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library(dplyr)
Attaching package: 'dplyr'
The following objects are masked from 'package:stats':
filter, lag
The following objects are masked from 'package:base':
intersect, setdiff, setequal, union
# 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>
graph1<-select(df,room_type, price)graph1%>%ggplot(aes(x=room_type,y=price, fill=room_type)) +geom_bar(stat="identity") +labs(x="Types of Airbnbs", y="Price", title="Price of Types of Airbnbs in DC", caption ="Source: Google Drive")
Warning: Removed 1488 rows containing missing values or values outside the scale range
(`geom_bar()`).
Insights:
The visualization I have is a bar graph that shows the shows the relationship between types of airbnbs and their prices. What I noticed from the graph is that hotel rooms and shared rooms are around the same price which makes sense since they typically have the same area of space. I also noticed that an entire home or apartment is significantly more expensive than the hotel room, shared and private room. This also makes sense since it has a significantly larger area of space than the other three types of airbnbs.
