Data Analysis of hotel_bookings Dataset Markdown Document
Daniel Xiaojing Yu
28/08/2021
Setting up my R environment by loading the ‘tidyverse’, ‘skimr’, and ‘janitor’ packages
Import data
hotel_bookings <- read_csv("~/Desktop/hotel_bookings.csv")## Rows: 119390 Columns: 32## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (13): hotel, arrival_date_month, meal, country, market_segment, distrib...
## dbl (18): is_canceled, lead_time, arrival_date_year, arrival_date_week_numb...
## date (1): reservation_status_date##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Inspect data
bookings_df <- read_csv("~/Desktop/hotel_bookings.csv")## Rows: 119390 Columns: 32## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (13): hotel, arrival_date_month, meal, country, market_segment, distrib...
## dbl (18): is_canceled, lead_time, arrival_date_year, arrival_date_week_numb...
## date (1): reservation_status_date##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.head(bookings_df)## # A tibble: 6 × 32
## hotel is_canceled lead_time arrival_date_ye… arrival_date_mo… arrival_date_we…
## <chr> <dbl> <dbl> <dbl> <chr> <dbl>
## 1 Resort Hotel 0 342 2015 July 27
## 2 Resort Hotel 0 737 2015 July 27
## 3 Resort Hotel 0 7 2015 July 27
## 4 Resort Hotel 0 13 2015 July 27
## 5 Resort Hotel 0 14 2015 July 27
## 6 Resort Hotel 0 14 2015 July 27
## # … with 26 more variables: 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>, assigned_room_type <chr>, booking_changes <dbl>,
## # deposit_type <chr>, agent <chr>, company <chr>, …str(bookings_df)## spec_tbl_df [119,390 × 32] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ hotel : chr [1:119390] "Resort Hotel" "Resort Hotel" "Resort Hotel" "Resort Hotel" ...
