Data Validation and Organization of Metadata for Local and Remote Tables
Set action levels: failure thresholds and functions to invoke
Activate one or more of an agent's validation steps
Put the current date into a file name
Put the current datetime into a file name
Did all of the validations fully pass?
Does the column count match that of a different table?
Do one or more columns actually exist?
Do the columns contain character/string data?
Do the columns contain R Date objects?
Do the columns contain R factor objects?
Do the columns contain integer values?
Do the columns contain logical values?
Do the columns contain numeric values?
Do the columns contain POSIXct dates?
Do columns in the table (and their types) match a predefined schema?
Generate a table column schema manually or with a reference table
Do column data lie between two specified values or data in other colum...
Are column data decreasing by row?
Are column data equal to a fixed value or data in another column?
Do column data agree with a predicate expression?
Are column data greater than a fixed value or data in another column?
Are column data greater than or equal to a fixed value or data in anot...
Are column data part of a specified set of values?
Are column data increasing by row?
Are column data less than a fixed value or data in another column?
Are column data less than or equal to a fixed value or data in another...
Is a set of values entirely accounted for in a column of values?
Is a set of values a subset of a column of values?
Do column data lie outside of two specified values or data in other co...
Are column data not equal to a fixed value or data in another column?
Are data not part of a specified set of values?
Are column data not NULL/NA?
Are column data NULL/NA?
Do strings in column data match a regex pattern?
Do values in column data fit within a specification?
Perform multiple rowwise validations for joint validity
Create a pointblank agent object
Create a pointblank informant object
Create a pointblank multiagent object
Get a table from a database
Deactivate one or more of an agent's validation steps
Draft a starter pointblank validation .R/.Rmd file with a data table
Conditionally send email during interrogation
Create an email object from a pointblank agent
Export an agent, informant, multiagent, or table scan to H...
Get a table from a local or remote file
Specify a file for download from GitHub
Get a summary report from an agent
Get the agent's x-list
Collect data extracts from a validation step
Get a table information report from an informant object
Get a summary report using multiple agents
Sunder the data, splitting it into 'pass' and 'fail' pieces
Get a parameter value from a summary table
Determine if one or more columns exist in a table
Given an informant object, update and incorporate table snippets
Add column information from another data table
Add information that focuses on aspects of a data table's columns
Add information that focuses on some key aspect of the data table
Generate a useful text 'snippet' from the target table
Add information that focuses on aspects of the data table as a whole
Given an agent that has a validation plan, perform an interrogation
Enable logging of failure conditions at the validation step level
Pipe operator
Print the action_levels object
Print the ptblank_agent object
Print the ptblank_informant object
Print the ptblank_multiagent_report.long object
Print the ptblank_multiagent object
Print the ptblank_tbl_scan object
Print the a table-prep formula
Print the tbl_store object
Print a single-step x-list to the console
Print an x-list comprising all validation steps to the console
Read pointblank agents stored on disk as a multiagent
Objects exported from other packages
Remove one or more of an agent's validation steps
Does the row count match that of a different table?
Are row data complete?
Are row data distinct?
Thoroughly scan a table to better understand it
Run several tests and a final validation in a serial manner
Set a data table to an agent or an informant
An SQLite version of the small_table dataset
A fn for info_snippet(): get the highest value from a column
A fn for info_snippet(): get a list of column categories
A fn for info_snippet(): get the lowest value from a column
A fn for info_snippet(): get an inline statistical summary
Perform a specialized validation with a user-defined function
Provide simple email message body components: body
Provide simple email message body components: footer
A specialized version of stopifnot() for pointblank : `stop_if_not()...
Obtain a materialized table via a table store
Does the target table match a comparison table?
Obtain a table-prep formula from a table store
Define a store of tables with table-prep formulas: a table store
Table Transformer: obtain a summary table for string columns
Table Transformer: obtain a summary stats table for numeric columns
Table Transformer: get a table's column names
Table Transformer: get the dimensions of a table
Table Transformer: shift the times of a table
Table Transformer: slice a table with a slice point on a time column
Perform pointblank validation testing within R Markdown documents
Transform a pointblank agent to a testthat test file
Read an agent, informant, multiagent, or table scan from d...
Write an agent, informant, multiagent, or table scan to di...
Get an agent from pointblank YAML and interrogate()
Display validation expressions using pointblank YAML
Display pointblank YAML using an agent or a YAML file
Execute all agent and informant YAML tasks
Get an informant from pointblank YAML and incorporate()
Read a pointblank YAML file to create an agent object
Read a pointblank YAML file to create an informant object
Write pointblank objects to YAML files
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.
Useful links