Exploratory Data Analysis Using R

Exploratory Data Analysis Using R provides a classroom-tested introduction to exploratory data analysis (EDA) and introduces the range of "interesting" – good, bad, and ugly – features that can be found in data, and why it is important to find them. It also introduces the mechanics of using R to explore and explain data.

The book begins with a detailed overview of data, exploratory analysis, and R, as well as graphics in R. It then explores working with external data, linear regression models, and crafting data stories. The second part of the book focuses on developing R programs, including good programming practices and examples, working with text data, and general predictive models. The book ends with a chapter on "keeping it all together" that includes managing the R installation, managing files, documenting, and an introduction to reproducible computing.

The book is designed for both advanced undergraduate, entry-level graduate students, and working professionals with little to no prior exposure to data analysis, modeling, statistics, or programming. it keeps the treatment relatively non-mathematical, even though data analysis is an inherently mathematical subject. Exercises are included at the end of most chapters, and an instructor's solution manual is available.

About the Author:

Ronald K. Pearson holds the position of Senior Data Scientist with GeoVera, a property insurance company in Fairfield, California, and he has previously held similar positions in a variety of application areas, including software development, drug safety data analysis, and the analysis of industrial process data. He holds a PhD in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology and has published conference and journal papers on topics ranging from nonlinear dynamic model structure selection to the problems of disguised missing data in predictive modeling. Dr. Pearson has authored or co-authored books including Exploring Data in Engineering, the Sciences, and Medicine (Oxford University Press, 2011) and Nonlinear Digital Filtering with Python. He is also the developer of the DataCamp course on base R graphics and is an author of the datarobot and GoodmanKruskal R packages available from CRAN (the Comprehensive R Archive Network).

《使用R進行探索性數據分析》是一本經過課堂驗證的介紹探索性數據分析（EDA）的書籍，並介紹了數據中可以找到的各種「有趣」的特徵，包括好的、壞的和醜陋的特徵，以及為什麼找到它們很重要。它還介紹了使用R進行數據探索和解釋的方法。

本書首先詳細介紹了數據、探索性分析和R，以及R中的圖形。然後探討了如何處理外部數據、線性回歸模型和製作數據故事。書的第二部分重點介紹了開發R程序，包括良好的編程實踐和示例、處理文本數據和一般預測模型。書的最後一章介紹了「保持一切井然有序」，包括管理R安裝、管理文件、文檔記錄和可重現計算的介紹。

本書旨在為高年級本科生、初級研究生和沒有接觸過數據分析、建模、統計或編程的專業人士提供設計。儘管數據分析本質上是一門數學科目，但本書的內容相對非數學化。大多數章節末尾都包含練習題，並提供教師解答手冊。

關於作者：
Ronald K. Pearson現任加利福尼亞州費爾菲爾德的財產保險公司GeoVera的高級數據科學家，之前在軟件開發、藥物安全數據分析和工業過程數據分析等各種應用領域擔任類似職位。他擁有麻省理工學院的電氣工程和計算機科學博士學位，並在非線性動態模型結構選擇和預測建模中的偽缺失數據問題等主題上發表了會議和期刊論文。Pearson博士還撰寫或合著了包括《在工程、科學和醫學中探索數據》（牛津大學出版社，2011年）和《使用Python進行非線性數字濾波》在內的書籍。他還是DataCamp基礎R圖形課程的開發者，並且是CRAN（綜合R存檔網絡）上可用的datarobot和GoodmanKruskal R軟件包的作者。