Extract Dictionary Meta Data as a Tibble — get

Returns a tibble of the dictionary contents, optionally formatting missing values and nominal variable categories. This can be useful for custom workflows that require dictionary information in tabular form.

Usage

get_dictionary(
  dictionary,
  format_missing = FALSE,
  format_categories = FALSE,
  as_code = FALSE
)

Arguments

dictionary: A data dictionary object
format_missing: Logical. If TRUE, missing metadata fields (like labels or units) are returned as stored in the dictionary. If FALSE (default), missing values are replaced with NA.
format_categories: Logical. If TRUE, nominal category information is returned exactly as stored in the dictionary. If FALSE (default), category levels and labels are returned as list-columns extracted from the variable objects.
as_code: Logical. If TRUE, text is returned as code that can be copy/pasted into an R script to construct dictionary. If FALSE (the default), unknowns are returned as a tibble.

Value

A tibble with one row per variable (as_code = FALSE), containing meta data fields such as label, description, units, divby_modeling, category_levels, and category_labels. If as_code = TRUE, a string containing code to construct the original dictionary.

Author

Byron Jaeger, Tyler Sagendorf

Examples

dd <- as_data_dictionary(iris)
get_dictionary(dd)
#> # A tibble: 5 × 7
#>   name    label description units divby_modeling category_levels category_labels
#>   <chr>   <chr> <chr>       <chr>          <dbl> <named list>    <named list>   
#> 1 Sepal.… NA    NA          NA                NA <NULL>          <NULL>         
#> 2 Sepal.… NA    NA          NA                NA <NULL>          <NULL>         
#> 3 Petal.… NA    NA          NA                NA <NULL>          <NULL>         
#> 4 Petal.… NA    NA          NA                NA <NULL>          <NULL>         
#> 5 Species NA    NA          NA                NA <chr [3]>       <chr [3]>      

# Code to recreate dd
dd_code <- get_dictionary(dd, as_code = TRUE)
dd_code
#> [1] "data_dictionary(.list = list(numeric_variable(name='Sepal.Length',label=NULL,description=NULL,units=NULL,divby_modeling=NULL),numeric_variable(name='Sepal.Width',label=NULL,description=NULL,units=NULL,divby_modeling=NULL),numeric_variable(name='Petal.Length',label=NULL,description=NULL,units=NULL,divby_modeling=NULL),numeric_variable(name='Petal.Width',label=NULL,description=NULL,units=NULL,divby_modeling=NULL),nominal_variable(name='Species',label=NULL,description=NULL,category_levels=c('setosa','versicolor','virginica'),category_labels=c('setosa','versicolor','virginica'))))"

# Create data dictionary from code string
dd_new <- eval(parse(text = dd_code))
all.equal(dd, dd_new) # TRUE
#> [1] TRUE