madshapR (version 2.0.0)

data_dict_evaluate: Generate an assessment report for a data dictionary

Description

Assesses the content and structure of a data dictionary and generates reports of the results. The report can be used to help assess data dictionary structure, presence of fields, coherence across elements, and taxonomy or data dictionary formats.

Usage

data_dict_evaluate(data_dict, taxonomy = NULL, is_data_dict_mlstr = TRUE)

Value

A list of data frames containing assessment reports.

Arguments

data_dict

A list of data frame(s) representing metadata to be evaluated.

taxonomy

An optional data frame identifying a variable classification schema.

is_data_dict_mlstr

Whether the input data dictionary should be coerced with specific format restrictions for compatibility with other Maelstrom Research software. TRUE by default.

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 function truncates each cell to a maximum of 10000 characters, to be readable and compatible with Excel.

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.

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

Examples

{

library(dplyr)

# use madshapR_examples provided by the package
data_dict <- madshapR_examples$`data_dictionary_example - errors`
eval_data_dict <- data_dict_evaluate(data_dict,is_data_dict_mlstr = TRUE)

glimpse(eval_data_dict)

}