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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.

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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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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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