R语言：利用相关性分析对复杂数据进行数据探索 / 憋错料

cor(1:5,1:5)

## [1] 1

cor(1:5,5:1)

## [1] -1

cor(1:5,c(1,2,3,4,4))

## [1] 0.9701

cor(1:5,c(1,2,3,1,4))

## [1] 0.6063

library(RCurl)

## Loading required package: bitops

urlfile<-"http://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data"

x<-getURL(urlfile,ssl.verifypeer=FALSE)

adults<-read.csv(textConnection(x),header=F)

head(adults,2)

## V1 V2 V3 V4 V5 V6

## 1 39 State-gov 77516 Bachelors 13 Never-married

## 2 50 Self-emp-not-inc 83311 Bachelors 13 Married-civ-spouse

## V7 V8 V9 V10 V11 V12 V13 V14

## 1 Adm-clerical Not-in-family White Male 2174 0 40 United-States

## 2 Exec-managerial Husband White Male 0 0 13 United-States

## V15

## 1 <=50K

## 2 <=50K

# 更改列名

names(adults)<-c("Age","Workclass","FinalWeight","Education","EducationNumer","MaritalStatus","Occupation","Relationship","Race","Sex","CapitalGain","CapitalLoss","HoursWeek","NativeCountry","Income")

head(adults,2)

## Age Workclass FinalWeight Education EducationNumer

## 1 39 State-gov 77516 Bachelors 13

## 2 50 Self-emp-not-inc 83311 Bachelors 13

## MaritalStatus Occupation Relationship Race Sex

## 1 Never-married Adm-clerical Not-in-family White Male

## 2 Married-civ-spouse Exec-managerial Husband White Male

## CapitalGain CapitalLoss HoursWeek NativeCountry Income

## 1 2174 0 40 United-States <=50K

## 2 0 0 13 United-States <=50K

adults$Income<-ifelse(adults$Income==" <=50K",0,1)

str(adults)

## ‘data.frame‘: 32561 obs. of 15 variables:

## $ Age : int 39 50 38 53 28 37 49 52 31 42 ...

## $ Workclass : Factor w/ 9 levels " ?"," Federal-gov",..: 8 7 5 5 5 5 5 7 5 5 ...

## $ FinalWeight : int 77516 83311 215646 234721 338409 284582 160187 209642 45781 159449 ...

## $ Education : Factor w/ 16 levels " 10th"," 11th",..: 10 10 12 2 10 13 7 12 13 10 ...

## $ EducationNumer: int 13 13 9 7 13 14 5 9 14 13 ...

## $ MaritalStatus : Factor w/ 7 levels " Divorced"," Married-AF-spouse",..: 5 3 1 3 3 3 4 3 5 3 ...

## $ Occupation : Factor w/ 15 levels " ?"," Adm-clerical",..: 2 5 7 7 11 5 9 5 11 5 ...

## $ Relationship : Factor w/ 6 levels " Husband"," Not-in-family",..: 2 1 2 1 6 6 2 1 2 1 ...

## $ Race : Factor w/ 5 levels " Amer-Indian-Eskimo",..: 5 5 5 3 3 5 3 5 5 5 ...

## $ Sex : Factor w/ 2 levels " Female"," Male": 2 2 2 2 1 1 1 2 1 2 ...

## $ CapitalGain : int 2174 0 0 0 0 0 0 0 14084 5178 ...

## $ CapitalLoss : int 0 0 0 0 0 0 0 0 0 0 ...

## $ HoursWeek : int 40 13 40 40 40 40 16 45 50 40 ...

## $ NativeCountry : Factor w/ 42 levels " ?"," Cambodia",..: 40 40 40 40 6 40 24 40 40 40 ...

## $ Income : num 0 0 0 0 0 0 0 1 1 1 ...

# 对因子变量进行哑变量处理

library(caret)

## Loading required package: lattice

## Loading required package: ggplot2

dmy<-dummyVars("~.",data=adults)

adultsTrsf<-data.frame(predict(dmy,newdata=adults))

dim(adults)

## [1] 32561 15

dim(adultsTrsf)

## [1] 32561 109

head(adultsTrsf)

