[R] Revolutions blog: October roundup

David Smith david at revolutionanalytics.com
Mon Nov 10 18:04:21 CET 2014

Revolution Analytics staff and guests write about R every weekday at
the Revolutions blog:
and every month I post a summary of articles from the previous month
of particular interest to readers of r-help.

In case you missed them, here are some articles related to R from the
month of October:

R hits a new milestone with 6,000 CRAN packages, and R 3.1.2 released:

Revolution Analytics announces Revolution R Open, a supported and
enhanced downstream distribution of R: http://bit.ly/1xDbKrn . (I'll
be presenting a webinar on this topic on Wednesday November 12:
http://bit.ly/1xDbKro )

Some benchmarks on the performance improvements that come from linking
Revolution R Open with the Intel Math Kernel Libraries:

Now available: the Reproducible R Toolkit: a package ("checkpoint")
and a server containing archived CRAN packages to make it easy to
reproduce the results of R code that uses packages:

Revolution Analytics has released DeployR Open, a new open-source
framework for integrating R into other applications:

The new MRAN website mran.revolutionanalytics.com provides a
dependency graph for every R package on CRAN: http://bit.ly/1xDbIQc

A new package miniCRAN makes it easy to create a local package
repository with a subset of CRAN packages: http://bit.ly/1xDbIQe

The ACM held an unconference near San Francisco, and featured a
comparison of principal components analysis in R and Python:

The author of the survival package, Dr Terry Therneau, on the state of
Type III tests in R: http://bit.ly/1xDbKrp

Using R to create a "fashion fingerprint" to visualize colours in a
fashion collection: http://bit.ly/1xDbIQg

The new "Rocker" project provides easy-to-use Docker containers
(similar to virtual machines) including R: http://bit.ly/1xDbIQf

HP releases "Distributed R", an R package to integrate with the HP
Vertica database: http://bit.ly/1xDbKrt

Some tips on controlling R resource usage when deployed in a
production environment: http://bit.ly/1xDbIQi

An exhortation to explore complex (and even not-so-complex)
statistical problems with simulation: http://bit.ly/1xDbIQh

Presentations from R user groups on image analysis, data mapping and
data journalism: http://bit.ly/1xDbIQj

The Fantasy Football Analytics blog suggests 14 reasons why R is
better than Excel for data analysis: http://bit.ly/1xDbKrs

An overview of the Tweedie distribution, which judging from citations
is becoming more widely used: http://bit.ly/1xDbKru

A look at the GLDEX package and the Generalized Lambda Distribution to
model financial returns: http://bit.ly/1xDbKrv

In a video interview, RStudio's Joe Cheng discusses how the design of
the R language supports the implementation of domain-specific
languages: http://bit.ly/1xDbIQm

Slides from the webinar "R and Data Science", presented by Joseph
Rickert: http://bit.ly/1xDbKry

An article in the New York Times contrasts Bayesian and Frequentist
statistics: http://bit.ly/1xDbKrx

General interest stories (not related to R) in the past month
included: a Halloween prank (http://bit.ly/1xDbIQn), teaching robots
to walk with a genetic algorithm (http://bit.ly/1xDbKrw), if dogs and
cats kept diaries (http://bit.ly/1xDbIQo), the "bookbook"
(http://bit.ly/1xDbIQp), and the direction techniques of David Fincher

Meeting times for local R user groups (http://bit.ly/eC5YQe) can be
found on the updated R Community Calendar at: http://bit.ly/bb3naW

If you're looking for more articles about R, you can find summaries
from previous months at http://blog.revolutionanalytics.com/roundups/.
You can receive daily blog posts via email using services like
blogtrottr.com, or join the Revolution Analytics mailing list at
http://revolutionanalytics.com/newsletter to be alerted to new
articles on a monthly basis.

As always, thanks for the comments and please keep sending suggestions
to me at david at revolutionanalytics.com or via Twitter (I'm

# David

David M Smith <david at revolutionanalytics.com>
Chief Community Officer, Revolution Analytics
Tel: +1 (650) 646-9523 (Chicago IL, USA)
Twitter: @revodavid


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