# Blogs

During the 2013 JSM (Joint Statistics Meetings) Conference in Montreal, Revolution Analytics conducted a survey of attendees from August 5 to August 8. The 865 respondents gave their opinions on the privacy and ethics related to data collection, and on their familiarity with statistical software used for the analysis of such data.

Out of the 865 statisticians surveyed:

In case you missed them, here are some articles from August of particular interest to R users:

A tutorial on parallel programming with the foreach, doMC and doSNOW packages.

Joe Rickert reviews R's capabilities for linear algebra, sparse matrices and big matrices.

The massively-online open course (MOOC) platform Coursera has already delivered two essential free courses for anyone who wants to learn the R language. *Computing for Data Analysis*, presented by Roger Peng, covers the basics of R programming. The follow-up course *Data Analysis*, presented by Jeff Leek, covers statistical modeling and data visualization with R.

One of the practical challenges of Bayesian statistics is being able to deal with all of the complex probability distributions involved. You begin with the likelihood function of interest, but once you combine it with the prior distributions of all the parameters, you end up with a complex posterior distribution that you need to characterize. Since you usually can't calculate that distribution analytically, the best you can do is to simulate from that distribution (generally, using Markov-Chain Monte-Carlo techniques).

If you enjoy data visualization and schadenfreude, here's a great Tumblr to spend some browsing through this weekend. WTF Visualizations features a steady diet of charts, graphs, infographics and data visualizations of all stripes that are poorly constructed, confusing, misleading or just make no sense. Here's just one example:

R has been featured in a couple of recent articles in the tech press. Last month, Data Informed's feature article 5 Key Considerations When Choosing Open Source Statistics Software suggested R for its analytics capabilities:

Last week, Revolution Analytics' US Chief Scientist Mario Inchiosa gave a presentation on high-performance predictive analytics in R and Hadoop, showing how Revolution R Enterprise 7 will bring the high-performance predictable algorithms of ScaleR to run on Cloudera and Hortonworks Hadoop clusters, while retaining the same easy-to-use interface from the R langua

The best way to learn any software is to use it, and if you're new to Hadoop and want to try using Hadoop with R the process of setting up your own Hadoop cluster can be daunting (to say the least). But if learning is the goal, the key is that you don't *need* to install a full cluster. All you need is your own machine, and the ability to install software from the shell command line.

KDDNuggets has completed its annual poll of top languages for analytics, data mining and data science, and just as in the prior two years the R language is ranked the most popular. R is used by almost 61% of respondents: