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R 4 data science quick reference : a pocket guide to APIs, libraries, and packages /
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,...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
New York, NY :
Apress,
[2022]
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| Edición: | Second edition. |
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 100 | 1 | |a Mailund, Thomas, |e author. | |
| 245 | 1 | 0 | |a R 4 data science quick reference : |b a pocket guide to APIs, libraries, and packages / |c Thomas Mailund. |
| 250 | |a Second edition. | ||
| 264 | 1 | |a New York, NY : |b Apress, |c [2022] | |
| 264 | 4 | |c Ã2022 | |
| 300 | |a 1 online resource (231 pages) : |b illustrations | ||
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| 500 | |a Includes index. | ||
| 520 | |a 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. | ||
| 505 | 0 | |a 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. | |
| 588 | |a Description based on online resource; title from digital title page (viewed on November 10, 2022). | ||
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| 650 | 7 | |a Statistics |x Data processing. |2 fast |0 (OCoLC)fst01132113 | |
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