A big thank you to Databricks for working with us and sharing:
rqdatatable are new
R packages for data wrangling; either at scale (in databases, or big data systems such as Apache Spark), or in-memory. The packages speed up both execution (through optimizations) and development (though a good mental model and up-front error checking) for data wrangling tasks.
Win-Vector LLC‘s John Mount will be speaking on the
rqdatatable packages at the The East Bay R Language Beginners Group Tuesday, August 7, 2018 (Oakland, CA).
In this note we will show how to speed up work in
R by partitioning data and process-level parallelization. We will show the technique with three different
dplyr. The methods shown will also work with base-
R and other packages.
For each of the above packages we speed up work by using
wrapr::execute_parallel which in turn uses
wrapr::partition_tables to partition un-related
data.frame rows and then distributes them to different processors to be executed.
rqdatatable::ex_data_table_parallel conveniently bundles all of these steps together when working with
The partitioning is specified by the user preparing a grouping column that tells the system which sets of rows must be kept together in a correct calculation. We are going to try to demonstrate everything with simple code examples, and minimal discussion.
rquery is an
R package for specifying data transforms using piped Codd-style operators. It has already shown great performance on
rqdatatable is a new package that supplies a screaming fast implementation of the
rquery system in-memory using the
rquery is already one of the fastest and most teachable (due to deliberate conformity to Codd’s influential work) tools to wrangle data on databases and big data systems. And now
rquery is also one of the fastest methods to wrangle data in-memory in
R (thanks to
data.table, via a thin adaption supplied by