2953 search results for "ggplot"

Introducing ggvis

June 23, 2014
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Introducing ggvis

Our first public release of ggvis, version 0.3, is now available on CRAN. What is ggvis? It’s a new package for data visualization. Like ggplot2, it is built on concepts from the grammar of graphics, but it also adds interactivity, a new data pipeline, and it renders in a web browser. Our goal is to

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Deploying a scoring engine for predictive analytics with OpenCPU

June 23, 2014
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Deploying a scoring engine for predictive analytics with OpenCPU

TLDR/abstract: See the tvscore demo app or this jsfiddle for all of this in action. This post explains how to use the OpenCPU system to setup a scoring engine for calculating real time predictions. In our example we use the predict.gam function from the mgcv package to make predictions based...

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Gender gap and visualisation challenge @ useR!2014

June 20, 2014
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Gender gap and visualisation challenge @ useR!2014

7 days to go for submissions in the DataVis contest at useR!2014 (see contest webpage). Note that the contest is open for all R users, not only conference participants. Submit your solution soon! PISA dataset allows to challenge some ,,common opinions”, like are boys or girls better in math / reading. But, how to compare

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Shiny 0.10

June 20, 2014
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Shiny 0.10

Shiny 0.10 is now available on CRAN. Interactive documents In this release, the biggest changes were under the hood to support the creation of interactive documents. If you haven’t had a chance to check out interactive documents, we really encourage you to do so—it may be the easiest way to learn Shiny. New layout functions

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PITFALL: Did you really mean to use matrix(nrow, ncol)?

June 17, 2014
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PITFALL: Did you really mean to use matrix(nrow, ncol)?

Are you a good R citizen and preallocates your matrices? If you are allocating a numeric matrix in one of the following two ways, then you are doing it the wrong way!x <- matrix(nrow=500, ncol=100)orx <- matrix(NA, nrow=500, ncol=100)Why? Becau...

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Studying Ted Talks, Anscombe’s Quartet, and Modern Languages Enrollment

June 17, 2014
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While the feed from a newer github/jekyll blogging platform (patilv.github.io) is registered with blog aggregators, here are snippets of three posts that were recently published at the new site. Please click on the titles to visit the corresponding page. 1. Frequent Speakers at Ted and Word Cloud of Talk Titles A recent article in openculture.com by Dan Colman mentioned...

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Can You Track Me Now? (Visualizing Xfinity Wi-Fi Hotspot Coverage) [Part 2]

June 13, 2014
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Can You Track Me Now? (Visualizing Xfinity Wi-Fi Hotspot Coverage) [Part 2]

This is the second of a two-part series. Part 1 set up the story and goes into how to discover, digest & reformat the necessary data. This concluding segment will show how to perform some basic visualizations and then how to build beautiful & informative density maps from the data and offer some suggestions as to how to...

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Proficiency levels @ PISA and visualisation challenge @ useR!2014

June 13, 2014
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Proficiency levels @ PISA  and visualisation challenge @ useR!2014

16 days to go for submissions in the DataVis contest at useR!2014 (see contest webpage). The contest is focused on PISA data and students’ skills. The main variables that reflect pupil skills in math / reading / science are plausible values e.g. columns PV1MATH, PV1READ, PV1SCIE in the dataset. But, these values are normalized to

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The Gilbreath’s Conjecture

June 12, 2014
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The Gilbreath’s Conjecture

317 is a prime, not because we think so, or because our minds are shaped in one way rather than another, but because it is so, because mathematical reality is built that way (G.H. Hardy) In 1958, the mathematician and magician Norman L. Gilbreath presented a disconcerting hypothesis conceived in the back of a napkin. Gilbreath

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Bar charts with percentage labels but counts on the y axis

June 11, 2014
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Bar charts with percentage labels but counts on the y axis

Bar charts and histograms are easily to understand. I often write for non-specialist audiences so I tend to use them a lot. People like percentages too, so a bar chart with counts on the y axis but percentage labels is a useful thing to be able to produce. But how to do them in our

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