3150 search results for "ggplot"

The beautiful R charts in London: The Information Capital

November 26, 2014
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The beautiful R charts in London: The Information Capital

If you've lived in or simply love London, a wonderful new book for your coffee-table is London: The Information Capital. In 100 beautifully-rendered charts, the book explores the data that underlies the city and its residents. To create most of these charts, geographer James Cheshire and designer Oliver Uberti relied on programs written in R. Using the R programming...

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Visualizing Historical & Most-likely First Snowfall Dates for U.S. Regions

November 26, 2014
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Visualizing Historical & Most-likely First Snowfall Dates for U.S. Regions

UPDATE: You can now run this as a local Shiny app by entering shiny::runGist("95ec24c1b0cb433a76a5", launch.browser=TRUE) at an R prompt (provided all the dependent libraries (below) are installed) or use it interactively over at Shiny Apps. The impending arrival of the first real snowfall of the year in my part of Maine got me curious about

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What’s the most popular German car where you live?

November 25, 2014
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What’s the most popular German car where you live?

by Tim Winke PhD student in Demography and Social Sciences in Berlin This post has been abstracted from Tim's entry to a contest that Dalia Research is running based on a global smarthpone survey that they are conducting. Tim's entry post is available as is all of the code behind it. - editor When people think about Germany, what...

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Circlizing Numbers

November 24, 2014
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Circlizing Numbers

She makes the sound the sea makes to calm me down (Dissolve Me, Alt-J) Searching how to do draw chord diagrams in the Internet with ggplot2 I found a very-easy-to-use package called circlize which does exactly that. A chord diagram shows relationships between things so the input to draw it is simply a matrix with the intensity … Continue reading...

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Pipeline to Plot Annual % Change

November 24, 2014
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Pipes in R make my life incredibly easy, and I think my code easier to read.   Note, there are a couple different flavors of pipes (see magrittr and pipeR).  For now, I choose pipeR.library(quantmod)library(pipeR)library(ggplot2)getSymbols("^GSPC",from="1900-01-01",auto.assign=F) %>>% #get S&P 500 from Yahoo!Finance ( . ) %>>% #get end of year ROC( type="discrete", n=1

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GTrendsR package to Explore Google trending for Field Dependent Terms

November 24, 2014
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GTrendsR package to Explore Google trending for Field Dependent Terms

My friend, Steve Simpson, introduced me to Philippe Massicotte and Dirk Eddelbuettel’s GTrendsR GitHub package this week. It’s a pretty nifty wrapper to the Google Trends API that enables one to search phrase trends over time. The trend indices that … Continue reading →

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When should I change to snow tires in Netherlands

November 23, 2014
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When should I change to snow tires in Netherlands

The Royal Netherlands Meteorological Institute has weather information by day for a number of Dutch stations. In this post I want to use those data for a practical problem: when should I switch to winter tires? (or is that snow tires? In any case nails...

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Information Density and Custom Chart Designs

November 21, 2014
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Information Density and Custom Chart Designs

I’ve been doodling today with a some charts for the Wrangling F1 Data With R living book, trying to see how much information I can start trying to pack into a single chart. The initial impetus came simply from thinking about a count of laps led in a particular race by each drive; this morphed

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Trading The Odds Volatility Risk Premium: Addressing Data Mining and Curve-Fitting

November 19, 2014
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Trading The Odds Volatility Risk Premium: Addressing Data Mining and Curve-Fitting

Several readers, upon seeing the risk and return ratio along with other statistics in the previous post stated that the … Continue reading →

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Visualizing (generalized) linear mixed effects models, part 2 #rstats #lme4

November 18, 2014
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Visualizing (generalized) linear mixed effects models, part 2 #rstats #lme4

In the first part on visualizing (generalized) linear mixed effects models, I showed examples of the new functions in the sjPlot package to visualize fixed and random effects (estimates and odds ratios) of (g)lmer results. Meanwhile, I added further features to the functions, which I like to introduce here. This posting is based on the

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