Publications by Joseph Rickert
The R Backpages 2
by Joseph Rickert In this roundup of R-related news: Domino enables data science collaboration; Plotly adds an R graphics gallery; Revolution Analytics R user group sponsorship applications are open; and Quandl adds new data sets. San Francisco startup takes on collaborative Data Science Domino, a San Francisco based startup, is inviting users to...
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2013 was a good year for R User Group Meetings
by Joseph Rickert R user groups are thriving. By our count of events listed on Revolution Analytics' Community Calendar, there were about 441 face-to-face R meetups worldwide in 2013; up 41% from 312 in 2012. The plot below indicates what the activity looks like on a monthly basis. (Download the data and R code behind this chart here.) My tak...
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A look at the distribution of R package dependencies
by Joseph Rickert For a knitty gritty look at what’s new with R there is no better place to start than Dirk Eddelbuettel’s CRANberries site. Since 2007, the site has been reliably providing near real-time status on CRAN packages. Now, every two hours, CRANberries displays updated information on new R packages, updated R packages and R package...
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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 los...
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Tips on Computing with Big Data in R
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, di...
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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. ...
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How to ask for R help
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 interact...
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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, ...
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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 paramete...
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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 clear that ...
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