How long does your Oracle RMAN backup take to complete? How does this vary over time? Are there patterns by week, week of month, or day of week? The gist below can help you evaluate questions like these.… Read more ›

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

A while ago I was playing around with the JavaScript package D3.js, and I began with this visualization—that I never really finished—of how a one-way ANOVA is calculated. I wanted to make the visualization interactive, and I did integrate some interactive elements. For instance, if you hover over a data point it will show the residual, and its value will be highlighted in...

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

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 decent...

Parallel computing is incredibly useful, but not every thing worths distribute across as many cores as possible. 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...

(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"))...

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