## Age Workclass... Workclass..Federal.gov Workclass..Local.gov

## 1 39 0 0 0

## 2 50 0 0 0

## 3 38 0 0 0

## 4 53 0 0 0

## 5 28 0 0 0

## 6 37 0 0 0

## Workclass..Never.worked Workclass..Private Workclass..Self.emp.inc

## 1 0 0 0

## 2 0 0 0

## 3 0 1 0

## 4 0 1 0

## 5 0 1 0

## 6 0 1 0

## Workclass..Self.emp.not.inc Workclass..State.gov Workclass..Without.pay

## 1 0 1 0

## 2 1 0 0

## 3 0 0 0

## 4 0 0 0

## 5 0 0 0

## 6 0 0 0

## FinalWeight Education..10th Education..11th Education..12th

## 1 77516 0 0 0

## 2 83311 0 0 0

## 3 215646 0 0 0

## 4 234721 0 1 0

## 5 338409 0 0 0

## 6 284582 0 0 0

## Education..1st.4th Education..5th.6th Education..7th.8th Education..9th

## 1 0 0 0 0

## 2 0 0 0 0

## 3 0 0 0 0

## 4 0 0 0 0

## 5 0 0 0 0

## 6 0 0 0 0

## Education..Assoc.acdm Education..Assoc.voc Education..Bachelors

## 1 0 0 1

## 2 0 0 1

## 3 0 0 0

## 4 0 0 0

## 5 0 0 1

## 6 0 0 0

## Education..Doctorate Education..HS.grad Education..Masters

## 1 0 0 0

## 2 0 0 0

## 3 0 1 0

## 4 0 0 0

## 5 0 0 0

## 6 0 0 1

## Education..Preschool Education..Prof.school Education..Some.college

## 1 0 0 0

## 2 0 0 0

## 3 0 0 0

## 4 0 0 0

## 5 0 0 0

## 6 0 0 0

## Education.Numer MaritalStatus..Divorced MaritalStatus..Married.AF.spouse

## 1 13 0 0

## 2 13 0 0

## 3 9 1 0

## 4 7 0 0

## 5 13 0 0

## 6 14 0 0

## MaritalStatus..Married.civ.spouse MaritalStatus..Married.spouse.absent

## 1 0 0

## 2 1 0

## 3 0 0

## 4 1 0

## 5 1 0

## 6 1 0

## MaritalStatus..Never.married MaritalStatus..Separated

## 1 1 0

## 2 0 0

## 3 0 0

## 4 0 0

## 5 0 0

## 6 0 0

## MaritalStatus..Widowed Occupation... Occupation..Adm.clerical

## 1 0 0 1

## 2 0 0 0

## 3 0 0 0

## 4 0 0 0

## 5 0 0 0

## 6 0 0 0

## Occupation..Armed.Forces Occupation..Craft.repair

## 1 0 0

## 2 0 0

## 3 0 0

## 4 0 0

## 5 0 0

## 6 0 0

## Occupation..Exec.managerial Occupation..Farming.fishing

## 1 0 0

## 2 1 0

## 3 0 0

## 4 0 0

## 5 0 0

## 6 1 0

## Occupation..Handlers.cleaners Occupation..Machine.op.inspct

## 1 0 0

## 2 0 0

## 3 1 0

## 4 1 0

## 5 0 0

## 6 0 0

## Occupation..Other.service Occupation..Priv.house.serv

## 1 0 0

## 2 0 0

## 3 0 0

## 4 0 0

## 5 0 0

## 6 0 0

## Occupation..Prof.specialty Occupation..Protective.serv Occupation..Sales

## 1 0 0 0

## 2 0 0 0

## 3 0 0 0

## 4 0 0 0

## 5 1 0 0

## 6 0 0 0

## Occupation..Tech.support Occupation..Transport.moving

## 1 0 0

## 2 0 0

## 3 0 0

## 4 0 0

## 5 0 0

## 6 0 0

## Relationship..Husband Relationship..Not.in.family

## 1 0 1

## 2 1 0

## 3 0 1

## 4 1 0

## 5 0 0

## 6 0 0

## Relationship..Other.relative Relationship..Own.child

## 1 0 0

## 2 0 0

## 