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Jeremy Leipzig and Xiao-Yi Li - Data Mashups in R

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Jeremy Leipzig and Xiao-Yi Li Data Mashups in R

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In 2017, Noon Passama developed a group of jewellery pieces influenced by the idea of a numeral system. The body of work has become the starting point of a collaboration with graphic design and art direction Studio Amanda Haas. The result is a publication titled after the jewellery series: 0123456789. It documents the precise information of each piece which equals a numeric value. The book is a pseudo-scientific archeological undertaking, describing a post-apocalyptic world. In this dusty sandy world, the remaining human beings occasionally encounter objects of wonder. This book contains a science-fictional text written by curator Gabriela Acha and otherworldly images of excavation sites by photographer Christian Hagemann. [...] Studio Amanda Haas [...] created the overall concept, story-line and set design of the photography. The book undertakes an experimental research that, while visually exploring the shapes of numbers and the dynamics they create, it also suggests an alternative understanding of their visual manifestations. Each contributor in this book looked at the particular jewellery series from a personal perspective, keeping in mind the overarching questions of possible origin, belonging and cultural affiliation. The fictional story-line established itself surrounding the following presumptions: What if a past or future tribe of (human) beings had created pieces of jewellery that in themselves are not only beautiful, but might even bear a secret force or knowledge beyond the evident? Could there be a transmission of knowledge long after the existence of such a tribe? And if so, what would this lost knowledge be?--Provided by the publisher.

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Data Mashups in R
Jeremy Leipzig
Xiao-Yi Li
Published by OReilly Media

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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 fashionsaving 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 Rencompassing 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.

Chapter 1. Mapping Foreclosures
Messy Address Parsing

To illustrate how to combine data from disparate sources for statistical analysis and visualization, lets focus on one of the messiest sources of data around: web pages.

The Philadelphia sheriffs office posts foreclosure auctions on its website each month. How do we collect this data, massage it into a reasonable form, and work with it? First, create a new folder (for example, ~/Rmashup) to contain our project files. It is helpful to change the R working directory to your newly created folder.

#In Unix/MacOS> setwd("~/Documents/Rmashup/")#In Windows> setwd("C:/~/Rmashup/")

We can download this foreclosure listings web page from within R (or you may instead choose to save the raw HTML from your web browser):

> download.file(url="http://www.phillysheriff.com/properties.html",destfile="properties.html")

Here is some of this web pages source HTML, with addresses highlighted:

6321 Farnsworth St. 62nd Ward 1,379.88 sq. ft. BRT# 621533500 Improvements: Residential Property
HOMER SIMPSON C.P. January Term, 2006 No. 002619 $27,537.87 Phelan Hallinan & Schmieg, L.L.P.
243-467
1402 E. Mt. Pleasant Ave. 50th Ward approximately 1,416 sq. ft. more or less BRT# 502440300 ...

The sheriffs raw HTML listings are inconsistently formatted, but with the right regular expression we can identify street addresses: notice how they appear alone on a line. Our goal is to submit viable addresses to the geocoder. Here are some typical addresses that our regular expression should match:

3509 N. Lee St. 2120-2128 E. Allegheny Ave. 7601 Crittenden St., #E-10 370 Tomlinson Place 2311 N. 33rd St. 6822-24 Old York Rd. 335 W. School House Lane

These are not addresses and should not be matched:

2,700 sq. ft. BRT# 124077100 Improvements: Residential Property C.P. June Term, 2009 No. 00575

R has built-in functions that allow the use of Perl-type regular expressions. For more info on regular expressions, see Mastering Regular Expressions (OReilly) and Regular Expression Pocket Reference (OReilly).

With some minor deletions to clean up address idiosyncrasies, we should be able to correctly identify street addresses from the mess of other data contained in properties.html . Well use a single regular expression pattern to do the cleanup. For clarity, we can break the pattern into the familiar elements of an address (number, name, suffix)

> stNum<-"^[0-9]{2,5}(\\-[0-9]+)?"> stName<-"([NSEW]\\. )?[0-9A-Z ]+"> stSuf<-"(St|Ave|Place|Blvd|Drive|Lane|Ln|Rd)(\\.?)$"> myStPat<-paste(stNum,stName,stSuf,sep=" ")

Note the backslash characters themselves must be escaped with a backslash to avoid conflict with R syntax. Lets test this pattern against our examples using Rs grep() function:

> grep(myStPat,"6822-24 Old York Rd.",perl=TRUE,value=FALSE,ignore.case=TRUE) [1] 1 > grep(myStPat,"2,700 sq. ft. BRT# 124077100 Improvements: Residential Property",perl=TRUE,value=FALSE,ignore.case=TRUE) integer(0)

The result, [1] 1, shows that the first of our target address strings matched; we tested only one string at a time. We also have to omit strings that we dont want with our address, such as extra punctuation (like quotes or commas), or sheriffs office designations that follow street names:

> badStrings<-"(\\r| a\\/?[kd]\\/?a.+$| - Premise.+$| assessed as.+$|,Unit.+||Apt\\..+| #.+$|[,\"]|\\s+$)"

Test this against some examples using Rs gsub() function:

> gsub(badStrings,'',"119 Hagy's Mill Rd. a/k/a 119 Spring Lane",perl=TRUE) [1] "119 Hagy's Mill Rd." > gsub(badStrings,'',"3229 Hurley St. - Premise A",perl=TRUE) [1] "3229 Hurley St."

Lets encapsulate this address parsing into a function that will accept an HTML file and return a vector, a one-dimensional ordered collection with a specific data type, in this case character. Copy and paste this entire block into your R console:

#input:html filename#returns:data frame of geocoded addresses that can be plotted by PBSmappinggetAddressesFromHTML<-function(myHTMLDoc){myStreets<-vector(mode="character",0)stNum<-"^[0-9]{2,5}(\\-[0-9]+)?"stName<-"([NSEW]\\. )?([0-9A-Z ]+)"stSuf<-"(St|Ave|Place|Blvd|Drive|Lane|Ln|Rd)(\\.?)$"badStrings<-paste("(\\r| a\\/?[kd]\\/?a.+$| - Premise.+$| assessed as.+$|,","Unit.+||Apt\\..+| #.+$|[,\"]|\\s+$)")
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