# 4032 search results for "git"

## Video: Data Mining with R

February 15, 2013
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Yesterday's Introduction to R for Data Mining webinar was a record setter, with more than 2000 registrants and more than 700 attending the live session presented by Joe Rickert. If you missed it, I've embedded the video replay below, and Joe's slides (with links to many useful resources) are also available. During the webinar, Joe demoed several examples of...

## Clustering Loss Development Factors

February 15, 2013
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Anytime I get a new hammer, I waste no time in trying to find something to bash with it. Prior to last year, I wouldn’t have known what a cluster was, other than the first half of a slang term used to describe a poor decision-making process. Now I’ve seen it in action a

## FillIn: a function for filling in missing data in one data frame with info from another

February 15, 2013
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Sometimes I want to use R to fill in values that are missing in one data frame with values from another. For example, I have data from the World Bank on government deficits. However, there are some country-years with missing data. I gathered data from ...

## January Seasonality Shiny web application

February 14, 2013
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Today, I want to share the January Seasonality application (code at GitHub). This example is based on the An Example of Seasonality Analysis post. This is the third application in the series of examples (I plan to share 5 examples) that will demonstrate the amazing Shiny framework and Systematic Investor Toolbox to analyze stocks, make

## GPS Basemaps in R Using get_map

February 14, 2013
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There are many different maps you can use for a background map for your gps or other latitude/longitude data (i.e. any time you're using geom_path, geom_segment, or geom_point.)get_mapHelpfully, there's just one function that will allow you to query Google Maps, OpenStreetMap, Stamen maps, or CloudMade maps: get_map in the ggmap package. You could also use either get_googlemap, get_openstreetmap, get_stamenmap, or get_cloudmademap, but...

## Version 1.0 of multilevelPSA Available on CRAN

February 14, 2013
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Version 1.0 of multilevelPSA has been released to CRAN. The multilevelPSA package provides functions to estimate and visualize propensity score models with multilevel, or clustered, data. The graphics are an extension of PSAgraphics package by Helmreich and Pruzek. The example below will investigate the differences between private and public school internationally using the Programme of International Student Assessment...

## Getting a simple tree via NCBI

February 14, 2013
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I was just at the Phylotastic hackathon in Tucson, AZ at the iPlant facilities at the UofA. A problem that needs to be solved is getting the incrasingly vast phylogenetic information to folks not comfortable building their own phylogenies. Phylomatic has made this super easy for people that want plant phylogenies (at least 250 or so papers...

## Getting a simple tree via NCBI

February 14, 2013
By

I was just at the Phylotastic hackathon in Tucson, AZ at the iPlant facilities at the UofA. A problem that needs to be solved is getting the incrasingly vast phylogenetic information to folks not comfortable building their own phylogenies. Phylomatic has made this super easy for people that want plant phylogenies (at least 250 or so papers...

## Multiple Stocks Plot Shiny web application

February 13, 2013
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Today, I want to share the Multiple Stocks Plot application (code at GitHub). This is the second application in the series of examples (I plan to share 5 examples) that will demonstrate the amazing Shiny framework and Systematic Investor Toolbox to analyze stocks, make back-tests, and create summary reports. The motivation for this series of

## Large claims, and ratemaking

February 13, 2013
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During the course, we have seen that it is natural to assume that not only the individual claims frequency can be explained by some covariates, but individual costs too. Of course, appropriate families should be considered to model the distribution of the cost , given some covariates .Here is the dataset we’ll use, > sinistre=read.table("http://freakonometrics.free.fr/sinistreACT2040.txt", + header=TRUE,sep=";") > sinistres=sinistre...