Posted on Categories Administrativia, data science, StatisticsTags , , , Leave a comment on Big News: vtreat 1.2.0 is Available on CRAN, and it is now Big Data Capable

Big News: vtreat 1.2.0 is Available on CRAN, and it is now Big Data Capable

We here at Win-Vector LLC have some really big news we would please like the R-community’s help sharing.

vtreat version 1.2.0 is now available on CRAN, and this version of vtreat can now implement its data cleaning and preparation steps on databases and big data systems such as Apache Spark.

vtreat is a very complete and rigorous tool for preparing messy real world data for supervised machine-learning tasks. It implements a technique we call “safe y-aware processing” using cross-validation or stacking techniques. It is very easy to use: you show it some data and it designs a data transform for you.

Thanks to the rquery package, this data preparation transform can now be directly applied to databases, or big data systems such as PostgreSQL, Amazon RedShift, Apache Spark, or Google BigQuery. Or, thanks to the data.table and rqdatatable packages, even fast large in-memory transforms are possible.

We have some basic examples of the new vtreat capabilities here and here.

Posted on Categories data science, Exciting Techniques, Opinion, Practical Data Science, Pragmatic Data Science, Pragmatic Machine Learning, Statistics, TutorialsTags , , , , Leave a comment on rqdatatable: rquery Powered by data.table

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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Posted on Categories data science, Opinion, Pragmatic Data Science, Statistics, TutorialsTags , , , 5 Comments on Talking about clinical significance

Talking about clinical significance

In statistical work in the age of big data we often get hung up on differences that are statistically significant (reliable enough to show up again and again in repeated measurements), but clinically insignificant (visible in aggregation, but too small to make any real difference to individuals).

An example would be: a diet that changes individual weight by an ounce on average with a standard deviation of a pound. With a large enough population the diet is statistically significant. It could also be used to shave an ounce off a national average weight. But, for any one individual: this diet is largely pointless.

The concept is teachable, but we have always stumbled of the naming “statistical significance” versus “practical clinical significance.”

I am suggesting trying the word “substantial” (and its antonym “insubstantial”) to describe if changes are physically small or large.

This comes down to having to remind people that “p-values are not effect sizes”. In this article we recommended reporting three statistics: a units-based effect size (such as expected delta pounds), a dimensionless effects size (such as Cohen’s d), and a reliability of experiment size measure (such as a statistical significance, which at best measures only one possible risk: re-sampling risk).

The merit is: if we don’t confound different meanings, we may be less confusing. A downside is: some of these measures are a bit technical to discuss. I’d be interested in hearing opinions and about teaching experiences along these distinctions.

Posted on Categories data science, Opinion, Programming, Statistics, TutorialsTags , , 2 Comments on WVPlots now at version 1.0.0 on CRAN!

WVPlots now at version 1.0.0 on CRAN!

Nina Zumel and I have been working on packaging our favorite graphing techniques in a more reusable way that emphasizes the analysis task at hand over the steps needed to produce a good visualization. We are excited to announce the WVPlots is now at version 1.0.0 on CRAN!

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Posted on Categories Opinion, Statistics, TutorialsTags , , Leave a comment on Ready Made Plots make Work Easier

Ready Made Plots make Work Easier

A while back Simon Jackson and Kara Woo shared some great ideas and graphs on grouped bar charts and density plots (link). Win-Vector LLC‘s Nina Zumel just added a graph of this type to the development version of WVPlots.

NewImage

Nina has, as usual, some great documentation here.

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Posted on Categories Administrativia, Opinion, Programming, StatisticsTags , , , , , 2 Comments on rquery: SQL from R

rquery: SQL from R

My BARUG rquery talk went very well, thank you very much to the attendees for being an attentive and generous audience.


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(John teaching rquery at BARUG, photo credit: Timothy Liu)

I am now looking for invitations to give a streamlined version of this talk privately to groups using R who want to work with SQL (with databases such as PostgreSQL or big data systems such as Apache Spark). rquery has a number of features that greatly improve team productivity in this environment (strong separation of concerns, strong error checking, high usability, specific debugging features, and high performance queries).

If your group is in the San Francisco Bay Area and using R to work with a SQL accessible data source, please reach out to me at jmount@win-vector.com, I would be honored to show your team how to speed up their project and lower development costs with rquery. If you are a big data vendor and some of your clients use R, I am especially interested in getting in touch: our system can help R users start working with your installation.

Posted on Categories Administrativia, data science, Exciting Techniques, Opinion, Pragmatic Data Science, Pragmatic Machine Learning, Statistics, TutorialsTags , , , , Leave a comment on Upcoming speaking engagments

Upcoming speaking engagments

I have a couple of public appearances coming up soon.

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Posted on Categories Coding, Opinion, Programming, Statistics, TutorialsTags , , , Leave a comment on R Tip: Use Slices

R Tip: Use Slices

R tip: use slices.

SliceOMatic

R has a very powerful array slicing ability that allows for some very slick data processing.

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Posted on Categories data science, Opinion, Practical Data Science, Pragmatic Data Science, Pragmatic Machine Learning, StatisticsTags , , Leave a comment on cdata Update

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.

Posted on Categories Opinion, Programming, StatisticsTags , , , 14 Comments on Neglected R Super Functions

Neglected R Super Functions

R has a lot of under-appreciated super powerful functions. I list a few of our favorites below.


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Atlas, carrying the sky. Royal Palace (Paleis op de Dam), Amsterdam.

Photo: Dominik Bartsch, CC some rights reserved.

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