R/03-harmonized_data_evaluate.R
harmonized_dossier_evaluate.RdAssesses the content and structure of a harmonized dossier and generates reports of the results. This function can be used to evaluate data structure, presence of specific fields, coherence across elements, and data dictionary formats.
harmonized_dossier_evaluate(
harmonized_dossier,
dataschema = attributes(harmonized_dossier)$`Rmonize::DataSchema`,
taxonomy = NULL,
as_dataschema_mlstr = TRUE
)A list containing the harmonized dataset(s).
A DataSchema object.
An optional data frame identifying a variable classification schema.
Whether the output DataSchema should be coerced with specific format restrictions for compatibility with other Maelstrom Research software. TRUE by default.
A list of data frames containing assessment reports for each harmonized dataset.
A harmonized dossier is a named list containing one or more data frames, which are harmonized datasets. A harmonized dossier is generally the product of applying processing to a dossier object The name of each harmonized dataset (data frame) is taken from the reference input dataset. A harmonized dossier also contains the DataSchema and Data Processing Elements used in processing as attributes.
A DataSchema is the list of core variables to generate across datasets and
related metadata. A DataSchema object is a list of data frames with elements
named 'Variables' (required) and 'Categories' (if any). The 'Variables'
element must contain at least the name column, and the 'Categories'
element must contain at least the variable and name columns to be usable
in any function. In 'Variables' the name column must also have unique
entries, and in 'Categories' the combination of variable and name columns
must also be unique.
A taxonomy is a classification schema that can be defined for variable
attributes. A taxonomy is usually extracted from an
Opal environment, and a
taxonomy object is a data frame that must contain at least the columns
taxonomy, vocabulary, and terms. Additional details about Opal
taxonomies are
available online.
The object may be specifically formatted to be compatible with additional Maelstrom Research software, in particular Opal environments.
{
#' # use Rmonize_DEMO provided by the package
library(dplyr)
glimpse(harmonized_dossier_evaluate(Rmonize_DEMO$harmonized_dossier))
}
#> - DATA DICTIONARY ASSESSMENT: data_dict --------------
#> Assess the standard adequacy of naming
#> Assess the uniqueness of variable names
#> Assess the presence of possible duplicated columns
#> Assess the presence of empty rows in the data dictionary
#> Assess the presence of empty columns in the data dictionary
#> Assess the completion of `label(:xx)` column in 'Variables'
#> Assess the `valueType` column in 'Variables'
#> Generate report
#>
#> The data dictionary contains no error/warning.
#>
#> - WARNING MESSAGES (if any): --------------------------------------------
#>
#> - DATASET ASSESSMENT: pooled_harmonized_dataset (empty dataset) --------------------------
#> Assess the standard adequacy of naming
#> Assess the presence of variable names both in dataset and data dictionary
#> Assess the presence of possible duplicated variable in the dataset
#> Assess the presence of duplicated participants in the dataset
#> Assess the presence of empty rows in the data dictionary
#> Assess the presence all NA(s) of columns in the data dictionary
#> Assess the presence of categories not in the data dictionary
#> Assess the `valueType` comparison in dataset and data dictionary
#> Generate report
#>
#> The dataset contains no error/warning.
#>
#> - WARNING MESSAGES (if any): -------------------------------------------------
#>
#> List of 1
#> $ Data dictionary summary: tibble [1 × 4] (S3: tbl_df/tbl/data.frame)
#> ..$ index : int 1
#> ..$ name : chr "adm_unique_id"
#> ..$ label:en : chr "Unique identification code of the participant."
#> ..$ valueType: chr "text"