constructive.example

This package serves as an example on how to extend {constructive}.

Implement a constructor for a new class

Let’s add support for a new class : “qr”. This is the class of the object we get after applying base::qr() to get the QR decomposition of a matrix.

The reconstruction of qr objects is not perfect due to rounding errors so the feature is not a good fit for {constructive} but is a good use case for an example.

First note that {constructive} works even if it doesn’t support directly a class, it will then use the next relevant constructor:

library(constructive)

# will work but use the list constructor
A <- matrix(1:6, nrow = 3)
qr_A <- qr(A)
construct(qr_A)
#> list(
#>   qr = matrix(
#>     c(
#>       -0x1.deeea11683f49p+1, 0.5345224838248488, 0.8017837257372732,
#>       -8.55235974119758, 1.9639610121239324, 0x1.fa35f928a0dfap-1
#>     ),
#>     nrow = 3L,
#>     ncol = 2L
#>   ),
#>   rank = 2L,
#>   qraux = c(1.26726124191242429, 1.1499536117281517),
#>   pivot = 1:2
#> ) |>
#>   structure(class = "qr")

We used the following workflow:

• Call constructive::.cstr_new_class(class = "qr", constructor = "qr")
• Save the script and call devtools::document() (see this commit).
• Update the code (see this commit)
• devtools::load_all()

After this we have:

library(constructive.example)
construct(qr_A)
#> {constructive} couldn't create code that reproduces perfectly the input
#> ℹ Call `construct_issues()` to inspect the last issues
#> qr(matrix(
#>   c(0.99999999999999967, 2, 3, 4.000000000000002, 5.000000000000001, 6.000000000000001),
#>   nrow = 3L,
#>   ncol = 2L
#> ))

The recreation was not perfect due to rounding errors but we’re pretty close

construct_issues()
#>     original$qr         | recreated$qr           
#> [1] -3.7416573867739413 | -3.7416573867739413 [1]
#> [2] 0.5345224838248488  | 0.5345224838248488  [2]
#> [3] 0.8017837257372732  | 0.8017837257372732  [3]
#> [4] -8.5523597411975807 - -8.5523597411975825 [4]
#> [5] 1.9639610121239324  - 1.9639610121239346  [5]
#> [6] 0.9886930334182005  - 0.9886930334182003  [6]
#> 
#>  `original$qraux`: 1.2672612419124243 1.1499536117281517
#> `recreated$qraux`: 1.2672612419124243 1.1499536117281519

We still get the previous behavior if we use the "next" constructor.

# fall back on the next method, which is for lists
construct(qr_A, opts_qr("next"))
#> list(
#>   qr = matrix(
#>     c(
#>       -0x1.deeea11683f49p+1, 0.5345224838248488, 0.8017837257372732,
#>       -8.55235974119758, 1.9639610121239324, 0x1.fa35f928a0dfap-1
#>     ),
#>     nrow = 3L,
#>     ncol = 2L
#>   ),
#>   rank = 2L,
#>   qraux = c(1.26726124191242429, 1.1499536117281517),
#>   pivot = 1:2
#> ) |>
#>   structure(class = "qr")

Thanks to our implementation of is_corrupted_qr() we don’t fail on corrupted objects but fall back to the next method.

corrupted <- structure(1:3, class = "qr")
construct(corrupted)
#> 1:3 |>
#>   structure(class = "qr")

Implement a new constructor for a supported class

We build a new constructor for the class “tbl_df” (tibbles), using the deprecated constructor tibble::data_frame(), equivalent to tibble::tibble().

We used the following workflow:

• Call constructive::.cstr_new_constructor(class = c("tbl_df", "tbl", "data.frame"), constructor = "tibble::data_frame")
• Save the script and call devtools::document(), see this commit
• Update the code (see this commit)
• devtools::load_all()

After this we have:

construct(dplyr::band_members, opts_tbl_df("data_frame"))
#> tibble::data_frame(name = c("Mick", "John", "Paul"), band = c("Stones", "Beatles", "Beatles"))

Note that our implementation of the “tibble” constructor in {constructive} is more sophisticated because tibble() cannot create columns named .rows or .name_repair, and we check if the object is corrupted.