While many content providers are working diligently to find ways to lock us in to their content and to exclude aggregation, it’s aggregation that I want!! I want to be able to read my content in the form and manner that fits my workflow of the moment.

Reeder for the iPad is a great example of an app that makes reading a pleasure. It’s fast; amazingly fast. I can read my Google Reader subscribed feeds, mark items for further follow-up, forward them to my Twitter feed (Note to Reeder devs: I maintain more than one Twitter account and more than one account on other social networking sites, to separate the personal from the professional roles vested in me), add it to my Instapaper account, and so on. In short, reading news feeds in Reeder is a pleasure; very nearly perfect.

Audacity is a free, cross-platform sound editor.

Notepad++ is a free, Windows-only text editor offering syntax coloring, line numbers, and other programmer-centric features.

TextWrangler is a free, Mac-only text editor with advanced features such as regular expression find and replace, syntax coloring, line numbering, etc., making it an excellent free choice for programmers.

G.I.M.P. is an open source, free, cross-platform alternative to commercial image editing software.

CMapTools is a free, cross-platform that allows the user to create and share concept maps for any topic they choose. Concept maps can be used for brainstorming activities, to assess students’ models of concept domains, or to capture individual or group domain knowledge.

The R Project provides a comprehensive, free, open source statistical programming language and environment based on the S language. R is the name of both the language and the environment in which you generally use the language. It’s an interactive environment where the commands you enter generate immediate results that you can use to guide your analyses.

> x <- c(5,4,3,2,5)
> mean(x)
3.8

There is an active development and support community using R and it’s been growing more popular in both academic and business communities.

Books & Articles on R

• Albert, J. (2009). Bayesian computation with R (2nd ed.). New York: Springer.
• Baayen, R. H. (2008). Analyzing linguistic data : a practical introduction to statistics using R. Cambridge, UK ; New York: Cambridge University Press.
• Braun, J., & Murdoch, D. J. (2007). A first course in statistical programming with R. Cambridge ; New York: Cambridge University Press.
• Crawley, M. J. (2007). The R book. Chichester, England ; Hoboken, N.J.: Wiley.
• Dalgaard, P. (2008). Introductory statistics with R (2nd ed.). New York: Springer.
• Gentle, J. E. (2009). Computational statistics. New York: Springer.
• Good, P. I. (2005). Introduction to statistics through resampling methods and R/S-PLUS. Hoboken, N.J.: Wiley-Interscience.
• Leipzig, J., & Li, X.-Y. (2009). Data mashups in R. from http://oreilly.com/catalog/9780596804770/
• Nolan, D. A., & Speed, T. P. (2000). Stat labs : mathematical statistics through applications. New York: Springer.
• Owen, W. J. (2006). Using R in an Undergraduate Mathematical Statistics Sequence [Electronic Version]. InterStat, from http://interstat.statjournals.net/YEAR/2006/abstracts/0605002.php
• Owen, W. J. (2007). The R Guide [Electronic Version], from http://cran.r-project.org/doc/contrib/Owen-TheRGuide.pdf
• Petris, G. (2009). Dynamic linear models with R. New York: Springer.
• Rossiter, D. G. (2009). Introduction to the R Project for Statistical Computing for use at ITC [Electronic Version], from http://cran.r-project.org/other-docs.html
• Torgo, L. (2003). Data Mining with R: Learning by Case Studies. LIACC-FEP, University of Porto.
• Trosset, M. W. (2009). An introduction to statistical inference and its applications with R. Boca Raton: Chapman & Hall/CRC.
• Vance, A. (2009, January 7). Data Analysts Captivated by R’s Power. New York Times, from http://www.nytimes.com/2009/01/07/technology/business-computing/07program.html
• Vance, A. (2009, January 8). R You Ready for R. New York Times, from http://bits.blogs.nytimes.com/2009/01/08/r-you-ready-for-r/
• Verzani, J. simpleR – Using R for Introductory Statistics.
• Warnes, G. R., & Gorjanc, G. (2008). gdata: Various R programming tools for data manipulation.

Support for R

• Nabble.com
• StackOverflow.com