02116cam a2200301Ii 4500001001300000003000600013007000300019008004100022020001800063020001500081035002100096050002600117100002400143245004900167260004400216300003700260490001100297504005100308505054100359520067300900650002701573650003401600650003301634830001101667942001201678952011101690999001301801ocn954429014OCoLCta210114s2016 sz a b 001 0 eng d a9783319455983 a3319455982 a(OCoLC)954429014 aQA276.45.R3bB64 20161 aBoehmke, Bradley C.10aData wrangling with R /cBradley C. Boehmke. aCham, Switzerland : bSpringer,cc2016. axii, 238 p. :bill. (some col.) 1 aUse R! aIncludes bibliographical references and index.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. 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. 4aMultivariate analysis. 4aR (Computer program language) 4aStatisticsxData processing. 0aUse R! 2lcccBK 00104070aPNLIBbPNLIBcGENd2021-06-17oQA276.45.R3 B64 2016pPNLIB21060073r2021-06-17w2021-06-17yBK c259d259