2183 search results for "Ggplot2"

sjPlot – data visualization for statistics (in social science) #rstats

November 26, 2013
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sjPlot – data visualization for statistics (in social science) #rstats

I’d like to announce the release of version 0.7 of my R package for data visualization and give a small overview of this package (download and installation instructions can be found on the package page). What does this package do? In short, the functions in this package mostly do two things: compute basic or advanced

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Generating Ticker Symbols with Markov Chains

November 25, 2013
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Generating Ticker Symbols with Markov Chains

Stock ticker symbols are short character string codes (AAPL for Apple, WMT for Walmart) that uniquely identify stock listings. They originate from the days when stock quotes were transmitted via telegraph. These symbols are often used as tools for branding and companies choose them to be memorable and easily recognized. As a fun and simple...

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Mapping Foodborne Chicago Reports

November 25, 2013
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Mapping Foodborne Chicago Reports

I help out with a project called Foodborne Chicago, where we search twitter for people in Chicago mentioning "food poisoning" and @reply to them with a link where they can file a report with the city of Chicago's Department of Public Health. The applic...

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Dutch Rainwater Composition 1992-2011

November 24, 2013
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Dutch Rainwater Composition 1992-2011

Last week I examined rainwater composition 1992 to 2005. There is additional data, but National Institute for Public Health has changed equipment in 2005. This week I will add those data.DataData is in a number of spreadsheets. The scrip...

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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 »