Package: pointblank 0.12.2.9000

Richard Iannone

pointblank: Data Validation and Organization of Metadata for Local and Remote Tables

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.

Authors:Richard Iannone [aut, cre], Mauricio Vargas [aut], June Choe [aut]

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NEWS

# Install 'pointblank' in R:
install.packages('pointblank', repos = c('https://rstudio.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/rstudio/pointblank/issues

Datasets:

On CRAN:

data-assertionsdata-checkerdata-dictionariesdata-framesdata-inferencedata-managementdata-profilerdata-qualitydata-validationdata-verificationdatabase-tableseasy-to-understandreporting-toolschema-validationtesting-toolsyaml-configuration

10.50 score 870 stars 250 scripts 9.9k downloads 185 exports 88 dependencies

Last updated 29 days agofrom:9effa69e64. Checks:OK: 3 NOTE: 4. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 23 2024
R-4.5-winOKOct 23 2024
R-4.5-linuxOKOct 23 2024
R-4.4-winNOTEOct 23 2024
R-4.4-macNOTEOct 23 2024
R-4.3-winNOTEOct 23 2024
R-4.3-macNOTEOct 23 2024

Exports:%>%action_levelsactivate_stepsaffix_dateaffix_datetimeall_passedbetweencase_whencol_count_matchcol_existscol_is_charactercol_is_datecol_is_factorcol_is_integercol_is_logicalcol_is_numericcol_is_posixcol_schemacol_schema_matchcol_vals_betweencol_vals_decreasingcol_vals_equalcol_vals_exprcol_vals_gtcol_vals_gtecol_vals_in_setcol_vals_increasingcol_vals_ltcol_vals_ltecol_vals_make_setcol_vals_make_subsetcol_vals_not_betweencol_vals_not_equalcol_vals_not_in_setcol_vals_not_nullcol_vals_nullcol_vals_regexcol_vals_within_specconjointlycreate_agentcreate_informantcreate_multiagentcredscreds_anonymouscreds_filecreds_keydb_tbldeactivate_stepsdraft_validationemail_blastemail_createexpect_col_count_matchexpect_col_existsexpect_col_is_characterexpect_col_is_dateexpect_col_is_factorexpect_col_is_integerexpect_col_is_logicalexpect_col_is_numericexpect_col_is_posixexpect_col_schema_matchexpect_col_vals_betweenexpect_col_vals_decreasingexpect_col_vals_equalexpect_col_vals_exprexpect_col_vals_gtexpect_col_vals_gteexpect_col_vals_in_setexpect_col_vals_increasingexpect_col_vals_ltexpect_col_vals_lteexpect_col_vals_make_setexpect_col_vals_make_subsetexpect_col_vals_not_betweenexpect_col_vals_not_equalexpect_col_vals_not_in_setexpect_col_vals_not_nullexpect_col_vals_nullexpect_col_vals_regexexpect_col_vals_within_specexpect_conjointlyexpect_row_count_matchexpect_rows_completeexpect_rows_distinctexpect_seriallyexpect_speciallyexpect_tbl_matchexport_reportexprfile_tblfrom_githubget_agent_reportget_agent_x_listget_data_extractsget_informant_reportget_multiagent_reportget_sundered_dataget_tt_paramhas_columnsincorporateinfo_columnsinfo_columns_from_tblinfo_sectioninfo_snippetinfo_tabularinterrogatelog4r_stepread_disk_multiagentremove_stepsrow_count_matchrows_completerows_distinctscan_dataseriallyset_tblsmall_table_sqlitesnip_highestsnip_listsnip_lowestsnip_statsspeciallystock_msg_bodystock_msg_footerstop_if_notstop_on_failtbl_gettbl_matchtbl_sourcetbl_storetest_col_count_matchtest_col_existstest_col_is_charactertest_col_is_datetest_col_is_factortest_col_is_integertest_col_is_logicaltest_col_is_numerictest_col_is_posixtest_col_schema_matchtest_col_vals_betweentest_col_vals_decreasingtest_col_vals_equaltest_col_vals_exprtest_col_vals_gttest_col_vals_gtetest_col_vals_in_settest_col_vals_increasingtest_col_vals_lttest_col_vals_ltetest_col_vals_make_settest_col_vals_make_subsettest_col_vals_not_betweentest_col_vals_not_equaltest_col_vals_not_in_settest_col_vals_not_nulltest_col_vals_nulltest_col_vals_regextest_col_vals_within_spectest_conjointlytest_row_count_matchtest_rows_completetest_rows_distincttest_seriallytest_speciallytest_tbl_matchtt_string_infott_summary_statstt_tbl_colnamestt_tbl_dimstt_time_shifttt_time_slicevalidate_rmdvarswarn_on_failwrite_testthat_filex_read_diskx_write_diskyaml_agent_interrogateyaml_agent_show_exprsyaml_agent_stringyaml_execyaml_informant_incorporateyaml_read_agentyaml_read_informantyaml_write

