利用R语言分析挖掘Titanic数据集(一)

简介

一个实际的数据挖掘项目包括6个阶段

1)提出正确的问题

问题本身确定了挖掘的对向与目标

2)数据采集

利用文件的i/o函数，JDBC/ODBC,网络爬虫技术从不同的系统，例如文件，数据库或internet采集数据，称为原始数据。由于原始数据存在存在格式的无序性与差异性问题，要利用分析工具与可视化程序来处理它们。

3)数据清洗

包括数据解析，排序，合并，筛选，缺失值插补以其其它各种数据转化和数据组织过程，最终得到一个合适数据分析的数据结构。

4)基础数据分析

进行基本的探索性数据分析，包括计算数据的汇总，采用基本的统计，聚类以及可视化方法来帮助用户更好的理解数据的特征，还可以通过图形来展现发现数据的主要性质，变化趋势，以及孤立点等。

5)高级数据分析

我们可以通过描述性统计得到有关数据特征的一个大概特征。但是我们希望从中得到一个大致推论，让用户以此为依据根据输入参数预测数据特征，这就必须借用机器学习的方法基于训练数据生成预测模型，在根据预测模型根据给定输入预测输出。

6)模型评估

为了评估生成的模型是否在给定领域能够得到最优的结果，还要进行模型的筛选。该任务通常包括多个步骤，包括参数的预处理，参数调优，机器学习算法切换。
下列样例子，我们将根据titanic获救乘客的数据，进行一个简单的数据挖掘，具体内容是从数据源kaggle取得数据（可能要fc),完成数据清洗，执行基本的数据分析分析，判断哪些属性对逃生概率有重要的影响。再执行深度数据挖掘，构建分类算法，根据给定的输入数据预测逃生概率。最后进行模型评估并取得预测模型。

从csv读取数据

1)从kaggle下载数据

https://www.kaggle.com/c/titanic/data

2)设置路径

> setwd("d:/R-TT")> getwd()[1] "d:/R-TT"

3)使用read.csv读取数据

#读取之后用str（）查看，""之间没有空格> train.data = read.csv("titanic.csv",na.strings = c("NA",""))> str(train.data)'data.frame':   891 obs. of  12 variables: $ PassengerId: int  1 2 3 4 5 6 7 8 9 10 ... $ Survived   : int  0 1 1 1 0 0 0 0 1 1 ... $ Pclass     : int  3 1 3 1 3 3 1 3 3 2 ... $ Name       : Factor w/ 891 levels "Abbing, Mr. Anthony",..: 109 191 358 277 16 559 520 629 417 581 ... $ Sex        : Factor w/ 2 levels "female","male": 2 1 1 1 2 2 2 2 1 1 ... $ Age        : num  22 38 26 35 35 NA 54 2 27 14 ... $ SibSp      : int  1 1 0 1 0 0 0 3 0 1 ... $ Parch      : int  0 0 0 0 0 0 0 1 2 0 ... $ Ticket     : Factor w/ 681 levels "110152","110413",..: 524 597 670 50 473 276 86 396 345 133 ... $ Fare       : num  7.25 71.28 7.92 53.1 8.05 ... $ Cabin      : Factor w/ 147 levels "A10","A14","A16",..: NA 82 NA 56 NA NA 130 NA NA NA ... $ Embarked   : Factor w/ 3 levels "C","Q","S": 3 1 3 3 3 2 3 3 3 1 ...> train.data$Survived = factor(train.data$Survived)> train.data$Pclass = factor(train.data$Pclass)> str(train.data)'data.frame':   891 obs. of  12 variables: $ PassengerId: int  1 2 3 4 5 6 7 8 9 10 ... $ Survived   : Factor w/ 2 levels "0","1": 1 2 2 2 1 1 1 1 2 2 ... $ Pclass     : Factor w/ 3 levels "1","2","3": 3 1 3 1 3 3 1 3 3 2 ... $ Name       : Factor w/ 891 levels "Abbing, Mr. Anthony",..: 109 191 358 277 16 559 520 629 417 581 ... $ Sex        : Factor w/ 2 levels "female","male": 2 1 1 1 2 2 2 2 1 1 ... $ Age        : num  22 38 26 35 35 NA 54 2 27 14 ... $ SibSp      : int  1 1 0 1 0 0 0 3 0 1 ... $ Parch      : int  0 0 0 0 0 0 0 1 2 0 ... $ Ticket     : Factor w/ 681 levels "110152","110413",..: 524 597 670 50 473 276 86 396 345 133 ... $ Fare       : num  7.25 71.28 7.92 53.1 8.05 ... $ Cabin      : Factor w/ 147 levels "A10","A14","A16",..: NA 82 NA 56 NA NA 130 NA NA NA ... $ Embarked   : Factor w/ 3 levels "C","Q","S": 3 1 3 3 3 2 3 3 3 1 ...

