1800 search results for "Tutorial"

Comment sections and help instructions at the Shiny Dev Center

May 28, 2014
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Comment sections and help instructions at the Shiny Dev Center

We’ve added two new features that make it easier to learn Shiny with the Shiny Dev Center. Disqus comments - Each lesson and article on the Dev Center now has its own comments section. Use the comments section to start a discussion or to leave feedback about the articles. How to get help with Shiny –

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Attempt at R version of Peter Norvig’s Spelling Corrector

May 26, 2014
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Attempt at R version of Peter Norvig’s Spelling Corrector

I recently came across a short tutorial by Peter Norvig on natural language processing using some interesting examples. I found it very interesting and really liked Peter Norvig’s explanations and the fact that it was all in ipython notebook for … Continue reading →

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Quick History 2: GLMs, R and large data sets

May 22, 2014
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by Joseph Rickert In last week’s post, I sketched out the history of Generalized Linear Models and their implementations. In this post I’ll attempt to outline how GLM functions evolved in R to handle large data sets. The first function to make it possible to build GLM models with datasets that are too big to fit into memory was...

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R beats Python! R beats Julia! Anyone else wanna challenge R?

May 21, 2014
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R beats Python!  R beats Julia!  Anyone else wanna challenge R?

Before I left for China a few weeks ago, I said my next post would be on our Rth parallel R package. It’s not quite ready yet, so today I’ll post one of the topics I spoke on last night at the Berkeley R Language Beginners Study Group. Thanks to the group for inviting me,

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R beats Python! R beats Julia! Anyone else wanna challenge R?

May 21, 2014
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R beats Python!  R beats Julia!  Anyone else wanna challenge R?

Before I left for China a few weeks ago, I said my next post would be on our Rth parallel R package. It’s not quite ready yet, so today I’ll post one of the topics I spoke on last night at the Berkeley R Language Beginners Study Group. Thanks to the group for inviting me,

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Deploying Shiny Server on Amazon: Some Troubleshoots and Solutions

May 18, 2014
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Deploying Shiny Server on Amazon: Some Troubleshoots and Solutions

I really enjoyed Treb Allen‘s tutorial on deploying a Shiny server on an Amazon Cloud Instance. I used this approach for my shiny app that is a map highlighting the economic impact of the recent shale oil and gas boom on the places where the actual extraction happens. The easiest way to proceed is to

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R has some sharp corners

May 15, 2014
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R has some sharp corners

R is definitely our first choice go-to analysis system. In our opinion you really shouldn’t use something else until you have an articulated reason (be it a need for larger data scale, different programming language, better data source integration, or something else). The advantages of R are numerous: Single integrated work environment. Powerful unified scripting/programming Related posts:

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hipsteR: re-educating people who learned R before it was cool

May 15, 2014
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hipsteR: re-educating people who learned R before it was cool

This morning, I started a tutorial for folks whose knowledge of R is (like mine) stuck in 2001. Yesterday I started reading the Rcpp book, and on page 4 there’s an example using the R function replicate, which (a) I’d never heard before, and (b) is super useful. I mean, I often write code like

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qqman: an R package for creating Q-Q and manhattan plots from GWAS results

May 15, 2014
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qqman: an R package for creating Q-Q and manhattan plots from GWAS results

Three years ago I wrote a blog post on how to create manhattan plots in R. After hundreds of comments pointing out bugs and other issues, I've finally cleaned up this code and turned it into an R package.The qqman R package is on CRAN: http://cran.r-project.org/web/packages/qqman/The source code is on GitHub: https://github.com/stephenturner/qqmanIf you'd like to cite the...

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Plotly and rOpenSci: Make ggplots shareable and interactive.

May 13, 2014
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Plotly and rOpenSci: Make ggplots shareable and interactive.

By Matt Sundquist Plotly's Co-Founder Here at Plotly, we are on a mission to build a platform where data scientists can analyze data, create beautiful graphs and collaborate: like a GitHub for data, where you can share and find plots, data, and code. The benefits are: Plots (including ggplot2 plots) are interactive and drawn with D3 (try zooming, panning,...

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