Overview
- 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)
Keywords
About this book
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
About the author
Bibliographic Information
Book Title: R Data Science Quick Reference
Book Subtitle: A Pocket Guide to APIs, Libraries, and Packages
Authors: Thomas Mailund
DOI: https://doi.org/10.1007/978-1-4842-4894-2
Publisher: Apress Berkeley, CA
eBook Packages: Professional and Applied Computing, Apress Access Books, Professional and Applied Computing (R0)
Copyright Information: Thomas Mailund 2019
eBook ISBN: 978-1-4842-4894-2Published: 07 August 2019
Edition Number: 1
Number of Pages: IX, 246
Number of Illustrations: 11 b/w illustrations
Topics: Programming Languages, Compilers, Interpreters, Programming Techniques, Big Data, Data Mining and Knowledge Discovery