iteration3

[moodymudskipper]

I prefer #2 that generalises this, but this is the simpler version you asked for @krlmlr . The output is the same. I didn't publish my iteration 2 using quosures because it's more complex and harder to generalise, I can if you want though.

semi-manual reprex:

library(dplyr, w = F)

mutate
#> function(.data, ...) {
#>   ..new_caller_env <- env_clone_lazy(parent.frame())
#>   ..call = sys.call()
#>   archive(
#>     call = ..call,
#>     env = ..new_caller_env
#>   )
#>   on.exit({
#>     env_cleanup(..new_caller_env)
#>   })
#>   ..call[[1]] <- function(.data, ...) {
#>     UseMethod("mutate")
#>   }
#>   eval(..call, ..new_caller_env)
#> }
#> <bytecode: 0x12c036f98>
#> <environment: namespace:dplyr>

starwars %>%
  select(name, mass) %>%
  mutate(
    mass2 = mass * 2,
    mass2_squared = mass2 * mass2
  )
#> # A tibble: 87 × 4
#>    name                mass mass2 mass2_squared
#>    <chr>              <dbl> <dbl>         <dbl>
#>  1 Luke Skywalker        77   154         23716
#>  2 C-3PO                 75   150         22500
#>  3 R2-D2                 32    64          4096
#>  4 Darth Vader          136   272         73984
#>  5 Leia Organa           49    98          9604
#>  6 Owen Lars            120   240         57600
#>  7 Beru Whitesun lars    75   150         22500
#>  8 R5-D4                 32    64          4096
#>  9 Biggs Darklighter     84   168         28224
#> 10 Obi-Wan Kenobi        77   154         23716
#> # ℹ 77 more rows

mtcars %>%
  group_by(cyl) %>%
  summarise(disp = mean(disp), sd = sd(disp))
#> # A tibble: 3 × 3
#>     cyl  disp    sd
#>   <dbl> <dbl> <dbl>
#> 1     4  105.    NA
#> 2     6  183.    NA
#> 3     8  353.    NA

qs::qsave(dplyr:::globals$archive, "archive.qs")
#> Warning in qs::qsave(dplyr:::globals$archive, "archive.qs"): 'package:dplyr'
#> may not be available when loading

#> Warning in qs::qsave(dplyr:::globals$archive, "archive.qs"): 'package:dplyr'
#> may not be available when loading

.rs.restartR()
archive <- qs::qread("archive.qs")

eval(archive$call[[1]], archive$env[[1]])
#> # A tibble: 87 × 4
#>    name                mass mass2 mass2_squared
#>    <chr>              <dbl> <dbl>         <dbl>
#>  1 Luke Skywalker        77   154         23716
#>  2 C-3PO                 75   150         22500
#>  3 R2-D2                 32    64          4096
#>  4 Darth Vader          136   272         73984
#>  5 Leia Organa           49    98          9604
#>  6 Owen Lars            120   240         57600
#>  7 Beru Whitesun lars    75   150         22500
#>  8 R5-D4                 32    64          4096
#>  9 Biggs Darklighter     84   168         28224
#> 10 Obi-Wan Kenobi        77   154         23716
#> # ℹ 77 more rows

eval(archive$call[[2]], archive$env[[2]])
#> # A tibble: 3 × 3
#>     cyl  disp    sd
#>   <dbl> <dbl> <dbl>
#> 1     4  105.    NA
#>.2     6  183.    NA
#> 3     8  353.    NA

[krlmlr]

Brilliant!

CC @hannes.

options(conflicts.policy = list(warn = FALSE))
library(dplyr)

mutate
#> function(.data, ...) {
#>   ..new_caller_env <- env_clone_lazy(parent.frame())
#>   ..call = sys.call()
#>   archive(
#>     call = ..call,
#>     env = ..new_caller_env
#>   )
#>   on.exit({
#>     env_cleanup(..new_caller_env)
#>   })
#>   ..call[[1]] <- function(.data, ...) {
#>     UseMethod("mutate")
#>   }
#>   eval(..call, ..new_caller_env)
#> }
#> <bytecode: 0x10879f6f8>
#> <environment: namespace:dplyr>

factor <- 2

starwars %>%
  select(name, mass) %>%
  mutate(
    mass2 = mass * factor,
    mass2_squared = mass2 * mass2
  )
#> # A tibble: 87 × 4
#>    name                mass mass2 mass2_squared
#>    <chr>              <dbl> <dbl>         <dbl>
#>  1 Luke Skywalker        77   154         23716
#>  2 C-3PO                 75   150         22500
#>  3 R2-D2                 32    64          4096
#>  4 Darth Vader          136   272         73984
#>  5 Leia Organa           49    98          9604
#>  6 Owen Lars            120   240         57600
#>  7 Beru Whitesun lars    75   150         22500
#>  8 R5-D4                 32    64          4096
#>  9 Biggs Darklighter     84   168         28224
#> 10 Obi-Wan Kenobi        77   154         23716
#> # ℹ 77 more rows

fun <- function() {
  three <- 3

  mtcars %>%
    group_by(cyl) %>%
    summarise(disp = mean(disp), sd = sd(disp), high = three + factor)
}

fun()
#> # A tibble: 3 × 4
#>     cyl  disp    sd  high
#>   <dbl> <dbl> <dbl> <dbl>
#> 1     4  105.    NA     5
#> 2     6  183.    NA     5
#> 3     8  353.    NA     5

suppressWarnings(qs::qsave(dplyr:::globals$archive, "archive.qs"))

callr::r(function() {
  archive <- qs::qread("archive.qs")

  list(
    eval(archive$call[[1]], archive$env[[1]]),
    eval(archive$call[[2]], archive$env[[2]])
  )
})
#> [[1]]
#> # A tibble: 87 × 4
#>    name                mass mass2 mass2_squared
#>    <chr>              <dbl> <dbl>         <dbl>
#>  1 Luke Skywalker        77   154         23716
#>  2 C-3PO                 75   150         22500
#>  3 R2-D2                 32    64          4096
#>  4 Darth Vader          136   272         73984
#>  5 Leia Organa           49    98          9604
#>  6 Owen Lars            120   240         57600
#>  7 Beru Whitesun lars    75   150         22500
#>  8 R5-D4                 32    64          4096
#>  9 Biggs Darklighter     84   168         28224
#> 10 Obi-Wan Kenobi        77   154         23716
#> # ℹ 77 more rows
#> 
#> [[2]]
#> # A tibble: 3 × 4
#>     cyl  disp    sd  high
#>   <dbl> <dbl> <dbl> <dbl>
#> 1     4  105.    NA     5
#> 2     6  183.    NA     5
#> 3     8  353.    NA     5

^{Created on 2024-04-10 with reprex v2.1.0}