2657 search results for "ggplot2"

Simulated Annealing Feature Selection

January 12, 2015
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Simulated Annealing Feature Selection

As previously mentioned, caret has two new feature selection routines based on genetic algorithms (GA) and simulated annealing (SA). The help pages for the two new functions give a detailed account of the options, syntax etc. The package already has functions to conduct feature selection using simple filters as well as recursive feature elimination (RFE). RFE...

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How to analyze smartphone sensor data with R and the BreakoutDetection package

January 10, 2015
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How to analyze smartphone sensor data with R and the BreakoutDetection package

Yesterday, Jörg has written a blog post on Data Storytelling with Smartphone sensor data. Here’s a practical approach on how to analyze smartphone sensor data with R. In this example I will be using the accelerometer smartphone data that Datarella provided in its Data Fiction competition. The dataset shows the acceleration along

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Interactive visualizations with R – a minireview

January 9, 2015
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Interactive visualization allows deeper exploration of data than static plots. Javascript libraries such as d3 have made possible wonderful new ways to show data. Luckily the R community has been active in developing R interfaces to some popular javascript libraries to enable R users to create interactive visualizations without knowing any javascript. In this post I have reviewed...

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In case you missed it: December 2014 roundup

January 9, 2015
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In case you missed them, here are some articles from November of particular interest to R users. R was featured in recent articles in Nature News and Mashable. A recap of the 6th Spanish R Users Conference. R was the recipient of a 2014 "Bossie" award for best open-source big data tools. A fractal Christmas tree created with R....

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Anomaly Detection with Wikipedia Page View Data

January 8, 2015
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Anomaly Detection with Wikipedia Page View Data

Today, the Twitter engineering team released another very interesting Open Source R package for working with time series data: “AnomalyDetection“. This package uses the Seasonal Hybrid ESD (S-H-ESD) algorithm to identify local anomalies (= variations inside seasonal patterns) and global anomalies (= variations that cannot be explained with seasonal patterns). As a

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New R Package: metricsgraphics

January 8, 2015
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Mozilla released the MetricsGraphics.js library back in November of 2014 (gh repo) and was greeted with great fanfare. It’s primary focus is on crisp, clean layouts for interactive time-series data, but they have support for other chart types as well (though said support is far from comprehensive). I had been pondering building an R package

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Nine Interactive 3D Graphs That Let You Zoom, Flip, & Spin

January 8, 2015
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Nine Interactive 3D Graphs That Let You Zoom, Flip, & Spin

Plotly’s interactive 3D graphs have new capabilities: multiple scenes, streaming graphs, and contour lines. Plot from our web app, Excel, Google Docs, Python, MATLAB, and R. Contact us if you’re interested in running Plotly on-premise to integrate your own applications. Click any image to go to the full-screen interactive version. Mount Bruno Elevation...

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Another release day: ggRandomForests V1.1.3

January 8, 2015
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Another release day: ggRandomForests V1.1.3

Continuing progress with the vignettes mean bug fixes in the code. Plus I’m presenting the regression random forest vignette to the stats group here tomorrow. http://cran.r-project.org/web/packages/ggRandomForests/index.html I’ve got another blog post percolating that will detail the biggest change in this… Continue reading →

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Top 77 R posts for 2014 (+R jobs)

January 7, 2015
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Top 77 R posts for 2014 (+R jobs)

R-bloggers.com is 5 years old this month! In celebration, this post share links to the top 77 most read R posts of 2014 (+stats on R-bloggers, + top R jobs for the beginning of 2015)

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Run scoring trends: using Shiny to create dynamic charts and tables in R

January 7, 2015
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Or, Retracing my stepsAs I’ve been learning the functionality of Shiny, the web app for R, I have used the helpful tutorials available from the developers at RStudio. At some point, though, one needs to break out and develop one’s own application.  My Shiny app “MLB run scoring trends” can be found at (

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