data_dict_pivot_wider: Transform column(s) of a data dictionary from long format to...

In madshapR: Support Technical Processes Following 'Maelstrom Research' Standards

Transform column(s) of a data dictionary from long format to wide format

Description

Transforms column(s) of a data dictionary from long format to wide format. If a taxonomy is provided, the corresponding columns in the data dictionary will be converted to a format with the taxonomy expanded. This operation is equivalent to performing a tidyr::pivot_wider() on these columns following the taxonomy structure provided. Variable names in the data dictionary must be unique.

Usage

data_dict_pivot_wider(data_dict, taxonomy = NULL)

Arguments

data_dict	A list of data frame(s) representing metadata to be transformed.
taxonomy	An optional data frame identifying a variable classification schema.

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.

A taxonomy is a classification schema that can be defined for variable attributes. A taxonomy is usually extracted from an Opal environment, and a taxonomy object is a data frame that must contain at least the columns taxonomy, vocabulary, and terms. Additional details about Opal taxonomies are available online.

Value

A list of data frame(s) identifying a data dictionary.

See Also

tidyr::pivot_wider(), as_data_dict()

Examples

{

# use madshapR_DEMO provided by the package

data_dict <- madshapR_DEMO$`data_dict_PARIS - collapsed`
taxonomy  <- madshapR_DEMO$taxonomy_PARIS
data_dict_pivot_wider(data_dict, taxonomy)

}

madshapR documentation built on May 29, 2024, 7:43 a.m.