3 0 0

## 4 0 0

## 5 0 0

## 6 0 0

## Relationship..Unmarried Relationship..Wife Race..Amer.Indian.Eskimo

## 1 0 0 0

## 2 0 0 0

## 3 0 0 0

## 4 0 0 0

## 5 0 1 0

## 6 0 1 0

## Race..Asian.Pac.Islander Race..Black Race..Other Race..White Sex..Female

## 1 0 0 0 1 0

## 2 0 0 0 1 0

## 3 0 0 0 1 0

## 4 0 1 0 0 0

## 5 0 1 0 0 1

## 6 0 0 0 1 1

## Sex..Male CapitalGain CapitalLoss HoursWeek NativeCountry...

## 1 1 2174 0 40 0

## 2 1 0 0 13 0

## 3 1 0 0 40 0

## 4 1 0 0 40 0

## 5 0 0 0 40 0

## 6 0 0 0 40 0

## NativeCountry..Cambodia NativeCountry..Canada NativeCountry..China

## 1 0 0 0

## 2 0 0 0

## 3 0 0 0

## 4 0 0 0

## 5 0 0 0

## 6 0 0 0

## NativeCountry..Columbia NativeCountry..Cuba

## 1 0 0

## 2 0 0

## 3 0 0

## 4 0 0

## 5 0 1

## 6 0 0

## NativeCountry..Dominican.Republic NativeCountry..Ecuador

## 1 0 0

## 2 0 0

## 3 0 0

## 4 0 0

## 5 0 0

## 6 0 0

## NativeCountry..El.Salvador NativeCountry..England NativeCountry..France

## 1 0 0 0

## 2 0 0 0

## 3 0 0 0

## 4 0 0 0

## 5 0 0 0

## 6 0 0 0

## NativeCountry..Germany NativeCountry..Greece NativeCountry..Guatemala

## 1 0 0 0

## 2 0 0 0

## 3 0 0 0

## 4 0 0 0

## 5 0 0 0

## 6 0 0 0

## NativeCountry..Haiti NativeCountry..Holand.Netherlands

## 1 0 0

## 2 0 0

## 3 0 0

## 4 0 0

## 5 0 0

## 6 0 0

## NativeCountry..Honduras NativeCountry..Hong NativeCountry..Hungary

## 1 0 0 0

## 2 0 0 0

## 3 0 0 0

## 4 0 0 0

## 5 0 0 0

## 6 0 0 0

## NativeCountry..India NativeCountry..Iran NativeCountry..Ireland

## 1 0 0 0

## 2 0 0 0

## 3 0 0 0

## 4 0 0 0

## 5 0 0 0

## 6 0 0 0

## NativeCountry..Italy NativeCountry..Jamaica NativeCountry..Japan

## 1 0 0 0

## 2 0 0 0

## 3 0 0 0

## 4 0 0 0

## 5 0 0 0

## 6 0 0 0

## NativeCountry..Laos NativeCountry..Mexico NativeCountry..Nicaragua

## 1 0 0 0

## 2 0 0 0

## 3 0 0 0

## 4 0 0 0

## 5 0 0 0

## 6 0 0 0

## NativeCountry..Outlying.US.Guam.USVI.etc. NativeCountry..Peru

## 1 0 0

## 2 0 0

## 3 0 0

## 4 0 0

## 5 0 0

## 6 0 0

## NativeCountry..Philippines NativeCountry..Poland NativeCountry..Portugal

## 1 0 0 0

## 2 0 0 0

## 3 0 0 0

## 4 0 0 0

## 5 0 0 0

## 6 0 0 0

## NativeCountry..Puerto.Rico NativeCountry..Scotland NativeCountry..South

## 1 0 0 0

## 2 0 0 0

## 3 0 0 0

## 4 0 0 0

## 5 0 0 0

## 6 0 0 0

## NativeCountry..Taiwan NativeCountry..Thailand

## 1 0 0

## 2 0 0

## 3 0 0

## 4 0 0

## 5 0 0

## 6 0 0

## NativeCountry..Trinadad.Tobago NativeCountry..United.States

## 1 0 1

## 2 0 1

## 3 0 1

## 4 0 1

## 5 0 0

## 6 0 1

## NativeCountry..Vietnam NativeCountry..Yugoslavia Income

## 1 0 0 0

## 2 0 0 0

## 3 0 0 0

## 4 0 0 0

## 5 0 0 0

## 6 0 0 0

str(adultsTrsf)

