Modify Data Using Externally Defined Modification Rules

Data cleaning scripts typically contain a lot of 'if this change that' type of statements. Such statements are typically condensed expert knowledge. With this package, such 'data modifying rules' are taken out of the code and become in stead parameters to the work flow. This allows one to maintain, document, and reason about data modification rules as separate entities.

Outliers Detection

Provides functions for detecting outliers in datasets using statistical methods. The package supports identification of anomalous observations in numerical data and is intended for use in data cleaning, exploratory data analysis, and preprocessing workflows.

Basic Pattern Analysis

Run basic pattern analyses on character sets, digits, or combined input containing both characters and numeric digits. Useful for data cleaning and for identifying columns containing multiple or nonstandard formats.

Easily Tidy Gapminder Datasets

A toolset that allows you to easily import and tidy data sheets retrieved from Gapminder data web tools. It will therefore contribute to reduce the time used in data cleaning of Gapminder indicator data sheets as they are very messy.

r Client for OpenRefine API

'OpenRefine' (formerly 'Google Refine') is a popular, open source data cleaning software. This package enables users to programmatically trigger data transfer between R and 'OpenRefine'. Available functionality includes project import, export and deletion.

Tidy Consultant Universe

Loads the 5 packages in the Tidy Consultant Universe. This collection of packages is useful for anyone doing data science, data analysis, or quantitative consulting. The functions in these packages range from data cleaning, data validation, data binning, statistical modeling, and file exporting.

Precision Agriculture Data Analysis

Precision agriculture spatial data depuration and homogeneous zones (management zone) delineation. The package includes functions that performs protocols for data cleaning management zone delineation and zone comparison; protocols are described in Paccioretti et al., (2020) .

Simple Data Frames

Provides a 'tbl_df' class (the 'tibble') with stricter checking and better formatting than the traditional data frame.

Tidy Messy Data

Tools to help to create tidy data, where each column is a variable, each row is an observation, and each cell contains a single value. 'tidyr' contains tools for changing the shape (pivoting) and hierarchy (nesting and 'unnesting') of a dataset, turning deeply nested lists into rectangular data frames ('rectangling'), and extracting values out of string columns. It also includes tools for working with missing values (both implicit and explicit).

Interface to the World Database on Protected Areas

Fetch and clean data from the World Database on Protected Areas (WDPA) and the World Database on Other Effective Area-Based Conservation Measures (WDOECM). Data is obtained from Protected Planet < https://www.protectedplanet.net/en>. To augment data cleaning procedures, users can install the 'prepr' R package (available at < https://github.com/prioritizr/prepr>). For more information on this package, see Hanson (2022) .