1856 search results for "RSTUDIO"

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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In case you missed it: February 2016 roundup

March 8, 2016
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In case you missed them, here are some articles from February of particular interest to R users. A tutorial on presenting interactive versions of R maps in PowerBI. An animation of Japan's population pyramid through 2050 based on US Census Bureau demographic projections. Interactive visualizations of multivariate data in R with the threejs package. New Zealand's tourism ministry uses...

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mockaRoo – making realistic test data in R

March 8, 2016
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When I’m building stuff in R like packages, models, etc. I find myself wishing for realistic looking test data without having to resort to getting data off my production server. To that end I’ve been on the hunt for a way of generating decent test data. A few months back I stumbled upon the neat The post

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BallR: Interactive NBA Shot Charts with R and Shiny

March 8, 2016
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BallR: Interactive NBA Shot Charts with R and Shiny

The NBA’s Stats API provides data for every single shot attempted during an NBA game since 1996, including location coordinates on the court. I built a tool called BallR, using R’s Shiny framework, to explore NBA shot data at the player-level. BallR lets you select a player and season, then creates a customizable chart that shows shot...

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Computerworld’s 10-step tutorial for creating interactive election maps with R

March 7, 2016
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Computerworld’s 10-step tutorial for creating interactive election maps with R

With the US election season in full swing, you can hardly browse a newspaper website without seeing some kind of map showing election or polling results, like this one from the New York Times. With election data (usually) accessible online, and a wealth of mapping tools available in the R language, you can fairly easily make similar maps yourself...

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ggplot2 2.1.0

March 4, 2016
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ggplot2 2.1.0

I’m very pleased to announce the release of ggplot2 2.1.0, scales 0.4.0, and gtable 0.2.0. These are set of relatively minor updates that fix a whole bunch of little problems that crept in during the last big update. The most important changes are described below. When mapping an aesthetic to a constant the default guide

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Dynamic Predictions using Joint Models

March 4, 2016
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Dynamic Predictions using Joint Models

What are Dynamic PredictionsIn this post we will explain the concept of dynamic predictions and illustrate how these can be computed using the framework of joint models for longitudinal and survival data, and the R package JMbayes. The type of dynamic ...

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Where People Live

March 3, 2016
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Where People Live

There was an interesting map on reddit this morning, with a visualisation of latitude and longituge of where people live, on Earth. So I tried to reproduce it. To compute the density, I used a kernel based approch > library(maps) > data("world.cities") > X=world.cities > liss=function(x,h){ + w=dnorm(x-X,0,h) + sum(X*w) + } > vx=seq(-80,80) > vy=Vectorize(function(x) liss(x,1))(vx) > vy=vy/max(vy)...

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Declutter a shiny report’s code v2.0

March 3, 2016
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I wrote a year ago on a way to declutter shiny report code which involved putting objects into a sourced file, however, at that point in time the solution was a bit brittle and clunky. Now there’s a better way to develop shiny applications – shiny modules. In October, RStudio introduced the concept of modules The post

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Learn R from the Ground Up

March 1, 2016
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Learn R from the Ground Up

R is an open source programming language which is made from the dialect of S. R programming is very power when dealing with Data Manipulation and Statistical Modelling. R is widely used by data scientist and can solve complex problems containing datasets with statistical computing. R has thousands of packages which shows how powerful R History + Installation

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