Monthly Archives: October 2016

Running the Numbers – How Can Hamilton Still Take the 2016 F1 Drivers’ Championship?

October 31, 2016
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Running the Numbers – How Can Hamilton Still Take the 2016 F1 Drivers’ Championship?

Way back in 2012, I posted a simple R script for trying to work out the finishing combinations in the last two races of that year’s F1 season for Fernando Alonso and Sebastien Vettel to explore the circumstances under which Alonso could take the championship (Paths to the F1 2012 Championship Based on How They

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ShinyProxy 0.6.0 released!

October 31, 2016
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ShinyProxy is a novel, open source platform to deploy Shiny apps for the enterprise or larger organizations. Why is this needed? There is currently no valid open source alternative that offers this functionality. What does it offer? authentication authorization securing traffic with TLS/SSL usage statistics ...

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Weighted Effect Coding: Dummy coding when size matters

October 31, 2016
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If your regression model contains a categorical predictor variable, you commonly test the significance of its categories against a preselected reference category. If all categories have (roughly) the same number of observations, you can also ...

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Poster/cheatsheet for R/BioC package genomation

October 31, 2016
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We prepared a poster/cheatsheet for the bioconductor package genomation, which is a package for summary and annotation of genomic intervals. Users can visualize and quantify genomic intervals over pre-defined functional regions, such as promoters, exons, introns, etc. The genomic intervals represent regions with a defined chromosome position, which may be associated with a score, such as aligned reads from HT-seq experiments,...

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Detecting outliers and fraud with R and SQL Server on my bank account data – Part 1

October 31, 2016
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Detecting outliers and fraud with R and SQL Server on my bank account data – Part 1

Detecting outliers and fraudulent behaviour (transactions, purchases, events, actions, triggers, etc.) takes a large amount of experiences and statistical/mathetmatical background. One of the samples Microsoft provided with release of new SQL Server 2016 was using simple logic of Benford’s law. This law works great with naturally occurring numbers and can be applied across any kind … Continue reading Detecting...

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ggmail + forecast = how many emails I will get tomorrow?

October 31, 2016
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ggmail + forecast = how many emails I will get tomorrow?

During the eRum 2016, Adam Zagdański gave a very good tutorial about time series modeling. Among other things I’ve learned that the forecast package (created by Rob Hyndman) got cool new plots based on the ggplot2 package. Let’s use it to play with mailbox statistics for my gmail account! 1. Get the data Follow this … Czytaj dalej ggmail...

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xlim_tree: set x axis limits for only Tree panel

October 30, 2016
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xlim_tree: set x axis limits for only Tree panel

A ggtree user recently asked me the following question in google group: I try to plot long tip labels in ggtree and usually adjust them using xlim(), however when creating a facet_plot xlim affects all plots and minimizes them. Is it possible to work around this and only affect the tree and it’s tip labels leaving the other plots in facet_plot...

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Fastest Way to Add New Variables to A Large Data.Frame

October 30, 2016
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Fastest Way to Add New Variables to A Large Data.Frame

(This article was first published on S+/R – Yet Another Blog in Statistical Computing, and kindly contributed to R-bloggers) pkgs

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Becoming The Intern

October 30, 2016
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Becoming The Intern

I was not always this famous… And with this I mean that a year ago only my colleagues knew I did stuff in R and now I’m reaching a slightly wider audience. Some of this is definitely due to me interning for Hadley Wickham and helping prepare the ne...

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ratio-of-uniforms [#2]

October 30, 2016
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ratio-of-uniforms [#2]

Following my earlier post on Kinderman’s and Monahan’s (1977) ratio-of-uniform method, I must confess I remain quite puzzled by the approach. Or rather by its consequences. When looking at the set A of (u,v)’s in R⁺×X such that 0≤u²≤ƒ(v/u), as discussed in the previous post, it can be represented by its parameterised boundary u(x)=√ƒ(x),v(x)=x√ƒ(x)    x

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