Posted on Categories data science, Opinion, Pragmatic Data Science, Programming, StatisticsTags , , , 3 Comments on Data Manipulation Corner Cases

Data Manipulation Corner Cases

Let’s try some "ugly corner cases" for data manipulation in R. Corner cases are examples where the user might be running to the edge of where the package developer intended their package to work, and thus often where things can go wrong.

Let’s see what happens when we try to stick a fork in the power-outlet.

Fork

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Posted on Categories data science, Programming, TutorialsTags , , , 1 Comment on rquery Substitution

rquery Substitution

The rquery R package has several places where the user can ask for what they have typed in to be substituted for a name or value stored in a variable.

This becomes important as many of the rquery commands capture column names from un-executed code. So knowing if something is treated as a symbol/name (which will be translated to a data.frame column name or a database column name) or a character/string (which will be translated to a constant) is important.

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Posted on Categories Administrativia, Programming, StatisticsTags , Leave a comment on Binning Data in a Database

Binning Data in a Database

Roz King just wrote an interesting article on binning data (a common data analytics step) in a database. They compare a case-based approach (where the bin divisions are stuffed into code) with a join based approach. They share code and timings.

Best of all: rquery gets some attention and turns out to be the dominant solution at all scales measured.

Here is an example timing (lower times better):

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So please check the article out.

Posted on Categories Coding, TutorialsTags , , 6 Comments on Getting Started With rquery

Getting Started With rquery

To make getting started with rquery (an advanced query generator for R) easier we have re-worked the package README for various data-sources (including SparkR!).

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Query Generation in R

R users have been enjoying the benefits of SQL query generators for quite some time, most notably using the dbplyr package. I would like to talk about some features of our own rquery query generator, concentrating on derived result re-use.

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Posted on Categories data science, TutorialsTags , , , 1 Comment on Collecting Expressions in R

Collecting Expressions in R

Not a full R article, but a quick note demonstrating by example the advantage of being able to collect many expressions and pack them into a single extend_se() node.

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Posted on Categories Opinion, ProgrammingTags , , , , , , ,

How to use rquery with Apache Spark on Databricks

A big thank you to Databricks for working with us and sharing:

rquery: Practical Big Data Transforms for R-Spark Users
How to use rquery with Apache Spark on Databricks

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rquery on Databricks is a great data science tool.

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John Mount speaking on rquery and rqdatatable

rquery and 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.


Rquery
Rqdatatable

Win-Vector LLC‘s John Mount will be speaking on the rquery and rqdatatable packages at the The East Bay R Language Beginners Group Tuesday, August 7, 2018 (Oakland, CA).

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Posted on Categories data science, ProgrammingTags , , , , , , 11 Comments on Speed up your R Work

Speed up your R Work

Introduction

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 R packages: rqdatatable, data.table, and 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 rquery pipelines.

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.

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Posted on Categories data science, Exciting Techniques, Opinion, Practical Data Science, Pragmatic Data Science, Pragmatic Machine Learning, Statistics, TutorialsTags , , , ,

rqdatatable: rquery Powered by data.table

rquery is an R package for specifying data transforms using piped Codd-style operators. It has already shown great performance on PostgreSQL and Apache Spark. rqdatatable is a new package that supplies a screaming fast implementation of the rquery system in-memory using the data.table package.

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).

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