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1. |
Record Nr. |
UNINA9910624380503321 |
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Autore |
Mailund Thomas |
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Titolo |
R 4 Data Science Quick Reference : A Pocket Guide to APIs, Libraries, and Packages / / by Thomas Mailund |
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Pubbl/distr/stampa |
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Berkeley, CA : , : Apress : , : Imprint : Apress, , 2022 |
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ISBN |
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Edizione |
[2nd ed. 2022.] |
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Descrizione fisica |
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1 online resource (231 pages) |
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Disciplina |
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Soggetti |
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Programming languages (Electronic computers) |
Big data |
Computer science |
Programming Language |
Big Data |
Computer Science |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Nota di contenuto |
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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. |
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Sommario/riassunto |
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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.. You will: Implement applicable R 4 programming language specification features Import data with readr |
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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. |
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