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© 2019

R Data Science Quick Reference

A Pocket Guide to APIs, Libraries, and Packages

  • 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

Book

Table of contents

  1. Front Matter
    Pages i-ix
  2. Thomas Mailund
    Pages 1-3
  3. Thomas Mailund
    Pages 5-31
  4. Thomas Mailund
    Pages 33-43
  5. Thomas Mailund
    Pages 45-69
  6. Thomas Mailund
    Pages 71-81
  7. Thomas Mailund
    Pages 83-107
  8. Thomas Mailund
    Pages 109-160
  9. Thomas Mailund
    Pages 161-180
  10. Thomas Mailund
    Pages 181-193
  11. Thomas Mailund
    Pages 195-203
  12. Thomas Mailund
    Pages 205-218
  13. Thomas Mailund
    Pages 219-238
  14. Thomas Mailund
    Pages 239-239
  15. Back Matter
    Pages 241-246

About this book

Introduction

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, tibble, forcates, lubridate, stringr, tidyr, magnittr, dplyr, purrr, ggplot2, modelr, broom, knitr, shiny, 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.  


You will:
  • Get started with RMarkdown and notebooks
  • 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 ggplot 2 and data fit for models using modelr and broom
  • Report results with markdown, knitr, shiny, and more

Keywords

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

Authors and affiliations

  1. 1.AarhusDenmark

About the authors

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.  

Bibliographic information

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