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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 project, you 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:

data/airbnb_price.csv This is a CSV file containing data on Airbnb listing prices and locations.

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

data/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

data/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
# We've loaded the necessary packages for you in the first cell. Please feel free to add as many cells as you like!
suppressMessages(library(dplyr)) # This line is required to check your answer correctly
options(readr.show_types = FALSE) # This line is required to check your answer correctly
library(readr)
library(readxl)
library(stringr)
library(lubridate, verbose = FALSE)
library(tidyr, verbose = FALSE)

# Importing the datasets
airbnb_price = read_csv("data/airbnb_price.csv", show_col_types = FALSE)
airbnb_room_type = read_excel("data/airbnb_room_type.xlsx")
airbnb_last_review = read_tsv("data/airbnb_last_review.tsv", show_col_types = FALSE)
glimpse(airbnb_price)
glimpse(airbnb_room_type)
glimpse(airbnb_last_review)

Let's see the range date of reviews in these listings

# Converting last_review into date format
airbnb_last_review = airbnb_last_review %>%
	mutate(last_review = parse_date_time(last_review, orders = "B d y"))

# Getting the dates of the earliest and most recent reviews
earliest_review = min(airbnb_last_review$last_review)
recent_review = max(airbnb_last_review$last_review)

earliest_review
recent_review

Now, let's see the types of room listed

airbnb_room_type %>% 
	count(room_type)

Oh no! The categories are quite messy so let's tidy them up and standaridize them a little.

# First make letter case consistent
airbnb_room_type = airbnb_room_type %>%
	mutate(room_type = str_to_lower(room_type))

Now, let's see if this was enough

# Let's see how much it improved
airbnb_room_type %>% 
	count(room_type)

Great! It seems to be working. Now, let's get private rooms only

private_rooms = sum(airbnb_room_type$room_type == "private room")
private_rooms

Now, let's explore the average listing price, but first, we should put the variable in an appropriate format.

# Separate the column into two: numeric_price and unit
airbnb_price = airbnb_price %>%
	separate(col = price, into = c("numeric_price", "unit"), sep = " ", remove = FALSE) %>%
	mutate(numeric_price = as.numeric(numeric_price))

# Average price
avg_price = mean(airbnb_price$numeric_price)
avg_price
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