2385 search results for "ggplot"

Fair weather fans? (An R scatter plot matrix)

July 18, 2013
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Fair weather fans? (An R scatter plot matrix)

(This article was first published on Bayes Ball, and kindly contributed to R-bloggers) The Victoria HarbourCats are roughly half way through their inaugural season in the West Coast League, and currently lead the league in average attendance.  In a recent conversation with one of the team’s staff, he mentioned that after the first game in early June, the fans...

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Lilac Bloomsday Run in Graphs

July 17, 2013
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Lilac Bloomsday Run in Graphs

The Lilac Bloomsday Run is a 7.46 miles (12 kms) race held annually in Spokane, Washington. The run was started in 1977 by Don Kardong and it saw 51,613 people register for it in 2013. In this post, I use R to collect data from their website, clean and...

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All My Roads Lead Back to Finance–PIMCO Sankey

July 17, 2013
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All My Roads Lead Back to Finance–PIMCO Sankey

Even though the route might be circuitous, my seemingly random journeys all seem to lead back to finance.  My fun with rCharts sankey diagrams (Exploring Networks with Sankey) has led me into an exploration of the PIMCO network.  Although PIMCO is best known for its fixed income products, PIMCO has broadened its product offerings...

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ngramr – an R package for Google Ngrams

July 16, 2013
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ngramr – an R package for Google Ngrams

The recent post How common are common words? made use of unusually explicit language for the Stubborn Mule. As expected, a number of email subscribers reported that the post fell foul of their email filters. Here I will return to the topic of n-grams, while keeping the language cleaner, and describe the R package I developed

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Rain in Netherlands during the past 100 years

July 14, 2013
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Rain in Netherlands during the past 100 years

Climate has my interest. But discussions on climate change seem to be focused on temperature. In real life, we look at temperature, rain, sunshine and wind. I was therefor happy to find a load of rain data on Royal Netherlands Meteorological Institute....

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Popularity bigdata / large data packages in R and ffbase useR presentation

Popularity bigdata / large data packages in R and ffbase useR presentation

(This article was first published on BNOSAC - Belgium Network of Open Source Analytical Consultants, and kindly contributed to R-bloggers) A few weeks ago, Rstudio released it's download logs, showing who downloaded R packages through their CRAN mirror. More info: http://blog.rstudio.org/2013/06/10/rstudio-cran-mirror/ This is very nice information and it can be used to show the popularity of packages with R, which has...

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Visualizing a tiny slice of India’s demographics with information from Wikipedia

July 10, 2013
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Visualizing a tiny slice of India’s demographics with information from Wikipedia

This post presents a tiny slice of a complex and diverse India using charts. (Data retrieval from Wikipedia on 9 July, 2013 and the analysis were performed using R; charts were generated using ggplot2, googleVis and wordcloud. More information can be f...

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rCharts version of d3 horizon

July 10, 2013
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rCharts version of d3 horizon

I love horizon plots.  My love shows up throughout my blog, and I have plotted horizon charts in base graphics, xtsExtra, lattice, and ggplot2.  Now with rCharts, we can implement Jason Davies d3.js horizon chart plugin to plot R data in...

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Unicode Tips in Python 2 and R

July 9, 2013
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Unicode Tips in Python 2 and R

Most of time, I don’t need to deal with different encodings at all. When possible, I use ASCII characters. And when there is a little processing in Chinese characters or other Unicode characters, I use .Net languages or JVM languages, in which every string is Unicode and of course when the characters are displayed they are displayed as characters...

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Interactive Heatmaps (and Dendrograms) – A Shiny App

July 7, 2013
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Interactive Heatmaps (and Dendrograms) – A Shiny App

Heatmaps are a great way to visualize data matrices. Heatmap color and organization can be used to  encode information about the data and metadata to help learn about the data at hand. An example of this could be looking at the raw data  or hierarchically clustering samples and variables based on their similarity or differences.

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