',
  name = 'my_connect_server'
)

# Authenticate with API key
rsconnect::connectApiUser(
  account = 'jsmith',
  server  = 'my_connect_server',
  apiKey  = Sys.getenv('CONNECT_API_KEY')
)

# Deploy
rsconnect::deployApp(server = 'my_connect_server')

รายการตรวจสอบก่อนนำไปใช้งาน

# Production-hardened app.R patterns

# Load data once at startup (not per-session)
app_data <- readRDS('data/processed.rds')  # outside server()

server <- function(input, output, session) {
  # Reference app_data directly — already in memory
  output$plot <- renderPlot({
    subset_data <- app_data[app_data$group == input$grp, ]
    plot(subset_data$x, subset_data$y)
  })

  # Sanitise error messages
  tryCatch({
    risky_operation()
  }, error = function(e) {
    showNotification('An error occurred.', type = 'error')
    # log the real error internally, not to user
    message('Error: ', conditionMessage(e))
  })
}

ข้อควรพิจารณาในการปรับขนาด

library(shiny)
library(pool)

# Create a database connection pool at startup
# (shared across all sessions)
pool <- dbPool(
  drv      = RPostgres::Postgres(),
  host     = Sys.getenv('DB_HOST'),
  dbname   = 'analytics',
  user     = Sys.getenv('DB_USER'),
  password = Sys.getenv('DB_PASSWORD'),
  minSize  = 2,
  maxSize  = 10
)

onStop(function() { poolClose(pool) })

server <- function(input, output, session) {
  output$tbl <- renderTable({
    dbGetQuery(pool, 'SELECT * FROM metrics LIMIT 100')
  })
}

ตรวจสอบความเข้าใจอย่างรวดเร็ว

สรุปการนำแอป Shiny ไปใช้งาน

# Minimal production-ready structure
# myapp/
#   app.R           <- ui + server + shinyApp()
#   data/           <- processed datasets
#   R/              <- modules and helpers
#   renv.lock       <- package version snapshot
#   .Renviron       <- local env vars (gitignored)

# Deploy:
library(rsconnect)
rsconnect::deployApp(
  appDir  = 'myapp',
  appName = 'my_production_app'
)