
R Data Science Quick Reference
A Pocket Guide to APIs, Libraries, and Packages
Thomas Mailund(Author)
Apress
Published on 8. August 2019
Book
Paperback/Softback
IX, 246 pages
978-1-4842-4893-5 (ISBN)
Article exhausted; check for reprint
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 LearnImport data with readrWork with categories using forcats, time and dates with lubridate, and strings with stringrFormat data using tidyr and then transform that data using magrittr and dplyrWrite functions with R for data science, data mining, and analytics-based applicationsVisualize data with ggplot2 and fit data to models using modelrWho This Book Is ForProgrammers new to R's data science, data mining, and analytics packages. Some prior coding experience with R in general is recommended.
More details
Edition
1st ed.
Language
English
Place of publication
CA
United States
Target group
Professional and scholarly
Illustrations
11 s/w Abbildungen
IX, 246 p. 11 illus.
Dimensions
Height: 23.5 cm
Width: 15.5 cm
Weight
454 gr
ISBN-13
978-1-4842-4893-5 (9781484248935)
DOI
10.1007/978-1-4842-4894-2
Schweitzer Classification
Other editions
New editions

Book
10/2022
2nd Edition
APress
€37.44
Shipment within 15-20 days
Additional editions

E-Book
08/2019
APress
€46.99
Available for download
Person
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.
Content
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