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R for Data Science: Import, Tidy, Transform, Visualize, and Model Data, 2nd Edition (Early Release)

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  • Дата: 15-12-2022, 13:48
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R for Data Science: Import, Tidy, Transform, Visualize, and Model Data, 2nd Edition (Early Release)Название: R for Data Science: Import, Tidy, Transform, Visualize, and Model Data, 2nd Edition (Early Release)
Автор: Hadley Wickham, Garrett Grolemund, Mine Cetinkaya-Rundel
Издательство: O’Reilly Media, Inc.
Год: 2022-12-14
Язык: английский
Формат: epub
Размер: 10.2 MB

Learn how to use R to turn data into insight, knowledge, and understanding. Ideal for current and aspiring data scientists, this book introduces you to doing data science with R and RStudio, as well as the tidyverse—a collection of R packages designed to work together to make data science fast, fluent, and fun. Even if you have no programming experience, this updated edition will have you doing data science quickly.

You'll learn how to import, transform, and visualize your data and communicate the results. And you'll get a complete, big-picture understanding of the data science cycle and the basic tools you need to manage the details. Each section in this edition includes exercises to help you practice what you've learned along the way.

Updated for the latest tidyverse best practices, new chapters dive deeper into visualization and data wrangling, show you how to get data from spreadsheets, databases, and websites, and help you make the most of new programming tools.

This chapter will teach you how to visualize your data using ggplot2. R has several systems for making graphs, but ggplot2 is one of the most elegant and most versatile. ggplot2 implements the grammar of graphics, a coherent system for describing and building graphs. With ggplot2, you can do more and faster by learning one system and applying it in many places.

You'll learn how to
Visualize—create plots for data exploration and communication of results
Transform—discover types of variables and the tools you can use to work with them
Import—get data into R and in a form convenient for analysis
Program—learn R tools for solving data problems with greater clarity and ease
Communicate—integrate prose, code, and results with Quarto

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