Data Mashups in R

Introduction

Programmers may spend a good part of their careers scripting code to conform to commercial statistics packages, visualization tools, and domain-specific third-party software. The same tasks can force end users to spend countless hours in copy-paste purgatory, each minor change necessitating another grueling round of formatting tabs and screenshots. Luckily, R scripting offers some reprieve. Because this open source project garners the support of a large community of package developers, the R statistical programming environment provides an amazing level of extensibility. Data from a multitude of sources can be imported into R and processed using R packages to aid statistical analysis and visualization. R scripts can also be configured to produce high-quality reports in an automated fashion—saving time, energy, and frustration.

This book will demonstrate how real-world data is imported, managed, visualized, and analyzed within R. Spatial mashups provide an excellent way to explore the capabilities of R—encompassing R packages, R syntax, and data structures. Instead of canned sample data, we will be plotting and analyzing actual current home foreclosure auctions. Through this exercise, we hope to provide an general idea of how the R environment works with R packages as well as its own capabilities in statistical analysis. We will be accessing spatial data in several formats (HTML, XML, shapefiles, and text) both locally and over the web, to produce a map of home foreclosures ...