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001 978-1-4614-7900-0
003 DE-He213
005 20140220082459.0
007 cr nn 008mamaa
008 131128s2014 xxu| s |||| 0|eng d
020 _a9781461479000
_9978-1-4614-7900-0
024 7 _a10.1007/978-1-4614-7900-0
_2doi
050 4 _aQA276-280
072 7 _aUFM
_2bicssc
072 7 _aCOM077000
_2bisacsh
082 0 4 _a519.5
_223
100 1 _aNolan, Deborah.
_eauthor.
245 1 0 _aXML and Web Technologies for Data Sciences with R
_h[electronic resource] /
_cby Deborah Nolan, Duncan Temple Lang.
264 1 _aNew York, NY :
_bSpringer New York :
_bImprint: Springer,
_c2014.
300 _aXXIV, 663 p. 65 illus., 51 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aUse R!,
_x2197-5736
505 0 _aData Formats XML and JSON -- Web Technologies, Getting Data from the Web -- General XML Application Areas -- Bibliography -- General Index -- R Function and Parameter Index -- R Package Index -- R Class Index -- Colophon.
520 _aWeb 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 GoogleDocs, 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.
650 0 _aStatistics.
650 0 _aComputer science.
650 0 _aMathematical statistics.
650 1 4 _aStatistics.
650 2 4 _aStatistics and Computing/Statistics Programs.
650 2 4 _aProgramming Languages, Compilers, Interpreters.
650 2 4 _aStatistics, general.
700 1 _aTemple Lang, Duncan.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9781461478997
830 0 _aUse R!,
_x2197-5736
856 4 0 _uhttp://dx.doi.org/10.1007/978-1-4614-7900-0
912 _aZDB-2-SMA
999 _c92069
_d92069