Function reference • madshapR

All functions
`as_category()`	Validate and coerce any object as a categorical variable.
`as_dataset()`	Validate and coerce any object as a dataset
`as_data_dict()`	Validate and coerce any object as a data dictionary
`as_data_dict_mlstr()`	Validate and coerce any object as an Opal data dictionary format
`as_data_dict_shape()`	Validate and coerce any object as a workable data dictionary structure
`as_dossier()`	Validate and coerce any object as a dossier (list of dataset(s))
`as_taxonomy()`	Validate and coerce any object as a taxonomy
`as_valueType()`	Validate and coerce any object according to a given valueType
`bookdown_open()`	Open a a web-based bookdown folder in a browser
`bookdown_render()`	Render a bookdown into a bookdown site
`bookdown_template()`	Create a bookdown template
`check_dataset_categories()`	Assess a data dictionary and associated dataset for category differences
`check_dataset_valueType()`	Assess a data dictionary and associated dataset for valueType differences
`check_dataset_variables()`	Assess a data dictionary and associated dataset for undeclared variables
`check_data_dict_categories()`	Assess a data dictionary for potential issues in categories
`check_data_dict_missing_categories()`	Assess categorical variables for non-Boolean values in 'missing' column
`check_data_dict_valueType()`	Assess a data dictionary for non-valid valueType values
`check_data_dict_variables()`	Assess a data dictionary for potential issues in variables
`check_name_standards()`	Assess variable names in a data dictionary for non-standard formats
`col_id()`	Return the id column names(s) of a dataset
`dataset_cat_as_labels()`	Apply data dictionary category labels to the associated dataset variables
`dataset_evaluate()`	Generate an assessment report for a dataset
`dataset_preprocess()`	Generate an evaluation of all variable values in a dataset
`dataset_summarize()`	Generate an assessment report and summary of a dataset
`dataset_visualize()`	Generate a web-based visual report for a dataset
`dataset_zap_data_dict()`	Remove labels (attributes) from a data frame, leaving its unlabelled columns
`data_dict_apply()`	Apply a data dictionary to a dataset
`data_dict_collapse()`	Transform multi-row category column(s) to single rows and join to "Variables"
`data_dict_evaluate()`	Generate an assessment report for a data dictionary
`data_dict_expand()`	Transform single-row category information to multiple rows as element
`data_dict_extract()`	Generate a data dictionary from a dataset
`data_dict_filter()`	Subset data dictionary by row values
`data_dict_group_by()`	Group listed data dictionaries by specified column names
`data_dict_group_split()`	Split grouped data dictionaries into a named list
`data_dict_list_nest()`	Bind listed data dictionaries
`data_dict_match_dataset()`	Inner join between a dataset and its associated data dictionary
`data_dict_pivot_longer()`	Transform column(s) of a data dictionary from wide format to long format
`data_dict_pivot_wider()`	Transform column(s) of a data dictionary from long format to wide format
`data_dict_ungroup()`	Ungroup data dictionary
`data_extract()`	Create an empty dataset from a data dictionary
`dossier_create()`	Generate a dossier from a list of one or more datasets
`dossier_evaluate()`	Generate an assessment report of a dossier
`dossier_summarize()`	Generate an assessment report and summary of a dossier
`drop_category()`	Validate and coerce any object as a non-categorical variable.
`is_category()`	Test if an object is a valid dataset
`is_dataset()`	Test if an object is a valid dataset
`is_data_dict()`	Test if an object is a valid data dictionary
`is_data_dict_mlstr()`	Test if an object is a valid Maelstrom data dictionary
`is_data_dict_shape()`	Test if an object is a workable data dictionary structure
`is_dossier()`	Test if an object is a valid dossier (list of dataset(s))
`is_taxonomy()`	Test if an object is a valid taxonomy
`is_valueType()`	Test if a character object is one of the valid valueType values
`madshapR_DEMO`	Built-in material allowing the user to test the package with demo data
`madshapR_website()`	Call to online documentation
`summary_variables()`	Provide descriptive statistics for variables in a dataset
`summary_variables_categorical()`	Provide descriptive statistics for variables of categorical in a dataset
`summary_variables_date()`	Provide descriptive statistics for variables of type 'date' in a dataset
`summary_variables_datetime()`	Provide descriptive statistics for variables of type 'datetime' in a dataset
`summary_variables_numeric()`	Provide descriptive statistics for variables of type 'numeric' in a dataset
`summary_variables_text()`	Provide descriptive statistics for variables of type 'text' in a dataset
`valueType_adjust()`	Attribute the valueType from a data dictionary to a dataset, or vice versa
`valueType_guess()`	Guess the first possible valueType of an object (Can be a vector)
`valueType_list`	Built-in data frame of allowed valueType values
`valueType_of()`	Return the valueType of an object
`valueType_self_adjust()`	Guess and attribute the valueType of a data dictionary or dataset variable
`variable_visualize()`	Generate a list of charts, figures and summary tables of a variable

Reference

All functions