Ucimlrepo Package Rdocumentation

ucimlrepo: Explore UCI ML Repository Datasets Description Find and import datasets from the University of California Irvine Machine Learning (UCI ML) Repository into R. Supports working with data from UCI ML repository inside of R scripts, notebooks, and 'Quarto'/'RMarkdown' documents. Access the UCI ML repository directly at https://archive.ics.uci.edu/. Author(s) Maintainer: James Joseph Balamuta james.balamuta@gmail.com (ORCID) [copyright holder] Authors: Philip Truong ucirepository@gmail.com [copyright holder] See Also Useful links: Report bugs at https://github.com/coatless-rpkg/ucimlrepo/issues Empty Fetch Response Description Internal function to create an empty fetch response.

Usage empty_fetch_response() Value A list containing dataset metadata, dataframes, and variable info in its properties. See Also Empty Search Response Description Internal function to create an empty search response. Usage empty_search_response() Value A data frame with three empty columns of 'id', 'name', and 'url'. See Also Fetch UCI ML Repository Dataset Description Loads a dataset from the UCI ML Repository, including the dataframes and metadata information. Usage fetch_ucirepo(name, id) Arguments Details Only provide name or id, not both.

Value A list containing dataset metadata, dataframes, and variable info in its properties. - data: Contains dataset matrices as pandas dataframes - ids: Dataframe of ID columns - features: Dataframe of feature columns - targets: Dataframe of target columns - original: Dataframe consisting of all IDs, features, and targets - headers: List of all variable names/headers - - metadata: Contains metadata information about the dataset.

uci_id: Unique dataset identifier for UCI repository - name: Name of dataset on UCI repository - repository_url: Link to dataset webpage on the UCI repository - data_url: Link to raw data file - abstract: Short description of dataset - area: Subject area e.g. life science, business - tasks: Associated machine learning tasks e.g. classification, regression - characteristics: Dataset types e.g.

multivariate, sequential - num_instances: Number of rows or samples - num_features: Number of feature columns - feature_types: Data types of features - target_col: Name of target column(s) - index_col: Name of index column(s) - has_missing_values: Whether the dataset contains missing values - missing_values_symbol: Indicates what symbol represents the missing entries (if the dataset has missing values) - year_of_dataset_creation: Year that data set was created - dataset_doi: DOI registered for dataset that links to UCI repo dataset page - creators: List of dataset creator names - intro_paper: Information about dataset's published introductory paper - external_url: URL to external dataset page.

This field will only exist for linked datasets i.e. not hosted by UCI - additional_info: Descriptive free text about dataset - summary: General summary - purpose: For what purpose was the dataset created? - funded_by: Who funded the creation of the dataset? - instances_represent: What do the instances in this dataset represent? - recommended_data_splits: Are there recommended data splits? - sensitive_data: Does the dataset contain data that might be considered sensitive in any way? - preprocessing_description: Was there any data preprocessing performed?

variable_info: Additional free text description for variables - citation: Citation Requests/Acknowledgements - - - variables: Contains variable details presented in a tabular/dataframe format - name: Variable name - role: Whether the variable is an ID, feature, or target - type: Data type e.g.

categorical, integer, continuous - demographic: Indicates whether the variable represents demographic data - description: Short description of variable - units: Variable units for non-categorical data - missing_values: Whether there are missing values in the variable's column - Examples # Access Data by Name iris_dl <- fetch_ucirepo(name = "iris") # Access original data iris_uci <- iris_dl$data$original # Access features and targets iris_features <- iris_dl$data$features iris_targets <- iris_dl$data$targets # Access Data by ID iris_dl <- fetch_ucirepo(id = 53) List Available Datasets from UCI ML Repository Description Prints a list of datasets that can be imported via the fetch_ucirepo function.

Usage list_available_datasets(filter, search, area) Arguments Value A data frame containing the list of available datasets with columns of: - id: Integer ID for the data set. - name: Name of Dataset - url: Download location of the data set In the event the search fails, the data frame returned will be empty. Examples list_available_datasets(search = "iris") list_available_datasets(area = "social science") list_available_datasets(filter = "python") # Required for now...

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