Importing data
This data set contains a single file which compares various booking information between hotels.
importing the data and reading the top 5 rows
# A tibble: 6 × 32
hotel is_canceled lead_time arrival_date_ye… arrival_date_mo…
<chr> <dbl> <dbl> <dbl> <chr>
1 Resort Hotel 0 342 2015 July
2 Resort Hotel 0 737 2015 July
3 Resort Hotel 0 7 2015 July
4 Resort Hotel 0 13 2015 July
5 Resort Hotel 0 14 2015 July
6 Resort Hotel 0 14 2015 July
# … with 27 more variables: arrival_date_week_number <dbl>,
# arrival_date_day_of_month <dbl>, stays_in_weekend_nights <dbl>,
# stays_in_week_nights <dbl>, adults <dbl>, children <dbl>,
# babies <dbl>, meal <chr>, country <chr>, market_segment <chr>,
# distribution_channel <chr>, is_repeated_guest <dbl>,
# previous_cancellations <dbl>,
# previous_bookings_not_canceled <dbl>, reserved_room_type <chr>, …skim() is used to for getting summary statistics about variables in dataframe,tibbles,datatablesand vectors. It is mostly used with grouped dataframes (source: https://cran.r-project.org/web/packages/skimr/vignettes/skimr.html)
| Name | hotel_bookings |
| Number of rows | 119390 |
| Number of columns | 32 |
| _______________________ | |
| Column type frequency: | |
| character | 13 |
| Date | 1 |
| numeric | 18 |
| ________________________ | |
| 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_month | 0 | 1 | 3 | 9 | 0 | 12 | 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 | ▇▂▁▁▁ |
| arrival_date_year | 0 | 1 | 2016.16 | 0.71 | 2015.00 | 2016.00 | 2016.00 | 2017 | 2017 | ▃▁▇▁▆ |
| arrival_date_week_number | 0 | 1 | 27.17 | 13.61 | 1.00 | 16.00 | 28.00 | 38 | 53 | ▅▇▇▇▅ |
| arrival_date_day_of_month | 0 | 1 | 15.80 | 8.78 | 1.00 | 8.00 | 16.00 | 23 | 31 | ▇▇▇▇▆ |
| 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 | ▇▁▁▁▁ |
from the above summary statistics we can see there are a total of 119390 rows and 32 columns in the hotel_bookings dataset. 13 character variables, 18 numeric variables, and 1 date variable. there are a total of 4 missing values in the children variable. for the analysis now i will be using hotel, market segment, stays_in_weekend_nights and stays_in_week_nights.
Varaible Description
hotel variable: type of hotel booked
market segment : Market segment designation. In categories, the term “TA” means “Travel Agents” and “TO” means “Tour Operators”
stays_in_weekend_nights : guest stayed at the hotel in weekend nights
stays_in_week_nights : guest stayed at the hotel in week nights
I am using the select() from the dplyr package which comes with tidyverse package and the piping for selecting columns
# A tibble: 119,390 × 4
hotel stays_in_weekend_ni… stays_in_week_nig… market_segment
<chr> <dbl> <dbl> <chr>
1 Resort Hotel 0 0 Direct
2 Resort Hotel 0 0 Direct
3 Resort Hotel 0 1 Direct
4 Resort Hotel 0 1 Corporate
5 Resort Hotel 0 2 Online TA
6 Resort Hotel 0 2 Online TA
7 Resort Hotel 0 2 Direct
8 Resort Hotel 0 2 Direct
9 Resort Hotel 0 3 Online TA
10 Resort Hotel 0 3 Offline TA/TO
# … with 119,380 more rows[1] "Resort Hotel" "City Hotel" [1] "Direct" "Corporate" "Online TA" "Offline TA/TO"
[5] "Complementary" "Groups" "Undefined" "Aviation" bookings in different market segments
city hotel
# A tibble: 8 × 2
market_segment n
<chr> <int>
1 Aviation 237
2 Complementary 542
3 Corporate 2986
4 Direct 6093
5 Groups 13975
6 Offline TA/TO 16747
7 Online TA 38748
8 Undefined 2
resort hotel
# A tibble: 6 × 2
market_segment n
<chr> <int>
1 Complementary 201
2 Corporate 2309
3 Direct 6513
4 Groups 5836
5 Offline TA/TO 7472
6 Online TA 17729
# A tibble: 14 × 3
# Groups: hotel, market_segment [14]
hotel market_segment n
<chr> <chr> <int>
1 City Hotel Aviation 237
2 City Hotel Complementary 542
3 City Hotel Corporate 2986
4 City Hotel Direct 6093
5 City Hotel Groups 13975
6 City Hotel Offline TA/TO 16747
7 City Hotel Online TA 38748
8 City Hotel Undefined 2
9 Resort Hotel Complementary 201
10 Resort Hotel Corporate 2309
11 Resort Hotel Direct 6513
12 Resort Hotel Groups 5836
13 Resort Hotel Offline TA/TO 7472
14 Resort Hotel Online TA 17729
how many number of days do people stay in the hotel?
Resort hotel