4)注意

na.strings 代表缺失数据的值(转化为NA)，不参与运算

3.根据数据类型进行转化

#将int型数据转化成factor型.与上面对比有两列数据转化为factor> train.data$Survived = factor(train.data$Survived)> train.data$Pclass = factor(train.data$Pclass)> str(train.data)'data.frame':   891 obs. of  12 variables: $ PassengerId: int  1 2 3 4 5 6 7 8 9 10 ... $ Survived   : Factor w/ 2 levels "0","1": 1 2 2 2 1 1 1 1 2 2 ... $ Pclass     : Factor w/ 3 levels "1","2","3": 3 1 3 1 3 3 1 3 3 2 ... $ Name       : Factor w/ 891 levels "Abbing, Mr. Anthony",..: 109 191 358 277 16 559 520 629 417 581 ... $ Sex        : Factor w/ 2 levels "female","male": 2 1 1 1 2 2 2 2 1 1 ... $ Age        : num  22 38 26 35 35 NA 54 2 27 14 ... $ SibSp      : int  1 1 0 1 0 0 0 3 0 1 ... $ Parch      : int  0 0 0 0 0 0 0 1 2 0 ... $ Ticket     : Factor w/ 681 levels "110152","110413",..: 524 597 670 50 473 276 86 396 345 133 ... $ Fare       : num  7.25 71.28 7.92 53.1 8.05 ... $ Cabin      : Factor w/ 147 levels "A10","A14","A16",..: NA 82 NA 56 NA NA 130 NA NA NA ... $ Embarked   : Factor w/ 3 levels "C","Q","S": 3 1 3 3 3 2 3 3 3 1 ...

Survived(0=NO,1=YES)和Pclass(1=1st,2=2nd,3=3rd)都属于定类变量，我们使用factor函数将这两个变量转化为类型因子

4.检查缺失值

1)做准备

将属性Survived和Pclass转换为因子类型
R用NA(not available)代表缺失值，用NaN代表（not a number）代表不存在的值

2)进行操作

#用is.na()判断当成属于值是否包含NA值 is.na(train.data$Age)  [1] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [16] FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE [31] FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE [46]  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE [61] FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [76] FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE [91] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE[106] FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE[121] FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE[136] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE[151] 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FALSE FALSE FALSE FALSE FALSE FALSE  TRUE[631] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE[646] FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE[661] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE[676] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE[691] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE[706] FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE[721] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE[736] FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE[751] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE[766] FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE  TRUE FALSE[781] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE[796] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE[811] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE[826]  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE[841] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE[856] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE[871] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE[886] FALSE FALSE FALSE  TRUE FALSE FALSE#is.na()进行了标记，用sum()统计缺失值总数> sum(is.na(train.data$Age) == TRUE)[1] 177#用缺失总数除以非缺失数来计算缺失比例> sum(is.na(train.data$Age) == TRUE)/length(train.data$Age)[1] 0.1986532#用sapply函数来计算所有属性缺失值的比例：> sapply(train.data, function(df){+   sum(is.na(df==TRUE))/length(df)+ })PassengerId    Survived      Pclass        Name         Sex         Age       SibSp       Parch 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.198653199 0.000000000 0.000000000      Ticket        Fare       Cabin    Embarked 0.000000000 0.000000000 0.771043771 0.002244669 #除了观察缺失值的比例，我们可以使用Amelia包对缺失值进行可视化处理> library(Rcpp)> library(Amelia)#使用missmap函数绘制缺失值示意图> missmap(train.data,main = "MISSINGMAP")

