R 4 Data Science Quick Reference
A Pocket Guide to APIs, Libraries, and Packages
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Beschreibung
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
- 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
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..
What You'll Learn
- 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
Programmers new to R's data science, data mining, and analytics packages. Some prior coding experience with R in general is recommended.
Focuses on data science using R version 4 release Covers the specific APIs and packages that let you build R-based data science applications Includes how to use these packages to do data, statistical analysis using R
Autor*in
Thomas Mailund
Themen in »R 4 Data Science Quick Reference«
Programming Language R 4 R data science tidyr analytics purrr ggplot modelr markdown knitr shiny tidyr stingr forcats