## $ is_canceled : num [1:119390] 0 0 0 0 0 0 0 0 1 1 ...
## $ lead_time : num [1:119390] 342 737 7 13 14 14 0 9 85 75 ...
## $ arrival_date_year : num [1:119390] 2015 2015 2015 2015 2015 ...
## $ arrival_date_month : chr [1:119390] "July" "July" "July" "July" ...
## $ arrival_date_week_number : num [1:119390] 27 27 27 27 27 27 27 27 27 27 ...
## $ arrival_date_day_of_month : num [1:119390] 1 1 1 1 1 1 1 1 1 1 ...
## $ stays_in_weekend_nights : num [1:119390] 0 0 0 0 0 0 0 0 0 0 ...
## $ stays_in_week_nights : num [1:119390] 0 0 1 1 2 2 2 2 3 3 ...
## $ adults : num [1:119390] 2 2 1 1 2 2 2 2 2 2 ...
## $ children : num [1:119390] 0 0 0 0 0 0 0 0 0 0 ...
## $ babies : num [1:119390] 0 0 0 0 0 0 0 0 0 0 ...
## $ meal : chr [1:119390] "BB" "BB" "BB" "BB" ...
## $ country : chr [1:119390] "PRT" "PRT" "GBR" "GBR" ...
## $ market_segment : chr [1:119390] "Direct" "Direct" "Direct" "Corporate" ...
## $ distribution_channel : chr [1:119390] "Direct" "Direct" "Direct" "Corporate" ...
## $ is_repeated_guest : num [1:119390] 0 0 0 0 0 0 0 0 0 0 ...
## $ previous_cancellations : num [1:119390] 0 0 0 0 0 0 0 0 0 0 ...
## $ previous_bookings_not_canceled: num [1:119390] 0 0 0 0 0 0 0 0 0 0 ...
## $ reserved_room_type : chr [1:119390] "C" "C" "A" "A" ...
## $ assigned_room_type : chr [1:119390] "C" "C" "C" "A" ...
## $ booking_changes : num [1:119390] 3 4 0 0 0 0 0 0 0 0 ...
## $ deposit_type : chr [1:119390] "No Deposit" "No Deposit" "No Deposit" "No Deposit" ...
## $ agent : chr [1:119390] "NULL" "NULL" "NULL" "304" ...
## $ company : chr [1:119390] "NULL" "NULL" "NULL" "NULL" ...
## $ days_in_waiting_list : num [1:119390] 0 0 0 0 0 0 0 0 0 0 ...
## $ customer_type : chr [1:119390] "Transient" "Transient" "Transient" "Transient" ...
## $ adr : num [1:119390] 0 0 75 75 98 ...
## $ required_car_parking_spaces : num [1:119390] 0 0 0 0 0 0 0 0 0 0 ...
## $ total_of_special_requests : num [1:119390] 0 0 0 0 1 1 0 1 1 0 ...
## $ reservation_status : chr [1:119390] "Check-Out" "Check-Out" "Check-Out" "Check-Out" ...
## $ reservation_status_date : Date[1:119390], format: "2015-07-01" "2015-07-01" ...
## - attr(*, "spec")=
## .. cols(
## .. hotel = col_character(),
## .. is_canceled = col_double(),
## .. lead_time = col_double(),
## .. arrival_date_year = col_double(),
## .. arrival_date_month = col_character(),
## .. arrival_date_week_number = col_double(),
## .. arrival_date_day_of_month = col_double(),
## .. stays_in_weekend_nights = col_double(),
## .. stays_in_week_nights = col_double(),
## .. adults = col_double(),
## .. children = col_double(),
## .. babies = col_double(),
## .. meal = col_character(),
## .. country = col_character(),
## .. market_segment = col_character(),
## .. distribution_channel = col_character(),
## .. is_repeated_guest = col_double(),
## .. previous_cancellations = col_double(),
## .. previous_bookings_not_canceled = col_double(),
## .. reserved_room_type = col_character(),
## .. assigned_room_type = col_character(),
## .. booking_changes = col_double(),
## .. deposit_type = col_character(),
## .. agent = col_character(),
## .. company = col_character(),
## .. days_in_waiting_list = col_double(),
## .. customer_type = col_character(),
## .. adr = col_double(),
## .. required_car_parking_spaces = col_double(),
## .. total_of_special_requests = col_double(),
## .. reservation_status = col_character(),
## .. reservation_status_date = col_date(format = "")
## .. )
## - attr(*, "problems")=<externalptr>glimpse(bookings_df)## Rows: 119,390
## Columns: 32
## $ hotel <chr> "Resort Hotel", "Resort Hotel", "Resort…
## $ is_canceled <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, …
## $ lead_time <dbl> 342, 737, 7, 13, 14, 14, 0, 9, 85, 75, …
## $ arrival_date_year <dbl> 2015, 2015, 2015, 2015, 2015, 2015, 201…
## $ arrival_date_month <chr> "July", "July", "July", "July", "July",…
## $ arrival_date_week_number <dbl> 27, 27, 27, 27, 27, 27, 27, 27, 27, 27,…
## $ arrival_date_day_of_month <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ stays_in_weekend_nights <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ stays_in_week_nights <dbl> 0, 