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pointblank (version 0.12.3)

Data Validation and Organization of Metadata for Local and Remote Tables

Description

Validate data in data frames, 'tibble' objects, 'Spark' 'DataFrames', and database tables. Validation pipelines can be made using easily-readable, consecutive validation steps. Upon execution of the validation plan, several reporting options are available. User-defined thresholds for failure rates allow for the determination of appropriate reporting actions. Many other workflows are available including an information management workflow, where the aim is to record, collect, and generate useful information on data tables.

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install.packages('pointblank')

Monthly Downloads

13,359

Version

0.12.3

License

MIT + file LICENSE

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Maintainer

Richard Iannone

Last Published

November 28th, 2025

Functions in pointblank (0.12.3)

col_exists

Do one or more columns actually exist?
col_count_match

Does the column count match that of a different table?
all_passed

Did all of the validations fully pass?
action_levels

Set action levels: failure thresholds and functions to invoke
activate_steps

Activate one or more of an agent's validation steps
affix_datetime

Put the current datetime into a file name
col_is_date

Do the columns contain R Date objects?
col_is_character

Do the columns contain character/string data?
col_is_factor

Do the columns contain R factor objects?
affix_date

Put the current date into a file name