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More Practical Data Science with R Book News

Some more Practical Data Science with R news.

Practical Data Science with R is the book we wish we had when we started in data science. Practical Data Science with R, Second Edition is the revision of that book with the packages we wish had been available at that time (in particular vtreat, cdata, and wrapr). A second edition also lets us also correct some omissions, such as not demonstrating data.table.

For your part: please help us get the word out about this book. Practical Data Science with R, Second Edition, R in Action, Second Edition, and Think Like a Data Scientist are Manning’s August 20th 2018 “Deal of the Day” (use code dotd082018au at https://www.manning.com/dotd).

For our part we are busy revising chapters and setting up a new Github repository for examples and code and other reader resources.

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Announcing Practical Data Science with R, 2nd Edition

We are pleased and excited to announce that we are working on a second edition of Practical Data Science with R!

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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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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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cdata Update

The R package cdata now has version 0.7.0 available from CRAN.

cdata is a data manipulation package that subsumes many higher order data manipulation operations including pivot/un-pivot, spread/gather, or cast/melt. The record to record transforms are specified by drawing a table that expresses the record structure (called the “control table” and also the link between the key concepts of row-records and block-records).

What can be quickly specified and achieved using these concepts and notations is amazing and quite teachable. These transforms can be run in-memory or in remote database or big-data systems (such as Spark).

The concepts are taught in Nina Zumel’s excellent tutorial.


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And in John Mount’s quick screencast/lecture.

link, slides

The 0.7.0 update adds local versions of the operators in addition to the Spark and database implementations. These methods should now be a bit safer for in-memory complex/annotated types such as dates and times.

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Four Years of Practical Data Science with R

Four years ago today authors Nina Zumel and John Mount received our author’s copies of Practical Data Science with R!

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Hangul/Korean edition of Practical Data Science with R!

Excited to see our new Hangul/Korean edition of “Practical Data Science with R” by Nina Zumel, John Mount, translated by Daekyoung Lim.

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R Tip: Use the vtreat Package For Data Preparation

If you are working with predictive modeling or machine learning in R this is the R tip that is going to save you the most time and deliver the biggest improvement in your results.

R Tip: Use the vtreat package for data preparation in predictive analytics and machine learning projects.

Vtreat

When attempting predictive modeling with real-world data you quickly run into difficulties beyond what is typically emphasized in machine learning coursework:

  • Missing, invalid, or out of range values.
  • Categorical variables with large sets of possible levels.
  • Novel categorical levels discovered during test, cross-validation, or model application/deployment.
  • Large numbers of columns to consider as potential modeling variables (both statistically hazardous and time consuming).
  • Nested model bias poisoning results in non-trivial data processing pipelines.

Any one of these issues can add to project time and decrease the predictive power and reliability of a machine learning project. Many real world projects encounter all of these issues, which are often ignored leading to degraded performance in production.

vtreat systematically and correctly deals with all of the above issues in a documented, automated, parallel, and statistically sound manner.

vtreat can fix or mitigate these domain independent issues much more reliably and much faster than by-hand ad-hoc methods.
This leaves the data scientist or analyst more time to research and apply critical domain dependent (or knowledge based) steps and checks.

If you are attempting high-value predictive modeling in R, you should try out vtreat and consider adding it to your workflow.

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Data Reshaping with cdata

I’ve just shared a short webcast on data reshaping in R using the cdata package.

(link)

We also have two really nifty articles on the theory and methods:

Please give it a try!

This is the material I recently presented at the January 2017 BARUG Meetup.

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rquery: Fast Data Manipulation in R

Win-Vector LLC recently announced the rquery R package, an operator based query generator.

In this note I want to share some exciting and favorable initial rquery benchmark timings.

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