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

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exceldata — by Lisa Avery, 2 years ago

Streamline Data Import, Cleaning and Recoding from 'Excel'

A small group of functions to read in a data dictionary and the corresponding data table from 'Excel' and to automate the cleaning, re-coding and creation of simple calculated variables. This package was designed to be a companion to the macro-enabled 'Excel' template available on the GitHub site, but works with any similarly-formatted 'Excel' data.

electionsBR — by Denisson Silva, a year 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 and 'CepespData' websites. Among other features, 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.

IAT — by Dan Martin, 9 years 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.

crimeutils — by Jacob Kaplan, 3 years ago

A Comprehensive Set of Functions to Clean, Analyze, and Present Crime Data

A collection of functions that make it easier to understand crime (or other) data, and assist others in understanding it. The package helps you read data from various sources, clean it, fix column names, and graph the data.

njtr1 — by Gavin Rozzi, 3 years ago

Download, Analyze & Clean New Jersey Car Crash Data

Download and analyze motor vehicle crash data released by the New Jersey Department of Transportation (NJDOT). The data in this package is collected through the filing of NJTR-1 form by police officers, which provide a standardized way of documenting a motor vehicle crash that occurred in New Jersey. 3 different data tables containing data on crashes, vehicles & pedestrians released from 2001 to the present can be downloaded & cleaned using this package.

trustmebro — by Annemarie Pläschke, 5 months ago

Inspect and Clean Subject-Generated ID Codes and Related Data

Makes data wrangling with ID-related aspects more comfortable. Provides functions that make it easy to inspect various subject-generated ID codes (SGIC) for plausibility. Also helps with inspecting other common identifiers, ensuring that your data stays clean and reliable.

messy.cats — by Harrison Karp, 3 years ago

Employs String Distance Tools to Help Clean Categorical Data

Matching with string distance has never been easier! 'messy.cats' contains various functions that employ string distance tools in order to make data management easier for users working with categorical data. Categorical data, especially user inputted categorical data that often tends to be plagued by typos, can be difficult to work with. 'messy.cats' aims to provide functions that make cleaning categorical data simple and easy.

reasonabletools — by Matthew Reusswig, 5 years ago

Clean Water Quality Data for NPDES Reasonable Potential Analyses

Functions for cleaning and summarising water quality data for use in National Pollutant Discharge Elimination Service (NPDES) permit reasonable potential analyses and water quality-based effluent limitation calculations. Procedures are based on those contained in the "Technical Support Document for Water Quality-based Toxics Control", United States Environmental Protection Agency (1991).

chessR — by Jason Zivkovic, 3 years ago

Functions to Extract, Clean and Analyse Online Chess Game Data

A set of functions to enable users to extract chess game data from popular chess sites, including 'Lichess'< https://lichess.org/> and 'Chess.com' < https://www.chess.com/> and then perform analysis on that game data.

clickR — by David Hervas Marin, 10 months ago

Semi-Automatic Preprocessing of Messy Data with Change Tracking for Dataset Cleaning

Tools for assessing data quality, performing exploratory analysis, and semi-automatic preprocessing of messy data with change tracking for integral dataset cleaning.