Airbnb DC Homework Assignment

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

library(ggplot2)
library(readxl)
df <- read_excel("Airbnb_DC_25.csv")
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>

room_counts <- count(df, room_type)

ggplot(room_counts, aes(x = room_type, y = n, fill = room_type)) +
  geom_col() +
  labs(
    title = "Airbnb Listings by Room Type in Washington DC",
    x = "Room Type",
    y = "Number of Listings",
    fill = "Room Type",
    caption = "Source: Airbnb_DC_25.csv"
  ) +
  theme_minimal()

The visualization I created is a bar graph showing the number of Airbnb listings by room type in Washington, D.C. I used different colors to represent each room type, making it easier to compare the categories. The graph shows how some room types have more listings than others. This helps us understand which types of Airbnb accommodations are more common in Washington, D.C.