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library(R6)
A <- R6Class(
  "A",
  public = list(
    f = function() {
      cat("Called A$f()\n")
      if (
        is.null(self$.__enclos_env__$super) ||
        identical(self$.__enclos_env__$super$g, self$g)
      ) {

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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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import time
import sys

# Get set of loaded modules
x = set(sys.modules)

start = time.time()
import plotnine
end = time.time()
end - start

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How to deal with the SystemRequirements: C++11 NOTE when running R CMD check

For the development version of R-devel which will become 4.3.0, there is a new warning in R CMD check that comes up for some packages:

* checking C++ specification ... NOTE
  Specified C++11: please drop specification unless essential

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# Example 1: running at top level. This works.
model_str1 <- "speed~dist"
model1 <- lm(formula=model_str1, data=cars)
stats::update(model1, y = TRUE)
#> 
#> Call:
#> lm(formula = model_str1, data = cars, y = TRUE)
#> 
#> Coefficients:
#> (Intercept)         dist  

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# This function recurses into `expr`, looking for `search_expr`. If it finds
# it, it will return `replace_expr`. Notably, `replace_expr` can contain rlang
# operators like `!!`, and perform rlang substitution on them.
modify_expr <- function(
  expr,
  search_expr,
  replace_expr
) {
  if (typeof(expr) != "language") {
    stop("modify_expr only works on language objects (AKA quoted expressions)!")

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# Test app for https://github.com/rstudio/shiny/pull/3666

# pkgload::load_all()

library(promises)
library(httpuv)
library(htmltools)
library(shiny)
library(later)
options(shiny.minified = FALSE)

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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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This is a test, v2!

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# Web REPL/console


# Note: 127.0.0.1 allows connections only from localhost. To listen on external
# ports, use 0.0.0.0.
web_repl <- function(host = "127.0.0.1", port = 8080) {
  library(httpuv)

  app <- list(
    call = function(req) {