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A User-Friendly Biodiversity Data Cleaning App for the Inexperienced R User
Provides features to manage the complete workflow for biodiversity data cleaning. Uploading data, gathering input from users (in order to adjust cleaning procedures), cleaning data and finally, generating various reports and several versions of the data. Facilitates user-level data cleaning, designed for the inexperienced R user. T Gueta et al (2018)
Tools for Easily Combining and Cleaning Data Sets
Tools for combining and cleaning data sets, particularly with grouped and time series data.
Weighted Correlation Network Analysis
Functions necessary to perform Weighted Correlation Network Analysis on high-dimensional data as originally described in Horvath and Zhang (2005)
An Automated Cleaning Tool for Semantic and Linguistic Data
Implements several functions that automates the cleaning and spell-checking of text data. Also converges, finalizes, removes plurals and continuous strings, and puts text data in binary format for semantic network analysis. Uses the 'SemNetDictionaries' package to make the cleaning process more accurate, efficient, and reproducible.
Clean Data Frames
Provides a friendly interface for modifying data frames with a sequence of piped commands built upon the 'tidyverse' Wickham et al., (2019)
Tools for Cleaning Rectangular Data
A dependency-free collection of simple functions for cleaning rectangular data. This package allows to detect, count and replace values or discard rows/columns using a predicate function. In addition, it provides tools to check conditions and return informative error messages.
Turn Clean Data into Messy Data
Take real or simulated data and salt it with errors commonly found in the wild, such as pseudo-OCR errors, Unicode problems, numeric fields with nonsensical punctuation, bad dates, etc.
Pulling Clean Data from Covid19india.org
Pull raw and pre-cleaned versions of national and state-level COVID-19 time-series data from covid19india.org < https://www.covid19india.org>. Easily obtain and merge case count data, testing data, and vaccine data. Also assists in calculating the time-varying effective reproduction number with sensible parameters for COVID-19.
Additional Tools for Splitting and Cleaning Data
Move between data frames and lists more efficiently with precision splitting via 'dplyr' verbs. Easily cast variables to different data types. Keep rows with NAs. Shift row values.
Automated Cleaning of Fossil Occurrence Data
Functions to automate the detection and resolution of taxonomic and stratigraphic errors in fossil occurrence datasets. Functions were developed using data from the Paleobiology Database.