Data Mashups in R.: A Case Study in Real-World Data Analysis

38 Pages·2011·4.931 MB·English

by  Jeremy Leipzig, Xiao-Yi Li

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Data Mashups in R
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Data Mashups in R
Jeremy Leipzig and Xiao-Yi Li
Beijing • Cambridge • Farnham • Köln • Sebastopol • Tokyo
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Data Mashups in R
by Jeremy Leipzig and Xiao-Yi Li
Copyright © 2011 Jeremy Leipzig and Xiao-Yi Li. All rights reserved.
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March 2011: First Edition.
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ISBN: 978-1-449-30353-2
[LSI]
1299253461
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Table of Contents
Introduction ................................................................ vii
1. Mapping Foreclosures ................................................... 1
Messy Address Parsing 1
Exploring “streets” 3
Obtaining Latitude and Longitude Using Yahoo 4
Shaking the XML Tree 5
The Many Ways to Philly (Latitude) 6
Using Data Structures 7
Using Helper Methods 7
Using Internal Class Methods 7
Exceptional Circumstances 8
The Unmappable Fake Street 8
No Connection 9
Taking Shape 9
Finding a Usable Map 10
PBSmapping 10
Developing the Plot 11
Preparing to Add Points to Our Map 12
Exploring R Data Structures: geoTable 14
Making Events of Our Foreclosures 15
Turning Up the Heat 15
Factors When You Need Them 16
Filling with Color Gradients 17
2. Statistics of Foreclosure ................................................. 19
Importing Census Data 19
Descriptive Statistics 22
Descriptive Plots 23
Correlation 25
Final Thoughts 26
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Appendix: Getting Started ..................................................... 27
vi | Table of Contents
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Introduction
Programmers may spend a good part of their careers scripting code to conform to com-
mercial statistics packages, visualization tools, and domain-specific third-party soft-
ware. The same tasks can force end users to spend countless hours in copy-paste pur-
gatory, 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 pro-
gramming environment provides an amazing level of extensibility. Data from a multi-
tude 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 auc-
tions. Through this exercise, we hope to provide an general idea of how the R envi-
ronment 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.
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