R語言數據分析與挖掘實戰手冊

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R語言數據分析與挖掘實戰手冊

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內容描述

本書系統地介紹了利用R 語言進行數據分析和挖掘的相關技術，
採用由淺入深的框架體系：開篇伊始介紹R 語言的基礎操作，
進而介紹回歸分析、方差分析等數據分析的方法，
以更好地探索數據內部結構，獲取數據所包含的信息；
更重要的是為後續的數據挖掘提供理論依據；
*後介紹典型數據挖掘工具和方法，採用理論基礎到算法介紹到案例實戰的佈局，
讓讀者深刻感知數據挖掘的精髓，在了解算法的同時更好地學以致用。

目錄大綱

第1 章R 語言簡介
1.1 R 語言軟件的安裝與運行.......................................... .................................................. .....1
1.1.1 R 語言軟件的安裝、啟動與關閉...................................... .....................................1
1.1.2 R 語言程輯包的安裝和使用...................................... ............................................4
1.2 R 語言的數據結構............................................ .................................................. ...............6
1.2.1 R 語言對象和類型.......................................... .................................................. .....6
1.2.2 向量.............................................. .................................................. ........................7
1.2.3 數組和矩陣............................................ .................................................. .............12
1.2.4 列表.............................................. .................................................. ......................17
1.2.5 數據框............................................. .................................................. ...................20
第2 章數據的讀取與保存
2.1 數據的讀取............................................. .................................................. .......................24
2.1.1 讀取內置數據集和文本文件....................................... .........................................24
2.1.2 讀取Excel 數據和CSV 格式的數據...................................... ..............................30
2.1.3 讀取R 語言格式數據和網頁數據...................................... ..................................33
2.1.4 讀取其他格式的數據......................................... .................................................. 34
2.2 數據保存............................................... .................................................. .........................36
2.2.1 寫出數據............................................ .................................................. ................36
2.2.2 使用函數cat().......................................... .................................................. ..........37
2.2.3 保存為R 語言格式文件......................................... ..............................................38
2.2.4 保存為其他類型文件.......................................... .................................................39
第3 章數據預處理
3.1 缺失值處理.............................................. .................................................. ......................40
3.1.1 缺失值判斷............................................ .................................................. .............40
3.1.2 缺失模型判斷............................................ .................................................. .........44
3.1.3 常用處理方法............................................ .................................................. .........48
3.2 數據整理............................................... .................................................. .........................53
3.2.1 數據合併............................................. .................................................. ...............53
3.2.2 選取子集............................................ .................................................. ................56
3.2.3 數據轉換............................................. .................................................. ...............59
第4 章數據的探索性分析
4.1 基本繪圖函數.............................................. .................................................. ..................66
4.2 探索單個變量.............................................. .................................................. ..................74
4.2.1 單組數據的圖形描述......................................... .................................................. 74
4.2.2 單組數據的描述性分析........................................ ...............................................79
4.3 探索多個變量............................................. .................................................. ...................81
4.3.1 兩組數據的圖形描述......................................... .................................................. 81
4.3.2 多組數據的圖形描述......................................... .................................................. 85
4.3.3 多組數據的描述性統計........................................ ...............................................88
4.4 其他圖像探索.............................................. .................................................. ..................90
第5 章回歸分析
5.1 一元線性回歸.............................................. .................................................. ..................94
5.1.1 模型簡介............................................. .................................................. ...............94
5.1.2 函數介紹............................................. .................................................. ...............96
5.1.3 綜合案例：iris 數據集的一元回歸建模.................................... ..........................97
5.2 多元線性回歸.............................................. .................................................. ..................99
5.2.1 模型簡介............................................. .................................................. ...............99
5.2.2 綜合案例：iris 數據集的多元回歸建模.................................... ........................100
5.3 變量的選擇.............................................. .................................................. ....................105
5.3.1 逐步回歸方法簡介及函數介紹........................................ ..................................105
5.3.2 綜合案例：swiss 數據集的逐步回歸建模.................................... .....................106
5.3.3 嶺回歸的方法簡介及函數介紹....................................... ...................................109
5.3.4 綜合案例：longley 數據集的嶺回歸探索..................................... .....................110
5.3.5 lasso 回歸方法簡介及函數介紹........................................ .................................114
5.3.6 綜合案例：longley 數據集的lasso 回歸建模.................................... ................115
5.4 Logistic 回歸............................................... .................................................. .................117
5.4.1 模型簡介............................................. .................................................. .............117
5.4.2 函數介紹............................................. .................................................. .............119
5.4.3 綜合案例：iris 數據集的邏輯回歸建模.................................... ........................120
第6 章方差分析
6.1 單因素方差分析............................................. .................................................. .............124
6.1.1 模型介紹............................................. .................................................. .............124
6.1.2 函數介紹............................................. .................................................. .............126
6.1.3 綜合案例：不同治療方法下膽固醇降低效果的差異性分析............................ 127
6.2 雙因素方差分析............................................. .................................................. .............130
6.2.1 模型介紹............................................. .................................................. .............130
6.2.2 綜合案例：不同劑量下老鼠妊娠重量的差異性分析.................................. ...... 132
6.3 協方差分析.............................................. .................................................. ....................136
6.3.1 模型簡介............................................. .................................................. .............136
6.3.2 函數介紹............................................. .................................................. .............136
6.3.3 綜合案例：hotdog 數據集的協方差分析..................................... ......................137
第7 章主成分分析和因子分析
