library(ggplot2)
#getwd()
#list.files()

melb_rent_data <- read.csv("MelbRent2024New.csv")

#print(melb_rent_data)

#head(rent_data$Date)

#rent_data$Date <- as.Date(rent_data$Date, format = "%d/%m/%Y")
melb_rent_data$Date <- as.Date(paste0("01-", melb_rent_data$Date), format = "%d-%b-%y")
#rent_data$Date <- as.Date(rent_data$Date, format = "%b-%y")
#print(rent_data)
#head(rent_data$Date)

#head(rent_data$Date)
#tail(rent_data$Date)

#p0 <- ggplot(data = melb_rent_data, aes(x = Date, y = Inner.Melb))
p0 <- ggplot(data = melb_rent_data) +
      
      geom_point(aes(x = Date, y = Inner.Melb)) +
  
      geom_point(aes(x = Date, y = Inner.East)) +
      
      geom_point(aes(x = Date, y = Southern.Melb)) +
    
      geom_point(aes(x = Date, y = Outer.West)) +
      
      geom_point(aes(x = Date, y = North.West)) +
  
      geom_point(aes(x = Date, y = North.East)) +
      
      geom_point(aes(x = Date, y = Outer.East)) +
  
      geom_point(aes(x = Date, y = South.East)) +
      
      theme_minimal()

#p0 <- p0 + ggplot(data = melb_rent_data, aes(x = Date, y = Inner.East)) + geom_point()
      
#p0 <- p0 + geom_point()
    

print(p0)

library(ggplot2)
#getwd()
#list.files()

rent_data <- read.csv("RentJune2024New.csv")

#print(rent_data)

#head(rent_data$Date)

#rent_data$Date <- as.Date(rent_data$Date, format = "%d/%m/%Y")
rent_data$Date <- as.Date(paste0("01-", rent_data$Date), format = "%d-%b-%y")
#rent_data$Date <- as.Date(rent_data$Date, format = "%b-%y")
#print(rent_data)
#head(rent_data$Date)

#head(rent_data$Date)
#tail(rent_data$Date)

p1 <- ggplot(data = rent_data, aes(x = Date, y = Inner.Melb))
      
p1 <- p1 + geom_point() + theme_minimal()
    

print(p1)

library(ggplot2)
#getwd()
#list.files()

count_data <- read.csv("CountJune2024New.csv")

#print(count_data)

#head(count_data$Date)

#rent_data$Date <- as.Date(rent_data$Date, format = "%d/%m/%Y")
count_data$Date <- as.Date(paste0("01-", count_data$Date), format = "%d-%b-%y")
#rent_data$Date <- as.Date(rent_data$Date, format = "%b-%y")
#print(count_data)
#head(count_data$Date)

#head(count_data$Date)
#tail(count_data$Date)

p2 <- ggplot(data = count_data, aes(x = Date, y = Inner.Melbourne))
      
p2 <- p2 + geom_point() + theme_minimal()
    

print(p2)

library(ggplot2)
#getwd()
#list.files()

melb_wage_data <- read.csv("Wages2024New.csv")

print(melb_wage_data)
##      Date     Wages Date.1       Rent
## 1  May-12  1.000000 Mar-12  1.0000000
## 2  Nov-12  2.532486 Jun-12  0.4734694
## 3  May-13  4.657118 Sep-12  0.4081633
## 4  Nov-13  5.719435 Dec-12  0.8163265
## 5  May-14  6.468747 Mar-13  0.8163265
## 6  Nov-14  7.085270 Jun-13  1.5020408
## 7  May-15  7.815612 Sep-13  1.9918367
## 8  Nov-15  8.773594 Dec-13  2.1632653
## 9  May-16  9.921275 Mar-14  1.7551020
## 10 Nov-16 10.547283 Jun-14  2.1224490
## 11 May-17 11.751873 Sep-14  2.5306122
## 12 Nov-17 13.051314 Dec-14  3.5510204
## 13 May-18 14.549938 Mar-15  4.1632653
## 14 Nov-18 16.143413 Jun-15  4.0408163
## 15 May-19 17.528218 Sep-15  4.4081633
## 16 Nov-19 19.112207 Dec-15  5.3061224
## 17 May-20 20.269373 Mar-16  7.5102041
## 18 Nov-20 21.587783 Jun-16  7.5102041
## 19 May-21 23.807265 Sep-16  7.7142857
## 20 Nov-21 25.922413 Dec-16  9.6326531
## 21 May-22 27.800436 Mar-17 10.4489796
## 22 Nov-22 30.522622 Mar-17 11.1836735
## 23 May-23 32.846438 Sep-17 12.8979592
## 24 Nov-23 36.071327 Dec-17 14.9387755
## 25 May-24 40.292137 Mar-18 16.0816326
## 26               NA Jun-18 16.0816326
## 27               NA Sep-18 16.0816326
## 28               NA Dec-18 16.0816326
## 29               NA Mar-19 17.5510204
## 30               NA Jun-19 17.5510204
## 31               NA Sep-19 19.0204082
## 32               NA Dec-19 19.1836735
## 33               NA Mar-20 19.6734694
## 34               NA Jun-20 17.5510204
## 35               NA Sep-20 12.6530612
## 36               NA Dec-20  5.6326531
## 37               NA Mar-21  0.1632653
## 38               NA Jun-21 -2.1224490
## 39               NA Sep-21 -3.1020408
## 40               NA Dec-21 -3.5918367
## 41               NA Mar-22  0.3265306
## 42               NA Jun-22  3.9918367
## 43               NA Sep-22  8.0816327
## 44               NA Dec-22 11.9183674
## 45               NA Mar-23 21.3061225
## 46               NA Jun-23 27.5102041
## 47               NA Sep-23 32.2448980
## 48               NA Dec-23 38.0408163
## 49               NA Mar-24 41.7142857
## 50               NA Jun-24 44.3265306
#head(rent_data$Date)

#rent_data$Date <- as.Date(rent_data$Date, format = "%d/%m/%Y")
melb_wage_data$Date <- as.Date(paste0("01-", melb_wage_data$Date), format = "%d-%b-%y")
melb_wage_data$Date.1 <- as.Date(paste0("01-", melb_wage_data$Date.1), format = "%d-%b-%y")
#rent_data$Date <- as.Date(rent_data$Date, format = "%b-%y")
#print(rent_data)
#head(rent_data$Date)

#head(rent_data$Date)
#tail(rent_data$Date)

#p0 <- ggplot(data = melb_rent_data, aes(x = Date, y = Inner.Melb))
p3 <- ggplot(data = melb_wage_data) +
      
      geom_point(aes(x = Date, y = Wages)) +
  
      geom_point(aes(x = Date.1, y = Rent)) +
      
      theme_minimal()

#p0 <- p0 + ggplot(data = melb_rent_data, aes(x = Date, y = Inner.East)) + geom_point()
      
#p0 <- p0 + geom_point()
    

print(p3)
## Warning: Removed 25 rows containing missing values or values outside the scale range
## (`geom_point()`).