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Data Mashups in R
Author: Jeremy Leipzig and Xiao-Yi Li
Pages: 29
Publisher: O'Reilly
ISBN: 978-0-596-55964-9
Summary: A showcase of the possibilities of R
Review Date: 4 November, 2010

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 the Springer Use R series, and 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.

Overall Rating: 9/10

Disclaimer: This E-Book was given to me as a free review copy by O'Reilly

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