Examples: visualization, C++, networks, data cleaning, html widgets, ropensci.

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DataClean — by Xiaorui(Jeremy) Zhu, a year ago

Data Cleaning

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.

DataCombine — by Christopher Gandrud, a year ago

Tools for Easily Combining and Cleaning Data Sets

Tools for combining and cleaning data sets, particularly with grouped and time series data.

editrules — by Edwin de Jonge, 2 years ago

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.

janitor — by Sam Firke, 5 months ago

Simple Tools for Examining and Cleaning Dirty Data

The main janitor functions can: perfectly format data.frame column names; provide quick one- and two-variable tabulations (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.

IAT — by Dan Martin, a year ago

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.

electionsBR — by Denisson Silva, 4 months ago

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.

dataMaid — by Claus Thorn Ekstrøm, 9 months ago

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.

deducorrect — by Mark van der Loo, 2 years ago

Deductive Correction, Deductive Imputation, and Deterministic Correction

A collection of methods for automated data cleaning where all actions are logged.

kineticF — by Dipesh E Patel, 2 years ago

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.

velociraptr — by Andrew A Zaffos, 7 months ago

Fossil Analysis

Functions for downloading, reshaping, culling, cleaning, and analyzing fossil data from the Paleobiology Database < https://paleobiodb.org>.