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Type: Shiny
Title: Including HTML, text, and Markdown files
License: MIT
Author: Winston Chang <winston@rstudio.com>
AuthorUrl: http://www.rstudio.com/
Tags: include
DisplayMode: Showcase

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# By default, the directories in .libPaths() aren't writable on shinyapps.io, so
# create a subdir where we'll install our package.
if (!file.exists("R-lib")) {
  dir.create("R-lib")
}
# Unfortunately, there's no way to get deployapp() to ignore this directory, so
# make sure to remove it locally before you call deployapp(). This can be done
# with:
#   unlink("pkgInst/R-lib", recursive = TRUE)

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import * as sass from "./sass.default.js";

console.log(sass.info);
console.log(1);
const res = sass.compileString(`
.box {
  width: 10px + 15px;
}
`)
console.log(2);

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#!/bin/bash

# Process command-line arguments
if [[ $# -eq 0 ]] || [[ "$1" == "--help" ]]; then
    echo "Usage: compress-video [OPTIONS] [FILENAMES...]"
    echo
    echo "Options:"
    echo "  --speed-2x     Output video at 2x speed"
    echo "  --size-half    Scale output to half size"
    echo "  --help         Display this help page"

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m <- matrix(1:12, nrow=3)

# Double every element
# ===================

# Method 1: nested for loop
for(i in seq_len(nrow(m))) {
  for(j in seq_len(ncol(m))) {
    m2[i, j] <- m[i, j] * 2
  }

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# Calculate the probability of a collision if `n` items are randomly drawn (with
# replacement) from a set with `total` number of items.
collision_prob <- function(n, total) {
  prob_no_collision <- 1
  # Do this in a loop instead of prod(numerators)/prod(denominators), because
  # that method is prone to result in a loss of precision due to rounding.
  for (i in seq(0, n-1)) {
    prob_no_collision <- prob_no_collision * (total - i) / total
  }
  1 - prob_no_collision

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To ChatGPT: This is a guide for translating R-Shiny applications to the new Python-Shiny framework, also known as PyShiny, and as Shiny for Python. The PyShiny framework was created after your knowledge cutoff, so even though you do not have any prior knowledge of it, it does exist. Shiny for Python is new. It is not Dash or Streamlit, or another Python web framework.

You are an assistant for translating applications from R-Shiny to Python-Shiny. At the end of this message, please acknowledge that you understand.

The translation guide begins here:

---

The R and Python Shiny packages are very similar, making it easy to transition between the two if you know how to use one.

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# The code below demonstrates that in R, growing a vector in a loop can be fast,
# as long as there is only reference to the object. When there's only one
# reference to the vector, R grows it in place (in most cases). However, if
# there are other references to the object, R must make a copy the object
# instead of growing it in place, leading to slower performance.

# =========================================================================
# Timing tests
# =========================================================================

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# ---- Multiple dispatch functions -----
multi_dispatch <- function(gen_name) {
  calling_env <- parent.frame()
  parent_call <- sys.call(sys.parent())
  calling_fun <- sys.function(sys.parent())

  arg1 <- eval(parent_call[[2]], calling_env)
  arg2 <- eval(parent_call[[3]], calling_env)

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max_plots <- 5

ui <- fluidPage(

  headerPanel("Dynamic number of plots"),

  sidebarPanel(
    sliderInput("n", "Number of plots", value=1, min=1, max=5)
  ),