Learn R Programming

⚠️There's a newer version (0.12.1) of this package.Take me there.

pointblank

Validate data in data frames, 'tibble' objects, and in 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.

Copy Link

Version

Install

install.packages('pointblank')

Monthly Downloads

5,369

Version

0.5.2

License

MIT + file LICENSE

Issues

Pull Requests

Stars

Forks

Maintainer

Richard Iannone

Last Published

August 28th, 2020

Functions in pointblank (0.5.2)

agent_yaml_show_exprs

Display pointblank expressions using a YAML file with a validation plan
agent_read

Read an agent from disk
agent_yaml_write

Write an agent's validation plan to a YAML file
agent_yaml_read

Read a YAML file to create a new agent with a validation plan
action_levels

Set action levels: failure thresholds and functions to invoke
agent_yaml_string

Display pointblank YAML using an agent or a YAML file
agent_write

Write an agent to disk
agent_yaml_interrogate

Read a YAML file to interrogate a target table immediately
all_passed

Did all of the validations fully pass?
col_exists

Do one or more columns actually exist?
col_is_numeric

Do the columns contain numeric values?
col_is_date

Do the columns contain R Date objects?
col_schema

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

Do the columns contain character/string data?
col_is_factor

Do the columns contain R factor objects?
col_is_posix

Do the columns contain POSIXct dates?
col_is_logical

Do the columns contain logical values?
col_vals_between

Are column data between two specified values?
col_schema_match

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

Do the columns contain integer values?
col_vals_expr

Do column data agree with a predicate expression?
col_vals_lte

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

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

Are column data equal to a specified value?
col_vals_gt

Are column data greater than a specified value?
col_vals_not_equal

Are column data not equal to a specified value?
col_vals_not_in_set

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

Are column data not between two specified values?
col_vals_gte

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

Get a preview of an email before actually sending that email
get_agent_x_list

Get the agent's x-list
print.x_list_n

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

Collect data extracts from a validation step
reexports

Objects exported from other packages
get_sundered_data

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

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

Do strings in column data match a regex pattern?
col_vals_lt

Are column data less than a specified value?
col_vals_not_null

Are column data not NULL/NA?
col_vals_null

Are column data NULL/NA?
get_agent_report

Get a summary report from an agent
small_table_sqlite

A SQLite version of the small_table dataset
stock_msg_footer

Provide simple email message body components: footer
create_agent

Create a pointblank agent object
email_blast

Send email at a step or at the end of an interrogation
print.ptblank_agent

Print the agent information to the console
set_tbl

Set a data table to an agent
print.x_list_i

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

Provide simple email message body components: body
small_table

A small table that is useful for testing
scan_data

Thoroughly scan the table data so as to understand it better
set_read_fn

Set a table-reading function to an agent
stop_if_not

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

Modify pointblank validation testing options within R Markdown documents
print.examination_page

rows_distinct

Are row data distinct?
rows_not_duplicated

Verify that row data are not duplicated (deprecated)
remove_tbl

Remove a data table associated with an agent
%>%

Pipe operator
conjointly

Perform multiple rowwise validations for joint validity
remove_read_fn

Remove a table-reading function associated with an agent