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PROJECT_2 AKIN

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
# Load the necessary packages
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)

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

# Load Excel file for room types
airbnb_room_type <- read_excel('data/airbnb_room_type.xlsx')

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

# Merge 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")

# What are the dates of the earliest and most recent reviews? 
# In order to use a function like max()/min() on last_review date column, format needs to be converted
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))

# 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


# 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()

# Load solution values into solution tibble:
# Note first_reviewed and last_reviewed columns in 
# review_dates were created earlier
review_dates$nb_private_rooms = nb_private_rooms
review_dates$avg_price = round(avg_price, 2)

print(review_dates)