R 4 data science quick reference : a pocket guide to APIs, libraries, and packages / Thomas Mailund.
Mailund, Thomas, author.
2022
QA276.45.R3 M35 2022
Available Online

Linked e-resources

Linked Resource
Online Access
Concurrent users
Unlimited
Authorized users
Authorized users
Document Delivery Supplied
Can lend chapters, not whole ebooks

Details

Title
R 4 data science quick reference : a pocket guide to APIs, libraries, and packages / Thomas Mailund.
Author
Mailund, Thomas, author.
Edition
Second edition.
ISBN
9781484287804 electronic book
1484287800 electronic book
9781484287798
1484287797
DOI
https://doi.org/10.1007/978-1-4842-8780-4
Published
New York, NY : Apress, [2022]
Language
English
Description
1 online resource (231 pages) : illustrations
Item Number
10.1007/978-1-4842-8780-4 doi
Call Number
QA276.45.R3 M35 2022
Dewey Decimal Classification
519.50285/536
Summary
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.
Note
Includes index.
Access Note
Access limited to authorized users.
Source of Description
Description based on online resource; title from digital title page (viewed on November 10, 2022).
Available in Other Form
Print version: 9781484287798
Linked Resources
Online Access
Record Appears in
Online Resources > Ebooks
All Resources
1. Introduction. - 2. Importing Data: readr
3. Representing Tables: tibble. - 4. Tidy+select, 5. Reformatting Tables: tidyr
6. Pipelines: magrittr
7. Functional Programming: purrr. - 8. Manipulating Data Frames: dplyr. - 9. Working with Strings: stringr
10. Working with Factors: forcats. - 11. Working with Dates: lubridate. - 12. Working with Models: broom and modelr. - 13. Plotting: ggplot2
14. Conclusions.

Browse Subjects

Show more subjects...

Statistics

from
to
Export