###1.
M= read.csv("Mortgage.csv", header = TRUE, sep = ",")
data1=data.frame(M)
print(head(data1))
## X rate age school networth interest points maturities years married
## 1 1 adjustable 38 22 7.558 13.62 2.33 1.5 1 no
## 2 2 adjustable 38 22 7.558 13.62 2.33 1.5 1 no
## 3 3 adjustable 38 22 7.558 13.62 2.33 1.5 1 no
## 4 4 adjustable 38 22 7.556 13.62 2.33 1.5 1 no
## 5 5 adjustable 41 16 7.821 14.00 1.75 1.0 4 no
## 6 6 adjustable 41 16 8.014 14.00 1.75 1.0 4 no
## first selfemp tdiff margin coborrower liability liquid
## 1 yes no 1.38 1.50 no 3.69 8.91
## 2 yes no 1.38 1.50 no 3.69 8.91
## 3 yes no 1.38 1.50 no 3.69 8.91
## 4 yes no 1.38 1.50 no 3.89 8.91
## 5 yes yes 1.38 5.50 no 50.93 12.50
## 6 yes yes 1.38 4.75 no 50.44 17.74
summary(data1)
## X rate age school
## Min. : 1.00 Length:78 Min. :24.00 Min. : 6.00
## 1st Qu.:20.25 Class :character 1st Qu.:31.00 1st Qu.:14.00
## Median :39.50 Mode :character Median :36.50 Median :16.00
## Mean :39.50 Mean :36.04 Mean :16.45
## 3rd Qu.:58.75 3rd Qu.:40.50 3rd Qu.:18.00
## Max. :78.00 Max. :65.00 Max. :25.00
## networth interest points maturities
## Min. :-0.0560 Min. :11.76 Min. :0.000 Min. :0.420
## 1st Qu.: 0.3585 1st Qu.:12.50 1st Qu.:1.000 1st Qu.:0.930
## Median : 0.7945 Median :13.50 Median :1.660 Median :1.000
## Mean : 3.5040 Mean :13.25 Mean :1.498 Mean :1.058
## 3rd Qu.: 7.5575 3rd Qu.:13.75 3rd Qu.:2.022 3rd Qu.:1.000
## Max. :17.8600 Max. :14.50 Max. :4.340 Max. :2.380
## years married first selfemp
## Min. : 1.000 Length:78 Length:78 Length:78
## 1st Qu.: 1.000 Class :character Class :character Class :character
## Median : 2.000 Mode :character Mode :character Mode :character
## Mean : 4.205
## 3rd Qu.: 4.000
## Max. :28.000
## tdiff margin coborrower liability
## Min. :1.380 Min. :-0.900 Length:78 Min. : 0.00
## 1st Qu.:1.450 1st Qu.: 1.500 Class :character 1st Qu.: 3.69
## Median :1.590 Median : 2.400 Mode :character Median : 8.44
## Mean :1.606 Mean : 2.292 Mean :13.42
## 3rd Qu.:1.640 3rd Qu.: 3.040 3rd Qu.:19.43
## Max. :2.040 Max. : 5.500 Max. :86.35
## liquid
## Min. : 0.0000
## 1st Qu.: 0.6675
## Median : 1.3550
## Mean : 5.6827
## 3rd Qu.: 8.9100
## Max. :93.4900
##2.
data2=data1[1:20,]
data2$idx=data2$interest-data2$margin
data3=data.frame(data2$rate, data2$age, data2$years, data2$networth, data2$idx)
head(data3)
## data2.rate data2.age data2.years data2.networth data2.idx
## 1 adjustable 38 1 7.558 12.12
## 2 adjustable 38 1 7.558 12.12
## 3 adjustable 38 1 7.558 12.12
## 4 adjustable 38 1 7.556 12.12
## 5 adjustable 41 4 7.821 8.50
## 6 adjustable 41 4 8.014 9.25
names(data3)=c("R","A","Y","N","I")
head(data3)
## R A Y N I
## 1 adjustable 38 1 7.558 12.12
## 2 adjustable 38 1 7.558 12.12
## 3 adjustable 38 1 7.558 12.12
## 4 adjustable 38 1 7.556 12.12
## 5 adjustable 41 4 7.821 8.50
## 6 adjustable 41 4 8.014 9.25
summary(data3)
## R A Y N
## Length:20 Min. :34.0 Min. : 1.0 Min. : 2.419
## Class :character 1st Qu.:38.0 1st Qu.: 1.0 1st Qu.: 7.558
## Mode :character Median :38.0 Median : 1.5 Median : 7.558
## Mean :40.6 Mean : 4.3 Mean : 8.355
## 3rd Qu.:42.5 3rd Qu.: 4.0 3rd Qu.: 8.014
## Max. :57.0 Max. :28.0 Max. :17.860
## I
## Min. : 8.50
## 1st Qu.:11.10
## Median :11.42
## Mean :11.29
## 3rd Qu.:12.12
## Max. :13.65
###3
hist(data3$A, main= "age")
hist(data3$I, main="indexhist")
boxplot(data3$Y, col="red", main= "mortgage years plot")
boxplot(data3$N, col="blue", main= "networth plot")
plot(x=data3$A, y=data3$N, main= "networth by age")
library(ggplot2)
sp <- ggplot(data3, aes(x=Y, y=I)) + geom_line()
sp
###5
data5=read.csv("https://raw.githubusercontent.com/Cnadour/HW3/main/Mortgage.csv")
head(data5)
## X rate age school networth interest points maturities years married
## 1 1 adjustable 38 22 7.558 13.62 2.33 1.5 1 no
## 2 2 adjustable 38 22 7.558 13.62 2.33 1.5 1 no
## 3 3 adjustable 38 22 7.558 13.62 2.33 1.5 1 no
## 4 4 adjustable 38 22 7.556 13.62 2.33 1.5 1 no
## 5 5 adjustable 41 16 7.821 14.00 1.75 1.0 4 no
## 6 6 adjustable 41 16 8.014 14.00 1.75 1.0 4 no
## first selfemp tdiff margin coborrower liability liquid
## 1 yes no 1.38 1.50 no 3.69 8.91
## 2 yes no 1.38 1.50 no 3.69 8.91
## 3 yes no 1.38 1.50 no 3.69 8.91
## 4 yes no 1.38 1.50 no 3.89 8.91
## 5 yes yes 1.38 5.50 no 50.93 12.50
## 6 yes yes 1.38 4.75 no 50.44 17.74