library(readxl)
setwd("/Users/precious/Downloads/DATASETS")
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>
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)


df_filtered <- df %>%
  filter(price < 500)

ggplot(df_filtered, aes(x = price, fill = room_type)) +
  geom_histogram(binwidth = 25, alpha = 0.7, position = "identity") +
  labs(
    title = "Airbnb Listing Prices in Washington, DC",
    x = "Price per Night ($)",
    y = "Number of Listings",
    caption = "Data Source: Airbnb_DC_25 Dataset"
  ) + theme(panel.background = element_rect(fill = "lightblue"))

This histogram displays the distribution of Airbnb listing prices in Washington, DC. The data was filtered to include only listings with prices below $500 per night to better visualize the most common price ranges. Different colors represent different room types, allowing comparisons between entire homes, private rooms, and shared rooms. One clear pattern in the plot is that most listings are from the lower price ranges, with far fewer listings at higher prices. This suggests that most people book cheaper Airbnb and the majority of Airbnb options in Washington, DC are relatively affordable compared to more expensive listings.