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Cleaning Validation Functions for Pharmaceutical Cleaning Process
Provides essential Cleaning Validation functions for complying with pharmaceutical cleaning process regulatory standards. The package includes non-parametric methods to analyze drug active-ingredient residue (DAR), cleaning agent residue (CAR), and microbial colonies (Mic) for non-Poisson distributions. Additionally, Poisson methods are provided for Mic analysis when Mic data follow a Poisson distribution.
Extract-Transform-Load Framework for Medium Data
A predictable and pipeable framework for performing ETL (extract-transform-load) operations on publicly-accessible medium-sized data set. This package sets up the method structure and implements generic functions. Packages that depend on this package download specific data sets from the Internet, clean them up, and import them into a local or remote relational database management system.
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.
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.
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)
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.