1878 search results for "ggplot2"

Predictive Modeling using R and the OpenScoring-Engine – a PMML approach

December 13, 2012
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Predictive Modeling using R and the OpenScoring-Engine – a PMML approach

On November, the 27th, a special post took my interest. Scott Mutchler presented a small framework for predictive analytics based on the PMML (Predictive Model Markup Language) and a Java-based REST-Interface. PMML is a XML based standard for the description and exchange of analytical models. The idea is that every piece of software which supports the corresponding...

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In case you missed it: November 2012 Roundup

December 12, 2012
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In case you missed them, here are some articles from November of particular interest to R users. In the webinar "Real-Time Predictive Analytics with Big Data", I showed how R fits into a real-time production system. R package developer Yihui Xie shares his favorite software and hardware in an interview with The Setup. Hadley Wickham created a handy tutorial...

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2012-12 Post-Processing grid Graphics

December 11, 2012
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Statistical plots drawn with the ggplot2 package generate numerous grid grobs and viewports which are labelled and organised into a coherent hierarchy. This report describes an example that shows how to manipulate the grobs and viewports in a ggplot2 plot … Continue reading →

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Visualizing Baltimore 3.1: Crime and Vacant Properties, Neighborhood Level, Bit More Polished

December 11, 2012
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Visualizing Baltimore 3.1: Crime and Vacant Properties, Neighborhood Level, Bit More Polished

Redos of the plots from this post: Bit more communicative, though the overplotting is a bit annoying. Code: ## gis libraries library(spBayes) library(MBA) library(geoR) library(fields) library(sp) library(maptools) library(rgdal) library(classInt) library(lattice) library(xtable) library(spatstat) library(splancs)   ## Other packages library(ggplot2) library(foreign) library(stringr)...

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Visualizing Baltimore 3: Crime and Vacant Properties, Neighborhood Level

December 10, 2012
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Visualizing Baltimore 3: Crime and Vacant Properties, Neighborhood Level

A few quick plots of West Baltimore neighborhoods, first Sandtown-Winchester: and Harlem Park: These aren’t very polished, I’ll put up better versions. Here’s the code for those that want it: ## gis libraries library(spBayes) library(MBA) library(geoR) library(fields) library(sp) library(maptools) library(rgdal)...

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Visualizing Baltimore 2: Vacant Property and Some More Crime

December 10, 2012
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Visualizing Baltimore 2: Vacant Property and Some More Crime

One of the key predictors in my model for this crime project I’m working on is vacant houses and lots. I’ll speak to some findings about the relationship between levels of the different types of crime and vacant property in...

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"Economics-style" graphs with bezier() from Hmisc

December 10, 2012
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"Economics-style" graphs with bezier() from Hmisc

So, I really think this one is pretty cool. We spend much of our time in R making graphs with data, but what if you have a theory that you’d like to express graphically? Something like what I’ll call “economics-style” graphs, i...

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Shiny apps are awesome

December 10, 2012
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Shiny apps are awesome

RStudio has a new product called Shiny that, quoting from their website, "makes it super simple for R users like you to turn analyses into interactive web applications that anyone can use". See here for more information. A Shiny basically consists of two files: a ui.r file and a server.r file. The ui.r file, as it says, provides...

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Shiny apps are awesome

December 10, 2012
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Shiny apps are awesome

RStudio has a new product called Shiny that, quoting from their website, "makes it super simple for R users like you to turn analyses into interactive web applications that anyone can use". See here for more information. A Shiny basically consists of two files: a ui.r file and a server.r file. The ui.r file, as it says, provides...

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Handling missing data with Amelia

December 9, 2012
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Handling missing data with Amelia

So, what if you have data, but some of the observations are missing? Many statistical techniques assume no missingness, so we might want to “fill in” or rectangularize our data, by replacing missing observations with plausible substitutes....

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