Monthly Archives: July 2013

Practical Data Science with R, deal of the day Aug 1 2013

July 31, 2013
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Practical Data Science with R, deal of the day Aug 1 2013

Deal of the Day August 1: Half off my book Practical Data Science with R. Use code dotd0801au at www.manning.com/zumel/ Related posts: Data Science, Machine Learning, and Statistics: what is in a name? Data science project planning Setting expectation...

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Slidify Did That… and That… and…

July 31, 2013
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In my exuberance for rCharts, I have not expressed my equal love for its older sibling slidify.  I adopted slidify a little more slowly than other R bloggers Create elegant, interactive presentations from R with Slidify Interactive slides with go...

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Estimating Ages from First Names Part 2 – Using Some Morbid Test Data

July 31, 2013
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Estimating Ages from First Names Part 2 – Using Some Morbid Test Data

In my last post, I wrote about how I compiled a US Social Security Agency data set into something usable in R, and mentioned some issues scaling it up to be usable for bigger datasets.  I also mentioned the need … Continue reading →

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A dirty hack for importing packages that use Depends

July 31, 2013
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A dirty hack for importing packages that use Depends

A dirty hack for importing packages that use Depends 2013-05-27 Source Scope This article is about R package development. Motivation As stated in the the Writing R Extensions manual and the Software for Data Analysis book (aka the R bible), packages should whenever possible use Imports instead of Depends, to avoid name collision (masking) and ensure trustworthy computations. See

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I made a mistake, please don’t shoot me

July 31, 2013
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The major difference between commercial/academic written software is the handling of user mistakes, or to be more exact what is considered to be a user mistake. In the commercial world the emphasis is on keeping the customer happy, which translates into trying hard to gracefully handle any ‘mistake’ the user makes. Academic software is generally

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Butler Analytics: Real Analysts use R

July 31, 2013
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In an overview of several predictive analytics platforms (including SPSS, Oracle and SAS), Butler Analytics offers this 4.5/5 star review of Revolution R Enterprise: Real analysts use R – well it sounds a bit macho, but actually there is some truth in it. R is the most widely used, and arguably the most powerful analysis software on the planet....

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Measuring Bias in Published Work

July 31, 2013
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Measuring Bias in Published Work

In a series of previous posts, I’ve spent some time looking at the idea that the review and publication process in political science—and specifically, the requirement that a result must be statistically significant in order to be scientifically notable or publishable—produces a very misleading scientific literature. In short, published studies of some relationship will tend

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Trivial, but useful: sequences with defined mean/s.d.

July 31, 2013
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Trivial, but useful: sequences with defined mean/s.d.

O.k., the following post may be (mathematically) trivial, but could be somewhat useful for people that do simulations/testing of statistical methods. Let’s say we want to test the dependence of p-values derived from a t-test to a) the ratio of means between two groups, b) the standard deviation or c) the sample size(s) of the

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R in Insurance: Presentations are online

July 31, 2013
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R in Insurance: Presentations are online

The programme and the presentation files of the first R in Insurance conference have been published on GitHub. Front slides of the conference presentations Additionally to the slides many presenters have made their R code available as well: Alexander McNeil shared the examples of the CreditRisk+ model he presented. Lola Miranda made a...

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R ecology workshop

July 31, 2013
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R ecology workshop

After my presentation yesterday to a group of grad students on R resources, I did a presentation today on intro to R data manipulation, visualizations, and analyses/visualizations of biparite networks and community level analyses (diversity, rarefactio...

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