A Magical Framework for Collaborative & Reproducible Data Analysis

A comprehensive data analysis framework for NIH-funded research that streamlines workflows for both data cleaning and preparing NIH Data Archive ('NDA') submission templates. Provides unified access to multiple data sources ('REDCap', 'MongoDB', 'Qualtrics') through interfaces to their APIs, with specialized functions for data cleaning, filtering, merging, and parsing. Features automatic validation, field harmonization, and memory-aware processing to enhance reproducibility in multi-site collaborative research as described in Mittal et al. (2021) .

Miscellaneous Functions for the Analysis of Educational Assessments

Miscellaneous functions for data cleaning and data analysis of educational assessments. Includes functions for descriptive analyses, character vector manipulations and weighted statistics. Mainly a lightweight dependency for the packages 'eatRep', 'eatGADS', 'eatPrep' and 'eatModel' (which will be subsequently submitted to 'CRAN'). The function for defining (weighted) contrasts in weighted effect coding refers to te Grotenhuis et al. (2017) . Functions for weighted statistics refer to Wolter (2007) .

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.

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.

Create Elegant Data Visualisations Using the Grammar of Graphics

A system for 'declaratively' creating graphics, based on "The Grammar of Graphics". You provide the data, tell 'ggplot2' how to map variables to aesthetics, what graphical primitives to use, and it takes care of the details.

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) .

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) .