rquery: Relational Query Generator for Data Manipulation at Scale

A piped query generator based on Edgar F. Codd's relational algebra, and on production experience using 'SQL' and 'dplyr' at big data scale. The design represents an attempt to make 'SQL' more teachable by denoting composition by a sequential pipeline notation instead of nested queries or functions. The implementation delivers reliable high performance data processing on large data systems such as 'Spark', databases, and 'data.table'. Package features include: data processing trees or pipelines as observable objects (able to report both columns produced and columns used), optimized 'SQL' generation as an explicit user visible modeling step, explicit query reasoning and checking.

Version: 1.1.0
Depends: R (≥ 3.4.0)
Imports: wrapr (≥ 1.6.2), utils, stats
Suggests: DBI, RSQLite, igraph, DiagrammeR, DiagrammeRsvg, rsvg, knitr, rmarkdown, testthat
Published: 2018-09-20
Author: John Mount [aut, cre], Win-Vector LLC [cph]
Maintainer: John Mount <jmount at win-vector.com>
License: GPL-3
URL: https://github.com/WinVector/rquery/, https://winvector.github.io/rquery/
NeedsCompilation: no
Materials: README NEWS
CRAN checks: rquery results

Downloads:

Reference manual: rquery.pdf
Vignettes: Assignment Partitioner
Pipeable SQL
Query Generation
rquery Introduction
Package source: rquery_1.1.0.tar.gz
Windows binaries: r-devel: rquery_1.1.0.zip, r-release: rquery_1.1.0.zip, r-oldrel: rquery_1.1.0.zip
OS X binaries: r-release: rquery_1.1.0.tgz, r-oldrel: rquery_1.1.0.tgz
Old sources: rquery archive

Reverse dependencies:

Reverse depends: rqdatatable
Reverse imports: cdata
Reverse suggests: vtreat

Linking:

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