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pointblank

Validate data in data frames, 'tibble' objects, 'Spark' 'DataFrames', and database tables (e.g., 'PostgreSQL' and 'MySQL'). 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

install.packages('pointblank')

Monthly Downloads

8,478

Version

0.6.0

License

MIT + file LICENSE

Issues

23

Pull Requests

5

Stars

242

Forks

16

Maintainer

Richard Iannone

Last Published

November 20th, 2020

Functions in pointblank (0.6.0)

col_is_logical

Do the columns contain logical values?
col_is_date

Do the columns contain R Date objects?
all_passed

Did all of the validations fully pass?
col_is_integer

Do the columns contain integer values?
action_levels

Set action levels: failure thresholds and functions to invoke
col_is_numeric

Do the columns contain numeric values?
col_exists

Do one or more columns actually exist?
col_is_posix

Do the columns contain POSIXct dates?
col_is_factor

Do the columns contain R factor objects?
col_is_character

Do the columns contain character/string data?
col_schema

Generate a table column schema manually or with a reference table
col_vals_equal

Are column data equal to a specified value?
col_vals_between

Are column data between two specified values?
col_vals_gte

Are column data greater than or equal to a specified value?
col_vals_in_set

Are column data part of a specified set of values?
col_vals_gt

Are column data greater than a specified value?
col_vals_expr

Do column data agree with a predicate expression?
col_vals_lt

Are column data less than a specified value?
col_vals_lte

Are column data less than or equal to a specified value?
col_schema_match

Do columns in the table (and their types) match a predefined schema?
create_informant

Create a pointblank informant object
db_tbl

Get a table from a database
col_vals_not_null

Are column data not NULL/NA?
get_data_extracts

Collect data extracts from a validation step
col_vals_not_in_set

Are data not part of a specified set of values?
get_informant_report

Get a table information report from an informant object
col_vals_null

Are column data NULL/NA?
info_snippet

Generate a useful text 'snippet' from the target table
%>%

Pipe operator
info_section

Add information that focuses on some key aspect of the data table
log4r_step

Enable logging of failure conditions at the validation step level
info_columns

Add information that focuses on aspects of a data table's columns
interrogate

Given an agent that has a validation plan, perform an interrogation
col_vals_not_between

Are column data not between two specified values?
incorporate

Given an informant object, update and incorporate table snippets
col_vals_regex

Do strings in column data match a regex pattern?
col_vals_not_equal

Are column data not equal to a specified value?
get_sundered_data

Sunder the data, splitting it into 'pass' and 'fail' pieces
remove_read_fn

Remove a table-reading function associated with an agent or informant
rows_distinct

Are row data distinct?
get_agent_report

Get a summary report from an agent
conjointly

Perform multiple rowwise validations for joint validity
set_read_fn

Set a table-reading function to an agent or informant
info_tabular

Add information that focuses on aspects of the data table as a whole
set_tbl

Set a data table to an agent or informant
small_table

A small table that is useful for testing
email_blast

Send email at a validation step or at the end of an interrogation
email_create

Create an email object from a pointblank agent
create_agent

Create a pointblank agent object
get_agent_x_list

Get the agent's x-list
print.examination_page

Print the reporting produced by scan_data()
print.ptblank_agent

Print the agent information to the console
yaml_informant_incorporate

Get an informant from pointblank YAML and incorporate()
remove_tbl

Remove a data table associated with an agent or informant
print.action_levels

Print the action_levels object
print.x_list_n

Print an x-list comprising all validation steps to the console
stop_if_not

The next generation of stopifnot()-type functions: stop_if_not()
yaml_agent_show_exprs

Display validation expressions using pointblank YAML
yaml_agent_string

Display pointblank YAML using an agent or a YAML file
stock_msg_footer

Provide simple email message body components: footer
snip_highest

A fn for info_snippet(): get the highest value from a column
snip_list

A fn for info_snippet(): get a list of column categories
scan_data

Thoroughly scan a table to better understand it
reexports

Objects exported from other packages
yaml_read_agent

Read a pointblank YAML file to create an agent object
small_table_sqlite

A SQLite version of the small_table dataset
snip_lowest

A fn for info_snippet(): get the lowest value from a column
yaml_read_informant

Read a pointblank YAML file to create an informant object
x_write_disk

Write a pointblank agent or informant to disk
yaml_agent_interrogate

Get an agent from pointblank YAML and interrogate()
yaml_write

Write an agent and informant to a pointblank YAML file
print.x_list_i

Print a single-step x-list to the console
x_read_disk

Read a pointblank agent or informant from disk
validate_rmd

Modify pointblank validation testing options within R Markdown documents
stock_msg_body

Provide simple email message body components: body
print.ptblank_informant

Print the table information report