Blog Archives

R User Groups on GitHub

January 28, 2016
By
R User Groups on GitHub

by Joseph Rickert Quite a few times over the past few years I have highlighted presentations posted by R user groups on their websites and recommended these sites as a source for interesting material, but I have never thought to see what the user groups were doing on GitHub. As you might expect, many people who make presentations at...

Read more »

Pipelining R and Python in Notebooks

January 26, 2016
By
Pipelining R and Python in Notebooks

by Micheleen Harris Microsoft Data Scientist As a Data Scientist, I refuse to choose between R and Python, the top contenders currently fighting for the title of top Data Science programming language. I am not going to argue about which is better or pit Python and R against each other. Rather, I'm simply going to suggest to play to...

Read more »

Getting Started with Markov Chains: Part 2

January 22, 2016
By
Getting Started with Markov Chains: Part 2

by Joseph Rickert In a previous post, I showed some elementary properties of discrete time Markov Chains could be calculated, mostly with functions from the markovchain package. In this post, I would like to show a little bit more of the functionality available in that package by fitting a Markov Chain to some data. In this first block of...

Read more »

A gentle introduction to parallel computing in R

January 19, 2016
By
A gentle introduction to parallel computing in R

by John Mount Ph.D. Data Scientist at Win-Vector LLC Let's talk about the use and benefits of parallel computation in R. IBM's Blue Gene/P massively parallel supercomputer (Wikipedia). Parallel computing is a type of computation in which many calculations are carried out simultaneously." Wikipedia quoting: Gottlieb, Allan; Almasi, George S. (1989). Highly parallel computing The reason we care is:...

Read more »

New Data Sources for R

January 14, 2016
By
New Data Sources for R

by Joseph Rickert Over the past few months, a number of new CRAN packages have appeared that make it easier for R users to gain access to curated data. Most of these provide interfaces to a RESTful API written by the data publishers while a few just wrap the data set inside the package. Some of the new packages...

Read more »

Microsoft R Server available free to students with DreamSpark

January 12, 2016
By
Microsoft R Server available free to students with DreamSpark

by Joseph Rickert Over the last 6 years, thousands of students and faculty have downloaded Revolution R Enterprise (RRE) from Revolution Analytics for free, making it possible for them to do statistical modeling on large data sets with the same R language used by savvy statisticians and data scientists in business and industry. In addition to this individual scholar...

Read more »

Getting Started with Markov Chains

January 7, 2016
By
Getting Started with Markov Chains

by Joseph Rickert There are number of R packages devoted to sophisticated applications of Markov chains. These include msm and SemiMarkov for fitting multistate models to panel data, mstate for survival analysis applications, TPmsm for estimating transition probabilities for 3-state progressive disease models, heemod for applying Markov models to health care economic applications, HMM and depmixS4 for fitting Hidden...

Read more »

7th Meeting of Spanish R Users. 5-6 November 2015. Salamanca (Spain)

January 5, 2016
By
7th Meeting of Spanish R Users. 5-6 November 2015. Salamanca (Spain)

By Virgilio Gómez Rubio, Spanish R Users Organizing Committee As every autumn since 2009, Spanish R users gathered at their annual meeting. It is organised by Spanish R users group ‘Comunidad R-Hispano’and took place in 5-6 November in the historic city of Salamanca. The 7th Meeting of Spanish R Users attracted more than 100 R entusiasts and provided a...

Read more »

Looking forward to 2016

December 24, 2015
By
Looking forward to 2016

by Joseph Rickert The following map of all of the R user groups listed in Microsoft's Local R User Group Directory is good way to visualize the R world as we rocket into 2016. As a member of the useR!2016 planning committee, foremost in my mind right now is that in just a few months people will be coming...

Read more »

Trade-offs to consider when reading a large dataset into R using the RevoScaleR package

December 15, 2015
By
Trade-offs to consider when reading a large dataset into R using the RevoScaleR package

by Seth Mottaghinejad, Data Scientist at Microsoft R and big data There are many R packages dedicated to letting users (or useRs if you prefer) deal with big data in R. (We will intentionally avoid using proper case for 'big data', because (1) the term has been somewhat hackneyed, and (2) for the sake of this article we can...

Read more »

Sponsors

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)