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Construct using only low level constructors

Source: R/construct_dput.R
construct_dput.Rd

  • construct_dput() is a closer counterpart to base::dput() that doesn't use higher level constructors such as data.frame() and factor().

  • construct_base() uses higher constructors, but only for the classes maintained in the default base R packages. This includes data.frame() and factor(), the S4 constructors from the 'method' package etc, but not data.table() and other constructors for classes from other packages.

Usage

construct_dput(
  x,
  ...,
  data = NULL,
  pipe = NULL,
  check = NULL,
  unicode_representation = c("ascii", "latin", "character", "unicode"),
  escape = FALSE,
  pedantic_encoding = FALSE,
  compare = compare_options(),
  one_liner = FALSE,
  template = getOption("constructive_opts_template")
)

construct_base(
  x,
  ...,
  data = NULL,
  pipe = NULL,
  check = NULL,
  unicode_representation = c("ascii", "latin", "character", "unicode"),
  escape = FALSE,
  pedantic_encoding = FALSE,
  compare = compare_options(),
  one_liner = FALSE,
  template = getOption("constructive_opts_template")
)

Arguments

x

An object, for construct_multi() a named list or an environment.

...

Constructive options built with the opts_*() family of functions. See the "Constructive options" section below.

data

Named list or environment of objects we want to detect and mention by name (as opposed to deparsing them further). Can also contain unnamed nested lists, environments, or package names, in the latter case package exports and datasets will be considered. In case of conflict, the last provided name is considered.

pipe

Which pipe to use, either "base" or "magrittr". Defaults to "base" for R >= 4.2, otherwise to "magrittr".

check

Boolean. Whether to check if the created code reproduces the object using waldo::compare().

unicode_representation

By default "ascii", which means only ASCII characters (code point < 128) will be used to construct strings and variable names. This makes sure that homoglyphs (different spaces and other identically displayed unicode characters) are printed differently, and avoid possible unfortunate copy and paste auto conversion issues. "latin" is more lax and uses all latin characters (code point < 256). "character" shows all characters, but not emojis. Finally "unicode" displays all characters and emojis, which is what dput() does.

escape

Boolean. Whether to escape double quotes and backslashes. If FALSE we use single quotes to surround strings (including variable and element names) containing double quotes, and raw strings for strings that contain backslashes and/or a combination of single and double quotes. Depending on unicode_representation escape = FALSE cannot be applied on all strings.

pedantic_encoding

Boolean. Whether to mark strings with the "unknown" encoding rather than an explicit native encoding ("UTF-8" or "latin1") when it's necessary to reproduce the binary representation exactly. This detail is normally of very little significance. The reason why we're not pedantic by default is that the constructed code might be different in the console and in snapshot tests and reprexes due to the latter rounding some angles, and it would be confusing for users.

compare

Parameters passed to waldo::compare(), built with compare_options().

one_liner

Boolean. Whether to collapse the output to a single line of code.

template

A list of constructive options built with opts_*() functions, they will be overriden by .... Use it to set a default behavior for {constructive}.

Value

An object of class 'constructive'.

Details

Both functions are valuable for object inspection, and might provide more stable snapshots, since supporting more classes in the package means the default output of construct() might change over time for some objects.

To use higher level constructor from the base package itself, excluding for instance stats::ts(), utils::person() or methods::classGeneratorFunction()), we can call construct(x, classes = "{base}"

Examples

construct_dput(head(iris, 2))
#> list(
#>   Sepal.Length = c(5.1, 4.9),
#>   Sepal.Width = c(3.5, 3),
#>   Petal.Length = c(1.4, 1.4),
#>   Petal.Width = c(0.2, 0.2),
#>   Species = c(1L, 1L) |>
#>     structure(levels = c("setosa", "versicolor", "virginica"), class = "factor")
#> ) |>
#>   structure(row.names = c(NA, -2L), class = "data.frame")
construct_base(head(iris, 2))
#> data.frame(
#>   Sepal.Length = c(5.1, 4.9),
#>   Sepal.Width = c(3.5, 3),
#>   Petal.Length = 1.4,
#>   Petal.Width = 0.2,
#>   Species = factor("setosa", levels = c("setosa", "versicolor", "virginica"))
#> )