Data Mashups in R
Publisher: O'Reilly Media
Final Release Date: June 2009

This article demonstrates how the realworld data is imported, managed, visualized, and analyzed within the R statistical framework. Presented as a spatial mashup, this tutorial introduces the user to R packages, R syntax, and data structures. The user will learn 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-locally and over the web to produce a map of home foreclosure auctions and perform statistical analysis on these events.

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oreillyData Mashups in R

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(2 of 4 customers found this review helpful)


A showcase of the possibilities of R

By Dan

from Orem, UT

About Me Analyst, Data Nut, Geek, Sys Admin

Verified Reviewer



      Best Uses

      • Intermediate
      • Novice

      Comments about oreilly Data Mashups in R:

      Data Mashups in R walks you through a complex mashup of several public data sources as a means to highlight the capabilities of R. Slurp some messy address data in HTML from the local Sherrif's website, geocode it with Yahoo APIs, parse the resulting XML, weed out fake addresses, watch out for bad netweork connections, slurp and digest some ESRI map data, plot the map, plot the address data on the map as a heat map, mix in some census data for some per captia insight and compare the result with economic demographics plotted on the same map. Do all the above in R.

      Some might consider R an obscure command-line tool for crusty statisticians, but Data Mashups in R might change your mind. If you're new to R, the authors will likely have you breathless by the end of their mad dash through a number of modern data sources while assembling their timely mashup. If you're one of the aforementioned crusty statisticians who lives and breathes R, you might find something new in their exploration of R's capabilities, but you'll likely not be surprised at what's possible with R.

      Data Mashups in R will likely get you excited about what you can do in R, but if you're new to the tool, the one-page appendix on getting started with R won't get you very far. For tutorial material you'll probably want to consult the manuals on the R website or other tutorial works, and obtain a good reference work like O'Reilly's R in a Nutshell.

      I found a few typos in Data Mashups in R, and some of the example code had errors in it, but nothing too hard to overcome if you are determined.

      All in all, for the price, it's a good overview of what's possible with R.

      Disclaimer: This document was provided to me as a review copy by O'Reilly.

      (12 of 14 customers found this review helpful)


      A useful, practical example of working with data in R

      By David Smith

      from Undisclosed

      Comments about oreilly Data Mashups in R:

      (Read the full review at the Revolutions blog ( .)

      This 30-page article is an excellent and very practical example of integrating messy data from varied sources, using R or REvolution R. It's not designed as a manual-style introduction to R. But while working through the fully-detailed example it presents, even programmers unfamiliar to R will get a good sense of the practical capabilities of R when working with real-life data sources.

      The example used is a particularly timely one: how to automate the process of downloading foreclosure data from a public website, and presenting it in graphical form, and involves working with data in HTML, XML and flat files.

      For experienced R users looking for tips on integrating messy data sources from the Web, or for programmers new to R looking for a practical example to work through as an introduction to R, Data Mashups in R is well worth the download.

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