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 Administrativia, Coding, ProgrammingTags , 1 Comment on wrapr 1.5.0 available on CRAN

wrapr 1.5.0 available on CRAN

The R package wrapr 1.5.0 is now available on CRAN.

wrapr includes a lot of tools for writing better R code:

I’ll be writing articles on a number of the new capabilities. For now I just leave you with the nifty operator coalesce notation.

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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.

Continue reading Upcoming speaking engagments

Posted on Categories Administrativia, data science, Opinion, Practical Data Science, Pragmatic Data Science, StatisticsTags , , , Leave a comment on Four Years of Practical Data Science with R

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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Posted on Categories Administrativia, Practical Data Science, StatisticsTags , , 2 Comments on Hangul/Korean edition of Practical Data Science with R!

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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Posted on Categories Administrativia, StatisticsTags , , , , ,

Speaking on New Tools for R at Big Data Scale

I would like to thank LinkedIn for letting me speak with some of their data scientists and analysts.


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John Mount discussing rquery SQL generation at LinkedIn.

If you have a group using R at database or Spark scale, please reach out ( jmount at win-vector.com ). We (Win-Vector LLC) have some great new tools I’d love to speak on and share. I’d love an invite, especially if your group is in the San Francisco Bay Area.

Note: we also now have a 1/2 to 1 day on-site “Spark for R Users” training offering. Again, please reach out if your team is interested.

Posted on Categories Administrativia, Coding, Statistics, TutorialsTags , , , 7 Comments on R Tip: Use [[ ]] Wherever You Can

R Tip: Use [[ ]] Wherever You Can

R tip: use [[ ]] wherever you can.

In R the [[ ]] is the operator that (when supplied a simple scalar argument) pulls a single element out of lists (and the [ ] operator pulls out sub-lists).

For vectors [[ ]] and [ ] appear to be synonyms (modulo the issue of names). However, for a vector [[ ]] checks that the indexing argument is a scalar, so if you intend to retrieve one element this is a good way of getting an extra check and documenting intent. Also, when writing reusable code you may not always be sure if your code is going to be applied to a vector or list in the future.

It is safer to get into the habit of always using [[ ]] when you intend to retrieve a single element.

Example with lists:

list("a", "b")[1]
#> [[1]]
#> [1] "a"

list("a", "b")[[1]]
#> [1] "a"

Example with vectors:

c("a", "b")[1]
#> [1] "a"

c("a", "b")[[1]]
#> [1] "a"

The idea is: in situations where both [ ] and [[ ]] apply we rarely see [[ ]] being the worse choice.


Note on this article series.

This R tips series is short simple notes on R best practices, and additional packaged tools. The intent is to show both how to perform common tasks, and how to avoid common pitfalls. I hope to share about 20 of these about every other day to learn from the community which issues resonate and to also introduce some of features from some of our packages. It is an opinionated series and will sometimes touch on coding style, and also try to showcase appropriate Win-Vector LLC R tools.

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

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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