Pulling Clean Data from Covid19india.org

Pull raw and pre-cleaned versions of national and state-level COVID-19 time-series data from covid19india.org < https://www.covid19india.org>. Easily obtain and merge case count data, testing data, and vaccine data. Also assists in calculating the time-varying effective reproduction number with sensible parameters for COVID-19.

Additional Tools for Splitting and Cleaning Data

Move between data frames and lists more efficiently with precision splitting via 'dplyr' verbs. Easily cast variables to different data types. Keep rows with NAs. Shift row values.

Downloading and Cleaning U.S. Macroeconomic Data

Utilities to retrieve and tidy U.S. macroeconomic data series from public government data providers. Functions streamline access to series from the Federal Reserve Bank of St. Louis Federal Reserve Economic Data (FRED), the Bureau of Labor Statistics flat files, and the Bureau of Economic Analysis National Income and Product Accounts tables, then return consistent, tidy data frames ready for modeling and graphics. The package includes helpers for date alignment, log-linear projections, and common macro diagnostics, along with convenience plot builders for quick publication-quality charts.

Automated Cleaning of Fossil Occurrence Data

Functions to automate the detection and resolution of taxonomic and stratigraphic errors in fossil occurrence datasets. Functions were developed using data from the Paleobiology Database.

Create Messy Data from Clean Data Frames

For the purposes of teaching, it is often desirable to show examples of working with messy data and how to clean it. This R package creates messy data from clean, tidy data frames so that students have a clean example to work towards.

Tools for Easily Combining and Cleaning Data Sets

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

An Automated Cleaning Tool for Semantic and Linguistic Data

Implements several functions that automates the cleaning and spell-checking of text data. Also converges, finalizes, removes plurals and continuous strings, and puts text data in binary format for semantic network analysis. Uses the 'SemNetDictionaries' package to make the cleaning process more accurate, efficient, and reproducible.

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', 'iPro 2', 'Diasend', 'Libre', or 'Carelink' 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 in either rows or columns. The column format of this spreadsheet is compatible with REDCap data upload. cgmreport() also iterates through a directory of cleaned data, and produces PDFs of individual and aggregate AGP plots. Please visit < https://github.com/childhealthbiostatscore/R-Packages/> to download the new-user guide.

Automation and Standardization of Cleaning Clinical Laboratory Data

Navigating the shift of clinical laboratory data from primary everyday clinical use to secondary research purposes presents a significant challenge. Given the substantial time and expertise required for lab data pre-processing and cleaning and the lack of all-in-one tools tailored for this need, we developed our algorithm 'lab2clean' as an open-source R-package. 'lab2clean' package is set to automate and standardize the intricate process of cleaning clinical laboratory results. With a keen focus on improving the data quality of laboratory result values and units, our goal is to equip researchers with a straightforward, plug-and-play tool, making it smoother for them to unlock the true potential of clinical laboratory data in clinical research and clinical machine learning (ML) model development. Functions to clean & validate result values (Version 1.0) are described in detail in 'Zayed et al. (2024)' . Functions to standardize & harmonize result units (added in Version 2.0) are described in detail in 'Zayed et al. (2025)' .

Extract and Clean World Football (Soccer) Data

Allow users to obtain clean and tidy football (soccer) game, team and player data. Data is collected from a number of popular sites, including 'FBref', transfer and valuations data from 'Transfermarkt'< https://www.transfermarkt.com/> and shooting location and other match stats data from 'Understat'< https://understat.com/> and 'fotmob'< https://www.fotmob.com/>. It gives users the ability to access data more efficiently, rather than having to export data tables to files before being able to complete their analysis.