{constructive} prints code that can be used to recreate R objects. In a sense it is similar to base::dput() or base::deparse() but {constructive} strives to use idiomatic constructors (factor for factors, as.Date() for dates, data.frame() for data frames etc), in order to get output readable by humans.
Some use cases are:
- Snapshot tests
- Exploring objects (alternative to
dput()orstr()) - Creating reproducible examples from existing data
- Comparing two objects (using
construct_diff())
Installation
Install it from cynkra R-universe:
install.packages('constructive', repos = c('https://cynkra.r-universe.dev', 'https://cloud.r-project.org'))Or install with:
remotes::install_github("cynkra/constructive")Comparison with dput()
A few examples compared to their dput() output.
library(constructive)
construct(head(iris, 2))
#> data.frame(
#> 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 = factor(c("setosa", "setosa"), levels = c("setosa", "versicolor", "virginica"))
#> )
dput(head(iris, 2))
#> structure(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 = structure(c(1L,
#> 1L), levels = c("setosa", "versicolor", "virginica"), class = "factor")), row.names = 1:2, class = "data.frame")
construct(.leap.seconds)
#> as.POSIXct(
#> c(
#> "1972-07-01", "1973-01-01", "1974-01-01", "1975-01-01", "1976-01-01",
#> "1977-01-01", "1978-01-01", "1979-01-01", "1980-01-01", "1981-07-01",
#> "1982-07-01", "1983-07-01", "1985-07-01", "1988-01-01", "1990-01-01",
#> "1991-01-01", "1992-07-01", "1993-07-01", "1994-07-01", "1996-01-01",
#> "1997-07-01", "1999-01-01", "2006-01-01", "2009-01-01", "2012-07-01",
#> "2015-07-01", "2017-01-01"
#> ),
#> tz = "GMT"
#> )
dput(.leap.seconds)
#> structure(c(78796800, 94694400, 126230400, 157766400, 189302400,
#> 220924800, 252460800, 283996800, 315532800, 362793600, 394329600,
#> 425865600, 489024000, 567993600, 631152000, 662688000, 709948800,
#> 741484800, 773020800, 820454400, 867715200, 915148800, 1136073600,
#> 1230768000, 1341100800, 1435708800, 1483228800), class = c("POSIXct",
#> "POSIXt"), tzone = "GMT")
library(dplyr, warn.conflicts = FALSE)
grouped_band_members <- group_by(band_members, band)
dput(grouped_band_members)
#> structure(list(name = c("Mick", "John", "Paul"), band = c("Stones",
#> "Beatles", "Beatles")), class = c("grouped_df", "tbl_df", "tbl",
#> "data.frame"), row.names = c(NA, -3L), groups = structure(list(
#> band = c("Beatles", "Stones"), .rows = structure(list(2:3,
#> 1L), ptype = integer(0), class = c("vctrs_list_of", "vctrs_vctr",
#> "list"))), class = c("tbl_df", "tbl", "data.frame"), row.names = c(NA,
#> -2L), .drop = TRUE))
construct(grouped_band_members)
#> tibble::tibble(name = c("Mick", "John", "Paul"), band = c("Stones", "Beatles", "Beatles")) |>
#> dplyr::group_by(band)We can provide to the data argument a named list, an environment, a package where to look for data, or an unnamed list of such items, so we don’t print more than necessary, for instance improving the previous example:
construct(grouped_band_members, data = "dplyr")
#> band_members |>
#> dplyr::group_by(band)Customize the output using constructive options
Some objects can be constructed in several ways, for instance a tibble might be constructed using tibble::tibble() or using tibble::tribble().
The opts_*() family of functions provides ways to tweak the output code, this includes setting the constructor (e.g. setting "tribble" rather than the default "tibble") is used for this purpose. Use these functionsconstruct()'s…`, for instance :
construct(band_members, opts_tbl_df("tribble"))
#> tibble::tribble(
#> ~name, ~band,
#> "Mick", "Stones",
#> "John", "Beatles",
#> "Paul", "Beatles",
#> )
construct(iris, opts_atomic(trim = 2))
#> {constructive} couldn't create code that reproduces perfectly the input
#> ℹ Call `construct_issues()` to inspect the last issues
#> data.frame(
#> Sepal.Length = c(5.1, 4.9, numeric(148)),
#> Sepal.Width = c(3.5, 3, numeric(148)),
#> Petal.Length = c(1.4, 1.4, numeric(148)),
#> Petal.Width = c(0.2, 0.2, numeric(148)),
#> Species = factor(rep(c("setosa", "versicolor", character(1)), each = 50L))
#> )These functions have their own documentation page and are referenced in ?construct.
For every class that doesn’t refer to an internal type a “next” constructor is available, so we can conveniently explor objects using lower level constructors.
construct(band_members, opts_tbl_df("next"))
#> data.frame(name = c("Mick", "John", "Paul"), band = c("Stones", "Beatles", "Beatles")) |>
#> structure(class = c("tbl_df", "tbl", "data.frame"))
construct(band_members, opts_tbl_df("next"), opts_data.frame("next"))
#> list(name = c("Mick", "John", "Paul"), band = c("Stones", "Beatles", "Beatles")) |>
#> structure(class = c("tbl_df", "tbl", "data.frame"), row.names = 1:3)Other functions
-
construct_issues()is used without arguments to check what were the issues encountered with the last reconstructed object, it can also be provided a specific constructive object. -
construct_diff()highlights the differences in the code used to produce 2 objects. -
construct_multi()constructs several objects from a named list, -
construct_dump()is similar tobase::dump(), it’s a wrapper aroundconstruct_multi()that writes to a file. -
construct_signature()constructs a function signature such as the one we see in the “usage” section of a function’s help file. outputs the code produced
-
deparse_call()is an alternative tobase::deparse()andrlang::expr_deparse()that handles additional corner cases and fails when encountering tokens other than symbols and syntactic literals .
Note about environments
Environments use reference semantics, they cannot be copied. An attempt to copy an environment would indeed yield a different environment and identical(env, copy) would be FALSE (read more about it in ?opts_environment).
In some case we can build code that points to a specific environment, for instance:
construct(globalenv())
#> .GlobalEnv
construct(environment(setNames))
#> asNamespace("stats")When it’s not possible we use constructive::.env() function for this purpose.
e1 <- new.env(parent = .GlobalEnv)
e1$x <- 1
construct(e1)
#> constructive::.env("0x1211bd9e0", parents = "global")constructive::.env() fetches the environment from its memory address. The parents argument doesn’t do anything, it provides as additional information the sequence of parents until we reach a special environment.
This strategy is convenient because it always works, but it’s not reproducible between sessions as the memory address is not stable. Moreover it doesn’t tell us anything about the environment’s content.
Depending on what compromise you’re ready to make, you might use different constructions in opts_environment(). For the case above, choosing "list2env" works well :
construct(e1, opts_environment("list2env"))
#> list2env(list(x = 1), parent = .GlobalEnv)constructive::.xptr() is the counterpart of constructive::.env() to construct "externalptr" objects from a memory address.