Web technologies are increasingly relevant to scientists working
with data, for both accessing data and creating rich dynamic and
interactive displays. The XML and JSON data formats are widely used
in Web services, regular Web pages and JavaScript code, and
visualization formats such as SVG and KML for Google Earth and
Google Maps. In addition, scientists use HTTP and other network
protocols to scrape data from Web pages, access REST and SOAP Web
Services, and interact with NoSQL databases and text search
applications. This book provides a practical hands-on introduction
to these technologies, including high-level functions the authors
have developed for data scientists. It describes strategies and
approaches for extracting data from HTML, XML, and JSON formats and
how to programmatically access data from the Web.
Along with these general skills, the authors illustrate several
applications that are relevant to data scientists, such as reading
and writing spreadsheet documents both locally and via Google Docs,
creating interactive and dynamic visualizations, displaying
spatial-temporal displays with Google Earth, and generating code
from descriptions of data structures to read and write data. These
topics demonstrate the rich possibilities and opportunities to do
new things with these modern technologies. The book contains many
examples and case-studies that readers can use directly and adapt
to their own work. The authors have focused on the integration of
these technologies with the R statistical computing environment.
However, the ideas and skills presented here are more general, and
statisticians who use other computing environments will also find
them relevant to their work.
Deborah Nolan is Professor of Statistics at University of
California, Berkeley.
Duncan Temple Lang is Associate Professor of Statistics at
University of California, Davis and has been a member of both the S
and R development teams."
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