Exploring the NYC Airbnb Market

Beta

Welcome to New York City, one of the most-visited cities in the world. There are many Airbnb listings in New York City to meet the high demand for temporary lodging for travelers, which can be anywhere between a few nights to many months. In this notebook, we will take a closer look at the New York Airbnb market by combining data from multiple file types like .csv, .tsv, and .xlsx (Excel files).

Recall that CSV, TSV, and Excel files are three common formats for storing data. Three files containing data on 2019 Airbnb listings are available to you:

datasets/airbnb_price.csv

listing_id: unique identifier of listing
price: nightly listing price in USD
nbhood_full: name of borough and neighborhood where listing is located

datasets/airbnb_room_type.xlsx This is an Excel file containing data on Airbnb listing descriptions and room types.

listing_id: unique identifier of listing
description: listing description
room_type: Airbnb has three types of rooms: shared rooms, private rooms, and entire homes/apartments

datasets/airbnb_last_review.tsv This is a TSV file containing data on Airbnb host names and review dates.

listing_id: unique identifier of listing
host_name: name of listing host
last_review: date when the listing was last reviewed

# Load the necessary packages

suppressMessages(library(dplyr)) # Do not change this line, as it is required to check your answer correctly
options(readr.show_types = FALSE) # Do not change this line, as it is required to check your answer correctly
library(readr)
library(readxl)
library(stringr)

# Import CSV for prices
airbnb_price <- read_csv('data/airbnb_price.csv', show_col_types=FALSE)

# Import TSV for room types
airbnb_room_type <- read_excel('data/airbnb_room_type.xlsx')

# Import Excel file for review dates
airbnb_last_review <- read_tsv('data/airbnb_last_review.tsv', show_col_types=FALSE)

# Join the three data frames together into one
listings <- airbnb_price %>%
  inner_join(airbnb_room_type, by = "listing_id") %>%
  inner_join(airbnb_last_review, by = "listing_id")

# Question 1: What is the average listing price? 

# To convert price to numeric, remove "dollars" from each value
avg_price <- listings %>%
  mutate(price_clean = str_remove(price, " dollars") %>%
        as.numeric()) %>%
  # Take the mean of price_clean
  summarize(avg_price = mean(price_clean)) %>%
  # Convert from a tibble to a single number
  as.numeric()

# Question 2: How many of the listings are private rooms? 

# Since there are differences in capitalization, make capitalization consistent
private_room_count <- listings %>%
  mutate(room_type = str_to_lower(room_type)) %>%
  # Then count the number of each room_type
  count(room_type) %>%
  # Get row containing count for private rooms only
  filter(room_type == "private room") 

# # Extract number of rooms
nb_private_rooms <- private_room_count$n

# # Question 3: Which listing was most recently reviewed? 

# In order to use a function like max()/min() on the last_review column, it needs to be converted to Date
review_dates <- listings %>%
  # Convert to date using the format 'Month DD YYYY'
  mutate(last_review_date = as.Date(last_review, format = "%B %d %Y")) %>%
  # Use max() and min() to take the latest and earliest dates
  summarize(first_reviewed = min(last_review_date),
            last_reviewed = max(last_review_date))

review_dates$nb_private_rooms = nb_private_rooms
review_dates$avg_price = avg_price
review_dates