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, name_standard = FALSE)

Arguments

object

A potential valid data dictionary to be coerced.

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

{

library(dplyr)

###### Example 1 : use the function to apply the attribute "data_dict" to the 
# object. 
data_dict <- 
  as_data_dict_mlstr(madshapR_examples$`data_dictionary_example`)

glimpse(data_dict)

###### Example 2 : use the function to shape the data dictionary formatted as
# data_dict_mlstr to data_dict object. The function mainly converts valueType 
# column into corresponding typeof/class columns in 'Variables', and converts
# missing column into "na_values" column. 
data_dict <- 
  as_data_dict_mlstr(madshapR_examples$`data_dictionary_example - as_data_dict`)
  
glimpse(data_dict)

}
#> List of 2
#>  $ Variables : tibble [9 × 8] (S3: tbl_df/tbl/data.frame)
#>   ..$ index                : int [1:9] 1 2 3 4 5 6 7 8 9
#>   ..$ name                 : chr [1:9] "part_id" "gndr" "height" "weight_ms" ...
#>   ..$ label:en             : chr [1:9] "id of the participant" "gndr" "height" "weight_ms" ...
#>   ..$ description:en       : chr [1:9] "id of the participant" "gender of the participant" "height of the participant" "weight of the participant - measured" ...
#>   ..$ valueType            : chr [1:9] "text" "integer" "integer" "integer" ...
#>   ..$ unit                 : chr [1:9] NA NA "cm" "kg" ...
#>   ..$ datacollection::type : chr [1:9] "automatic" "declared" "declared" "measured" ...
#>   ..$ datacollection::level: chr [1:9] "high" "high" "moderate" "moderate" ...
#>  $ Categories: tibble [11 × 4] (S3: tbl_df/tbl/data.frame)
#>   ..$ variable: chr [1:11] "gndr" "gndr" "gndr" "weight_ms" ...
#>   ..$ name    : chr [1:11] "1" "2" "-77" "-99" ...
#>   ..$ label:en: chr [1:11] "Male" "Female" "Don’t want to answer" "Don’t know" ...
#>   ..$ missing : logi [1:11] FALSE FALSE TRUE TRUE TRUE FALSE ...
#>  - attr(*, "madshapR::class")= chr "data_dict_mlstr"
#> List of 2
#>  $ Variables : tibble [9 × 8] (S3: tbl_df/tbl/data.frame)
#>   ..$ index                : int [1:9] 1 2 3 4 5 6 7 8 9
#>   ..$ name                 : chr [1:9] "part_id" "gndr" "height" "weight_ms" ...
#>   ..$ label:en             : chr [1:9] "id of the participant" "gndr" "height" "weight_ms" ...
#>   ..$ description:en       : chr [1:9] "id of the participant" "gender of the participant" "height of the participant" "weight of the participant - measured" ...
#>   ..$ valueType            : chr [1:9] "text" "integer" "integer" "integer" ...
#>   ..$ unit                 : chr [1:9] NA NA "cm" "kg" ...
#>   ..$ datacollection::type : chr [1:9] "automatic" "declared" "declared" "measured" ...
#>   ..$ datacollection::level: chr [1:9] "high" "high" "moderate" "moderate" ...
#>  $ Categories: tibble [11 × 4] (S3: tbl_df/tbl/data.frame)
#>   ..$ variable: chr [1:11] "gndr" "gndr" "gndr" "weight_ms" ...
#>   ..$ name    : chr [1:11] "1" "2" "-77" "-99" ...
#>   ..$ label:en: chr [1:11] "Male" "Female" "Don’t want to answer" "Don’t know" ...
#>   ..$ missing : logi [1:11] FALSE FALSE TRUE TRUE TRUE FALSE ...
#>  - attr(*, "madshapR::class")= chr "data_dict_mlstr"