Exercise 2

Housing Prices

Run Housing Prices Data

if(!file.exists("HousePrices.csv")){
  dir.create("HousePrices.csv")
}
download.file("https://www.biz.uiowa.edu/faculty/jledolter/datamining/HousePrices.csv", "HousePrices.csv",method="curl")
HousePr<-read.csv("https://www.biz.uiowa.edu/faculty/jledolter/datamining/HousePrices.csv")
attach(HousePr)
library(nutshell)
library(lattice)
library(latticeExtra)

offers.ho=table(HousePr$Offers)
offers.ho

## 
##  1  2  3  4  5  6 
## 23 36 46 19  3  1

barchart(offers.ho,horizontal=FALSE,ylab="Houses",xlab="Number of Offers",col="purple")

Price<-HousePr$Price

densityplot(~Price,groups=HousePr$Neighborhood,data=HousePr,plot.points=FALSE)

Direct Marketing Data

Run Direct Marketing Data

if(!file.exists("DirectMarketing.csv")){
  dir.create("DirectMarketing.csv")
}
download.file("https://www.biz.uiowa.edu/faculty/jledolter/datamining/DirectMarketing.csv", "DirectMarketing.csv",method="curl")
DirectMk<-read.csv("https://www.biz.uiowa.edu/faculty/jledolter/datamining/DirectMarketing.csv")
attach(DirectMk)

smoothScatter(DirectMk$Salary,DirectMk$AmountSpent,ylab="Amount Spent", xlab="Salary")

HM.tbl=table(HM=DirectMk$OwnHome)
HM.tbl

## HM
##  Own Rent 
##  516  484

barchart(HM.tbl)

Gender Discrimination

Run Gender Discrimination Data

if(!file.exists("GenderDiscrimination.csv")){
  dir.create("GenderDiscrimination.csv")
}
download.file("https://www.biz.uiowa.edu/faculty/jledolter/datamining/GenderDiscrimination.csv", "GenderDiscrimination.csv",method="curl")
GenDsc<-read.csv("https://www.biz.uiowa.edu/faculty/jledolter/datamining/GenderDiscrimination.csv")
attach(GenDsc)

smoothScatter(GenDsc$Experience,GenDsc$Salary,ylab="Salary",xlab="Experience")

xyplot(GenDsc$Experience~GenDsc$Salary|GenDsc$Gender,data=GenDsc,ylab="Experience",xlab="Salary",layout=c(1,2),col="black")

Loan Data

Run Loan Data

if(!file.exists("LoanData.csv")){
  dir.create("LoanData.csv")
}
download.file("https://www.biz.uiowa.edu/faculty/jledolter/datamining/LoanData.csv", "LoanData.csv",method="curl")
LoanData<-read.csv("https://www.biz.uiowa.edu/faculty/jledolter/datamining/LoanData.csv")
attach(LoanData)

boxplot(LoanData$Borrower.Rate~LoanData$Age,data=LoanData,ylab="Borrower Rate",xlab="Age")

cred.gr=table(LoanData$Credit.Grade)
cred.gr

## 
##    A   AA    B    C    D    E   HR   NC 
##  424  451  553  843  927 1129 1217   67

barchart(cred.gr,horizontal=FALSE,xlab="Credit Grade",ylab="Frequency",col="Black")

Financial Indicators

Run Financial Indicators Data

if(!file.exists("FinancialIndicators.csv")){
  dir.create("FinancialIndicators.csv")
}
download.file("https://www.biz.uiowa.edu/faculty/jledolter/datamining/FinancialIndicators.csv", "FinancialIndicators.csv",method="curl")
FinInd<-read.csv("https://www.biz.uiowa.edu/faculty/jledolter/datamining/FinancialIndicators.csv")
attach(FinInd)

Cntry.name=table(FinInd$Country)
Cntry.name

## 
## Foreign      US 
##    1783    5329

barchart(Cntry.name,horizontal=FALSE,xlab="Country Name",ylab="Frequency",col="Black")

fact=factor(FinInd$Country)
rest=FinInd$Growth.in.Revenue..last.year
t3=tapply(rest,fact,mean,na.rm=TRUE)
t3

##    Foreign         US 
## 0.13100953 0.09507975