Dependencies:askpassbase64encbigDbitopsblastulablobbriobslibcachemcallrclicolorspacecommonmarkcpp11crayoncurlDBIdbplyrdescdiffobjdigestdplyrevaluatefansifarverfastmapfontawesomefsgenericsgetPassgluegtherehighrhtmltoolshtmlwidgetshttrjquerylibjsonlitejuicyjuiceknitrlabelinglifecyclemagrittrmarkdownmemoisemimemunsellopensslpillarpkgbuildpkgconfigpkgloadpraiseprocessxpspurrrR6rappdirsRColorBrewerRcppreactablereactRrematch2rlangrmarkdownrprojrootrstudioapisassscalesstringistringrsystestthattibbletidyrtidyselecttinytexutf8uuidV8vctrsviridisLitewaldowithrxfunxml2yaml

Readme and manuals

Help Manual

Help pageTopics
Set action levels: failure thresholds and functions to invokeaction_levels stop_on_fail warn_on_fail
Activate one or more of an _agent_'s validation stepsactivate_steps
Put the current date into a file nameaffix_date
Put the current datetime into a file nameaffix_datetime
Did all of the validations fully _pass_?all_passed
Does the column count match that of a different table?col_count_match expect_col_count_match test_col_count_match
Do one or more columns actually exist?col_exists expect_col_exists test_col_exists
Do the columns contain character/string data?col_is_character expect_col_is_character test_col_is_character
Do the columns contain R 'Date' objects?col_is_date expect_col_is_date test_col_is_date
Do the columns contain R 'factor' objects?col_is_factor expect_col_is_factor test_col_is_factor
Do the columns contain integer values?col_is_integer expect_col_is_integer test_col_is_integer
Do the columns contain logical values?col_is_logical expect_col_is_logical test_col_is_logical
Do the columns contain numeric values?col_is_numeric expect_col_is_numeric test_col_is_numeric
Do the columns contain 'POSIXct' dates?col_is_posix expect_col_is_posix test_col_is_posix
Generate a table column schema manually or with a reference tablecol_schema
Do columns in the table (and their types) match a predefined schema?col_schema_match expect_col_schema_match test_col_schema_match
Do column data lie between two specified values or data in other columns?col_vals_between expect_col_vals_between test_col_vals_between
Are column data decreasing by row?col_vals_decreasing expect_col_vals_decreasing test_col_vals_decreasing
Are column data equal to a fixed value or data in another column?col_vals_equal expect_col_vals_equal test_col_vals_equal
Do column data agree with a predicate expression?col_vals_expr expect_col_vals_expr test_col_vals_expr
Are column data greater than a fixed value or data in another column?col_vals_gt expect_col_vals_gt test_col_vals_gt
Are column data greater than or equal to a fixed value or data in another column?col_vals_gte expect_col_vals_gte test_col_vals_gte
Are column data part of a specified set of values?col_vals_in_set expect_col_vals_in_set test_col_vals_in_set
Are column data increasing by row?col_vals_increasing expect_col_vals_increasing test_col_vals_increasing
Are column data less than a fixed value or data in another column?col_vals_lt expect_col_vals_lt test_col_vals_lt
Are column data less than or equal to a fixed value or data in another column?col_vals_lte expect_col_vals_lte test_col_vals_lte
Is a set of values entirely accounted for in a column of values?col_vals_make_set expect_col_vals_make_set test_col_vals_make_set
Is a set of values a subset of a column of values?col_vals_make_subset expect_col_vals_make_subset test_col_vals_make_subset
Do column data lie outside of two specified values or data in other columns?col_vals_not_between expect_col_vals_not_between test_col_vals_not_between
Are column data not equal to a fixed value or data in another column?col_vals_not_equal expect_col_vals_not_equal test_col_vals_not_equal
Are data not part of a specified set of values?col_vals_not_in_set expect_col_vals_not_in_set test_col_vals_not_in_set
Are column data not 'NULL'/'NA'?col_vals_not_null expect_col_vals_not_null test_col_vals_not_null
Are column data 'NULL'/'NA'?col_vals_null expect_col_vals_null test_col_vals_null
Do strings in column data match a regex pattern?col_vals_regex expect_col_vals_regex test_col_vals_regex
Do values in column data fit within a specification?col_vals_within_spec expect_col_vals_within_spec test_col_vals_within_spec
Perform multiple rowwise validations for joint validityconjointly expect_conjointly test_conjointly
Create a *pointblank* _agent_ objectcreate_agent
Create a *pointblank* _informant_ objectcreate_informant
Create a *pointblank* _multiagent_ objectcreate_multiagent
Get a table from a databasedb_tbl
Deactivate one or more of an _agent_'s validation stepsdeactivate_steps
