LEADER 03352nam 22006255 450 001 9910624380503321 005 20251009105818.0 010 $a9781484287804 010 $a1484287800 024 7 $a10.1007/978-1-4842-8780-4 035 $a(MiAaPQ)EBC7127626 035 $a(Au-PeEL)EBL7127626 035 $a(CKB)25219243200041 035 $a(OCoLC)1349468003 035 $a(OCoLC-P)1349468003 035 $a(CaSebORM)9781484287804 035 $a(DE-He213)978-1-4842-8780-4 035 $a(PPN)265863988 035 $a(Perlego)4514046 035 $a(EXLCZ)9925219243200041 100 $a20221028d2022 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aR 4 Data Science Quick Reference $eA Pocket Guide to APIs, Libraries, and Packages /$fby Thomas Mailund 205 $a2nd ed. 2022. 210 1$aBerkeley, CA :$cApress :$cImprint: Apress,$d2022. 215 $a1 online resource (231 pages) 300 $aIncludes index. 311 08$aPrint version: Mailund, Thomas R 4 Data Science Quick Reference Berkeley, CA : Apress L. P.,c2022 9781484287798 327 $a1. 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. 330 $aIn 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.. You will: Implement applicable R 4 programming language specification features 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 ggplot2 and fit data to models using modelr. 606 $aProgramming languages (Electronic computers) 606 $aBig data 606 $aComputer science 606 $aProgramming Language 606 $aBig Data 606 $aComputer Science 615 0$aProgramming languages (Electronic computers) 615 0$aBig data. 615 0$aComputer science. 615 14$aProgramming Language. 615 24$aBig Data. 615 24$aComputer Science. 676 $a519.502855133 700 $aMailund$b Thomas$0846442 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910624380503321 996 $aR 4 Data Science Quick Reference$92962674 997 $aUNINA