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Iterators — object-oriented programming constructs that act as a pointer in an ordered sequence — are familiar to programmers of languages like Python, but are not a standard part of the R language. Nonetheless, by installing the iterators package (an open-source contribution by Revolution Analytics) you can create and manipulate iterator objects in R.

Here's a fabulous visualization of 15 sorting algorithms. But don't just watch — turn up the volume and listen:


Last month I joined Gregory Piatetsky (KDnuggets editor) for a webinar presentation Data Science: Not Just for Big Data, hosted by Kalido.

If you missed yesterday's webinar, Big Data Analytics with Teradata and Revolution Analytics, you missed hearing from Michele Chambers (Chief Strategy Officer, Revolution Analytics) and Bill Franks (Chief Analytics Officer, Teradata) describing how you can run the advanced statistical modeling algorithms of Revolution R Enteprise ScaleR on data in Teradata databases, with

R is designed as an in-memory application: all of the data you work with must be hosted in the RAM of the machine you're running R on. This optimizes performance and flexibility, but does place contraints on the size of data you're working with (since it must all work in RAM). When working with large data sets in R, it's important to understand how R allocates, duplicates and consumes memory.

In case you missed them, here are some articles from October of particular interest to R users:

Joe Rickert recounts the R presence at the Strata + Hadoop World conference, including slides from the R and Hadoop tutorial.

A little bit of inspiration for the weekend, as this mouse attempts to make off with a cracker (no, Jezebel, it's not a biscuit or a cookie):


It just goes to show that sometimes, perserverance does pay off! Have a great weekend — we'll be back on Monday.

Prof. Ripley today announced on the r-devel mailing list that CRAN now has it's 5000th R package:

Package 'quint' brought the number of packages on CRAN (for all platforms: some are Windows-only or non-Windows only) to 5000 a few minutes ago: see

The new book Analyzing Baseball Data with R by Max Marchi and Jim Albert is now available, and the authors have also launched a companion blog to share some of the analyses from the book.