Inspect and Clean Subject-Generated ID Codes and Related Data

Makes data wrangling with ID-related aspects more comfortable. Provides functions that make it easy to inspect various subject-generated ID codes (SGIC) for plausibility. Also helps with inspecting other common identifiers, ensuring that your data stays clean and reliable.

Employs String Distance Tools to Help Clean Categorical Data

Matching with string distance has never been easier! 'messy.cats' contains various functions that employ string distance tools in order to make data management easier for users working with categorical data. Categorical data, especially user inputted categorical data that often tends to be plagued by typos, can be difficult to work with. 'messy.cats' aims to provide functions that make cleaning categorical data simple and easy.

Clean Water Quality Data for NPDES Reasonable Potential Analyses

Functions for cleaning and summarising water quality data for use in National Pollutant Discharge Elimination Service (NPDES) permit reasonable potential analyses and water quality-based effluent limitation calculations. Procedures are based on those contained in the "Technical Support Document for Water Quality-based Toxics Control", United States Environmental Protection Agency (1991).

Download, Clean, Classify, Enrich and Export Biodiversity Occurrence Data

Downloads, imports, cleans, classifies, enriches and exports biodiversity occurrence data, with an emphasis on reproducible Global Biodiversity Information Facility (GBIF) < https://api.gbif.org/v1/> workflows. The package supports batch occurrence downloads, taxonomic standardisation, coordinate cleaning, optional spatial thinning, spatial attribution and structured export of processed occurrence records and audit outputs. Terrestrial and freshwater workflows can join records to administrative units, protected areas, freshwater ecoregions, basins, rivers, lakes, reservoirs, wetlands and other contextual spatial overlays. Marine workflows support offshore and coastal records through joins to Marine Regions < https://www.marineregions.org/> style layers, Exclusive Economic Zone (EEZ) units, marine ecoregions, Large Marine Ecosystems and user-supplied marine overlays. The package also supports native-range and invasive-status evidence workflows using the World Register of Marine Species (WoRMS) < https://www.marinespecies.org/>, evidence derived from Standardising and Integrating Alien Species (SInAS) < https://zenodo.org/records/18220953>, and Global Register of Introduced and Invasive Species (GRIIS) < https://griis.org/> style species-country records. These tools are intended for biodiversity, macroecological and invasion-biology analyses where occurrence records need to be processed consistently, transparently and reproducibly.

Functions to Extract, Clean and Analyse Online Chess Game Data

A set of functions to enable users to extract chess game data from popular chess sites, including 'Lichess'< https://lichess.org/> and 'Chess.com' < https://www.chess.com/> and then perform analysis on that game data.

Prepare and Explore Data for Palaeobiological Analyses

Provides functionality to support data preparation and exploration for palaeobiological analyses, improving code reproducibility and accessibility. The wider aim of 'palaeoverse' is to bring the palaeobiological community together to establish agreed standards. The package currently includes functionality for data cleaning, binning (time and space), exploration, summarisation and visualisation. Reference datasets (i.e. Geological Time Scales < https://stratigraphy.org/chart/>) and auxiliary functions are also provided. Details can be found in: Jones et al., (2023) .

Semi-Automatic Preprocessing of Messy Data with Change Tracking for Dataset Cleaning

Tools for assessing data quality, performing exploratory analysis, and semi-automatic preprocessing of messy data with change tracking for integral dataset cleaning.

Cleans Spectrophotometry Data Obtained from the Denovix DS-11 Instrument

Cleans spectrophotometry data obtained from the Denovix instrument. The package also provides an option to normalize the data in order to compare the quality of the samples obtained.

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

Functions to support data cleaning, evaluation, and description, developed for integration with Maelstrom Research software tools. 'madshapR' provides functions primarily to evaluate and manipulate datasets and data dictionaries in preparation for data harmonization with the package 'Rmonize' and to facilitate integration and transfer between RStudio servers and secure Opal environments. 'madshapR' functions can be used independently but are optimized in conjunction with ‘Rmonize’ functions for streamlined and coherent harmonization processing.

Import, Clean and Update Data from the New Zealand Freshwater Fish Database

Access the New Zealand Freshwater Fish Database from R and a few functions to clean the data once in R.