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 and perform
statistical analysis on these events.