Functions to Support Data Management and Processing Using the Maelstrom Research Approach

Functions to support data cleaning, evaluation, and description, developed for integration with Maelstrom Research software tools. 'madshapR' provides functions primarily to evaluate and manipulate datasets and data dictionaries in preparation for data harmonization with the package 'Rmonize' and to facilitate integration and transfer between RStudio servers and secure Opal environments. 'madshapR' functions can be used independently but are optimized in conjunction with ‘Rmonize’ functions for streamlined and coherent harmonization processing.

Import, Clean and Update Data from the New Zealand Freshwater Fish Database

Access the New Zealand Freshwater Fish Database from R and a few functions to clean the data once in R.

Prepare and Explore Data for Palaeobiological Analyses

Provides functionality to support data preparation and exploration for palaeobiological analyses, improving code reproducibility and accessibility. The wider aim of 'palaeoverse' is to bring the palaeobiological community together to establish agreed standards. The package currently includes functionality for data cleaning, binning (time and space), exploration, summarisation and visualisation. Reference datasets (i.e. Geological Time Scales < https://stratigraphy.org/chart>) and auxiliary functions are also provided. Details can be found in: Jones et al., (2023) .

Deductive Correction, Deductive Imputation, and Deterministic Correction

A collection of methods for automated data cleaning where all actions are logged.

United States Copyright Office Product Management Division SR Audit Data Dataset Cleaning Algorithms

Intended to be used by the United States Copyright Office Product Management Division Business Analysts. Include algorithms for the United States Copyright Office Product Management Division SR Audit Data dataset. The algorithm takes in the SR Audit Data excel file and reformat the spreadsheet such that the values and variables fit the format of the online database. Support functions in this package include clean_str(), which cleans instances of variable AUDIT_LOG; clean_data_to_excel(), which cleans and output the reorganized SR Audit Data dataset in excel format; clean_data_to_dataframe(), which cleans and stores the reorganized SR Audit Data data set to a data frame; format_from_excel(), which reads in the outputted excel file from the clean_data_to_excel() function and formats and returns the data as a dictionary that uses FIELD types as keys and NON-FIELD types as the values of those keys. format_from_dataframe(), which reads in the outputted data frame from the clean_data_to_dataframe() function and formats and returns the data as a dictionary that uses FIELD types as keys and NON-FIELD types as the values of those keys; support_function(), which takes in the dictionary outputted either from the format_from_dataframe() or format_from_excel() function and returns the data as a formatted data frame according to the original U.S. Copyright Office SR Audit Data online database. The main function of this package is clean_format_all(), which takes in an excel file and returns the formatted data into a new excel and text file according to the format from the U.S. Copyright Office SR Audit Data online database.

A Grammar of Data Manipulation

A fast, consistent tool for working with data frame like objects, both in memory and out of memory.

Exploratory Data Analysis for FishNet2 Data

Provides data processing and summarization of data from FishNet2.net in text and graphical outputs. Allows efficient filtering of information and data cleaning.

Modifying Rules on a DataBase

Apply modification rules from R package 'dcmodify' to the database, prescribing and documenting deterministic data cleaning steps on records in a database. The rules are translated into SQL statements using R package 'dbplyr'.

Clinical Publication

Accelerate the process from clinical data to medical publication, including clinical data cleaning, significant result screening, and the generation of publish-ready tables and figures.

Language Mapping and Geospatial Analysis of Linguistic and Cultural Data

Streamlined workflows for geolinguistic analysis, including: accessing global linguistic and cultural databases, data import, data entry, data cleaning, data exploration, mapping, visualization and export.