| hotel_bookings {bayesrules} | R Documentation |
Hotel Bookings Data
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/.