R Data Science Quick Reference
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R Data Science Quick Reference

A Pocket Guide to APIs, Libraries, and Packages
 eBook
Sofort lieferbar | Lieferzeit: Sofort lieferbar I
ISBN-13:
9781484248942
Veröffentl:
2019
Einband:
eBook
Seiten:
246
Autor:
Thomas Mailund
eBook Typ:
PDF
eBook Format:
Reflowable eBook
Kopierschutz:
Digital Watermark [Social-DRM]
Sprache:
Englisch
Beschreibung:

In this handy, practical book you will cover each concept concisely, with many illustrative examples. You'll be introduced to several R data science packages, with examples of how to use each of them. In this book, you'll learn about the following APIs and packages that deal specifically with data science applications: readr, dibble, forecasts, lubridate, stringr, tidyr, magnittr, dplyr, purrr, ggplot2, modelr, and more.After using this handy quick reference guide, 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.  What You Will LearnImport data with readrWork with categories using forcats, time and dates with lubridate, and strings with stringrFormat data using tidyr and then transform that data using magrittr and dplyrWrite functions with R for data science, data mining, and analytics-based applicationsVisualize data with ggplot2 and fit data to models using modelrWho This Book Is ForProgrammers new to R's data science, data mining, and analytics packages.  Some prior coding experience with R in general is recommended.  
In this handy, practical book you will cover each concept concisely, with many illustrative examples. You'll be introduced to several R data science packages, with examples of how to use each of them. 

In this book, you’ll learn about the following APIs and packages that deal specifically with data science applications: readr, dibble, forecasts, lubridate, stringr, tidyr, magnittr, dplyr, purrr, ggplot2, modelr, and more.

After using this handy quick reference guide, 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.  


What You Will Learn
  • 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

Who This Book Is For

Programmers new to R's data science, data mining, and analytics packages.  Some prior coding experience with R in general is recommended.  
1. Introduction.- 2. Importing Data: readr.- 3. Representing Tables: tibble.- 4. Reformatting Tables: tidyr.- 5. Pipelines: magrittr.- 6. Functional Programming: purrr.- 7. Manipulating Data Frames: dplyr.- 8. Working with Strings: stringr.- 9. Working with Factors: forcats.- 10. Working with Dates: lubridate.- 11. Working with Models: broom and modelr.- 12. Plotting: ggplot2.- 13. Conclusions.

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