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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.
Simple Tools for Examining and Cleaning Dirty Data
The main janitor functions can: perfectly format data.frame column names; isolate duplicate records; and provide quick one- and two-variable tabulations (i.e., frequency tables and crosstabs). Other janitor functions nicely format the results of these tabulations. 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.
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 that automatically downloads and aggregates election data from Brazil, directly from the Superior Electoral Court website. Among others, there are data available on local and federal elections for all positions (city councillor, mayor, state deputy, federal deputy, governor, and president) disaggregated by state of the Federation, city, zone, and polling stations. In addition, data on the verification (blank votes, null votes, abstention), candidates' and voters' backgrounds are also available.
A Suite of Checks for Identification of Potential Errors in a Data Frame as Part of the Data Cleaning Process
Data cleaning is an important first step of any statistical analysis. dataMaid provides an extendable suite of test for common potential errors in a dataset. It produces a document with a thorough summary of the checks and the results that a human can use to identify possible errors.
Deductive Correction, Deductive Imputation, and Deterministic Correction
A collection of methods for automated data cleaning where all actions are logged.
Framework for the Analysis of Kinetic Visual Field Data
Data cleaning, processing, visualisation and analysis for manual (Goldmann) and automated (Octopus 900) kinetic visual field data.