1037 search results for "Rstudio"

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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New data packages

July 23, 2014
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New data packages

I’ve released four new data packages to CRAN: babynames, fueleconomy, nasaweather and nycflights13. The goal of these packages is to provide some interesting, and relatively large, datasets to demonstrate various data analysis challenges in R. The package source code (on github, linked above) is fully reproducible so that you can see some data tidying in

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Announcing Packrat v0.4

July 22, 2014
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Announcing Packrat v0.4

We’re excited to announce a new release of Packrat, a tool for making R projects more isolated and reproducible by managing their package dependencies. This release brings a number of exciting features to Packrat that significantly improve the user experience: Automatic snapshots ensure that new packages installed in your project library are automatically tracked by

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Introducing tidyr

July 22, 2014
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Introducing tidyr

tidyr is new package that makes it easy to “tidy” your data. Tidy data is data that’s easy to work with: it’s easy to munge (with dplyr), visualise (with ggplot2 or ggvis) and model (with R’s hundreds of modelling packages). The two most important properties of tidy data are: Each column is a variable. Each

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Notes from the 2nd R in Insurance Conference

July 22, 2014
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Notes from the 2nd R in Insurance Conference

The 2nd R in Insurance conference took place last Monday, 14 July, at Cass Business School London. This one-day conference focused once more on applications in insurance and actuarial science that use R. Topics covered included reserving, pricing, loss...

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UseR 2014, days 3-4

July 21, 2014
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UseR 2014, days 3-4

Three weeks ago, I’d commented on the first two days of the UseR 2014 conference. I’m finally back to talk about the second half. Dirk Eddelbuettel on Rcpp Dirk Eddelbuettel gave a keynote on Rcpp . The goal of Rcpp is to have “the speed of C++ with the ease and clarity of R.” He

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Master interactive documents at the Shiny Dev Center

July 21, 2014
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Master interactive documents at the Shiny Dev Center

We’ve added a new section of articles to the Shiny Development Center. These articles explain how to create interactive documents with Shiny and R Markdown. You’ll learn how to Use R Markdown to create reproducible, dynamic reports. R Markdown offers one of the most efficient workflows for writing up your R results. Create interactive documents

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