02585nam 2200505 450 991062438050332120230318122131.01-4842-8780-010.1007/978-1-4842-8780-4(MiAaPQ)EBC7127626(Au-PeEL)EBL7127626(CKB)25219243200041(OCoLC)1349468003(OCoLC-P)1349468003(CaSebORM)9781484287804(PPN)265863988(EXLCZ)992521924320004120230318d2022 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierR 4 data science quick reference a pocket guide to APIs, libraries, and packages /Thomas MailundSecond edition.New York, New York :Apress,[2022]©20221 online resource (231 pages)Includes index.Print version: Mailund, Thomas R 4 Data Science Quick Reference Berkeley, CA : Apress L. P.,c2022 9781484287798 1. Introduction. - 2. Importing Data: readr -- 3. Representing Tables: tibble. - 4. Tidy+select, 5. Reformatting Tables: tidyr -- 6. Pipelines: magrittr -- 7. Functional Programming: purrr. - 8. Manipulating Data Frames: dplyr. - 9. Working with Strings: stringr -- 10. Working with Factors: forcats. - 11. Working with Dates: lubridate. - 12. Working with Models: broom and modelr. - 13. Plotting: ggplot2 -- 14. Conclusions.In this handy, quick reference book you'll be introduced to several R data science packages, with examples of how to use each of them. All concepts will be covered concisely, with many illustrative examples using the following APIs: readr, dibble, forecasts, lubridate, stringr, tidyr, magnittr, dplyr, purrr, ggplot2, modelr, and more. With R 4 Data Science Quick Reference, 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. All source code used in the book is freely available on GitHub.R (Computer program language)StatisticsData processingR (Computer program language)StatisticsData processing.519.502855133Mailund Thomas846442MiAaPQMiAaPQMiAaPQBOOK9910624380503321R 4 Data Science Quick Reference2962674UNINA