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010   $a1-5231-5042-4
010   $a1-4842-4894-5
024 7 $a10.1007/978-1-4842-4894-2
035   $a(CKB)4100000008959112
035   $a(MiAaPQ)EBC5848646
035   $a(DE-He213)978-1-4842-4894-2
035   $a(CaSebORM)9781484248942
035   $a(EXLCZ)994100000008959112
100   $a20190807d2019      u|         0
101 0 $aeng
135   $aurcnu||||||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aR Data Science Quick Reference $eA Pocket Guide to APIs, Libraries, and Packages /$fby Thomas Mailund
205   $a1st ed. 2019.
210  1$aBerkeley, CA :$cApress :$cImprint: Apress,$d2019.
215   $a1 online resource (246 pages)
300   $aIncludes index.
311 08$a1-4842-4893-7 
327   $a1. Introduction -- 2. Importing Data: readr -- 3. Representing Tables: tibble -- 4. Reformatting Tables: tidyr -- 5. Pipelines: magrittr -- 6. Functional Programming: purrr -- 7. Manipulating Data Frames: dplyr -- 8. Working with Strings: stringr -- 9. Working with Factors: forcats -- 10. Working with Dates: lubridate -- 11. Working with Models: broom and modelr -- 12. Plotting: ggplot2 -- 13. Conclusions.
330   $aIn this handy, practical book you will cover each concept concisely, with many illustrative examples. You'll be introduced to several R data science packages, with examples of how to use each of them. In this book, you'll learn about the following APIs and packages that deal specifically with data science applications: readr, tibble, forcates, lubridate, stringr, tidyr, magnittr, dplyr, purrr, ggplot2, modelr, broom, knitr, shiny, and more. After using this handy quick reference guide, you'll have the code, APIs, and insights to write data science-based applications in the R programming language. You'll also be able to carry out data analysis. You will: Get started with RMarkdown and notebooks Import data with readr Work with categories using forcats, time and dates with lubridate, and strings with stringr Format data using tidyr and then transform that data using magrittr and dplyr Write functions with R for data science, data mining, and analytics-based applications Visualize data with ggplot 2 and data fit for models using modelr and broom Report results with markdown, knitr, shiny, and more.
606   $aProgramming languages (Electronic computers)
606   $aComputer programming
606   $aBig data
606   $aData mining
606   $aR (Computer program language)
606   $aProgramming Languages, Compilers, Interpreters$3https://scigraph.springernature.com/ontologies/product-market-codes/I14037
606   $aProgramming Techniques$3https://scigraph.springernature.com/ontologies/product-market-codes/I14010
606   $aBig Data$3https://scigraph.springernature.com/ontologies/product-market-codes/I29120
606   $aData Mining and Knowledge Discovery$3https://scigraph.springernature.com/ontologies/product-market-codes/I18030
615  0$aProgramming languages (Electronic computers)
615  0$aComputer programming.
615  0$aBig data.
615  0$aData mining.
615  0$aR (Computer program language)
615 14$aProgramming Languages, Compilers, Interpreters.
615 24$aProgramming Techniques.
615 24$aBig Data.
615 24$aData Mining and Knowledge Discovery.
676   $a005.7
700   $aMailund$b Thomas$4aut$4http://id.loc.gov/vocabulary/relators/aut$0846442
906   $aBOOK
912   $a9910338231403321
996   $aR Data Science Quick Reference$92511549
997   $aUNINA