000			01993cam a2200289Ii 4500
001			ocn954429014
003			OCoLC
007			ta
008			210114s2016 sz a b 001 0 eng d
020			_a9783319455983
020			_a3319455982
035			_a(OCoLC)954429014
050			_aQA276.45.R3 _bB64 2016
100	1		_aBoehmke, Bradley C.
245	1	0	_aData wrangling with R / _cBradley C. Boehmke.
260			_aCham, Switzerland : _bSpringer, _cc2016.
300			_axii, 238 p. : _bill. (some col.)
490	1		_aUse R!
504			_aIncludes bibliographical references and index.
505	0		_aThe role of data wrangling -- Introduction to R -- The basics -- Dealing with numbers -- Dealing with character strings -- Dealing with regular expressions -- Dealing with factors -- Dealing with dates -- Data structure basics -- Managing vectors -- Managing lists -- Managing matrices -- Managing data frames -- Dealing with missing values -- Importing data -- Scraping data -- Exporting data -- Functions -- Loop control statements -- Simplify your code with %>% -- Reshaping your data with tidyr -- Transforming your data with dplyr.
520			_aThis guide for practicing statisticians, data scientists, and R users and programmers will teach the essentials of preprocessing: data leveraging the R programming language to easily and quickly turn noisy data into usable pieces of information. Data wrangling, which is also commonly referred to as data munging, transformation, manipulation, janitor work, etc., can be a painstakingly laborious process. Roughly 80% of data analysis is spent on cleaning and preparing data; however, being a prerequisite to the rest of the data analysis workflow (visualization, analysis, reporting), it is essential that one become fluent and efficient in data wrangling techniques.
650		4	_aMultivariate analysis.
650		4	_aR (Computer program language)
650		4	_aStatistics _xData processing.
830		0	_aUse R!
942			_2lcc _cBK
999			_c259 _d259