R data science quick reference : a pocket guide to APIs, libraries, and packages
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Bibliographic Information
R data science quick reference : a pocket guide to APIs, libraries, and packages
Apress , Springer Science+Business Media [Distributor], c2019
- : pbk
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Note
Includes index
Description and Table of Contents
Description
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.
Table of Contents
1. Introduction2. Importing Data: readr3. Representing Tables: tibble4. Reformatting Tables: tidyr5. Pipelines: magrittr6. Functional Programming: purrr7. Manipulating Data Frames: dplyr8. Working with Strings: stringr9. Working with Factors: forcats10. Working with Dates: lubridate11. Working with Models: broom and modelr12. Plotting: ggplot213. Conclusions
by "Nielsen BookData"
