Abstract
R, extended with the Tidyverse, is a powerful language for data science. There is strong support for each step along a data analysis pipeline. After reading this book, you should have a good grasp of how the Tidyverse packages work and how you use them. The book did not cover which data science and machine learning model to use on particular data, but only how they could fit into pipelines. Covering actual data analysis and the methods to use—and packages supporting them—is beyond the scope of this book. Each statistical or machine learning method could fill a book in itself, and there are many such models in R packages. The book is mainly a syntax guide and what it covered can be used with any model you need to apply to your data, so it is a good foundation for how to adapt your data analysis into the Tidyverse framework.
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R, extended with the Tidyverse, is a powerful language for data science. There is strong support for each step along a data analysis pipeline. After reading this book, you should have a good grasp of how the Tidyverse packages work and how you use them. The book did not cover which data science and machine learning model to use on particular data, but only how they could fit into pipelines. Covering actual data analysis and the methods to use—and packages supporting them—is beyond the scope of this book. Each statistical or machine learning method could fill a book in itself, and there are many such models in R packages. The book is mainly a syntax guide and what it covered can be used with any model you need to apply to your data, so it is a good foundation for how to adapt your data analysis into the Tidyverse framework.
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© 2019 Thomas Mailund
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Mailund, T. (2019). Conclusions. In: R Data Science Quick Reference. Apress, Berkeley, CA. https://doi.org/10.1007/978-1-4842-4894-2_13
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DOI: https://doi.org/10.1007/978-1-4842-4894-2_13
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Publisher Name: Apress, Berkeley, CA
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