3155 search results for "map"

Analysing Dracula

April 14, 2015
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Analysing Dracula

I recently read Bram Stoker’s Dracula, mostly because I wanted to rewatch the 1990s movie of the same name and see how badly they’d mutilated the story.  [Update 26/04/2015: I rewatched the 1992 movie and while it’s the most faithful version to the book, it has some weird additions and is utterly appalling.  People have been … Continue reading...

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Scale back or transform back multiple linear regression coefficients: Arbitrary case with ridge regression

April 10, 2015
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SummaryThe common case in data science or machine learning applications, different features or predictors manifest them in different scales. This could bring difficulty in interpreting the resulting coefficients of linear regression, such as one featur...

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Where are the R users?

April 9, 2015
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Where are the R users?

by Joseph Rickert A recent post by David Smith included a map that shows the locations of R user groups around the world. While is exhilarating to see how R user groups span the globe, the map does not give any idea about the size of the community at each location. The following plot, constructed from information on the...

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Six Ways You Can Make Beautiful Graphs (Like Your Favorite Journalists)

April 8, 2015
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Six Ways You Can Make Beautiful Graphs (Like Your Favorite Journalists)

This post shows how to make graphs like The Economist, New York Times, Vox, 538, Pew, and Quartz. And you can share–embed your beautiful, interactive graphs in apps, blog posts, and web sites. Read on to learn how. If you like interactive graphs and need to securely collaborate with your team, contact us about Plotly Enterprise.

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Mixing Numbers and Symbols in Time Series Charts

April 8, 2015
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Mixing Numbers and Symbols in Time Series Charts

One of the things I’ve been trying to explore with my #f1datajunkie projects are ways of representing information that work both in a glanceable way as well as repaying deeper reading. I’ve also been looking at various ways of using text labels rather than markers to provide additional information around particular data points. For example,

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Exploring San Francisco with choroplethrZip

April 7, 2015
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Exploring San Francisco with choroplethrZip

by Ari Lamstein Introduction Today I will walk through an analysis of San Francisco Zip Code Demographics using my new R package choroplethrZip. This package creates choropleth maps of US Zip Codes and connects to the US Census Bureau. A choropleth is a map that shows boundaries of regions (such as zip codes) and colors those regions according to...

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SWMPr 2.0.0 now on CRAN

April 5, 2015
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SWMPr 2.0.0 now on CRAN

I’m pleased to announce that my second R package, SWMPr, has been posted on CRAN. I developed this package to work with water quality time series data from the System Wide Monitoring Program (SWMP) of the National Estuarine Research Reserve System (NERRS). SWMP was established in 1995 to provide continuous environmental data at over 300

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Seeing the Forest and the Trees – a parallel machine learning example

April 1, 2015
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Seeing the Forest and the Trees – a parallel machine learning example

Parallelizing Random Forests in R with BatchJobs and OpenLava By: Gord Sissons and Feng Li In his series of blogs about machine learning, Trevor Stephens focuses on a survival model from the Titanic disaster and provides a tutorial explaining how decision trees tend to over-fit models yielding anomalous predictions. How do we build a better

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Configuring the R BatchJobs package for Torque batch queues

March 31, 2015
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Configuring the R BatchJobs package for Torque batch queues

I was asked recently to look at some R code which performs “embarrassingly parallel” computations (the same function, multiple times, different parameters) and see whether I could modify it to run on one of our high-performance computing clusters. The machine has 63 virtual compute nodes and uses the TORQUE batch queue system to allocate nodes

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Targeted Learning R Packages for Causal Inference and Machine Learning

March 31, 2015
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Targeted Learning R Packages for Causal Inference and Machine Learning

by Sherri Rose Assistant Professor of Health Care Policy Harvard Medical School Targeted learning methods build machine-learning-based estimators of parameters defined as features of the probability distribution of the data, while also providing influence-curve or bootstrap-based confidence internals. The theory offers a general template for creating targeted maximum likelihood estimators for a data structure, nonparametric or semiparametric statistical model,...

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