This section is elementary, but things really pick up speed as later on (also available in a paid preview).
We have two new chapters of Practical Data Science with R, Second Edition online and available for review!
The newly available chapters cover:
Data Engineering And Data Shaping – Explores how to use R to organize or wrangle data into a shape useful for analysis. The chapter covers applying data transforms, data manipulation packages, and more.
Choosing and Evaluating Models – The chapter starts with exploring machine learning approaches and then moves to studying key model evaluation topics like mapping business problems to machine learning tasks, evaluating model quality, and how to explain model predictions.
If you haven’t signed up for our book’s MEAP (Manning Early Access Program), we encourage you to do so. The MEAP includes a free copy of Practical Data Science with R, First Edition, as well as early access to chapter drafts of the second edition as we complete them.
For those of you who have already subscribed — thank you! We hope you enjoy the new chapters, and we look forward to your feedback.
Please help share our news and this discount.
The second edition isn’t finished yet, but chapters 1 through 4 are available in the Manning Early Access Program (MEAP), and we have finished chapters 5 and 6 which are now in production at Manning (so they should be available soon). The authors are hard at work on chapters 7 and 8 right now.
The discount gets you half off. Also the 2nd edition comes with a free e-copy the first edition (so you can jump ahead).
Here are the details in Tweetable form:
Deal of the Day January 13: Half off Practical Data Science with R, Second Edition. Use code dotd011319au at http://bit.ly/2SKAxe9.
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.