This collection of web pages is intended to be a guide to some of the resources for the analysis of spatial data using R, and other associated software. Corrections and contributions are very welcome, and may be made through the mailing list R-sig-Geo, or directly to the site maintainer. Another useful resource is the CRAN Spatial Task View. Please note that all software and documentation here provided or described is done so "as is" without warranty of any kind.

Project Goals The broad goals of the Bioconductor project are: To provide widespread access to a broad range of powerful statistical and graphical methods for the analysis of genomic data. To facilitate the inclusion of biological metadata in the analysis of genomic data, e.g. literature data from PubMed, annotation data from Entrez genes. To provide a common software platform that enables the rapid development and deployment of extensible, scalable, and interoperable software. To further scientific understanding by producing high-quality documentation and reproducible research. To train researchers on computational and statistical methods for the analysis of genomic data

R is a software package especially suitable for data analysis and graphical representation. Functions and results of analysis are all stored as objects, allowing easy function modification and model building. R provides the language, tool, and environment in one convenient package. It is very flexible and highly customizable. Excellent graphical tools make R an ideal environment for EDA (Exploratory Data Analysis). Since most high level functions are written in R language itself, you can learn the language by studying the function code.

Data ExplorationDecision Treesk-means ClusteringHierarchical ClusteringOutlier DetectionTime Series AnalysisTime Series Clustering and ClassificationAssociation RulesText MiningSocial Network AnalysisParallel ComputingOther Examples

Here at Revolution Analytics, we’d like to thank all of the data scientists who participated in our first R in Business competition and congratulate the winners. Each of the applications selected demonstrates how predictive analytics with R goes well beyond traditional business intelligence to empower business decision-makers to evaluate critical success factors within important business processes early and often. We see from the winning entries that a wide variety of industries and processes – from marketing, heavy manufacturing, clinical trial design, and IT project management all share a need for ongoing predictive analysis. Revolution Analytics delivers the only R-based enterprise analytics framework, and by examining undiscovered applications of the R language, Revolution Analytics can further refine R tools to meet all organizations’ deep analysis needs.

Learn more about the winning entries in the slide show below: