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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'.
Weighted Correlation Network Analysis
Functions necessary to perform Weighted Correlation Network Analysis on high-dimensional data as originally described in Horvath and Zhang (2005)
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.
Modify Data Using Externally Defined Modification Rules
Data cleaning scripts typically contain a lot of 'if this change that' type of statements. Such statements are typically condensed expert knowledge. With this package, such 'data modifying rules' are taken out of the code and become in stead parameters to the work flow. This allows one to maintain, document, and reason about data modification rules as separate entities.
Basic Pattern Analysis
Run basic pattern analyses on character sets, digits, or combined input containing both characters and numeric digits. Useful for data cleaning and for identifying columns containing multiple or nonstandard formats.
Easily Tidy Gapminder Datasets
A toolset that allows you to easily import and tidy data sheets retrieved from Gapminder data web tools. It will therefore contribute to reduce the time used in data cleaning of Gapminder indicator data sheets as they are very messy.
r Client for OpenRefine API
'OpenRefine' (formerly 'Google Refine') is a popular, open source data cleaning software. This package enables users to programmatically trigger data transfer between R and 'OpenRefine'. Available functionality includes project import, export and deletion.