2508 search results for "Ggplot2"

R: Mapping Super Typhoon Yolanda (Haiyan) Track

November 24, 2013
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R: Mapping Super Typhoon Yolanda (Haiyan) Track

After reading Enrico Tonini post, I decided to map the super typhoon Haiyan track using OpenStreetMap, maptools, and ggplot2. If mapping with googleVis was possible with 13 lines only, that can also be achieved with the packages I used; but because I p...

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The homogenization of scientific computing, or why Python is steadily eating other languages’ lunch

November 18, 2013
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Over the past two years, my scientific computing toolbox been steadily homogenizing. Around 2010 or 2011, my toolbox looked something like this: Ruby for text processing and miscellaneous scripting; Ruby on Rails/JavaScript for web development; Python/Numpy (mostly) and MATLAB (occasionally) for numerical computing; MATLAB for neuroimaging data analysis; R for statistical analysis; R for plotting

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Dutch Rainwater Composition 1992-2005.

November 17, 2013
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Dutch Rainwater Composition 1992-2005.

After reading Blog About Stats' Open Data Index Blog Post I decided to browse a bit in the Open Data Index. Choosing Netherlands and following Emission of Pollutants I ended on a page from National Institute for Public Health. The page&n...

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An intuitive interpretation of the beta distribution

November 15, 2013
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An intuitive interpretation of the beta distribution

First of all this text is not just about an intuitive perspective on the beta distribution but at least as much about the idea of looking behind a measured empirical probability and thinking of it as a product of chance itself. … Continue reading → The post An intuitive interpretation of the beta distribution appeared first on

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advanced r programming topics + rapache course

advanced r programming topics + rapache course

Advanced R programming topics Similarly as last year, BNOSAC is offering the short course on 'Advanced R programming topics' at the Leuven Statistics Research Center (Belgium).  The course is now part of FLAMES (Flanders Training Network for Methodology and Statistics) and can be found here http://www.flames-statistics.eu/training/advanced-r-programming-topics. Subscription is no longer possible unless you ask kindly to LStat. RApache and developing web applications with...

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Trying to reduce the memory overhead when using mclapply

November 14, 2013
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Trying to reduce the memory overhead when using mclapply

I am currently trying to understand how to reduce the memory used by mclapply. This function is rather complicated and others have explained the differences versus parLapply (A_Skelton73, 2013; Read more »

Plot matrix with the R package GGally

November 13, 2013
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Plot matrix with the R package GGally

I am glad to have found the R package GGally. GGally is a convenient package built upon ggplot2 that contains templates for different plots to be combined into a plot matrix through the function ggpairs. It is a nice alternative to the more limited pairs function. The package has also functions to deal with parallel

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A slightly different introduction to R, part V: plotting and simulating linear models

November 11, 2013
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A slightly different introduction to R, part V: plotting and simulating linear models

In the last episode (which was quite some time ago) we looked into comparisons of means with linear models. This time, let’s visualise some linear models with ggplot2, and practice another useful R skill, namely how to simulate data from known models. While doing this, we’ll learn some more about the layered structure of a

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Hurricanes in South Carolina

November 10, 2013
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Hurricanes in South Carolina

In a recent post, I discussed the occurrence of hurricanes in the North Atlantic basin. The data comes from the National Oceanic and Atmospheric Association, a member of the US federal government. The data spans a bit more than 150 years. In that post, I make the observation that the data supports a model wherein

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Box Me

November 8, 2013
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Box Me

Here’s a short R function I wrote to turn a long data set into a wide one for viewing. It’s not the most exciting function ever but I find it quite useful when my screen is wide and short. It simply cuts the data set horizontally into equal size pieces and puts them side by … Continue reading...

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