Monthly Archives: June 2013

R Plotting Financial Time Series

June 19, 2013
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In my little world of finance, data almost always is a time series.  Through both quiet iteration and significant revolutions, the volunteers of R have made analyzing and charting time series pleasant.  As a mini-tribute to all those who have...

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Generating Tables Using Pander, knitr, and Rmarkdown

June 19, 2013
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Generating Tables Using Pander, knitr, and Rmarkdown I use a pretty common workflow (I think) for producing reports on a day to day basis. I write them in rmarkdown using RStudio, knit them into .html and .md documents using knitr, then convert the resulting .md file to a .docx file using pander, which is really just a way of communicating with Pandoc via my R terminal. This workflow...

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highlight 0.4.2

June 19, 2013
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highlight 0.4.2 is on CRAN. This fixes a few bugs reported by users. The main improvement is that we can now use highlight as a vignette engine, using the functionality introduced in R 3.0.0. In the Rcpp universe, we use … Continue reading →

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Dallas R Users: Creating R Packages this Saturday, 6/29

June 18, 2013
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I’ll be presenting at the Dallas R Users Group next Saturday at 10:00AM at the University of Dallas on how to reproduce your R code. We’ll review how to use R scripts, how to embed R code in reproducible documents, and then introduce how to create your own R packages based on your R code.

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Visualising Crime Hotspots in England and Wales using {ggmap}

June 18, 2013
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Visualising Crime Hotspots in England and Wales using {ggmap}

Two weeks ago, I was looking for ways to make pretty maps for my own research project. A quick search led me to some very informative blog posts by Kim Gilbert, David Smith and Max Marchi. Eventually, I Google'd the excellent crime weather map exa...

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Le Monde puzzle [#825]

June 18, 2013
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Le Monde puzzle [#825]

Yet another puzzle which first part does not require R programming, even though it is a programming question in essence: Given five real numbers x1,…,x5, what is the minimal number of pairwise comparisons needed to rank them? Given 33 real numbers, what is the minimal number of pairwise comparisons required to find the three largest ones?

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My R package’s worldmap of downloads!

June 18, 2013
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My R package’s worldmap of downloads!

Last week, a colleague draw my attention on this new log files from the Rstudio cloud CRAN mirror, through a post from Tal Galili. This CRAN mirror is a little different, as it uses Amazon CloudFront to deliver the downloads … Continue reading →

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The Fallacy of 1/N and Static Weight Allocation

June 18, 2013
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The Fallacy of 1/N and Static Weight Allocation

In the last few years there has been a increasing tendency to ignore the value of a disciplined quantitative approach to the portfolio allocation process in favor of simple and static weighting schemes such as equal weighting or some type of adjusted volatility weighting. The former simply ignores the underlying security dynamics, assuming equal risk-return,

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The Fallacy of 1/N and Static Weight Allocation

June 18, 2013
By
The Fallacy of 1/N and Static Weight Allocation

In the last few years there has been a increasing tendency to ignore the value of a disciplined quantitative approach to the portfolio allocation process in favor of simple and static weighting schemes such as equal weighting or some type of adjusted volatility weighting. The former simply ignores the underlying security dynamics, assuming equal risk-return,

Read more »

Create SAS Code from R ‘tree’ Objects

June 18, 2013
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Create SAS Code from R ‘tree’ Objects

I recently was faced with the desire to port some tree models developed in R to SAS so I could score a large database. To me this makes sense as SAS is better with large files (or at least so as not to offend anyone, I am better with large files in SAS). I started

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