hotel_bookings: Hotel Bookings Data

In bayesrules: Datasets and Supplemental Functions from Bayes Rules! Book

Description

A random subset of the data on hotel bookings originally collected by Antonio, Almeida and Nunes (2019) and distributed through the R for Data Science TidyTuesday project.

Usage

hotel_bookings

Format

A data frame with 1000 hotel bookings and 32 variables on each booking.

hotel

"Resort Hotel" or "City Hotel"

is_canceled

whether the booking was cancelled

lead_time

number of days between booking and arrival

arrival_date_year

year of scheduled arrival

arrival_date_month

month of scheduled arrival

arrival_date_week_number

week of scheduled arrival

arrival_date_day_of_month

day of month of scheduled arrival

stays_in_weekend_nights

number of reserved weekend nights

stays_in_week_nights

number of reserved week nights

adults

number of adults in booking

children

number of children

babies

number of babies

meal

whether the booking includes breakfast (BB = bed & breakfast), breakfast and dinner (HB = half board), or breakfast, lunch, and dinner (FB = full board)

country

guest's country of origin

market_segment

market segment designation (eg: TA = travel agent, TO = tour operator)

distribution_channel

booking distribution channel (eg: TA = travel agent, TO = tour operator)

is_repeated_guest

whether or not booking was made by a repeated guest

previous_cancellations

guest's number of previous booking cancellations

previous_bookings_not_canceled

guest's number of previous bookings that weren't cancelled

reserved_room_type

code for type of room reserved by guest

assigned_room_type

code for type of room assigned by hotel

booking_changes

number of changes made to the booking

deposit_type

No Deposit, Non Refund, Refundable

agent

booking travel agency

company

booking company

days_in_waiting_list

number of days the guest waited for booking confirmation

customer_type

Contract, Group, Transient, Transient-party (a transient booking tied to another transient booking)

average_daily_rate

average hotel cost per day

required_car_parking_spaces

number of parking spaces the guest needed

total_of_special_requests

number of guest special requests

reservation_status

Canceled, Check-Out, No-Show

reservation_status_date

when the guest cancelled or checked out

Source

Nuno Antonio, Ana de Almeida, and Luis Nunes (2019). "Hotel booking demand datasets." Data in Brief (22): 41-49. https://github.com/rfordatascience/tidytuesday/blob/master/data/2020/2020-02-11/hotels.csv/.

bayesrules documentation built on Sept. 25, 2021, 9:06 a.m.