High-Performance Open-Source Archive
Functionality to create perturbed table using BigQuery including validation process to be able to work with large datasets in a Google Cloud Platform environment.
Generate record keys from ons_id as default where ons_id exists, with an option to turn it off.
Created functions to generate sample microdata, ptable_10_5, and random record keys for testing purposes, with alternative parameters allowed. The sample microdata included in this package is identical to the sample in the python version (cell-key-perturbation). However, a new sample of microdata created in R with the same parameters would not be identical to the sample in python, due to differences in Random Number Generation.
Validation moved to a separate module with improved and additional validation checks.
Tabulation with missing values: Missing values will be included in the frequency table, treating missingness as a category. Added a check function for missing values in tabulation variables, which returns a warning message if any of the tabulation variables contains missing values and suggests to consider removing them.
Re-ordered columns and standardised the data type for output tables.
New threshold parameter added (with default of 10) for the create_perturbed_table function. Counts < threshold will be set to missing. This means that the user will not need to go through an additional process to suppress small counts after applying perturbation.
Warnings if any records are missing record keys and exception raised if percentage with record keys < 50%.
The ‘record_key_arg’ parameter renamed to ‘record_key’ to match the Python implementation.
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