0, 1, 1, 2, 2, 2, 2, 3, 3, 4, 4, 4, …
## $ adults <dbl> 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, …
## $ children <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ babies <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ meal <chr> "BB", "BB", "BB", "BB", "BB", "BB", "BB…
## $ country <chr> "PRT", "PRT", "GBR", "GBR", "GBR", "GBR…
## $ market_segment <chr> "Direct", "Direct", "Direct", "Corporat…
## $ distribution_channel <chr> "Direct", "Direct", "Direct", "Corporat…
## $ is_repeated_guest <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ previous_cancellations <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ previous_bookings_not_canceled <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ reserved_room_type <chr> "C", "C", "A", "A", "A", "A", "C", "C",…
## $ assigned_room_type <chr> "C", "C", "C", "A", "A", "A", "C", "C",…
## $ booking_changes <dbl> 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ deposit_type <chr> "No Deposit", "No Deposit", "No Deposit…
## $ agent <chr> "NULL", "NULL", "NULL", "304", "240", "…
## $ company <chr> "NULL", "NULL", "NULL", "NULL", "NULL",…
## $ days_in_waiting_list <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ customer_type <chr> "Transient", "Transient", "Transient", …
## $ adr <dbl> 0.00, 0.00, 75.00, 75.00, 98.00, 98.00,…
## $ required_car_parking_spaces <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ total_of_special_requests <dbl> 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 3, …
## $ reservation_status <chr> "Check-Out", "Check-Out", "Check-Out", …
## $ reservation_status_date <date> 2015-07-01, 2015-07-01, 2015-07-02, 20…colnames(bookings_df)## [1] "hotel" "is_canceled"
## [3] "lead_time" "arrival_date_year"
## [5] "arrival_date_month" "arrival_date_week_number"
## [7] "arrival_date_day_of_month" "stays_in_weekend_nights"
## [9] "stays_in_week_nights" "adults"
## [11] "children" "babies"
## [13] "meal" "country"
## [15] "market_segment" "distribution_channel"
## [17] "is_repeated_guest" "previous_cancellations"
## [19] "previous_bookings_not_canceled" "reserved_room_type"
## [21] "assigned_room_type" "booking_changes"
## [23] "deposit_type" "agent"
## [25] "company" "days_in_waiting_list"
## [27] "customer_type" "adr"
## [29] "required_car_parking_spaces" "total_of_special_requests"
## [31] "reservation_status" "reservation_status_date"skim_without_charts(bookings_df)| Name | bookings_df |
| 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 |
|---|---|---|---|---|---|---|---|---|---|
| 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 |
Cleaning data
Notes: Focus on in the following variables: ‘hotel’, ‘is_canceled’, and ‘lead_time’. Then create a new data frame with just those columns as called it ‘trimmed_df’ by adding the variable names to this code chunk, and rename the variable ‘hotel’ to be named ‘hotel_type’ to be easy understand
trimmed_df <- bookings_df %>%
select(hotel, is_canceled, lead_time)
trimmed_df %>%
select(hotel, is_canceled, lead_time) %>%
rename(hotel_type = hotel)## # A tibble: 119,390 × 3
## hotel_type is_canceled lead_time
## <chr> <dbl> <dbl>
## 1 Resort Hotel 0 342
## 2 Resort Hotel 0 737
## 3 Resort Hotel 0 7
## 4 Resort Hotel 0 13
## 5 Resort Hotel 0 14
## 6 Resort Hotel 0 14
## 7 Resort Hotel 0 0
## 8 Resort Hotel 0 9
## 9 Resort Hotel 1 85
## 10 Resort Hotel 1 75
## # … with 119,380 more rowshead(trimmed_df)## # A tibble: 6 × 3
## hotel is_canceled lead_time
## <chr> <dbl> <dbl>
## 1 Resort Hotel 0 342
## 2 Resort Hotel 0 737
## 3 Resort Hotel 0 7
## 4 Resort Hotel 0 13
## 5 Resort Hotel 0 14
## 6 Resort Hotel 0 14Notes: combine the arrive month and year into one column using the unite() function
trimmed_df <- bookings_df %>%
select(arrival_date_year, arrival_date_month) %>%
unite(arrival_month_year, c("arrival_date_month",
"arrival_date_year"),
sep = " ")
head(trimmed_df)## # A tibble: 6 × 1
## arrival_month_year
## <chr>
## 1 July 2015
## 2 July 2015
## 3 July 2015
## 4 July 2015
## 5 July 2015
## 6 July 2015Notes: create a new column that summed up all the adults, children, and babies on a reservation for the total number of people
trimmed_df <- bookings_df %>%
