Monthly Archives: April 2013

Stamen maps with spplot

Stamen maps with spplot

Several R packages provide an interface to query map services (Google Maps, Stamen Maps or OpenStreetMap) to obtain raster images …Continuar leyendo »

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Announcing Revolution R Enterprise 6.2

April 24, 2013
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Announcing Revolution R Enterprise 6.2

We are pleased to announce that Revolution R Enterprise Release 6.2 is available to new subscribers today. This new software release from Revolution Analytics includes a number of key new features: Support for open source R 2.15.3, the latest stable release of R. Since Release 2.14.2, the R Project has added 89 new features, 11 performance enhancements and 139...

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Bank of England Fan Charts in R

April 24, 2013
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Bank of England Fan Charts in R

I managed to catch David Spiegelhalter’s Tails You Win on BBC iplayer last week. I missed it the first time round, only for my parents on my last visit home to tell me about a Statistician jumping out of a … Continue reading →

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Prefer = for assignment in R

April 23, 2013
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Prefer = for assignment in R

We share our opinion that = should be preferred to the more standard <- for assignment in R. This is from a draft of the appendix of our upcoming book. This has the risk of becoming an R version of Javascript’s semicolon controversy, but here you have it. R has five common assignment operators: “=“, Related posts:

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Disaggregating Annual Losses into Each Quarter

April 23, 2013
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Disaggregating Annual Losses into Each Quarter

In loss forecasting, it is often necessary to disaggregate annual losses into each quarter. The most simple method to convert low frequency to high frequency time series is interpolation, such as the one implemented in EXPAND procedure of SAS/ETS. In the example below, there is a series of annual loss projections from 2013 through 2016.

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Learn how to analyze data with R with Coursera’s "Data Analysis" videos

April 23, 2013
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If you didn't manage to catch Coursera's Data Analysis course, don't despair. Instructor Jeff Leek has made the course videos available on YouTube, which you can review at your leisure to learn how to plan, carry out, and communicate analyses of real data sets with R. (The course assumes you already have familiarity with R, so if you're new...

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Python Complements R’s Shortcomings

April 23, 2013
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Python Complements R’s Shortcomings

I’m a big fan of open-source software for research. For example, R-statistics, Qgis, and Grass GIS are awesome programs. R can do any statistical tests and numerical modeling you can imagine; if there’s not a built-in function you can write … Continue reading →

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Slides from my R intro seminar

April 23, 2013
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Slides from my R intro seminar

Here are my slides from a short introductory seminar on R (essentially going through part I of the R tutorial) last week. As magic lantern pictures go, they’re hideously ugly, but they were mostly there for future reference. Most of the seminar was spent showing RStudio. This Friday, we’ll practice some uses of qplot and make

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The Foundation for Open Access Statistics

April 23, 2013
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Earlier this month we blogged about Harvard Professors Gary King and Stuart Shieber providing advice to graduate students about open access, dissertations, and journal publishing. We also mentioned some of the great initiatives that facilitate open access publishing in the statistics community, like the Journal of Statistical Software (JSS), The R Journal and arxiv.org. The ...

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wapply: A faster (but less functional) ‘rollapply’ for vector setups

April 23, 2013
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wapply: A faster (but less functional) ‘rollapply’ for vector setups

For some cryptic reason I needed a function that calculates function values on sliding windows of a vector. Googling around soon brought me to ‘rollapply’, which when I tested it seems to be a very versatile function. However, I wanted to code my own version just for vector purposes in the hope that it may

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