Draft a starter *pointblank* validation .R/.Rmd file with a data tabledraft_validation
Conditionally send email during interrogationemail_blast
Create an email object from a *pointblank* _agent_email_create
Export an _agent_, _informant_, _multiagent_, or table scan to HTMLexport_report
Get a table from a local or remote filefile_tbl
Specify a file for download from GitHubfrom_github
A table with game revenue datagame_revenue
A table with metadata for the 'game_revenue' datasetgame_revenue_info
Get a summary report from an agentget_agent_report
Get the agent's *x-list*get_agent_x_list
Collect data extracts from a validation stepget_data_extracts
Get a table information report from an _informant_ objectget_informant_report
Get a summary report using multiple agentsget_multiagent_report
Sunder the data, splitting it into 'pass' and 'fail' piecesget_sundered_data
Get a parameter value from a summary tableget_tt_param
Determine if one or more columns exist in a tablehas_columns
Given an _informant_ object, update and incorporate table snippetsincorporate
Add information that focuses on aspects of a data table's columnsinfo_columns
Add column information from another data tableinfo_columns_from_tbl
Add information that focuses on some key aspect of the data tableinfo_section
Generate a useful text 'snippet' from the target tableinfo_snippet
Add information that focuses on aspects of the data table as a wholeinfo_tabular
Given an agent that has a validation plan, perform an interrogationinterrogate
Enable logging of failure conditions at the validation step levellog4r_step
Read *pointblank* _agents_ stored on disk as a _multiagent_read_disk_multiagent
Remove one or more of an _agent_'s validation stepsremove_steps
Does the row count match that of a different table?expect_row_count_match row_count_match test_row_count_match
Are row data complete?expect_rows_complete rows_complete test_rows_complete
Are row data distinct?expect_rows_distinct rows_distinct test_rows_distinct
Thoroughly scan a table to better understand itscan_data
Run several tests and a final validation in a serial mannerexpect_serially serially test_serially
Set a data table to an _agent_ or an _informant_set_tbl
A small table that is useful for testingsmall_table
An SQLite version of the 'small_table' datasetsmall_table_sqlite
A 'fn' for 'info_snippet()': get the highest value from a columnsnip_highest
A 'fn' for 'info_snippet()': get a list of column categoriessnip_list
A 'fn' for 'info_snippet()': get the lowest value from a columnsnip_lowest
A 'fn' for 'info_snippet()': get an inline statistical summarysnip_stats
Perform a specialized validation with a user-defined functionexpect_specially specially test_specially
A table containing data pertaining to various specificationsspecifications
Provide simple email message body components: bodystock_msg_body
Provide simple email message body components: footerstock_msg_footer
A specialized version of 'stopifnot()' for *pointblank*: 'stop_if_not()'stop_if_not
Obtain a materialized table via a table storetbl_get
Does the target table match a comparison table?expect_tbl_match tbl_match test_tbl_match
Obtain a table-prep formula from a table storetbl_source
Define a store of tables with table-prep formulas: a table storetbl_store
Table Transformer: obtain a summary table for string columnstt_string_info
Table Transformer: obtain a summary stats table for numeric columnstt_summary_stats
Table Transformer: get a table's column namestt_tbl_colnames
Table Transformer: get the dimensions of a tablett_tbl_dims
Table Transformer: shift the times of a tablett_time_shift
Table Transformer: slice a table with a slice point on a time columntt_time_slice
Perform *pointblank* validation testing within R Markdown documentsvalidate_rmd
Transform a *pointblank* agent to a *testthat* test filewrite_testthat_file
Read an _agent_, _informant_, _multiagent_, or table scan from diskx_read_disk
Write an _agent_, _informant_, _multiagent_, or table scan to diskx_write_disk
Get an _agent_ from *pointblank* YAML and 'interrogate()'yaml_agent_interrogate
Display validation expressions using *pointblank* YAMLyaml_agent_show_exprs
Display *pointblank* YAML using an agent or a YAML fileyaml_agent_string
Execute all agent and informant YAML tasksyaml_exec
Get an _informant_ from *pointblank* YAML and 'incorporate()'yaml_informant_incorporate
Read a *pointblank* YAML file to create an _agent_ objectyaml_read_agent
Read a *pointblank* YAML file to create an _informant_ objectyaml_read_informant
Write *pointblank* objects to YAML filesyaml_write