| data_dict_evaluate {madshapR} | R Documentation |
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, as_data_dict_mlstr = TRUE)
Arguments
data_dict |
A list of data frame(s) representing metadata to be evaluated. |
taxonomy |
An optional data frame identifying a variable classification schema. |
as_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.
Value
A list of data frames containing assessment reports.
Examples
{
# use madshapR_DEMO provided by the package
library(dplyr)
data_dict <- madshapR_DEMO$`data_dict_TOKYO - errors`
glimpse(data_dict_evaluate(data_dict))
}