2217 search results for "ggplot"

Shiny Server on CentOS

June 29, 2013
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I’ve been enjoying working with Joe Cheng’s Shiny Server and wanted to create a quick step-by-step guide on installing it on an AWS CentOS EC2 instance as the standard Shiny Server instructions assume the typical dependencies are installed: 1. Shiny’s instructions say to install libssl-dev (sudo yum install libssl-dev), here is the CentOS equivalent : sudo yum install openssl-devel

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Using R: Two plots of principal component analysis

June 26, 2013
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Using R: Two plots of principal component analysis

PCA is a very common method for exploration and reduction of high-dimensional data. It works by making linear combinations of the variables that are orthogonal, and is thus a way to change basis to better see patterns in data. You either do spectral decomposition of the correlation matrix or singular value decomposition of the data

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-omics in 2013

June 24, 2013
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-omics in 2013

Just how many (bad) -omics are there anyway? Let’s find out. 1. Get the raw data It would be nice if we could search PubMed for titles containing all -omics: However, we cannot since leading wildcards don’t work in PubMed search. So let’s just grab all articles from 2013: and save them in a format

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Visualising Crime Hotspots in England and Wales using {ggmap}

June 24, 2013
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Visualising Crime Hotspots in England and Wales using {ggmap}

Two weeks ago, I was looking for ways to make pretty maps for my own research project. A quick search led me to some very informative blog posts by Kim Gilbert, David Smith and Max Marchi. Eventually, I Google'd the excellent crime weather map exa...

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Opel Corsa Diesel Usage

June 24, 2013
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Opel Corsa Diesel Usage

I wanted to extend my car weight distribution calculation of June 16 from only 2000 to years 2000 to 2013. Unfortunately, come Sunday afternoon the code seemed too slow and not even the beginning of a post. So, I went on to another calculation I w...

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Creating a BI Dashboard: Part 1

June 23, 2013
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Creating a BI Dashboard: Part 1

Introduction A few first posts of this blog will demonstrate how to build each report hosted by the business intelligence (BI) application dashboard shown below (see Fig. 1). This application uses the following tools and technologies R – a free software environment for statistical computing and graphics, ASP.NET MVC4 – a free framework for building

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Revisualizing the best cities in the US in 2012- Shiny + googleVis = Incredibly powerful

June 23, 2013
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Revisualizing the best cities in the US in 2012- Shiny + googleVis = Incredibly powerful

This is the last time I will talk about visualizing the best cities of 2012 based on Bloomberg Businessweek's rankings. In an earlier post on this topic, interactive applications to plot bar graphs and histograms for different characteristics...

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Principal Components Analysis Shiny App

June 23, 2013
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Principal Components Analysis Shiny App

I’ve recently started experimenting with making Shiny apps, and today I wanted to make a basic app for calculating and visualizing principal components analysis (PCA). Here is the basic interface I came up with. Test drive the app for yourself using the code below or  check out the the R code HERE. Above is an example of the

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Time Is on My Side – A Small Example for Text Analytics on a Stream

June 23, 2013
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Time Is on My Side – A Small Example for Text Analytics on a Stream

Introduction and Background While my last posting was about recommendation in the context of Location Based Social Networks there are also other interesting topics regarding the analysis of unstructured data. The most established one is probably Text Analytics/Mining focusing on all sorts of text data.For me, coming from spatial analysis, these topic is relatively new but I couldn’t help noticing...

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Generating Alerts From Guardian University Tables Data

June 23, 2013
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Generating Alerts From Guardian University Tables Data

One of the things I’ve been pondering with respect to the whole data journalism process is how journalists without a lot of statistical training can quickly get a feel for whether there may be interesting story leads in a dataset, or how they might be able to fashion “alerts” that bring attention to data elements

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