5.插补缺失值

1）执行以下操作增补缺失值

#首先列出出发港口的分布。增加了useNA = "alays"的参数设置，展示train.data中最大的两个港口个数> table(train.data$Embarked,useNA = "always")   C    Q    S <NA>  168   77  644    2 #将其中两个缺失值处理为概率最大的两个港口> train.data$Embarked[which(is.na(train.data$Embarked))] = 'S'> table(train.data$Embarked,useNA = "always")   C    Q    S <NA>  168   77  646    0 #AGE有很多缺失值，考虑到age与称乎Name在很大的相关性，我们可以根据将其所属年龄的平均值进行插补。#首先将name 转化成character>train.data$Name = as.character(train.data$Name)#strsplit（）把字符串按照某个规则进行拆分，\\s表示   空格,回车,换行等空白符,  +号表示一个或多个的意思,这里我们只摘抄了后面几行展示分类规则。>strsplit(train.data$Name,"\\s+").......[[883]][1] "Dahlberg," "Miss."     "Gerda"     "Ulrika"   [[884]][1] "Banfield," "Mr."       "Frederick" "James"    [[885]][1] "Sutehall," "Mr."       "Henry"     "Jr"       [[886]][1] "Rice,"     "Mrs."      "William"   "(Margaret" "Norton)"  [[887]][1] "Montvila," "Rev."      "Juozas"   [[888]][1] "Graham,"  "Miss."    "Margaret" "Edith"   [[889]][1] "Johnston,"  "Miss."      "Catherine"  "Helen"      "\"Carrie\""[[890]][1] "Behr,"  "Mr."    "Karl"   "Howell"[[891]][1] "Dooley," "Mr."     "Patrick"#生成的strsplit是列表形式，unlist()取消列表，这里我们只摘抄了后面几行展示这个过程>unlist(strsplit(train.data$Name,"\\s+"))......[3598] "Mr."                     "Henry"                   "Jr"                     [3601] "Rice,"                   "Mrs."                    "William"                [3604] "(Margaret"               "Norton)"                 "Montvila,"              [3607] "Rev."                    "Juozas"                  "Graham,"                [3610] "Miss."                   "Margaret"                "Edith"                  [3613] "Johnston,"               "Miss."                   "Catherine"              [3616] "Helen"                   "\"Carrie\""              "Behr,"                  [3619] "Mr."                     "Karl"                    "Howell"                 [3622] "Dooley,"                 "Mr."                     "Patrick"  #用table计算其出现的频次，table_words = table(unlist(strsplit(train.data$Name,"\\s+")))#我们用（"\\."）做为一种正则表达式，做为一种筛选的条件，sort()进行排序> sort(table_words [grep("\\.",names(table_words))],decreasing = TRUE)      Mr.     Miss.      Mrs.   Master.       Dr.      Rev.      Col.    Major.     Mlle.       517       182       125        40         7         6         2         2         2     Capt. Countess.      Don. Jonkheer.        L.     Lady.      Mme.       Ms.      Sir.         1         1         1         1         1         1         1         1         1 #为了找到包含缺失值的不同人群，我们使用stringr包提供的str_match函数来匹配包含符号“.的子字符串，然后使用cbind函数将列拼在一起，最后，用table函数来获得缺失值的统计信息，并对每种人群进行计数> library(stringr)> tb = cbind(train.data$Age,str_match(train.data$Name,"[a-zA-Z]+\\."))#tb的左侧列出了年龄包括缺省值，右侧列出了正式表达形式> tb       [,1]   [,2]         [1,] "22"   "Mr."        [2,] "38"   "Mrs."       [3,] "26"   "Miss."      [4,] "35"   "Mrs."       [5,] "35"   "Mr."        [6,] NA     "Mr."        [7,] "54"   "Mr."        [8,] "2"    "Master."    [9,] "27"   "Mrs."      [10,] "14"   "Mrs."      [11,] "4"    "Miss."     [12,] "58"   "Miss."     [13,] "20"   "Mr."       [14,] "39"   "Mr."       [15,] "14"   "Miss."     [16,] "55"   "Mrs."      [17,] "2"    "Master."   [18,] NA     "Mr."       [19,] "31"   "Mrs."      [20,] NA     "Mrs."   #将为含有缺失值的对应列找出来> tb[is.na(tb[,1]),2]  [1] "Mr."     "Mr."     "Mrs."    "Mr."     "Miss."   "Mr."     "Mrs."    "Miss."   "Mr."     [10] "Mr."     "Mr."     "Mr."     "Miss."   "Mr."     "Mr."     "Mr."     "Master." "Mr."     [19] "Mr."     "Miss."   "Mr."     "Mr."     "Mr."     "Mr."     "Miss."   "Mr."     "Mr."     [28] "Miss."   "Mrs."    "Mr."     "Mr."     "Master." "Mrs."    "Mr."     "Master." "Miss."   [37] "Mr."     "Mr."     "Mrs."    "Mr."     "Miss."   "Mr."     "Mr."     "Mr."     "Miss."   [46] "Miss."   "Miss."   "Miss."   "Mr."     "Mrs."    "Mr."     "Miss."   "Mr."     "Miss."   [55] "Mr."     "Mr."     "Mr."     "Mr."     "Miss."   "Mr."     "Miss."   "Mr."     "Miss."   [64] "Mr."     "Miss."   "Mrs."    "Mr."     "Mrs."    "Mr."     "Mr."     "Miss."   "Miss."   [73] "Mr."     "Mrs."    "Miss."   "Mrs."    "Mr."     "Mr."     "Miss."   "Mr."     "Mr."     [82] "Mr."     "Mrs."    "Mr."     "Mr."     "Mr."     "Mrs."    "Mr."     "Mr."     "Mr."     [91] "Mrs."    "Mr."     "Mr."     "Mr."     "Mr."     "Mr."     "Mr."     "Mr."     "Miss."  [100] "Mr."     "Mr."     "Mr."     "Miss."   "Mr."     "Mr."     "Mr."     "Mr."     "Mr."    [109] "Mr."     "Mr."     "Mrs."    "Mr."     "Mr."     "Mr."     "Mr."     "Mr."     "Mr."    [118] "Miss."   "Mr."     "Miss."   "Mrs."    "Mr."     "Mr."     "Miss."   "Miss."   "Mr."    [127] "Mr."     "Mr."     "Mr."     "Miss."   "Mr."     "Mr."     "Mr."     "Mr."     "Mr."    [136] "Mr."     "Mr."     "Miss."   "Mr."     "Mr."     "Mrs."    "Mr."     "Miss."   "Mr."    [145] "Miss."   "Master." "Mr."     "Mr."     "Miss."   "Mr."     "Mr."     "Mr."     "Mr."    [154] "Mr."     "Dr."     "Mr."     "Mr."     "Mr."     "Mr."     "Mr."     "Mr."     "Miss."  [163] "Mr."     "Mr."     "Mr."     "Mr."     "Mr."     "Mr."     "Mr."     "Mr."     "Mr."    [172] "Mrs."    "Mr."     "Miss."   "Mr."     "Mr."     "Miss."  #对其进行统计> table(tb[is.na(tb[,1]),2])    Dr. Master.   Miss.     Mr.    Mrs.       1       4      36     119      17 #如果某个人群包含缺失值，插补的方式是将每一类人群平均值计算出来（不包含缺失值）,grepl检索目标行命令，"Mr\\."的\\表示绝对匹配> mean.mr = mean(train.data$Age[grepl("Mr\\.",train.data$Name)&!is.na(train.data$Age)])> > mean.mrs = mean(train.data$Age[grepl("Mrs\\.",train.data$Name)&!is.na(train.data$Age)])> > mean.dr = mean(train.data$Age[grepl("Dr\\.",train.data$Name)&!is.na(train.data$Age)])> > mean.miss = mean(train.data$Age[grepl("Miss\\.",train.data$Name)&!is.na(train.data$Age)])> > mean.master = mean(train.data$Age[grepl("Master\\.",train.data$Name)&!is.na(train.data$Age)])#将每类人的人属性均值插补到缺失值中> train.data$Age[grepl("Mr\\.",train.data$Name)&is.na(train.data$Age)] = mean.mr> > train.data$Age[grepl("Mrs\\.",train.data$Name)&is.na(train.data$Age)] = mean.mrs> > train.data$Age[grepl("Dr\\.",train.data$Name)&is.na(train.data$Age)] = mean.dr> train.data$Age[grepl("Miss\\.",train.data$Name)&is.na(train.data$Age)] = mean.miss>train.data$Age[grepl("Master\\.",train.data$Name)&is.na(train.data$Age)] = mean.master#对于缺失值，我们考虑他们的身份，将其所属人群的年龄平均值来插补缺失值，不过。对于Cabin属性，由于该属性缺失太多，没有办法从其它参考属性来推断，进一步分析中不在尝试使用该属性。