Extensions of 'DTwrappers'

Offers functionality which provides methods for data analyses and cleaning that can be flexibly applied across multiple variables and in groups. These include cleaning accidental text, contingent calculations, counting missing data, and building summarizations of the data.

Testbench for Univariate Time Series Cleaning

A reliable and efficient tool for cleaning univariate time series data. It implements reliable and efficient procedures for automating the process of cleaning univariate time series data. The package provides integration with already developed and deployed tools for missing value imputation and outlier detection. It also provides a way of visualizing large time-series data in different resolutions.

County-Level Estimates of Fertilizer Application in USA

Compiled and cleaned the county-level estimates of fertilizer, nitrogen and phosphorus, from 1945 to 2012 in United States of America (USA). The commercial fertilizer data were originally generated by USGS based on the sales data of commercial fertilizer. The manure data were estimated based on county-level population data of livestock, poultry, and other animals. See the user manual for detailed data sources and cleaning methods. 'usfertilizer' utilized the tidyverse to clean the original data and provide user-friendly dataframe. Please note that USGS does not endorse this package. Also data from 1986 is not available for now.

Data Validation Infrastructure

Declare data validation rules and data quality indicators; confront data with them and analyze or visualize the results. The package supports rules that are per-field, in-record, cross-record or cross-dataset. Rules can be automatically analyzed for rule type and connectivity. Supports checks implied by an SDMX DSD file as well. See also Van der Loo and De Jonge (2018) , Chapter 6 and the JSS paper (2021) .

A Tidy Solution for Epidemiological Data

Offers a tidy solution for epidemiological data. It houses a range of functions for epidemiologists and public health data wizards for data management and cleaning.

Cases of COVID-19 in France

Imports and cleans 'opencovid19-fr' < https://github.com/opencovid19-fr/data> data on COVID-19 in France.

Flexibly Reshape Data: A Reboot of the Reshape Package

Flexibly restructure and aggregate data using just two functions: melt and 'dcast' (or 'acast').

Dams in the United States from the National Inventory of Dams (NID)

The single largest source of dams in the United States is the National Inventory of Dams (NID) < http://nid.usace.army.mil> from the US Army Corps of Engineers. Entire data from the NID cannot be obtained all at once and NID's website limits extraction of more than a couple of thousand records at a time. Moreover, selected data from the NID's user interface cannot not be saved to a file. In order to make the analysis of this data easier, all the data from NID was extracted manually. Subsequently, the raw data was checked for potential errors and cleaned. This package provides sample cleaned data from the NID and provides functionality to access the entire cleaned NID data.

Infrastructure for Running, Cycling and Swimming Data from GPS-Enabled Tracking Devices

Provides infrastructure for handling running, cycling and swimming data from GPS-enabled tracking devices within R. The package provides methods to extract, clean and organise workout and competition data into session-based and unit-aware data objects of class 'trackeRdata' (S3 class). The information can then be visualised, summarised, and analysed through flexible and extensible methods. Frick and Kosmidis (2017) , which is updated and maintained as one of the vignettes, provides detailed descriptions of the package and its methods, and real-data demonstrations of the package functionality.

Fossil Analysis

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