VCASMO - drewconway

useR! 2010 - Local R User Group Panel

2010-07-24T23:54:45Z

Panel discussion about forming local R user groups from useR! 2010. Panelists include, Jim Porzak (San Francisco Bay Area), David Smith (REvolution Analytics), John C. Nash (Ottawa, Canada), Drew Conway (New York City), Szilard Pafka (Los Angeles), March Vaisman (DC Metro), and JD Long (Chicago); and was moderated by Derek Norton (Atlanta). The panelists discuss their experiences organizing user groups in their areas, and field several questions from the audience.

Data munging with SQL and R (Rosetta Stone Talk - 3/3)

2010-01-18T00:35:17Z

Presentation given by Josh Reich at the NYC R Statistical Meetup on January 7, 2010. The January Meetup was the "R Rosetta Stone," and this talk covers R, SQL and Postgres.

Use Rapache: It Works!

2010-01-19T12:15:19Z

Presentations given at the Bay Area useR Group on January 10, 2010 by Jeff Horner, creator of the Rapache module, on "R-driven web applications".

Introduction to the Grammar of Graphics with ggplot2 in R

2009-12-29T06:51:21Z

A detailed introduction to the Grammar of Graphics as implemented in R with the data visualization library ggplot2. This talk was given by Harlan Harris to the NYC R Statistical Meetup on December 3, 2009.

Advanced debugging techniques in R

2010-06-05T00:08:20Z

Harlan Harris provides an interactive introduction to more advanced debugging techniques using traceback(), trace() and debug(). This talk was presented to the NYC R Statistical Programming Meetup on June 3, 2010.

Code from Harlan's talk is available here: http://gist.github.com/423333

Debugging basics in R

2010-06-04T23:57:35Z

Jay Emerson, professor of statistics at Yale University, introduces basic core functions for simple debugging in R, including print(), cat() and browser(). This talk was presented at the NYC R Statistical Programming Language Meetup on June 3, 2010.

Spatial Statistics in R: An Introduction

2010-02-09T06:03:55Z

Presentation given by John Myles White on February 4, 2010 to the NYC R Statistical Meetup. During that talk John covers several techniques for performing spatial analytics in R.

Portfolio Correlation Analysis Tool

2010-04-15T10:59:42Z

Andrew Ilardi presents R project that analyzes a list of stocks to the NYC R Statistical Programming Meetup on April 8, 2010. Andrew's tool reaches out to the web to pull historical stock prices, then plots Quarter over Quarter correlations and a year over year scatter plot.

Map-reduce in R with Amazon EMR

2010-05-28T00:09:56Z

James Long discusses his experience performing large distributed calculations in R on Amazon's Elastic Map-reduce service. James also provides a live demonstration of using R with EMR to stochastically calculate pi.

Mining and Analyzing Online Social Graph Data

2010-05-19T19:16:46Z

Drew Conway, PhD student in NYU's Department of Politics, provides an introduction to mining social graph data from the Internet that focuses on the technical, substantive and ethical concerns related to this type of analysis.

How I came to R

2010-05-07T03:03:52Z

J. D. Long will be giving an introduction to R - how he got into it when doing data analysis and what drew him to use it instead of complicated excel models.

Solving optimization problems numerically in R with optim()

2010-04-15T23:06:01Z

Often in game theory (and presumably other applied math settings) we are interested in the behavior of equations with no explicit solution. In this talk, Andrew Little demonstrates how I use the optim() and other functions in R for such situations in my research to the NYC R Statistical Programming Meetup on April 8, 2010.

Zelig and Matching in R with an Application to Conflict and Leader Tenure

2010-04-15T11:43:32Z

Andrew Little discusses two econometric packages developed by Gary King of Harvard, and how he has used them in his research at the August, 2009 NYC R Statistical Programming Meetup.

Zelig - a single, easy-to-use package that can estimate, help interpret, and present the results of a large range of statistical methods.

MatchIt - implements a wide range of sophisticated matching methods, making it possible to greatly reduce the dependence of causal inferences on hard-to-justify, but commonly made, statistical modeling assumptions.

Biomarker detection in cancer (gene expression analysis)

2010-04-15T10:19:15Z

Pankaj Chopra discusses using R and Bioconductor (http://www.bioconductor.org/) for biomarker detection in cancer to the NYC R Statistical Programming Meetup on April 8, 2010.