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.
Version:
0.12.2
Depends:
R (≥ 3.5.0)
Imports:
base64enc (≥ 0.1-3),
blastula (≥ 0.3.3),
cli (≥ 3.6.0),
DBI (≥ 1.1.0),
digest (≥ 0.6.27),
dplyr (≥ 1.0.10),
dbplyr (≥
2.3.0),
fs (≥ 1.6.0),
glue (≥ 1.6.2),
gt (≥ 0.9.0),
htmltools (≥ 0.5.4),
knitr (≥ 1.42),
rlang (≥ 1.0.3),
magrittr,
scales (≥ 1.2.1),
testthat (≥ 3.1.6),
tibble (≥
3.1.8),
tidyr (≥ 1.3.0),
tidyselect (≥ 1.2.0),
yaml (≥
2.3.7)
Suggests:
arrow,
bigrquery,
data.table,
duckdb,
ggforce,
ggplot2,
jsonlite,
log4r,
lubridate,
RSQLite,
RMySQL,
RPostgres,
readr,
rmarkdown,
sparklyr,
dittodb,
odbc
Published:
2024年10月23日
Maintainer:
Richard Iannone <rich at posit.co>
NeedsCompilation:
no
Documentation:
Downloads:
Reverse dependencies:
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