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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Quick R tip: ggplot in functions needs some extra care

July 31, 2013
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When building visualizations with ggplot2 in R I decided to create specialized functions that encapsulate plotting logic for some of my creations. In this case instead of commonly used aes function I had to use its alternative -  aes_string - for aesthetic mapping from a string.And now goes this handy tip:while original aesthetic mapping function aes accepts x and y parameters by...

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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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Reflections

July 31, 2013
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Reflections

We know much as a species yet we have so much more to learn. Each of us alive today could just as easily have been born into consciousness centuries or millenia ago, and have had little idea that the Earth revolved around the Sun nor that our solar system was just one of billions in… Continue reading...

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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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