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

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epidm — by Alex Bhattacharya, 2 years ago

UK Epidemiological Data Management

Contains utilities and functions for the cleaning, processing and management of patient level public health data for surveillance and analysis held by the UK Health Security Agency, UKHSA.

squashinformr — by Hayden MacDonald, 3 years ago

Politely Web Scrape Data from SquashInfo

Scrape SquashInfo < http://www.squashinfo.com/> for data on the Professional Squash Association World Tour and other squash events. 'squashinformr' functions scrape, parse, and clean data associated with players, tournaments, and rankings.

matchmaker — by Zhian N. Kamvar, 5 years ago

Flexible Dictionary-Based Cleaning

Provides flexible dictionary-based cleaning that allows users to specify implicit and explicit missing data, regular expressions for both data and columns, and global matches, while respecting ordering of factors. This package is part of the 'RECON' (< https://www.repidemicsconsortium.org/>) toolkit for outbreak analysis.

rdataretriever — by Henry Senyondo, 5 months ago

R Interface to the Data Retriever

Provides an R interface to the Data Retriever < https://retriever.readthedocs.io/en/latest/> via the Data Retriever's command line interface. The Data Retriever automates the tasks of finding, downloading, and cleaning public datasets, and then stores them in a local database.

furniture — by Tyson S. Barrett, a year ago

Furniture for Quantitative Scientists

Contains four main functions (i.e., four pieces of furniture): table1() which produces a well-formatted table of descriptive statistics common as Table 1 in research articles, tableC() which produces a well-formatted table of correlations, tableF() which provides frequency counts, and washer() which is helpful in cleaning up the data. These furniture-themed functions are designed to simplify common tasks in quantitative analysis. Other data summary and cleaning tools are also available.

quadcleanR — by Dominique Maucieri, 2 years ago

Cleanup and Visualization of Quadrat Data

A tool that can be customized to aid in the clean up of ecological data collected using quadrats and can crop quadrats to ensure comparability between quadrats collected under different methodologies.

upstartr — by Tom Cardoso, a year ago

Utilities Powering the Globe and Mail's Data Journalism Template

Core functions necessary for using The Globe and Mail's R data journalism template, 'startr', along with utilities for day-to-day data journalism tasks, such as reading and writing files, producing graphics and cleaning up datasets.

standartox — by Andreas Scharmüller, 2 years ago

Ecotoxicological Information from the Standartox Database

The < http://standartox.uni-landau.de> database offers cleaned, harmonized and aggregated ecotoxicological test data, which can be used for assessing effects and risks of chemical concentrations found in the environment.

fixr — by Ambu Vijayan, 2 years ago

Fixing Data Made Easy for Statistical Analysis

A set of functions that facilitate basic data manipulation and cleaning for statistical analysis including functions for finding and fixing duplicate rows and columns, missing values, outliers, and special characters in column and row names and functions for checking data consistency, distribution, quality, reliability, and structure.

postGGIR — by Wei Guo, 3 years ago

Data Processing after Running 'GGIR' for Accelerometer Data

Generate all necessary R/Rmd/shell files for data processing after running 'GGIR' (v2.4.0) for accelerometer data. In part 1, all csv files in the GGIR output directory were read, transformed and then merged. In part 2, the GGIR output files were checked and summarized in one excel sheet. In part 3, the merged data was cleaned according to the number of valid hours on each night and the number of valid days for each subject. In part 4, the cleaned activity data was imputed by the average Euclidean norm minus one (ENMO) over all the valid days for each subject. Finally, a comprehensive report of data processing was created using Rmarkdown, and the report includes few exploratory plots and multiple commonly used features extracted from minute level actigraphy data.