400 search results for "shiny"

TTTAR2: My First Shiny App with Bootstrap – #RUGSMAPS

August 26, 2014
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TTTAR2: My First Shiny App with Bootstrap – #RUGSMAPS

Thing To Try After useR! part 2 (TTTAR2) Originally, this post was supposed to be a sequel to TTTAR1 about h2o machine learning. Since TTTAR1 I have been carrying out more h2o tests both locally and on the cloud with the very kind support of Nick Elprin from Domino. The more...

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Goodbye static graphs, hello shiny, ggvis, rmarkdown (vs JS solutions)

August 18, 2014
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Goodbye static graphs, hello shiny, ggvis, rmarkdown (vs JS solutions)

One of the very exciting and promising developments from RStudio is the rmarkdown/shiny/ggvis combination of tools. We’re on the verge of static graphs and presentations being as old-fashioned as overhead transparencies. I’ve spent the last couple of days giving these tools a test spin. Lots of comments and links to examples appear below. I came

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A Simple Shiny App for Monitoring Trading Strategies – Part II

August 7, 2014
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This is a follow up on my previous post “A Simple Shiny App for Monitoring Trading Strategies“.  I added a few improvements that make the app a bit better (at least for me!). Below is the list of new features : A sample  .csv file (the one that contains the raw data) A “EndDate”  drop

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Introducing the Shiny App DThiring

August 4, 2014
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Well it has a been a long time since I have written anything on this blog.  I am long overdue.  I've been terribly busy learning new things and getting on with life.  One of the things I have learned is building R applications using Shin...

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Review of “Building interactive graphs with ggplot2 and shiny”

August 4, 2014
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Recently, Packt published a video course with the above title, and I've just spent a pleasant morning reviewing it on Packt's request. Pleasant, because I think the course gives an excellent introduction to both ggplot2 and shiny. The course is … Continue reading →

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Shiny 0.10.1

July 31, 2014
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Shiny 0.10.1

Shiny v0.10.1 has been released to CRAN. You can either install it from a CRAN mirror, or update it if you have installed a previous version. install.packages('shiny', repos = 'http://cran.rstudio.com') # or update your installed packages # update.packages(ask = FALSE, repos = 'http://cran.rstudio.com') The most prominent change in this patch release is that we added full

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drinkR: Estimate your Blood Alcohol Concentration using R and Shiny.

July 30, 2014
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drinkR: Estimate your Blood Alcohol Concentration using R and Shiny.

Inspired by events that took place at UseR 2014 last month I decided to implement an app that estimates one’s blood alcohol concentration (BAC). Today I present to you drinkR, implemented using R and Shiny, Rstudio’s framework for building web apps using R. So, say that I had a good dinner, drinking a couple of glasses...

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Building Interactive Graphs with ggplot2 and Shiny

July 30, 2014
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Building Interactive Graphs with ggplot2 and Shiny

Some time ago, I was contacted from guys at Packt Publishing. Their just published the Building Interactive Graphs with ggplot2 and Shiny online course and they ask me my (humble) opinion. I am proud of their request, and I will review shortly … Continue reading →

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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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