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R Data Science Quick Reference

A Pocket Guide to APIs, Libraries, and Packages

  • Book
  • © 2019

Overview

Authors:
  1. Thomas Mailund

    1. Aarhus, Denmark

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  • The first quick reference of its kind dealing with data science using R
  • Covers the specific APIs and packages that let you build R-based data science applications
  • Also covers how to use these packages to do data analysis using R

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Table of contents (13 chapters)

  1. Front Matter

    Pages i-ix
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  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
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Keywords

About this book

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.  

Authors and Affiliations

  • Aarhus, Denmark

    Thomas Mailund

About the author

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.  

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