Skip to Content


by Matt Sundquist, Plotly Co-founder

It's delightfully smooth to publish R code, plots, and presentations to the web. For example:

You're probably familiar with the classic Travelling Salesman problem: given (say) 20 cities, what is shortest route you can take that passes through all 20 cities and returns to the starting point? It's a difficult problem to solve, because you need to try all possible routes to find the minimum, and there are a LOT of possibilities. For a 20-city tour there are more than 1 trillion trillion routes to try — and that's a fairly small problem!

Rrrr! It's International Talk Like a Pirate day again, mateys, the day all landlubbers should talk in pirate lingo. (If you're unsure how, R can help.) It's also the day where you can pick up some great O'Reilly R books for half price

A quick heads up that if you'd like to get a great introduction to doing data science with the R language, Joe Rickert will be giving a free webinar next Thursday, September 25: Data Science with R. Regular readers of the blog will be familiar with Joe's posts on this topic.

by Joseph Rickert

While preparing for the DataWeek R Bootcamp that I conducted this week I came across the following gem. This code, based directly on a Max Kuhn presentation of a couple years back, compares the efficacy of two machine learning models on a training data set.

I'm speaking at the DataWeek conference in San Francisco today. My talk follows Skylar Lyon from Accenture — I'm really looking forward to hearing how he uses Revolution R Enterprise with Teradata Database to run R in-database with 400 million rows of data. Update: Here are Skylar's slides.


The R Foundation for Statistical Computing, the Vienna-based non-profit organization that oversees the R Project, has just added several new "ordinary members".

Graduate student Clay McLeod decided to find out what makes a post on the social-sharing site Reddit popular. These are the questions he seeks to answer:

There's a new online lifestyle magazine for data scientists with a machine-learning bent: ML Daily. (Thanks to reader SG for the tip.)

Check it out for lots of useful articles, including:

Google has just released a new package for R: CausalImpact. Amongst many other things, this package allows Google to resolve the classical conundrum: how can we asses the impact of an intervention (for example, the effect of an advertising campaign on website clicks) when we can't know what would have happened if we hadn't run the campaign?