Install Packages and Load Dataset

library(readxl)
library(tidyverse)
## Warning: package 'ggplot2' was built under R version 4.5.2
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.4     ✔ readr     2.1.5
## ✔ forcats   1.0.0     ✔ stringr   1.5.1
## ✔ ggplot2   4.0.2     ✔ tibble    3.3.0
## ✔ lubridate 1.9.4     ✔ tidyr     1.3.1
## ✔ purrr     1.1.0     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
df <- read_excel("Airbnb_DC_25.csv")

Use dplyr Commands to Summarize Prices by Room Type

room_price <- df |>
  group_by(room_type) |>
  summarize(avg_price = mean(price, na.rm = TRUE))
head(room_price)
## # A tibble: 4 × 2
##   room_type       avg_price
##   <chr>               <dbl>
## 1 Entire home/apt      181.
## 2 Hotel room           346.
## 3 Private room         103.
## 4 Shared room         1455.

Bar Graph of Showing Average Airbnb Prices For Each Room Type

ggplot(room_price, aes(x = room_type, y = avg_price, fill = room_type)) +
  geom_bar(stat = "identity") +
  labs(
    title = "Average Airbnb Price by Room Type in Washington DC",
    x = "Room Type",
    y = "Average Price (USD)",
    caption = "Data Source: Airbnb_DC_25 Dataset"
  ) +
  theme_minimal()

# Reflection

This visualization that I created is a bar graph showing the average Airbnb prices by room type in Washington DC. I used “fill =” to create different colors and the legend, “stat=identity” to use the actual values of the dataset. Each bar represents a different room type, and the colors help determine which room type is which. The x-axis shows the room type and the y-axis shows the average price of those room types. One key insight about this plot is that shared rooms have the highest average price, while private rooms have the lowest average price. I think this happened because in Washington D.C, entire homes and private rooms are very rare and there are not a lot of them in the city, which means that Airbnb has more shared rooms than private rooms on their app, which makes the average price so low.