mutate(guests = adults + children + babies)
head(trimmed_df)## # A tibble: 6 × 33
## hotel is_canceled lead_time arrival_date_ye… arrival_date_mo… arrival_date_we…
## <chr> <dbl> <dbl> <dbl> <chr> <dbl>
## 1 Resort Hotel 0 342 2015 July 27
## 2 Resort Hotel 0 737 2015 July 27
## 3 Resort Hotel 0 7 2015 July 27
## 4 Resort Hotel 0 13 2015 July 27
## 5 Resort Hotel 0 14 2015 July 27
## 6 Resort Hotel 0 14 2015 July 27
## # … with 27 more variables: 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>, assigned_room_type <chr>, booking_changes <dbl>,
## # deposit_type <chr>, agent <chr>, company <chr>, …Notes: represent average leading time
trimmed_df <- bookings_df %>%
summarize(number_canceled = sum(is_canceled),
average_lead_time = mean(lead_time))
head(trimmed_df)## # A tibble: 1 × 2
## number_canceled average_lead_time
## <dbl> <dbl>
## 1 44224 104.Manipulating data
Notes: arrange the data by most lead time to least lead time
arrange(hotel_bookings, lead_time)## # A tibble: 119,390 × 32
## hotel is_canceled lead_time arrival_date_year arrival_date_month
## <chr> <dbl> <dbl> <dbl> <chr>
## 1 Resort Hotel 0 0 2015 July
## 2 Resort Hotel 0 0 2015 July
## 3 Resort Hotel 0 0 2015 July
## 4 Resort Hotel 0 0 2015 July
## 5 Resort Hotel 0 0 2015 July
## 6 Resort Hotel 0 0 2015 July
## 7 Resort Hotel 0 0 2015 July
## 8 Resort Hotel 0 0 2015 July
## 9 Resort Hotel 0 0 2015 July
## 10 Resort Hotel 0 0 2015 July
## # … with 119,380 more rows, and 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>, assigned_room_type <chr>, …Notes: need to specifically tell it when to order by desending order
arrange(hotel_bookings, desc(lead_time))## # A tibble: 119,390 × 32
## hotel is_canceled lead_time arrival_date_year arrival_date_month
## <chr> <dbl> <dbl> <dbl> <chr>
## 1 Resort Hotel 0 737 2015 July
## 2 Resort Hotel 0 709 2016 February
## 3 City Hotel 1 629 2017 March
## 4 City Hotel 1 629 2017 March
## 5 City Hotel 1 629 2017 March
## 6 City Hotel 1 629 2017 March
## 7 City Hotel 1 629 2017 March
## 8 City Hotel 1 629 2017 March
## 9 City Hotel 1 629 2017 March
## 10 City Hotel 1 629 2017 March
## # … with 119,380 more rows, and 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>, assigned_room_type <chr>, …Notes: store the arranged data in a data frame named ‘hotel_bookings_v2’
head(hotel_bookings)## # A tibble: 6 × 32
## hotel is_canceled lead_time arrival_date_ye… arrival_date_mo… arrival_date_we…
## <chr> <dbl> <dbl> <dbl> <chr> <dbl>
## 1 Resort Hotel 0 342 2015 July 27
## 2 Resort Hotel 0 737 2015 July 27
## 3 Resort Hotel 0 7 2015 July 27
## 4 Resort Hotel 0 13 2015 July 27
## 5 Resort Hotel 0 14 2015 July 27
## 6 Resort Hotel 0 14 2015 July 27
## # … with 26 more variables: 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>, assigned_room_type <chr>, booking_changes <dbl>,
## # deposit_type <chr>, agent <chr>, company <chr>, …hotel_bookings_v2 <- arrange(hotel_bookings, desc(lead_time))
head(hotel_bookings_v2)## # A tibble: 6 × 32
## hotel is_canceled lead_time arrival_date_ye… arrival_date_mo… arrival_date_we…
## <chr> <dbl> <dbl> <dbl> <chr> <dbl>
## 1 Resort Hotel 0 737 2015 July 27
## 2 Resort Hotel 0 709 2016 February 9
## 3 City Hotel 1 629 2017 March 13
## 4 City Hotel 1 629 2017 March 13
## 5 City Hotel 1 629 2017 March 13
## 6 City Hotel 1 629 2017 March 13
## # … with 26 more variables: 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>, assigned_room_type <chr>, booking_changes <dbl>,
## # deposit_type <chr>, agent <chr>, company <chr>, …Notes: getting to know maximum and minimum lead time, then getting to know what the average lead time for booking is because I want to know how early I should run promotions for hotel rooms as included as v2 data set as well
max(hotel_bookings$lead_time)## [1] 737min(hotel_bookings$lead_time)## [1] 0mean(hotel_bookings_v2$lead_time)## [1] 104.0114mean(hotel_bookings$lead_time)## [1] 104.0114Notes: getting to know what the average lead time before booking is for just city hotels
hotel_bookings_city <- filter(hotel_bookings, hotel_bookings$hotel=="City Hotel")
