Assignment 3

library(ggplot2)

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


melb_rent_data$Date <- as.Date(paste0("01-", melb_rent_data$Date), format = "%d-%b-%y")

p0 <- ggplot(data = melb_rent_data) +
      
      geom_point(aes(x = Date, y = Inner.Melb), colour = "#4D8261") +
  
      geom_point(aes(x = Date, y = Inner.East), colour = "#78824D") +
      
      geom_point(aes(x = Date, y = Southern.Melb), colour = "#824E4F") +
    
      geom_point(aes(x = Date, y = Outer.West), colour = "#764E81") +
      
      geom_point(aes(x = Date, y = North.West), colour = "#4E6481") +
  
      geom_point(aes(x = Date, y = North.East), colour = "#4E8161") +
      
      geom_point(aes(x = Date, y = Outer.East), colour = "#915E67") +
  
      geom_point(aes(x = Date, y = South.East), colour = "#97AF87") +
  
      labs(
          title = "Melbourne Rent", 
          subtitle = "Weekly Rent Price since 2000", 
          y = "Rent\nPrice\n(AUD)",
          x = "Date") +
      
      scale_x_date(date_breaks = "5 years", date_labels = "%Y") +
  
      theme_minimal() +
      theme(
        panel.grid = element_blank(),
        axis.line = element_line(colour = "#B06148"),
        axis.text.x = element_text(colour = "#B06148"),
        axis.text.y = element_text(colour = "#B06148"),
        axis.title.x = element_text(colour = "#7d4533"),
        axis.title.y = element_text(angle = 0, colour = "#7d4533"),
        plot.title = element_text(colour = "#7d4533"),
        plot.subtitle = element_text(colour = "#B06148"),
        panel.background = element_rect(fill = "#F7CB99", colour = "#F7CB99"),
        plot.background = element_rect(fill = "#F7CB99", colour = "#F7CB99")
        
      )

p0

library(ggplot2)

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


rent_data$Date <- as.Date(paste0("01-", rent_data$Date), format = "%d-%b-%y")

p1 <- ggplot(data = rent_data, aes(x = Date, y = Inner.Melb))
      
p1 <- p1 + geom_point(colour = "#4D8261") + 
  
      labs(
          title = "Inner Melbourne", 
          subtitle = "Weekly Rent Price since 2000", 
          y = "Rent\nPrice\n(AUD)",
          x = "Date") +
  
      scale_x_date(date_breaks = "5 years", date_labels = "%Y") +

      theme_minimal() +
      theme(
        panel.grid = element_blank(),
        axis.line = element_line(colour = "#B06148"),
        axis.text.x = element_text(colour = "#B06148"),
        axis.text.y = element_text(colour = "#B06148"),
        axis.title.x = element_text(colour = "#7d4533"),
        axis.title.y = element_text(angle = 0, colour = "#7d4533"),
        plot.title = element_text(colour = "#7d4533"),
        plot.subtitle = element_text(colour = "#B06148"),
        panel.background = element_rect(fill = "#F7CB99", colour = "#F7CB99"),
        plot.background = element_rect(fill = "#F7CB99", colour = "#F7CB99")
        
      )

# Grey - #CACED0 
# Tan - #F7CB99
# Green - #4D8261
# Brown - #B06148

p1

library(ggplot2)

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

count_data$Date <- as.Date(paste0("01-", count_data$Date), format = "%d-%b-%y")

p2 <- ggplot(data = count_data, aes(x = Date, y = Inner.Melbourne))
      
p2 <- p2 + geom_point(colour = "#4D8261") + 
  
      labs(
          title = "Inner Melbourne", 
          subtitle = "Availabilities since 2000", 
          y = "Count",
          x = "Date") +
  
      scale_x_date(date_breaks = "5 years", date_labels = "%Y") +

      theme_minimal() +
      theme(
        panel.grid = element_blank(),
        axis.line = element_line(colour = "#B06148"),
        axis.text.x = element_text(colour = "#B06148"),
        axis.text.y = element_text(colour = "#B06148"),
        axis.title.x = element_text(colour = "#7d4533"),
        axis.title.y = element_text(angle = 0, colour = "#7d4533"),
        plot.title = element_text(colour = "#7d4533"),
        plot.subtitle = element_text(colour = "#B06148"),
        panel.background = element_rect(fill = "#F7CB99", colour = "#F7CB99"),
        plot.background = element_rect(fill = "#F7CB99", colour = "#F7CB99")
        
      )
p2

library(ggplot2)

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


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")

p3 <- ggplot(data = melb_wage_data) +
      
      geom_point(aes(x = Date, y = Wages), colour = "#9D3733") +

      geom_point(aes(x = Date.1, y = Rent), colour = "#4D8261") +
      
      labs(
          title = "Wage Growth vs Rent Prices", 
          subtitle = "Proportional Increase since 2012", 
          y = "_________",
          x = "Date") +
  
      scale_x_date(date_breaks = "2 years", date_labels = "%Y") +

      theme_minimal() +
      theme(
        panel.grid = element_blank(),
        axis.line = element_line(colour = "#B06148"),
        axis.text.x = element_text(colour = "#B06148"),
        axis.text.y = element_text(colour = "#B06148"),
        axis.title.x = element_text(colour = "#7d4533"),
        axis.title.y = element_text(angle = 0, colour = "#7d4533"),
        plot.title = element_text(colour = "#7d4533"),
        plot.subtitle = element_text(colour = "#B06148"),
        panel.background = element_rect(fill = "#F7CB99", colour = "#F7CB99"),
        plot.background = element_rect(fill = "#F7CB99", colour = "#F7CB99")
        
      )

p3

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