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library(knitr)library(readxl)df <-read_excel("C:/Users/ss671/OneDrive/Documents/Airbnb_DC_25 (1).csv")avg_price<-df%>%group_by(room_type)%>%summarize(avg_price=mean(price,na.rm=TRUE))ggplot(avg_price,aes(x=room_type,y=avg_price,fill=room_type))+geom_bar(stat="identity")+labs(title="Average price of Airbnb by room type in Dc",x="Type of Room",y="Average price",fill="Type of Room",caption="Airbnb_DC_25 dataset" )
df
# A tibble: 6,257 × 18
id name host_id host_name neighbourhood_group neighbourhood latitude
<dbl> <chr> <dbl> <chr> <lgl> <chr> <dbl>
1 3686 Vita's Hi… 4645 Vita NA Historic Ana… 38.9
2 3943 Historic … 5059 Vasa NA Edgewood, Bl… 38.9
3 4197 Capitol H… 5061 Sandra NA Capitol Hill… 38.9
4 4529 Bertina's… 5803 Bertina NA Eastland Gar… 38.9
5 5589 Cozy apt … 6527 Ami NA Kalorama Hei… 38.9
6 7103 Lovely gu… 17633 Charlotte NA Spring Valle… 38.9
7 11785 Sanctuary… 32015 Teresa NA Cathedral He… 38.9
8 12442 Peaches &… 32015 Teresa NA Cathedral He… 38.9
9 13744 Heart of … 53927 Victoria NA Columbia Hei… 38.9
10 14218 Quiet Com… 32015 Teresa NA Cathedral He… 38.9
# ℹ 6,247 more rows
# ℹ 11 more variables: longitude <dbl>, room_type <chr>, price <dbl>,
# minimum_nights <dbl>, number_of_reviews <dbl>, last_review <dttm>,
# reviews_per_month <dbl>, calculated_host_listings_count <dbl>,
# availability_365 <dbl>, number_of_reviews_ltm <dbl>, license <chr>
I chose to compare the average price of Airbnb for the different room types in Washington DC using the Airbnb_DC_25 dataset. The x-axis shows the type of room offered, while the y-axis shows the average listing price available. I used fill=roomtype to create the legend and to assign different colors to the different room types. One interesting thing I noticed was that the shared room was more expensive than any of the other rooms. I would assume that the shared rooms are primarily rented in an expensive neighborhood.
