Recent developments in Edinburgh regarding the growth of Airbnb and its impact on the housing market means a better understanding of the Airbnb listings is needed. Using data provided by Airbnb, we can explore how Airbnb availability and prices vary by neighbourhood.

edibnb

Format

A tibble with 13,245 rows and 10 variables:

id

ID number of the listing

price

Price, in GBP, for one night stay

neighbourhood

Neighbourhood listing is located in

accommodates

Number of people listing accommodates

bathrooms

Number of bathrooms

bedrooms

Number of bedrooms

beds

Number of beds (which can be different than the number of bedrooms)

review_scores_rating

Average rating of property

number_of_reviews

Number of reviews

listing_url

Listing URL

Source

https://www.kaggle.com/thoroc/edinburgh-inside-airbnb/version/2

Details

The data come from the Kaggle database, and was originally distributed by Inside Airbnb on 25 June 2019.

The data has been modified to better serve the goals of introductory data science education.

Examples

library(ggplot2) ggplot(edibnb, aes(x = price)) + geom_histogram(binwidth = 50)
#> Warning: Removed 199 rows containing non-finite values (stat_bin).