7 Shiny Development

BBS Course: Good Software Engineering Practice for R Packages

Daniel

March 24, 2023

Why are best practices important for Shiny?

  • Shiny is in itself more complex than usual R code
  • Small apps can quickly evolve into giant apps
  • It seems first possible to test everything by hand, clicking around
  • For one-off, throw-away, one-screen Shiny apps that is ok
  • Here we talk about best practices for more-than-once used Shiny apps

Design

Design: Collaborate with your customer

  • Involve your customer from the first ideation
  • Identify the roles and responsibilities
  • Make reasonable assumptions about user skills and knowledge
  • Show early wireframes, prototypes, beta versions and get feedback

UI Design: Wireframing

  • Wireframing is powerful for aligning in customer discussions
  • Focus on the vision and functionality
  • Keep the UI as simple as possible
  • Know your target as developer during coding and review process
  • Wireframing software:

UI Design: Example wireframe

Architecture

Architecture: Minimize code in Shiny

  • Minimize the code inside the Shiny UI/server
  • Because it will always be easier to test things that live outside of Shiny
  • Let’s look at an example from the Mastering Shiny book

Minimize Shiny Example: Before

server <- function(input, output, session) {
  data <- reactive({
    req(input$file)
    name <- input$file$name
    path <- input$file$datapath
    ext <- tools::file_ext(name)
    switch(ext,
      csv = vroom::vroom(path, ","),
      tsv = vroom::vroom(path, "\t"),
      validate("Invalid file")
    )
  })
  
  output$head <- renderTable({
    head(data(), input$n)
  })
}

The ext and switch() part validates the file extension and then loads the file.

Independent of Shiny reactive!

Minimize Shiny Example: After

load_file <- function(name, path) {
  ext <- tools::file_ext(name)
  switch(ext,
    csv = vroom::vroom(path, ","),
    tsv = vroom::vroom(path, "\t"),
    validate("Invalid file")
  )
}

server <- function(input, output, session) {
  data <- reactive({
    req(input$file)
    load_file(
      input$file$name, 
      input$file$datapath
    )
  })
  
  output$head <- renderTable({
    head(data(), input$n)
  })
}

  1. normal function, validate() will give a simple error outside of Shiny

  2. server is much easier to read now and only half the size

  3. can test load_file() interactively in console

  4. can unit tests load_file() separately (business as usual)

Minimize Shiny: Separate packages

  • On the macro level, this also applies to packages
  • Larger Shiny apps will need to live as an R package
  • The business logic best lives in another R package
  • That package can then also be used without the Shiny interface
  • You can use {staged.dependencies} to allow “joint” pull requests
  • Example from the NEST package ecosystem:
    • {teal.modules.clinical} as the Shiny package
    • {tern} as a business logic package

Modules

Modules: Building blocks of the Shiny app

  • Shiny modules generalize functions
  • Allows to coordinate UI and server code
  • Breaks the app code into reusable and testable units
  • Let’s look at an example from the Mastering Shiny book

Modules Example: Before

  • Difficult to understand what is going on
  • Need longer names / parentheses to differentiate tables / plots / variables in different places
  • Hard to test

Modules Example: After

  • Can easily see the 4 different parts of the app
  • Naming becomes simpler for vars / tables / plots
  • Can reuse the modules separately in other apps
  • Can test the modules separately

Modules: Considerations similar to Functions

  • What should be configurable?
    • Think about reuse needs for module
  • How to organize arguments?
    • Use consistent naming conventions
    • Keep the right order of the arguments
  • Avoid dependencies between arguments
    • Use instead parameter object pattern
  • How to write good and maintainable code?
    • Follow all the clean code rules from the previous chapter!

Reactivity

Reactivity

When building production shiny apps you won’t get around learning reactivity.

