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
wrapr). A second edition also lets us also correct some omissions, such as not demonstrating
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.
We are pleased and excited to announce that we are working on a second edition of Practical Data Science with R!
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.
Win-Vector LLC‘s John Mount will be speaking on the
rqdatatable packages at the The East Bay R Language Beginners Group Tuesday, August 7, 2018 (Oakland, CA).
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
Apache Spark, or
Google BigQuery. Or, thanks to the
rqdatatable packages, even fast large in-memory transforms are possible.
wrapr includes a lot of tools for writing better
%.>%(dot arrow pipe)
data.framebuilders and formatters )
:=(named map builder)
%.|%(reduce/expand args) NEW!
DebugFnW()(function debug wrappers)
λ()(anonymous function builder)
I’ll be writing articles on a number of the new capabilities. For now I just leave you with the nifty operator coalesce notation.
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 firstname.lastname@example.org, 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.
I have a couple of public appearances coming up soon.
- The East Bay R Language Beginners Group: Preparing Datasets – The Ugly Truth & Some Solutions, Tuesday, May 1, 2018 at Robert Half Technologies, 1999 Harrison Street, Oakland, CA, 94612.
- Official May 2018 BARUG Meeting: rquery: a Query Generator for Working With SQL Data, Tuesday, May 8, 2018 at Intuit, Building 20
2600 Marine Way · Mountain View, CA.
Four years ago today authors Nina Zumel and John Mount received our author’s copies 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.