Fabricate from a list of quosures

The programmatic entry point, for a caller that has already captured the expressions it wants fabricated. [fabricate()] captures its ... with enquos(), which such a caller cannot use: splicing captured quosures back in with !!! renders them as formulas, and fabricate() then captures those, one level too deep. fabricate_with_dots() takes the captured list as it stands and builds the frame [fabricate()] would have built from the same expressions.

Usage

fabricate_with_dots(data = NULL, dots, ID_label = "ID")

Arguments

data

Optional existing data frame to start from, as in [fabricate()].

dots

A named list of quosures, such as [rlang::quos()] or [rlang::enquos()] returns. A quosure named N sets the number of rows and does not become a column, exactly as fabricate(N = ) does. Every other element needs a name.

ID_label

Name of the unit ID column, or NA to suppress it. See [fabricate()].

Value

A tibble, the same one [fabricate()] returns for the same expressions.

Details

DeclareDesign is the caller this exists for. Its declare_model(), declare_measurement(), declare_assignment() and declare_sampling() each capture their own arguments, so that a design carries its expressions as a value rather than as a view onto the environment it was written in, and then pass the captured list here.

See also

[fabricate()], which is what to call when you hold the expressions rather than their captures.

Examples

dots <- rlang::quos(N = 10, Y = rnorm(N), X = Y > 0)
fabricate_with_dots(dots = dots)
#> # A tibble: 10 × 3
#>    ID         Y X    
#>    <chr>  <dbl> <lgl>
#>  1 01    -0.446 FALSE
#>  2 02     0.104 TRUE 
#>  3 03     1.41  TRUE 
#>  4 04     1.69  TRUE 
#>  5 05     2.29  TRUE 
#>  6 06     2.72  TRUE 
#>  7 07     2.17  TRUE 
#>  8 08     0.180 TRUE 
#>  9 09    -1.69  FALSE
#> 10 10    -0.789 FALSE

# The same frame without the row id, which is how a step that adds columns
# to data already in hand calls it.
fabricate_with_dots(dots = rlang::quos(Y2 = Y * 2),
                    data = fabricate_with_dots(dots = dots),
                    ID_label = NA)
#> # A tibble: 10 × 4
#>    ID         Y X         Y2
#>    <chr>  <dbl> <lgl>  <dbl>
#>  1 01    -0.870 FALSE -1.74 
#>  2 02    -1.25  FALSE -2.50 
#>  3 03    -0.279 FALSE -0.559
#>  4 04    -0.590 FALSE -1.18 
#>  5 05     1.18  TRUE   2.36 
#>  6 06    -0.917 FALSE -1.83 
#>  7 07     0.227 TRUE   0.453
#>  8 08    -0.435 FALSE -0.870
#>  9 09    -1.01  FALSE -2.02 
#> 10 10     0.828 TRUE   1.66