AE 06: Data types and classes

Application exercise
Modified

September 19, 2024

Packages

We will use the following packages in this application exercise.

  • tidyverse: For data import, wrangling, and visualization.
  • skimr: For summarizing the entire data frame at once.
  • scales: For better axis labels.

Hotel bookings

Antonio, Almeida, and Nunes (2019) collected detailed information on hotel bookings from two hotels (one resort hotel and one city hotel) in Portugal. The data set contains information such as when the booking was made, length of stay, number of adults, number of children, and number of available parking spaces.

Load the data

The data is stored in data/hotels-tt.csv. Let’s load the data file and examine it’s contents. Since the dataset is substantially large (nearly 30 variables and over 100,000 observations), we’ll use skimr::skim() to provide a compact summary of the data.

hotels <- read_csv("data/hotels-tt.csv")
skim(hotels) # much more useful to run interactively in the console
Data summary
Name hotels
Number of rows 119390
Number of columns 29
_______________________
Column type frequency:
character 13
Date 1
numeric 15
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
hotel 0 1 10 12 0 2 0
arrival_date 0 1 11 18 0 793 0
meal 0 1 2 9 0 5 0
country 0 1 2 4 0 178 0
market_segment 0 1 6 13 0 8 0
distribution_channel 0 1 3 9 0 5 0
reserved_room_type 0 1 1 1 0 10 0
assigned_room_type 0 1 1 1 0 12 0
deposit_type 0 1 10 10 0 3 0
agent 0 1 1 4 0 334 0
company 0 1 1 4 0 353 0
customer_type 0 1 5 15 0 4 0
reservation_status 0 1 7 9 0 3 0

Variable type: Date

skim_variable n_missing complete_rate min max median n_unique
reservation_status_date 0 1 2014-10-17 2017-09-14 2016-08-07 926

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
is_canceled 0 1 0.37 0.48 0.00 0.00 0.00 1 1 ▇▁▁▁▅
lead_time 0 1 104.01 106.86 0.00 18.00 69.00 160 737 ▇▂▁▁▁
stays_in_weekend_nights 0 1 0.93 1.00 0.00 0.00 1.00 2 19 ▇▁▁▁▁
stays_in_week_nights 0 1 2.50 1.91 0.00 1.00 2.00 3 50 ▇▁▁▁▁
adults 0 1 1.86 0.58 0.00 2.00 2.00 2 55 ▇▁▁▁▁
children 4 1 0.10 0.40 0.00 0.00 0.00 0 10 ▇▁▁▁▁
babies 0 1 0.01 0.10 0.00 0.00 0.00 0 10 ▇▁▁▁▁
is_repeated_guest 0 1 0.03 0.18 0.00 0.00 0.00 0 1 ▇▁▁▁▁
previous_cancellations 0 1 0.09 0.84 0.00 0.00 0.00 0 26 ▇▁▁▁▁
previous_bookings_not_canceled 0 1 0.14 1.50 0.00 0.00 0.00 0 72 ▇▁▁▁▁
booking_changes 0 1 0.22 0.65 0.00 0.00 0.00 0 21 ▇▁▁▁▁
days_in_waiting_list 0 1 2.32 17.59 0.00 0.00 0.00 0 391 ▇▁▁▁▁
adr 0 1 101.83 50.54 -6.38 69.29 94.58 126 5400 ▇▁▁▁▁
required_car_parking_spaces 0 1 0.06 0.25 0.00 0.00 0.00 0 8 ▇▁▁▁▁
total_of_special_requests 0 1 0.57 0.79 0.00 0.00 0.00 1 5 ▇▁▁▁▁

How does the Average Daily Rate (ADR) change over time? Are there differences between the city and resort hotel?

Your turn: Create a visualization that shows the average daily rate (ADR) over time for the city and resort hotels. Calculate the average (mean) ADR for each hotel by month based on when the guest(s) are scheduled to arrive, then visualize using a line graph. Ensure the \(x\)-axis is ordered chronologically.

Note

Use the lubridate package to restructure the data and determine the month when each stay began.

# add your code here

How often is each meal package booked?

Your turn: meal reports the type of meal booked with the hotel stay. Categories are presented in standard hospitality meal packages:

  • Undefined/SC – no meal package
  • BB – Bed & Breakfast
  • HB – Half board (breakfast and one other meal – usually dinner)
  • FB – Full board (breakfast, lunch and dinner)

Create a bar chart reporting the total number of bookings for each meal package. Order the bars by frequency (i.e. most frequent meal package on the left, least frequent meal package on the right).

Note

forcats will be your friend in preparing the data for the visualization.

# add your code here

Acknowledgments