1855 search results for "rstudio"

SSH tunnels on Windows for R

March 14, 2016
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Recently I’ve had to get to grips with SSH tunnels. SSH tunnels are really useful for maintaining remote network integrity and work in a secure fashion. It is, however, a pain to open PuTTY and log in all the time, mainly because I couldn’t script it in R! It’s been a trial, but like most The post

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Install all required R packages on your Shiny server

March 13, 2016
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Install all required R packages on your Shiny server

Abstract I give a walkthrough of a bash script that installs all of the R packages required by an R program (e.g., Shiny app, R file, R markdown file). This is useful for speeding up the workflow of adding a new Shiny app to a server. Why do we need a script? As explained in Dean Attali’s excellent post on how...

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ggplot2で字幕 [Subtitles in ggplot2]

March 12, 2016
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ggplot2で字幕 [Subtitles in ggplot2]

Subtitles aren’t always necessary for plots, but I began to use them enough that I whipped up a function for ggplot2 that does a decent job adding a subtitle to a finished plot object. More than a few folks have tried their hand at this in the past and this is just my incremental contribution

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R 3.2.4 is released

March 11, 2016
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R 3.2.4 is released

R 3.2.4 (codename “Very Secure Dishes”) was released today. You can get the latest binaries version from here. (or the .tar.gz source code from here). The full list of new features and bug fixes is provided below. Upgrading to R 3.2.4 on Windows If you are using Windows you can easily upgrade to the latest version of R using the installr … Continue reading...

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An Introduction to XGBoost R package

Introduction XGBoost is a library designed and optimized for boosting trees algorithms. Gradient boosting trees model is originally proposed by Friedman et al. The underlying algorithm of XGBoost is similar, specifically it is an extension of the classic gbm algorithm. By employing multi-threads and imposing regularization, XGBoost is able to utilize more computational power and get more accurate prediction....

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Some Light Image Processing & Creation With R

March 10, 2016
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Some Light Image Processing & Creation With R

A friend, we’ll call him Alen put a call out for some function that could take an image and produce a per-row “histogram” along the edge for the number of filled-in points. That requirement eventually scope-creeped to wanting “histograms” on both the edge and bottom. In, essence there was a desire to be able to

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Learn R by Intensive Practice

March 10, 2016
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Learn R by Intensive Practice

Learn R by Intensive Practice is an introductory R course built especially for beginners who are completely new to R or even to basic programming. This is the first part of a multi-part video lessons aimed to give hands-on learning experience throughout the course. In this and the coming parts, I have covered the essential 01. Install R...

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Developing a R Tutorial shiny dashboard app

March 9, 2016
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Developing a R Tutorial shiny dashboard app

Through this post, I would like to describe a R Tutorial Shiny app that I recently developed. You can access the app here. (Please open the app on Chrome as some of the features may not work on IE. The app also includes a “ReadMe” introduction which provides a quick overview on how to use Related Post

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R on Travis-CI

March 9, 2016
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R on Travis-CI

Support for building R projects on Travis has recently undergone improvements which we hope will make it an even better tool for the R community. Feature highlights include: Support for Travis’ container-based infrastructure. Package dependency caching (on the container-based builds). Building with multiple R versions (R-devel, R-release (3.2.3) and R-oldrel (3.1.3)). Log filtering to improve

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Introduction to XGBoost R package

Introduction to XGBoost R package

Introduction XGBoost is a library designed and optimized for boosting trees algorithms. Gradient boosting trees model is originally proposed by Friedman et al. The underlying algorithm of XGBoost is similar, specifically it is an extension of the classic gbm algorithm. By employing multi-threads and imposing regularization, XGBoost is able to utilize more computational power and get more accurate prediction....

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