Some guidance:

  • Keep user interface simple → often keeps reactivity simple → often less confusing to user
  • Resolve reactive inputs early on & validate generously
  • Design towards “stringy” reactivity graphs
  • Don’t interrupt reactivity
  • Preferably use
    • reactive over reactiveValues
    • observeEvent over observe

Reactivity: Resolve early & validate

server <- function(input, output, session) {
  output$plot <- renderPlot({
    # Resolve reactive values.
    xvar <- input$xvar
    yvar <- input$yvar
    req(xvar, yvar)
    
    # Validate – is it likely that the plot will be meaningful?
    validate(
      need(xvar %in% names(df), glue("xvar \"{xvar}\" does not exist")),
      need(yvar %in% names(df), glue("yvar \"{yvar}\" does not exist")),
      need(nrow(df) > 3, "too few data points for meaningful plot")
    )
    
    my_special_plot(df[[xvar]], df[[yvar]])
  })
}

Reactivity: Debugging techniques

  • Put browser() into the functions
  • Jump into browser mode upon error with options(shiny.error = browser)
  • Just put print() inside the reactives
  • Save variables to global environment via .GlobalEnv$var <- var and then check
  • Simplify things until it works and then go back

Reactivity: Graph visualization

To understand the order of execution we can visualize the reactive graph, which describes how sources and endpoints are connected via conductors.

e.g. input

e.g. reactive

e.g. output

Reactivity: “Stringy” reactivity graph

Bad Many edges, difficult to anticipate reactivity behaviour

Good This “stringy” reactivity graph is easier to understand and debug

Testing

Testing: From Icecream to Pyramid

Bad

  • cannot always test manually
  • can easily introduce bugs

Good

  • manual testing is reduced
  • only for “playing around”

Testing: Snapshot tests for UI

test_that("myInput UI works", {
  input <- "foo"
  set.seed(123)
  datasets <- mock_datasets()
  expect_snapshot(myInput(
    "my_test",
    datasets = datasets,
    input = input
  ))
})
  • Shiny UI functions return shiny.tag objects which print as HTML code
  • Can use snapshot tests to avoid accidental changes
  • Value is limited
  • Can create issues sometimes (non-reproducible hashes)

Testing: Shiny server tests

test_that("server works", {
  testServer(server, {
    session$setInputs(...)

    print(reactive1())
    print(output$output1)
    # etc

    # To interactively play:
    # browser()
  
    expect_equal(...)
    # etc.
  })
})
  • Code is run inside the server function
  • Can access reactives, outputs, etc.
  • Session object simulates user actions and time
  • Note: need to insert browser()
  • Also works for module server functions
  • Limitation: No UI, no JavaScript

Testing: Shiny app tests

library(shinytest2)
test_that("my app works", {
  app <- AppDriver$new(
    app_dir = "myAppDir",
    load_timeout = 1e5,
    variant = platform_variant()
  ) # app$get_logs()
  app$wait_for_idle(timeout = 1e5)
  app$get_screenshot()
  app$set_inputs(name = "Hadley")
  app$get_value("greeting")
  app$click("reset")
  app$get_value("greeting")
})
  • Usually the app is defined in myAppDir/app.R
  • New app instance app from AppDriver$new()
  • Full Shiny app in a headless chromote browser
  • Can look into it via app$get_screenshot()
  • We can set inputs, get values of inputs/outputs, click buttons etc.

Additional Topics

Security

  • Never include passwords in your source code! Instead:
    • Use environment variables
    • Or use the {config} package
    • Any files containing passwords must be added to .gitignore
  • For user authentication, start with existing solutions that are IT approved
    • See some best practices here and here.
  • Code run in server() is isolated and cannot be seen by other users
    • But global environment, e.g. options, are shared across sessions
  • Check all user input via assertions or validations to avoid JavaScript hacking

Packaging

The app should live in an R package. Why? Because we need:

  • Metadata and dependencies: from DESCRIPTION + NAMESPACE files
  • Code split into functions and modules: in files in the the R directory
  • Documentation via vignettes, README, and function and modules documentation
  • Automated tests: using testthat and the native R CMD check
  • Able to share the package as tarball or via repositories for installation

Deployment Options

Deployment Process

You’ll need few extra steps:

  1. Instruct the server how to run the app in the package. Simplest is app.R in package root folder containing:

    pkgload::load_all(".")
myApp() # = function in your package calling shinyApp()

  2. Ignore the app.R for package build

  3. Include shiny and pkgload to the Imports in the DESCRIPTION file

  4. Run rsconnect::deployApp() to share the updated app version!

References

License information

Good Software Engineering Practice for R Packages - Home | Slides | License