## ‘data.frame‘: 32561 obs. of 109 variables:

## $ Age : num 39 50 38 53 28 37 49 52 31 42 ...

## $ Workclass... : num 0 0 0 0 0 0 0 0 0 0 ...

## $ Workclass..Federal.gov : num 0 0 0 0 0 0 0 0 0 0 ...

## $ Workclass..Local.gov : num 0 0 0 0 0 0 0 0 0 0 ...

## $ Workclass..Never.worked : num 0 0 0 0 0 0 0 0 0 0 ...

## $ Workclass..Private : num 0 0 1 1 1 1 1 0 1 1 ...

## $ Workclass..Self.emp.inc : num 0 0 0 0 0 0 0 0 0 0 ...

## $ Workclass..Self.emp.not.inc : num 0 1 0 0 0 0 0 1 0 0 ...

## $ Workclass..State.gov : num 1 0 0 0 0 0 0 0 0 0 ...

## $ Workclass..Without.pay : num 0 0 0 0 0 0 0 0 0 0 ...

## $ FinalWeight : num 77516 83311 215646 234721 338409 ...

## $ Education..10th : num 0 0 0 0 0 0 0 0 0 0 ...

## $ Education..11th : num 0 0 0 1 0 0 0 0 0 0 ...

## $ Education..12th : num 0 0 0 0 0 0 0 0 0 0 ...

## $ Education..1st.4th : num 0 0 0 0 0 0 0 0 0 0 ...

## $ Education..5th.6th : num 0 0 0 0 0 0 0 0 0 0 ...

## $ Education..7th.8th : num 0 0 0 0 0 0 0 0 0 0 ...

## $ Education..9th : num 0 0 0 0 0 0 1 0 0 0 ...

## $ Education..Assoc.acdm : num 0 0 0 0 0 0 0 0 0 0 ...

## $ Education..Assoc.voc : num 0 0 0 0 0 0 0 0 0 0 ...

## $ Education..Bachelors : num 1 1 0 0 1 0 0 0 0 1 ...

## $ Education..Doctorate : num 0 0 0 0 0 0 0 0 0 0 ...

## $ Education..HS.grad : num 0 0 1 0 0 0 0 1 0 0 ...

## $ Education..Masters : num 0 0 0 0 0 1 0 0 1 0 ...

## $ Education..Preschool : num 0 0 0 0 0 0 0 0 0 0 ...

## $ Education..Prof.school : num 0 0 0 0 0 0 0 0 0 0 ...

## $ Education..Some.college : num 0 0 0 0 0 0 0 0 0 0 ...

## $ Education.Numer : num 13 13 9 7 13 14 5 9 14 13 ...

## $ MaritalStatus..Divorced : num 0 0 1 0 0 0 0 0 0 0 ...

## $ MaritalStatus..Married.AF.spouse : num 0 0 0 0 0 0 0 0 0 0 ...

## $ MaritalStatus..Married.civ.spouse : num 0 1 0 1 1 1 0 1 0 1 ...

## $ MaritalStatus..Married.spouse.absent : num 0 0 0 0 0 0 1 0 0 0 ...

## $ MaritalStatus..Never.married : num 1 0 0 0 0 0 0 0 1 0 ...

## $ MaritalStatus..Separated : num 0 0 0 0 0 0 0 0 0 0 ...

## $ MaritalStatus..Widowed : num 0 0 0 0 0 0 0 0 0 0 ...

## $ Occupation... : num 0 0 0 0 0 0 0 0 0 0 ...

## $ Occupation..Adm.clerical : num 1 0 0 0 0 0 0 0 0 0 ...

## $ Occupation..Armed.Forces : num 0 0 0 0 0 0 0 0 0 0 ...

## $ Occupation..Craft.repair : num 0 0 0 0 0 0 0 0 0 0 ...

## $ Occupation..Exec.managerial : num 0 1 0 0 0 1 0 1 0 1 ...

## $ Occupation..Farming.fishing : num 0 0 0 0 0 0 0 0 0 0 ...

## $ Occupation..Handlers.cleaners : num 0 0 1 1 0 0 0 0 0 0 ...

## $ Occupation..Machine.op.inspct : num 0 0 0 0 0 0 0 0 0 0 ...

