R 4 Data Science Quick Reference: A Pocket Guide to Apis, Libraries, and Packages

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..
What You'll Learn

• Implement applicable R 4 programming language specification features
• 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.

在這本方便的快速參考書中，您將介紹幾個 R 資料科學套件，並提供每個套件的使用示例。所有概念都將簡潔地介紹，並使用以下 API 進行許多實例示範：readr、dibble、forecasts、lubridate、stringr、tidyr、magrittr、dplyr、purrr、ggplot2、modelr 等等。

透過《R 4 Data Science Quick Reference》，您將獲得在 R 程式語言中撰寫基於資料科學的應用程式所需的程式碼、API 和洞察力。您還可以進行資料分析。書中使用的所有原始碼都可以在 GitHub 上免費取得。

您將學到什麼

• 實現適用於 R 4 程式語言規範的功能


• 使用 readr 匯入資料


• 使用 forcats 處理類別、使用 lubridate 處理時間和日期、使用 stringr 處理字串


• 使用 tidyr 格式化資料，然後使用 magrittr 和 dplyr 轉換資料


• 使用 R 為資料科學、資料探勘和分析應用程式編寫函式


• 使用 ggplot2 對資料進行視覺化，並使用 modelr 將資料擬合到模型中

本書適合對象

對 R 的資料科學、資料探勘和分析套件新手程式設計師。建議具備一般 R 程式編碼經驗。

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 on R and C programming.

Thomas Mailund是丹麥奧胡斯大學的副教授。他的背景是數學和計算機科學。在過去的十年中，他主要專注於遺傳學和進化研究，特別是比較基因組學、物種形成和新興物種之間的基因流動。他在Apress出版了《R語言的數據科學入門》、《R語言的函數式編程》和《R語言的元編程》等書籍，還有其他關於R和C編程的書籍。