View source: R/04-harmonized_data_summarize.R
harmonized_dossier_summarize | R Documentation |
Assesses and summarizes 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, and to summarize additional information about variable distributions and descriptive statistics.
harmonized_dossier_summarize(harmonized_dossier)
harmonized_dossier |
A list containing the harmonized dataset(s). |
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.
The Data Processing Elements specifies the input elements and processing algorithms
to generate harmonized variables in the DataSchema formats. It is also
contains metadata used to generate documentation of the processing.
A Data Processing Elements object is a data frame with specific columns
used in data processing: dataschema_variable
, input_dataset
,
input_variables
, Mlstr_harmo::rule_category
and Mlstr_harmo::algorithm
.
To initiate processing, the first entry must be the creation of a harmonized
primary identifier variable (e.g., participant unique ID).
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 valueType is a declared property of a variable that is required in
certain functions to determine handling of the variables. Specifically,
valueType refers to the
OBiBa data type of a variable.
The valueType is specified in a data dictionary in a column 'valueType' and
can be associated with variables as attributes. Acceptable valueTypes
include 'text', 'integer', 'decimal', 'boolean', datetime', 'date'. The full
list of OBiBa valueType possibilities and their correspondence with R data
types are available using valueType_list
. The valueType can be used to
coerce the variable to the corresponding data type.
A list of data frames containing overall assessment reports and summaries grouped by harmonized dataset.
# Use Rmonize_examples to run examples.
library(dplyr)
# Perform data processing
harmonized_dossier <- Rmonize_examples$`harmonized_dossier`
summary_harmo <- harmonized_dossier_summarize(harmonized_dossier)
glimpse(summary_harmo)
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