R: Functions to Support Data Management and Processing Using the Maelstrom Research Approach

# Functions to Support Data Management and Processing Using the Maelstrom Research Approach

## Documentation for package ‘madshapR’ version 2.0.0

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS
- Package README

## Help Pages

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 | Objects exported from other packages
bookdown_render | Objects exported from other packages
bookdown_template | Objects exported from other packages
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
color_palette_maelstrom | Built-in data frame of colors used in the graphs and charts.
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_trim_labels | Add shortened labels to data dictionary
data_dict_ungroup | Ungroup data dictionary
data_dict_update | Update a data dictionary from a dataset
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.
first_label_get | Get the first label from a data dictionary
has_categories | Test if an object has categorical variables.
is_category | Test and validate if an object is a categorical variable.
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_examples | Built-in material allowing the user to test the package with example 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
typeof_convert_to_valueType | Convert typeof (and class if any) into its corresponding valueType
valueType_adjust | Attribute the valueType from a data dictionary to a dataset, or vice versa
valueType_convert_to_typeof | Convert valueType into its corresponding typeof and class in R representation
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 | Self-adjust the valueType from a data dictionary or a dataset.
variable_visualize | Generate a list of charts, figures and summary tables of a variable