R Data Science Quick Reference

A Pocket Guide to APIs, Libraries, and Packages

Thomas Mailund

PDF
ca. 46,99
Amazon iTunes Thalia.de Hugendubel Bücher.de ebook.de kobo Osiander Google Books Barnes&Noble bol.com Legimi yourbook.shop Kulturkaufhaus ebooks-center.de
* Affiliatelinks/Werbelinks
Hinweis: Affiliatelinks/Werbelinks
Links auf reinlesen.de sind sogenannte Affiliate-Links. Wenn du auf so einen Affiliate-Link klickst und über diesen Link einkaufst, bekommt reinlesen.de von dem betreffenden Online-Shop oder Anbieter eine Provision. Für dich verändert sich der Preis nicht.

Apress img Link Publisher

Naturwissenschaften, Medizin, Informatik, Technik / Programmiersprachen

Beschreibung

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.  

Weitere Titel in dieser Kategorie

Kundenbewertungen

Schlagwörter

magrittr, analytics, modelr, tibble, knitr, R, broom, purrr, ggplot, stingr, tidyr, readr, markdown, lubridate, data science, RMarkdown, shiny, dplyr, forcats