GPUs (Graphic Processing Units) have become much more popular in recent years for computationally intensive calculations. Despite these gains, the use of this hardware has been very limited in the R programming language. Although possible, the prospect of programming in either OpenCL or CUDA is difficult for many programmers unaccustomed to working with such a low-level interface. Creating bindings for R’s high-level programming that abstracts away the complex GPU code would make using GPUs far more accessible to R users.
With the recent release of Apache Spark 1.4.1 on July 15th, 2015, I wanted to write a step-by-step guide to help new users get up and running with SparkR locally on a Windows machine using command shell and RStudio. SparkR provides an R frontend to Apache Spark and using Spark’s distributed computation engine allows R-Users to run large scale data analysis from the R shell. The steps listed here are also documented in my online book title “Getting Started with SparkR for Big Data Analysis” which can be accessed at: http://www.danielemaasit.com/getting-started-with-sparkr/. These steps will get you up and running in less than 5 mins.
On Monday, we compared the performance of several different ways of calculating a distance matrix in R. Now there's another method to add to the list: using GPU acceleration in R. A GPU is a dedicated, high-performance chip available on many computers today. Unlike the CPU, it's not used for general computations, but rather for specialized tasks that benefit from a massively multi-threaded architecture. Video-game graphics is the usual target for GPUs, but in recent years they've been used for certain high-performance computing tasks as well. The problem is that GPUs require specialized programming, and because they have limited access...
R 3.1.3 (codename “Smooth Sidewalk”) 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 3.1.3 on Windows If you are using Windows you can easily upgrade to the latest version of R using the installr package. … Continue reading R 3.1.3 is released (+ easy upgrading for Windows users with the installr package)
Microsoft announced today that it will acquire Revolution Analytics. Revolution Analytics is an open-source analytics company with a strong focus on the highly popular R programming language for statistical computing.
by Anusua Trivedi, Microsoft Data Scientist Background and Approach This blog series is based on my upcoming talk on re-usability of Deep Learning Models at the Hadoop+Strata World Conference in Singapore. This blog series will be in several parts – where I describe my experiences and go deep into the reasons behind my choices. Deep learning is an emerging field of research, which has its application across multiple domains. I try to show how transfer learning and fine tuning strategy leads to re-usability of the same Convolution Neural Network model in different disjoint domains. Application of this model acros
It's official! R Tools for Visual Studio, until now only available as a private preview, is now in public preview and available to everyone as a free, open-source download. RTVS is an add-in for Microsoft Visual Studio, which adds R language development capabilities to the popular Windows-based IDE. If you don't already have Visual Studio, you can download Visual Studio Community for free. Then, download and install RTVS to add the "R Tools" menu to Visual Studio. RTVS in action. Weather chart R code adapted from Bradley Boehmke. To get started with R in Visual Studio, just create a new...
Written by Nicole White What’s New in RNeo4j? RNeo4j is Neo4j’s R driver – it allows you to quickly and easily interact with a Neo4j database from your R environment. Some recent updates to RNeo4j include: My contributions Functionality for… Learn More »
Today’s guest post is written by Vincent Warmerdam of GoDataDriven and is reposted with Vincent’s permission from blog.godatadriven.com. You can learn more about how to use SparkR with RStudio at the 2015 EARL Conference in Boston November 2-4, where Vincent will be speaking live. This document contains a tutorial on how to provision a spark […]
By David Smith I was on a panel back in 2009 where Bow Cowgill said, "The best thing about R is that it was written by statisticians. The worst thing about R is that it was written by statisticians." R is undeniably quirky — especially to computer scientists — and yet it has attracted a huge following for a domain-specific language, with more than two million users wordwide. So why has R become so successful, despite being outside the mainstream of programming languages? John Cook adeptly tackles that question in a 2013 lecture, "The R Language: The Good The Bad...
I’ve written a package for image processing in R, with the goal of providing a fast API in R that lets you do things in C++ if you need to. The package is called imager, and it’ on Github. The whole thing is based on CImg, a very nice C++ library for image processing by […]
By Douglas Ashton, Consultant One of the big successes of data analytics is the cultural change in how business decisions are being made. There is now wide spread acceptance of the role that data science has to play in decision making.
The twitteR package, released back in 2010, has long provided the means to access and analyze your Twitter social network data with R. But until recently, there hasn't been anything comparable for the Facebook social network. But now, thanks to Pablo Barbera, there is the RFacebook package which provides a collection of R functions to access data from your Facebook social network. To use RFacebook, you first need to sign up for a Facebook developer account, which is quick and easy as long as you already have a Facebook profile. JulianHi provides an excellent step-by-step tutorial on getting started with...
by Joseph Rickert Apache Spark, the open-source, cluster computing framework originally developed in the AMPLab at UC Berkeley and now championed by Databricks is rapidly moving from the bleeding edge of data science to the mainstream. Interest in Spark, demand for training and overall hype is on a trajectory to match the frenzy surrounding Hadoop in recent years. Next month's Strata + Hadoop World conference, for example, will offer three serious Spark training sessions: Apache Spark Advanced T
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