Google Developers R Programming Video Lectures
Speed up your R code using a just-in-time (JIT) compiler Speed up your R code using a just-in-time (JIT) compiler This post is about speeding up your R code using the JIT (just in time) compilation capabilities offered by the new (well, now a year old) {compiler} package. Specifically, dealing with the practical difference between enableJIT and the cmpfun functions. If you do not want to read much, you can just skip to the example part. As always, I welcome any comments to this post, and hope to update it when future JIT solutions will come along.
Taking R to the Limit (High Performance Computing in R), Part 1 -- ...
rCharts rCharts Recently, I had blogged about two R packages, rCharts and rNVD3 that provided R users a lattice like interface to create interactive visualizations using popular javascript libraries. There was a lot of repeated code between the two packages, which lead me to think that it might be possible to integrates multiple JS libraries into a single package with a common lattice like interface. After heavy refactoring, I finally managed to implement three popular JS libraries in rCharts: Polycharts, NVD3 and MorrisJS. rCharts uses reference classes, which I believe is one of the best things to happen to R. It allowed me to keep the code base pretty concise, while implementing a fair degree of functionality. The current structure of rCharts should make it easy to integrate any JS visualization library that uses a configuration variable to create charts.
Package samplingVarEst
Taking R to the Limit (High Performance Computing in R), Part 2 -- ...
statistics.org.il/wp-content/uploads/2010/04/Big_Memory V0.pdf
36,797 views This is an interactive introduction to R. ... This is an interactive introduction to R. R is an open source language for statistical computing, data analysis, and graphical visualization. An Interactive Introduction To R (Programming Language For Statistics) An Interactive Introduction To R (Programming Language For Statistics)
rserve-php - Rserve client php library
If you are into large data and work a lot with package ff One of the main reasons why I prefer to use it above other packages that allow working with large datasets is that it is a complete set of tools. If you disagree, do comment. Next to that there are some extra goodies allowing faster grouping by - not restricted to the ff package alone (Fast groupwise aggregations: bySum, byMean, binned_sum, binned_sumsq, binned_tabulate) > require(ffbase) If you are into large data and work a lot with package ff
Mapping Public Opinion: A Tutorial Posted by d sparks on July 18, 2012 · 6 Comments At the upcoming 2012 summer meeting of the Society of Political Methodology, I will be presenting a poster on Isarithmic Maps of Public Opinion. Since last posting on the topic, I have made major improvements to the code and robustness of the modeling approach, and written a tutorial that illustrates the production of such maps. This tutorial is in a very rough draft form, but I will post it here when it is finalized. Mapping Public Opinion: A Tutorial « David B. Sparks Mapping Public Opinion: A Tutorial « David B. Sparks
R Offerings R Offerings Oracle has adopted R as a language and environment to support Statisticians, Data Analysts, and Data Scientists in performing statistical data analysis and advanced analytics, as well as generating sophisticated graphics. In addressing the enterprise and the need to analyze Big Data, Oracle provides R integration through four key technologies: Why Oracle for Advanced Analytics?
An unabashedly narcissistic data analysis of my own tweets. The… pie( table( whence.i.tweet )) qplot( whence ) + coord_polar() pie( log( table( whence )))+RColorBrewer ggplot (see below) plot( density( tweets.len )) qplot(... stat="density") + geom_density qplot(...stat="bin") + geom_text(...) tweeple tweep... Read more » Data Viz (R news & tutorials) Data Viz (R news & tutorials)
A big list of the things R can do A big list of the things R can do R is an incredibly comprehensive statistics package. Even if you just look at the standard R distribution (the base and recommended packages), R can do pretty much everything you need for data manipulation, visualization, and statistical analysis. And for everything else, there's more than 5000 packages on CRAN and other repositories, and the big-data capabilities of Revolution R Enterprise. A As a result, trying to make a list of everything R can do is a difficult task.
I guess a lot of us actually use many tools to accomplish various things in their everyday life. There is the (not that uncommon) case where you have to build something that others will use in their everyday business life to get insights, information and/or take decisions. The basic implementation scenario here would be to build an excel workbook where you will feed the data and have a overview sheet, named Dashboard…If things are on your side you could set-up a connection to a database (an existing one or one you will create for the data in discussion) and pull data from there. You can build powerful and visually elegant things using this approach. A cool resource to generate tears of joy among colleagues is Chandoo.org. Step up your R capabilities with new tools for increased productivity « Stats raving mad Step up your R capabilities with new tools for increased productivity « Stats raving mad
Fun with the googleVis Package for R | rbresearch
Writing Fast R Code - Part 1

R-bloggers | R news & tutorials from the web

R-bloggers | R news & tutorials from the web Scraping organism metadata for Treebase repositories from GOLD using Python and R Scraping organism metadata for Treebase repositories from GOLD using Python and RI recently wanted to get hold of habitat/phenotype/sequencing metadata for the individual organisms of an archived Read more » Two R tutorials for beginners Two R tutorials for beginnersI am currently in the process of rescuing some of the pages from my now defunct datajujitsu.co.uk blogger blog and moving to this Github/Clojure/Bootstrap version.... Read more »