R Data Science Quick Reference

1. Front Matter

   Pages i-ix

   PDF

2. Introduction

   • Thomas Mailund

   Pages 1-3

3. Importing Data: readr

   • Thomas Mailund

   Pages 5-31

4. Representing Tables: tibble

   • Thomas Mailund

   Pages 33-43

5. Reformatting Tables: tidyr

   • Thomas Mailund

   Pages 45-69

6. Pipelines: magrittr

   • Thomas Mailund

   Pages 71-81

7. Functional Programming: purrr

   • Thomas Mailund

   Pages 83-107

8. Manipulating Data Frames: dplyr

   • Thomas Mailund

   Pages 109-160

9. Working with Strings: stringr

   • Thomas Mailund

   Pages 161-180

10. Working with Factors: forcats

    • Thomas Mailund

    Pages 181-193

11. Working with Dates: lubridate

    • Thomas Mailund

    Pages 195-203

12. Working with Models: broom and modelr

    • Thomas Mailund

    Pages 205-218

13. Plotting: ggplot2

    • Thomas Mailund

    Pages 219-238

14. Conclusions

    • Thomas Mailund

    Pages 239-239

15. Back Matter

    Pages 241-246

    PDF

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.  

• R
• data science
• tidyr
• analytics
• purrr
• ggplot
• modelr
• broom
• markdown
• knitr
• shiny
• tidyr
• stingr
• forcats
• lubridate
• magrittr
• dplyr
• tibble
• readr
• RMarkdown

• Aarhus, Denmark

  Thomas Mailund

Thomas Mailund is an associate professor at Aarhus University, Denmark. He has a background in math and computer science.  For the last decade, his main focus has been on genetics and evolutionary studies, particularly comparative genomics, speciation, and gene flow between emerging species.  He has published Beginning Data Science in R, Functional Programming in R, and Metaprogramming in R with Apress as well as other books.  

Policies and ethics