rstats, Statistics

Rolling with R in 2011

Interest in the open source statistical package R has grown over the last few years as researchers discover its powerful graphic capabilities, a suite of packages that extend its functionality, and its data import capabilities.  While several courses use R to teach introductory statistics, most researchers arrive at R with some statistical experience.  The following selected resources represent a growing number of books and websites designed to help orient users to the capabilities of R.

Quick-R Homepagequick_r
This website tries to provide a quick overview of basic data management and statistical capabilities of R for current SAS, SPSS, Stata, and Systat users.  The stress is on providing a brief overview of R commands for common data analysis needs.

R for Stata UsersR for Stata
A comprehensive guide for getting started in R using Stata as point of reference.

R for SAS and SPSS Users (not pictured)
Similar concept as R for Stata users.

Using R for Introductory Statistics
John Verzani’s R for Introductory Statistics is one of several introductions to using R for basic statistics. Examples are available as an R package.

Do you have other sources that you like for R?  Let us know in the comments.

GIS

Making Data Flow

As water quality and questions of water supply have grown more salient in the Triangle, Duke researchers have tried to contribute to the growing debate over water quality using the latest digital mapping (GIS) tools.  In the fall of 2009, Data and GIS Services in Perkins Library provided GIS analysis support for a stream and watershed assessment project that developed strategies to reverse the impact of poor urban stormwater management, degraded water quality, and the loss of natural habitats on the Duke campus.

Data/GIS helped the researchers access critical spatial data for the characterization of the contributing watershed’s current land use patterns.  This data enabled the students to analyze the watershed’s area of impervious surface and hydrologic flow paths, and helped inform the understanding of the water quality issues faced at the stream site.

The GIS map below illustrates how digital mapping tools can be used to summarize a large amount of complex data into a compelling presentation.

Special thanks to the interdisciplinary team of environmental and civil engineers, biology and environmental science majors, and a Nicholas MEM student who shared their project results: Alicia Burtner, Matt Ball, Nari Sohn, Avni Patel, Will Bierbower, Adam Nathan, Mike Schallmo, Justine Jackson-Ricketts, and Jai Singh.

Uncategorized

Welcome!

Welcome to the Perkins Data and GIS blog!  Our goal is to highlight Duke research, collections, policies, and tools surrounding empirical
data and digital maps of interest to the research community.  We hope that this blog will serve as a catalyst to link researchers and resources across the Duke community and beyond!