Monthly Archives: June 2014

Be Careful with Using Model Design in R

June 25, 2014
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Be Careful with Using Model Design in R

In R, useful functions for making design matrices are model.frame and model.matrix. I will to discuss some of the differences of behavior across and within the two functions. I also have an example where I have run into this problme and it caused me to lose time. Using model.frame for a design matrix Whenever I

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rlist: a new package for working with list objects in R

June 25, 2014
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In recent years, non-relational data have attracted more and more attentions. Roughly speaking, all datasets that are hard to put into a rectangular table with rows and columns are non-relational datasets. The following data is a very simple non-relational dataset in JSON format. The dataset contains some information of three programmers, each of whom has a name, an age, some...

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A Simple Shiny App for Monitoring Trading Strategies

June 25, 2014
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A Simple Shiny App for Monitoring Trading Strategies

In a previous post I showed how to use  R, Knitr and LaTeX to build a template strategy report. This post goes a step further by making  the analysis  interactive. Besides the interactivity, the Shiny App also solves two problems : I can now access all my trading strategies from a single point regardless of the instrument traded.

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Boolean 3 (finally) on CRAN

June 25, 2014
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I have finally managed to get boolean3 accepted to CRAN. You can find it here: boolean3 on CRAN. To summarize: boolean3 provides a means of estimating partial-observability binary response models following boolean logic. boolean3 was developed by Jason W. Morgan under the … Continue reading →

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What Would Cohen Have Titled “The Earth is Round (p < .05)” in 2014?

June 25, 2014
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What Would Cohen Have Titled “The Earth is Round (p < .05)” in 2014?

The area of bibliometrics is not my area of expertise but is still of interest as a researcher. I sometimes think about how Google has impacted the way we title articles. Gone are the days of witty, snappy titles. Title … Continue reading →

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R Scrabble: Part 2

June 25, 2014
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R Scrabble: Part 2

Ivan Nazarov and Bartek Chroł gave very interesting comments to my last post on counting number of subwords in NGSL words. In particular they proposed large speedups of my code. So I thought to try checking a larger data set. So today I will work with TWL2006 - the official word authority for tournament Scrabble...

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Interactive, web-ready ggplot2-style graphics with ggvis

June 25, 2014
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Interactive, web-ready ggplot2-style graphics with ggvis

Hadley Wickham's been working on the next-generation update to ggplot2 for a while, and now it's available on CRAN. The ggvis package is completely new, and combines a chaining syntax reminiscent of dplyr with the grammar of graphics concepts of ggplot2. The resulting charts are web-ready in scalable SVG format, and can easily be made interactive thanks to RStudio's...

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Cleaning up oversized github repositories for R and beyond

June 25, 2014
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Cleaning up oversized github repositories for R and beyond

The version control system Git is an amazing piece of software for tracking every change that you make to a project and saving its entire history. It is incredibly useful, for users of R and other programming languages, leading it shoot from 0 market share in 2005 (when it was first released) to market domination in one short decade. However, Git can cause confusion....

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Identify Sleepers in Fantasy Football using Statistics and Wisdom of the Crowd

June 24, 2014
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Identify Sleepers in Fantasy Football using Statistics and Wisdom of the Crowd

In this post, I demonstrate how to statistically identify sleepers in fantasy football using the wisdom of the crowd. The R Scripts The R Script for the “Wisdom of the The post Identify Sleepers in Fantasy Football using Statistics and Wisdom of the Crowd appeared first on Fantasy Football Analytics.

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Do you believe in World Cup superstition?

June 24, 2014
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Do you believe in World Cup superstition?

If you believe in supernatural causality, you will love what the numbers of the World Cup have to say which team is going to win this Cup in Brazil. According to this numerology approach neither Brazil nor Germany or Netherlands will be the winner, but Uruguay. The table below shows my reason. This was produced

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