## $ Occupation..Other.service : num 0 0 0 0 0 0 1 0 0 0 ...

## $ Occupation..Priv.house.serv : num 0 0 0 0 0 0 0 0 0 0 ...

## $ Occupation..Prof.specialty : num 0 0 0 0 1 0 0 0 1 0 ...

## $ Occupation..Protective.serv : num 0 0 0 0 0 0 0 0 0 0 ...

## $ Occupation..Sales : num 0 0 0 0 0 0 0 0 0 0 ...

## $ Occupation..Tech.support : num 0 0 0 0 0 0 0 0 0 0 ...

## $ Occupation..Transport.moving : num 0 0 0 0 0 0 0 0 0 0 ...

## $ Relationship..Husband : num 0 1 0 1 0 0 0 1 0 1 ...

## $ Relationship..Not.in.family : num 1 0 1 0 0 0 1 0 1 0 ...

## $ Relationship..Other.relative : num 0 0 0 0 0 0 0 0 0 0 ...

## $ Relationship..Own.child : num 0 0 0 0 0 0 0 0 0 0 ...

## $ Relationship..Unmarried : num 0 0 0 0 0 0 0 0 0 0 ...

## $ Relationship..Wife : num 0 0 0 0 1 1 0 0 0 0 ...

## $ Race..Amer.Indian.Eskimo : num 0 0 0 0 0 0 0 0 0 0 ...

## $ Race..Asian.Pac.Islander : num 0 0 0 0 0 0 0 0 0 0 ...

## $ Race..Black : num 0 0 0 1 1 0 1 0 0 0 ...

## $ Race..Other : num 0 0 0 0 0 0 0 0 0 0 ...

## $ Race..White : num 1 1 1 0 0 1 0 1 1 1 ...

## $ Sex..Female : num 0 0 0 0 1 1 1 0 1 0 ...

## $ Sex..Male : num 1 1 1 1 0 0 0 1 0 1 ...

## $ CapitalGain : num 2174 0 0 0 0 ...

## $ CapitalLoss : num 0 0 0 0 0 0 0 0 0 0 ...

## $ HoursWeek : num 40 13 40 40 40 40 16 45 50 40 ...

## $ NativeCountry... : num 0 0 0 0 0 0 0 0 0 0 ...

## $ NativeCountry..Cambodia : num 0 0 0 0 0 0 0 0 0 0 ...

## $ NativeCountry..Canada : num 0 0 0 0 0 0 0 0 0 0 ...

## $ NativeCountry..China : num 0 0 0 0 0 0 0 0 0 0 ...

## $ NativeCountry..Columbia : num 0 0 0 0 0 0 0 0 0 0 ...

## $ NativeCountry..Cuba : num 0 0 0 0 1 0 0 0 0 0 ...

## $ NativeCountry..Dominican.Republic : num 0 0 0 0 0 0 0 0 0 0 ...

## $ NativeCountry..Ecuador : num 0 0 0 0 0 0 0 0 0 0 ...

## $ NativeCountry..El.Salvador : num 0 0 0 0 0 0 0 0 0 0 ...

## $ NativeCountry..England : num 0 0 0 0 0 0 0 0 0 0 ...

## $ NativeCountry..France : num 0 0 0 0 0 0 0 0 0 0 ...

## $ NativeCountry..Germany : num 0 0 0 0 0 0 0 0 0 0 ...

## $ NativeCountry..Greece : num 0 0 0 0 0 0 0 0 0 0 ...

## $ NativeCountry..Guatemala : num 0 0 0 0 0 0 0 0 0 0 ...

## $ NativeCountry..Haiti : num 0 0 0 0 0 0 0 0 0 0 ...

## $ NativeCountry..Holand.Netherlands : num 0 0 0 0 0 0 0 0 0 0 ...

## $ NativeCountry..Honduras : num 0 0 0 0 0 0 0 0 0 0 ...

## $ NativeCountry..Hong : num 0 0 0 0 0 0 0 0 0 0 ...

## $ NativeCountry..Hungary : num 0 0 0 0 0 0 0 0 0 0 ...

## $ NativeCountry..India : num 0 0 0 0 0 0 0 0 0 0 ...

## $ NativeCountry..Iran : num 0 0 0 0 0 0 0 0 0 0 ...

