商品描述
Exploratory Data Analysis Using R provides a classroom-tested introduction to exploratory data analysis (EDA), and this revised edition is accompanied by the R package ExploreTheData that implements many of the approaches described. As before, the primary focus of the book is on identifying "interesting" features - good, bad, and ugly - in a dataset, why it is important to find them, how to treat them, and more generally, the use of R to explore and explain datasets and the analysis results derived from them.
The book begins with a brief overview of exploratory data analysis using R, followed by a detailed discussion of creating various graphical data summaries in R. Then comes a thorough introduction to exploratory data analysis, and a detailed treatment of 13 data anomalies, why they are important, how to find them, and some options for addressing them. Subsequent chapters introduce the mechanics of working with external data, structured query language (SQL) for interacting with relational databases, linear regression analysis (the simplest and historically most important class of predictive models), and crafting data stories to explain our results to others. These chapters use R as an interactive data analysis platform, while Chapter 9 turns to writing programs in R, focusing on creating custom functions that can greatly simplify repetitive analysis tasks. Further chapters expand the scope to more advanced topics and techniques: special considerations for working with text data, a second look at exploratory data analysis, and more general predictive models.
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.
商品描述(中文翻譯)
《使用 R 進行探索性資料分析》提供了一個經過課堂測試的探索性資料分析 (EDA) 介紹，這個修訂版附帶了 R 套件 ExploreTheData，實現了許多所描述的方法。與之前一樣，本書的主要重點是識別資料集中的「有趣」特徵——好的、壞的和醜陋的——為什麼找到它們很重要，如何處理它們，以及更一般地使用 R 來探索和解釋資料集及其分析結果。
本書首先簡要概述了使用 R 進行探索性資料分析，接著詳細討論了在 R 中創建各種圖形資料摘要。然後是對探索性資料分析的徹底介紹，以及對 13 種資料異常的詳細處理，說明它們為什麼重要、如何找到它們，以及一些解決它們的選項。隨後的章節介紹了處理外部資料的機制、用於與關聯資料庫互動的結構化查詢語言 (SQL)、線性回歸分析（最簡單且歷史上最重要的預測模型類別），以及編寫資料故事以向他人解釋我們的結果。這些章節使用 R 作為互動資料分析平台，而第 9 章則轉向在 R 中編寫程式，重點是創建自定義函數，以大大簡化重複的分析任務。後續章節擴展到更高級的主題和技術：處理文本資料的特殊考量、對探索性資料分析的再次檢視，以及更一般的預測模型。
本書旨在為高年級本科生、入門級研究生以及對資料分析、建模、統計或程式設計幾乎沒有接觸的在職專業人士設計。儘管資料分析本質上是一個數學主題，但本書的處理相對不具數學性。大多數章節末尾都包含練習題，並提供了教師解答手冊。
作者簡介
Ronald K. Pearson holds a PhD in Electrical Engineering and Computer Science from the Massachussetts Institute of Technology and has more than 40 years professional experience in exploratory data analysis. Dr. Pearson has held industrial, business, and academic positions in the fields of industrial process control, bioinformatics, drug safety data analysis, software development, and insurance. He has authored or co-authored books including Exploring Data in Engineering, the Sciences, and Medicine (Oxford University Press, 2011) and Mining Imperfect Data with Examples in R and Python (SIAM, 2020).
作者簡介(中文翻譯)
羅納德·K·皮爾森擁有麻省理工學院的電機工程與計算機科學博士學位，並在探索性數據分析領域擁有超過40年的專業經驗。皮爾森博士曾在工業過程控制、生物信息學、藥物安全數據分析、軟體開發和保險等領域擔任工業、商業和學術職位。他著有或合著的書籍包括《工程、科學與醫學中的數據探索》（牛津大學出版社，2011年）和《不完美數據挖掘：以R和Python為例》（SIAM，2020年）。
