1820 search results for "tutorial"

Interactive visualizations with R – a minireview

January 9, 2015
By
2015-01-10 00_34_37-Clipboard

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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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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WrightMap: Multifaceted models

January 7, 2015
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WrightMap: Multifaceted models

We received an email from a user who was interested in displaying results from a multifaceted model in WrightMap. In the WrightMap manual, we show how to use multifaceted results from ConQuest: fpath <- system.file("extdata", package = "WrightMap") model4 <- CQmodel(file.path(fpath, "ex4a.mle"), file.path(fpath, "ex4a.shw")) wrightMap(model4, item.table = "rater", interactions = "rater*topic", step.table = "topic") (See this tutorial for more details.) But...

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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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Mapping Seattle Crime

January 6, 2015
By
Mapping Seattle Crime

Last week I published a data visualization of San Francisco crime. This week, I’m mapping Seattle crime data. The map above is moderately complicated to create, so I’ll start this tutorial with a simpler case: the dot distribution map. Seattle crime map, simplified version First, we’ll start by loading the data. Note that I already The post

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The Top 7 Beautiful Data Blog Posts in 2014

January 3, 2015
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2014 was a great year in data science – and also an exciting year for me personally from a very inspirational Strata Conference in Santa Clara to a wonderful experience of speaking at PyData Berlin to founding the data visualization company DataLion. But it also was a great year blogging about data science. Here’s the

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Locality Sensitive Hashing In R Part 1

January 1, 2015
By

Introduction In the next series of posts I will try to explain base concepts Locality Sensitive Hashing technique. Note, that I will try to follow general functional programming style. So I will use R’s Higher-Order Functions instead of traditional R’s *apply functions family (I suppose this post will be more readable for non R users). Also I will use brilliant...

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Locality Sensitive Hashing in R

January 1, 2015
By

Introduction In the next series of posts I will try to explain base concepts Locality Sensitive Hashing technique. Note, that I will try to follow general functional programming style. So I will use R’s Higher-Order Functions instead of traditional R’s *apply functions family (I suppose this post will be more readable for non R users). Also I will use brilliant...

Read more »

Plot with ggplot2 and plotly within knitr reports

December 30, 2014
By
Plot with ggplot2 and plotly within knitr reports

Plotly is a platform for making, editing, and sharing graphs. If you are used to making plots with ggplot2, you can call ggplotly() to make your plots interactive, web-based, and collaborative. For example, see plot.ly/~marianne2/166, shown below. Notice the hover text! The “plotly” R package lets you use plotly with R. Want to...

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