7.1 降維的基本方法：主成分分析........................................ .............................................139
7.1.1 理論基礎：原始變量的線性組合....................................... ...............................139
7.1.2 模型介紹............................................. .................................................. .............141
7.1.3 函數介紹............................................. .................................................. .............143
7.1.4 綜合案例：longley 數據集的變量降維及回歸................................... ...............144
7.1.5 綜合案例：longley 數據集的變量降維及回歸（主成分回歸）....................... 148
7.2 推廣發展：因子分析............................................ .................................................. ......150
7.2.1 理論基礎：多個變量綜合為少數因子..................................... ..........................150
7.2.2 模型介紹............................................. .................................................. .............151
7.2.3 函數介紹............................................. .................................................. .............153
7.2.4 綜合案例：能力和智商測試的因子分析探索.................................... ...............154
第8 章判別分析
8.1 距離判別法.............................................. .................................................. ....................160
8.1.1 理論基礎：離誰近，就屬於誰..................................... .....................................160
8.1.2 函數介紹............................................. .................................................. .............162
8.1.3 綜合案例：基於距離判別的iris 數據集分類.................................... ................164
8.2 Bayes 判別法.............................................. .................................................. .................168
8.2.1 理論基礎：先驗概率與錯判損失..................................... .................................168
8.2.2 函數介紹............................................. .................................................. .............170
8.2.3 綜合案例：基於iris 數據集的Bayes 判別分析.................................... ............171
8.3 Fisher 判別法.............................................. .................................................. .................171
8.3.1 理論基礎：投影........................................... .................................................. ....171
8.3.2 函數介紹............................................. .................................................. .............173
8.3.3 綜合案例：基於Fisher 判別的iris 數據集分類.................................... ............174
第9 章常規聚類分析
9.1 深入了解聚類分析............................................ .................................................. ..........178
9.1.1 差異與分類............................................ .................................................. ...........178
9.1.2 主流的聚類算法.......................................... .................................................. .....179
9.2 動態聚類.............................................. .................................................. ........................180
9.2.1 聚類的基本過程.......................................... .................................................. .....180
9.2.2 函數介紹............................................. .................................................. .............183
9.2.3 綜合案例：基於隨機生成序列的動態聚類.................................... ...................184
9.3 層次聚類.............................................. .................................................. ........................194
9.3.1 聚類的基本過程.......................................... .................................................. .....194
9.3.2 函數介紹............................................. .................................................. .............197
9.3.3 綜合案例：基於UScitiesD 數據集的層次聚類.................................... .............199
9.4 密度聚類.............................................. .................................................. ........................202
9.4.1 聚類的基本過程.......................................... .................................................. .....202
9.4.2 函數介紹............................................. .................................................. .............202
9.4.3 綜合案例：基於隨機生成序列的密度聚類.................................... ...................203
9.5 EM 聚類.............................................. .................................................. .........................204
9.5.1 聚類的基本過程.......................................... .................................................. .....205
9.5.2 函數介紹............................................. .................................................. .............205
9.5.3 綜合案例：基於iris 數據集的EM 聚類.................................... ........................206
第10 章關聯規則
10.1 簡單關聯規則.............................................. .................................................. ..............210
10.1.1 基本概念與表示形式.......................................... .............................................210
10.1.2 評價簡單關聯規則的有效性和實用性..................................... ........................211
10.2 序列關聯規則.............................................. .................................................. ..............212
10.2.1 差異與基本概念........................................... .................................................. ..212
10.2.2 生成序列關聯規則........................................... ................................................213
10.3 Apriori 算法............................................... .................................................. ................214
10.3.1 算法介紹：挖掘頻繁項集........................................ ........................................214
10.3.2 函數介紹............................................. .................................................. ...........215
10.3.3 綜合案例：基於Titanic 數據集的關聯規則挖掘.................................... ........216
10.4 Eclat 算法............................................... .................................................. ...................224
10.4.1 算法介紹：自底向上的搜索....................................... .....................................224
10.4.2 函數介紹............................................. .................................................. ...........224
10.4.3 綜合案例：基於美國人口調查數據的關聯規則挖掘................................... ... 225
10.5 SPADE 算法............................................... .................................................. ................230
10.5.1 算法介紹：基於序列格的搜索和連接..................................... ........................231
10.5.2 函數介紹............................................. .................................................. ...........232
10.5.3 綜合案例：基於zaki 數據集的序列關聯規則挖掘................................... ......233
第11 章神經網絡
11.1 深入了解人工神經網絡............................................ .................................................. .239
11.1.1 生物神經元............................................ .................................................. .........240
11.1.2 人工神經元模型........................................... .................................................. ..241
11.1.3 人工神經網絡種類........................................... ................................................244
11.1.4 建立模型的一般步驟.......................................... ..............................................247
11.2 BP 反向傳播網絡............................................ .................................................. .........248
11.2.1 BP 反向傳播網絡模型......................................... ...........................................248
11.2.2 算法介紹............................................. .................................................. ............249
11.2.3 函數介紹............................................. .................................................. ............250
11.3 綜合案例：基於Boston 數據的波士頓郊區房價預測建模.................................... .... 252

作者介紹

程靜
畢業於重慶大學，目前就職於西部估值最高的互聯網公司豬八戒網，
擔任高級數據分析工程師，負責行業數據蒐集、整理、分析，
並依據數據做出行業研究、評估和預測，
擅長大數據分析及數據挖掘的各種算法熟練使用R語言及Python語言。