Languages tweeted around Germany: red, blue, green, yellow, grey are for German, French, English, Dutch and other respectively. See here for a zoomable version.Motivated by the project twitter languages of New York I wanted to...

In my previous post (http://statcompute.wordpress.com/2013/05/25/test-drive-of-parallel-computing-with-r) on 05/25/2013, I’ve demonstrated the power of parallel computing with various R packages. However, in the real world, it is not straight-forward to utilize these powerful tools in our day-by-day computing tasks without carefully formulate the problem. In the example below, I am going to show how to use the

A list of interesting R/Stats quickies to keep the mind distracted: A long draft Advanced Data Analysis from an Elementary Point of View by Cosma Shalizi, in which he uses R to drive home the message. Not your average elementary point of view. Good notes by Frank Davenport on starting using R with data from

Most computers nowadays have few cores that incredibly help us with our daily computing duties. However, when statistical softwares do use parallelization for analyzing data faster? R, my preferred analytical package, does not take too much advantage of multicore processing by default. In fact, R has been inherently a “single-processor” package until nowadays. Stata, another

(This article was first published on Timely Portfolio, and kindly contributed to R-bloggers) A quick glimpse at the US 10y Treasury Bond rate since 2000 seems benign with low volatility and a general downward trend.require(latticeExtra)require(quantmod)US10y <- getSymbols("^TNX", from = "2000-01-01", auto.assign = FALSE)asTheEconomist(xyplot(US10y, scales = list(y = list(rot = 1)), main = "US 10y Yield Since 2000"))From TimelyPortfolioHowever,...

Occasionally, the need arises to communicate with R via another process. There are packages available to facilitate this communication, but for simple problems, a socket connection may be the answer. Nearly all software languages have a socket communic...

Regression is a mainstay of ecological and evolutionary data analysis. For example, a disease ecologist may use body size (e.g. a weight from a scale with measurement error) to predict infection. Classical linear regression assumes no error in covariates; they are known exactly. This is rarely the case in ecology, and ignoring error in covariates can bias regression coefficient...

It’s Memorial Day and my dissertation defense is tomorrow. This week I’m phoning in my blog. I had the opportunity to teach a short course last week that was part of a larger workshop focused on ecosystem restoration. A fellow grad student and I taught a session on Excel and R for basic data analysis.