MARC record from Internet Archive

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035 $a(NNC)13626009
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020 $a9781491910368$q(electronic bk.)
020 $a1491910364$q(electronic bk.)
020 $a9781491910337$q(electronic bk.)
020 $a149191033X$q(electronic bk.)
020 $a9781491910344$q(electronic bk.)
020 $a1491910348$q(electronic bk.)
020 $a1491910399
020 $a9781491910399
020 $z9781491910399
035 $a(OCoLC)966429425$z(OCoLC)959883998$z(OCoLC)967081506$z(OCoLC)968205795$z(OCoLC)990606789$z(OCoLC)1048155777$z(OCoLC)1066689704$z(OCoLC)1103251335$z(OCoLC)1117188708$z(OCoLC)1153034857$z(OCoLC)1171429669$z(OCoLC)1192346405$z(OCoLC)1240517992
037 $a17A40AC7-2948-4F90-9C18-207341CE0160$bOverDrive, Inc.$nhttp://www.overdrive.com
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049 $aZCUA
100 1 $aWickham, Hadley,$eauthor.
245 10 $aR for data science :$bimport, tidy, transform, visualize, and model data /$cHadley Wickham & Garrett Grolemund.
250 $aFirst edition.
264 1 $aSebastopol, CA :$bO'Reilly Media,$c2016.
264 4 $c©2017
300 $a1 online resource :$billustrations (some color)
336 $atext$btxt$2rdacontent
337 $acomputer$bc$2rdamedia
338 $aonline resource$bcr$2rdacarrier
588 0 $aOnline resource; title from PDF title page (EBSCO, viewed December 22, 2016).
504 $aIncludes bibliographical references and index.
505 0 $aCopyright; Table of Contents; Preface; What You Will Learn; How This Book Is Organized; What You Won't Learn; Big Data; Python, Julia, and Friends; Nonrectangular Data; Hypothesis Confirmation; Prerequisites; R; RStudio; The Tidyverse; Other Packages; Running R Code; Getting Help and Learning More; Acknowledgments; Online Version; Conventions Used in This Book; Using Code Examples; O'Reilly Safari; How to Contact Us; Part I. Explore; Chapter 1. Data Visualization with ggplot2; Introduction; Prerequisites; First Steps; The mpg Data Frame; Creating a ggplot; A Graphing Template; Exercises.
505 8 $aAesthetic MappingsExercises; Common Problems; Facets; Exercises; Geometric Objects; Exercises; Statistical Transformations; Exercises; Position Adjustments; Exercises; Coordinate Systems; Exercises; The Layered Grammar of Graphics; Chapter 2. Workflow: Basics; Coding Basics; What's in a Name?; Calling Functions; Exercises; Chapter 3. Data Transformation with dplyr; Introduction; Prerequisites; nycflights13; dplyr Basics; Filter Rows with filter(); Comparisons; Logical Operators; Missing Values; Exercises; Arrange Rows with arrange(); Exercises; Select Columns with select(); Exercises.
505 8 $aAdd New Variables with mutate()Useful Creation Functions; Exercises; Grouped Summaries with summarize(); Combining Multiple Operations with the Pipe; Missing Values; Counts; Useful Summary Functions; Grouping by Multiple Variables; Ungrouping; Exercises; Grouped Mutates (and Filters); Exercises; Chapter 4. Workflow: Scripts; Running Code; RStudio Diagnostics; Exercises; Chapter 5. Exploratory Data Analysis; Introduction; Prerequisites; Questions; Variation; Visualizing Distributions; Typical Values; Unusual Values; Exercises; Missing Values; Exercises; Covariation.
505 8 $aA Categorical and Continuous VariableExercises; Two Categorical Variables; Exercises; Two Continuous Variables; Exercises; Patterns and Models; ggplot2 Calls; Learning More; Chapter 6. Workflow: Projects; What Is Real?; Where Does Your Analysis Live?; Paths and Directories; RStudio Projects; Summary; Part II. Wrangle; Chapter 7. Tibbles with tibble; Introduction; Prerequisites; Creating Tibbles; Tibbles Versus data.frame; Printing; Subsetting; Interacting with Older Code; Exercises; Chapter 8. Data Import with readr; Introduction; Prerequisites; Getting Started; Compared to Base R; Exercises.
505 8 $aParsing a VectorNumbers; Strings; Factors; Dates, Date-Times, and Times; Exercises; Parsing a File; Strategy; Problems; Other Strategies; Writing to a File; Other Types of Data; Chapter 9. Tidy Data with tidyr; Introduction; Prerequisites; Tidy Data; Exercises; Spreading and Gathering; Gathering; Spreading; Exercises; Separating and Pull; Separate; Unite; Exercises; Missing Values; Exercises; Case Study; Exercises; Nontidy Data; Chapter 10. Relational Data with dplyr; Introduction; Prerequisites; nycflights13; Exercises; Keys; Exercises; Mutating Joins; Understanding Joins; Inner Join.
520 $a"This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience"--$cProvided by publisher.
650 0 $aElectronic data processing.
650 0 $aR (Computer program language)
650 0 $aDatabases.
650 0 $aBig data.
650 0 $aData mining.
650 12 $aData Mining
650 6 $aR (Langage de programmation)
650 6 $aDonnées volumineuses.
650 6 $aExploration de données (Informatique)
650 7 $aCOMPUTERS$xComputer Literacy.$2bisacsh
650 7 $aCOMPUTERS$xComputer Science.$2bisacsh
650 7 $aCOMPUTERS$xData Processing.$2bisacsh
650 7 $aCOMPUTERS$xHardware$xGeneral.$2bisacsh
650 7 $aCOMPUTERS$xInformation Technology.$2bisacsh
650 7 $aCOMPUTERS$xMachine Theory.$2bisacsh
650 7 $aCOMPUTERS$xReference.$2bisacsh
650 7 $aData mining.$2fast$0(OCoLC)fst00887946
650 7 $aBig data.$2fast$0(OCoLC)fst01892965
650 7 $aDatabases.$2fast$0(OCoLC)fst00888065
650 7 $aElectronic data processing.$2fast$0(OCoLC)fst00906956
650 7 $aR (Computer program language)$2fast$0(OCoLC)fst01086207
650 7 $aR$gProgramm$2gnd
655 0 $aElectronic books.
655 4 $aElectronic books.
700 1 $aGrolemund, Garrett,$eauthor.
776 08 $iPrint version:$z9781491910368
856 40 $uhttp://www.columbia.edu/cgi-bin/cul/resolve?clio13626009$zAll EBSCO eBooks
852 8 $blweb$hEBOOKS