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Simple Tools for Examining and Cleaning Dirty Data
The main janitor functions can: perfectly format data.frame column names; provide quick counts of variable combinations (i.e., frequency tables and crosstabs); and explore duplicate records. Other janitor functions nicely format the tabulation results. These tabulate-and-report functions approximate popular features of SPSS and Microsoft Excel. This package follows the principles of the "tidyverse" and works well with the pipe function %>%. janitor was built with beginning-to-intermediate R users in mind and is optimized for user-friendliness.
Fast and Easy Data Cleaning
Data cleaning functions for classes logical, factor, numeric, character, currency and Date to make data cleaning fast and easy. Relying on very few dependencies, it provides smart guessing, but with user options to override anything if needed.
Includes functions that researchers or practitioners may use to clean raw data, transferring html, xlsx, txt data file into other formats. And it also can be used to manipulate text variables, extract numeric variables from text variables and other variable cleaning processes. It is originated from a author's project which focuses on creative performance in online education environment. The resulting paper of that study will be published soon.
Parsing, Applying, and Manipulating Data Cleaning Rules
Facilitates reading and manipulating (multivariate) data restrictions (edit rules) on numerical and categorical data. Rules can be defined with common R syntax and parsed to an internal (matrix-like format). Rules can be manipulated with variable elimination and value substitution methods, allowing for feasibility checks and more. Data can be tested against the rules and erroneous fields can be found based on Fellegi and Holt's generalized principle. Rules dependencies can be visualized with using the 'igraph' package.
Biodiversity Data Cleaning
It brings together several aspects of biodiversity
data-cleaning in one place. 'bdc' is organized in thematic modules
related to different biodiversity dimensions, including 1) Merge
datasets: standardization and integration of different datasets; 2)
Pre-filter: flagging and removal of invalid or non-interpretable
information, followed by data amendments; 3) Taxonomy: cleaning,
parsing, and harmonization of scientific names from several taxonomic
groups against taxonomic databases locally stored through the
application of exact and partial matching algorithms; 4) Space:
flagging of erroneous, suspect, and low-precision geographic
coordinates; and 5) Time: flagging and, whenever possible, correction
of inconsistent collection date. In addition, it contains
features to visualize, document, and report data quality – which is
essential for making data quality assessment transparent and
reproducible. The reference for the methodology is Bruno et al. (2022)
Occurrence Data Cleaning
Flags and checks occurrence data that are in Darwin Core
format. The package includes generic functions and data as well as
some that are specific to bees. This package is meant to build upon
and be complimentary to other excellent occurrence cleaning packages,
including 'bdc' and 'CoordinateCleaner'. This package uses datasets
from several sources and particularly from the Discover Life Website,
created by Ascher and Pickering (2020). For further information,
please see the original publication and package website. Publication
- Dorey et al. (2023)
Fast and Easy Data Cleaning
A wrapper around the new 'cleaner' package, that allows data cleaning functions for classes 'logical', 'factor', 'numeric', 'character', 'currency' and 'Date' to make data cleaning fast and easy. Relying on very few dependencies, it provides smart guessing, but with user options to override anything if needed.
Data Cleaning for Psychological Analyses
Useful for preparing and cleaning data. It includes functions to center data, reverse coding, dummy code and effect code data, and more.
Interactive and Reproducible Data Cleaning
Flexible and efficient cleaning of data with interactivity. 'datacleanr' facilitates best practices in data analyses and reproducibility with built-in features and by translating interactive/manual operations to code. The package is designed for interoperability, and so seamlessly fits into reproducible analyses pipelines in 'R'.
Scrubbing and Other Data Cleaning Routines for fMRI
Data-driven fMRI denoising with projection scrubbing (Pham et al