ShinyApp

This presentation is being created as part of the peer assessment for the coursera developing data products class. The shiny app is focusing on Melbourne Housing Market Data.The assignemnt consists in 2 parts:

• A shiny App https://gpol93.shinyapps.io/realestateinmelbourne/
• A documentation

IT contains the data related to housing market in Melbourne (e.g price, number of bedrooms/bathrooms, latitude, liongitude). The goal of the app was to enable a user firendaly app where one would be able to look easily for a property by selecting and filtering the properties based on some characteristics and then visualize the results on an interactive map.

The shiny app is available here: https://gpol93.shinyapps.io/realestateinmelbourne/

• Then the reults will be displayed on an interactive map where the user can brush and click on the points to see the main characteristics of the property.
• The box number of goods diplays the number of properties mathcing the request.
• The box mensuality diplays the monthly amoujt the user whould have to pay if he takes a 20 year loan with 1% interest rate (To diplay it you have to click in the table on the price)

data <- read.csv("data.csv")
keep <- c("Regionname", "Lattitude","Longtitude", "Price",
          "Type", "Rooms", "Bedroom2", "Bathroom", "Car", "Landsize", "BuildingArea", "Date"
)
data <- data[, keep]
data$Date <- as.Date(as.character(data$Date), format = "%d/%m/%Y")
head(data)

             Regionname Lattitude Longtitude   Price Type Rooms Bedroom2
1 Northern Metropolitan  -37.8014   144.9958      NA    h     2        2
2 Northern Metropolitan  -37.7996   144.9984 1480000    h     2        2
3 Northern Metropolitan  -37.8079   144.9934 1035000    h     2        2
4 Northern Metropolitan  -37.8114   145.0116      NA    u     3        3
5 Northern Metropolitan  -37.8093   144.9944 1465000    h     3        3
6 Northern Metropolitan  -37.7969   144.9969  850000    h     3        3
  Bathroom Car Landsize BuildingArea       Date
1        1   1      126           NA 2016-09-03
2        1   1      202           NA 2016-12-03
3        1   0      156           79 2016-02-04
4        2   1        0           NA 2016-02-04
5        2   0      134          150 2017-03-04
6        2   1       94           NA 2017-03-04