Blog Archives

A First Look at rxDForest()

January 30, 2014
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A First Look at rxDForest()

by Joseph RIckert Last July, I blogged about rxDTree() the RevoScaleR function for building classification and regression trees on very large data sets. As I explaned then, this function is an implementation of the algorithm introduced by Ben-Haim and Yom-Tov in their 2010 paper that builds trees on histograms of data and not on the raw data itself. This...

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Quantitative Finance Applications in R – 3: Plotting xts Time Series

January 28, 2014
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Quantitative Finance Applications in R – 3: Plotting xts Time Series

by Daniel Hanson, QA Data Scientist, Revolution Analytics Introduction and Data Setup Last time, we included a couple of examples of plotting a single xts time series using the plot(.) function (ie, said function included in the xts package). Today, we’ll look at some quick and easy methods for plotting overlays of multiple xts time series in a single...

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Book review: "Doing Data Science" by Rachel Schutt and Cathy O’Neil

January 23, 2014
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by Joseph Rickert Every once in a while a single book comes to crystallize a new discipline. If books still have this power in the era of electronic media, "Doing Data Science, Straight Talk from the Frontline" by Rachel Schutt and Cathy O’Neil: O'Reilly, 2013 might just be the book that defines data science. "Doing Data Science", which is...

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Topological Data Analysis with R

January 16, 2014
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Topological Data Analysis with R

by Joseph Rickert When I was in graduate school in the mid '70s Mathematics departments were still under the spell of abstraction for its own sake. At that time, Algebraic Topology which uses concepts from Abstract Algebra to study topological spaces was a major gateway to the realm of abstraction. On my first visit, it was not at all...

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Quantitative Finance Applications in R – 2

January 9, 2014
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Quantitative Finance Applications in R – 2

by Daniel Hanson QA Data Scientist, Revolution Analytics Some Applications of the xts Time Series Package In our previous discussion, we looked at accessing financial data using the quantmod and Quandl R packages. As noted there, the data series returned by quantmod comes in the form of an xts time series object, and Quandl provides a parameter that sets...

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Forecasting By Combining Expert Opinion

January 3, 2014
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Forecasting By Combining Expert Opinion

by Michael Helbraun Michael is member of Revolution Analytics Sales Support team. In the following post, he shows how to synthesize a probability distribution from the opinion of multiple experts: an excellent way to construct a Bayesian prior. There are lots of different ways to forecast. Depending on whether there’s historical data, trend, or seasonality you might choose to...

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How to ask for R help

January 2, 2014
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by Stephen Welle, Senior Support Engineer at Revolution Alalytics and Joseph Rickert For someone trying to learn any new technology getting help with a problem on a public forum can be stressful. Knowing where to go, deciding how to pose a question and figuring out how to deal with a response can be challenging. Moreover, an unpleasant interaction could...

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Quantitative Finance Applications in R

December 27, 2013
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Quantitative Finance Applications in R

by Daniel Hanson, QA Data Scientist, Revolution Analytics Extracting Financial Data from Internet Source Using R (first in a series) Earlier this month, a colleague and I attended a presentation on Computational Finance in R, given by Guy Yollin of the University of Washington Applied Mathematics faculty, at a meeting of the Seattle useR Group. The first among several...

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Tips on Computing with Big Data in R

December 26, 2013
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by Joseph Rickert The Revolution R Enterprise 7.0 Getting started Guide makes a distinction between High Performance Computing (HPC) which is CPU centric, focusing on using many cores to perform lots of processing on small amounts of data, and High Performance Analytics (HPA), data centric computing that concentrates on feeding data to cores, disk I/O, data locality, efficient threading,...

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Catastrophe Modeling for the Insurance Industry

December 19, 2013
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Catastrophe Modeling for the Insurance Industry

by Joseph Rickert At a Bay Area R User Group (BARUG) meeting this month hosted by Cisco, Dag Lohmann (the co-founder of Katrisk) gave an electrifying talk on catastrophe modeling for the insurance industry. Catastrophes: cyclones, hurricanes, floods earthquakes, terrorist attacks are rare events (from a statistical point of view) that cause losses and human suffering over large geographic...

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