Monthly Archives: May 2013

Version 0.9 of timeline on CRAN

May 9, 2013
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Version 0.9 of timeline on CRAN

The initial version of the timeline package has been released to CRAN. This package provides creates timeline plots using ggplot2 in a style similar to Preceden. I would considered this beta quality as there are more features I would like to add but has enough functionality to possibly be useful to others. install.packages('timeline',repos='http://cran.r-project.org') require(timeline) data(ww2) timeline(ww2, ww2.events, event.spots=2, event.label='', event.above=FALSE) Timeline...</p><p><a href=Read more »

A Shiny web app to find out how much medical procedures cost in your state.

May 8, 2013
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A Shiny web app to find out how much medical procedures cost in your state.

Today the front page of the Huffington Post featured the new data available from the CMS that shows the cost of many popular procedures broken down by hospital. We here at Simply Statistics think you should be able to explore … Continue reading →

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What’s new in Revolution R Enterprise 6.2 (video)

May 8, 2013
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If you missed last week's webinar, product manager Thomas Dinsmore shared details of the new features in Revolution R Enterprise 6.2 in the video below: You can also download slides of the presentation at the link below. Revolution Analytics webinars: What's New in Revolution R Enterprise 6.2

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Computed columns for dataframes

May 8, 2013
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Computed columns for dataframes

Everyone loves to aggregate data. Everyone loves to create new columns based on other columns. Everyone hates to do the same thing twice. In my continuing work on multilevel view of loss reserving, I reached a point where I realized that I needed a robust mechanism to aggregate computed columns. SQL server and (I’m assuming)

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Feature Selection 2 – Genetic Boogaloo

May 8, 2013
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Feature Selection 2 – Genetic Boogaloo

Previously, I talked about genetic algorithms (GA) for feature selection and illustrated the algorithm using a modified version of the GA R package and simulated data. The data were simulated with 200 non-informative predictors and 12 linear effects and three non-linear effects. Quadratic discriminant analysis (QDA) was used to model the data. The last set of...

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3D Mapping in R

May 8, 2013
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3D Mapping in R

This tutorial has been kindly contributed by Robin Edwa

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SAS, SPSS, Stata Users: Learn R from Home June 17

May 8, 2013
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SAS, SPSS, Stata Users: Learn R from Home June 17

Has learning R been driving you a bit crazy? If so, it may be that you’re “lost in translation.” On June 17 and 19, I’ll be teaching a webinar, R for SAS, SPSS and Stata Users. With each R concept, … Continue reading →

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Building a custom database of country time-series data using Quandl

May 8, 2013
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Building a custom database of country time-series data using Quandl

Encouraged by this post I had another look at quandl for collecting datasets from different agencies. Right now I need to get data for four countries on a couple of dozen indicators. This graphic is just a quick example with only two indicators of what I am aiming to be able to do. The process

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An accept-reject sampler using RcppArmadillo::sample()

May 8, 2013
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An accept-reject sampler using RcppArmadillo::sample()

The recently added RcppArmadillo::sample() functionality provides the same algorithm used in R’s sample() to Rcpp-level code. Because R’s own sample() is written in C with minimal work done in R, writing a wrapper around RcppArmadillo::sample() to then call in R won’t get you much of a performance boost. However, if you need to repeatedly call sample(), then calling a...

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An accept-reject sampler using RcppArmadillo::sample()

May 8, 2013
By
An accept-reject sampler using RcppArmadillo::sample()

The recently added RcppArmadillo::sample() functionality provides the same algorithm used in R’s sample() to Rcpp-level code. Because R’s own sample() is written in C with minimal work done in R, writing a wrapper around RcppArmadillo::sample() to then call in R won’t get you much of a performance boost. However, if you need to repeatedly call sample(), then calling a...

Read more »