# Monthly Archives: December 2013

## Statistics unplugged

December 27, 2013
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How much does statistical software help and how much it interferes when teaching statistical concepts? Software used in the practice of statistics (say R, SAS, Stata, etc) brings to the party a mental model that it’s often alien to students, while being highly optimized for practitioners. It is possible to introduce a minimum of distraction

## Analemma graphs

December 27, 2013
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Continuing on the theme of solar angles, the code given below produces an analemma diagram similar to that of Lynch (2012, figure 2). 1 2 3 4 5 6 7 8 9 10 library(oce) loc <- list(lon=-0.0015, lat=51.4778) # Greenwich Observatory t <- seq.POSIXt(as.POSIXct("2014-01-01 12:00:00", tz="UTC"), as.POSIXct("2015-01-01...

## Coxcomb plots and ‘spiecharts’ in R

December 27, 2013
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I was contacted recently by a housing organisation who wanted an attractive visualisation of their finances, arranged in a circular form. Because there were two 4 continuous variables to include, all of which were proportions of each other, the client suggested a plot similar to a pie chart, but with each segment extending out a different radius from the segment. I realised...

## The performance gains from switching R’s linear algebra libraries

What is often forgotten in the so-called data analysis "language wars” is that, across most of these languages, many common computations are performed using outsourced dynamically linked math libraries. For example, R; Python's Numpy; Julia; Matlab; and Mathematica all make heavy use of the BLAS linear algebra API. As a result, R can't be properly »more

## 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,...

## Julia is lightning fast: bubble sort revisited

December 26, 2013
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I had heard the name of the new technical computing language Julia buzzing around for some time already. Now during Christmas I had some time on my hands, and implemented the bubble sort algorithm that I have already posted about… See more ›

## MCMSki IV, Jan. 6-8, 2014, Chamonix (news #14)

December 26, 2013
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The programs of the talks, posters and workshop are now printed and available on Speaker Deck (talks, posters, workshop). Please let me know if you spot anything wrong (even though it will not be reprinted!). This is presumably the last news item till Jan. 5 as I am almost off to Chamonix for a week

## Exploring census and demographic data with R

December 25, 2013
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I wanted to use R to explore how to access and visualize census data, home value data and school rating data using Indianapolis metro area as an example. My learning objectives here are to learn how to use R to: … Continue reading →

## The Mascots of Bayesian Statistics

December 25, 2013
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Why would Bayesian statistics need a mascot/symbol/logo? Well, why not? I don’t know of any other branch of statistics that has a mascot but many programming languages have. R has an “R”, Python has a python snake, and Go has an adorable little gopher. While Bayesian statistics isn’t a programming language it could be characterized as...

## Response Time Percentiles for Multi-server Applications

December 25, 2013
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In a previous post, I applied my rules-of-thumb for response time (RT) percentiles (or more accurately, residence time in queueing theory parlance), viz., 80th percentile: \$R_{80}\$, 90th percentile: \$R_{90}\$ and 95th percentile: \$R_{95}\$ to a cellphone application and found that the performance measurements were not completely consistent. Since the data appeared in a journal blog, I...