Monthly Archives: October 2012

Debugging parameter mismatch across RAC database instances with R, dba_hist, and gv$parameter

October 9, 2012
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Debugging parameter mismatch across RAC database instances with R, dba_hist, and gv$parameter

Did you find this post useful?  Does your organization need Oracle services?  We can help.   Much of this morning went into investigating strange ADDM reports on a two-node Oracle RAC database.  For some reason, there were statistically improbable differences…Read more ›

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Conditional Colors and Shapes in plot() with ifelse()

October 9, 2012
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Conditional Colors and Shapes in plot() with ifelse()

Here’s an example of how to color your plot shapes and pch using an ifelse() statement. Ifelse() is handy as it creates an easy way to branch a function. The syntax is: ifelse(logical.condition, option1, option2). Meaning, if the logical condi...

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Upcoming events

October 9, 2012
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Upcoming events

Featured I’ll be leading two courses in the near future: Value-at-Risk versus Expected Shortfall 2012 October 30-31, London. 30th: “Addressing the critical challenges and issues raised by the Basel proposal to replace VaR with Expected Shortfall” 31st: “Variability in Value-at-Risk and Expected Shortfall” led by Patrick Burns Details at CFP Events. Finance with R Workshop … Continue reading...

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What’s in My Pocket? Read it now! (or Read It Later)

October 9, 2012
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What’s in My Pocket? Read it now! (or Read It Later)

IntroductionYou know what's awesome? Pocket.I mean, sure, it's not the first. I think Instapaper existed a little before (perhaps). And there are alternatives, like Google Reader. But Pocket is still my favorite. It's pretty awesome at what it does.Pocket (or Read It Later, as it used to be known) has fundamentally changed the...

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New season of Grey’s Anatomy with Exponential Random Graph Models

October 8, 2012
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In a previous post we used the web of sexual contacts among characters on the Grey’s Anatomy television show to look at some social network analysis using R. To celebrate the beginning of the new season, Ben Lind has put … Continue reading →

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Laplace’s liberation army

October 8, 2012
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Laplace’s liberation army

Great as it is, Google does not always give the "best", or "right" (ie "most appropriate") results on a given search. For example, if you google "jags" (using google.co.uk), the first results are a girls' independent school and a sports club. The real JAGS (OK: real in a geeky, nerdy, statistical sense) only comes...

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lubridate 1.2.0 now on CRAN

October 8, 2012
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lubridate 1.2.0 now on CRAN

The latest version of lubridate offers some powerful new features and huge speed improvements. Some areas, such as date parsing are more than 50 times faster. lubridate 1.2.0 also fixes those pesky NA bugs in 1.1.0. Here’s some of what you’ll find: Parsers can now handle a wider variety date formats, even within the same

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In case you missed it: September 2012 Roundup

October 8, 2012
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In case you missed them, here are some articles from September of particular interest to R users. You can now browse the R-devel sources and changelogs at GitHub. R is used to create a 3-D animation of the Antarctic ice cap. At the DataWeek SF conference, R users from eBay, Intuit, Minted and other companies describe how R is...

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Another R + iButton script

October 8, 2012
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Previously I’ve detailed R scripts that automate the launching and downloading Maxim iButton thermochron data loggers. I’m typically doing the launching and downloading at separate times in my workflow, since I have duplicate iButtons to swap out, so separate scripts work for me. Ryan Knowles recently contributed a combined version of these scripts that downloads

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Product revenue prediction with R – part 2

October 8, 2012
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Product revenue prediction with R – part 2

After development of predictive model for transactional product revenue -(Product revenue prediction with R – part 1), we can further improvise the model prediction by modifications in the model. In this post, we will see what are the steps required for model improvement. With the help of a set of model summary parameters, the data

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