Package 'ucimlrepo'

Title: 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/>.
Authors: James Joseph Balamuta [aut, cre, cph] , Philip Truong [aut, cph]
Maintainer: James Joseph Balamuta <[email protected]>
License: MIT + file LICENSE
Version: 0.0.2
Built: 2024-11-18 03:23:10 UTC
Source: https://github.com/coatless-rpkg/ucimlrepo

Help Index


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

name

Character. Dataset name, or substring of name.

id

Integer. Dataset ID for UCI ML Repository.

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

filter

Character. Optional query to filter available datasets based on a label.

search

Character. Optional query to search for available datasets by name.

area

Character. Optional query to filter available datasets based on subject area.

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...