dataset_summarize {madshapR}

Generate an assessment report and summary of a dataset

Description

Assesses and summarizes the content and structure of a dataset 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.

Usage

dataset_summarize(
  dataset,
  data_dict = data_dict_extract(dataset),
  group_by = NULL,
  taxonomy = NULL,
  dataset_name = .dataset_name,
  valueType_guess = FALSE,
  .dataset_name = NULL
)

Arguments

dataset

A dataset object.

data_dict

A list of data frame(s) representing metadata of the input dataset. Automatically generated if not provided.

group_by

A character string identifying the column in the dataset to use as a grouping variable. Elements will be grouped by this column.

taxonomy

An optional data frame identifying a variable classification schema.

dataset_name

A character string specifying the name of the dataset (internally used in the function dossier_evaluate()).

valueType_guess

Whether the output should include a more accurate valueType that could be applied to the dataset. FALSE by default.

.dataset_name

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 dataset is a data table containing variables. A dataset object is a data frame and can be associated with a data dictionary. If no data dictionary is provided with a dataset, a minimum workable data dictionary will be generated as needed within relevant functions. Identifier variable(s) for indexing can be specified by the user. The id values must be non-missing and will be used in functions that require it. If no identifier variable is specified, indexing is handled automatically by the function.

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.

Value

A list of data frames containing assessment reports and summaries.

See Also

dossier_evaluate()

Examples

{

# use madshapR_DEMO provided by the package
library(dplyr)

#' ###### Example : Any data frame can be summarized
dataset <- iris['Sepal.Width']
glimpse(dataset_summarize(dataset))
 
}