R 4 Data Science Quick Reference

In this handy, quick reference book you'll be introduced to several R data science packages, with examples of how to use each of them. All concepts will be covered concisely, with many illustrative examples using the following APIs: readr, dibble, forecasts, lubridate, stringr, tidyr, magnittr, dplyr, purrr, ggplot2, modelr, and more.

With R 4 Data Science Quick Reference, 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. All source code used in the book is freely available on GitHub..

WHAT YOU'LL LEARN

* Implement applicable R 4 programming language specification features
* 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. 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 on R and C programming.

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