The Berlin_Listings App features all Airbnb listings for the city of Berlin in Germany, and allows the user to filter the original database, by selecting neighbourhood groups, room types, a price range and an availability range.

• The user can visualize the information via a leaflet-generated map.
• A particular listing can be selected in the map and display aditional information by clicking on its marker
• The aditional information to be displayed on a particular marker popup: Listing Name, Listing ID, Neighbourhood, Host, Room type, and Price.
• A price summary is included for the selected listings.

The Berlin_Listings app can be foud here

• The original dataset was scraped on November 07th, 2018 and contains detailed listings data of current Airbnb listings in Berlin.
• This data was created by Murray Cox and his Inside Airbnb project which can be found here.
• Data overview:

## 'data.frame':    22552 obs. of  16 variables:
##  $ id                            : int  2015 2695 3176 3309 7071 9991 14325 16401 16644 17409 ...
##  $ name                          : Factor w/ 21874 levels "","'attic with a view'",..: 3411 15624 9734 3867 5313 10763 1957 2033 12067 9430 ...
##  $ host_id                       : int  2217 2986 3718 4108 17391 33852 55531 59666 64696 67590 ...
##  $ host_name                     : Factor w/ 5998 levels "","'Tom","(Email hidden by Airbnb)",..: 2304 3903 859 2521 850 4519 1046 3859 4699 5811 ...
##  $ neighbourhood_group           : Factor w/ 12 levels "Charlottenburg-Wilm.",..: 5 7 7 11 7 7 7 2 2 7 ...
##  $ neighbourhood                 : Factor w/ 136 levels "Adlershof","Albrechtstr.",..: 19 97 100 109 50 100 97 33 77 100 ...
##  $ latitude                      : num  52.5 52.5 52.5 52.5 52.5 ...
##  $ longitude                     : num  13.4 13.4 13.4 13.3 13.4 ...
##  $ room_type                     : Factor w/ 3 levels "Entire home/apt",..: 1 2 1 2 2 1 1 2 1 2 ...
##  $ price                         : int  60 17 90 26 42 180 70 120 90 45 ...
##  $ minimum_nights                : int  4 2 62 5 2 6 90 30 60 3 ...
##  $ number_of_reviews             : int  118 6 143 25 197 6 23 0 48 279 ...
##  $ last_review                   : Factor w/ 1313 levels "","2010-09-16",..: 1303 1276 724 1230 1310 1206 1307 1 986 1306 ...
##  $ reviews_per_month             : num  3.76 1.42 1.25 0.39 1.75 0.15 0.23 NA 0.47 2.83 ...
##  $ calculated_host_listings_count: int  4 1 1 1 1 1 3 1 2 1 ...
##  $ availability_365              : int  141 0 220 297 26 137 129 365 159 42 ...

• This app has several input and output components, as well as ui elements to help visualize the information and documentation.

An example of an operation performed by the app is the price summary (here performed on the entire dataset).

summary(listings$price)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    0.00   30.00   45.00   67.14   70.00 9000.00

The code example for the server.R file:

library(shiny)
library(dplyr)
# Define server logic 
shinyServer(function(input, output) {
        lstDa <- reactive({
                filter(listings, 
                       price>=input$priceSlider[1],
                       price<= input$priceSlider[2],
                       neighbourhood_group %in% input$location,
                       room_type %in% input$roomType,
                       availability_365>= input$availabilitySlider[1],
                       availability_365 <= input$availabilitySlider[2]
                )
        })
                
        output$map1 <- renderLeaflet({
                listdata <- lstDa()
                leaflet(listdata ) %>%
                        addTiles() %>%
                        addMarkers(popup = paste0(
                                "<br/>Name: ", "<strong>",(listdata$name),"</strong>",
                                "<br/>id: ", listdata$id,
                                "<br/>Neighbourhood: ", listdata$neighbourhood,
                                "<br/>Host: ", listdata$host_name,
                                "<br/>Room Type: ", listdata$room_type,
                                "<br/>Price: ", "$",format(listdata$price, big.mark = ",", nsmall = 2, decimal.mark = ".")
                                ),
                                label = listdata$name,
                                clusterOptions = markerClusterOptions(),
                                lng = listdata$lng, lat = listdata$lat
                                )
        })
        output$priceTable <- renderPrint({
                listdata <- lstDa()
                summary(listdata$price)
        })
})

• The code for both the ui.R and the global.R file can be found at the GitHub repo for this app.