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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 isolate 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. Advanced R users can already do everything covered here, but with janitor they can do it faster and save their thinking for the fun stuff.
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.
Tools for Easily Combining and Cleaning Data Sets
Tools for combining and cleaning data sets, particularly with grouped and time series data.
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.
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.
Cleaning and Visualizing Implicit Association Test (IAT) Data
Implements the standard D-Scoring algorithm (Greenwald, Banaji, & Nosek, 2003) for Implicit Association Test (IAT) data and includes plotting capabilities for exploring raw IAT data.
R Functions to Download and Clean Brazilian Electoral Data
Offers a set of functions to easily download and clean Brazilian electoral data from the Superior Electoral Court website. Among others, the package retrieves data on local and federal elections for all positions (city councilor, mayor, state deputy, federal deputy, governor, and president) aggregated by state, city, and electoral zones.
An Automated Cleaning Tool for Semantic and Linguistic Data
Implements several functions that automatize the cleaning, removal of plurals and continuous strings, making the data binary, converging, and finalizing of linguistic data for semantic network analysis.
Clean and Analyze Continuous Glucose Monitor Data
This code provides several different functions for cleaning and analyzing continuous glucose monitor data. Currently it works with 'Dexcom' (< https://www.dexcom.com>), 'iPro 2' (< http://professional.medtronicdiabetes.com/ipro2-professional-cgm>), Diasend (< https://diasend.com//us>), Libre (< https://www.freestylelibre.us/>) or Carelink (< https://www.medtronicdiabetes.com/products/carelink-personal-diabetes-software>) data. The cleandata() function takes a directory of CGM data files and prepares them for analysis. cgmvariables() iterates through a directory of cleaned CGM data files and produces a single spreadsheet with data for each file. This spreadsheet is compatible with REDCap data upload ("--1" is added to each subject ID automatically for double data entry). cgmreport() also iterates through a directory of cleaned data, and produces PDFs of individual and aggregate AGP plots.
Weighted Correlation Network Analysis
Functions necessary to perform Weighted Correlation Network Analysis on high-dimensional data as originally described in Horvath and Zhang (2005)