It’s Christmas Eve tomorrow and so I declare the year over. My Christmas gift to you is a summary of activity in 2009 at the FriendFeed Life Scientists group. It’s crafted using R + Ruby, with raw data and some code snippets available. If you want to see the most popular items from the group 
I was prompted by a thread on the apparent decline of FriendFeed to look for evidence of declining participation in my networks. First, a quick and dirty Ruby script, tls.rb to grab the Life Scientists feed and count the likes and comments: #!/usr/bin/ruby require 'rubygems' require 'json/pure' require 'net/http' require 'open-uri' def format_date(d) if d 
I’m looking at ways to provide access to R via a web application. First rule: see what’s available first, before you reinvent the wheel. It’s not pretty.
From the R Web Interfaces FAQ:
Software
Brief notes
Rweb
Page last updated 1999. Of the 3 example links on the page one ran very slowly, the second not at 
Named rjson, appropriately. It’s quite basic just now, but contains methods for interconversion between R objects and JSON. Something like this:
> library(rjson)
> data <- list(a=1,b=2,c=3)
> json <- toJSON(data)
> json
"{\"a\":1,\"b\":2,\"c\":3}"
> cat(json, file="data.json")
Use cases? I wonder if RApache could be used to build an API that serves R data in JSON format?
Posted in 
R is terrific, of course, for all your statistical needs. But those data structures! “Everything is a list.” Leading to such wondrous ways to access variables as “p <- Meta(gds)$platform", or "last <- mylist]])]".
Sometimes, you want something more familiar. An array, a hash, a hash of arrays. Or, you may 
Plotting and charting libraries for Ruby (on Rails) abound. However, few are sophisticated enough for scientists and many are not actively maintained. Plotting in R, on the other hand, is about as sophisticated as it comes.
Can we bridge Ruby and R? Yes we can, thanks to Alex Gutteridge’s RSRuby. The next 
It’s been ages since I visited the R website, so I don’t know how long they’ve had a wiki. It’s built using DokuWiki, one of my personal favourites.
This is a great leap forward for R documentation, which is somewhat notorious for being (a) difficult to find and (b) difficult to understand when you find 
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
