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>.

Manage Tick-by-Tick Transaction Data

Manager of tick-by-tick transaction data that performs 'cleaning', 'aggregation' and 'import' in an efficient and fast way. The package engine, written in C++, exploits the 'zlib' and 'gzstream' libraries to handle gzipped data without need to uncompress them. 'Cleaning' and 'aggregation' are performed according to Brownlees and Gallo (2006) . Currently, TAQMNGR processes raw data from WRDS (Wharton Research Data Service, < https://wrds-web.wharton.upenn.edu/wrds/>).

Geographic and Taxonomic Occurrence R-Based Scrubbing

Streamlines downloading and cleaning biodiversity data from Integrated Digitized Biocollections (iDigBio) and the Global Biodiversity Information Facility (GBIF).

Public COVID-19 Data for Norway

Publicly available COVID-19 data for Norway cleaned and merged into one dataset, including PCR confirmed cases, tests, hospitalisation and vaccination.

Connection wrapper to SQLite databases

sqliter helps users, mainly data munging practioneers, to organize their sql calls in a clean structure. It simplifies the process of extracting and transforming data into useful formats.

'pguIMP'

Reproducible cleaning of bio-medical laboratory data using methods of visualization,error correction and transformation implemented as interactive R-notebooks.

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.

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.