# 3983 search results for "git"

## GeoCoding,R, and The Rolling Stones – Part 2

March 20, 2013
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Welcome to Part 2 of the GeoCoding, R, and the Rolling Stones blog. Let’s apply some of the things we learned in Part 1 to a practical real world example. Mapping the Stones – A Real Example The Rolling Stones have toured for many years. You can go to Wikipedia and see information on the

## GeoCoding,R, and The Rolling Stones – Part 2

March 20, 2013
By

Welcome to Part 2 of the GeoCoding, R, and the Rolling Stones blog. Let’s apply some of the things we learned in Part 1 to a practical real world example. Mapping the Stones – A Real Example The Rolling Stones have toured for many years. You can go to Wikipedia and see information on the

## GeoCoding, R, and The Rolling Stones – Part 1

March 20, 2013
By

In this article I discuss a general approach for Geocoding a location from within R, processing XML reports, and using R packages to create interactive maps. There are various ways to accomplish this, though using Google’s GeoCoding service is a good place to start. We’ll also talk a bit about the XML package that is

## GeoCoding, R, and The Rolling Stones – Part 1

March 20, 2013
By

In this article I discuss a general approach for Geocoding a location from within R, processing XML reports, and using R packages to create interactive maps. There are various ways to accomplish this, though using Google’s GeoCoding service is a good place to start. We’ll also talk a bit about the XML package that is

## Optimal Meeting Point on the Paris Metro

March 20, 2013
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tl;dr: Play with the app here When you live in Paris, chances are you are (home or work) very close to a metro station, so when you want to meet with some friends, you usually end up picking another metro station as a meeting point. Yet, finding the optimal place to meet can easily become a complex problem considering...

## Animating neural networks from the nnet package

March 19, 2013
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My research has allowed me to implement techniques for visualizing multivariate models in R and I wanted to share some additional techniques I’ve developed, in addition to my previous post. For example, I think a primary obstacle towards developing a useful neural network model is an under-appreciation of the effects model parameters have on model

March 19, 2013
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This post serves three function: It allows me to revisit an old blogpost It let's me test out the new-ish knitr function knti2wp and RWordPress It enables me to avoid the massive ammount of reading I need to do and … Continue reading →

## Analyzing Local Data with a Shiny Web App

March 19, 2013
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A great. recent enhancement for the Shiny App is the ability to upload local files. Now, in addition to users being able to interact with data provided on the host e.g. Soccer Tables or via the web, Wikipedia Search Rates they can use apps to view and analyse their own data I have knocked up

## Veterinary Epidemiologic Research: GLM – Evaluating Logistic Regression Models (part 3)

March 19, 2013
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$Veterinary Epidemiologic Research: GLM – Evaluating Logistic Regression Models (part 3)$

Third part on logistic regression (first here, second here). Two steps in assessing the fit of the model: first is to determine if the model fits using summary measures of goodness of fit or by assessing the predictive ability of the model; second is to deterime if there’s any observations that do not fit the