head(hotel_bookings_city)## # A tibble: 6 × 32
## hotel is_canceled lead_time arrival_date_ye… arrival_date_mo… arrival_date_we…
## <chr> <dbl> <dbl> <dbl> <chr> <dbl>
## 1 City Hotel 0 6 2015 July 27
## 2 City Hotel 1 88 2015 July 27
## 3 City Hotel 1 65 2015 July 27
## 4 City Hotel 1 92 2015 July 27
## 5 City Hotel 1 100 2015 July 27
## 6 City Hotel 1 79 2015 July 27
## # … with 26 more variables: arrival_date_day_of_month <dbl>,
## # stays_in_weekend_nights <dbl>, stays_in_week_nights <dbl>, adults <dbl>,
## # children <dbl>, babies <dbl>, meal <chr>, country <chr>,
## # market_segment <chr>, distribution_channel <chr>, is_repeated_guest <dbl>,
## # previous_cancellations <dbl>, previous_bookings_not_canceled <dbl>,
## # reserved_room_type <chr>, assigned_room_type <chr>, booking_changes <dbl>,
## # deposit_type <chr>, agent <chr>, company <chr>, …mean(hotel_bookings_city$lead_time)## [1] 109.7357Notes: how the information different among resort hotels and city hotels
hotel_summary <-
hotel_bookings %>%
group_by(hotel) %>%
summarize(average_lead_time=mean(lead_time),
min_lead_time=min(lead_time),
max_lead_time=max(lead_time))
head(hotel_summary)## # A tibble: 2 × 4
## hotel average_lead_time min_lead_time max_lead_time
## <chr> <dbl> <dbl> <dbl>
## 1 City Hotel 110. 0 629
## 2 Resort Hotel 92.7 0 737Aesthetics and Visualization data
Notes: install and load the ‘ggplot2’ package
Notes: using a bar chart getting to know what distribution type has the most number of bookings
ggplot(data = hotel_bookings) +
geom_bar(mapping = aes(x = distribution_channel))
Notes: driving deeper into bar chart in order to know if the number of bookings for each distribution type is different depending on wether or not there was a deposit or what market segment they represent
ggplot(data = hotel_bookings) +
geom_bar(mapping = aes(x = distribution_channel, fill=deposit_type))
ggplot(hotel_bookings) +
geom_bar(mapping = aes(x = distribution_channel, fill=market_segment))
Notes: facets galore as in order to create separate charts for each deposit type and market segment to help stakeholder understand the differents more clearly
ggplot(data = hotel_bookings) +
geom_bar(mapping = aes(x = distribution_channel)) +
facet_wrap(~deposit_type)
ggplot(data = hotel_bookings) +
geom_bar(mapping = aes(x = distribution_channel)) +
facet_wrap(~market_segment)
Notes: using ‘facet_grid’ function that will include plots even if they are empty
ggplot(data = hotel_bookings) +
geom_bar(mapping = aes(x = distribution_channel)) +
facet_grid(~deposit_type)
ggplot(data = hotel_bookings) +
geom_bar(mapping = aes(x = distribution_channel)) +
facet_grid(~market_segment)
Notes: putting all into one chart and explore the differences by deposit type and market segment
ggplot(data = hotel_bookings) +
geom_bar(mapping = aes(x = distribution_channel)) +
facet_wrap(~deposit_type~market_segment)
Filters and Facets data
Notes: create a bar chart showing each hotel type and market segment by using different colors to represent each market segment
ggplot(data = hotel_bookings) +
geom_bar(mapping = aes(x = hotel, fill = market_segment))
Notes: decide to use the facet_wrap() function to create a separte plot for each market segment
ggplot(data = hotel_bookings) +
geom_bar(mapping = aes( x = hotel)) +
facet_wrap(~market_segment)
Notes: load ’tidyverse’package then filtering a dataset to just city hotels that are online TA
onlineta_city_hotels <- filter(hotel_bookings,
(hotel=="City Hotel" &
hotel_bookings$market_segment=="Online TA"))Notes: plot the data to showing the relationship of variables between lead_time and stays_in_week_nights
ggplot(data = onlineta_city_hotels) +
geom_point(mapping = aes(x = lead_time, y = stays_in_week_nights))
Notes: create a plot to determine if people with children book hotel rooms in advance (on x-axis, the plot shows how far in advance a booking is made, with the bookings furthest to the right happening the most in advance; on the y-axis it shows how many children there are in a party)
ggplot(data = hotel_bookings) +
geom_point(mapping = aes(x = lead_time, y = children))