Methods to reliably use dplyr on remote data sources in R (SQL databases,
Spark 2.0.0 and above) in a generic fashion.
replyr is going into maintenance mode. It has been hard to track
shifting dplyr/dbplyr/rlang APIs and data structures post dplyr 0.5.
Most of what it does is now done better in one of the newer non-monolithic packages:
Programming and meta-programming tools: wrapr https://CRAN.R-project.org/package=wrapr.
Adapting dplyr to standard evaluation interfaces: seplyr https://CRAN.R-project.org/package=seplyr.
Big data data manipulation: rquery https://CRAN.R-project.org/package=rquery and cdata https://CRAN.R-project.org/package=cdata.
replyr helps with the following:
Summarizing remote data (via replyr_summarize).
Facilitating writing "source generic" code that works similarly on multiple 'dplyr' data sources.
Providing big data versions of functions for splitting data, binding rows, pivoting, adding row-ids, ranking, and completing experimental designs.
Packaging common data manipulation tasks into operators such as the gapply function.
Providing support code for common SparklyR tasks, such as tracking temporary handle IDs.
replyr is in maintenance mode. Better version of the functionality have been ported to the following packages:
wrapr, cdata, rquery, and seplyr.
To learn more about replyr, please start with the vignette:
vignette('replyr','replyr')