Part 2

The online hospitality company Airbnb has made publicly available a number of datasets. This part of the assignment makes use of the listings.csv dataset which is available at

http://data.insideairbnb.com/australia/vic/melbourne/2019-04-08/visualisations/listings.csv.

It consists of a number of parameters related to properties available for lodging in the Melbourne metropolitan area and can be visualised at http://insideairbnb.com/melbourne.

Write R code to answer the following.

1. Give the five neighbourhoods with the most listings (list them along with the counts in descending order). 

2. How many listings contain the following words (upper or lower case or mixed) in the name column?

a. Beautiful

b. Quiet

c. Amazing

d. <another adjective of your choice with at least 200 instances> 

3. How many listings are there with last review in 2016? Give month by month counts for the year 2016.

4. Create a new column of the table which calculates the number of ids that correspond to the given host_id . Your answer will match the calculated_host_listings_count column (only use this column to check your answer).

5. Write a function that inputs a listing id and outputs a score that is the sum of points according to the following criteria:

a. Points for the neighbourhood: (average number of bedrooms per dwelling in 2016) × 50 (this comes from the data set in Part 1)

b. Points for the room type: 200 for Entire home/apt, 100 for Private room, 0 for Shared room

c. Points for minimum nights: 50 for 1 night, 25 for 2 nights, 0 for 3 or more nights

d. Points for availability: (availability_365) divided by 5

e. Points for review frequency: 50 × (reviews per month), but no more than 100

f. Points for price: (300 minus price)

Which id (ids if more than one) has the highest score according to the above system?