## $ NativeCountry..Ireland : num 0 0 0 0 0 0 0 0 0 0 ...

## $ NativeCountry..Italy : num 0 0 0 0 0 0 0 0 0 0 ...

## $ NativeCountry..Jamaica : num 0 0 0 0 0 0 1 0 0 0 ...

## $ NativeCountry..Japan : num 0 0 0 0 0 0 0 0 0 0 ...

## $ NativeCountry..Laos : num 0 0 0 0 0 0 0 0 0 0 ...

## $ NativeCountry..Mexico : num 0 0 0 0 0 0 0 0 0 0 ...

## $ NativeCountry..Nicaragua : num 0 0 0 0 0 0 0 0 0 0 ...

## $ NativeCountry..Outlying.US.Guam.USVI.etc.: num 0 0 0 0 0 0 0 0 0 0 ...

## $ NativeCountry..Peru : num 0 0 0 0 0 0 0 0 0 0 ...

## $ NativeCountry..Philippines : num 0 0 0 0 0 0 0 0 0 0 ...

## $ NativeCountry..Poland : num 0 0 0 0 0 0 0 0 0 0 ...

## $ NativeCountry..Portugal : num 0 0 0 0 0 0 0 0 0 0 ...

## [list output truncated]

# 计算变量间相关系数的p值

cor.prob<-function(X,dfr=nrow(X)-2){

R<-cor(X,use="pairwise.complete.obs")

above<-row(R)<col(R)

r2<-R[above]^2

Fstat<-r2*dfr/(1-r2)

R[above]<-1-pf(Fstat,1,dfr)

R[row(R)==col(R)]<-NA

}

# 将数据整理成data.frame形式

flattenSquareMatrix<-function(m){

if((class(m) !="matrix") | (nrow(m) !=ncol(m))) stop("Must be asquare matrix.")

if(!identical(rownames(m),colnames(m))) stop("Row and column names must be equal.")

ut<-upper.tri(m)

data.frame(i=rownames(m)[row(m)[ut]],

j=rownames(m)[col(m)[ut]],

cor=t(m)[ut],

p=m[ut])

}

corMasterList<-flattenSquareMatrix(cor.prob(adultsTrsf))

dim(corMasterList)

## [1] 5886 4

head(corMasterList)

## i j cor p

## 1 Age Workclass... 0.04263 1.410e-14

## 2 Age Workclass..Federal.gov 0.05123 0.000e+00

## 3 Workclass... Workclass..Federal.gov -0.04261 1.454e-14

## 4 Age Workclass..Local.gov 0.06090 0.000e+00

## 5 Workclass... Workclass..Local.gov -0.06407 0.000e+00

## 6 Workclass..Federal.gov Workclass..Local.gov -0.04568 1.110e-16

corList<-corMasterList[order(-abs(corMasterList$cor)),]

head(corList)

## i j

## 1953 Sex..Female Sex..Male

## 597 Workclass... Occupation...

## 1256 MaritalStatus..Married.civ.spouse Relationship..Husband

## 1829 Race..Black Race..White

## 527 MaritalStatus..Married.civ.spouse MaritalStatus..Never.married

## 1881 Relationship..Husband Sex..Female

## cor p

## 1953 -1.0000 0

## 597 0.9980 0

## 1256 0.8932 0

## 1829 -0.7887 0

## 527 -0.6449 0

## 1881 -0.5801 0

#选择与Income相关系数绝对值大于0.2的数据集

selectedSub<-subset(corList,(abs(cor)>0.2 & j =="Income"))

bestSub<-as.character(selectedSub$i[c(1,3,5,6,8,9)])

library(psych)

## Attaching package: ‘psych‘

## 下列对象被屏蔽了from ‘package:ggplot2‘:

## %+%

pairs.panels(adultsTrsf[c(bestSub,"Income")])

时间： 2024-10-29 03:50:51

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