604 search results for "shiny"

Situational Baseball: Analyzing Runs Potential Statistics

May 26, 2015
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By Mark Malter A few weeks ago, I wrote about my Baseball Stats R shiny application, where I demonstrated how to calculate runs expectancies based on the 24 possible bases/outs states for any plate appearance. In this article, I’ll explain how I expanded on that to calculate the probability of winning the game, based on the current score/inning/bases/outs state....

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New Version of RStudio (v0.99) Available Now

May 26, 2015
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New Version of RStudio (v0.99) Available Now

We’re pleased to announce that the final version of RStudio v0.99 is available for download now. Highlights of the release include: A new data viewer with support for large datasets, filtering, searching, and sorting. Complete overhaul of R code completion with many new features and capabilities. The source editor now provides code diagnostics (errors, warnings, etc.) as

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Teaching R course? Use analogsea to run your customized RStudio in Digital Ocean!

May 20, 2015
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Teaching R course? Use analogsea to run your customized RStudio in Digital Ocean!

Two years ago I taught an introductory R/Shiny course here at The Jackson Lab. We all learnt a lot. Unfortunately not about Shiny itself, but rather about incompatibilities between its versions and trouble with its installation to some machines.And it ...

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Interactive charts in R

May 19, 2015
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Interactive charts in R

I’m giving a talk tomorrow at the Edinburgh R usergroup (EdinbR) on how to get started building interactive charts in R. I’ll talk about rCharts as a great general entry point to quickly generating interactive charts, and also the newer htmlwidgets … Continue reading →

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The perfect t-test

May 18, 2015
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The perfect t-test

I've created an easy to use R script that will import your data, and performs and writes up a state-of-the-art dependent or independent t-test. The goal of this script is to examine whether more researcher-centered statistical tools (i.e., a one-click analysis script that checks normality assumptions, calculates effect sizes and their confidence intervals, creates good figures,...

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Analyzing R-Bloggers’ posts via Twitter

May 18, 2015
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Analyzing R-Bloggers’ posts via Twitter

For those who don’t know, every time a new blog post gets added to R-Bloggers, it gets a corresponding tweet by @Rbloggers, which gets seen by Rbloggers’ ~20k followers fairly fast. And every time my post gets published, I can’t help but check up on how many people gave that tweet some Twitter love, ie. “favorite”d or...

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In case you missed it: April 2015 roundup

May 8, 2015
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In case you missed them, here are some articles from April of particular interest to R users. Joseph Rickert reviews the inaugural New York City R User Conference, featuring Andrew Gelman. Engineer Vineet Abraham compares performance benchmarks for R and Revolution R Open on OS X and Ubuntu. R was featured in the keynotes for the BUILD developer’s conference....

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A Link Between topicmodels LDA and LDAvis

May 8, 2015
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Carson Sievert and Kenny Shirley have put together the really nice LDAvis R package. It provides a Shiny-based interactive interface for exploring the output from Latent Dirichlet Allocation topic models. If you've never used it, I highly recommend checking out their XKCD example (this paper also has some nice background). LDAvis doesn't fit topic models, it...

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Extract values from numerous rasters in less time

May 7, 2015
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These days I was working with a Shiny app for which the computation time is a big problem.Basically this app takes some coordinates, extract values from 1036 rasters for these coordinates and make some computations.  As far as I can (and please correct me if I'm wrong!) tell there are two ways of doing this task:1) load all the...

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choroplethr v3.1.0: Better Summary Demographic Data

May 5, 2015
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choroplethr v3.1.0: Better Summary Demographic Data

Today I am happy to announce that choroplethr v3.1.0 is now on CRAN. You can get it by typing the following from an R console: install.packages("choroplethr") This version adds better support for summary demographic data for each state and county in the US. The data is in two data.frames and two functions. The data.frames are:

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