rqdatatable — Wrangling Lots Of Data, Fast

Kevin Feasel



John Mount explains the motivation behind rqdatatable and puts together a performance test:

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 rqdatatable).

Teaching rquery and fully benchmarking it is a big task, so in this note we will limit ourselves to a single example and benchmark. Our intent is to use this example to promote rquery and rqdatatable, but frankly the biggest result of the benchmarking is how far out of the pack data.tableitself stands at small through large problem sizes. This is already known, but it is a much larger difference and at more scales than the typical non-data.table user may be aware of.

Click through for the benchmark and information on how to grab the package before it goes into CRAN.

Related Posts

Interactive ggplot Plots with plotly

Laura Ellis takes us through ggplotly: As someone very interested in storytelling, ggplot2 is easily my data visualization tool of choice. It is like the Swiss army knife for data visualization. One of my favorite features is the ability to pack a graph chock-full of dimensions. This ability is incredibly handy during the data exploration […]

Read More

Goodbye, gather and spread; Hello pivot_long and pivot_wide

John Mount covers a change in tidyr which mimics Mount and Nina Zumel’s pivot_to_rowrecs and unpivot_to_blocks functions in the cdata package: If you want to work in the above way we suggest giving our cdatapackage a try. We named the functions pivot_to_rowrecs and unpivot_to_blocks. The idea was: by emphasizing the record structure one might eventually internalize what the transforms […]

Read More


June 2018
« May Jul »