1002 search results for "RStudio"

Creating an analysis as a package and vignette

Creating an analysis as a package and vignette Following from my last post, I am going to go step by step through the process I use to generate an analysis as a package vignette. This will be an analysis of the tweets from the 2012 and 2014 ISMB conference (thanks to Neil and Stephen for...

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Data Caching

July 28, 2014
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Data caching is not new. It is often necessary to save intermediate data files when the process of loading and/or manipulating data takes a considerable amount of time. This problem is further complicated when working with dynamic data that changes regularly. In these situations it often sufficient to use data that is current with in some time frame (e.g....

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Analyses as Packages

Analyses as Packages TL;DR Instead of writing an analysis as a single or set of R scripts, use a package and include the analysis as a vignette of the package. Read below for the why, the how is in the next post. Analyses and Reports As data science or statistical researchers, we tend to do a lot of analyses, whether for our...

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A Few Notes on UseR! 2014

July 25, 2014
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A Few Notes on UseR! 2014

It has been a month since the UseR! 2014 conference, and I'm probably the last one who writes about it. UseR! is my favorite conference because it is technical and not too big. I have completely lost interest in big and broad conferences like JSM (to me, it has become Joint Sightseeing Meetings). Karl has written two blog posts...

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Things to try after useR! – Part 1: Deep Learning with H2O

July 25, 2014
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Things to try after useR! – Part 1: Deep Learning with H2O

Annual R User Conference 2014The useR! 2014 conference was a mind-blowing experience. Hundreds of R enthusiasts and the beautiful UCLA campus, I am really glad that I had the chance to attend! The only problem is that, after a few days of non-stop R talks, I was (and still am) completely overwhelmed...

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Announcing Shiny Server Pro 1.2

July 24, 2014
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Announcing Shiny Server Pro 1.2

RStudio is very pleased to announce the general availability of Shiny Server Pro 1.2. Download a free 45 day evaluation of Shiny Server Pro 1.2 Shiny Server Pro 1.2 adds support for R Markdown Interactive Documents in addition to Shiny applications. Learn more about Interactive Documents by registering for the Reproducible Reporting webinar August 13

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Interactive visualization of non-linear logistic regression decision boundaries with Shiny

Interactive visualization of non-linear logistic regression decision boundaries with Shiny

(skip to the shiny app) Model building is very often an iterative process that involves multiple steps of choosing an algorithm and hyperparameters, evaluating that model / cross validation, and optimizing the hyperparameters. I find a great aid in this process, for classification tasks, is not only to keep track of the accuracy across models, »more

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Agent Based Models and RNetLogo

July 24, 2014
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Agent Based Models and RNetLogo

by Joseph Rickert If I had to pick just one application to be the “killer app” for the digital computer I would probably choose Agent Based Modeling (ABM). Imagine creating a world populated with hundreds, or even thousands of agents, interacting with each other and with the environment according to their own simple rules. What kinds of patterns and...

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Six of One (Plot), Half-Dozen of the Other

July 24, 2014
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Six of One (Plot), Half-Dozen of the Other

This is a guest post by Randy Zwitch (@randyzwitch), a digital analytics and predictive modeling consultant in the Greater Philadelphia area. Randy blogs regularly about Data Science and related technologies at http://randyzwitch.com. He’s blogged at Bad Hessian before here. For those of you with WordPress blogs and have the Jetpack Stats module installed, you’re intimately familiar… Continue reading →

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magrittr: Simplifying R code with pipes

July 23, 2014
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magrittr: Simplifying R code with pipes

R is a functional language, which means that your code often contains a lot of ( parentheses ). And complex code often means nesting those parentheses together, which make code hard to read and understand. But there's a very handy R package — magrittr, by Stefan Milton Bache — which lets you transform nested function calls into a simple...

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