Turn Clean Data into Messy Data

Take real or simulated data and salt it with errors commonly found in the wild, such as pseudo-OCR errors, Unicode problems, numeric fields with nonsensical punctuation, bad dates, etc.

Tools for Cleaning High-Frequency Real-Time Location Tracking Data

Provides data cleaning and preprocessing tools for high-frequency positional data from real-time location tracking systems (UWB, RFID, and similar technologies), with functions for ID mapping, time period marking, data standardization, and two-phase conditional gap interpolation. See Bilevicius (2026) .

Wrapper Functions Collection Used in Data Pipelines

The goal of this package is to provide wrapper functions in the data cleaning and cleansing processes. These function helps in messages and interaction with the user, keep track of information in pipelines, help in the wrangling, munging, assessment and visualization of data frame-like material.

Helpful Functions for Cleaning Surveillance Data

Helpful functions for the cleaning and manipulation of surveillance data, especially with regards to the creation and validation of panel data from individual level surveillance data.

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.

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.

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.

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.