Validate and coerce any object as an Opal data dictionary format

Validates the input object as a valid data dictionary compliant with formats used in Maelstrom Research ecosystem, including Opal, and returns it with the appropriate madshapR::class attribute. This function mainly helps validate input within other functions of the package but could be used to check if an object is valid for use in a function.

as_data_dict_mlstr(object, as_data_dict = FALSE, name_standard = FALSE)

Arguments

object

A potential valid data dictionary to be coerced.

as_data_dict

Whether the input data dictionary should not be coerced with specific format restrictions for compatibility with other Maelstrom Research software. FALSE by default.

name_standard

Whether the input data dictionary has variable names compatible with Maelstrom Research ecosystem, including Opal)or not. FALSE by default.

Value

A list of data frame(s) with madshapR::class 'data_dict_mlstr'.

Details

A data dictionary contains the list of variables in a dataset and metadata about the variables and can be associated with a dataset. A data dictionary object is a list of data frame(s) named 'Variables' (required) and 'Categories' (if any). To be usable in any function, the data frame 'Variables' must contain at least the name column, with all unique and non-missing entries, and the data frame 'Categories' must contain at least the variable and name columns, with unique combination of variable and name.

The object may be specifically formatted to be compatible with additional Maelstrom Research software, in particular Opal environments.

See also

For a better assessment, please use data_dict_evaluate().

Examples

{

# use madshapR_DEMO provided by the package

data_dict <- madshapR_DEMO$data_dict_MELBOURNE
as_data_dict_mlstr(madshapR_DEMO$data_dict_MELBOURNE)

}
#> $Variables
#> # A tibble: 6 × 6
#>   name       `label:en` valueType index `description:en`                   unit 
#>   <chr>      <chr>      <chr>     <chr> <chr>                              <chr>
#> 1 id         id         text      1     id of the participant              NA   
#> 2 Gender     Gender     integer   2     Gender                             NA   
#> 3 BMI        BMI        decimal   3     Body Mass Index                    kg/m…
#> 4 age        age        integer   4     Age of Participant                 years
#> 5 smo_status smo_status integer   5     Whether the participant is a curr… NA   
#> 6 prg_curr   prg_curr   integer   6     Are you currently pregnant ?       NA   
#> 
#> $Categories
#> # A tibble: 12 × 4
#>    variable   name  `label:en`             missing
#>    <chr>      <chr> <chr>                  <lgl>  
#>  1 Gender     1     Male                   FALSE  
#>  2 Gender     2     Female                 FALSE  
#>  3 age        -888  don't want to answer   TRUE   
#>  4 smo_status 1     never smoked           FALSE  
#>  5 smo_status 2     current smoker         FALSE  
#>  6 smo_status 3     former smoker          FALSE  
#>  7 smo_status -77   don't want to answer   TRUE   
#>  8 prg_curr   0     not currently pregnant FALSE  
#>  9 prg_curr   1     currently pregnant     FALSE  
#> 10 prg_curr   8     Don’t want to answer   TRUE   
#> 11 prg_curr   9     Don’t know             TRUE   
#> 12 prg_curr   -77   not applicable         TRUE   
#> 
#> attr(,"madshapR::class")
#> [1] "data_dict_mlstr"